3.4.4. Changes in Endogenous GA3

As indicated by Figure 3D, for the treatment groups and the control group, the contents of GA3 exhibited a continuous decrease over time. During the initiation period, the GA3 content of ABT-1—1000 mg·L−1 reached the highest level, namely 7.0 ng·g−1. After callus formation, the GA3 content of both treatment and control groups decreased, with that of treatment groups decreasing obviously, possibly due to the high demand for GA3 in the growth of root primordia [24]. During the adventitious root formation and elongation period, the GA3 contents of treatment groups were again observed as decreasing, but there was no obvious change in the GA3 content of the control group. At this point, adventitious roots were formed. As the root tip emerged from the skin pore, the GA3 content gradually decreased, probably because high concentrations of GA3 inhibited its elongation growth.


**8. Effect of cornual ectopic treatment on future fertility**

With regard future fertility, cornual ectopic is associated with higher risk of recurrent ectopic compared with other types of ectopic pregnancy. If the uterus is conserved, there is an increased incidence of uterine rupture at the surgical site in future pregnancies in the 2nd and 3rd trimesters especially in the cases where the sac excision leads to defective myometrium &/or the uterine cavity has been opened. However, the data about the absolute increase in such risk is still conflicting. (Lau S& Tulandi T, 1999; Weissman A& Fishman A, 1992)

There is a view about reinforcing the uterine wall with the use of round ligament especially if the cavity is opened. Again, no evidence yet has shown a proven benefit of such techniques.( Chatterjee J et al, 2009)

With regard the mood of delivery in subsequent pregnancy, caesarean section is recommended by many clinicians however, no evidence yet available to evaluate the safety of caesarean section versus vaginal delivery after cornual ectopic treatment. (Downey GP& Tuck S, 1994)


**Heterotopic Gastric Mucosal Patch of the Proximal Esophagus**

Vui Heng Chong

*Division of Gastroenterology and Hepatology, Department of Medicine, Raja Isteri Pengiran Anak Saleha (RIPAS) Hospital, Bandar Seri Begawan BA 1710, Brunei Darussalam*


**2.1 Changes in fluid properties**

The introduction of a secondary liquid phase into a traditional gas–liquid reactor can result in a change of fluid properties. For example, the surface tension between the primary liquid phase and the gas phase can change depending on the degree of solubility of the secondary liquid phase in the primary liquid phase. To characterise this, surface tension values based on mutually saturated liquids have been reported in literature [16, 18]. Such values have generally been obtained by mixing the primary liquid phase with the additional liquid phases for long periods followed by a separation of the phases and surface tension measurements using a tensiometer [18]. **Table 2** illustrates selected results from literature.

The results in **Table 2** generally point to a decrease in the saturated surface tension (*σsat*) with addition of the secondary liquid phase. Furthermore, this change in surface tension has been observed to be greater when the secondary liquid phase is more soluble in the primary liquid phase [18]. As a decrease in the surface tension results in smaller gas bubbles, an enhancement in mass transfer can be expected. However, this is not always the case. For example, Kundu et al. [18] observed a negative impact on mass transfer upon the addition of toluene, anisole, decyl alcohol and 2-ethyl-1-hexanol despite a decrease in *σsat* (refer to **Table 2**). This points to the presence of additional factors that need to be taken into account for a proper description of mass transfer in such systems.

Two additional points need to be highlighted with regard to surface tension. First, discrepancies in the reported values of *σsat* exist as seen in **Table 2**. For example, Ngo & Schumpe [16] measured an insignificant change in *σsat* (71.8 mN/m) upon the addition of n-Heptane whereas Kundu et al. [18] measured a significant change (*σsat* = 65 mN/m). Such discrepancies point to a need for additional experimental measurements of *σsat*.

#### **Table 2.**

*Saturated surface tension for various liquid–liquid combinations.*

A second point to note is that researchers have also attempted to report on dynamic values of the surface tension [21]. This was achieved by preparation of a "stable" liquid–liquid dispersion followed by measurement of the surface tension [21]. The values obtained were in the range of 17–26 mN/m for an n-C10-C13 alkane cut mixed with water [21]. These values were lower than those reported in **Table 2** and tended towards the surface tension values of pure alkanes (23.9 mN/m for n-Decane, 25.41 mN/m for n-Dodecane [18]). Consequently, it may be suggested that a degree of separation of the liquid–liquid dispersion occurred during surface tension measurements despite the best efforts of the researchers. In this case, the settled-out and less dense alkane phase would form an intervening layer between the gas phase and the dispersion.

Besides the above changes to the surface tension, the presence of a secondary liquid phase can lead to changes in *effective* fluid properties such as the effective density, the effective viscosity as well as the effective solubility. The pre-qualifying term, "*effective*", is used in this case since such properties are defined based on a view of the liquid–liquid dispersion as a single pseudo-homogenous liquid. This view permits for a simplification of the 3-phase gas–liquid–liquid reactor to an effective 2-phase reactor. Consequently, correlations derived for traditional 2-phase reactors, such as Eq. (1) with an empirical basis and Eq. (2) with a theoretical basis [5], can be used as a starting point for the design of 3-phase reactors. In these equations, *KLa* represents the overall volumetric mass transfer coefficients in a 2-phase reactor whereas *xg* and *dg* respectively represent the gas hold up and the bubble diameters (see Eq. (3) and (4)). Other variables are as defined in the Nomenclature.

$$K\_L \mathfrak{a} = \Gamma \cdot \left(\frac{P\_{\mathfrak{g}}}{V}\right)^{\times} \nu\_{\mathfrak{s}}^{\mathfrak{y}} \tag{1}$$

$$K\_L a = \Lambda \cdot \sqrt{D\_c} \left(\frac{P\_\text{g}/V}{\mu\_c}\right)^{0.25} \cdot \left(\frac{6\text{x}\_\text{g}}{d\_\text{g}}\right) \tag{2}$$

*The Use of Computational Fluid Dynamics in the Analysis of Gas-Liquid-Liquid Reactors DOI: http://dx.doi.org/10.5772/intechopen.99157*

$$\mathbf{d}\_{\mathbf{g}} = \mathbf{0}.7 \left( \frac{\sigma\_{\rm gc}^{0.6}}{\rho\_{\rm c}^{0.2} \left( \frac{\mathbf{p}\_{\rm c}}{\nabla} \right)^{0.4}} \right) \left( \frac{\mu\_{\rm c}}{\mu\_{\rm g}} \right)^{0.1} \tag{3}$$

$$\frac{\varkappa\_{\rm g}}{1-\varkappa\_{\rm g}} = 0.819 \frac{\nu\_{\rm s}^{0.67} N^{0.4} T^{0.267}}{\mathcal{g}^{0.33}} \left(\frac{\rho\_{\rm c}}{\sigma}\right)^{0.2} \left(\frac{\rho\_{\rm c}}{\rho\_{\rm c} - \rho\_{\rm g}}\right) \left(\frac{\rho\_{\rm c}}{\rho\_{\rm g}}\right)^{-0.067} \tag{4}$$

Effective fluid properties can be specified on the basis of simple mixing rules such as Eq. (5) for the effective density (*ρeff* ). Such equations consider the contributions of the individual liquid phases based on their volumetric proportions (*xi*) but neglect non-ideal effects that may arise from molecular interactions, commonly found in homogeneous mixtures, which may lead to excess volumes. This notwithstanding, experimental evidence suggests that such equations are sufficient for the effective density [22] and the effective solubility (see next section). This may be due to the poor mutual solubility of liquid phases typically tested for gas–liquid– liquid reactor applications: the properties of the individual liquid phases remain unchanged by the presence of a second, immiscible phase and the bulk properties can be estimated by simple volume averaging.

$$\rho\_{\rm eff} = \sum\_{i=1}^{n} \mathbf{x}\_i \rho\_i \tag{5}$$

With regard to effective viscosity (*μeff* ), models of a more complicated form than that given in Eq. (5) are required. This is due to the need to account for the perturbation of fluid flow in the presence of droplets of the secondary liquid phase. Models varying in complexity have been proposed. For example, the Taylor model (see Eq. (6)) has been proposed for dilute dispersions of spherical droplets [23]. Additionally, models such as those given in Eq. (7) and (8) have been proposed for non-dilute dispersions where the hydrodynamic interactions among droplets need to be accounted for [23, 24]. Besides these, however, models have also been proposed to account for non-zero shear rates in the fluid which introduce effects such as droplet deformation [23, 25].

$$\frac{\mu\_{\rm eff}}{\mu\_{c}} = 1 + 2.5 \mathbf{x}\_{d} \left[ \frac{\mathbf{0}.4 + \mu\_{r}}{\mathbf{1} + \mu\_{r}} \right]; \mathbf{x}\_{d} \to \mathbf{0} \tag{6}$$

$$\frac{\mu\_{\rm eff}}{\mu\_{\rm c}} \left[ \frac{2 \frac{\mu\_{\rm eff}}{\mu\_{\rm c}} + 5 \mu\_{\rm r}}{2 + 5 \mu\_{\rm r}} \right]^{1.5} = \exp \left( \frac{2.5 \varkappa\_{\rm d}}{1 - \varkappa\_{\rm d}/\varkappa\_{\rm m}} \right) \tag{7}$$

$$\frac{\mu\_{\rm eff}}{\mu\_c} \left[ \frac{2 \frac{\mu\_{\rm eff}}{\mu\_c} + 5 \mu\_r}{2 + 5 \mu\_r} \right]^{1.5} = \left( 1 - \frac{\varkappa\_d}{\varkappa\_m} \right)^{-2.5 \varkappa\_m} \tag{8}$$

In the equations above, *μr* refers to the ratio of viscosity of the secondary liquid phase (*μd*) to that of the primary liquid phase (*μc*). Variables *xd* and *xm*, on the other hand, refer to the volume fraction of the secondary liquid phase and the maximum packing limit of its droplets respectively.

#### **2.2 Changes in mass transfer properties and pathways**

Properties of interest affecting mass transfer include the solubility and diffusivity of a species within the liquid–liquid dispersion. Taking the liquid–liquid dispersion as a pseudo-homogenous liquid, effective properties have been defined. For example, it has been observed that the effective solubility of a species (*C*∗ *eff* ) can be specified according to the volumetric proportions of the liquids involved, once again recognising that the presence of immiscible phases has a negligible effect on the solubilities associated with individual phases [22, 26]. This is illustrated in Eq. (9) below, with *C*∗ *i* representing the solubility in liquid phase *i* and *n* representing the total number of liquid phases.

$$\mathbf{C}\_{\mathbf{eff}}^{\*} = \sum\_{i=1}^{n} \mathbf{x}\_{i} \mathbf{C}\_{i}^{\*} \tag{9}$$

Given the application fields of gas–liquid–liquid reactors (such as biological gas stripping), the effective solubility is usually higher than the solubility of a species in the primary liquid phase (*C*∗ *eff* >*C*∗ *c* ). This implies that a longer duration is required to saturate the liquid–liquid dispersion with a given species in comparison to the time required to saturate the pure primary liquid phase [27, 28]. Consequently, for a fixed mass transfer rate, lower values of the overall volumetric mass transfer coefficients can be obtained upon the addition of a secondary liquid phase [28, 29].

With regard to the molecular diffusion of a species, the effective diffusivity *Deff* � � has been defined as illustrated in Eq. (10) for dilute liquid–liquid dispersions [30]. This equation, being of a general nature, has also been used to describe the effective diffusivity of solid–liquid suspensions [31]. The variable *Dr* in this equation represents the diffusivity ratio whereas *Dd* and *Dc* represent the diffusivity of the species in the secondary and primary liquid phases respectively.

$$D\_{\rm eff} = D\_c \left[ \frac{D\_r(1 + 2\varkappa\_d) + 2(1 - \varkappa\_d)}{D\_r(1 - \varkappa\_d) + (2 + \varkappa\_d)} \right]; D\_r = \frac{D\_d}{D\_c} \tag{10}$$

It should be noted that the effective properties defined by Eqs. (9) and (10) can be, in a general sense, regarded as bulk properties. Appropriate as it may be to define them, a full description of mass transfer in a liquid–liquid dispersion also requires an examination of changes introduced by the secondary liquid phase at the mass-transfer interface. Tied to this are the questions whether the secondary liquid phase will be present at the interface and whether it is involved in active uptake of dissolving species at the interface. Indeed, the concept of a pseudo-homogenous liquid implies that the secondary liquid phase will be present, not only in the bulk of the primary liquid, but also at the interface. Furthermore, homogeneity implies that a similar distribution of the secondary liquid phase is found at the interface as is found in the bulk of the primary liquid phase. However, researchers have considered different possible configurations of the interface as illustrated in **Figure 2**. In this way, the requirement for homogeneity has been relaxed at the interface while being maintained in the bulk.

Assuming active uptake by the secondary liquid phase, different possible pathways of mass transfer have arisen. These have included, for example, parallel mass transfer and series mass transfer with or without the shuttling of the droplets of the secondary liquid phase [14, 30, 32, 33]. These various pathways have been associated with an enhancement in mass transfer besides that occurring due to changes discussed earlier

*The Use of Computational Fluid Dynamics in the Analysis of Gas-Liquid-Liquid Reactors DOI: http://dx.doi.org/10.5772/intechopen.99157*

**Figure 2.**

*Mass transfer interface illustrating possible configurations – (A) parallel mass transfer, (B) series mass transfer without shuttling and (C) series mass transfer with shuttling.*

(refer to Eq. (9) and (10)). A review of these pathways as well as their associated enhancement factors can be found in the work by Dumont & Delmas [19].

#### **2.3 Changes in reactor hydrodynamics**

The hydrodynamics of a reactor are taken to refer to the mean velocity field and the turbulence field within a reactor. These affect mass transfer in various ways. For example, the transport of gas bubbles within a reactor (and hence overall gas hold up) is dependent on the magnitude and orientation of the mean velocity field. Additionally, mass transfer at the interface between the gas bubbles and the liquid phase will depend on the prevailing local turbulence. Consequently, a change in the reactor hydrodynamics will lead to a change in the mass transfer.

The addition of a secondary liquid phase has been observed to change both the mean velocity field and the turbulence levels in stirred tank reactors [34–36]. Direct measurements of the velocity through techniques such as Particle Image Velocimetry [34, 36] and Laser Doppler Anemometry [35] have revealed that a secondary liquid phase can dampen the mean velocities [34, 35] while either increasing or decreasing the turbulence levels [34–36]. Further evidence in literature for a change in the hydrodynamics has been largely indirect – inferred from examining the change in, for example, mixing time upon addition of a secondary liquid phase [37, 38].

Two schools of thought have been postulated to explain the interaction of the secondary liquid phase with the hydrodynamics of a reactor. The first has been focused on the change in effective fluid properties. In this line of thinking, it has been suggested that a decrease in the effective density should lead to an increase in the velocities [34]. On the other hand, it has been suggested that an increase in the effective viscosity should have a dampening effect on both the mean velocities and the turbulence levels [34, 35].

The second school of thought, on the other hand, has been focused on the augmentation or dampening of turbulence by the droplets of the secondary liquid phase [34, 35]. Various mechanisms have been suggested in this regard although these are still the subject of active research [39–42]. For example, whether a particle (solid, liquid or gas) augments or dampens turbulence has been traditionally associated with the size of the particle (*dp*) in relation to the integral (or large) scales of turbulence (*l*) [43]. Large particles *dp=l*>0*:*1 with characteristically large particle's Reynolds numbers *Re p* >400 have generally been associated with turbulence augmentation through mechanisms such as vortex shedding in the wakes behind the particles [43, 44]. On the other hand, small particles *dp=l* <0*:*1 have generally been associated with a dampening of turbulent kinetic energy (TKE) that occurs as the particles are accelerated/dragged by the flow [39, 43].

It should be noted, however, that the above observations do not represent a fixed rule; exceptions have been observed. For example, it has been observed that particles of a size *dp=l*<0*:*1 can both augment and dampen turbulence depending on the flow's Reynolds number [45]. Furthermore, it has been observed that turbulence can be augmented by particles of a size in the order of the Kolmogorov (smallest) scales of turbulence [46–50]. In the latter cases, however, the nonuniform modification of the spectrum of TKE by particles was considered with the augmentation of TKE observed to occur at the small scales of turbulence [46–50]. Mechanisms that were proposed for the turbulence augmentation included flow forcing due to the inertia [50] or buoyancy [49] of small particles that were well correlated with the fluctuating fluid flow.

## **3. Computational fluid dynamics for gas–liquid–liquid reactors**

As illustrated in the previous sections, the introduction of a secondary liquid phase into a traditional gas–liquid reactor can result in a variety of changes. Consequently, the modelling of mass transfer in gas–liquid–liquid reactors requires a comprehensive modelling framework that can account for the different changes. An empirical approach, such as illustrated in Eq. (11) [51], may not suffice as the numerous effects introduced by the secondary liquid phase are reduced into a single term with an adjustable exponent requiring optimisation (compare to Eq. (1)). This is not an easily generalizable approach.

$$K\_L a' = \Gamma \cdot \left(\frac{P\_\mathcal{g}}{V}\right)^\times \nu\_i^\prime \left(1 - \varkappa\_d\right)^\times \tag{11}$$

Computational Fluid Dynamics (CFD), on the other hand, offers a fundamental framework that can be built upon to incorporate as much level of detail (or physics) as necessary/desired. This is the case since CFD offers an approximate/numerical solution to the fundamental equations of flow governing a system/reactor [7, 52]. Consequently, the hydrodynamics of a reactor are resolved in space and time and such hydrodynamic data can be coupled to fundamental models of mass transfer so as to predict parameters of interest such as the overall volumetric mass transfer coefficient, *KLa*0 .

As CFD-based approaches are inherently computationally intensive, a major constraint to such approaches is the computational resources available [7, 52]. Thus, a compromise has to be made between the level of physics to be captured and computational resources available [7]. For example, prior consideration must be given as to the level of resolution to be employed for the flow field. Similarly, an appropriate continuum description of the phases involved must be chosen *a priori*. Such compromises notwithstanding, a higher level of detail, accuracy and generality is still maintained with a CFD-based approach in comparison to empirical approaches. This will be illustrated in Section 4.

#### **3.1 Modelling frameworks for hydrodynamics**

As noted in the introduction to CFD above, several prior considerations have to be made with regard to the modelling approach. These considerations tend to give rise to different modelling frameworks or techniques. For example, as relates to the

#### *The Use of Computational Fluid Dynamics in the Analysis of Gas-Liquid-Liquid Reactors DOI: http://dx.doi.org/10.5772/intechopen.99157*

resolution of flow fields, it is possible to simulate the usually turbulent flow in a reactor at all length- and time-scales using Direct Numerical Simulations but the associated computational expense is prohibitive [53]. Therefore, filtering or averaging of the flow field is usually done [53]. Filtering techniques such as those used in Large Eddy Simulations offer an enhanced resolution of the flow field as compared to averaging techniques [53]. However, they are still considered computationally expensive and their use has been largely limited to single-phase reactors [53]. Averaging techniques such as those used to generate the Reynolds-Averaged Navier–Stokes (RANS) equations, on the other hand, lead to tractable simulations [53] and are a practical choice for the modelling of multiphase reactors.

With regard to the description of the phases, an Eulerian framework is typically used for the continuous phase (primary liquid phase) and it involves describing the flow based on a fixed observer position [54]. For the dispersed phases (gas and secondary liquid phase), on the other hand, either an Eulerian framework or a Lagrangian framework can be employed [54]. The Lagrangian framework involves the tracking of individual particles of the dispersed phase within the flow field of the continuous phase [54]. The particles either follow the flow field without interaction (one-way coupling) or interact with it thus modifying it (two-way coupling) [55].

The Lagrangian framework provides a greater degree of detail and as can be expected, it is costly to implement [54]. Consequently, an Eulerian framework for both the continuous and dispersed phases tends to provide a practical choice for modelling. An Eulerian description of both phases assumes that the phases involved can be treated as a continuum [56]. In this case, a phase indicator function is introduced into the governing equations of flow to account for the possible realisation of a phase *i* at a given position and a given time [56]. Averaging of the governing equations after decomposing the instantaneous flow field into its mean and fluctuating components results in Eq. (12) and (13) [57].

$$\frac{D}{\mathbf{Dt}}(a\_i \rho\_i) = \mathbf{0} \tag{12}$$

$$\frac{D}{Dt}(a\_i\rho\_i\overline{V\_i}) = -a\_i\nabla p + \nabla \cdot \overline{\overline{\tau}}\_i + a\_i\rho\_i\overline{\underline{g}} + \sum\_{j=1}^n \overline{R}\_{ji} \tag{13}$$

In Eqs. (12) and (13), subscripts *i* and *j* represent individual phases whereas *α* represents the respective phase volume fraction. This volume fraction is based on the total volume of all phases as opposed to the total liquid volume (see variable *x* in earlier equations). The respective phases can be either the gas phase, the primary liquid phase or the secondary liquid phase. Alternatively, if the liquid–liquid dispersion is treated as a pseudo-homogenous liquid, then the subscripts would refer to either the gas phase or the pseudo-homogenous liquid.

Other variables in Eqs. (12) and (13) such as *ρ* and *V* represent the density and the mean velocity of the respective phases whereas *p* represents the shared pressure field. Additionally, *g* represents the gravitational acceleration while *R* represents the interphase momentum exchange terms. These terms include the lift force, the drag force, the turbulent dispersion force and the added mass force among others [56].

There are two key points to note regarding the interphase momentum exchange terms. First, their significance varies with the set up being considered. For example, the drag force has been observed to be the most significant interphase exchange term in the bulk of a stirred tank reactor [58, 59]. On the other hand, terms such as the lift force have been observed to significantly affect the flow in a bubble column reactor [60]. Consequently, modelling can be simplified by only accounting for significant terms.

The second point to note touches on the various models that have been proposed to specify the interphase momentum exchange terms. Such models chiefly consider 2-phase interactions, that is, gas–liquid or liquid–liquid interactions [61–63]. Though such models have been improved upon to consider effects such as particleparticle interaction at high volume fractions of a single dispersed phase [61–63], there is still a need for appropriate models that specify the interphase exchange terms when more than two phases are present. To this end, work such as that by Baltussen et al. [64, 65] investigating the effective gas–liquid drag in the presence of an additional solid phase may provide direction.

Finally, *τ* in Eq. (13) represents the stress tensor accounting for both viscous and turbulent (Reynolds') stresses. The specification of the stress tensor is non-trivial due to the Reynolds'stresses that need to be solved directly or modelled. A direct solution of the Reynolds'stresses provides a greater amount of detail and is able to resolve complex features of the turbulence field such as anisotropy [53, 54]. However, the computational costs associated with this approach as well as reported solution difficulties favour the specification of the stress tensor using alternative simplified approaches [53, 54]. Modelling of the stress tensor using the Boussinesq hypothesis is one such alternative approach [53, 54]. In the latter, the Reynolds'stresses are related to the gradients of the mean velocity with the eddy/turbulent viscosity (*μt*) arising as a proportionality constant (see Eq. (14)) [57].

$$\bar{\bar{\tau}}\_{i} = a\_{i} (\mu\_{i} + \mu\_{t,i}) \left( \nabla \overline{V}\_{i} + \nabla \overline{V}\_{i}^{T} \right) - \frac{2}{3} a\_{i} ( (\mu\_{i} + \mu\_{t,i}) \nabla \cdot \overline{V}\_{i} + \rho\_{i} k\_{i} ) \bar{\bar{I}} \tag{14}$$

$$
\mu\_{t,i} = \mathcal{C}\_{\mu} \rho\_i \frac{k\_i^2}{c\_i} \tag{15}
$$

The eddy viscosity can be modelled in various ways such as that illustrated in Eq. (15) [53, 57]. In this case, it is related to the turbulent kinetic energy (*k*) and its dissipation rate (*ϵ*), both of which need to be solved for. Various models have been proposed for these latter parameters (*k* and *ϵ*) such as the dispersed *k*–*ϵ* turbulence model, the mixture *k*–*ϵ* turbulence model and the per-phase *k*–*ϵ* turbulence model [57]. These models represent an extension of the single phase *k*–*ϵ* turbulence model to multiphase situations and their applicability depends on the expected/prevailing type of flow [57]. For example, the mixture *k*–*ϵ* model is recommended for stratified flow whereas the dispersed *k*–*ϵ* model is recommended where there is one clear continuous phase and the other phases are dispersed within it [57].

It should be noted that irrespective of the choice of turbulence model, one key point to consider is the interaction of the dispersed phases with the prevailing turbulence. As noted in Section 2.3, the dispersed phase can modify the prevailing turbulence and this needs to be captured. To this end, various models have been proposed in literature and these vary in complexity depending on the level of detail captured. Various authors have recently examined the sufficiency of these models and their work is recommended [66–71].

#### **3.2 Modelling frameworks for mass transfer**

Similar to the modelling of hydrodynamics, the modelling of mass transfer involves the selection of appropriate frameworks prior to the actual simulation. The choice of a particular framework involves a compromise between the level of detail captured and computational cost involved. Furthermore, the choice of a modelling framework for mass transfer tends to be influenced by choices made during the

*The Use of Computational Fluid Dynamics in the Analysis of Gas-Liquid-Liquid Reactors DOI: http://dx.doi.org/10.5772/intechopen.99157*

modelling of the hydrodynamics. For example, the use of an Eulerian description of the phases together with averaged equations of flow offers a practical choice for the modelling of a reactor hydrodynamics. However, this approach involves a loss of specifics on the mass transfer interface thus necessitating the prescription of models to approximate the expected mass transfer behaviour. On the other hand, a Lagrangian tracking of particles coupled with interface tracking algorithms better resolves the mass transfer interface but is unfeasible for reactors with a large number of dispersed particles (droplets or bubbles). Consequently, the discussion below focuses on the modelling of mass transfer within the context of an Eulerian description of the phases and the modelling of hydrodynamics using averaged equations of flow.

Mass transfer in a gas–liquid–liquid reactor can be solved for by tracking the concentration of a species in each phase within the reactor. This results in three equations as given in Eqs. (16)–(18). In these equations, *C* represents the concentration of the species in the respective phase (subscripts *g*, *c*, *d*) whereas *J* and *S* represent the diffusive flux and the interphase mass transfer source terms respectively. Closure models are required for the interphase mass transfer source terms depending on the expected mass transfer behaviour.

$$\frac{D}{Dt} \left( a\_{\text{g}} \mathbf{C}\_{\text{g}} \right) = -\nabla \cdot \left( a\_{\text{g}} \mathbf{\tilde{J}}\_{\text{g}} \right) - \mathbf{S}\_{\text{gc}} - \mathbf{S}\_{\text{g}d} \tag{16}$$

$$\frac{D}{Dt}(a\_c \mathbf{C}\_c) = -\nabla \cdot \left(a\_c \mathbf{\tilde{J}}\_c\right) + \mathbf{S}\_{\rm gc} - \mathbf{S}\_{cd} \tag{17}$$

$$\frac{D}{Dt}(a\_d \mathbf{C}\_d) = -\nabla \cdot \left(a\_d \overline{f}\_d\right) + \mathbf{S}\_{\rm gd} + \mathbf{S}\_{\rm cd} \tag{18}$$

In the most general case, mass transfer can be assumed to occur between the gas phase and both the primary and the secondary liquid phases (S*gc* 6¼ 0, S*gd* 6¼ 0). This would correspond to the case of parallel mass transfer as illustrated in **Figure 2(A)**. Evidence for this mass transfer pathway has been provided based on observations of the formation by oil films on gas bubbles during "static" experiments [72]. The question arises, however, as to whether sufficient time for film formation occurs in a dynamic/agitated reactor [72, 73].

Series mass transfer is often taken to be the more probable mass transfer pathway in an agitated reactor ðcases (B) or (C) in **Figure 2** with S*gd* ¼ 0) [34]. In addition, the formation of small droplets of the secondary liquid phase with a large interfacial area implies that mass transfer between the respective liquid phases is usually faster than that occurring between the gas phase and the primary liquid phase [72]. Thus, based on the timescale of mass transfer between the gas phase and the primary liquid phase, it may be assumed that equilibrium conditions exist between the respective liquid phases. Furthermore, one need only track the concentration of the species in two phases – the gas phase (Eq. (16)) and the primary liquid phase (Eq. (17) with S*cd* ¼ 0). With these simplifying assumptions, the only unknown left is the interphase source term between the gas phase and the primary liquid phase (S*gc*). Gakingo et al. [13, 28] proposed definitions for S*gc* that account for possible mass transfer pathways at the gas–liquid interface plus an apparent decrease in the overall volumetric mass transfer coefficients that occurs due to a larger total solubility of the species in the liquid–liquid dispersion (refer to Section 2.2). This illustrated in Eqs. (19)–(21) below [13, 28].

$$\mathbf{S}\_{\mathfrak{g}^c} = K\_L a' \left( \mathbf{C}\_c^\* - \mathbf{C}\_c \right) \tag{19}$$

$$K\_{L}a' \approx E' \cdot \Lambda \cdot \sqrt{D\_{\epsilon}} \left(\frac{\rho\_{\epsilon}\varepsilon\_{\epsilon}}{\mu\_{\epsilon}}\right)^{0.25} \cdot \left(\frac{6a\_{\text{g}}}{d\_{\text{g}}}\right) \tag{20}$$

$$E' = \frac{1}{(1 - a\_d + a\_d m)^{\phi}} \tag{21}$$

In the equations above, *KLa*0 represents the overall volumetric mass transfer coefficient in the presence of the secondary liquid phase. The latter has been expanded in Eq. (20) based on an eddy cell model [74] with Λ and *E*0 representing a constant and the enhancement factor respectively. The enhancement factor is given in Eq. (21) where *m* represents the solubility ratio (*m* ¼ *C*∗ *d =C*∗ *c* ) and the exponent *φ* varies between 0.5 and 1. A value of *φ* ¼ 1 represents the series mass transfer pathway without shuttling whereas a value of *φ* ¼ 0*:*5 represents the series mass transfer pathway with shuttling [13, 28]. Other variables are as previously defined.

#### **4. Case studies**

Few studies have reported on the CFD-based modelling of gas–liquid–liquid reactors. Two such studies are reviewed in this section with the one having focussed on the modelling of mixing time [12] while the other focussed on the modelling of mass transfer [13].

In the case studies of interest, stirred tank reactors were considered as illustrated in **Table 3**. In addition, the modelling in both cases was done based on an Eulerian description of the phases involved (refer to Eqs. (12) and (13)). There were certain similarities in the modelling and these included, for example, a consideration of drag force as the only interphase momentum exchange term. The latter was specified according to Eq. (22) [13] with different models for the drag coefficient (*CD*) employed as illustrated in **Table 4**. Constant sizes were also assumed for both the gas phase bubbles and the droplets of the secondary liquid phase and these were predicted by Eqs. (3) and (23) respectively [12, 13]. Finally, turbulence was modelled based on the dispersed *k*–*ϵ* model (see Eqs. (24) and (25)) though the use of the Reynolds stress model was additionally considered in one study [12].

#### **Table 3.** *Details of experimental setups used in the studies of Cheng et al. [12] and Gakingo et al. [13].*

*The Use of Computational Fluid Dynamics in the Analysis of Gas-Liquid-Liquid Reactors DOI: http://dx.doi.org/10.5772/intechopen.99157*

**Table 4.**

*Sub-models used in the modelling work of Cheng et al. [12] and Gakingo et al. [13].*

$$\overline{R}\_{\vec{\mu}} = -\overline{R}\_{\vec{\mu}} = K\_{\vec{\mu}} (\overline{\nabla\_j} - \overline{\nabla\_i});\\ K\_{\vec{\mu}} = \frac{3}{4} a\_i a\_j \rho\_i \frac{C\_D}{d\_j} \left| \overline{\nabla\_j} - \overline{\nabla\_i} \right| \tag{22}$$

$$\frac{d\_d}{T} = \boldsymbol{\Omega} \cdot (\boldsymbol{\Omega} + \boldsymbol{\chi} \cdot \boldsymbol{\alpha}\_{\text{o,ave}}) \left(\frac{\sigma\_{cd}}{\rho\_c \boldsymbol{N}^2 T^3}\right)^{0.6} \tag{23}$$

$$\frac{D}{Dt}(a\_{\varepsilon}\rho\_{\varepsilon}k\_{\varepsilon}) = \nabla \cdot \left(a\_{\varepsilon}\left(\mu\_{\varepsilon} + \frac{\mu\_{t\varepsilon}}{\sigma\_{k}}\right)\nabla k\_{\varepsilon}\right) + a\_{\varepsilon}\mathbf{G}\_{k\varepsilon} - a\_{\varepsilon}\rho\_{\varepsilon}\varepsilon\_{\varepsilon} + a\_{\varepsilon}\rho\_{\varepsilon}\Pi\_{k\_{\varepsilon}} \tag{24}$$

$$\frac{D}{Dt}(a\_{\varepsilon}\rho\_{\varepsilon}\mathbf{c}\_{\varepsilon}) = \nabla \cdot \left(a\_{\varepsilon}\left(\mu\_{\varepsilon} + \frac{\mu\_{t\varepsilon}}{\sigma\_{\varepsilon}}\right)\nabla\epsilon\_{\varepsilon}\right) + a\_{\varepsilon}\frac{\varepsilon\_{\varepsilon}}{k\_{\varepsilon}}(\mathbf{C}\_{1\varepsilon}\mathbf{G}\_{k\cdot\varepsilon} - \mathbf{C}\_{2\varepsilon}\rho\_{\varepsilon}\mathbf{c}\_{\varepsilon}) + a\_{\mathbb{C}}\rho\_{\varepsilon}\Pi\_{\mathbf{c}\_{\varepsilon}} \tag{25}$$

The above similarities, notwithstanding, there were some notable differences between the two studies. For example, Gakingo et al. [13] tested two approaches in the modelling of the liquid–liquid dispersion. The first approach involved the treatment of the dispersion as a pseudo-homogenous liquid (herein referred to as the P-HOM approach) and the mixture properties were obtained from Eq. (5) for effective density or through experimental measurements for effective viscosity. The second approach, also used by Cheng et al. [12], involved a consideration of the heterogeneous nature of the dispersion and a modelling of each individual liquid phase (herein referred to as the HET approach). In this second approach, there was a need to specify models for turbulence modulation by the dispersed phases (Π*kc* in Eq. (24)) and different models were employed as illustrated in **Table 4**. Last but not least, mass transfer was considered in only one study [13] where the authors employed the previously reviewed frameworks, that is, Eqs. (16) and (17) with S*gd* ¼ 0, S*cd* ¼ 0 and Eqs. (19)–(21). Further specifics on the implementation of the modelling approaches (meshing, boundary conditions and solver settings) can be found in the respective studies [12, 13].

Several key observations can be made from the reported modelling works [12, 13]. First, it was observed that the hydrodynamics of a gas–liquid–liquid stirred tank reactor were better captured based on the HET modelling approach as opposed to the P-HOM modelling approach [13]. The failure by the latter approach was attributed to the fact that the hydrodynamics in a turbulent stirred tank reactor are dominated by turbulence as opposed to the effective (mixture) viscosity. To this end, it was reported that the ratio of turbulence viscosity to mixture viscosity was in the order of *O*ð10 � 100) based on the P-HOM modelling approach [13]. Consequently, a minimal change in the hydrodynamics was observed despite an almost 2-fold increase in the values of mixture viscosity [13]. This is illustrated through the minimal change in gas hold up trends shown in **Figure 3**.

As pertains to the HET modelling approach, the better performance at capturing the hydrodynamics and hence gas hold up trends (see **Figure 3**) was attributed to the capture of turbulence modulation by droplets of the secondary liquid phase [13]. In particular, it was reported that there was an increase in the turbulence viscosity which served to dampen the mean velocity field (see **Figure 4**) thus reducing the effective drag and dispersion experienced by the gas bubbles [13]. Cheng et al. [12], on the other hand, did not make an explicit mention of changes in the turbulence viscosity. Rather, they hypothesised that their mixing time was reduced at low volume fractions of the secondary liquid phase due to an increase in turbulence caused by the droplets of the latter [12]. Furthermore, they hypothesised that an increase in mixing time at high volume fractions of the secondary liquid phase was due to a dampening of the turbulence arising from an increase in the effective viscosity [12]. This reference to notable changes due to the effective viscosity stands in contrast with the findings on the P-HOM modelling approach [13].

A recent study on a gas–liquid–solid reactor has reported similar gas hold up trends as those reported for the HET approach [75]. Furthermore, the modelling of such a reactor based on non-Newtonian models for the liquid–solid slurry has been observed to result in the formation of regions of localised fluid motion near the impeller (caverns) with stagnant fluid elsewhere in the tank [75, 76]. This corresponds to the reports of a dampened mean velocity field according to the HET approach as seen in **Figure 4**. Observations such as these suggest that the dampening of the mean velocity field may be a common feature in three-phase reactors. However, questions arise as to the appropriate manner of describing such effects. For example, the question may be posed as to whether liquid–liquid dispersions in stirred tanks should be treated based on non-Newtonian models with a yield stress and shear-thinning behaviour rather than equations of the type given in Section 2.1 (Eqs. (6)–(8)). It is to be noted that Eqs. (6)–(8) predict the effective viscosity of the dispersion assuming no non-Newtonian behaviour. Furthermore, the

#### **Figure 3.**

*Gas hold up versus volume fraction of secondary liquid phase at 600 rpm. P-HOM refers to the pseudohomogenous modelling approach whereas HET refers to the individual treatment of the liquid phases. Data from Gakingo et al. [13].*

*The Use of Computational Fluid Dynamics in the Analysis of Gas-Liquid-Liquid Reactors DOI: http://dx.doi.org/10.5772/intechopen.99157*

**Figure 4.**

*Velocity contours based on the HET modelling approach on a mid-baffle plane at 600 rpm. Left – 0% alkane volume fraction (2-phase). Right* �*10% alkane volume fraction.*

experimental effective viscosity values that were used for the P-HOM modelling approach were in close agreement with those predicted by these equations [13].

Further observations made in the case studies of interest touched on the trends of pumping capacity and power drawn by the impellers. It was observed that the pumping capacity of the impellers decreased at high volume fractions of the secondary liquid phase [12, 13]. This was attributed to a decrease in the mean velocity field as dissipated by an increasing turbulence viscosity [13]. As for the power drawn, both an increase and a decrease in the power was reported. The decrease in power, as noted by Cheng et al. [12], was attributed to a decrease in the effective density upon addition of the secondary liquid phase. On the other hand, Gakingo et al. [13] attributed a noted increase in power drawn to increased energy dissipation by the droplets of the secondary liquid phase. This latter effect was not reported by Cheng et al. [12] and this could have been due to the use of different models to capture turbulence modulation by the dispersed phases (see **Table 4**). Cheng et al. [12] used a model by Katoaoka et al. [77] which considers only the mean velocity differences V*i* � V*c* in computing the work done by interfacial drag. Gakingo et al. [13], on the other hand, employed the model by Simonin et al. [57, 78–80] which considers not only the effects arising from mean velocity differences but also the effects arising from the fluctuating velocities of the dispersed phase particles. These latter effects were expected to become more significant than the former as the size of the dispersed phase particles decreased (V*i* � V*c* ! 0 as *dp* ! 0) [68]. Consequently, it may be suggested that the use of the model by Simonin et al. [57, 78–80] was more appropriate as it is more comprehensive.

With regard to mass transfer, it was illustrated that potentially accurate predictions can be obtained by using the HET modelling approach for hydrodynamics and Eqs. (16), (17) and (19)–(21) for mass transfer with S*gd* ¼ S*cd* ¼ 0 (see **Figure 5**) [13]. These equations correspond to a case where the concentrations of a species in the two liquid phases are at equilibrium and no direct uptake by the secondary liquid phase occurs at the gas–liquid interface. Though this scenario implied the occurrence of either series mass transfer or series mass transfer with shuttling, minimal differences were reported based on a consideration of these two

#### **Figure 5.**

*Comparison of predicted versus experimental overall volumetric mass transfer coefficients at 600 rpm and varying alkane concentrations. Experimental measurements have been obtained by the pressure step method. Data from Gakingo et al. [13].*

#### **Figure 6.**

*Contours of local KLa*0 *values predicted based on the HET modelling approach on a mid-baffle plane at 600 rpm. Left – 0% alkane volume fraction (2-phase). Right* �*10% alkane volume fraction.*

#### *The Use of Computational Fluid Dynamics in the Analysis of Gas-Liquid-Liquid Reactors DOI: http://dx.doi.org/10.5772/intechopen.99157*

alternatives [13]. Thus, it may be suggested that mass transfer in gas–liquid–liquid reactors is less sensitive to the assumed configuration at the gas–liquid interface and more sensitive to other varying parameters such as the energy dissipation rate and the gas hold up. More evidence for or against this suggestion should, however, be provided based on a re-examination of mass transfer using a different set of modelling assumptions.

In conclusion, the usefulness of a CFD-based mass transfer model may be glimpsed from the amount of detail that it can generate. For example, spatial resolutions of variables of interest can be quickly generated as illustrated in **Figure 6** to support the visualisation of changes made during *in situ* reactor design. This is indeed the chief advantage of a CFD-based approach over an empirical approach. However, before the full potential of the CFD-based approach can be realised, a number of areas will need improvement or further investigation. More on this is presented in the subsequent section.


**2. Sustainable design: from buildings to the city**

influence of the urban form on local microclimate.1

38 Sustainable Urbanization

overall performance.

1

(UK).

arranged on a site heavily affects their owners' energy performances and environmental behaviours. The growing awareness of these issues is gradually attracting the attention of both international organizations and international researchers towards the urban dimension (urban block or district), thus broadening the field of architectural interventions. In this light, urban design, which lies in‐between urban planning and architectural design, has become the appropriate tool through which to operate in achieving sustainability's goals. This recommen‐ dation, however, is not novel. In 1976, the European Commission suggested it during the first United NationsConference on HumanSettlements, 'Habitat' (Vancouver), in whichthe 'design of human settlement' was recognized as a strategic matter in contrasting the «social, economi‐ cal, ecological and environmental deterioration» of urban areas [1]. Nevertheless, the latest 'sustainable urban design' seems to increase its own complexity due to the involvement of different interrelated disciplines, such as fluid dynamics, climatology, technical physics and computer engineering. This interdisciplinary perspective demands updates to the design processinordertoincorporateexternalcontributionswhilemaintainingarchitectureattheheart of the process. In particular, both environmental data and analyses need to be reintegrated into the urban project from the design's initial phases. In fact, this has been an ordinary considera‐ tion in the history of architecture, in which several pieces of historical evidence prove in a clear waythein‐depthrelationshipbetweenurbanformandlocalclimaticconditions.Examplesoccur throughouthistory,startingfromvernacularsettlementsuntiltheModernMovement'smasters, and most of the 'environmental' knowledge of past designers is also stated in important recommendations contained in ancient manuscripts (Vitruvius, Aristotle, Varrone, Columel‐ la, Palladio, etc.). Nevertheless, most of the established abilities have been forgotten in favour of building technology systems, with an illusory belief in their supremacy. The energy and environmental crises of the 1970s clearly displayed their failure, encouraging designers to rediscover projects' environmental components as a function of a sustainable future. In this light, the past's lessons on 'sustainable practices' have grown in importance, especially when coupled with scientific and informatic progress. Computer advances, in fact, make it possible to study increasingly broad areas (CitySim, ENVI‐met, Virtual Environment) and contribute to supporting urban design's processes with environmental analysis software. The latter acts as useful'feedback'tools,abletoverify(qualitatively)theenvironmentalbehaviouroftheproject's concept by taking into account different climatic data (airtemperature,relative humidity, wind velocity and main directions, etc.) and form parameters (sky view factor, aspect ratio). In this way, designers experiment with a new methodology in the project's development, one that allows them to evaluate the initial urban proposal and, gradually, to modify it in relation to the main criticalities that emerged from previous environmental analyses' results. Nevertheless, modifications must respect a project's fundamentals and collaborate in the improvement of its

 Examples are the Martin Centre for Architectural and Urban Studies of Cambridge; the 'Urban Physics' research group (ETH, TU Delft and Cypro University); the Centre for Advance Urbanism and the SENSEable Lab of MIT; the Harvard Centre forGreenBuildingsandCitiesandtheCityFormLab;theEPA'sEPFLSolarEnergyandBuildingPhysicsLaboratory (Luisanne); the Environment People and Design (ePad) Research Group in Nottingham (UK); the Berkeley's Center for Environmental Design Research (U.S.A.) and the UCL-Institute for Environmental Design and Engineering in London

In fact, the way in which buildings are

As stated above, the relationship between a building and its context has a heavy influence on both local microclimatic conditions and a building's energy‐environmental impact, shifting designers' and planners' attention from buildings to the block dimension. The urban structure substantially affects a building's access to sunlight and ventilation, defining its capability to exploit passively the heat produced by solar radiation on vertical surfaces, the natural daylight and ventilation of inner spaces, as well as the reduction in environmental and noise pollution. The necessity of this 'change of scale' has been affirmed, in the last few years, in several national and international scientific works, in which authors highlighted the achievable benefits both in relation to climate change goals and energy issues. Among the most significant attestations, the U.S. Green Buildings Council stated that the neighbourhood level was «the primary scale at which building professionals can begin to address impacts of climate change». In fact, «the design and pattern of neighbourhoods […] play an important role in amplifying or dampening climate effects» [2]. Evidence of this is the Urban Heat Island phenomenon (UHI), fostered by cities' higher densities and the *albedo* properties of common urban materials, and the more frequent extreme weather events, such as flooding, strengthened by urban pavements' waterproofing and inadequate storm‐water runoff.

Furthermore, in relation to the energy issues, several pieces of national and international research supported the block dimension as the only possible way to decrease buildings' consumption and, therefore, to improve their environmental impacts. A recent study by Hachem, Athienitis and Fazio [3] showed how different layouts of the same building typology (terraced houses) modify their energy consumption, increasing their values between +6 to 8 and +25%. Orientation and buildings' arrangements are considered central in the project's process by its authors, conferring on the initial phase of the design huge energy responsibilities, which were estimated as being between 65 and 85% of successive building energy demands.

2 The International Congress of Modern Architecture focused on the work of Hannes Meyer, Mart Stam, Walter Gropius, Erns May, and Alexander Klein on *Existenzminimum* ("minimum dwelling"), aimed at reducing building costs (including the cost of land consumption) by a reduction of the worthless surfaces of houses. Research on typology reached some standard formulations of the new working-class building environment, in which the amounts of daylight, fresh air, heat, and silence were radically maximized.

The results of this study are comprehensible in relation to the microclimatic consequences of different buildings' arrangements. The influence of the modification of urban geometry on the urban microclimate was well known until the 1970s, in the main works of the climatologists T.R. Oke [4] and H. Langsberg [5], which became the theoretical basis of reference for most of the successive work on environmental design. As stated by Givoni in 1989 [6], and reported later in Steemers' and Ratti's work [7], the influence of climatic change on outdoor temperature, solar radiation and wind speed, caused «by 'the structure' of the city […] result in modified energy consumption». An urban grid's orientation, density and a building's typology heavily influence the wind pattern and the solar access in urban areas, while also defining the microclimatic conditions of outdoor and inner spaces. Further significant studies on the energy and environmental 'costs' of urban form have been developed during the last few years by several scholars (K. Steemers [7–10]; V. Gupta [11, 12], R. Compagnon [13], R. Knowles [14], E. Ng [15, 16], F. Allard [17, 18] and B. Blocken's research group [19, 20], including J. Carmeliet and M.K.A. Neophytou [21]).

Interest in this 'change of scale' in sustainable design is slowly extending from scientific research into concrete actions. In 2010, the European Commission introduced the *Advisory Group ICT Infrastructure for Energy‐Efficient Buildings and Neighbourhoods for Carbon‐Neutral Cities* [22] and, in June 2014, the European Urban Knowledge Network (EUKN), together with the Cellule Nationale d'Information pour la Politique Urbaine (CIPU), presented the *Certifi‐ cation Systems for Sustainable Urban Neighbourhoods* «as tools to evaluate and maintain high quality standards in urban development» [23]. Although practical initiatives are still few in number, they clearly envisage architects and planners' future goals.


**5. Correct identification of malaria vectors and** *Plasmodium* **detection**

High levels of malaria transmission occurring in forest-fringe areas of Southeast Asia is explained by movements of people in search of forest products and exposure to many highly efficient vector species that have adapted to forest ecotypes [66, 85, 102, 103]. The wide diversity of both the deep-forest (e.g., Leucosphyrus Group of mosquitoes), forest-fringe and deforested area main vectors (e.g., *An. minimus*, *An. maculatus s.l.*, *An. culicifacies s.l.*, *An. fluviatilis s.l.*, *An. letifer*, *An. donaldi*), as well as their great potential to adapt to habitat changes, means that the consequences of deforestation on malaria transmission in Southeast Asia are difficult to predict and unlikely to be unidirectional [104]. Whilst *An. dirus* and *An. baimaii*, main vectors of the Dirus complex, can find tree-crop plantations suitable for breeding, a close association between malaria and rubber plantations has been demonstrated [4, 105–108], contributing to high larval and pupal density during the rainy season [90, 91] and low numbers during the cool-dry season [92, 109], or provide conditions that are similar to this vector's natural habitat [110]. This ecological adaptation in human settlements and shaded plantations contributes to outdoor transmission among rubber tappers.

The identification of secondary or incidental vector species poses new challenges as shown by mixed results of sporozoite-positivity using nested Polymerase chain reaction (PCR) and routine circumsporozoite enzyme-linked immunosorbent assay (CSP-ELISA) (**Table 2**). Confirmation of all positive CSP-ELISA results by a second CSP-ELISA test on the heated ELISA lysate, especially in zoophilic species showed a relatively high proportion of false positives (40%) [93]. On the other hand, PCR analysis of Deoxyribonucleic acid (DNA) extracted from the head and thorax alone, along with sequence data, revealed five *Anopheles* species (*An. hyrcanus*, *An. barbirostris s.s.*, *An. barbirostris* clade III, *An. nivipes*, and *An. peditaeniatus*) infected with *Plasmodium falciparum,* which are not considered major vectors in the GMS [94]. Similarly, out of 11 *P. falciparum* CSP positive samples from Bangladesh, seven turned out to be positive by PCR suggesting that *An. maculatus*, *An. jeyporiensis* and *An. nivipes* play important roles in malaria transmission in Kuhalong District [95]. In Vietnam, the role of a secondary vector, *An. pampanai* infected with *P. vivax*, was also reported in the Binh Phuoc Province [96]. Morphological misidentification of the closely related sympatric species, such as *An. aconitus*, *An. pampanai* and *An. varuna* are common [99, 100]. Morphological identification of *Anopheles* specimens prior to PCR assays allows them to be sort out at the group or complex level but does not permit species identification [85]. PCR assays must be applied for a reliable identification to the species level, which ensures that data received by malaria vector control programmes are suitable for targeting the correct vector species [101]. Given the low infection rates among many of these species especially in elimination phase, it is important for field entomologists to assess various

species' role in malaria transmission in the eco-epidemiological context. When changing objectives from control to elimination of malaria in Southeast Asia, the need to focus not only in the so-called main vector species, but also on secondary vectors is increasingly important.

Molecular identification was specifically conducted on *Anopheles barbirostris s.s*. and *An. barbirostris* clade III; *An. hyrcanus* and *An. hyrcanus s.s*; *An. peditaeniatus* and *An. nivipes*, and morphological identification for the other *Anopheles* species.

**Table 2.** Sporozoite infectivity rates of less known (secondary) vectors along the Bangladesh-Thailand-Cambodia

**Morphological**  *Anopheles* **species\***

*An. maculatus*

*An. annularis*

*An. barbirostris*

*s.l*

*s.l.*

*s.s.*

*An. peditaeniatus*

*An. philippinensis*

\*

corridor.

**Nested PCR, Cambodia [93]**

> **Positive/ total**

**Total collection (%)**

*An. hyrcanus* 0.09 2/2

**Circumsporozoite ELISA, Thailand [97]**

**Total collection (%)**

*An. kochi* 0.93 1/44

*An. vagus* 41.9 25/1978

*An. karwari* 5.16 11/244 1.7

**Prior heating of eluate and circumsporozoite ELISA, Bangladesh**

21.43 4/640 4.3 2/97

5.08 3/139

**Positive /total**

**PCR confirmation of ELISA-positives Bangladesh [95]**

> **Positive / total**

105

**Total collection (%)**

http://dx.doi.org/10.5772/intechopen.76901

**[98]**

**Total collection (%)**

Human and Simian Malaria in the Greater Mekong Subregion and Challenges for Elimination

**Positive /total**

14.43 3/431 0.78 1/19

6.6 3/55 3.52 1/105 2.9 1/140 7.4 1/186

*An. nigerrimus* 0.87 1/21 4.1 1/104

*An. nivipes* 10.8 1/264 *An. jeyporiensis* 3.1 1/142 18.9 2/479

3/219 24.7 25/1169

Deforestation may deplete the populations of deep-forest vectors and so initially reduce malaria transmission; in some localities this depletion may be followed by the invasion of other efficient vector species resulting in increased transmission. With the exception of two longitudinal studies examining the effects of progressive land use changes from pre-development forest to oil palm cultivation on the distribution of disease vectors and malaria incidence [111], there is a striking lack of primary research directly measuring the impact of deforestation on malaria in Southeast Asia [104]. Recent studies showed that *An. dirus s.l.* was abundant in rubber plantations in Myanmar [109] and *An. baimaii* (molecularly identified) adults were caught from human landing collections in Wae Kha Mi, Mon State, the site of an acceptability study of permethrintreated clothing [110]. In Lao PDR, a total of 46 *An. dirus s.l.* were collected, of which 31 were

cassava, fruit orchards). On return to their usual settlements, they contribute to the spread of malaria within and across international borders [41, 43]. By creating hot-spots of malaria and disproportionately affecting people with certain high-risk occupations [86, 89], residual trans-

High levels of malaria transmission occurring in forest-fringe areas of Southeast Asia is explained by movements of people in search of forest products and exposure to many highly efficient vector species that have adapted to forest ecotypes [66, 85, 102, 103]. The wide diversity of both the deep-forest (e.g., Leucosphyrus Group of mosquitoes), forest-fringe and deforested area main vectors (e.g., *An. minimus*, *An. maculatus s.l.*, *An. culicifacies s.l.*, *An. fluviatilis s.l.*, *An. letifer*, *An. donaldi*), as well as their great potential to adapt to habitat changes, means that the consequences of deforestation on malaria transmission in Southeast Asia are difficult to predict and unlikely to be unidirectional [104]. Whilst *An. dirus* and *An. baimaii*, main vectors of the Dirus complex, can find tree-crop plantations suitable for breeding, a close association between malaria and rubber plantations has been demonstrated [4, 105–108], contributing to high larval and pupal density during the rainy season [90, 91] and low numbers during the cool-dry season [92, 109], or provide conditions that are similar to this vector's natural habitat [110]. This ecological adaptation in human settlements and shaded plantations contributes to out-

The identification of secondary or incidental vector species poses new challenges as shown by mixed results of sporozoite-positivity using nested Polymerase chain reaction (PCR) and routine circumsporozoite enzyme-linked immunosorbent assay (CSP-ELISA) (**Table 2**). Confirmation of all positive CSP-ELISA results by a second CSP-ELISA test on the heated ELISA lysate, especially in zoophilic species showed a relatively high proportion of false positives (40%) [93]. On the other hand, PCR analysis of Deoxyribonucleic acid (DNA) extracted from the head and thorax alone, along with sequence data, revealed five *Anopheles* species (*An. hyrcanus*, *An. barbirostris s.s.*, *An. barbirostris* clade III, *An. nivipes*, and *An. peditaeniatus*) infected with *Plasmodium falciparum,* which are not considered major vectors in the GMS [94]. Similarly, out of 11 *P. falciparum* CSP positive samples from Bangladesh, seven turned out to be positive by PCR suggesting that *An. maculatus*, *An. jeyporiensis* and *An. nivipes* play important roles in malaria transmission in Kuhalong District [95]. In Vietnam, the role of a secondary vector, *An. pampanai* infected with *P. vivax*, was also reported in the Binh Phuoc Province [96]. Morphological misidentification of the closely related sympatric species, such as *An. aconitus*, *An. pampanai* and *An. varuna* are common [99, 100]. Morphological identification of *Anopheles* specimens prior to PCR assays allows them to be sort out at the group or complex level but does not permit species identification [85]. PCR assays must be applied for a reliable identification to the species level, which ensures that data received by malaria vector control programmes are suitable for targeting the correct vector species [101]. Given the low infection rates among many of these species especially in elimination phase, it is important for field entomologists to assess various

mission under these circumstances has so far hindered progress towards elimination.

**5. Correct identification of malaria vectors and** *Plasmodium*

**detection**

104 Towards Malaria Elimination - A Leap Forward

door transmission among rubber tappers.

\* Molecular identification was specifically conducted on *Anopheles barbirostris s.s*. and *An. barbirostris* clade III; *An. hyrcanus* and *An. hyrcanus s.s*; *An. peditaeniatus* and *An. nivipes*, and morphological identification for the other *Anopheles* species.

**Table 2.** Sporozoite infectivity rates of less known (secondary) vectors along the Bangladesh-Thailand-Cambodia corridor.

species' role in malaria transmission in the eco-epidemiological context. When changing objectives from control to elimination of malaria in Southeast Asia, the need to focus not only in the so-called main vector species, but also on secondary vectors is increasingly important.

Deforestation may deplete the populations of deep-forest vectors and so initially reduce malaria transmission; in some localities this depletion may be followed by the invasion of other efficient vector species resulting in increased transmission. With the exception of two longitudinal studies examining the effects of progressive land use changes from pre-development forest to oil palm cultivation on the distribution of disease vectors and malaria incidence [111], there is a striking lack of primary research directly measuring the impact of deforestation on malaria in Southeast Asia [104]. Recent studies showed that *An. dirus s.l.* was abundant in rubber plantations in Myanmar [109] and *An. baimaii* (molecularly identified) adults were caught from human landing collections in Wae Kha Mi, Mon State, the site of an acceptability study of permethrintreated clothing [110]. In Lao PDR, a total of 46 *An. dirus s.l.* were collected, of which 31 were from immature rubber plantations, nine from mature rubber plantations, five from secondary forests and one from the rural village [105] (Tangena Julie-Ann, personal communication).

which acquired infection from the macaques. Perhaps even likely given that confirmed vectors of human plasmodia in Southeast Asia also become naturally infected by the monkey malaria species [127]. A recent case control study conducted in Sabah revealed that the age group >15, predominantly males, working in farms, plantations, forested areas, and with travel history, were independently associated with the risk of acquiring knowlesi malaria [128]. It also highlighted

Human and Simian Malaria in the Greater Mekong Subregion and Challenges for Elimination

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107

There are only few investigations on record in understanding bionomics of vectors transmitting *P. knowlesi* malaria. In order to implement vector control activities, the bionomics of the vectors must be understood. Based on few studies, it has been shown that the vectors are biting in the early part of the night from 18:00 h to 21:00 h and mostly outdoors [121–123, 129]. In these rural areas, people go to bed by 22:00 h and they are up by 05:00 h. The results showed that only 39.79% of *An. balabacensis* [123], 43.8% of *An. latens* [121] and 12.8% of *An. cracens* [122] were found biting during this sleeping time. Thus, current vector control measures like IRS and ITNs are not appropriate for the exophagic and exophilic vectors. The forests in Southeast Asia is providing a favorable environment with high percentage of macaques being positive for *P. knowlesi* [130–132], and with the presence of the vectors, it is going to be a daunting task to eliminate malaria. On a global scale, malaria has been reduced to low levels due to the scaling up of ITNs, IRS, ACTs, and intermittent preventive treatment to infants and pregnant women [133]. Thus, it is obvious that new tools are urgently required for successful malaria elimination.

It is known that the two human malaria species (*P. falciparum* and *P. vivax*), which infects millions of people actually were of zoonotic origin (from the African apes), which evolved thousands of years ago [134, 135]. Thus, there is always a possibility that in the future *P. knowlesi* and other simian malarias may become established in humans, especially when human malaria is eliminated. However, currently human-to-human transmission of knowlesi malaria by mosquitoes has not been established. This is crucial in the light of malaria elimination and more focused research is needed on this topic if we are to succeed with malaria elimination.

Changing landscape affects *Anopheles* distribution, mosquito density and diversity in Malaysia, and more globally Southeast Asia [105, 111, 136–138]. It has been shown that with loss of forest cover, cases of *P. knowlesi* have increased in Sabah [119]. Land use change has also led to increase of malaria cases due to various factors such as increase of macaques in small forest patches along with the colonization of the main vectors [119, 136]. It is interesting to note that *An. balabacensis,* the predominant vector of human and simian malaria, was found in great abundance in logged forest, followed by thinly logged virgin jungle reserve and was lowest in primary forest [136]. This vector was also found to be biting humans more at ground level compared to canopy level [136]. It is therefore important to include both the public health and agro-forestry sectors in controlling malaria vectors in the country. Studies from Thailand also indicate that if landscape management should be used for malaria control in northern Thailand, large-scale reduction and fragmentation of forest cover would be needed [139, 140]. Such drastic actions, however, do not

align with current global objectives concerning forest and biodiversity conservation.

The vectors of simian malaria described to date were *An. hackeri* (Leucosphyrus Group) [141] recorded biting mainly the macaques and large numbers were collected resting on Nipah palm trees in Selangor in 1960s; *An. cracens* (Dirus Complex) [122] biting both macaques and humans and found mainly in the forest and farms; *An. latens* (Leucosphyrus Complex) [121] was the

that IRS was associated with decrease of risk [128].


**1. Introduction**

In modern times, mass communication, mass media, and networking technologies have enabled access to vast amounts of knowledge that are distributed across many continents and time-zones, thus allowing web-active end-users to achieve great feats.

Web-active end-users (also referred to as end-users or end-user programmers) are people who lack programming experience but are engaged in internet activities [1]. There is a substantial number of web-active end-users and their number is continuously growing. The end-users often create applications to complete tasks such as finding apartments to rent in a certain location, tracking flights, and alerting

drivers regarding traffic jams. One approach to create such applications is utilizing web mashups programming environments.

Web mashup programming environments allow for creating applications from distributed heterogeneous web sources and functions. Most of the mashup programming environments are visual in nature. Some examples include Yahoo! Pipes [2], IBM mashup maker [3], xfruit [4], Apatar [5], Deri pipes [6], and JackBe [7]. The visual nature of these programming environments allows application creation using code abstraction to ease the programming process. However, the abstraction of code can add complexity of accessing the information, debugging, and comprehending large programs within these environments [1, 8, 9].

Further, end-users create mashup applications by seeking information from the complex ecosystem of the web, which is composed of evolving heterogeneous formats, services, standards, and languages [8]. Seeking information on the web is challenging, as the relevant information is scattered across numerous web sources that end-users must find and manually analyze, an information-seeking problem that costs both time and cognitive effort.

In this chapter, we observe the behavior of end-users while debugging, one of the most difficult aspects of programming [10]. Debugging mashup programs is even more challenging as end-user programmers must locate bugs within the abstract web mashup environment and then locate solutions on the web to fix bugs. The lack of debugging support within mashup environments increases the complexity of finding bugs [9]. Further, finding correct solutions to fix bugs is complicated as the web is a huge compilation of heterogeneous resources.

Currently, it is not clear how web-active end-users seek for bugs in their program and their solutions on the web. Hence, we used an information seeking theory called Information Foraging Theory.

Information Foraging Theory (IFT) can expand our understanding of the information-seeking problems of web-active end-user programmers while debugging. IFT posits that people seek information in the same manner as predators forage for their prey, where predators are the end-users, and the prey is the bugs or bug fixes they are searching for. The hunting grounds or 'patches' where webactive end-users search for these bugs or fixes would be their IDE or the websites they visit and the scents the web-active end-users follow are given by different cues (e.g., links) found on the web [11–15]. IFT has been applied successfully to diverse domains such as documents, the web, user interfaces, and programming environments [15–23].

Past research on web mashups have focused on creating web tools that increase the ease and effectiveness of creating applications by end-user programmers [24–28]. While past IFT research on programming environments has investigated debugging and navigational behavior of professional programmers [19–21]. No prior research exists to understand the debugging behavior of web-active end-user programmers. The only research relevant to this chapter is our own [8], where we created a debugging support for web mashups and investigated the debugging behavior of end-user programmers using IFT with and without the support. Based on this prior research, we found IFT to be the most relevant choice to understand the information-seeking behavior during mashup debugging.

To understand the debugging behavior of end-user programmers we conducted a controlled lab study of eight students who were not computer science majors. The study participants completed their tasks using Yahoo! Pipes, a mashup environment, as it provided the best debugging support at the time. The participants completed two debugging tasks using a think-aloud protocol. We investigated how end-users forage for information within the IDE as well as the web using IFT

*How Do Web-Active End-User Programmers Forage? DOI: http://dx.doi.org/10.5772/intechopen.97765*

theory. Our analyses discovered new cues and strategies that end-user programmers pursued while locating the bugs in the mashup environment and foraging the web for fixing the bugs.

This chapter is organized as follows. Section 2 describes the debugging behavior of end-user programmers. Section 3 describes Information Foraging Theory, IFT terminologies from Yahoo! Pipes, and relevant literature. Section 4 describes the background and related work on web mashups, and Yahoo! Pipes. Section 5 describes the methodology and results from the lab study. This section discusses the cues utilized by end-user programmers and their behavior during debugging tasks and provides recommendations. Section 6 summarizes our findings and suggests how web mashup environments can improve the debugging process.


**1. Introduction**

Trophic relationships between organisms are the mechanisms responsible for most of energy and nutrient transfers; they allow the functioning of the ecosystem. These relationships, known as food webs, caught the attention of naturalists before the concepts of evolution and ecology were about to be determined.

Initially, the diet of a species and its skills to obtain it were recognized as the leading factors for the prevalence of the fittest. Additionally, it is one of the main forces leading to evolution of that species in the long term [1]. Furthermore, competition for food became one of the favorite hypotheses to explain species exclusion; it states that when two species seemed to feed on the same resources, the best suited ultimately leads its competitor to extinction in the long term [2]. This idea has been around for many years and has not been completely discarded or proved [1].

Examining phototrophs, also known as primary producers, is the dominant starting point to analyze food webs. They use the incoming sun's energy and inorganic nutrients to generate their biomass. This is the most important mechanism, as it initiates the cycling of nutrients and energy flux in aquatic food webs. There is primary productivity involving chemolithotrophs dominating in places devoid of sun's light [3]. These places were mostly known to be, until recent times, around underwater volcanoes more than 1000 meters deep [3, 4].

Primary production is at the base of all consumers concurring in the environment. However, macroscopic food webs tend to be very short, with few levels of consumers because these organisms dissipate matter and energy efficiently [5]. All metazoans invest their energy looking for food, ingesting it, digesting,

repairing themselves, mating, and reproducing. These activities make multicellular organisms to get around 10% of biomass fixation efficiency. Thus, 1,000 kgs of the primary producer will be needed to produce 100 kgs of herbivorous animals, only 10 kgs of small carnivores, 1 kgs of medium-sized carnivores, and only 0.1 kgs of top carnivores, following a pattern known as pyramid of productivity [5]. Adding a predatory species at any level would destabilize the food web, as this will consume higher amounts of biomass [6]. Energy dissipation is even larger, meaning that the entropy produced during the functioning of the food web is very high. However, only 1% of the incoming sunlight is used for primary production, stressing the importance of the environmental factors limiting biomass productivity to sustain food webs.

Primary productivity varies along seasons. When it reaches its peak, productivity is controlled by the top predator's consumption (top-down), and when it reaches its lowest level, productivity is controlled by phototrophs (bottom-up). There are places that are permanently bottom-up controlled such as the deep ocean communities depending on the "organic matter rain" from dead organisms living in the photic zone in places near the equator are almost always top-down controlled, where productivity may be at its peak for most of the year. All other places experience top-down/bottom-up controls alternatively, depending on the productivity seasons.

Unicellular algae lead primary productivity in marine environments, sustaining the great diversity of organisms, especially in places receiving nutrient inputs from lands. Heterotrophic unicellular organisms forage on algae and both phototrophs (phytoplankton) and heterotrophs (zooplankton) conform to the plankton. However, unicellular organisms span in sizes less than 1 μm to hundreds of micrometers, and the species' diversity of plankton, including microbial eukaryotes and bacteria, ranges in the order of thousands. Species of microorganisms are much more numerous than the metazoans. With such a great diversity of microorganisms, it become apparent that the microbial food webs may function differently from the macroscopic food webs.

It was believed that food webs would get destabilized if the number of species increases at any level above the primary producers. However, microbial food web seemed to get more stability with the increasing number of species, contradicting what was observed in macroscopic food webs [7]. Thus, the higher number of species of bacterial and microbial eukaryotes in aquatic food webs seemed to contradict that assumption; this phenomenon was named as "The paradox of microbial loop." It was paradoxical that productivity and efficiency of nutrients and energy transformation is increased by adding more species, promoting the stabilization of the food web [8].

It's been a long road since the recognition of the "paradox of the microbial loop" in the aquatic food webs. Nowadays, it is referred only as the "microbial loop," after being integrated into the food web conceptualization in both terrestrial and aquatic environments [7].

The complexity of microbial food webs needs to be approached from the analysis of different functioning capacities and nutritional needs of the participating species. It has been normal to assign very general feeding habits to protists and metazoans, like bacterivores for example. This nomenclature implicates that a single species of protist can feed on any one or indistinctly on all the thousands of bacteria species. However, observation of feeding habits has revealed that protists and metazoans prefer feeding on specific kind of bacteria while avoiding other species. Pigmented bacteria [9], for example, has fewer predators than non-pigmented ones. On the other hand, there are several species of protists, mostly amoebae, small flagellates and *Colpoda steinii* that feed on pigmented ones [10].

#### *Food Webs DOI: http://dx.doi.org/10.5772/intechopen.97252*

One explanation for pigmented bacteria to have fewer predators relied on the toxicity or poisonous effect of those pigments for many protists, pointing out the importance of the biochemical warfare that bacteria must synthetize to defend themselves. However, chemicals used for evading enemies attract other ones looking for those same compounds, putting bacteria in a situation where there is no way out for bacterial preys. Indeed, there is no way out of being preyed upon, as every living being has predators, or at least other species which may feed on them or use them as a resource.

Is there a single factor determining the feeding preferences? The short answer is "No." Remember that "bacterivorous" or "algivorous" are labels used to recognize the kind of food that protists and metazoans may prefer to feed on, and it involves many species. From the beginning, this was a non-exclusive way to label the category of food that may be used to group the highest quantity of species to simplify and conceptualize the food webs. Furthermore, during the first half of the XX century [11], there were many very interesting studies trying to determine the "diets" of several species of protists [11, 12], with the aim of designing a chemically defined culture media, as is the case of several recipes for culturing *Tetrahymena pyriformis, Glaucoma sp,* or *Paramecium* sp., culminating with 3 books edited by Lewandowsky and Hutner (1979), approaching the field of protists' biochemistry (at that time it was biochemistry of protozoa).

Designing a culture media for protists or bacteria was a major task, as numerous factors about their nutritional needs were unknown (and remain unknown). These attempts to cultivate bacteria and protists lead to one important conclusion: different species cannot synthesize one or several molecules needed for their metabolism and have to take those molecules, as such, from their ingested food [12] or from other microorganisms that live within the biofilm (such as the case of NAD+ \*\*, which the bacteria has to consume from other species of bacteria for both of them to grow). Microbial biologists named this phenomenon as "auxotroph" [13]. In this way, the molecule(s) a bacterial species is auxotroph for must be added to the culture media, to keep a culture of such species [14]. The kind of molecules, their diversity, and their macro- and micronutrient composition form a universe comparable to the one containing the species' diversity on the planet.


**1. Introduction**

Nitrogen (N) and potassium (K) are essential elements for plants life being part of multiple structural compounds and participating in many vital processes [1]. On the other hand, silicon (Si) is not considered an essential element for plants, however, its absorption can produce beneficial effects in some crops, such as resistance to pests and diseases [2], attenuating abiotic stress [3, 4] and hydric stress [5]. Among the main accumulative crops of Si, are gramineous as sugar cane (*Saccharum* spp.), rice (*Oriza sativa* L.), and corn (*Z. mays* L.) [6].

Some research report about the effect of the combinations of silicon and nitrogen on plants [7–10] and silicon and potassium on the development of plants and yields [11], mainly in accumulative crops of silicon. However, there are not enough results on the effects produced by the interaction of silicon, nitrogen, and potassium on the accumulation of these elements inside the vegetables and their implications on the growth of the plants.

High nitrogen concentration and low concentrations of potassium increase the susceptibility of the corn plants to the noxious agents because it diminishes the absorption of Si. This fact is important because it is well-known that silicon can induce higher resistance to the plants in front of the pests [2].

In an accumulative crop of Si like rice, Andreotti et al. [7], informed that the supply of Si had a small influence on the production of dry matter, although it increased the number of panicles per plant at the highest concentrations of Si, and on the other hand, the concentration of Si decreased with the increment of the dose of fertilization with urea [11].

However, some researchers have stated that there is a lack of information on the use of silicon in general and in particular in corn crops that justifies the need to carry out further research on this subject [12]. Castellanos et al. [13] demonstrated the positive effect of the application of Si on the damages of *Spodoptera frugiperda* Smith in corn to an intermediate dose of N, while Matías and García-Montalvo [14] pointed out that the positive role of the silicon in the resistance to the foliar insects in *Zea diploperennis* L., however in *Z. mayz* this is not yet well explained.

González et al. [15] verified the increment of the green forage in one variety of corn and not in others when the application of an intermediate dose of Si was made, while any varieties had a response at the highest dose.

In front of this situation, two hypotheses arise, one that the unbalanced management of N and K, or the excessive use of N associated with the insufficient application of potassium can diminish the silicon absorption and the production of dry biomass in the plants, and other, that the use of silicon can improve the response of the plant in dry biomass production in function of the application of N and K.

To confirm one of those hypotheses the aim of the present investigation was to evaluate the effect of the interaction of silicon, potassium, and nitrogen on the foliar area and the accumulation of these elements and the dry biomass in corn plants.

### **2. Material and methods**

The investigation was performed in a greenhouse located at the department of soils and fertilizers, FCAV/UNESP Jaboticabal Campus, SP, with geographic coordinates of 21° 15′ 22" South, 48° 18′ 58" West and an elevation of 600 m between March and June of 2014, in a hydroponic floating system.

Two silicon concentrations were evaluated (0 and 2 mmol L−1) using as source silicate of calcium; two concentrations of potassium (1 and 12 mmol L−1) corresponding to 16 and 200% of the solution of K proposed by Hoagland and Arnon [16], using as source monobasic potassium phosphate, and four nitrogen concentrations: 1, 10, 15, and 20 mmol L−1 corresponding at 10, 100, 150, and 200% of the solution of Hoagland and Arnon [16], respectively. The added nitrogen corresponded the 25% to ammoniac form (from ammonium chloride) and the 75% to nitric form (from calcium nitrate).

The rest of the macronutrients and micronutrients were incorporated into de nutrient solutions as were proposed by Hoagland and Arnon [16], balancing the concentrations of calcium and phosphorous. The nutrient solution was maintained under continuous oxygenation by means of an air compression system.

Treatments were arranged in one 2 × 2 × 4 factorial scheme with three repetitions. Each experimental unit consisted of a polypropylene pot with a lid, measuring 48 cm long, 11 cm wide at the lower base, 16 cm wide at the upper base, and 17 cm tall, containing 8 L of nutrient solution and six corn plants (Hybrid 30A77HX). The plants were developed in a greenhouse. Initially, the sowing of the *Silicon, Potassium and Nitrogen Accumulation and Biomass in Corn under Hydroponic… DOI: http://dx.doi.org/10.5772/intechopen.100628*

corn was carried out in vermiculite on isospory trays, irrigated for 15 days, time in that plants reached five leaves.

Water used in the hydroponic system was distilled and deionized, where solution levels were completed daily in each pot with stock solutions corresponding to each treatment. Values of pH were adjusted to between 6.0 ± 0.2 using solutions of HCl 1.0 mol L−1 or NaOH 1.0 mol L−1.

At 45 days after transplanted, the foliar area of the plants was evaluated. For that, all the leaves of the six plants of each pot were collected, being used an integrative apparatus of scanning the foliar area (LI-COR®modelo LI-3000C).

Later on, the dry biomass was determined from the collection of the roots and the aerial part of each pot. For this, the picked-up material was placed in paper bags and dried off in an oven with forced air circulation (65°C) until they reached a constant weight to determine the dry biomass content by pot (aerial part, roots, and the total).

The dry material was ground for chemical analysis of N and K content according to the methodology described by Battaglia et al. [8] and silicon according to Kraska and Breitenbech [17]. Using data of concentration of N, K, and Si in the dry biomass from the aerial part, from the root, and from the total, for each pot, the accumulation of each element per pot was calculated and expressed in mg per plant.

The data of the active foliar area, dry biomass, nitrogen, potassium, and silicon accumulation in the aerial part of the plants were submitted to variance analysis. The media was compared by means of the Tukey test (P < 0.05). The statistical package SPSS version 21 was used [18].

### **3. Results**

The application of silicon in the nutritious solution increased the biomass of the plants of corn and the accumulation of N, Si, and K in this, however, the foliar area did not increase, while a high-dose of K caused an increment of all evaluated variables. The foliar area, the biomass of the plants, and the accumulation of the three elements in the plants were influenced in some way by nitrogen dose (**Table 1**). Foliar and total dry matter and the accumulation of K in the plants were influenced by the interaction of the treatments of Si and K, but not the other variables, while

### **Table 1.**

*Effect of nitrogen (N) and potassium (K) concentrations in the presence or not of silicon (Si), on the nitrogen, potassium, and silicon accumulation in the aerial part of corn plants, the foliar area to and biomass, under hydroponic conditions.*

dry biomass of the root and accumulation of K were influenced by the interaction Si × N. The interaction of N and K had influenced on the accumulation of Si and K in the plants and on the increment of the dry biomass of the aerial part of the plants and the total.

The nitrogen accumulation was increased at the highest concentrations of this nutrient with a relationship at the lowest dose while this did not happen at the lowest dose (1 mmol L−1). The silicon accumulation was higher at the concentration at 10 mmol L−1 of N, with a statistical difference with the treatments at 15 and 20 mmol L−1 of N in the nutrient solution being lower at 1 mmol L−1. A similar situation was observed in relation to the influence of N doses for the foliar area (**Table 2**).

The higher values in accumulation of K were observed at the concentrations of 15 and 20 mmol L−1 of N, and the lower at 1 mmol L−1. A similar situation to that it was observed for the total dry biomass, however, the roots dry biomass showed lower values at 1 mmol L−1 of N in relation to the highest concentrations. The interaction of the application of Si with the highest concentration of K promoted an increase in the accumulation of K and the foliar and total biomass. However, there was no difference for the foliar biomass between the treatment of Si at 2 mmol L−1 combined with the treatment with K at 12 mmol L−1 compared with K at 12 mmol L-1 without the application of Si (**Table 3**).

The accumulation of K was increased in the treatments that received 2 mmol L−1 of Si and 10 and 15 mmol L−1 of N, without statistic difference with the treatment that received N at 10 mmol L−1 without the application of Si. The dry biomass of the root was increased in the interaction of Si at 2 mmol L-1 and N at 10 mmol L-1 in relation to the treatment that received 1 mmol L−1 of N in the nutrient solution

*Different letters in the columns indicate significant differences according to the Tukey test for P < 0.05.*

### **Table 2.**

*Effect of the nitrogen (N) concentrations on the N, K, and Si accumulation in the aerial part of corn plants, the foliar area (FA), and biomass under hydroponic conditions.*

### **Table 3.**

*Effect of the interaction of nitrogen (N) and potassium (K) concentrations on the N, K and Si accumulation in the aerial part of corn plants, the foliar area and biomass under hydroponic conditions.*

*Silicon, Potassium and Nitrogen Accumulation and Biomass in Corn under Hydroponic… DOI: http://dx.doi.org/10.5772/intechopen.100628*

without the application of Si, but not in the relation of the rest of the treatments. The role of silicon in the absorption of the K was verified, and at the same time that the beneficial effect of silicon was not evidenced in front of low and high concentrations of N (**Table 4**).

The accumulation of Si and the dry biomass in the air part of the plant manifested an increment at the higher concentration of K (12 mmol L−1) combined with 1, 10, and 15 mmol L−1 of N, while the accumulation of K and the biomass of the air part showed increments at 1 mmol L−1 of K and 10 mmol L−1 of N and at 12 mmol L−1 of K and at 10, 15 and 20 mmol L−1 of N (**Table 5**).

The influence of the concentration of K in the nutritious solution on the absorption of Si was verified. The application of low and high N concentrations caused the less accumulation of Si in the plant. In the same way, the results demonstrated that the treatments that stood out for a bigger absorption of Si also stood out for higher total biomass, while those treatments that had shown higher accumulation levels of potassium also manifested higher levels of dry biomass in the air part of the plant.

*Different letters in the columns indicate significant differences according to the Tukey test for P < 0.05.*

### **Table 4.**

*Effect of the interaction of silicon (Si) and nitrogen (N) concentrations on K accumulation and root biomass under hydroponic conditions.*

### **Table 5.**

*Effect of the interaction of potassium (K) and nitrogen (N) concentrations on the Si and K accumulation in the aerial part of corn plants and biomass under hydroponic conditions.*


*3.2.6 Meeting 6: storytelling in the first or third person*

Several short stories were analyzed in class. The class was then asked to develop creative writing stories in the first and third person. We tried to make the students understand that the narrator is chosen according to the effect that we want to achieve in the reader. In fact, first-person narration is emotionally stronger and more intense at the cost, however, of a certain subjectivity, while third-person narration is more objective but less empathetic.

### *3.2.7 Meeting 7: the rewriting of literature texts*

The students were invited, at first, to analyze some important texts of Italian literature (The Betrothed in particular) and then to rewrite parts of the stories or novels by changing the setting and introducing invented characters and situations.


**2. Materials and Methods**

This study consists of (1) the introduction of the "precision index" for use in the quality control assessment of GNSS-R data; (2) a daily rasterization demonstration over the Mekong Delta based on the precision index obtained from the Lv. 2 product; (3) a demonstration of the temporal Kalman smoother over the Mekong Delta using the precision index as the reciprocal number of observation errors (Lv. 3 product); and (4) cross-validation with PALSAR-2 quadruple polarimetric data that have been preprocessed with a polarimetric decomposition method and ground observation data. A flowchart presenting the methodology of this study is illustrated in Figure 1.


**2.2 Harvesting of** *Daphnia*

*Daphnia* gut contents were minimized by allowing the microcrustaceans to feed on copolymer microspheres of 4.3-micron mean diameter (Duke Scientific, Fremont, CA, USA) for one hour prior to harvesting. Microspheres were fed at a concentration equal to the number of algal cells previously supplied. *Daphnia* were harvested by filtration through 250 um Nitex mesh (Sefar America, Depew, NY, USA) and flash frozen. Average mass of adult *Daphnia pulex* was 0.1158 + 8.3 mg fully hydrated and 0.05285 + 10.60 mg dehydrated (*n* = 50). Average mass of adult *D. magna* was 1.37+ 0.46 mg fully hydrated and 0.23 + 0.06 mg when dehydrated (*n* = 64).


*2.3.1 Dermoscopy of inverse psoriasis*

Dermoscopic features of inverse psoriasis are characterized by regularly distributed dotted vessels on reddish background (**Figure 21**). In contrast with other variants, scales are absent. Absence of scales enhances visualization of vascular structures. Consequently, dermoscopic differential diagnosis of flexural dermatosis mainly leans on evaluation of vascular structures.

 Anatomical localization: Inframammary (10).*

### *2.3.2 Dermoscopic differential diagnosis of inverse psoriasis*

Differential diagnosis of inverse psoriasis should be done with skin diseases, which present with erythematous patches/plaques in flexural and genital areas. Mechanical intertrigo, seborrheic dermatitis, lichen planus inversus, and fungal/bacterial infections may count as differential. Because no clear dermoscopic features have been defined for mechanical intertrigo and flexural infections, we will discuss dermoscopic features of seborrheic dermatitis and lichen planus inversus under this topic.

The main dermoscopic features of seborrheic dermatitis of flexural areas are irregularly distributed linear, blurry vessels [22]. As we mentioned before, we do not see classical yellowish scales of seborrheic dermatitis in flexuras.

When we review the literature so far, there are only three reports about dermoscopic features of lichen planus inversus. In all of these reports, dermoscopic features of only pigmented variant of lichen planus inversus were evaluated and defined as diffuse brown patches containing multiple granular gray-brown dots [23–25]. In our clinical practice, we see non-pigmented lichen planus inversus more than pigmented subtype. According to our dermoscopic experience, Wickham striae, which is seen in lichen planus inversus, tends to be in "starry sky" or "radial streaming" pattern rather than reticular pattern. Background is usually pinkish or violaceous. Dotted vessels usually encircle Wickham striae (**Figure 22**).

Dermoscopic features of inverse psoriasis and its differentials are summarized in **Table 3**.