**3. Results**

#### *3.1. Infections*

Three or more yearly upper respiratory infections (URI) (rhinorrhea, nasal congestion, and productive cough), requiring antibiotic therapy for at least two consecutive years, were recorded in 18 (34.6%) of the SIgED patients. Sixteen patients (30.7%) had suffered one or more episodes of pneumonia (range 1 to 3). Median IgG, IgM, and IgA levels were not significantly different in patients with or without URI or pneumonia (data not shown). In two patients, chronic respiratory infection with *M. avium* complex was identified. Recurrent episodes of otitis were recorded in four patients. Three patients had suffered from herpesvirus infections, and one had suffered from chronic pyelonephritis.


**Star network:** In this topology, one particle called as focal particle [23] is in connection with others as shown in **Figure 3c**. Focal particle defined as equivalent to a leader adjusts trajectory of a swarm by comparing positions of others.

**Tree network:** In this network structure, all particles are taken shape of a tree and, each of nodes of tree consists of exactly one particle as illustrated in **Figure 3d** [24]. One particle at the node is connected to both child nodes bottom in a tree and a parent node directly above in a tree. If one of the child node particle has a solution better than its parent solution, position of both particles are replaced. In this topology, parent particle is defined as equivalent to a leader.

#### **3. Pareto multiobjective optimization**

In multiobjective optimization problems (MOOP) indicated as an optimization of more than one objective function, "trade-off" solutions that are conflicted to each other are obtained rather than single solution. MOOP is generally defined to obtain decision variables

**Figure 4.**

*Conceptual representation of the non-dominated and dominated particles in objective function space. Here, f* 1ð Þ *x and f* 2ð Þ *x are objective functions. [8].*

*Pareto-Based Multiobjective Particle Swarm Optimization: Examples in Geophysical Modeling DOI: http://dx.doi.org/10.5772/intechopen.97067*

in *p* dimensional of model space *S*, while simultaneously optimizing of a vector

$$f(\mathbf{x}) = \begin{bmatrix} f\_1(\mathbf{x}), f\_2(\mathbf{x}), f\_3(\mathbf{x}) \dots \dots f\_N(\mathbf{x}) \end{bmatrix} \tag{5}$$

including all objective functions [25]. In MOOP, all objective functions in *f x*ð Þ vector may not be optimized simultaneously. Therefore Pareto optimality sometimes called as Pareto dominance approach is used to find set of possible solutions. According to Pareto approach, if *x* is better solution than *y* in minimization problem, *f x*ð Þ dominates *f y*ð Þ, if and only if *f k*ð Þ *x* ≤ *f k*ð Þ*y* , *k* ¼ 1, … *:*, *N*. If there is no other *x*∈ *S* satisfies a condition such that *f*ð Þ *x* <*f x*ð Þ without deteriorating any other objective function, non-dominated solutions called Pareto optimal set *P*∗ ∈*S* or Pareto front *PF* ∗ ¼ f j *f x*ð Þ∈ *x*∈*P*∗ g exists in an objective function space as schematically illustrated in **Figure 4**.

All particles of Pareto front generally spreads in two ways which are concave and L-shaped curves. Concave shaped spreading indicates that one objective function does not be ameliorated without deteriorating any other objective function(s). On the other hand, L-shaped spreading indicates that complete optimal solution which means that all objective functions can be optimized simultaneously [26]. Other remarkable distribution of Pareto front is its deviation from symmetry referenced by utopia point [0,0]. Deviation is an indication that one objective function has many local minimums relatively the others (minimum one or more) and/or modeling has not properly accomplished with defined parameter search space and used methods [27].

#### **4. Pareto-based multiobjective particle swarm optimization**

Several considerations that are increasing the diversity, maintaining the nondominated particles and selecting a leader should be taken into account when using MOPSO. [20] A general MOPSO algorithm modified from PSO algorithm by these considerations outlined below is shown in **Figure 5**.

**Increasing of diversity:** In MOPSO, it is expected to increase of diversity of all particles, while dominated particles are required to converge towards to Pareto-front. Mutation, niching and clustering techniques are generally used to increase diversity. Mutation operator is originally a stage of the genetic algorithm and it prevents to trap of particles a local minima by altering decision variables within the bounds of possibility [28, 29]. suggested that polynomial and dynamic mutation rate which should be changed from higher to lower degree to keep increasing diversity in an entire search space. This feature allows particles to be explored, even in far field on this space [30]. Niching method, which was also developed for evolutionary algorithms [31], prevents a premature convergence that is a leading cause of a movement of particles towards a solution [32]. In NichePSO presented by [33], alteration of each particle is kept track after each iteration. If some particles do not change after several iterations, niche formed as a sub-swarm including both these particles and their surroundings is generated with a radius called *σshare* as illustrated in **Figure 6**. By comparing of two particles in niches, a control mechanism is provided to be adding particles to a subswarm or to be merging of sub-swarms [34]. In clustering technique introduced by [35], main swarm is divided into *k* sub-swarms and determined leader in each subswarm is moved to centroid of its sub-swarm. Diversity is provided by a migration of leaders to different sub-swarms [36].

**Maintaining non-dominated particles:** Maintaining non-dominated particles is key component of MOPSO. External archive technique allowing a storage of nondominated particles is generally used to spread and maintain Pareto-front. Global

#### **Figure 5.**

*General MOPSO algorithm.*

best, personal best or local best can be stored to an archive when extending PSO to MOPSO. However, as dominated particles tending to converge and join to Paretofront, a rapid increase number of particles in an archive is a disadvantage [20]. A restriction is applied to prevent rapidly growing number of non-dominated particles. In [37], they proposed *ϵ*-dominance value defined as set of boxes in objective function space as shown in **Figure 7** controls adding of dominated particles to Pareto-front by sizing of an archive. In [38], their attempts to update of external archive is composed niching method used to add or remove particles from archive when it is full. In [30], geographically-based global repository approach is used to update of an archive based on removing of non-dominated solutions from cells which have fewer particles. Li [39] proposed non-dominated sorting method to determine Pareto-front in each iteration. In his study, rather than comparing of a new position with a personal best, temporary swarms have been generated by combining both of them. In fact, the fundamental condition maintaining nondominated particles is that a new archive should be dominant over previous archives. *ϵ*-dominance approach is one of the most feasible method to provide this condition [20].

**Leader selection:** Unlike one leader that guides a swarm in the PSO, nondominated particles of Pareto front set indicate presence of multiple leaders in MOPSO. Neighbor density and kernel density estimator methods based on density measurement techniques are suggested to determine particles that are most likely to be selected as a leader. In [40], they studied and further supported the concept of crowding factor according to nearest neighbors of a given particle. The value of

*Pareto-Based Multiobjective Particle Swarm Optimization: Examples in Geophysical Modeling DOI: http://dx.doi.org/10.5772/intechopen.97067*

**Figure 6.**

*Niches having radius indicated by σshare are generated to compare of particles that have not change after several iterations.*

**Figure 7.**

*ϵ-dominance concept for minimizing two objective functions. Particle A, B and C are incommensurable particles, however, particle a is considered to be dominant to B and C due to more closer the lower left hand corner.*

estimators are determined by perimeter of a rectangular formed between one particle and neighbors as illustrated in **Figure 8**. Particles which have larger value of estimator are preferred to select as a leader.

The kernel density estimator is based on the niching method. A niche which has radius as *σshare* is generated for each of non-dominated particles. It is preferable selected a leader from the least crowded niche [41]. As for leader selection from preferred area, different ways are used. For instance, neighbor density estimator has been used by Raquel and Naval [42] to select a leader randomly from top a list ranking in descending order according to estimator values. If an external archive is full, particle has been removed by randomly selecting from bottom of the list. To select a leader, Coello Coello et al. [30] suggested to fitness values based on the number of particles in each hypercube formed by division of an objective function space. A leader is randomly selected from a hypercube determined by roulette-wheel selection scheme which its lengths of divided segments proportional to fitness values.

#### **5. Selecting the methods and parameters for Pareto-based multiobjective particle swarm optimization**

We joint modeled two synthetic seismological data obtained by response of fivelayered models by using MOPSO. In one synthetic model, shear wave velocities increase smoothly with depth (SM-1), while the other has a noticeable velocity contrast at third layer interface (SM-2). In the modeling stage, we simultaneously optimized two objective functions related with Rayleigh wave dispersion (RWD) and horizontal to vertical spectral ratio (HVSR) methods, which have different sensivities to physical phenomena. The estimated parameters were shear-wave velocities and depth obtained by cumulative sum of layer thicknesses in each layer. Parameter search space and more technical details were be given in [8].

Minimization was carried out between observed data obtained by response of synthetic models (*yobs i* Þ and calculated data (*ycal i* Þ obtained by response of models

*Pareto-Based Multiobjective Particle Swarm Optimization: Examples in Geophysical Modeling DOI: http://dx.doi.org/10.5772/intechopen.97067*

which their parameters changing with particle movements. Fitness value ð Þ *α* minimized at each of the objective function was calculated using,

$$a = \left[\frac{1}{n}\sum\_{i=1}^{n} \left(\boldsymbol{y}\_i^{obs} - \boldsymbol{y}\_i^{cal}\right)^2\right]^{1/2},\tag{6}$$

where *n* is the number of observations. Number of iterations and particles was selected as 200 and 100, respectively. Number of particles was selected as 100 referenced by 10-fold the number of model parameters (thicknesses and velocities for 5 layers). We used PSO parameters defined in [43] with velocity equation in Eq. (3) which provides an optimization without a velocity limitation. A mutation operator was used to increase diversity and mutation rate was selected as two percent. *ϵ*-dominance approach was selected to maintain of non-dominated particles and the value of *ϵ* was used as 0.01 for each of the objective functions. Number of hypercubes was selected as 30 for 100 particles recommended by [30].

#### **Figure 9.**

*Results for SM-1. Synthetic model and a model obtained from the Pareto optimum particle and the parameter search space (a); the fit between the observed and calculated RWD (b), and HVSR (c); the Pareto optimum solution marked as + and Pareto front (dark dots) with the dominated particles (light dots) for all iterations (d); and the Pareto optimum solution (+) with Pareto front and dominated particles at the last iteration (e) [8].*

Hypercubes having more particles were preferred for leader selection. After a fitness hypercube was determined by using Roulette-wheel approach, a leader was selected randomly from this hypercube.

#### **6. Results and discussions**

The results of synthetic models (SM-1 and SM-2) are shown in **Figure 9** and **Figure 10**, respectively. As can be seen in **Figure 9a** and **Figure 10a**, MOPSO was successful in proving to obtain the real models with defined methods and parameters. These tests showing matched perfectly between synthetic data and model

#### **Figure 10.**

*Results for SM-2. Synthetic model and a model obtained from the Pareto optimum particle and the parameter search space (a); the fit between the observed and calculated RWD (b), and HVSR (c); the Pareto optimum solution marked as + and Pareto front (dark dots) with the dominated particles (light dots) for all iterations (d); and the Pareto optimum solution (+) with Pareto front and dominated particles at the last iteration (e) [8].*

*Pareto-Based Multiobjective Particle Swarm Optimization: Examples in Geophysical Modeling DOI: http://dx.doi.org/10.5772/intechopen.97067*

responses as seen in **Figure 9b**, **Figure 9c**, **Figure 10b** and **Figure 10c**, further strengthened our confidence in MOPSO modeling such seismological data. The most conspicuous observation to emerge from the result of both models was the distribution of dominated particles and Pareto front. Dominated particles in **Figure 9d** show a clear and balanced distribution for SM-1, however, as in **Figure 10d** dominated particles tend to towards the *αRWD* axis for SM-2. This results showed that if only HVSR optimization was done, many local minima and nonunique solutions could be obtained in modeling of data obtained by response of such as SM-2 model. In contrast to earlier findings in [27], deviation from symmetry and divergence from an objective function axis were not only related to a improperly accomplished modeling, but also to the characteristic of HVSR optimization in case of model such as SM-2 that its optimization indicated non-uniqueness solutions. In addition, as shown in **Figure 9b**, spreading of Pareto front in SM-1 modeling showed a concave shaped curve that means an objective function could not further minimized without maximized to other objective function. However, for SM-2 model, Pareto front showed a similar distribution to L-shape, indicating that both objective functions were minimized independently and simultaneously.

#### **7. Conclusion**

This chapter considered is an overview of methods and parameters generally used in PSO and Pareto-based MOPSO, in the first step. The parameters and methods used in the literature are reliable but do not have an obvious superiority to each other. In spite of that MOPSO has been widely applied in many real world engineering problems, a few attempts have been made in order to modeling geophysical data. Until our previous study, this methodology have not been applied modeling seismological data. A set of solutions demonstrated in this chapter support the idea that MOPSO provides a powerful methodology for joint modeling of data having different sensivity. The present findings have important implications in order to solve weighting problem encountered in joint modeling approach. A clear benefit of MOPSO in the prevention of weighting-sum approaches could be clearly identified in this analysis. A further important implication is that divergence of particles from an objective function axis is not only related to properly defined parametrization and accomplished modeling, but also to non-uniqueness solutions. Our investigations into this point are still ongoing and seem likely to confirm our hypothesis. The evidence from this study implies that MOPSO is considered to be very attractive for joint modeling geophysical data in the future. However, further work needs to be performed to confirm whether MOPSO is beneficial to joint modeling of different types of geophysical data.

#### **Acknowledgements**

I am grateful to Prof. Dr. Abdullah Karaman and Assoc. Prof. Dr. Ekrem Zor for their help and valuable suggestions and discussions. Support was given by Istanbul Technical University and The Council of Higher Education (YÖK), who funded the work in its initial stages.

*Optimisation Algorithms and Swarm Intelligence*

#### **Author details**

Ersin Büyük Department of Geophysics, İstanbul Technical University, İstanbul, Turkey

\*Address all correspondence to<EMAIL_ADDRESS>
© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Pareto-Based Multiobjective Particle Swarm Optimization: Examples in Geophysical Modeling DOI: http://dx.doi.org/10.5772/intechopen.97067*

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## *Edited by Nodari Vakhania and Mehmet Emin Aydin*

Optimisation is one of the unavoidable key subjects in engineering and other realworld problems, which attracts researchers' and practitioners' attention for decades. On the other hand, computational algorithms nowadays play a definitive role in most real-life applications, from mobile phones to supercomputers, Internet servers, manufacturing, etc. An intelligent method for the enumeration of feasible solutions may lead to efficient computational algorithms. Swarm intelligence emerges as a rather new and novel of field computational intelligence that turned into a hot spot in optimization studies last two decades.

This book brings together a number of research articles within the intersection of these two prominent subjects, which introduces techniques and approaches in detail and demonstrates how optimisation problems can be solved with heuristic and swarm intelligence approaches. It contains a few contributions on Particle Swarm Optimisation (PSO) area, which is one of renown swarm optimisation approaches that will shed light to issues around optimisation with swarm intelligence to guide junior researchers with implementation details provided.

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

Optimisation Algorithms and Swarm Intelligence

Optimisation Algorithms and

Swarm Intelligence

*Edited by Nodari Vakhania*

*and Mehmet Emin Aydin*


Contents

Preface

Before beginning to consider to edit this book, I must confess a very genuine doubt as to my competency to undertake an edited book project and discourse on global social work. For the last twenty-five years I have been involved in social work teaching, curriculum development, research, and practice in Asia and more recently in Canada. From when the question was asked by Abraham Flexner in 1915 whether social work was a profession, we have come a long way to answer Flexner. Social work has grown in the last 100 years and we need waste no time in answering the same question. But perhaps we need to ask new questions. Social work education, theory, and practice have evolved over the century to be transformative as the profession is informed by contemporary debates, critical reflections, and research. I have had the opportunity to live, work, and teach in different countries, cultures, and professional settings. My involvement with the International Association of Schools of Social Work (IASSW) as member at large (2012-2016) and with the Asian Pacific Association of Social Work Education (APASWE) provided me with unique insights in to social work as an international and global profession. In the last fifty years, there has been a lot of research on global social work being published, focusing on the international dimension of social work that recognizes

the complexity of working across borders and cultures. This book aims to

When the invitation to edit this book was offered to me by the IntechOpen, I took it is an opportunity and risk and accepted the offer even while open book publishing in social work is not common. We received a huge response and educators from Canada, USA, South Africa, Ireland, Alaska, and other counties shared their research insights and scholarship that reflects the current globalization and its impacts on social work education and practice. The chapters included in this book challenge current thinking and thus generate new brave ideas for further development of global social work as a distinct social work practice. Aa a reader you will find an eclectic mix of ideas and unique identities that inform global social work practices. The book is divided in to two parts/sections. The first section is on Decolonizing Social Work. The second part focused on Empowering Social Work Research and Field Practice. In exploring these themes*, Global Social Work - Cutting Edge Issues and Critical Reflections* addresses issues regarding

will be a useful addition to the ongoing efforts.

cooperation, and passion.

contribute to further the existing knowledge base on global social work. There are currently increasing numbers of social work programs both in the global south and north offering a wide range of international content and I do hope that this book

community dynamics, colonization, race, immigrants, human rights, social action, and politics of populism. The authors have challenged traditional views about professional imperialism and were able to introduce global social work discourse in new and engaging directions. I am very grateful to the IntechOpen team and for the opportunity given to me to edit this book. I owe sincere thanks to the chapter contributors, who despite many constraints contributed to this collective effort that is now an example of a global collaborative project based on collegiality,

*by Ann-Christin Torpsten*


*2.4. In Vitro Cell Culturing, Drug Treatment, Nuclear Factor-2 Erythroid-2 (Nrf2) Gene Silencing by Small Interfering RNA (siRNA) and Western Blot Analysis*

The mouse hippocampal HT22 and murine BV2 microglial cells were cultured in Dulbecco's modified Eagle's medium (DMEM) supplemented with FBS (Fetal bovine serum) (10%) and penicillin/streptomycin (1%) in a 5% CO2 incubator at 37 ◦C. After attaining a confluency of 70%, the cells were pretreated for 1 h with ethanol (100 μM), followed by curcumin (2 μM) or Nrf2 siRNA for 24 h, or TAK242 (TLR4 specific inhibitor). The Nrf2 gene was knocked down with Nrf2 siRNA at a concentration of 10 μM per transfection for 36 h, as directed (SC: 37049, Santa Cruz Biotechnology, Inc., Dallas, TX, USA). The transfection was conducted with lipofectamine™2000 reagent (Invitrogen, Waltham, MA, USA) when the cells culture reached to 75–80%. The control group cells were treated with 0.01% Dimethyl sulfoxide (DMSO).


**Author details**

Imane Ait Oumghar1,2\*, Abdelwahed Barkaoui1 and Patrick Chabrand2

1 LERMA Laboratory, International University of Rabat, El Jadida, Morocco

2 University of Aix-Marseille, ISM, Marseille Cedex, France

\*Address all correspondence to<EMAIL_ADDRESS>
© 2023 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


**1. Introduction**

Air pollution and its quality in cities are the major concerns worldwide and China is no exception. Air pollution has been a serious environmental issue in China since extensive industrial production and other anthropogenic activities exponentially increased the concentration of major atmospheric pollutants that damaged the environmental quality and harmfully impacted human health [1–5]. The World Health Organization (WHO) reiterates that environmental and health risk factors from outdoor air pollution are substantial. In 2016, 91% of the world population was living in places where the WHO air quality guidelines levels were not met [6]. New estimates in 2018 revealed that 9 out of 10 people breathe air containing high levels of pollutants [7]. Over the last four decades,

**Citation:** Rahaman, S.; Jahangir, S.; Chen, R.; Kumar, P. Restricted Anthropogenic Activities and Improved Urban Air Quality in China: Evidence from Real-Time and Remotely Sensed Datasets Using Air Quality Zonal Modeling. *Atmosphere* **2022**, *13*, 961. https://doi.org/ 10.3390/atmos13060961

Academic Editors: Duanyang Liu, Kai Qin and Honglei Wang

Received: 7 May 2022 Accepted: 30 May 2022 Published: 14 June 2022

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**Copyright:** © 2022 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/).

rapid economic development, industrialization, and urbanization across China has caused serious air pollution problems [8–10]. China's National Western Development Strategies (the 'Go West' movement) by the Chinese government in 1999 also contributed to the spatial expansion of industrialization and urbanization [11]. This has resulted in the GDP reaching around RMB 8270 billion, to which industrial production contributes 34% as the most significant contributing factor (http://www.stats.gov.cn/tjsj/ndsj/, accessed on 18 April 2020). Furthermore, due to rapid industrialization and urbanization air pollution has become the fourth primary health risk factor for all deaths in China after heart attack, dietary risk, and smoking [12]. The recent COVID-19 pandemic at the global and regional level has substantially affected the spatial and temporal characteristics of significant air pollutants, such as NO2, CO, O3, PM10, PM2.5, and SO2 across China [13–15]. In this context, the present study aims to assess the overall air quality from ground-based monitoring and remotely sensed datasets using air quality zonal modeling in 15 major cities of China before and after pandemic lockdown periods. Here, we estimated the changes in pollutant concentration and the spatio-temporal variation characteristics for 60 days before and after the shutting down of the nation. Section 2 discusses data and methods where we mentioned data sources, how we analyzed the data, and what techniques we used. Next are the results in Section 3 where we analyzed the spatio-temporal distribution of NO2 before and after the pandemic; total NO2 load over the area using air quality zonal modeling, and trends of air pollutants in China before and after the pandemic. Section 4 presents the discussion where we critically evaluate the results and link our study with other relevant studies with reasons and evidence.

NO2 is primarily released by anthropogenic emissions, which contain the industrial burning of fossil fuels such as coal, oil, natural gas, vehicle exhaust, biomass burning, and electricity generation [16–18]. Pollutants such as carbon monoxide (CO) and sulphur dioxide (SO2) are mainly released from industrial plants and heating processes due to anthropogenic activities [19]. SO2 is regarded as one of the major air pollutants in cities because of its negative effects on human health and the ecosystem [20]. Particulate Matter (PM10, PM2.5) is a common proxy indicator for air pollution and it affects more people than any other pollutant. The major components of PM are sulfate, nitrates, ammonia, sodium chloride, black carbon, mineral dust, and water [6]. Tropospheric O3 is produced by emissions of nitrogen oxides (NOx = NO + NO2) and volatile organic compounds (VOCs) in the presence of sunlight [21]. The distribution of O3 varies with space and time and is lower in urban polluted areas than elsewhere because it disappears when it reacts with other pollutants [22,23]. Among NOx elements, NO2 is the most important precursor and quencher of O3 through NOx titration particularly during wintertime [24]. The daytime relationship between NO2 and O3 concentration in Northern China demonstrates significant ozone titration. However, this titration effect has been considerably attenuated with increasing PM2.5, which further reduces the incoming solar radiation, during the lockdown period [25]. It is not only harmful to human health but also poses adverse impacts on plants and ecosystems [1,3,26,27].

Various studies have been carried out to investigate the spatial and temporal distribution patterns of NO2, CO, O3. PM10, PM2.5, and SO2 at national, regional, and local levels [2,28–42] along with their driving forces in the recent past [27,43–46]. Numerous studies have shown a strong correlation between level of exposure to atmospheric pollutants and human health [2,4,5]; increased mortality [47–49]; sleeping disorders among elderly [50]; body weight and obesity [51]. To mitigate air pollution, the Chinese government has imposed stringent air quality standards [52] and strengthened emission controls of major atmospheric pollutants [53–57].

The understanding of the spatial distribution of air pollution in China has been recently improved by the application of advanced assessment tools, such as satellite remote sensing. The satellite-retrieved products have many advantages, including global coverage, high spatial-temporal resolution, and historical datasets [41,42,47]. Amid the coronavirus pandemic in China, NASA and European Space Agency (ESA) pollution monitoring satellites

have detected significant decreases in nitrogen dioxide (NO2) over mainland China [13–15]. There is evidence that the change is at least partly related to the economic slowdown following the outbreak of the COVID-19 virus. According to NASA scientists, the reduction in NO2 pollution was first apparent near Wuhan, but eventually broke out across the country [58]. In line with the previous studies of holiday effects on pollution [59–62], the main purpose of this work is to quantify spatial and temporal changes in primary pollutants such as NO2, CO, O3, PM10, PM2.5, and SO2 over China's mainland due to shutdown of anthropogenic activities.


*2.5. Quantification of GTs*

All experiments were conducted using plant samples grown under different light supplements for two weeks. At the bottom, middle, or top of the leaf on both the abaxial and adaxial sides, six locations were imaged using a stereomicroscope (WRAYMER 820T, WRAYMER, Osaka, Japan). In the same manner, the number of GTs on the second and third leaves was compared. An area of 4 mm2 was chosen randomly from each picture, and the total number of GTs was counted. The data were analyzed independently from sixteen plants as biological replications.


**1. Introduction**

Oil palm, *Elaeis guineensis,* is cultivated on a vast scale as a source of oil in West and Central Africa, where it originated, and in Malaysia, Indonesia and Thailand, where it predominates and thrives luxuriantly, and has become wellestablished over years [1]. The explosive expansion of oil palm plantation in these regions and countries has generated enormous amount of vegetable waste, creating problems in re-planting operations and tremendous environmental concern. Thus, oil palm biomass refers to agricultural by-products generated from oil palm industries during re-planting, pruning and milling activities, which in most cases is left to decompose in the fields [2]. In essence, fibres of empty fruit bunches are either by-products of the process of extracting palm oil form the palm fruits, cultivation activities or remains of the trees at the end of their useful life [3]. The wastes are usually disposed indiscriminately or used by the locals as cooking fuel, both of which are not environmentally friendly During oil extraction process, the fruit bunches are left empty as residues after fruit extraction. The presence of these empty fruit bunches (EFB) at mill's gate is unavoidable, but the great headache these biomass residues cause mill management cannot be ignored in waste management and disposal strategies [4].

Millions of tons of oil palm empty fruit bunches on the average are generated annually in different countries across the globe. In Malaysia, for instance, over 5.2 million tons of EFB were generated in 2002 [5]. Thus, oil palm industry is the largest contributor of biomass in Malaysia. These biomass residues are continually generated in large quantities annually with only a small fraction being converted into value-added products while a large percentage are left underutilised [2]. Globally, in 2014, 22.4 million tons of EFB were estimated to have been produced [6], as waste from crude oil palm (COP) processing, the amount of which is abundantly high [7].

The abundance of oil palm empty fruit bunches (OPEFB) has created enormous environmental issue such as fouling and attraction of pests, thereby posing very serious threats to humans and environment. In the context of the afore-mentioned challenges, examining oil palm EFB as an alternative fibrous material to other known pulpable resources such as wood, bamboos, bagasse, straws and grasses, strikes an important concordant note in converting waste to wealth and enhancing environmental wellness.

In countries like Malaysia and Indonesia, oil palm is one of the non-woody plants that have shown great potential as papermaking raw materials. Therefore, this book chapter will attempt to examine the fibre morphological characteristics of OPEFB, the chemical nature of oil palm fibre, the papermaking potentials of empty fruit bunches (EFB) and ultimately their impact on the environment.


**1. Introduction**

The proliferation of smartphone devices has enabled the expansion of Location Based Services (LBS) [1]. With the increasing popularity of LBS applications, there is a growing demand for more accurate localization solutions. Wireless MIMO localization is an alternative solution to the widely accepted Global Positioning System (GPS) in environments where GPS falls short. Specifically, GPS faces a challenge in maintaining accuracy and availability with urban canyons and indoor environments

[2]. Wireless MIMO systems already exist in these environments for communication purposes. Therefore, the existing wireless communication infrastructure can also be leveraged to provide localization services without investing in additional equipment. In fact, many LBS applications are enabled by wireless MIMO localization. While compiling a comprehensive list of these applications would be difficult, the following subsections provide an overview of some interesting LBS applications.

### **1.1 Applications**

#### *1.1.1 Emergency services*

The purpose of emergency services is to identify a caller's location and provide this information to the emergency responders. Emergency service is the oldest LBS application. The need to position mobile users was first advocated back in 1996 when the Federal Communication Commission (FCC) announced its mandate to enhance emergency services. During that time, the main motivation was mostly centered around locating emergency calls [3]. Since then, both FCC Enhanced 911 (E911) and 3rd Generation Partnership Project (3GPP) requirements for localization accuracy have become more stringent [4, 5].

#### *1.1.2 Autonomous vehicles and urban air mobility*

Precise positioning systems play a crucial role in autonomous vehicles and Unmanned Aerial Systems (UASs) [6]. The purpose of these positioning systems is to provide accurate estimations of the vehicle's location and orientation relative to the road and other vehicles (whether terrestrial or aerial). Moreover, the localization systems facilitate tracking of other vehicles, pedestrians, and obstacles in the surroundings. This information is utilized to plan safe and efficient routes, and to avoid collisions. The wireless MIMO system can provide primary location estimation or a backup in the event of GPS failure or loss of other proximity sensors [2]. Several studies have explored using MIMO localization for vehicles [7–11] and UASs [12–14].

#### *1.1.3 Field surveying and mapping*

Field surveying and mapping has both civilian and military applications including creating detailed topographical maps, measuring land boundaries, and collecting data on natural resources. For example, in construction surveying, positioning and localization systems are used to ensure that buildings and infrastructures are positioned and aligned correctly. In military applications, these systems can be used for reconnaissance of enemy territory and targeting of enemy or enemy assets. Simultaneous Localization and Mapping (SLAM) is often employed in these types of applications. SLAM is an active area of research and over the past few years, various surveys have been published that summarize the state-of-the-art SLAM solutions [15–17].

#### *1.1.4 Indoor tracking and localization*

Indoor tracking and localization technology have numerous practical applications across various industries. In healthcare, it can be used to track the location of medical

#### *Localization Techniques in Multiple-Input Multiple-Output Communication: Fundamental… DOI: http://dx.doi.org/10.5772/intechopen.112037*

equipment, staff and patients, ensuring efficient use of resources and timely delivery of care [18]. In the retail industry, it can help to optimize store layouts and improve the customer experience by providing personalized recommendations and targeted advertising. In industrial settings, it can improve warehouse logistics and inventory management by providing real-time tracking of goods and equipment [19]. Additionally, indoor tracking and localization can be used to enhance the safety of buildings and occupants by detecting and responding to emergencies, such as fires or security breaches. The technology also has potential applications in the field of smart architectures (smart homes [20], smart buildings [21], smart cities [22], and smart grids [23]) where it can be used to automate and optimize tasks and energy consumption.

#### *1.1.5 Agriculture*

Highly accurate localization systems have a wide range of applications in agriculture, including precision farming, autonomous equipment, livestock tracking, and soil mapping [24–28]. In precision farming, localization systems are used to collect data on soil conditions, crop growth, weather patterns, and other factors, which can then be analyzed to make informed decisions about crop management, including planting, fertilization, irrigation, and harvesting. Moreover, the accurate localization systems are also used to guide autonomous equipment to carry out tasks such as planting, spraying, and harvesting with greater precision and efficiency.

#### *1.1.6 Social networking*

LBS-enabled social networking applications aim to connect people who are located near each other and share similar interests. These applications use location data to recommend nearby events, activities, or groups that users might be interested in, and facilitate connections with others who are nearby. This approach offers benefits for both individuals and businesses. Some popular LBS-enabled social networking applications include Meetup, Foursquare, Yelp, and Facebook Places.

### **2. Wireless MIMO system**

#### **2.1 Sub-6 GHz and mmWave massive MIMO systems**

Fifth-Generation and Beyond (5G&B) mobile networks offer the potential for significantly greater communication capacity and ultra high-speeds that exceed those of previous generations by several orders of magnitude [29]. The large number of antennas in massive MIMO allows for more precise control of the signals, leading to increased capacity, better coverage, improved energy efficiency and reliability [30, 31]. Specifically, massive MIMO antennas enable the generation of narrow and highly directional signal beams. A beam can be steered towards a user to provide a high-quality signal that is less susceptible to interference and fading.

Sub-6 GHz bands are typically between 1 and 6 GHz. This frequency range is commonly used for wireless communication technologies such as cellular networks (3G, 4G, and 5G), Wi-Fi, Bluetooth, and other wireless communication standards. Sub-6 GHz systems are typically implemented using small-scale MIMO antennas. Regarding the sub-6 GHz channel, several measurement campaigns have been carried out to characterize it [32–34]. The propagation that depends on path-loss and shadowing results in large-scale fading, and multi-path propagation, results in small-scale fading [35].

The massive increase in data traffic has made the sub-6 GHz spectrum congested. This results in limited bandwidth for users, causing slower and unreliable connections [36]. One solution to this problem is to move to a different frequency band such as milimeter-Wave (mmWave) frequency channels. The channels are called mmWave because their wavelength ranges between 1 mm and 10 mm, which is equivalent to a frequency range between 30 GHz and 300 GHz. The mmWave channels can provide significantly more bandwidth compared to sub-6 GHz, which will be required for next generation wireless communication systems. Therefore, mmWave frequency has been identified as a key technology-enabler in 5G&B [30, 35, 36]. However, there are some disadvantages in mmWave communication such as severe signal attenuation and blockage. The signals cannot penetrate obstacles and tend to get absorbed by rain [37, 38].

In an experimental study, a comprehensive channel measurement campaign was conducted in Europe in 2014–2016 in numerous indoor and outdoor scenarios. The study showed that geometry of the main propagation paths at sub-6 GHz and mmWave bands are almost similar [39]. However, the blockage at mmWave band causes higher losses, rendering the path completely blocked. This experimental outcome has motivated several recent studies to use sub-6 GHz channel information for mmWave applications [40–42].