*6.4.2 Ensured supply of dairy farm machinery*

The most updated dairy farm machinery and equipment are available in the country now. Many commercial companies are either preparing the required equipment at local market level or import from abroad. These companies not only provide inputs but also services and consultancy. The important farm machinery and farm structures may include, but not limited to, dairy sheds and layouts, milking machines, lines and parlors, milk chillers and utensils, forage harvesters, choppers and inoculants mixtures, and hay and silage bailers (based on the data provided by the sector).

### **6.5 Farmers' education on modern management practices**

 As mentioned earlier, several farmers education programs related to dairy farming are in place now. In the recent past, Engro Foods Ltd. started and completed a large farmers and extension worker training program titled "Big Push," in which they trained a total of 12,000 Basic Livestock Workers for basic dairy farming, keeping in hygienic, and clean milk production (http://www.engrofoods. com/bigpush.html: accessed October 15, 2018). Among those trained, 750 persons got special training as livestock extension workers (LEW), artificial insemination technicians, and farm supervisors and village milk collectors. This program was completed in collaboration with Punjab Skills Development Foundation during a period of one and half year (February, 2017–June, 2018).

Similarly, Nestle Pakistan Ltd. has taken several initiatives to rural development by taking several steps to increase the knowledge and skill level of farmers for enhanced and good quality milk production and quality of life (https://www. nestle.pk/asset-library/documents/financial\_reports/csv\_report\_2016.pdf: accessed October 19, 2018). The company's initiatives positively impacted the lives of small dairy farmers by imparting training to around 77,000 farmers through farmer help camps. Nestle, through its famous Dairy and Rural Development Foundation (DRDF) trained 48,600 dairy farmers and 500 commercial farmers and farm managers on best dairy farm management practices, trained and established 2450 AITs, trained and established 7000 Women LEWs as entrepreneurs serving farmers in 10,000 villages in South Punjab. They started a street theater and mass awareness campaign, which reached approximately 3 million dairy households to increase knowledge of best dairy farm practices. They upgraded 118 local farms to model farms to meet rural communities' requirements and now serving as service and supply hubs for small farmers.


**Testing Physical and Mathematical Criteria in a New Meandering Autocorrelation Function**

Charles R.P. Szinvelski, Lidiane Buligon, Michel Baptistella Stefanello, Silvana Maldaner, Debora R. Roberti and Gervásio Annes Degrazia

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/67920

#### Abstract

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S0022112093000096

An alternative formulation for the low wind speed-meandering autocorrelation function is presented. Employing distinct theoretical criteria, this mathematical formulation, from a physical point of view, is validated. This expression for the meandering autocorrelation function reproduces well-observed wind-meandering data measured in a micrometeorological site located in a pampa ecosystem area (South Brazil). The comparison shows that the alternative relation for the meandering autocorrelation function is suitable to provide meandering characteristic parameters. Employing MacLaurin's series expansion of a lateral dispersion parameter that represents cases in which turbulence and oscillatory movements associated to the meandering events coexist, a new formulation for the turbulence/meandering dissipation rate has been presented.

Keywords: wind meandering, meandering autocorrelation function, physical and mathematical criteria, turbulent energy spectrum, meandering period

## 1. Introduction

The autocorrelation function is a physical quantity important both for diffusion investigation and to provide basic relations concerning the turbulence processes in the planetary boundary layer (PBL). The employment of autocorrelation functions derived from observed data for distinct movement patterns in the PBL allows to evaluate relevant quantities used to understand complex phenomena in geophysical flow. Therefore, the utilization of the autocorrelation function in the Taylor statistical diffusion theory provides directly the dispersion parameters

© 2017 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

and also formulations for the turbulence dissipation rate, which are utilized, respectively, in Eulerian (Gaussian diffusion plume) and Lagrangian dispersion models.

Simulating turbulent diffusion and species scalar and vector transport in low wind speed situations is a challenging problem. For conditions, in which the mean wind speed tends to be negligible [1], it is very difficult to identify a defined mean wind direction. In this case, large-scale motions, such as submeso motions, control, in a dominate manner, the atmospheric contaminant dispersion [2]. These large horizontal wind oscillations are called meandering and are responsible for the fact that measured autocorrelation functions of the horizontal wind velocity components show a looping behavior, characterized by the presence of negative lobes [3]. This looping pattern with the presence of negative lobes in autocorrelation functions, which characterize the meandering observed phenomenon, can be well reproduced by particular mathematical formulations. With this good fit, it is possible to estimate the principal physical variables that specify the meandering flow [4, 5].

Recently, Moor et al. [6] proposed to utilize a new mathematical formulation to fit the observed low wind speed-meandering autocorrelation functions. This functional form for the horizontal wind velocity components u and v may be written as follows:

$$R(\tau) = \frac{\cos\left(\frac{m\tau}{(m^2+1)T\_\perp}\right)}{\left(1 + \frac{\tau}{(m^2+1)T\_\perp}\right)^2} \tag{1}$$

where τ is the time lag, TL is the decorrelation time scale, and m is the loop parameter. The mathematical formulation, as provided by Eq. (1), is composed of the product of the binomial function (describing the autocorrelation function for the turbulent degrees of freedom [7]) by the cosine (describing the influence of the submeso motions in the autocorrelation function [8]). Therefore, this formulation presents a heuristical flexibility that allows describing the observed characteristics of the atmospheric multiple-scale interaction. Eq. (1) can also be written in a distinct way, namely

$$R(\tau) = \frac{\cos(\mathbf{q}\tau)}{\left(1 + \mathbf{p}\tau\right)^2} \tag{2}$$

with

$$p = \frac{1}{(m^2 + 1)T\_L} \tag{3}$$

and

$$q = \frac{m}{(m^2 + 1)T\_L} \tag{4}$$

q and p are hybrid relations described in terms of the turbulent (TL) and meandering parameters (m) and are originated from the Frenkiel autocorrelation function [9]. Furthermore, a meandering characteristic time scale (meandering period) can be represented as

Testing Physical and Mathematical Criteria in a New Meandering Autocorrelation Function http://dx.doi.org/10.5772/67920 207

$$T^\* = \frac{2\pi}{q} \tag{5}$$

and will be calculated by

and also formulations for the turbulence dissipation rate, which are utilized, respectively, in

Simulating turbulent diffusion and species scalar and vector transport in low wind speed situations is a challenging problem. For conditions, in which the mean wind speed tends to be negligible [1], it is very difficult to identify a defined mean wind direction. In this case, large-scale motions, such as submeso motions, control, in a dominate manner, the atmospheric contaminant dispersion [2]. These large horizontal wind oscillations are called meandering and are responsible for the fact that measured autocorrelation functions of the horizontal wind velocity components show a looping behavior, characterized by the presence of negative lobes [3]. This looping pattern with the presence of negative lobes in autocorrelation functions, which characterize the meandering observed phenomenon, can be well reproduced by particular mathematical formulations. With this good fit, it is possible to estimate the principal

Recently, Moor et al. [6] proposed to utilize a new mathematical formulation to fit the observed low wind speed-meandering autocorrelation functions. This functional form for the horizontal

> cos mτ ðm2þ1ÞTL

1 þ τ ðm2þ1ÞTL

where τ is the time lag, TL is the decorrelation time scale, and m is the loop parameter. The mathematical formulation, as provided by Eq. (1), is composed of the product of the binomial function (describing the autocorrelation function for the turbulent degrees of freedom [7]) by the cosine (describing the influence of the submeso motions in the autocorrelation function [8]). Therefore, this formulation presents a heuristical flexibility that allows describing the observed characteristics of the atmospheric multiple-scale interaction. Eq. (1) can also be written in a

> RðτÞ ¼ cosðqτÞ ð1 þ pτÞ

p ¼ 1

q ¼ m

meandering characteristic time scale (meandering period) can be represented as

ðm2 þ 1ÞTL

ðm2 þ 1ÞTL

q and p are hybrid relations described in terms of the turbulent (TL) and meandering parameters (m) and are originated from the Frenkiel autocorrelation function [9]. Furthermore, a

2 ð1Þ

2 ð2Þ

ð3Þ

ð4Þ

Eulerian (Gaussian diffusion plume) and Lagrangian dispersion models.

206 Turbulence Modelling Approaches - Current State, Development Prospects, Applications

physical variables that specify the meandering flow [4, 5].

wind velocity components u and v may be written as follows:

distinct way, namely

with

and

RðτÞ ¼

$$T^\* = \frac{2\pi \ (m^2 + 1)T\_L}{m} \tag{6}$$

The purpose of this study is to verify that the expression given by Eq. (1) fills the mathematical and physical requirements that must be fulfilled by an autocorrelation function. An additional aim is to employ the best-fitting curves, obtained from Eq. (1), to calculate the loop parameter and the meandering characteristic time scale. The present investigation also provides a new relation for the turbulence/meandering dissipation rate.

## 2. Discussion of the physical and mathematical criteria to the autocorrelation function

In this part of the chapter, we use four established criteria [10] to validate the autocorrelation function, as given by the mathematical formulation described in Eq. (1). Therefore, these criteria are applied to stationary and homogeneous turbulence, which are mathematical descriptions of respective physical requirements for the validation of the autocorrelation function applied to the studies of turbulence. Eq. (1) is described in terms of a parameter m, which indicates the intensity of the meandering phenomena. This parameter is responsible to introduce variations in the usual results that are those normally expected in the classical forms that represent the autocorrelation functions. In sequence, the four criteria are discussed in detail as follows:

	- a. |R(τ)| ≤ = R(0), it is limited in the origin and its maximum value 1 occurs in τ = 0. This fact agrees with the theoretical consideration presented in [11, 12]. The authors suggest that R must osculate the theoretical parabolic form of R in the origin (τ ≈ 0).
	- b. The next property takes into account the qualitative behavior to large values of autocorrelation time. In this case, R(τ) ! 0 when |τ| ! ∞. In Eq. (1), this consideration results of confront theorem applied in jRðjτjÞj ≤ ð1 þ pjτjÞ�2 ;
	- c. To define the integral time scale, it is necessary to calculate the integral ð∞ 0 RðτÞdτ, namely the integral must be convergent. Thus,

$$\int\_0^\infty R(\tau)d\tau \le \left| \int\_0^\infty R(\tau)d\tau \right| \le \int\_0^\infty |R(\tau)|d\tau = \frac{1}{p} = (m^2+1)T\_L\tag{7}$$

is a finite value.

	- the integral resolution is1

$$\int\_0^\infty \frac{\cos\left(q\pi\right)}{\left(1+p\pi\right)^2} d\pi = \left(1+m^2\right)T\_L\left[1-m\left(\sin(m)\text{Ci}(m)-\cos\left(m\right)\left(\text{Si}(m)-\frac{\pi}{2}\right)\right)\right] \tag{8}$$

$$= (1-\beta\_\text{m})(1+m^2)T\_L$$

Therefore, using the above considerations, this result suggests the following interpretation: the case m = 0 will establish the autocorrelation time TL to a fully developed turbulence hypothesis. On the other hand, when m 6¼ 0, the time scale will be proportional to TL by the following correction factor (1 � βm) yielding

$$T\_{L\mathfrak{m}} = (1 - \mathfrak{B}\_{\mathfrak{m}})(1 + m^2)T\_L \tag{9}$$

(IV) To validate these criteria, basically, we must verify the following relation [15]:

$$E(\omega) = \frac{2\langle \mu^2 \rangle}{\pi} \int\_0^\varkappa R(\tau) \cos \left(\omega \tau\right) d\tau \approx \omega^{-2} \tag{10}$$

1 Ci(m) is the cosine integral function and Si(m) is the sine integral function.

In this case, applying the Fourier transform in Eq. (1) results

(II) RðτÞ∈C0

process.

ð∞ 0

cos ðqτÞ ð1 þ pτÞ

as ð∞ 0

1

ðRÞ, wherever the smoothness is not valid in τ because the lateral derivatives in

origin are not defined. This fact yields the angular functional form of the autocorrelation function to τ = 0, and its physical implication is that this autocorrelation function is not suitable to investigate the viscous microstructure of turbulence [13]. Therefore, to practical applications, where the hypothesis of fully developed turbulence is admitted (Re ≫ 0), the validity of this criteria is not relevant by the fact that the energy-containing and the characteristics scales of eddies in this region are not relevant in the scalar diffusion

(III) As already mentioned, the integral time scale TL is well defined and its value is obtained

2 dτ ¼ ð1 þ m2ÞTL 1 � m sinðmÞCiðmÞ � cos ðmÞ SiðmÞ � π

Therefore, using the above considerations, this result suggests the following interpretation: the case m = 0 will establish the autocorrelation time TL to a fully developed turbulence hypothesis. On the other hand, when m 6¼ 0, the time scale will be propor-

¼ ð1 � βmÞð1 þ m2ÞTL

h i � � � �

2

ÞTL ð9Þ

RðτÞcos ðωτÞdτ ≈ ω�2 ð10Þ

ð8Þ

RðτÞdτ. Here, it is necessary to make some considerations:

208 Turbulence Modelling Approaches - Current State, Development Prospects, Applications

integration recovers dimensions of integral scale;

tional to TL by the following correction factor (1 � βm) yielding

TLm ¼ ð1 � βmÞð1 þ m2

(IV) To validate these criteria, basically, we must verify the following relation [15]:

ð∞ 0

EðωÞ ¼ 2〈u2〉 π

Ci(m) is the cosine integral function and Si(m) is the sine integral function.

$$\begin{split} \frac{E(\omega)}{\langle \mu^2 \rangle T\_L} &= \frac{(m^2 + 1)}{\pi} \left\{ 2 - A \left[ \cos \left( A \right) \left( \frac{\pi}{2} - \text{Si}(A) \right) + \sin \left( A \right) \text{Ci}(A) \right] \right. \\ &\left. - B \left[ \cos \left( B \right) \left( \frac{\pi}{2} - \text{Si}(B) \right) + \sin \left( B \right) \text{Ci}(B) \right] \right\} \end{split} \tag{11}$$

with A ¼ ðm2 þ 1ÞðωTLÞ þ m and B ¼ ðm2 þ 1ÞðωTLÞ � m:

Eq. (11) does not express the form ω�2 , explicitly, but this behavior is founded in its graphic representation (see Figure 1).

The present analysis, based on mathematical and physical criteria extensively discussed in the literature, showed that the meandering autocorrelation function, as given by Eq. (1), can be employed to investigate the physical patterns associated to the meandering multiplescale flow.

Figure 1. Turbulent energy spectrum from the autocorrelation functions (Eq. (1)) for distinct values of the m parameter.

#### 3. Reproducing observed meandering autocorrelation functions

In this part of the study, we use meandering data observed in a low wind speed PBL to calculate the experimental autocorrelation functions. These experimental meandering autocorrelation functions were obtained from the following relation:

$$R(\tau) = \frac{\langle u(t)u(t+\tau) \rangle}{\langle u^2 \rangle} \tag{12}$$

where 〈u2〉 ¼ σ2 u is the variance of the velocity. These observed functions are fitted by Eq. (1). Therefore, Eq. (1) is evaluated as a mathematical representation to reproduce the observed negative lobes in the meandering autocorrelation functions and utilized to calculate the loop parameter and the meandering period.

The low wind speed data were collected at the Federal University of Santa Maria (Rio Grande do Sul, Brazil) meteorological site. The wind velocity components were sampled at a frequency of 10 Hz by a sonic anemometer installed at a height of 3 m located in a pampa ecosystem area.

Figures 2 and 3 establish the comparison among the autocorrelation function obtained from the low wind speed data set (continuous line) and the correspondent best fit (dashed line)

Figure 2. Autocorrelation function for the horizontal wind-velocity component u.

Testing Physical and Mathematical Criteria in a New Meandering Autocorrelation Function http://dx.doi.org/10.5772/67920 211

Figure 3. Autocorrelation function for the horizontal wind-velocity component v.

RðτÞ ¼ 〈uðtÞuðt þ τÞ〉

210 Turbulence Modelling Approaches - Current State, Development Prospects, Applications

Therefore, Eq. (1) is evaluated as a mathematical representation to reproduce the observed negative lobes in the meandering autocorrelation functions and utilized to calculate the loop

The low wind speed data were collected at the Federal University of Santa Maria (Rio Grande do Sul, Brazil) meteorological site. The wind velocity components were sampled at a frequency of 10 Hz by a sonic anemometer installed at a height of 3 m located in a pampa ecosystem area. Figures 2 and 3 establish the comparison among the autocorrelation function obtained from the low wind speed data set (continuous line) and the correspondent best fit (dashed line)

u is the variance of the velocity. These observed functions are fitted by Eq. (1).

where 〈u2〉 ¼ σ2

parameter and the meandering period.

Figure 2. Autocorrelation function for the horizontal wind-velocity component u.

〈u2〉 ð12Þ

provided from Eq. (1). These figures were calculated from 1-h time series presenting the mean horizontal speed ðu ¼ 0:96 ms�1Þ. They show that the autocorrelation function, provided by the mathematical formulation (Eq. (1)), represents fairly well the observed form of the meandering autocorrelation functions including their negative lobes. This oscillatory behavior, generating negative lobes in the autocorrelation functions, calls attention for the presence of multiple scales in the horizontal wind-meandering flow. The action of the submeso [8] and turbulent scale motions provoke, for short-time intervals, large variations in the horizontal wind direction.

Employing Eqs. (3)–(5) and a best-fit curve condition, it is possible to calculate, respectively, the magnitudes for the looping parameter and meandering period (a complete discussion about this development can be seen in [1]). These values are presented in Table 1.

The magnitudes for mu and mv in Table 1 agree very well with the results obtained by Moor et al. [6] that employed wind-meandering data which were collected in a nocturnal stable PBL in a Brazilian Amazon Large Scale Biosphere-Atmosphere Project. The meandering periods T� u and T� v in Table 1 present similar magnitudes with the mean values found by [4]. The characteristic values of the meandering phenomenon are exhibited in Table 1, which can be used in Eqs. (6) and (9) to obtain the new turbulent scales, yielding TL,mu ≈ 70 s and TL,mv ≈ 62 s. These magnitudes of TL,m are in agreement with the results estimated by Moor et al. [6].

Table 1. Meandering period and loop parameter values for the u and v components.

## 4. Turbulence/meandering dissipation rate

The lateral dissipation parameter σy is a statistical quantity fundamental for the dispersion modeling and for the derivation of turbulence/meandering dissipation rate functional form [16, 17]. From the Taylor statistical diffusion theory, this relevant parameter may be described as

$$
\sigma\_y^2(t) = 2\sigma\_v^2 \Big|\_{0}^{t} (t - \tau) R\_v(\tau) d\tau \tag{13}
$$

where σv is the standard deviation of the turbulent lateral velocity.

Taylor [18] considered an exponential form for the autocorrelation function and first derived an expression for σ2 y

$$
\sigma\_y^2(t) = 2\sigma\_v^2 T\_{Lv}^2 \left(\frac{t}{T\_{Lv}} - 1 + \mathcal{e}^{\frac{-t}{T\_{Lv}}}\right) \tag{14}
$$

where t is the travel time of the fluid particle, TLv is the lateral Lagrangian integral time scale (defined in Criteria III).

Tennekes [16] used Eq. (14) and inertial subrange similarity arguments [15, 12] to derive the following fundamental expression for the turbulence dissipation rate ε:

$$
\varepsilon = \frac{2}{\mathcal{C}\_0} \frac{\sigma\_v^2}{T\_{Lv}} \tag{15}
$$

where C0 is the Kolmogorov constant.

Following the above development and employing the autocorrelation function as given by Eq. (1) into Eq. (13) yields

Testing Physical and Mathematical Criteria in a New Meandering Autocorrelation Function http://dx.doi.org/10.5772/67920 213

$$\frac{\sigma\_y^2(t)}{2\sigma\_v^2} = -\frac{\cos\left(qt\right)}{p^2} + \frac{1+pt}{p^2} - \frac{q(1+pt)}{p^3} \left[ \sum\_{n=0}^{\infty} \left( \frac{(pt)^{2n+2}}{2n+2} - \frac{(pt)^{2n+3}}{2n+3} \right) \sum\_{k=0}^{n} \frac{(-1)^k m^{2k+1}}{(2k+1)!} \right] \tag{16}$$

$$-\frac{1}{p^2} \left[ \sum\_{n=0}^{\infty} \left( \frac{(pt)^{2n+1}}{2n+1} - \frac{(pt)^{2n+2}}{2n+2} \right) \sum\_{k=0}^{n} \frac{(-1)^k m^{2k}}{(2k)!} \right]$$

The magnitudes for mu and mv in Table 1 agree very well with the results obtained by Moor et al. [6] that employed wind-meandering data which were collected in a nocturnal stable PBL in a Brazilian Amazon Large Scale Biosphere-Atmosphere Project. The meandering periods T�

The lateral dissipation parameter σy is a statistical quantity fundamental for the dispersion modeling and for the derivation of turbulence/meandering dissipation rate functional form [16, 17]. From the Taylor statistical diffusion theory, this relevant parameter may be described as

1337 1337 5.2 4.2

Taylor [18] considered an exponential form for the autocorrelation function and first derived

where t is the travel time of the fluid particle, TLv is the lateral Lagrangian integral time scale

Tennekes [16] used Eq. (14) and inertial subrange similarity arguments [15, 12] to derive the

Following the above development and employing the autocorrelation function as given by

ε ¼ 2 C0 σ2 v TLv

t TLv

� 1 þ e �t TLv

� �

ðt � τÞRvðτÞdτ ð13Þ

vðsÞ mu mv

magnitudes of TL,m are in agreement with the results estimated by Moor et al. [6].

4. Turbulence/meandering dissipation rate

σ2

Table 1. Meandering period and loop parameter values for the u and v components.

212 Turbulence Modelling Approaches - Current State, Development Prospects, Applications

where σv is the standard deviation of the turbulent lateral velocity.

σ2

yðtÞ ¼ 2σ2

following fundamental expression for the turbulence dissipation rate ε:

vT2 Lv

yðtÞ ¼ 2σ2 v ðt 0

v in Table 1 present similar magnitudes with the mean values found by [4]. The characteristic values of the meandering phenomenon are exhibited in Table 1, which can be used in Eqs. (6) and (9) to obtain the new turbulent scales, yielding TL,mu ≈ 70 s and TL,mv ≈ 62 s. These

and T�

T�

an expression for σ2

(defined in Criteria III).

Eq. (1) into Eq. (13) yields

y

uðsÞ T�

where C0 is the Kolmogorov constant.

u

ð14Þ

ð15Þ

For t ≪ TLv, MacLaurin's series expansions of the meandering and turbulent dissipation parameter (Eq. (16)) can be approximated by

$$
\sigma\_y^2(t) = \sigma\_v^2 t^2 - \frac{2p\sigma\_v^2}{3}t^3 \tag{17}
$$

The confrontation of Eq. (17) with the Taylor statistical diffusion theory (Eq. (13)), for dispersion periods that are very smaller than the Lagrangian integral time scale, demonstrates that the negative expression in Eq. (17) represents a term that reduces the hybrid dispersion parameter. This negative contribution results in the suppression of a number of degrees of freedom of the turbulent field associated to the high-frequency harmonics [17]. As a consequence, it is reasonable to relate the term 2pσ2 v 3 t 3 to the Kolmogorov spectrum high-frequency eddies. This comparison was accomplished by [16] employing the Lagrangian structure function, the Lagrangian autocorrelation function, and the inertial subrange Lagrangian turbulent spectrum. Tenneke's derivation provides the following relationship for the lateral dispersion parameter in terms of inertial subrange quantities:

$$
\sigma\_y^2(t) = \sigma\_v^2 t^2 - \frac{\mathbb{C}\_{0\mathcal{E}}}{6} t^3 \tag{18}
$$

The comparison of Eq. (17) with Eq. (18) leads to the following fundamental relation:

$$\varepsilon = \frac{4}{(m^2 + 1)} \frac{\sigma\_v^2}{\mathbb{C}\_0 T\_{Lv}} \tag{19}$$

The new expression, as given by Eq. (19), maintains the basic argument that turbulent motion is dissipated at a rate proportional to the kinetic energy and inversely proportional to the memory effect associated to the energy-containing eddies. Therefore, Eq. (19) is described in terms of the looping parameter m. Such a parameter, associated to the meandering phenomenon, becomes the new dissipation rate distinct of those applied to the purely turbulent cases. The looping parameter m determines the magnitudes of the dissipation rate. Hence, the dissipation rate increases when the turbulence is dominant. On the other hand, the dissipation rate decreases when submeso motions, associated to the meandering phenomenon, control the geophysical flow.

## 5. Conclusion

In this study, a mathematical relation to represent observed meandering autocorrelation functions, derived from the heuristic arguments, is tested and validated using well-known mathematical and physical criteria. Therefore, it is employed to describe experimental windmeandering data. From this approach and utilizing best-fitting curves, it was possible to obtain characteristic values for the loop parameter and the meandering period. These quantities, which characterize the wind-meandering phenomenon, are shown in Table 1.

An important aspect in the present development has been the derivation of Eq. (11) which represents a theoretical formulation to model observed wind-meandering spectral data. Thus, Eq. (8) is able to describe the effect of the submeso and turbulent scales and hence allow to identify the low frequencies associated to the meandering spectral peaks.

Another relevant result is provided by Eq. (19). Therefore, this expression is able to evaluate dissipation rates generated from complex flows. Complex flows, such as meandering motions, are those in which there are interactions between distinct circulations characterized by movements presenting different spatial and time scales. As a consequence, Eq. (19) allows to describe this atmospheric phenomenon type.

## Acknowledgements

The authors would like to thank the CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico) and CAPES ( Coordenação de Aperfeiçoamento de Pessoal de Nível Superior) for partial financial support of this work.

## Appendix A

In this appendix, the general criteria to validate mathematical formulation for the autocorrelation function are presented. These criteria are a summary of physical and mathematical requirements applied in the statistical treatment of the turbulence founded in the classical literature [10, 13].

In particular, Manomaiphiboon and Russell [10] presented four criteria for the validation of the ACF to homogeneous and stationary turbulence, as follows:

(I) R(τ) is limited at origin (neighborhood of origin) jRðτÞj ≤ 1 ¼ Rð0Þ. Besides, limjτj!∞RðτÞ

$$=0 \text{ and } \int\_0^\infty |R(\tau)|d\tau < \infty.$$

In fact, the generic form of the autocorrelation function is given by the Eq. (12)

following R(0) = 1, which indicates that the maximum correlation is τ = 0 and, consequently to any other correlation time, is given by |R(τ)| ≤ 1.

On the other hand, the condition that ð∞ 0 jRðτÞjdτ must be finite will permit to obtain the Lagrangian integral scale time, which will be defined in Criteria III.

 $\text{(III)} \quad R(\tau) \text{ is smooth in } \tau. \text{ At origin}$  $\frac{d R(\tau)}{d \tau} \Big|\_{\tau=0} = 0 \text{ and } \frac{d^2 R(\tau)}{d \tau^2} \Big|\_{\tau=0} < 0.$

5. Conclusion

In this study, a mathematical relation to represent observed meandering autocorrelation functions, derived from the heuristic arguments, is tested and validated using well-known mathematical and physical criteria. Therefore, it is employed to describe experimental windmeandering data. From this approach and utilizing best-fitting curves, it was possible to obtain characteristic values for the loop parameter and the meandering period. These quanti-

An important aspect in the present development has been the derivation of Eq. (11) which represents a theoretical formulation to model observed wind-meandering spectral data. Thus, Eq. (8) is able to describe the effect of the submeso and turbulent scales and hence allow to

Another relevant result is provided by Eq. (19). Therefore, this expression is able to evaluate dissipation rates generated from complex flows. Complex flows, such as meandering motions, are those in which there are interactions between distinct circulations characterized by movements presenting different spatial and time scales. As a consequence, Eq. (19) allows to

The authors would like to thank the CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico) and CAPES ( Coordenação de Aperfeiçoamento de Pessoal de Nível Superior)

In this appendix, the general criteria to validate mathematical formulation for the autocorrelation function are presented. These criteria are a summary of physical and mathematical requirements applied in the statistical treatment of the turbulence founded in the classical

In particular, Manomaiphiboon and Russell [10] presented four criteria for the validation of the

(I) R(τ) is limited at origin (neighborhood of origin) jRðτÞj ≤ 1 ¼ Rð0Þ. Besides, limjτj!∞RðτÞ

following R(0) = 1, which indicates that the maximum correlation is τ = 0 and, conse-

In fact, the generic form of the autocorrelation function is given by the Eq. (12)

ties, which characterize the wind-meandering phenomenon, are shown in Table 1.

identify the low frequencies associated to the meandering spectral peaks.

214 Turbulence Modelling Approaches - Current State, Development Prospects, Applications

describe this atmospheric phenomenon type.

for partial financial support of this work.

ACF to homogeneous and stationary turbulence, as follows:

quently to any other correlation time, is given by |R(τ)| ≤ 1.

jRðτÞjdτ < ∞.

Acknowledgements

Appendix A

literature [10, 13].

¼ 0 and

ð∞ 0

The mathematical properties described by Criteria II result of the Taylor series expansion of R(τ) in the neighborhood of origin (τ ≈ 0),

$$R(\tau) = \sum\_{n=0}^{\infty} \frac{d^n R(\tau)}{d\tau^n} \Big|\_{\tau=0} \frac{\tau^n}{n!} \,. \tag{20}$$

Due to the parity of R(τ) (stationary turbulence), the odd-order derivatives are null, in particular, dRðτÞ dτ j τ¼0 ¼ 0. Therefore, from Eq. (20), it results as follows:

$$R(\tau) = 1 + \frac{1}{2} \frac{d^2 R(\tau)}{d\tau^2} \bigg|\_{\tau=0} \tau^2 + O(\tau^4) \tag{21}$$

and, consequently, 1 2 d2 RðτÞ dτ2 j τ¼0 < 0. The test of the second derivative in an interval centered at the origin (τ ≈ 0) ensures a representation of R(τ) by quadratic polynomial and, at τ = 0, agrees with the first criteria.

Following [12], to introduce a time scale τL, which contains temporal values close to the origin, the Taylor's autocorrelation function can be approximated by

$$R(\tau) \approx 1 - \frac{\tau^2}{\tau\_L^2} \tag{22}$$

meaning an osculating parabola R(τ) at origin. This expression defines the time scale τL by the relation

$$\frac{1}{\tau\_L^2} = \left. -\frac{1}{2} \frac{d^2 R(\tau)}{d\tau^2} \right|\_{\tau=0}. \tag{23}$$

$$R(\tau) = \frac{1}{\langle \iota^2 \rangle} \Big|\_{0}^{\approx} E(\omega) \cos \left( \omega \tau \right) d\omega \tag{24}$$

and

$$E(\omega) = \frac{2\langle \mu^2 \rangle}{\pi} \Bigg| \overset{\rightsquigarrow}{\sim}\_{0} \mathbf{R}(\tau) \cos \left(\omega \tau\right) d\tau \tag{25}$$

where ω is the turbulent frequency.

According to the inertial subinterval theory (K41) [20], E(ω) can be expressed by EðωÞ ¼ κEω�2 ∝ ω�2, to 1 ≪ ωTL ≪ TL τη , where κ is the dimensional universal constant, E is the average rate of turbulent energy dissipation, and τη is the Kolmogorov time scale.

## Author details

Charles R.P. Szinvelski, Lidiane Buligon, Michel Baptistella Stefanello, Silvana Maldaner, Debora R. Roberti and Gervásio Annes Degrazia\*

\*Address all correspondence to<EMAIL_ADDRESS>
Universidade Federal de Santa Maria, Santa Maria, Brazil

## References

and

Author details

References

EðωÞ ¼ 2〈u2〉 π

216 Turbulence Modelling Approaches - Current State, Development Prospects, Applications

where ω is the turbulent frequency.

EðωÞ ¼ κEω�2 ∝ ω�2, to 1 ≪ ωTL ≪ TL

Debora R. Roberti and Gervásio Annes Degrazia\*

\*Address all correspondence to<EMAIL_ADDRESS>
Universidade Federal de Santa Maria, Santa Maria, Brazil

Atmospheric Environment. 2006;40:7234-7245

2016;161(1):165-182. DOI: 10.1007/s10546-016-0165-6

speed conditions. Physica A. 2015;438:286-292

ð ∞

RðτÞcos ðωτÞdτ ð25Þ

τη , where κ is the dimensional universal constant, E is

0

According to the inertial subinterval theory (K41) [20], E(ω) can be expressed by

the average rate of turbulent energy dissipation, and τη is the Kolmogorov time scale.

Charles R.P. Szinvelski, Lidiane Buligon, Michel Baptistella Stefanello, Silvana Maldaner,

[1] Mortarini L, Anfossi D. Proposal of an empirical velocity spectrum formula in low-wind speed conditions. Quarterly Journal of the Royal Meteorological Society. 2015;141:85-97

[2] Anfossi D, Alessandrini S, Trini Castelli S, Ferrero E, Oettl D, Degrazia G. Tracer dispersion simulation in low wind speed conditions with a new 2-D Langevin equation system.

[3] Steeneveld GJ, Holtslag AAM. Meteorological aspects of air quality. In: Air Quality in the

[4] Mortarini L, Stefanello M, Degrazia G, Roberti D, Castelli ST, Anfossi D. Characterization of wind meandering in low-wind-speed conditions. Boundary-Layer Meteorology.

[5] Cava D, Mortarini L, Giostra U, Richiardone R, Anfossi D. A wavelet analysis of lowwind-speed submeso motions in a nocturnal boundary layer. Quarterly Journal of the

[6] Moor L, Degrazia G, Stefanello M, Mortarini L, Acevedo O, Maldaner S, Szinvelski C, Roberti D, Buligon L, Anfossi D. Proposal of a new autocorrelation function in low wind

21st Century. New York: Nova Science Publishers; 2009. pp. 67-114

Royal Meteorological Society. 2017;143:661-669. DOI: 10.1002/qj.2954


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## Contents


**4. Experiments and results**

#### **4.1 Annotation results**

In the first trial, Cohen's *kappa* and Krippendorff's *alpha* are obtained at 0.80 and 0.78 respectively. Which are highly acceptable in the study since the scores measured the overall attribute and polarity. To identify the category that has the largest variation between two coders, Cohen's *kappa* for each label was calculated separately. Results (**Table 1**) indicated that Polarity had the highest agreement, while attribute showed lower agreement among two annotators. At the end of the first trial, both coders discussed the issues they encountered when they were annotating the corpus and make efforts to improve the preliminary annotation schema. The problems include dealing with the sentence that is difficult to assign the aspects.

Based on the revisions of the annotation schema, the coders conducted the second trial. With the revised annotation schema, the Cohen's *kappa* for the attribute and polarity is obtained at 0.89 and 0.91 respectively. In addition, Cohen's *kappa* and Krippendorff's *alpha* for the aspect-sentiment pair is computed by the end of the second trial, with 0.82 and 0.81 respectively, which indicated that the annotation schema in this study is valid.

#### **4.2 Model training**

The experiment was conducted on the dataset of TripAdvisor hotel reviews which contains 5506 sentences, where the numbers of positive, neutral, and negative sentiment samples are 3032, 2986, and 2725, respectively. Given a dataset, maximizing the predictive performance and training efficiency of a model requires finding the optimal network architecture and tuning hyper-parameters. In addition, the samples can significantly affect the performance of the model. To investigate the effect of

**Table 1.**

*Cohen's kappa for categories of aspect and polarity.*

sentiment sample fractions on the model performance, four sub-datasets with 4000 sentiment samples subjected to different sentiment fractions were resampled from the TripAdvisor hotel dataset as the train sets, one is a balanced dataset and three are unbalanced datasets that the sample fraction of sentiment positive, neutral, and negative dominated, respectively. In addition, it is observed that the average number of the aspects in a sentence is about 1.4, and the average length of the aspects in a sentence is about 8.0, which indicates that one sentence normally contains more than one aspect and the aspect averagely contains eight characters. The number of aspects in train and test sets is more than 850 and 320, respectively, which confirms the diversity of aspects in the dataset of TripAdvisor hotel reviews. For each train set, 20% of reviews were selected as the validation set.

Attention-based gated RNN models including LSTM and GRU were used for ABSA. Attention-based GRU/LSTM without and with aspect embedding were referred to as AT-GRU/AT-LSTM and ATAE-GRU/ATAE-LSTM, respectively. The details of the configurations and used hyper-parameters are summarized in **Table 2**. In the experiments, all word embeddings with the dimension of 300 were initialized by GloVe [17]. The word embeddings were pre-trained on an unlabeled corpus of which size is about 840 billion. The dimension of hidden layer vectors and aspect embedding are 300 and 100 respectively. The weight matrices are initialized with the uniform distribution U (0.1, 0.1), and the bias vectors are initialized to zero. The learning rate and mini-batch size are 0.001 and 16 respectively. The best optimizer and number of epochs were obtained from {SGD, Adam, AdaBelief} and {100, 300, 500} respectively via grid search. The optimal parameters based on the best performance on the validation set were kept and the optimal model is used for evaluation in the test set.

The aim of the training is to minimize the cross-entropy error between the target sentiment distribution *y* and the predicted sentiment distribution ^*y*. However, overfitting is a common issue during training. In order to avoid the over-fitting, regularization procedures including L2-regularization, early stopping as well as dropout were used in the experiment. L2-regularization adds "squared magnitude" of coefficient as a penalty term to the loss function.

#### **Table 2.**

*Details of configurations and used hyper-parameters.*

*Tourist Sentiment Mining Based on Deep Learning DOI: http://dx.doi.org/10.5772/intechopen.98836*

$$\text{loss} = -\sum\_{i} \sum\_{j} y\_i^j \log \hat{y}\_i^j + \lambda ||\theta||^2 \tag{15}$$

where *i* is the index of review; *j* is the index of sentiment class, and the classification in this paper is three-way; *λ* is the L2-regularization term, which modified the learning rule to multiplicatively shrink the parameter set on each step before performing the usual gradient update; *θ* is the parameter set.

On the other hand, early stopping is a commonly used and effective way to avoid over-fitting. It reliably occurs that the training error decreases steadily over time, but validation set error begins to rise again. Therefore, early stopping terminates when no parameters have improved over the best-recorded validation error for a pre-specified number of iterations. Additionally, dropout is a simple way to prevent the neural network from overfitting, which refers to temporarily removing cells and their connections from a neural network [55]. In an RNN model, dropout can be implemented on input, output, and hidden layers. In this study, only the output layer with a dropout ratio of 0.5 was followed by a linear layer to transform the feature representation to the conditional probability distribution.

Optimizers are algorithms used to update the attributes of the neural network such as parameter set and learning rate to reduce the losses to provide the most accurate results possible. Three optimizers namely SGD [56], Adam [57], and AdaBelief [58] were used in the experiment to search for the best performance. The standard SGD uses a randomly selected batch of samples from the train set to compute derivate of loss, on which the update of the parameter set is dependent. The updates in the case of the standard SGD are much noisy because the derivative is not always toward minima. As result, the standard SGD may have a more time complexity to converge and get stuck at local minima. In order to overcome this issue, SGD with momentum is proposed by Polyak [56] (1964) to denoise derivative using the previous gradient information to the current update of the parameter set. Given a loss function *f*ð Þ*θ* to be optimized, the SGD with momentum is given by:

$$
\sigma\_{t+1} = \beta v\_t - a \mathbf{g}\_t \tag{16}
$$

$$
\theta\_{t+1} = \theta\_t + \nu\_{t+1} \tag{17}
$$

where *α* >0 is the learning rate; *β* ∈ ½ � 0, 1 is the momentum coefficient, which decides the degree to which the previous gradient contributing to the updates of the parameter set, and *gt* ¼ ∇*f*ð Þ *θt* is the gradient at *θt*.

Both Adam and AdaBelief are adaptive learning rates optimizer. Adam records the first moment of gradient *mt* which is similar to SGD with momentum and second moment of gradient *vt* in the meanwhile. *mt* and *vt* are updated using the exponential moving average (EMA) of *gt* and *g*2 *t* , respectively:

$$m\_{t+1} = \beta\_1 m\_t + (1 - \beta\_1) \mathbf{g}\_t \tag{18}$$

$$
\sigma\_{t+1} = \beta\_2 \upsilon\_t + (1 - \beta\_2) \mathbf{g}\_t^2 \tag{19}
$$

where *β*1 and *β*2 are exponential decay rates.

The second moment of gradient *st* in AdaBelief is updated using the EMA of *gt* � *mt* � �2 , which is easily modified from Adam without extra parameters:

$$\mathbf{s}\_{t+1} = \beta\_2 \mathbf{s}\_t + (\mathbf{1} - \beta\_2) \left(\mathbf{g}\_t - m\_t\right)^2 \tag{20}$$

The update rules for parameter set using Adam and AdaBelief are given by Eqs. (23) and (24), respectively:

$$
\theta\_{t+1} = \theta\_t - \frac{am\_t}{\sqrt{v\_t} + \varepsilon} \tag{21}
$$

$$
\theta\_{t+1} = \theta\_t - \frac{am\_t}{\sqrt{s\_t} + \varepsilon} \tag{22}
$$

where *ε* is a small number, typically set as 10�8.

Specifically, the update direction in Adam is *mt=* ffiffiffiffi *vt* p , while the update direction in AdaBelief is *mt=* ffiffiffi *st* p . Intuitively, 1*=* ffiffiffi *st* p is the "belief" in the observation, viewing *mt* as the prediction of *gt* , AdaBelief takes a large step when observation *gt* is close to prediction *mt*, and a small step when the observation greatly deviates from the prediction.

It is noted that the best models in the validation set were obtained by returning to the parameter set at the point in time with the lowest validation set error.

#### **4.3 Results and analysis**

As for the confusion matrix for a multi-class classification task, accuracy is the most basic evaluation measure of classification. The evaluation measure accuracy represents the proportion of the correct predictions of the trained model, and it can be calculated as:

$$Accuracy = \frac{\sum\_{1}^{C} TP\_i}{N} \tag{23}$$

where *C* is the number of classes (*C* equals to 3 in this study); *N* is the sample number of the test set; *TPi* is the number of true predictions for the samples of the *i th* class, which is diagonally positioned in the confusion matrix. In addition to accuracy, classification effectiveness is usually evaluated in terms of macro precision and recall, which are aimed at a class with only local significance. As **Figure 1** illustrates, the class that is being measured is referred to as the positive class and the rest classes are uniformly referred to as the negative classes. The macro precision is the proportion of correct predictions among all predictions with the positive class, while macro recall is the proportion of correct predictions among all positive instances. The macro F1-score is the harmonic mean of macro precision and recall. The macro-average measures take evaluations of each class into consideration, which can be computed as:

$$\text{MacroPrecision} = \frac{1}{\text{C}} \sum\_{i=1}^{C} \frac{TP\_i}{TP\_i + FP\_i} \tag{24}$$

$$\text{MacroRecall} = \frac{1}{\text{C}} \sum\_{i=1}^{\text{C}} \frac{T P\_i}{T P\_i + F \text{N}\_i} \tag{25}$$

$$Macro-F1 = \frac{2 \times \text{MacroPrecision} \times \text{MacroRecall}}{\text{MacroPrecision} + \text{MacroRecall}} \tag{26}$$

where *FPi* and *FNi* are the number of false predictions for the positive and negative samples of the *i th* class, respectively.

**Figure 1.** *Summary of model performance.*

This study computed accuracy (A), macro precision (P), macro recall (R), and macro F1-score (F) of AT-GRU, ATAE-GRU, AT-LSTM, and ATAE-LSTM trained with various optimizers and epochs. The results show: (1) Attention-based models (AT-GRU and AT-LSTM) performed better than attention-based models with aspect embedding (ATAE-GRU and ATAE-LSTM). Taken Dataset 1 for example, the best accuracy in the test set using AT-GRU was 80.7%, while the best accuracy using ATAE-GRU was 75.3%; (2) Attention-based GRU performed better than attention-based LSTM. Taken AT-GRU and AT-LSTM for example, the accuracy and macro F1-score of AT-GRU for all datasets were higher than those of AT-LSTM; (3) The balanced dataset (Dataset 1) achieved the best predictive performance for all models. For the unbalanced datasets, the accuracy was exactly close to that of the balanced dataset. However, the macro precision, recall, and F1-score were significantly lower than those of the balanced dataset, which confirmed that the balanced dataset had the best generalization and stability in this study; (4) For Dataset 3 in which the neutral sentiment samples dominated, all of the models exhibited the worst predictive performance compared with other datasets. The candidate model for each dataset is illustrated in **Figure 1**. It is noted that the candidate model was selected according to accuracy. However, the model with a higher macro F1-score was selected as the candidate model instead when the accuracies of models were similar. Among 16 models, AT-GRU trained with the optimizer of AdaBelief and epoch of 300 in Dataset 1 achieved the highest accuracy of 80.7% and macro F1-score of 75.0% in the meanwhile. **Figure 2** illustrates the normalized confusion matrix of the best predictive model of which diagonal represented for the precisions. The precisions of positive and negative sentiment classification were about 20% higher than that of neutral sentiment classification, which confirmed that the need to boost the precision of neutral sentiment classification in order to globally improve the accuracy of the model in future work.

Early stopping was used in this research to avoid overfitting and save training time. **Figure 3** illustrates the learning history of AT-GRU using early stopping in four

**Figure 2.** *Normalized confusion matrix of model with best predictive performance.*

**Figure 3.** *Learning history of AT-GRU using early stopping.*

#### *Tourist Sentiment Mining Based on Deep Learning DOI: http://dx.doi.org/10.5772/intechopen.98836*

datasets, where the training stopped when the validation loss kept increasing for 5 epochs (i.e., "patience" equals to 5 in this study). For all datasets, the validation accuracy was exactly close to the training validation during the training procedure, which confirmed that early stopping was able to effectively avoid overfitting. Experimental results of A/P/R/F obtained based on training AT-GRU and AT-LSTM using early stopping. The accuracies obtained by AT-GRU and AT-LSTM were similar. For the balanced dataset, the accuracy and macro F1-score obtained by early stopping were significantly lower than that obtained by the corresponding model without early stopping. This is because the loss function probably found the local minima if the training stopped when the loss started to rise for 5 epochs. All of the optimizers used in this study were aimed at avoiding the loss function sticking at the local minima to find the global loss minima, therefore, using more epochs in the training was effective to obtain the best predictive performance model. On the other hand, for the unbalanced datasets, the accuracy and macro F1-score obtained by early stopping were similar to that obtained by the corresponding model without early stopping, which indicated that early stopping was effective to avoid overfitting as the loss converged fast in the unbalanced dataset. Although early stopping is a straightforward way of avoiding overfitting and improving training efficiency, the trade-off is that the model for test set possibly returns at the time point when reaching the local minima of loss function especially for the balanced dataset, and a new hyper-parameter of "patience" which is sensitive to the results is introduced.

Three optimizers were used in this study to find the best model. **Figure 4** illustrates the learning history of AT-GRU in four datasets. The gap between training and

**Figure 4.** *Learning history of AT-GRU.*

validation accuracy was the largest, which indicated that the worst generalization of Adam among three optimizers in this study although it converged quickly at the very beginning except for Dataset 3. Both SGD and AdaBelief can achieve good predictive performance with good generalization, however, AdaBelief converged faster than SGD, and the best results were achieved by AdaBelief.


**1. Introduction**

Arid and semi-arid areas are extremely sensitive to climate change and human activities, and the forest-steppe ecotone in these areas is among the most vulnerable ecosystems in the world [1–3]. Until now, climate change has posed a serious threat to the forest-steppe ecotone in arid and semi-arid areas by causing tree growth decline and tree mortality [2,4,5]. The latest forecasts suggest that the expansion of arid and semi-arid areas will potentially exacerbate regional warming and habitat fragmentation in the future [3,6]. Therefore, there is an urgent need to pay attention to the forest-steppe ecotone in such ecologically fragile areas.

Pollen taxa concentrates in mountains in arid regions, which are also key areas for ecosystem services. Mountain areas typically have rich and unique pollen taxa [7]. Generally, mountain vegetation belts in arid central Asia have various types, ranging from desert to grassland, forest and alpine meadows as the elevation rises [8]. Among them, mountain forest-steppes, typically situated between the forest and steppe belts, are located at the driest edge of forest distribution vertically [9,10]. As such, it is critical to understand the dynamics of the mountain forest-steppe ecotone under climate change in arid regions. Yet, how the change of pollen reflects the migration of the forest-steppe ecotone remains unclear, despite the series of traditional pollen assemblage studies carried out in some hotspots, including the Tibetan Plateau, the Altai Mountains, and the Taibai Mountains [11–13].

Pollen taxa diversity can serve as a comprehensive indicator reflecting the dynamics of the vegetation belts [4]. Whether past climate change increased or decreased pollen taxa diversity has not yet been fully determined, and changes in the pollen taxa diversity potentially include clues for vegetation belt change, especially for the migration of the mountain forest-steppe ecotone. Thus, the Holocene is an ideal period to explore the migration of the forest-steppe ecotone, wherein which climate change was mainly caused by natural variability.

Moreover, it is important to focus on the response modes of mountain vegetation belts to Holocene climate change and to improve the corresponding mechanisms, which are the key to exploring the history of forest-steppe migration. The position of the mountainous vertical vegetation belt is very sensitive to climate change [11,12], because the vertical belt is the epitome of the horizontal belt (about 1/1000). The vertical belt in arid areas is unique, as the upper and lower limits of the forest are a ffected by temperature and moisture, respectively. Mountain vegetation responds to temperature change by position migration, while it responds to moisture change through changes in species composition [4,5]. These two modes are mostly adopted, even though they might not fully explain the mechanism between mountain vegetation belts and changes in temperature and moisture. Studies have indicated that the width of the mountain vegetation belt and climate are not completely in equilibrium [14,15]. As the climate mode can be warm and dry, warm and wet, cold and dry, and cold and wet, both the upper and the lower limits of the forest belt, which are in di fferent climatic conditions, may respond di fferently [12], which ultimately leads to potential changes in the belt width and position. As such, variations in the width of vegetation belts may also be one of the most typical responses to particular climate change, and may further lead to the change in the forest-steppe ecotone. Therefore, it is of importance to see whether these kinds of response patterns exist in the vegetation belts and can be detected through the change of pollen taxa diversity in arid mountain areas.

The Tien Mountains, the largest mountain system in the world's arid regions, is located in the hinterland of Eurasia, a ffected by the westerlies [16–18], where the vertical di fferentiation of vegetation belts is apparent and the typical mountain forest-steppe exists [19]. Due to the complex topographical conditions, limited range size and arid environments, the forest-steppe ecotone in the Tien Mountains appears to be most threatened by climate deterioration. However, few studies have specifically focused on the migration of the forest-steppe ecotone in response to the e ffect of climate change in the Tien Mountains, despite many studies exploring its climate and vegetation changes at multiple time scales [20–23]. Therefore, there is an urgent need to comprehensively study the response pattern of the forest-steppe ecotone in the Tien Mountains to Holocene climate change based on evidence from both surface pollen and fossil pollen, as pollen can provide a long history of pollen taxa diversity with good temporal continuity [4].

Based on the above description, we aim to explore the following scientific questions: (1) How did the mountainous pollen taxa diversity evolve during the Holocene? (2) How did the pollen taxa diversity reflect the changes in vegetation belts in the Tien Mountains during the Holocene? (3) How did the forest-steppe ecotone migrate in response to the Holocene temperature and moisture change in the Tien Mountains?

#### **2. Materials and Methods**

#### *2.1. Study Area*

The Tien Mountains, the largest mountain system in the world's arid regions, are located in the hinterland of Eurasia, influenced by westerlies (Figure 1a,b) [16]. The annual mean precipitation on the northern slopes changes from 175 mm in the desert zone to 465 mm in the mid-altitude zone [24].

**Figure 1.** The location of the Tien Mountains in the hinterland of Eurasia, influenced by westerly winds. (**a**) Digital elevation model image of the study area. The red solid dot represents the location of Sayram Lake, while the blue one represents the location of Aibi Lake. (**b**) The red dotted area represents the range of subgraph. The blue dotted line represents the modern limit of the East Asian summer monsoon, which also illustrates the horizontal limit of the westerlies effect. (**c**) Vegetation belts on the northern slope of the Tien Mountains.

The Tien Mountains have the most typical and complete vertical natural belt spectrum for temperate arid areas around the world, reflecting the distribution characteristics and changes in the mountain pollen taxa diversity and ecological processes. The northern slope of the Tien Mountains can be divided into the following vegetation belts [14] (Figure 1c): sparse and low cushion vegetation is distributed in high mountain belts above 3400 m a.s.l.; alpine and subalpine meadows dominate from 2700 to 3400 m a.s.l.; conifer forest is distributed on the middle elevations from 1700 to 2700 m a.s.l.; typical steppe dominates from 700 to 1700 m a.s.l., represented by steppe on the sunny slope and patchy forest on the shady slope; desert vegetation is distributed below 700 m a.s.l. Notably, the forest-steppe ecotone, also known as the transition zone between the forest belt and the steppe belt, is currently situated around 1700 m a.s.l. on the northern slope of the Tien Mountains [14].

#### *2.2. Modern Vegetation Survey*

To investigate the changes in the richness of modern plant species along the elevational gradient, typical plots representing the characteristic vegetation composition in each vegetation belt were selected, based on the natural geographical pattern of the Tien Mountains [14]. In the mountainous area, with an elevation from 700 to 1700 m a.s.l., plots were sampled every 100 m along the elevational gradient. Due to a flat terrain in the oasis and the downstream desert area, plots were sampled every 50 km in the horizontal direction. Finally, 40 valid plots were surveyed. During the sample survey, the forest plot area was 10 m × 10 m, and the grassland or meadow community area was 2 m × 2 m. Then the detailed information about vegetation habitat conditions, species composition, coverage and height were recorded.

#### *2.3. Surface Pollen*

Surface soil samples were collected every 100 m in the range of 200 to 3500 m a.s.l. on the northern slope of the Tien Mountains, and 34 samples were obtained to investigate the changes in surface pollen composition and evenness in each vegetation belt [14]. Pollen was extracted by treating with HCl (10%) and NaOH (10%), then sieved through 180 μm and 10 μm sieves, and floated with heavy liquid (KI + HI + Zn) using standard techniques [25]. Each sample was counted to contain at least 250 pollen grains under an optical microscope at 400× magnification.

#### *2.4. Fossil Pollen*

We selected two typical fossil pollen sites with high resolution on the northern slope of the Tien Mountains to explore the evolution of pollen taxa diversity. We digitized all pollen taxa using GetData Graph Digitizer v2.25 software [26] and then used them as analytical paleo data. Considering the location representativeness, age, temporal resolution and pollen identification of fossil pollen cores, the following two sites can meet our analysis needs.

The first sediment core, named SLMH-2009 (44◦35 N, 81◦09 E), was from Sayram Lake, with an elevation of 2072 m (Figure 1a,b) [22], and was located in the middle-low part of the modern conifer forest belt. This core was dated back to the Late Glacial to early Holocene transition, with 11 AMS14C dates, and had a temporal resolution of less than 100 years for each pollen sample. The authors analyzed 150 fossil pollen samples in total, with a minimum of 400 pollen grains identified for each sample.

The second sediment core, named A-01, was from Aibi Lake (44◦54–45◦08 N, 82◦35–83◦10 E) (Figure 1a,b) [23], located in the desert vegetation belt at 200 m a.s.l. This core was dated back to the Late Glacial to early Holocene transition with eight AMS14C dates and had a temporal resolution of less than 150 years for each pollen sample. The authors analyzed 195 fossil pollen samples in total, with a minimum of 350 pollen grains identified for each sample.

#### *2.5. Pollen Taxa Diversity Index*

We chose the Shannon-Wiener index and Simpson index to calculate the pollen taxa diversity, as these two indices include the heterogeneity of the measured plant community. They consider both the richness and evenness of the species in the community. Specifically, the Shannon-Wiener diversity index was used to estimate the level of pollen taxa diversity in the modern ecosystem and in the Holocene, which is a comprehensive index reflecting richness and evenness. Its formula is as follows [27].

$$\text{pH} = -\sum\_{i=1}^{r} \text{Pi} \ln \text{Pi} \tag{1}$$

*Pi* = *ni*/*N*, indicating the relative richness of species.

*ni* represents the number of individuals of each pollen taxon.

*N* represents the total number of individuals of all pollen taxa in the community.

It should be noted that communities with low richness and high evenness, and communities with high richness and low evenness, have low diversity indices.

The Simpson diversity index was also used to present the pollen taxa diversity. Unlike the Shannon-Wiener index, the Simpson index measures the probability that two random entities from the pollen group represent di fferent pollen taxa, which can be quantified by the following equation [28].

$$\mathbf{D} = 1 - \sum\_{i=1}^{N} P\_i^2 \tag{2}$$

*Pi* = *ni*/*N*, indicating the relative richness of species. *ni* represents the number of individuals of each pollen taxon. *N* represents the total number of individuals of all pollen taxa in the community.

#### *2.6. Climate Data*

Holocene temperature change was acquired from the integrated results of paleoclimate records for 30–90◦ N [29,30], and Holocene moisture change was derived from integrated results of paleoclimate records in arid central Asia [16].


**1. Introduction**

In today's competitive business environment, more than 80% of businesses offer their products on various short-term, interest-free credit terms (that is, a credit payment) with a view to stimulate sales and to reduce inventory in the United Kingdom and the United States of America. Likewise, trade credit financing is used by approximately 60% of international trade transactions, rendering it to be the second after that of banks and other financial institutions in the United States of America. Additionally, in order to avoid the risk of order cancellation and non-payment risks, the business frequently offers a partial credit period to the downstream members, who must pay a portion of the procurement amount at the time of placing an order and then receive a permissible delay on the rest of the outstanding amount (that is, a cash-credit payment). On the other hand, granting trade credit increases not only the opportunity loss, but also the default risk from the viewpoint of the business, so the powerful businesses may ask the downstream members to prepay the entire or a fraction of the procurement amount before the delivery to mitigate interest loss and default risks (that is, an advance payment). For example, insurance companies generally require an advance payment in order to extend coverage to the insured party.

In existing literatures, Zhang [1] proposed an advance payment plan because it may save time and money for a customer to prepay, for example 80.00 USD for 4 months of water bills, instead of paying 20.00 USD each month for 4 months. All the above mentioned payment types can be combined such that, for example, the supplier demands the retailer to prepay 5 to 10% of the total procurement cost as a good-faith deposit when both sign a contract of agreement to install some item(s). Upon delivery of the item(s), a cashon-delivery payment to cover the supplier's cost of the item(s) is then required. In this contractual arrangement, the retailer will pay the remainder of the total cost after the work is completed.

In reality, the deteriorating items have a maximum lifetime due to their physical nature and must be disposed of after the expiration date, due to the fact that consumers evaluate the freshness of a deteriorating item by checking its expiration date before making a purchase, and the willingness to purchase a deteriorating item decreases throughout its shelf-life. Furthermore, the expiration date is the most important factor that is time-bound and plays an important role in developing the inventory model. In practice, most of the products maintain their quality or original situation over a span; that is, during this span, deterioration does not occur, and then they begin to deteriorate in the next period. It is observed that foodstuffs, first-hand vegetables, and fruits have a short span during which fresh quality is maintained and there is almost no spoilage. These processes are defined as the non-instantaneous deterioration of the product.

The quality of the products is considered to be another direct factor to affect a consumer's purchase decision as well. Furthermore, this article assumes that the retailer receives the items with a time-varying deterioration rate depending on its expiration date, such as seasonal products, and that an inspection will occur during the state of no deterioration in order to manage the quality of the products. By performing the screening process, the retailer detects the imperfect items and throws them out.

In this article, we first establish an inventory model for non-instantaneous deteriorating items with expiration dates and imperfect quality in which we assume a 100% screening process to identify imperfect items. We then consider that the supplier asks the retailer to prepay a fraction of the procurement cost when signing a contract to buy products, to pay another fraction of the procurement cost in cash upon receiving the ordered quantity, and

to receive a short-term interest-free credit term on the remaining procurement cost (that is, an advance-cash-credit payment). Likewise, the retailer gives the customer the opportunity to pay a fraction of the procurement cost after delivery of the ordered items and then to pay the remaining procurement cost at a later date without any additional charges to reduce the risk of cancellations of the order from customers (that is, a cash-credit payment). It is worth mentioning that, by the usage of the mathematical analytic solution procedures, the present article shows that the total annual cost function is convex by exploring the functional properties of the total annual cost function such as, for example, the continuity, convexity, monotonicity (increasing and decreasing), and differentiability properties, whereby one can also see the symmetry aspects of the total annual const function. Furthermore, by applying the mathematical analytic solution procedures again, we prove that the retailer's optimal replenishment cycle not only exists, but it also is unique. With a view to illustrating and validating the proposed inventory model, we have considered numerical examples involving different fixed markup rates. Finally, in the concluding section of this article (Section 6), we have briefly discussed the limitations and shortcomings of this investigation in that we have concentrated upon the inventory system without shortage and that it can affect the supply chain from the producer to the retailer. Furthermore, this model has the potential to be extended to incorporate inflation and quantity discount effects, different demand forms such as credit-linked promotion-dependent demand and other issues under the system with shortages. Additionally, this article has considered the deterministic situation, so considering the stochastic situation, such as stochastic demand, can be another future research direction on the subject of this article.


**1. Introduction**

Chromogenic assays, also named as color detection, result in colored reaction products. By detecting the color change before and after the reaction, substance concentration could be determined by the naked eye, light microscopes, and spectrophotometers.

Nowadays, a large number of food analyses and biochemical detections could be performed by color detection and were exampled as below:

Protein is one of the main ingredients of food. Bradford method, developed by Marion M. Bradford in 1976 [1], is the most widely used colorimetric method for protein detection. The anionic dyes used in this method is Coomassie brilliant blue (such as G-250, R-150, R-250, R-350). For example, the Coomassie brilliant blue G-250 exists in three forms with different colors including cationic (red), neutral (green), and anionic (blue). After binding with protein under acidic conditions, the color of Coomassie brilliant blue G-250 dye converts from red to blue, and the protein concentration is assayed. Up to now, the relative reference of the Bradford method was cited by more than 240,000 literature [1].

The reducing sugars could be assayed by using 3,5-dinitrosalicylic acid method (known as DNS method). 3,5-Dinitrosalicylic acid, an aromatic compound, was used as the test reagent and reacted with reducing sugar to form the 3-amino-5-nitrosalicylic acid, since the 3-amino-5-nitrosalicylic acid could strongly absorb light at 540 nm. The amount of the reducing sugar could be measured by spectrophotometry [2–4].

The anthrone method is another colorimetric detection method for both reducing and nonreducing sugars assay [5]. Anthrone is a tricyclic aromatic ketone. In the acidic condition, the anthrone regent reacts with sugar, resulting in yielding a blue-green color. The absorbance of the above blue-green color solution could be measured at 620 nm.

Phenol-sulfuric acid method is also a colorimetric method for the detection of carbohydrate in food [6]. While reacting with phenol and sulfuric acid, the carbohydrate sample solution to be tested became yellow-orange in color. Since the sulfuric acid converted the nonreducing sugars to reducing sugars, the total amount of carbohydrate could be detected by this method.

The pH indicator paper could be used to measure the acidity of the solution to be tested. The main components of pH indicator paper contain methyl red [pH 4.4 (red)–6.2 (yellow)], bromocresol green [pH 3.6 (yellow)–5.4 (green)], and thyme blue [pH 6.7 (yellow)–7.5 (blue)]. The pH indicator paper that dropped different acidity solution exhibit different color, by using colorimetric card the range of the acidity could be detected naked eye.

The sulfur dioxide as well as sulfite was widely used during the food processing. Sulfur dioxide and sulfite exhibit a high effect in inhibiting nonenzymatic browning in the process of food processing and could be used as preservative to inhibit mold and bacteria. Thus, it is important for the detection of the content of sulfite. The sulfite could be reactive with o-phthaldialdehyde under alkaline environment and formed dark blue complex [9]. The sulfite content could be detected by the increase of the absorbance value at 628 nm.

Jabbari [7] developed a sensitive kinetic method for the determination of sulfite (0.05–2.5 μg/ml). There is an addition reaction between methyl green and the sulfite at pH 8 and 25°C. This results in the color fading of the methyl green. Thus, by detecting the decrease in the absorbance of the dyestuff at 625 nm by the fixed time method, the concentration of the sulfite could be spectrophotometrically monitored. The result showed that the limit of the detection and the relative standard deviation are 0.05 μg/ml and 2.88%, respectively.

The phosphorus in food could be detected by phosphor vanadium molybdenum yellow spectrophotometry [8]. Under acidic condition, the phosphorus reacts with vanadium ammonium molybdate and formed a stable yellow color vanadium-molybdenum-yellow.

Apart from the above, color detection exhibits great potential in the field of quality monitoring, chemical technology, nanophysics, and clinical medicine, because of their rapid, direct, specific, convenient, and sensitive features. The intention of this book is to provide readers with a comprehensive overview for the principles, features, and applications of color detection.


**Author details**

Laura Conejero† , Paula Saz-Leal† and José Luis Subiza\* Inmunotek, Alcalá de Henares, Madrid, Spain

\*Address all correspondence to<EMAIL_ADDRESS>
† Contributed equally.

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

*Trained Immunity-Based Vaccines: A Ready-to-Act Strategy to Tackle Viral Outbreaks DOI: http://dx.doi.org/10.5772/intechopen.95765*


**5. Conclusion**

*Landraces - Traditional Variety and Natural Breed*

Nevertheless, there is not a large number of studies carried out in landraces (**Table 1**). For example, in the International Rice Research Institute (IRRI) programme, an improved line (IR68144-3B-2-2-3) with a high concentration of Fe in the grain was obtained through a cross between a high-yield variety ('IR72') and a traditional variety (Zawa Bonday) from India. This new variety was reported to have about 80% more Fe than the commercial variety 'IR64' [90]. Useful information have been collected about the Zn content of different mapping populations of rice including wild germplasm, landraces and varieties, as well as hybrids [91]. Using 'IR64' as one of the parents, the hybrid with the highest Zn content (26.9 mg/kg) resulted from a cross with the landrace Chittimuthyalu. A collection of 14 hybrids between different landraces of eggplant has also been characterised [62]. These hybrids exhibited a higher average content of phenolics, as well as Fe and Zn, than commercial varieties. Zn average concentration was also higher in the hybrids than in the landraces tested. A maize hybrid with a high carotenoid content has also been identified [92]. It is a single-cross hybrid developed from the landrace ITA0370005 and it is currently being used by an Italian beer brewer. The metabolite profile and the antioxidant activity of the tomato hybrid Torpedino di Fondi (TF), developed from the landrace San Marzano (SM), has been characterised in two ripening stages, pink and red, both considered ideal for fresh consumption. In comparison with SM, pink TF tomatoes exhibited the highest content of total polyphenols, tannins, and flavonoids besides the greatest antioxidant activity [93]. Within a breeding programme, the eggplant landrace Almagro, known to contain higher values of vitamin C and total phenolics than regular varieties, but also having higher prickle presence, was used as recurrent parent in a backcross, whereas three non-prickly eggplant accessions were used as donors of this desirable trait [94]. Finally, an improved pure line (H15) with the

Almagro eggplant ideotype and reduced prickliness was developed.

Biofortification can be tackled through the genetic transformation of crops to express desirable genes from a plant species, independently of their taxonomic status, or even from other type of organisms, in the plant of interest to increase their nutrient content and bioavailability. This approach overcomes the limitation of the availability of genetic variability, allows the transfer of several genes simultaneously, and makes possible to biofortify crops with particular nutrients that are not naturally produced by themselves. Biofortification through transgenesis implies large investment of time, resources and researching: it is necessary to identify and characterise gene functions previously, and then, use these genes to transform crops. However, once the crop has been biofortified, it becomes a cost-effective approach [96]. The cisgenesis is a very interesting alternative to the transgenesis. With this approach, only genetic material from either the same species, or close relatives that hybridise naturally with it, is introduced [97]. In this way, the pool of genes available is exactly the same than when classical breeding methods are used. Cisgenic crops are subject to the same regulation than transgenic crops, but the EFSA (European Food Safety Authority) have concluded that cisgenics pose similar risks than plants obtained by conventional breeding [98]. Furthermore, the consumer's

Furthermore, the application of modern biotechnological techniques to landraces also allows the development of crops with higher yield, as it has been achieved recently [95]. The CRISPR-Cas9 technique was applied to the African rice landrace Kabre, considered a valuable resource, obtaining mutants with significantly improved seed yield and low lodging by disrupting genes known to control seed size and/or yield (**Table 1**).

*4.2.2 Biofortification through modern biotechnological techniques*

acceptance of cisgenics is greater than of transgenics [99].

**108**

In spite of not having been widely used in fortification and biofortification, especially with modern biotechnological approaches, crop landraces and traditional varieties could be key to improve the nutritional quality of food crops, as they can provide the desired genetic variability without sexual incompatibility barriers to overcome. Hopefully, in the near future there could be less restrictive regulations about the use of these biotechnological tools in crop breeding.


2.1.2. "Cellular Scurvy"

Even under conditions of severe vitamin C deficiency, *i.e.*, scurvy, some ascorbate is still present in cells and tissues of humans *in vivo*. As discussed above, cell culture media are not usually supplemented with ascorbic acid, due to its inherent instability in these media. As a consequence, many researchers have reported that cells in culture are devoid of any detectible amounts of ascorbate, even with the use of extremely sensitive HPLC techniques [24,33–38]. Similarly, many complete cell culture media containing fetal bovine serum (FBS) have no detectable amounts of ascorbate [33,34,36,38], and our own analysis of various commercial sources of media and FBS has shown identical results (unpublished observations). The effects of these ascorbate-free conditions are not well defined or understood. However, it should be recognized that many immortalized cell lines likely have been maintained under scorbutic conditions for generations. In this manner, a whole host of cell culture artifacts may be expected when ascorbate is reintroduced into the system.

Cells in culture can be maintained without ascorbic acid because it is not essential to cell growth and division. The biological functions of ascorbate as an electron donor in enzymatic synthesis pathways do not have an absolute requirement for ascorbate [3,39,40]. These enzymes can use other reducing substrates as sources of electrons [39,41], and enzyme activity can still occur in the absence of ascorbate, albeit at a far decreased rate [42]. In particular, the α-ketoglutarate-dependent dioxygenases, such as those involved in collagen synthesis and regulation of hypoxia-inducible factor 1α (HIF-1α), do not require ascorbate as part of the normal catalytic cycle; ascorbate is only needed to rescue the enzyme should an uncoupled enzymatic reaction occur [3]. It has also been suggested that ascorbate may function to maintain intracellular iron in the ferrous state, making it available to replenish or replace ferric iron in the active site of these enzymes [43]. It is possible that cells in culture adapt by increasing ferrous iron uptake and turn-over of iron-containing proteins, partially circumventing the need for ascorbic acid. Regardless of the mechanism, it is evident from cell culture studies that "ascorbate-requiring" enzymes, such as those involved in collagen synthesis [44], degradation of HIF-1α [43], norepinephrine and α-amidated peptide synthesis [45], and histone and DNA demethylase activity [46], still exhibit some residual activity in the absence of ascorbate. However, these enzymes have diverse effects in different tissues, and their activity in an ascorbate-free environment may not be reflective of their roles *in vivo*.

On the other hand, normal physiological functioning of cells can be recapitulated when ascorbate is provided. Ascorbate appears to play an important role in the normal function of cultured endothelial cells, raising antioxidant protection, reducing oxidative stress and damage, and increasing eNOS activity when compared to cells devoid of vitamin C [33,38]. These effects on eNOS, at least, appear dependent on the ability of ascorbate to enhance the stability of tetrahydrobiopterin [34] and influence AMP-activated kinase (AMPK) activity [47]. In addition, ascorbate supplementation of cultured endothelial cells tightens cell-to-cell junctions that are critical for maintaining an endothelial barrier *in vivo* [48] and regulates NADPH oxidase activity [49], a critical component of the inflammatory response.

It is important to note that the effect of ascorbate supplementation may also greatly vary by cell type. Many of the aforementioned effects of ascorbate are observed in primary cell lines. Although propagated in the absence of ascorbate, the response to ascorbate supplementation in these cells reflects responses seen *in vivo*. Cancer cell lines and other immortalized cells, however, often show cytotoxic effects in response to ascorbate addition that are not observed in primary cell lines [24]. This may be the result of adaptations that have accumulated in these cells due to the "culture shock" that alters the normal physiological responses to stimuli [6], possibly involving iron dysregulation or aberrant cell signaling responses.


**Abstract**

The research and development of new implant models modifying the micro and macro design has increased significantly in the last decades. With the advancement of knowledge about the biological behavior of these materials when implanted in living tissue, a great search for morphological changes at macrogeometric, microgeometric and even nanogeometric levels was started, to accelerate the process of osseointegration of implants, reducing the time for the rehabilitation treatment. This chapter will seek to demonstrate, through scientific evidence, the potential effect of the morphological characteristics of implants on osseointegration. Modifications in the surface treatment of implants will be discussed to improve the osseointegration process in terms of quality and time reduction, changes in the surgical technique used for the osteotomy of the implant installation site, and macrogeometric changes in the shape of the implant body.

**Keywords:** implant design, implant microgeometry, implant macrogeometry, rapid osseointegration, titanium implants

## **1. Introduction**

Dental implants have become a predictable and safe form of treatment for the replacement of missing teeth. Surely, implants have revolutionized dentistry practice in worldwide, enabling the rehabilitation of patients who have lost single teeth to patients with loss of all teeth. Thus, various types of treatments made possible by improving the quality of life and patient satisfaction. Among these treatments that had the greatest representation, we can mention: the cases of totally edentulous people, who could receive implants to improve the fixation of removable dentures or even receive fixed dentures; patients with partial losses with a lack of posterior pillars (teeth) who had to wear removable dentures and could receive fixed dentures; and patients who had unit losses where it was possible to rehabilitate them without wearing out natural healthy teeth.

Since its diffusion by Branemark in the 60s [1, 2], dental implants have been the object of many studies and, consequently, have undergone several changes. However, the base material for its manufacture, titanium, continues to be used due to its

excellent biological and mechanical characteristics. Surgical techniques have also undergone several advances and modifications. Initially, a waiting time for the beginning of the rehabilitation procedures of 6 months was recommended, with implants installed in the bone tissue and covered by mucosa during this waiting period for osseointegration. With the advancement of knowledge, it was proposed that for implants installed in the mandible, the waiting time could be less than in the maxilla, due to the difference in density between the two anatomical sectors. However, Gehrke and Tavares da Silva Neto [3], showed in a clinical study that the evolution of osseointegration is the same in the 2 types of bone (maxilla and mandible) and that the implants could be loaded in both arches with the same waiting time, as long as these sites who received implants were in adequate condition. On the other hand, new techniques aiming to speed up the treatment time and provide greater comfort to patients, such as post-extraction implants (immediate), immediate loading on the implants, implants with simultaneous bone regeneration, among others, were proposals and studied and, currently, are widely used.

Different changes at nano-, micro- and macro-structural levels have been researched and proposed with the aim of improving and/or accelerating the processes involved in the osseointegration of dental implants. Such possibilities became possible with the evolution of scientific knowledge about the events involved in the healing process of peri-implant tissues after implant insertion. In this sense, several types of surface treatment have been proposed in order to promote a physical–chemical stimulation capable of accelerating the initial phases of bone neoformation on the implant [4, 5]. Among the main methods used to produce surface roughness of implants are the addition processes (e.g., titanium plasma spray, hydroxyapatite coating) and subtraction processes (e.g., acid etching, microparticle blasting, laser). Among all of them, the most used procedure currently by most of the world industry is the subtraction methods, as they have shown good results and are less costly. On the other hand, the addition of ions (e.g., Calcium, Magnesium, hydroxyapatite) on these surfaces at nano- and/or micrometric levels has shown good results for the osseointegration.

Initially, the implants had a cylindrical macrogeometry, being later proposed implants with macrogeometry with conical designs. Tapered shaped implants had advantages over cylindrical ones, especially regarding the surgical process, where they were shown to generate less trauma to the bone tissue resulting from the drilling process used for this type of implant. In addition to this change in the body of the implant body, changes in the shape of the turns, which were initially triangular and with little depth, received other shapes, such as trapezoidal, square, and with greater depth and distance in the thread pitch. Also, changes in the cervical portion of the implants, which are in contact with the cortical bone, have been proposed. Among these changes, we can mention the presence of smooth (polished) surfaces, treated (rough) surfaces and the presence of micro-turns. Regarding the prosthetic connections, the implants had several alterations, being proposed different models of fittings, always with the intention of improving the stability of the rehabilitation in the long term. **Figure 1** shows different types of implants and designs proposed in recent decades.

More recently, with scientific evidence that the installation of implants with less compression of the bone tissue could benefit and accelerate the osseointegration process, new macrogeometric models of implants were proposed. In this sense, it was shown that the presence of free spaces of bone tissue or the presence of uncompressed fragments can facilitate the cellular work of phagocytosis and bone matrix neoformation, as shown schematically in the **Figure 2**. This type of condition, creating a space

*Characteristics of Implant Systems That Can Accelerate and Improve the Osseointegration Process DOI: http://dx.doi.org/10.5772/intechopen.99937*

**Figure 1.**

*Available dental implants with different design and connections. (courtesy of Implacil De Bortoli company, Brazil).*

#### **Figure 2.**

*Schematic image depicting the installation of an implant in a conventional technique (left) causing bone compression over an extensive area, and the implant insertion with spaces between the bone and the implant (right) decreasing bone compression area.*

between the bone tissue and the implant, is achieved through a modification in the surgical technique, that is, in the relationship between the diameter of the last reamer used and the diameter of the implant to be inserted into the bone. Thus, in this chapter we will describe and discuss some advances resulting from these changes in the structure of dental implants during the last decades.