**3. Materials and methods**

*Aerodynamics*

global scale).

surface temperature differences. As the zone of maximum solar heating migrates with the seasons—moving northward during the Northern Hemisphere summer and southward as winter approaches—the wind patterns that make up the general circulation also migrate latitudinally. Airflow (or wind) can be divided into three broad categories: waves, turbulence, and mean wind. Each can exist in the boundary layer, where transport of quantities such as moisture, heat, momentum, and pollutants is dominated in the vertical by turbulence and horizontal by the mean wind [22]. Each

The earth's highly integrated wind system can be thought of as a series of deep rivers of air that encircle the planet. Embedded in the main currents are vortices of various sizes, including hurricanes, tornadoes, and midlatitude cyclones. Like eddies in a stream, these rotating wind systems develop and die out with somewhat predictable regularity. In general, the smallest eddies, such as dust devils, last only a few minutes, whereas larger and more complex systems, such as midlatitude cyclones and hurricanes, may survive for several days [21]. The scales of atmospheric motion shown in **Table 1** illustrate the three major categories of atmospheric circulation: microscale, mesoscale, and macroscale (synoptic scale and

In short, wind is the movement of air from an area of high pressure to an area of low pressure. In fact, wind exists because the sun unevenly heats the surface of the earth. As hot air rises, cooler air moves in to fill the void. As long as the sun shines, the wind will blow. And wind has long served as a power source to humans [23]. Wind spins the blades, which turn a shaft connected to a generator that produces electricity, in other words, wind turbines convert kinetic energy contained in the wind first into mechanical and then into electrical energy [24]. Wind is a clean source of renewable energy that produces no air or water pollution. And since the wind is free, operational costs are nearly zero once a turbine is erected. Mass production and technology advances are making turbines cheaper, and many govern-

ments offer tax incentives to spur wind-energy development [23].

sustainable source of energy that can successfully replace fossil fuels [6].

Mesoscale 1.0–100 km Tornadoes, waterspouts, dust devils, land/sea

**Scale Typical size Phenomenon Life span** Microscale 0–1.0 km Small turbulent eddies, thunderstorms Seconds to

breeze, mountain/valley breeze

100–5000 km Hurricanes, tropical storms Days to weeks

Longwaves in the westerlies, trade wind Weeks to years

minutes

Minutes to hours/ days

Nowadays, wind turbine technology is considered matured, and the costs of wind energy are low [6]. Industry experts predict that if this pace of growth continues, by 2050 one third of the world's electricity needs will be fulfilled by wind power [23]. Though wind power has performed well in recent years, it also creates a strong environmental impact, such as visual impact, climatic impact, and noise. Although these impacts seem minor when compared with nonrenewable energy, its effect on humans should not be overlooked, due to its potential great development in usage. In short, with proper and supportive policies toward wind power and a good understanding of its environmental impact, wind energy can be a clean and

can exist in the presence of any of the others or separately.

**222**

**Table 1.**

Synoptic scale

Global scale 1000–

40,000 km

*Scales of atmospheric motion (adapted from [20, 21]).*

In this chapter, we use computational intelligence by artificial neural networks for the next-step prediction of one climatic variable: wind speed. ANN was trained to perform the forecasting of 1 h ahead, and then, using it, the trained network was applied to recursively infer the forecasting for the next 6 h of the wind speed (nowcasting), following the methodology explained in [13]. The activation functions that define the outputs of the neurons in terms of their activity levels, inserted in this simulation, were the sigmoidal function in the form of the hyperbolic tangent function (characterized as continuous, increasing, differentiable, and nonlinear) for hidden layers and linear function to the output layer.

To train the RNN and validate the technique, anemometer data (average hourly values of wind speed, wind direction, and temperature) for 1 year (August 08, 2014, and August 07, 2015) are collected by one tower with anemometer installed at height of 101.8 m to Colonia Eulacio (Uruguay), and data (average hourly values of wind speed, wind direction, temperature, humidity, and pressure) for 1 month (November 30, 2015, until December 31, 2015) are collected by one tower with anemometers installed at height of 100.0 m to Mucuri (Brazil), using the same criteria as in [13], namely, 70% for training/validating data and 30% for simulation. The reason for choosing these periods is these are the months with the totality of data available for the realization of this study.

The Mucuri city (Bahia, Brazil) is located at an altitude of 7.0 m in relation to the sea level, and it has a territorial area of 1775 km2 , approximately. Mucuri's anemometer tower is located in a coastal plain, at a distance of 340.0 m from the sea, with latitude 18°1′31.52″ S and longitude 39°30′51.69″ W (**Figure 2**).

As for Colonia Eulacio Tower in Uruguay, according to datum WGS84, it is located at 33o 16′ S, 57o 31′ W [25]. The altitude of the installation location (see **Figure 3**) is approximately 100.0 m, and the location is surrounded by fields with plains; thus, it is characterized by noncomplex terrain. The station is owned by the Administración Nacional de Usinas y Transmissiones Eléctricas (UTE), which is a state-owned company in Uruguay that is responsible for the generation, distribution, and commercialization of electrical energy in the country, as cited in [13].

**Figure 2.** *Location of the Mucuri Tower in Bahia, Brazil.*

**Figure 3.** *Location of the Colonia Eulacio Tower in Soriano Department, Uruguay [21].*

The insertion of meteorological parameters as input data contributes to efficient training of the ANN. Seven different ANN configurations are applied for each site and height; then, a quantitative analysis is conducted, and the statistical results (MAE, MSE, and RMSE) are evaluated to select the configuration that best predicts the real data. The proposed ANN configurations to be analyzed are the following ones. For Mucuri (Brazil) the best ANN configuration was Configuration 1 and for Colonia Eulacio (Uruguay) was the Configuration 4.

**225**

*Study of the Wind Speed Forecasting Applying Computational Intelligence*

percentage error (MAPE), mean absolute error (MAE), mean squared error (MSE), and root mean square error (RMSE). Pearson's correlation coefficient ranges from −1.0 to 1.0. Values close to 0.0 are adequate for the MAE, MSE, and RMSE, values close to 0.0% are adequate for the MAPE, and values close to 1.0 are adequate for the R-squared. The software used to program and perform this computational procedure was MATLAB version 7.10.0 (2010) (personal computer, 8 GB RAM), as

Computational intelligence (CI) is the theory, design, application, and development of biologically and linguistically motivated computational paradigms. Over the last few years, there has been an explosion of research on machine learning and deep learning. Nowadays, deep learning has become the core method for artificial intelligence (AI) [26]. AI is one of the newest fields in science and engineering. AI currently encompasses a huge variety of subfields, ranging from the general (learning and perception) to the specific, such as playing chess, proving mathematical theorems, writing poetry, driving a car on a crowded street, diagnosing diseases, and predicting the conditions of the atmosphere for a given location and time.  In fact, some of the

In the early days of artificial intelligence, the field rapidly tackled and solved problems that are intellectually difficult for human beings but relatively straightforward for computers—problems that can be described by a list of formal, mathematical rules [28]. Many artificial intelligence tasks can be solved by designing the right set of features to extract for that task, then providing these features to a simple machine learning algorithm. The most widely used artificial neuron model is the perceptron proposed in [29, 30]. This model defines a neuron composed of inputs, a summation and an activation function. The value of each input is multiplied by a weight, and the weighted values of the inputs are summed to yield the result of the sum which is used as the input of the activation function. To teach (train) the neuron, the weights are modified so that the output obtained corresponds to the

The multilayer perceptron (MLP) consists of a system of simple interconnected neurons, or nodes, which is a model representing a nonlinear mapping between an input vector and an output vector. The architecture of a MLP is variable but in general will consist of several layers of neurons. The input layer plays no computational role but merely serves to pass the input vector to the network. The terms input and output vectors refer to the inputs and outputs of the MLP and can be represented as single vectors [31]. Moreover, in relation to recurrent neural network (RNN), the definition is that they are powerful sequence-processing models that are equipped with a memory from recurrent feedback connections. One of the current main challenges of RNN is to dynamically adapt to multiple temporal resolutions and scales in order to learn hierarchical representations in time. Since they operate in discrete time steps and update at every time step, it is generally difficult to learn temporal features that have a significantly different resolution than

Predicting the short-term power output of a wind turbine (wind energy converter) is an important task for the efficient management of smart grids. Shortterm forecasting at the minute scale also is known as nowcasting. By definition nowcasting refers to short lead time weather forecasts, the US National Weather Service specifies zero to 3 h, though forecasts up to 6 h may be called nowcasts by

or R-squared), mean absolute

*DOI: http://dx.doi.org/10.5772/intechopen.89758*

the methodology applied in [13].

(r or Pearson's r), coefficient of determination (R2

**3.1 Computational intelligence and nowcasting**

most successful AI systems are based on CI.

desired value [30].

their input frequency [32].

For statistical analysis of wind speed prediction results at the above sites, the following statistical indicators were applied: Pearson's correlation coefficient

*Study of the Wind Speed Forecasting Applying Computational Intelligence DOI: http://dx.doi.org/10.5772/intechopen.89758*

(r or Pearson's r), coefficient of determination (R2 or R-squared), mean absolute percentage error (MAPE), mean absolute error (MAE), mean squared error (MSE), and root mean square error (RMSE). Pearson's correlation coefficient ranges from −1.0 to 1.0. Values close to 0.0 are adequate for the MAE, MSE, and RMSE, values close to 0.0% are adequate for the MAPE, and values close to 1.0 are adequate for the R-squared. The software used to program and perform this computational procedure was MATLAB version 7.10.0 (2010) (personal computer, 8 GB RAM), as the methodology applied in [13].

## **3.1 Computational intelligence and nowcasting**

*Aerodynamics*

**Figure 3.**

The insertion of meteorological parameters as input data contributes to efficient training of the ANN. Seven different ANN configurations are applied for each site and height; then, a quantitative analysis is conducted, and the statistical results (MAE, MSE, and RMSE) are evaluated to select the configuration that best predicts the real data. The proposed ANN configurations to be analyzed are the following ones. For Mucuri (Brazil) the best ANN configuration was Configuration 1 and for

1.Configuration 1: three layers, nine input nodes (site, Brazil) or seven input nodes (site, Uruguay), nine hidden neurons, and one output node

2.Configuration 2: three layers, nine input nodes (site, Brazil) or seven input

3.Configuration 3: three layers, nine input nodes (site, Brazil) or seven input nodes (site, Uruguay), three hidden neurons, and one output node

4.Configuration 4: three layers, nine input nodes (site, Brazil) or seven input

5.Configuration 5: four layers, nine input nodes (site, Brazil) or seven input

6.Configuration 6: four layers, nine input nodes (site, Brazil) or seven input nodes (site, Uruguay), six hidden neurons (first hidden layer) and three

7.Configuration 7: four layers, nine input nodes (site, Brazil) or seven input nodes (site, Uruguay), one hidden neuron (first hidden layer) and one hidden

For statistical analysis of wind speed prediction results at the above sites, the following statistical indicators were applied: Pearson's correlation coefficient

nodes (site, Uruguay), nine hidden neurons (first hidden layer) and six hidden

nodes (site, Uruguay), six hidden neurons, and one output node

nodes (site, Uruguay), one hidden neuron, and one output node

neurons (second hidden layer), and one output node

neuron (second hidden layer), and one output node

hidden neurons (second hidden layer), and one output node

Colonia Eulacio (Uruguay) was the Configuration 4.

*Location of the Colonia Eulacio Tower in Soriano Department, Uruguay [21].*

**224**

Computational intelligence (CI) is the theory, design, application, and development of biologically and linguistically motivated computational paradigms. Over the last few years, there has been an explosion of research on machine learning and deep learning. Nowadays, deep learning has become the core method for artificial intelligence (AI) [26]. AI is one of the newest fields in science and engineering. AI currently encompasses a huge variety of subfields, ranging from the general (learning and perception) to the specific, such as playing chess, proving mathematical theorems, writing poetry, driving a car on a crowded street, diagnosing diseases, and predicting the conditions of the atmosphere for a given location and time.  In fact, some of the most successful AI systems are based on CI.

In the early days of artificial intelligence, the field rapidly tackled and solved problems that are intellectually difficult for human beings but relatively straightforward for computers—problems that can be described by a list of formal, mathematical rules [28]. Many artificial intelligence tasks can be solved by designing the right set of features to extract for that task, then providing these features to a simple machine learning algorithm. The most widely used artificial neuron model is the perceptron proposed in [29, 30]. This model defines a neuron composed of inputs, a summation and an activation function. The value of each input is multiplied by a weight, and the weighted values of the inputs are summed to yield the result of the sum which is used as the input of the activation function. To teach (train) the neuron, the weights are modified so that the output obtained corresponds to the desired value [30].

The multilayer perceptron (MLP) consists of a system of simple interconnected neurons, or nodes, which is a model representing a nonlinear mapping between an input vector and an output vector. The architecture of a MLP is variable but in general will consist of several layers of neurons. The input layer plays no computational role but merely serves to pass the input vector to the network. The terms input and output vectors refer to the inputs and outputs of the MLP and can be represented as single vectors [31]. Moreover, in relation to recurrent neural network (RNN), the definition is that they are powerful sequence-processing models that are equipped with a memory from recurrent feedback connections. One of the current main challenges of RNN is to dynamically adapt to multiple temporal resolutions and scales in order to learn hierarchical representations in time. Since they operate in discrete time steps and update at every time step, it is generally difficult to learn temporal features that have a significantly different resolution than their input frequency [32].

Predicting the short-term power output of a wind turbine (wind energy converter) is an important task for the efficient management of smart grids. Shortterm forecasting at the minute scale also is known as nowcasting. By definition nowcasting refers to short lead time weather forecasts, the US National Weather Service specifies zero to 3 h, though forecasts up to 6 h may be called nowcasts by

#### *Aerodynamics*

some agencies [33]. Nowcasting is critical when managing operations of the smart grid, such as system integration, ensuring power continuity and managing ramp rates [34]. In this chapter, nowcasting refers to short-term wind speed forecasting 6 h ahead. In [35] they described and evaluated a proposal for nowcasting wind speed for wind farm locations from historical time series, based on the method of regression by support vectors (in short, nowcasting of wind speed using support vector regression: experiments with time series from Gran Canaria).


**4.4 Smelling "Zuko" incense and putting the hands together showed larger changes than smelling incense odor with putting the hands together**

We already have the results of the previous MEG experiments for smelling incense odor with putting the hands together as shown in our paper published in IntechOpen [23]. From these results of previous MEG experiments, it was obtained that smelling incense odor with putting the hands activates few specific brain areas.

On the other hand, in these MEG experiments, it was obtained that the results of smelling "Zuko" incense into the hands and putting the hands promote to excite few same specific brain areas as shown in **Figure 15** in Section 3.4.

From the results of **Tables 1** and **2**, we can find out that subject B5 obtained in orbitofrontal cortex OFC, subject A5, and B4 obtained in inner frontal area, subject A3, A4, and B2 obtained in occipital cortex V1 as an estimation active area in the brain clearly show almost all larger value ofα-amylase after smelling "Zuko" and putting the hands and MEG experiments before these experiments. These results are considered to show something stress by smelling "Zuko" incense and putting the hands for the response of subject A3, A4, B2, B3, B4, and B5 which showed large activity in a few special areas in the brain.

From the comparison with these two experimental results and analysis, we can obtain that smelling "Zuko" incense and putting the hands together showed clearly larger changes than smelling incense odor with putting the hands together. We can

group) and no habit (B-group) of putting the hands in daily life. The larger active effects for smelling "Zuko" incense rubbing into the hands and putting the hands were obtained more than the previous smelling incense odor activity with putting

**4.1 A possibility for improving cognitive ability by smelling "Zuko" incense from the results of P300m responses of "auditory oddball paradigm"**

We try to discuss a possibility for improving cognitive ability by smelling "Zuko" incense from the results of the following examples of P300m responses of

*4.1.1 The comparison of a P300m peak of MEG response with nonsmelling and smelling*

Though the typical examples are the results of P300m MEG responses of only

1.As a cognitive P300m response, it was shown that the result with smelling

2.As a cognitive P300m response, it was shown that the result with smelling

3.As a cognitive P300m response, it was shown that the result with smelling "Zuko" had larger active wave area S than without smelling "Zuko."

From these typical results of P300m responses, it can be considered that a possibility of the improving cognitive ability of P300 peak was shown by smelling

**4.2 Effects of stress on the comparison with before and after smelling "Zuko"**

alpha-amylase value in saliva from the results of **Table 1**, **Figures 13** and **14**. From these results of alpha-amylase value in saliva, it was shown that the

We can find out few effects of stress for smelling "Zuko" incense by measuring

1.This report shows the results of the statistical comparison with habit and no habit with putting the hands by using alpha-amylase value in saliva.

2.We could not find out a significant difference among the sex in 10 Japanese

3.We could not find out a significant difference among the habit and no habit with smelling "Zuko" incense and putting hands together in daily life.

4. In only female subjects, we found out a significant difference (P < 0.079) in Ttests among before and after smelling "Zuko" incense rubbing into the hands.

subjects as the statistical value of alpha-amylase in saliva.

"Zuko" had shorter latency time than without smelling "Zuko."

"Zuko" had bigger peak height than without smelling "Zuko."

the hands from the results of summaries in **Figure 15** in Section 3.4.

"auditory oddball paradigm" MEG experiments.

*Sino-Nasal and Olfactory System Disorders*

two subjects, we can find out the results as follows:

"Zuko" incense using "auditory oddball paradigm."

following discussions were obtained:

**194**

**incense by measuring alpha-amylase value in saliva**

**4. Discussions**

*"Zuko" incense*

find larger changes in the brain, for example, from the right area to the left area for subject A5, B1, and B2 and from the left area to right area for subject A2 as shown in **Figure 15**. And we can also find out other larger changes in the brain in the same laterality, for example, from the occipital area to the prefrontal area for subject A3 and from the inferior parietal area to the occipital area for subject A4 at the left side in the brain.

brain clearly show almost all larger value of alpha-amylase after smelling "Zuko" and putting the hands and MEG experiments before these experiments. These results are considered to show something stress by smelling "Zuko" incense and putting the hands for the response of subjects A3, A4, B2, B3, B4, and B5 which

*Smelling "Zuko": Incense Rubbing into the Hands and Smelling the Hands Activates…*

From the above results, we consider that the F5 language area in the left frontal cortex and V1 visual area were promoted to activate by smelling "Zuko" incense

Alpha-amylase showed something stress like for smelling "Zuko" incense with

We concluded that new specific effects both in smelling "Zuko" incense into the hands and imitating the behavior of putting the hands together can be considered to promote excitation of the higher activities and to make larger changes in the brain

We sincerely thank Mr. Kimiyoshi Yoshino and Mr. Masaru Yamamoto in Nippon Kodo Co. Ltd. in Japan for the subject's attendance and the supply of "Zuko"

This study was supported by the Awards of Alzheimer's Disease in Osaka-Gas Co. and Grants for Alzheimer's Disease in Osaka Research Association in Japan.

Department of Medical Engineering, Faculty of Health Science, Aino University,

© 2019 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,

\*Address all correspondence to<EMAIL_ADDRESS>
provided the original work is properly cited.

putting the hands, and the specific mirror neuron and default-mode network

activities dynamically in a few specific regions in the human brain.

showed large activity in a few special areas in the brain.

rubbing into the hands with putting the hands together.

showed activity of the special areas in the brain.

*DOI: http://dx.doi.org/10.5772/intechopen.88987*

**Acknowledgements**

incense powder.

**Author details**

Mitsuo Tonoike

**197**

Ibaraki, Osaka, Japan

These above results of larger changes show that the usage of "Zuko" incense rubbing into the hands promote to activate the brain more than smelling incense odor.


**5.2.3 Spherical wave system**

46 Advanced Holography – Metrology and Imaging

This system has two sets of two-beam illuminations. One set, that is, an optical setup for displacement measurement using two-beam illuminations is shown in Figure 21. A collimated light beam from a laser is divided into an object wave and a reference wave by beam splitter 1. Additionally, the object wave is divided into object wave 1 and object wave 2 by beam splitter 2. Each object wave has a shutter on the light path. From the two holograms, the *x*-directional in-plane and the *z*-directional out-of-plane displacements are analyzed. Another set of two-beam illuminations provides the *y*-directional in-plane

(a) x (b) z (c) y (d) z

(e) *u* x (f) *u*y (g) ex (h) ey

Fig. 22. Phase-difference displacement and strain distributions measured by collimated

**5.2.2 Collimated wave system**

Fig. 21. Optical using two beam illuminations

displacement.

beam system

In this experiment, three holograms using light sources 1, 2 and 3 are recorded on a CCD simultaneously as shown in Figure 23. Although the phases of the three reference waves are shifted simultaneously seven times, the phase-shift amounts during the seven times are different for each light source. The total phase-shifts are 2, 4 and 6for the light sources 1, 2 and 3, respectively. The each fringe pattern by the light sources 1, 2 and 3 is extracted from the continuous seven holograms using the Fourier transformation of the brightness change at each pixel of the holograms.

Let us explain the procedures. Figure 24 illustrates the captured brightness changes at a pixel and the brightness change corresponding to each of the three light sources. The discrete Fourier transformation of the captured brightness along the time axis provides the Fourier spectrum shown in Figure 25. It has seven frequency components from -3 to 3. Here, the components of the frequencies 1, 2 and 3 arise from the light sources 1, 2 and 3, respectively. By extracting these components and calculating the amplitudes and the phases of the components, the complex amplitudes of the three holographic fringe patterns are obtained separately.

Fig. 23. Optical setup using three spherical waves

Three-Dimensional Displacement and Strain Measurements

The strain distribution in the x-direction is shown in Figure 27.

Fig. 26. Phase-difference distributions by each light sources

Fig. 27. Strain distribution in *x*-direction

**6. Conclusions**

cantilever.

by Windowed Phase-Shifting Digital Holographic Interferometry 49

A strain distribution measurement of a T-shaped cantilever with a fixed line is performed by the system shown in Figure 23. The size of the cantilever is 5 mm width, 15 mm length, and 5 mm thickness. A load is applied on the cantilever at the point 12.5 mm from the base of the

Seven phase-shifted holograms are captured before and after deformation. The Fourier components corresponding to each light source are separated by the phase-shifting method using Fourier transformation and each phase value is obtained. The phase-difference distributions of the light sources 1, 2 and 3 are shown in Figs. 28 (a), (b) and (c), respectively.

In order to reduce the effect of speckle noise, the windowed digital holographic interferometry was proposed. In the method, the effect of number of windows, or window size on accuracy, spatial resolution and calculation time were examined. The results are that the number of windows increases, the accuracy becomes better at first because of speckle noise reduction and after that it becomes worse because of larger speckle size. The best accuracy is sub-nanometer when the number of windows is 1024 in our experiment.

Fig. 24. Brightness change of composite wave

Fig. 25. Fourier spectrum of composite wave

Fig. 24. Brightness change of composite wave

Fig. 25. Fourier spectrum of composite wave

A strain distribution measurement of a T-shaped cantilever with a fixed line is performed by the system shown in Figure 23. The size of the cantilever is 5 mm width, 15 mm length, and 5 mm thickness. A load is applied on the cantilever at the point 12.5 mm from the base of the cantilever.

Seven phase-shifted holograms are captured before and after deformation. The Fourier components corresponding to each light source are separated by the phase-shifting method using Fourier transformation and each phase value is obtained. The phase-difference distributions of the light sources 1, 2 and 3 are shown in Figs. 28 (a), (b) and (c), respectively. The strain distribution in the x-direction is shown in Figure 27.

Fig. 26. Phase-difference distributions by each light sources

Fig. 27. Strain distribution in *x*-direction


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The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

  *See also* Land tenure; Tree tenure *Terminalia ivorensis*, 62, 80 *Terminalia superba*, 63, 80 Tetteh Quarshie, 100 Timber trees, 13, 14, 95, 102, 106–108, 110, 111, 139, 155 Transform zone, 7, 129, 134, 151, 152, 159, 162 Tree diversity, 130, 132–137, 138–140, 152–154 Tree planting, 3, 106, 149, 161 Tree rights, 14, 26, 103, 109–111 Tree species, 3, 12, 26, 27, 38, 41, 49, 62, 63, 65, 67, 72–81, 95, 103, 106, 128, 138, 152–154, 156, 159–161 Tree tenure, 14, 111–113, 126 *Triplochiton scleroxylon*, 39, 81

#### **W**

Water potential, 41, 42 Water-use efficiency, 37 West Africa, 6, 8–11, 37, 41, 48, 49, 61, 62, 68, 95–97, 99, 100, 122–125, 150, 161

**Y**

Yield, 3, 6–10, 12, 19, 22, 23, 25, 26, 37, 38, 40, 48–50, 61–65, 67, 68, 70, 71, 96, 99, 103, 124, 126, 127, 131–134, 137, 150–153, 156, 159 Youth, 13, 111, 148, 154–155


*Article* **Humidity Sensitivity Behavior of CH3NH3PbI3 Perovskite**

**Xuefeng Zhao, Yuting Sun, Shuyu Liu, Gaifang Chen, Pengfei Chen, Jinsong Wang, Wenjun Cao and Chunchang Wang \***

> Laboratory of Dielectric Functional Materials, School of Materials Science & Engineering, Anhui University, Hefei 230601, China<EMAIL_ADDRESS>(X.Z<EMAIL_ADDRESS>(Y.S<EMAIL_ADDRESS>(S.L<EMAIL_ADDRESS>(G.C<EMAIL_ADDRESS>(P.C<EMAIL_ADDRESS>(J.W<EMAIL_ADDRESS>(W.C.)

**\*** Correspondence<EMAIL_ADDRESS>
**Abstract:** The CH3NH3PbI3 (MAPbI3) powders were ground by PbI2 and CH3NH3I prepared by ice bath method. The humidity sensitive properties of an impedance-type sensor based on MAPbI3 materials were systematically studied. Our results indicate that the MAPbI3-based sensor has superior sensing behaviors, including high sensitivity of 5808, low hysteresis, approximately 6.76%, as well as good stability. Water-molecule-induced enhancement of the conductive carrier concentration was argued to be responsible for the excellent humidity sensitive properties. Interestingly, the humidity properties can be affected by red light sources. The photogenerated carriers broke the original balance and decreased the impedance of the sensor. This work promotes the development of perovskite materials in the field of humidity sensing.

**Keywords:** humidity sensing; impedance-type sensors; organometallic halide perovskite


**Abstract**

Corrosion is an unavoidable fact in everyday life but always receive attention to control due to its technical, economical, and esthetical importance. Corrosion inhibitors are one of the most widely used and economically viable methods protecting metals and alloys against corrosion. Typical corrosion inhibitors are bio-toxic organic compounds, which have serious issue on toxicity. Considering the toxicity of the inhibitors, there is a tremendous interest in searching for an eco-friendly, and non-toxic green corrosion inhibitor. This chapter briefly discusses the importance and different methods of corrosion inhibitors with a particular emphasis given to the discussion on the different characteristic feature of the green corrosion inhibitors reported in the literature as a comparative view of organic inhibitors.

**Keywords:** corrosion inhibitors, mild steel, green corrosion, organic, adsorption


**Author details**

Jane O'Sullivan1 \* and Donal O'Connor2

### **References**

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[2] Dummer R, Hauschild A, Lindenblatt N, Pentheroudakis G, Keilholz U. Cutaneous melanoma: ESMO clinical practice guidelines for diagnosis, treatment and follow-up. Annals of Oncology. 2015;**26**(5):126-132

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106 Human Skin Cancers - Pathways, Mechanisms, Targets and Treatments

**Chapter 5**

**Provisional chapter**

**Possibilities for the Therapy of Melanoma: Current**

This chapter presents an overview of possibilities for the therapy of melanoma, current knowledge and future direction. Skin cancer is one of the most frequent types of cancers. Melanoma is much less common than basal cell and squamous cell skin cancers, but it is far more dangerous. Detailed knowledge of melanoma at the molecular level allows to develop new treatment alternatives and to design effective new drugs. There are two approaches in therapy of melanoma in the present based on immunotherapy and targeted therapy or their combination. Immunotherapy includes immune checkpoint blockades, whereas targeted therapy is represented by protein kinase inhibitors, such as BRAF inhibitors, MEK inhibitors, and NRAS inhibitors. Detailed knowledge of protein structure and the understanding of its role in key signaling pathways in melanoma development lead to the designation of new protein kinase inhibitors in targeted therapy. **Keywords:** melanoma, chemotherapy, immunotherapy, targeted therapy, protein kinase

**Possibilities for the Therapy of Melanoma: Current**

DOI: 10.5772/intechopen.70368

© 2016 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,

© 2018 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.

and reproduction in any medium, provided the original work is properly cited.

The incidence of melanoma is increasing worldwide. Melanomas represent 3% of all skin cancers but 65% of skin cancer deaths [1]. Melanoma is currently the fifth and sixth most common solid malignancy diagnosed in men and women, respectively [2]. The rates of melanoma have been rising for at least 30 years [3]. Although melanoma is no longer considered just 'one disease', pathologists will continue to have important role in identifying and describing tumor subtypes [4]. More detailed understanding of melanoma allows the development

**Knowledge and Future Directions**

**Knowledge and Future Directions**

Marcela Valko-Rokytovská, Jana Šimková, Mária Milkovičová and Zuzana Kostecká

Marcela Valko-Rokytovská, Jana Šimková, Mária Milkovičová and Zuzana Kostecká

Additional information is available at the end of the chapter

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.70368

**Abstract**

inhibitors

**1. Introduction**

**Provisional chapter**