**About the Editors**

**Alisson Mendes Rodrigues** is currently a researcher at the Federal University of Campina at the Materials Technology Laboratory (Brazil). He is a multidisciplinary researcher with a chemistry degree from the State University of Maranhao (Brazil), an MEng in materials engineering from the ˜ Federal Institute of Education, Science and Technology of Maranhao (Brazil), and a D.Sc. in materials ˜ science and engineering from the Federal University of Sao Carlos (Brazil). He completed his ˜ doctoral internship at the University of California (EUA), worked as a researcher at the University of Rostock (Germany), and was a researcher at the Sao Carlos School of Engineering, which is ˜ part of the University of Sao Paulo (Brazil). Among his scientific interests, the following stand ˜ out: glass science covering fundamental and applied research on isothermal and non-isothermal crystallization kinetics in stoichiometric and non-stoichiometric glasses, glass technology in the development and characterization of bioactive glasses and glass ceramics, the manufacture of ceramic coatings by physical vapor deposition (PVD) techniques such as high-power impulse magnetron sputtering (HiPIMS), and the development of eco-friendly and sustainable materials from urban and mining waste.

**Gelmires de Araujo Neves** is a full professor at the Federal University of Campina Grande (UFCG), located in Brazil. He holds a BEng degree in mining engineering from the Federal University of Para´ıba (Brazil), an MEng degree in chemical engineering from the Federal University of Para´ıba, and Dr-Ing in process engineering from the Federal University of Campina Grande. He has experience in the areas of materials and metallurgical engineering, with emphasis on ceramic materials and material recycling, working mainly on the following topics: waste recycling, construction materials, ceramic material processing, organophilic clays for use in drilling fluid, iron ore pelletizing, adsorption of dyes and nanometric fillers, and characterization of ceramic raw materials. He has also coordinated several research and development projects funded by various funding agencies and companies, has published 211 articles in indexed journals, and has supervised hundreds of students (graduate, master, and doctoral level). He was the head of the Department of Materials Engineering, a member of the Full Board of the UFCG, the coordinator of the Postgraduate Program in Materials Science and Engineering, and a member of the Postgraduate Chamber of the UFCG.

**Romualdo Rodrigues Menezes** is an associated professor and the head materials science and engineering graduate program at the Department of Materials Engineering, Federal University of Campina Grande (Brazil). Before, he was an assistant professor of materials engineering at the Federal University of Paraiba (Brazil). He holds a BEng degree in materials engineering from the Federal University of Paraiba, an MEng degree in chemical engineering from the Federal University of Campina Grande, and D.Sc. in materials science and engineering from the Federal University of Sao˜ Carlos (Brazil). The research activities of Prof. Menezes are in the broad areas of ceramic processing, nanotechnology, biomaterials, and waste recycling. He is the author or co-author of more than 150 scientific papers. He is a member of the Brazilian Ceramic Association and the Latin American Society of Artificial Organs, Biomaterials and Tissue Engineering.


**2. Papers Details**

Chen et al. [1] present the numerical solution of an improved double-layer foundation consolidation theory, aiming at understanding the characteristics of the marine soft soil double-layer foundation structure with complex drainage conditions. They introduced continuous drainage boundary conditions into the traditional consolidation theory by applying the Laplace's transform and Stehfest algorithm in the derivation process of the model equations. This improved model was validated and analyzed by the degenerated model of the perfectly permeable and the semi-permeable boundary conditions. Moreover, they applied the model to a marine soft soil foundation project in Guangxi, China for the consolidation analysis. The validation of the results against field data shows good performance of the model in the analysis of consolidation and settlement of marine soft soil foundations with complex drainage conditions.

Yu et al. [2] investigate the effect of an existing footprint on the stability of jack-up platform reinstallation. In this study, a large deformation finite element analyses with the coupled Eulerian–Lagrangian (CEL) method was carried out. Attention is focused on the effects of footprint geometry, reinstallation eccentricity, and the roughness between spudcan and soil on the profiles of vertical forces, horizontal forces, and bending moments. They also underline the role of the soil profiles, soil properties, geometry of footprints and spudcans, leg details, use of spigots (or not), etc., in site-specific analyses.

Li et al. [3] investigate the accumulation of pore water pressure in a soft clay seabed around a suction anchor under cyclic loading conditions. They propose a three-dimensional damage-dependent bounding surface model, in which a damage parameter and initial anisotropic tensors are introduced to represent the remolding of the soil structure and initial anisotropy, respectively. The proposed model is validated against available triaxial test data and provides a better description of the cyclic behaviors of soft clay. Li et al. [3] also propose a new structure that can reduce the accumulation of excess pore water pressure based on the numerical simulations.

Han et al. [4] investigate the flow field dynamics and corresponding response of a porous seabed around an immersed tunnel under wave loading combined current with various velocities. They developed a 2D numerical model, which is composed of two sub-models: The flow model with OpenFOAM and the seabed model with a Meshfree method, to simulate the fluid–structure–seabed interaction. The integrated model is well validated by comparison with the analytical solution and experimental data. The numerical results confirmed that the existence of the immersed tunnel affected surrounding seabed dynamic behaviors, and thus weakened the displacement and dynamic pore pressure change nearby. The parametric studies also reveal the significant impact of wave characteristics, soil properties, and current velocities on the liquefaction behavior in the vicinity of an immersed tunnel.

Li et al. [5] present a numerical investigation on the influence of flap geometric parameters on the aerodynamic characteristics of two-element wingsail. They propose a two-element wingsail model by using the steady and unsteady RANS approach with the SST k-ω turbulence model. Existing experimental data are used to ensure the accuracy of the numerical simulation. Moreover, it provides a quantitative evaluation regarding the aerodynamic characteristics of the wingsail with different structural parameters including camber, the rotating axis position of the flap, angle of attack, and flap thickness.

Cui et al. [6] illustrate a framework to analyze the hydrodynamic dispersion characteristics of coral sands in marine environments. In particular, they explore how the dispersion characteristics of solute in calcareous sands are influenced by the particle size, degree of compactness, and gradation of porous media. They used coral sands collected from a reef in the South China Sea and conducted a series of laboratory tests and theoretical analyses. Results reveal a direct correlation between the dispersion coefficients of coral sands and the above-mentioned factors. Moreover, they find that the dispersion mechanisms in coral sands could be determined by the flow velocity of the pore fluid.

Wang et al. [7] explore the instability behavior of the fine-grained seabed under wave action. In particular, they carried out a series of in-lab flume experiments using the sediment collected in the subaqueous Yellow River delta. Results reveal an oscillation failure type characterized by an arc-shaped sliding surface. Moreover, they find that the presence of gas plays a key role in promoting the occurrence of submarine slope failures.

Lu et al. [8] investigate the remobilization of heavy metal Cu under wave action, especially during sediment liquefaction. They conducted a series of controlled wave flume experiments using the sandy silts collected from the Yellow River Delta. The experimental results indicate that sediment liquefaction significantly promotes the release of Cu from internal sediment to the overlying water. Moreover, they analyzed the pattern and mechanism of migration and diffusion of dissolved Cu in different stages. The study may provide a reference for understanding changes of the marine ecological environment in the subaqueous Yellow River Delta.

Wu et al. [9] investigate the dynamic response characteristics of the pile–soil–structure system in coral sand under earthquake. They conducted a series of shaking table tests of three-story structures with a nine-pile foundation in coral sand and Fujian sand, respectively. The influence of shaking intensity on the dynamic response of the system has been taken into consideration by the authors. In this way, they are able to compare the similarities and differences between dynamic characteristics of coral and Fujian sand based on the results of pore water pressure, acceleration, displacement, and dynamic bending moment. This work may provide some references for the seismic design of coral reef projects.

Liu et al. [10] examine the possibility of using the pressure sensing technique for observing seabed deformation caused by submarine sand wave migration. They developed two pressure sensing tools, a fixed-depth total pressure recorder and a surface synchronous bottom pressure recorder, to make observations in laboratory flume experiments. The proposed pressure sensor techniques have been proven by the results to effectively reflect elevation caused by submarine sand wave migration with an accuracy more than 90%. Moreover, authors discuss the reliability and limitations of using the two methods in practical observations. This work suggests an important way for guiding the monitoring and early detection of bedforms and sediment mobility and better understanding the mechanisms of submarine sand wave migration.

Guo et al. [11] present an exploration into identifying the interactions between ocean waves and the continental margin in the generation of double-frequency (DF, 0.1–0.5 Hz) microseisms. They collected a total of 10 days of ambient noise data at 33 stations across the East Coast of USA. Both the observations and correlation analyses led to a hypothesis on the frequency dependent interactions of ocean waves with the continental margin and the origination of DF microseisms. This work suggests a potential contribution of the frequency-dependent interactions between the ocean waves and the continental margin determined in analyzing the wave-induced mass wasting on the continental margin.

Wu et al. [12] present an experimental investigation on the small-strain stiffness of marine silty sand. They conducted a series of bender element tests on marine silty sand with fines content ranging from 0% to 30% under isotropic consolidation. Moreover, they discuss the influence of parameters in the established framework based on clean sand. In particular, a binary packing state concept was implemented to modify the Hardin model for evaluation of the small-strain stiffness of marine silty sand. The proposed model has been calibrated by the independent test data for different silty sand published in the literature.

Jun and Kwon [13] propose the representative constitutive relationship equations of marine soft soil in Korea. They collected samples at 23 dredged reclaimed construction sites in three regions in Korea. In particular, the consolidation simulations were carried out at a high void ratio using the centrifugal experiment to realize high water content and in-field stress conditions. Then the void ratio–effective stress and void ratio–permeability coefficient were estimated using the back analysis. The constitutive relationship for Korean soft soil was determined to be a reasonable power function equation. They also underline the importance of a design chart based on the constitutive relationship equations.

Huang and Han [14] summarize seismic liquefaction field investigations in marine engineering to provide a systematic understanding of the historical cases published over recent decades. Particularly, they identified the effects of seawater and the gas component in the marine seabed layers, leading to a larger liquefied area than when liquefaction occurs on land. Moreover, they review mitigation strategies of novel marine foundation structures considering their resistance of liquefaction.

**Author Contributions:** Conceptualization, X.L. and D.-S.J.; Writing–original draft, X.L.; Writing– review & editing, D.-S.J., Q.Y., Y.W. and H.S. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.

**Acknowledgments:** We want to express our sincere thanks to all the authors and the reviewers.

**Conflicts of Interest:** The authors declare no conflict of interest.


*3.1. System Overview*

The main challenges associated with PWD detection are a lack of data for training the DNN in various stages of infection, and the existence of background objects that are similar to the object of interest. We collected a large dataset consisting of observations from various districts in South Korea. The suppression of ambiguous background objects is particularly important in this domain [**?** ]. The hard negative samples for PWD were further divided into six categories according to their appearance and texture information. The problem-solving strategy was found to be beneficial for guiding an object detector, particularly in instances where the object of interest was highly correlated with other background objects.

As shown in Figure **??**, we first augmented the training samples with suitable augmentation methods and then trained our first DNN, which was designed to distinguish the background and PWD objects. The detector was robust at detecting most background regions, including healthy trees, land, buildings, lakes, etc., but PWD-infected trees confused with "disease-like" areas (FP) such as yellow land or maple trees. In this phase, it is easy to collect a lot of "disease-like" areas, which is referred to as the mining of hard negative samples [**?** ]. We further categorized those ambiguous FP samples into six distinct categories (Figure **??**). The disease and "disease-like" samples were passed through the DNN to perform a fine-level object detection. The UAV images in our system included disease regions that varied in size from 12 × 8 to 360 × 300 pixels. Accordingly, we applied a feature pyramid network (FPN) to capture arbitrary scale features based on both bottom-up and top-down connections. ResNet was selected as a backbone network, and the features in each residual block were processed for pyramidal representation. The bottom-up pathway produced the feature map hierarchy and the top-down pathway fused higher resolution

features through upsampling the spatially coarse ones. This combined bottom-up and top-down process helps generate semantically stronger features. For the feature maps in each hierarchical stage, we appended a 3 × 3 convolution layer to reduce the aliasing effect of upsampling. Then, the set of merged features of each FPN stage was finally used for predictions. In the inference stage, we cropped the 800 × 800 image patch from the large sized orthophotograph (with "\*.tif" extension). We assembled the results of augmented inference images and fused them by weighted boxes fusion algorithm to enhance the location and classification accuracies. A more detailed description of each module is provided later.

**Figure 2. Left figure**: the object of hard negative samples. **Middle figure**: the appearance relationship among "disease-like" objects and ground truth disease. **Right figure**: the reasons that the network predicted the different types of false negative samples. In high-quality image, branches and leaves are clear and easily identifiable. However, in low-resolution image, the real PWD-infected trees appear similar to the maple tree and the yellow land.


**1. Introduction**

Water is an essential component of growing crops. Even in humid climates, precipitation is not enough to meet plant water requirements. Thus, additional water is applied through irrigation. Irrigation management can be complicated with unpredictable precipitation patterns. Not watering at the right time and correct amount can result in plant water stress and reduce the quality and yields of crops. On the other hand, over-watering can increase the risk of nutrients leaching below the root zone, waste resources (water, energy, and nutrients), and environmental impacts. Therefore, it is important to apply irrigation at the right timing and correct amount. Determining the appropriate amount of irrigation and the optimal timing of irrigation are challenging due to unpredictable weather conditions and climate changes.

Irrigation scheduling is a method of determining the appropriate amount of water to be applied to a crop at the correct time to achieve full crop production potential. Scheduling irrigation water has been based on the soil moisture measurement and/or weather data that are estimates of evapotranspiration.

This chapter reviews the various existing and recent advances in irrigation scheduling methods. The irrigation scheduling methods are:

• Feel and appearance.

#### **2. Irrigation scheduling method**

#### **2.1 Feel and appearance**

The most popular and quickest method is based on the feel and appearance of the soil. A soil probe is typically used to take soil samples. **Table 1** shows the soil moisture and appearance relationship. **Table 1** shows an approximate relationship between field capacity and wilting point. The top of each soil type corresponds to the condition of zero soil moisture deficiency, also known as field capacity. The bottom of each soil type corresponds to the condition of maximum soil moisture deficiency, also known as wilting point. The soil moisture deficiency also presents the available moisture range of the soil. The table provides general numbers for a specific group of soils and may not apply to all soil groups. This method is not quantitative and is judged by the individual, which lacks precision.

#### **2.2 Gravimetric method**

Soil moisture content is an important parameter for understanding the water movement in the soil. Taking soil samples is the direct method to measure the actual soil moisture level. This method requires weighing a sample of a known volume of soil and then reweighing it after drying in an oven at 105°C to calculate the mass of water lost by drying [2]. This method allows for calculating gravimetric water content (g/g) and soil bulk density (g/cm3 ). Multiplying the gravimetric water content by the soil bulk density allows for calculating the volumetric water content (cm3 /cm3 ) [3]. The equation of volumetric water content is described in (Eq. (1)). Soil sample collection method is accurate, but it requires intense labor, time, and soil disturbance. Therefore, continuous soil moisture monitoring through soil sample collection on farmland can be difficult and limited.

$$\theta\_v = \frac{\theta\_\text{g} \* \rho\_{soil}}{\rho\_{water}} = \frac{\left(\left(\frac{\left(M\_{water} - M\_{dry}\right)}{M\_{dry}}\right) \* \left(\frac{M\_{dry}}{V\_{soil}}\right)\right)}{\rho\_{water}}\tag{1}$$

Where,

*θv* = Volumetric water content (cm3 /cm3 ).

*θg* = Gravimetric water content (g/g).

*Irrigation Scheduling Methods: Overview and Recent Advances DOI: http://dx.doi.org/10.5772/intechopen.107386*

#### **Table 1.**

*Relationship between soil moisture and appearance [1].*

*ρsoil* = Soil bulk density (g/cm3 ). *ρwater* = Density of water = 1 (g/cm3 ). *Mwater* = Weight of wet soil (g). *Mdry* = Weight of dry soil (g). *Vsoil* = Volume of soil sample (cm3 ).

#### **2.3 Weather-based irrigation scheduling method**

Weather-based irrigation scheduling method is based on the weather condition. Four major weather parameters determine evapotranspiration (ET), which drives the weather-based irrigation scheduling method. The weather parameters are solar radiation, air temperature, relative humidity, and wind speed. Higher the solar radiation, the greater ET. This is because sunlight is the main energy source for evaporating water. The warmer the air, the greater ET, because it can hold more water vapor. The drier the air, the greater the ET, because there is less water vapor it already holds. The greater wind, the greater the ET. In humid climate regions, solar radiation and air temperatures play a significant role in determining daily ET.

ET can be estimated in several ways. One method that is accepted as an international standard is the Penman–Monteith Equation, which is used to calculate the reference potential ET (rPET) using comprehensive weather data. The weather data includes net radiation, soil heat flux, average air temperature, wind speed, saturation vapor pressure, actual vapor pressure, the slope of vapor pressure curve, and psychrometric constant. rPET assumes a four grass-covered surface that is well-watered and unshaded. The actual ET of a crop at any given time depends on the amount of leaf area and the developmental stage, so to calculate the ET for a specific crop type, at a specific developmental stage, the rPET values must first be multiplied by a crop. Kc changes with crops as they grow, for example, the Kc of fruit trees increases rapidly in the spring as the trees leaf out to full canopy. **Figure 1** shows the change of Kc as a soybean grows. To estimate actual crop ET, the rPET is multiplied by the crop coefficient Kc to determine the actual water lost from the crop via ET (see (Eq. (2)).

$$ET\_C = K\_C \* rPET \tag{2}$$

Where,

*ETC* ¼Actual Crop Evapotranspiration (in/day).

*KC* ¼Crop Coefficient (unitless multiplier).

*rPET* ¼Reference Potential Evapotranspiration (in/day).

Based on each day of the actual crop evapotranspiration, the suggested irrigation amount can be calculated to ensure that the soil has adequate moisture for plant growth and improve irrigation water use efficiency. For example, if last week's cumulative actual crop evapotranspiration was 2.5 cm, the farmer should apply 2.5 cm of irrigation to maximize crop production and minimize environmental impacts.

#### **2.4 Soil moisture sensor-based irrigation scheduling method**

An alternative way to measure soil moisture content is using a soil moisture sensor. A typical soil moisture sensor estimates the volumetric water content (cm3/cm3) in soils. Soil moisture sensors allow monitoring the changes of soil moisture level over time without disturbing the soil. The sensors can be installed at multiple depths of soil to monitor the water flow in soil. In general, there are two types of soil moisture sensors. Soil tension sensors measure the required force for roots to pull water out of the soil. Volumetric water content sensor based on the electrical properties of the soil

**Figure 1.** *Crop coefficient (kc) changes as the soybean grows.*

to estimate the soil moisture level. Knowing some common terminologies used in soil moisture sensor-based irrigation scheduling would be helpful in interpreting these sensor data.

The descriptions of some useful terminologies follow:

**Saturation:** All soil pore spaces are filled with water.

**Field Capacity (FC):** Maximum amount of water that soil can hold after drainage. **Wilting point (WP):** Soil moisture level where there is no available water for the crop.

**Available Water Capacity (AWC):** The difference between FC and WP, is often expressed in inches of water per foot of soil. AWC of a soil is primarily related to the soil texture, organic matter content, and bulk density. The equation of AWC is shown in (Eq. (3)).

$$A\text{WC} = \frac{\rho\_{soil} \ast T \ast P\_W}{\rho\_{water} \ast 100} \tag{3}$$

Where,

AWC = Available water capacity in inches.

*ρsoil*= Soil bulk density.

T = thickness of soil horizon under consideration in inches.

*PW* = Moisture content between field capacity and wilting point in percentage by weight.

*ρwater* = Density of water = 1 (g/cm3 ).

**Maximum Allowable Depletion (MAD):** The amount of available water that can be safely depleted without causing drought stress, which depends on the crop and the growth stage.

**Soil Matric Potential (SMP):** Physical force required for the plant to move water into its root system.


**Author details**

Kasturi Vasudevan1 \*, Surendra Kota1 , Lov Kumar1 and Himanshu Bhusan Mishra2

1 Department of Electrical Engineering, Indian Institute of Technology, Kanpur, India

2 Department of Electronics Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, India

\*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.

*New Results on Single User Massive MIMO DOI: http://dx.doi.org/10.5772/intechopen.112469*


*3.8. Aflatoxin Analyses from Cultures of Aspergillus Species*

To verify the protocol of the AF quantification from the cultures, two common strains of aflatoxin-producing fungi, *A. flavus* NRRL 3357 and *A. parasiticus* NRRL 2999, were used and tested for aflatoxin production. In addition, three of the non-aflatoxin producing *Aspergillus* strains were used as a negative control: *A. oryzae* M2040, *A. oryzae* NRRL RIB40, and *A. oryzae* NRRL 3483. Fungal strains were grown in three culture conditions in triplicate, as mentioned previously; samples were then harvested after 5 days of incubation, and AFs were extracted and analyzed by HPLC coupled with DAD and FLD.


**Abstract**

There is a principal and controversial debate on the so-called 'Greening-Regreening' of the Sahel. There still is the old philosophy of an expanding/shrinking ecosystem Sahara versus Sahel. In some concepts, it is presented as annual. Another concept is based on a general degradation of the Sahelian savannas – in some cases with a decline to a lower state of ecological equilibrium after a short period of resilience. Anyhow, there are also signs of still ongoing regeneration processes of vegetation and soil. The main problem, however, lies in the principal lack of terrestrial observation and in the confusion of terms. This mostly concern on vegetation units and their dynamics. The goal of this article is to explain the general nature of the Sahara and the Sahel based on maps and graphs. We try to analyse the dynamics of boundaries during the last 200 years. The main results are the tripartite nature of the Sahara, divided into semidesert, desert and Saharan savanna with relatively stable boundaries. A reconstruction of the vegetation for the last 200 years confirmed the position of these borderlines even under different states of the plant cover. It also revealed the nature of Sahelian savannas as cultural landscapes – in higher diversity and density. It is also possible that the North Sahelian savannas had been for long times under the dynamics of elephant landscapes. A high-resolution sediment and pollen record from the Middle Sahel of Niger evidenced the high diversity and resilience up to the severe drought of the 1970s. It was a definite stroke from which these savannas never reached again their former diversity despite a slide recovery named 'Regreening'. The various projects for regeneration or conservation in Sahara or Sahel differ in two types of projects. The one is the installation of Nature Reserves/National Parks with special reserves for emblematic animals as keystone organisms and an auto-regeneration of vegetation and soil. The other type consists of pasture rotation projects such as in the Malian Gourma or in the Central Air Mts. The first initiative resulted in the decade-long protection against the severe degradations, which were typical for the surrounding regions. The rotation system was based on timewise open wells and of observed pasture status. It was conceived together with the local populations and has been respected until the invasion of northern cattle keepers during the peak of drought in 1984. After severe quarrels, the system collapsed and the savannas degraded heavily. A comparable project worked in the central Air Mts. for 5 years. Remarkable results have been, but the

rebellion of the 1990s, put a sudden end on it. The general insecurity of the last decades caused by civil war and/or various terrrorist groups led to a re-evaluation of a great number of regeneration initiatives including the pharaonic 'Great Green Wall', a continent wide forest belt. However, smaller projects on the village level may better develop as they are under the responsability of local population, which can reactivate their long experience. The 'regreening' might be restricted to the region of the southern Sahara and the northern Sahel as well as to the traditional park systems. Anyhow, even if a long-time amelioration of production systems will happen, the former must be regarded on the background of a rapidly increasing demography.

**Keywords:** Sahara, Sahel, vegetation, landscape types, present situation, historical development, stability of limits, cultural, landscapes, degradation, regeneration potential


*3.2.3 ATP binding protein ComA (PDB: 3VX4)*

Quorum sensing is mediated by a signaling molecule autoinducer [53]. This system in some streptococcal species such as **S. mutans** is the ComABCDE pathway, in which inducing peptides are processed from the ComC precursor and exported to the extracellular space by ComA and ComB [43, 54]. ComA is a bifunctional ATP-binding cassette transporter comprising three domains: an N-terminal peptidase domain (PEP), a transmembrane domain, and a C-terminal nucleotide linker domain [55–57]. PEP is a peptidase belonging to the cysteine protease family [55, 58–60].

Docking with the ATP binding protein ComA PDB id: 3VX4 identified as the best ligands the compounds: licorisoflavan A (16) (Edock = −-132.56 kJ/mol), licoricidin (15) (Edock = −128.75 kJ/mol), and methoxyficifolinol (1) (Edock = −127.50 kJ/ mol). When observing the interactions of the best ligands in the formed complexes, it was observed that hydrogen bonds with residues Thr568 and Ser563 and steric interactions with Lys567 are common, indicating that these interactions contributed to the reduction of the interaction energy and stabilization of the complexes (**Figure 7**).

#### **Figure 7.**

*Representations of the interactions between the three best ligands (compounds 16, 15, and 1) and the amino acid residues of the ATP binding protein ComA PDB id: 3VX4. Blue dashed lines represent hydrogen bonds and red dashed lines represent steric interactions.*

*Molecular Docking of Phytochemicals against* Streptococcus mutans *Virulence Targets… DOI: http://dx.doi.org/10.5772/intechopen.101506*

#### **3.3 Exoenzymes**

#### *3.3.1 Glucanosyucrase (PDB id: 3AIC)*

Glucansucrases or glycosyltransferases (GTFs) are extracellular enzymes, produced by various bacteria, including **S. mutans**, that cleave sucrose into glucose and fructose and build sticky biofilm chains. The growth of the glucan chain was associated with adherence of one bacteria to another and the dental surface. Furthermore, modulate the diffusion of substances through the biofilm, which could occasionally serve as an extracellular energy reserve [61].

The glucanosucrase in *S. mutans* allows the metabolism of sucrose into lactic acid, which reduces the pH around the tooth, facilitating the dissolution of calcium phosphate from tooth enamel, which induces tooth decay [62]. These characteristics make the **S. mutans** glucanosucrase as one of the main and most studied targets for the development of new agents useful in the prevention of dental caries.

The best ligands that interacted with glucansucrase PDB id: 3AIC in the docking simulation were: erycrystagallin (10) (Edock = −145.72 kJ/mol), malvidin-3,5-diglucoside (20) (Edock = −138.84 kJ/mol), and erystagallin (11) (Edock = −136.44 kJ/ mol). Hydrogen bonds with residues Asp480, Asp481, Asn537, and steric interactions with residues Leu433, Glu515, and Trp517 are common to the two best ligands and seem to be important for reducing the energy of formation of these complexes (**Figure 8**).

The docking study conducted out by Kim et al. [63] between rubusoside and **S. mutans** glucanosucrase (PDB id: 3AIC), identified residues Leu 433, Leu434, Ala478, Asp480, Glu515, Trp517, and Tyr916 as the main ones involved in the stabilization of the complex, and validated these residues as important anchoring sites for potential inhibitors of this enzyme.

Bhagavathy, Mahendiran, and Kanchana [64], performed molecular docking between seven phytochemical isolates of *Psidium guajava* and *S. mutans* glucanosucrase (PDB id: 3AIB) and demonstrated that the main residues involved in the formation of the complexes were Thr426, Ile427, Gln553, and Tyr978. These residues diverged from those identified in this study.

#### **Figure 8.**

*Representations of the interactions between the three best ligands (compounds 10, 20, and 11) and the amino acid residues of the glucanoscarase PDB id: 3AIC. Blue dashed lines represent hydrogen bonds and red dashed lines represent steric interactions.*

Opposing, Islam et al. [65] performed a molecular docking study between epigallocatechin gallate (EGCG) and the same **S. mutans** enzyme, glucanosucrase (PDB id: 3AIB). The results showed that the main interactions that stabilize the complex of the ligand (EGCG) with the enzyme occurred between the amino acid residues Glu515 and Trp517, which were the same residues identified in our work, reinforcing the importance of these residues for the stabilization of the complex.

#### *3.3.2 Dextranase (PDB id: 3VMO)*

**S. mutans** dextranase is an enzyme that hydrolyzes the α-1,6 bonds of dextran and produces isomalto-oligosaccharides of different sizes for metabolic use [66, 67]. This protein is composed of 850 aa residues with a molecular mass of 94.5 kDa, but it has multiple native and recombinant forms [68, 69]. According to the sequencing of several enzymes in this family, dextranases are divided into five regions: a signal peptide sequence (N-terminal with 24 aa), a variable N-terminal region (Ser25-Asn99), a conserved region (Gln100-Ala615), a glucan binding site (Leu616-Ile732), and a C-terminal variable region (Asn733-Asp850) [70, 71].

Some biochemical studies, based on the comparison of amino acid sequences with other glycosyltransferases, revealed that the Asp385 residue is essential for the catalytic reaction [72]. Besides, it was observed that Asp270 from cycloisomalto oligosaccharide glucanotransferases from *Bacillus circulans* T3040 [73] and Asp243 from endodextranase from *Thermotoga lettingae* TMO [74], corresponding to Asp385 from dextranase from **Streptococcus mutans**, were recognized as **S. mutans** residues catalytic.

Molecular docking performed with the dextranase PDB id: 3VMO identified as the best ligands: licorisoflavan A (16) (Edock = −138.02 kJ/mol), malvidin-3,5-diglucoside (20) (Edock = −136.94 kJ μg/mol), and licoricidin (15) (Edock = −129.73 kJ/ mol). Compounds 15 and 16 showed steric interactions in common with residues Tyr257 and Ala559 and showed steric interactions and hydrogen bonds with the key residue Asp385 which has already been identified as essential for catalytic reaction. Diglucoside 20, on the other hand, had a lower energy conformation distinct from compounds 15 and 16 and interacted with other amino acid residues in the active site of the enzyme (**Figure 9**).

#### **Figure 9.**

*Representations of the interactions between the three best ligands (compounds 16, 20, and 15) and the amino acid residues of dextranase PDB id: 3VMO. Blue dashed lines represent hydrogen bonds and red dashed lines represent steric interactions.*

*Molecular Docking of Phytochemicals against* Streptococcus mutans *Virulence Targets… DOI: http://dx.doi.org/10.5772/intechopen.101506*

#### **Figure 10.**

*Representations of the interactions between the three best ligands (compounds 10, 11, and 1) and the amino acid residues of hemolysin PDB id: 2RK5. Blue dashed lines represent hydrogen bonds and red dashed lines represent steric interactions.*

#### *3.3.3 Hemolysin (PDB id: 2RK5)*

Hemolysins are exotoxins capable of promoting erythrocyte lysis. They are toxins produced by some species of streptococci [75] and contribute to the virulence process of **S. mutans** [76]. In **S. mutans***,* alpha- and gamma-hemolytic strains are described [77], as well as beta-hemolytic [78].

Docking with hemolysin PDB id: 3VMO identified as the best ligands the compounds: erycristagallin (10) (Edock = −112.64 kJ/mol), erystagallin (11) (Edock = −104.10 kJ/mol), and methoxyficifolinol (1) (Edock = −100.63 kJ/mol). The steric interactions and hydrogen bonds with Asn21, Asn24, and Asp30 residues were common for compounds 10 and 11, and seem to be important for the stabilization of the complexes. Compound 1, despite belonging to the same chemical class as compounds 10 and 11, showed a more stable conformation in another position of the active site, consequently, is stabilized by interactions with different amino acid residues, but which contributed less to the stabilization of the complex (**Figure 10**).


**About the Editors**

**Ermina Begovic** graduated from Zagreb University, where she also finished her MSc degree. She completed her PhD at University of Naples Federico II, where, at present, she holds the position of Associate Professor of Ship Seakeeping. Her main research interests are high speed and planing craft seakeeping, safety and comfort on board of high speed ships, experimental hydrodynamics, hydrodynamic loads acting on intact and damaged ships, and high speed hull form design and optimization. Ermina Begovic is the author of more than 90 papers published in international journals, conference proceedings, and book chapters, and a member of the ISSC Specialist Committee on Special Craft and of Stability Research and Development Committee.

**Simone Mancini**, since 2020, has been the numerical team leader at the Department of Hydro and Aerodynamics at the FORCE Technology, Denmark. Furthermore, he was, for 15 years, a technical officer of the Italian Navy and, after periods of experience on board, he served as a project manager at the Ship Design Office of the Italian Navy General Staff. In 2012, he completed his MSc in Naval Architect and Marine Engineering at the University of Naples "Federico II", where he also completed his PhD in Computational Fluid Dynamics, in 2016. He is the author of more than 40 papers published in international journals, conference proceedings, and book chapters. He has served as a reviewer for more than 30 journals, and he is a member of the editorial board of two international journals. Since 2015, he has been a Contract Professor at the University "Giustino Fortunato". He is a SNAME member.


*Edited by Takemi Otsuki*

The issue of asbestos exposure and the resulting health problems is now a global problem. The World Health Organization (WHO) has made recommendations on the eradication of asbestos-related diseases. However, malignant mesothelioma, mainly due to asbestos exposure, is a refractory malignant tumor, and technological innovation in diagnosis and treatment is required. In this context, this book describes the immunological effects of asbestos exposure, blood biomarkers, the pathology of malignant mesothelioma, and the status of immune checkpoint drugs in the treatment of malignant mesothelioma, along with the status of MMP mesothelioma. Concerns about health hazards associated with asbestos exposure may persist for many years to come. We hope this book will help researchers in this area.

ISBN 978-1-83880-832-7

Asbestos-related Diseases

Published in London, UK © 2020 IntechOpen © rep0rter / iStock


*Article* **Influence of Dysprosium Compounds on the Extraction Behavior of Dy from Nd-Dy-Fe-B Magnet Using Liquid Magnesium**

**Sun-Woo Nam 1,2, Sang-Min Park 1, Mohammad Zarar Rasheed 1, Myung-Suk Song 1, Do-Hyang Kim 2 and Taek-Soo Kim 1,\***

**Abstract:** During the liquid metal extraction reaction between a Nd-Dy-Fe-B magnet and liquid Mg, Nd rapidly diffuses out of the magnet, whereas Dy is not extracted due to the reaction with the matrix and the formation of Dy2Fe17 phase. In addition, theDy2O3 phase exists at the grain boundaries. Until now, only the effect of the Dy2O3 phase on the extraction of Dy has been reported. In this study, the effect of the Dy2Fe17 phase on the extraction of Dy from the Nd-Dy-Fe-B magnet was investigated in liquid Mg. The formation of the Dy2Fe17 phase during the reaction between Mg and matrix (RE2Fe14B) was first examined using a thermodynamical approach and confirmed by microstructural analysis. It was observed that Dy extraction was dominated by Dy2Fe17 phase decomposition from 3 h to 24 h, followed by Dy2O3 phase dominant reaction with Mg. Comparing the activities of the Dy2Fe17 phase and the Dy2O3 phase, the reaction of Dy2Fe17 is dominant, as compared to the Dy2O3 phase. Finally, at 48 h, the high Dy extraction percentage of 93% was achieved. As a result, in was concluded that the Dy2Fe17 phase acts as an obstacle in the extraction of Dy. In the future, if research to control the Dy2Fe17 phase proceeds, it will be of great importance to advance the recycling of Dy.

**Keywords:** recycling; pyrometallurgy; dysprosium; liquid metal extraction; phase transformation


Arbuscular Mycorrhizal Fungi in Agriculture - New Insights

*Edited by Rodrigo Nogueira de Sousa*

Published in London, United Kingdom

Arbuscular Mycorrhizal Fungi in Agriculture - New Insights http://dx.doi.org/10.5772/intechopen.104271 Edited by Rodrigo Nogueira de Sousa

#### Contributors

Addisu Fufa Ebbisa, YingWu Shi, Hongmei Yang, Ming Chu, XinXiang Niu, Huifang Bao, Ning Wang, Faqiang Zhan, Xuanqi Long, Rong Yang, Qing Lin, Kai Lou, Jamal Salih Alkobaisy, Muhammad Hanif, Zubairia Ashraf, Samar Bashir, Fatima Riaz, Rizwan Amanat, Samina Sarwar, Nousheen Yousaf, Malik A. Aziz, Shayesta Islam, Gousia Gani, Zaffar M. Dar, Amajad Masood, Syed H. Baligah, Stephen Larbi-Koranteng, Frederick Kankam, Joseph Adomako, Muntala Abdulai, Joanna F. Dames, Sílvia Natal David Maússe Sitoe, Yangrong Cao, Jiashan Wu, Weiyun Wang, Hui Zhu, Allal Douira, Amina Ouazzani Touhami, Zineb Sellal, Jamila Dahmani, Soukaina Maazouzi, Najoua Mouden, Mohamed Chliyeh, Karima Selmaoui, Rachid Benkirane, Cherkaoui El Modafar, Julio Alves Cardoso Filho, Nidhi Verma, Yeshveer Singh, Tanvi Singh, Anupam Patra, Sarah Otun, Ikechukwu Achilonu, Samah Ourras, Ismail El Aymani, Soumaya El Gabardi, Manal Adnani, Mariam Artib, Rodrigo Nogueira de Sousa

#### © The Editor(s) and the Author(s) 2023

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First published in London, United Kingdom, 2023 by IntechOpen IntechOpen is the global imprint of INTECHOPEN LIMITED, registered in England and Wales, registration number: 11086078, 5 Princes Gate Court, London, SW7 2QJ, United Kingdom

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**Abstract**

There is a broad range of alternatives in terms of cerebral palsy treatment (intrathecal baclofen (ITB), selective dorsal rhizotomy (SDR), and deep brain stimulation (DBS)). In order to reduce dystonia and spasticity, ITB bump insertion, SDR, and DBS are the main neurosurgical treatment approaches. In ITB treatment, a baclofen pump is implanted in the abdomen and is connected to spine via a thin tube. The pump is refilled regularly. It may require a replacement surgery. SDR includes cut of sensory nerves in affected site. Globus pallidus is the target in DBS surgery, the main advantage of this technique is that, it is reversible and adjustable. In this chapter, neurosurgical treatment alternatives for cerebral palsy will be discussed.

**Keywords:** cerebral palsy, deep brain stimulation, dystonia, intrathecal baclofen pump, selective dorsal rhizotomy, spasticity