Meet the editors

Mustafa Ergen graduated from the Department of Landscape Architecture, Abant İzzet Baysal University, Turkey, in 2000. In 2005, he completed his first master's degree in Urban and Regional Planning at the Gebze Institute of Technology, Turkey. He completed his second master's degree in Landscape Architecture at Anhalt University of Applied Sciences, Germany, in 2006. During 2007–2008, he studied Geographic Information Systems

and Remote Sensing at the Mediterranean Agronomic Institute of Chania, Greece, and was granted a specialization diploma in Environmental Management. He also received a Dr.Eng from the Technical University of Dortmund, Germany. Currently, he works in the Department of Landscape Architecture, Faculty of Agriculture, Sakarya University of Applied Sciences, Turkey.

Yasar Bahri Ergen graduated from Istanbul State Academy of Engineering and Architecture, Department of Architecture, in 1973. In 1976 he completed his master's degree in Urban Planning at the Istanbul State Academy of Engineering and Architecture. From 1983 to 1984 he lived in the USA to conduct research in his field of expertise. He received a doctorate degree from Gazi University in 1987. His academic life started in 1982 and took a turn

from the USA to Gazi University. In 1995 he established the Department of Urban and Regional Planning at Bozok University and in 2009 he established the Department of Urban and Regional Planning at Amasya University. In 2017 he established the Faculty of Fine Arts and Design at Siirt University In 2018 he received the title of associate professor. After that, he started to work at Aksaray University in 2021 and still works at Aksaray University.


**3. Results and discussion**

In this section, we present the results and discuss the three analyses performed in this chapter described above.

### **3.1. k-Means used with Euclidean distance**

In this section we present how results of appliance of k-means clustering with Euclidean distance function over NDVI monthly values extracted from the study area can assist the monitoring of sugarcane fields.

Months from December to May correspond to the period of maximum vegetative growth of sugarcane. In **Figure 3J**, **L** and **B**, pixels that appear in yellow and red colors correspond to the maximum NDVI values, being included in the clusters 3 and 4, respectively. On the other hand,

calculate the distance between multidimensional arrays and vectors. The dynamic time warping (DTW) is a very efficient distance function to compare time series [22]. Its main objective is to keep close time series that have similar behavior but are delayed or distorted along the time axis. Thus, this technique presents a proper way of working to warping, because the comparisons between corresponding points are not rigid. DTW is a tool with two of the main issues raised by high-temporal-resolution satellite image time series, namely, the irregular sampling in the temporal dimension and the need for comparison of pairs of time series hav-

First: k-Means used with Euclidean distance, when we considered only monthly NDVI values. These values of sugarcane fields were extracted using geographical coordinates (latitude and longitude) provided by the Canasat/INPE Project (www.canasat.inpe.br). In this approach, each element of the dataset corresponds to one NDVI value, which refers to a month value in a given location (pixel), in order to obtain monthly analysis of the region of interest. Considering similarity among NDVI values, elements were assigned to different clusters. Five clusters were generated for each month of the crop season (2004–2005), being able to follow the development stage of the crop per month. For example, whether crop is in maturing phase, it has already been harvested, and there are not spectral mixing with other

Second: k-Means used with DTW distance function, when we have generated series of NDVI values corresponding to one or more sugarcane crop series. The clustering was determined by five clusters for each crop season (2001–2010) for annual crop monitoring according to the type of planting in each crop season, for example, sugarcane ratoon, sugarcane expansion,

Third: k-Means used with DTW distance function of three dimensional (multivariate) time series database, extracted from 324 monthly images of NDVI, albedo and surface temperature. Since DTW calculates the distance between pairs of data points using Euclidean distance, DTW method can be applied to multivariate time series. The whole dataset had 220,238 data series, being each observation a triplet of NDVI, albedo and surface temperature values

In this section, we present the results and discuss the three analyses performed in this chapter

In this section we present how results of appliance of k-means clustering with Euclidean distance function over NDVI monthly values extracted from the study area can assist the

sugarcane renewed, sugarcane under renewing and not defined [13, 24].

of study area in a given month, with 108 values per time series [25].

ing different numbers of samples [23].

28 Time Series Analysis and Applications

crops or vegetation;

**3. Results and discussion**

monitoring of sugarcane fields.

**3.1. k-Means used with Euclidean distance**

described above.

We will show next the three clustering analyses performed:

**Figure 3.** Monthly MVC NDVI images and clustering of NDVI (five clusters) of sugarcane planting area in the state of São Paulo for months from April 2004 to march 2005. (A) NDVI/NOAA 2004 April; (B) Clustering 2004 April; (C) NDVI/ NOAA 2004 July; (D) Clustering 2004 July; (E) NDVI/NOAA 2004 September; (F) Clustering 2004 September; (G) NDVI/ NOAA 2004 November; (H) Clustering 2004 November; (I) NDVI/NOAA 2005 January; (J) Clustering 2005 January; (K) NDVI/NOAA 2005 March; (L) Clustering 2005 March.

months of August, September and October correspond to harvest season. In these months (**Figure 3F**), pixels in magenta and blue, with minimum NDVI values, correspond to clusters 0 and 1, respectively. Cluster 2 (green) corresponds to sugarcane intermediate stage of growth.

These clusters can be validated in the MVC NDVI images. The black squares over the satellite images in the left correspond to the main sugarcane planting areas. Analyzing the MVC NDVI images in the northeastern region of São Paulo, the evolution of the sugarcane vegetative growth cycle can be seen (**Figure 3**). Planting begins in August represented in the images by pixels in shades of green and blue located in the northeastern region of the state. These colors represent low NDVI values (around 0.2) characterizing areas with exposed soil and sparse vegetation. Similar pattern also occurs in the months from September to November. From December, when sugarcane begins to grow up and acquire more biomass, these regions are shades of yellow, orange and red. Months from January to May show shades of dark red, when sugarcane reaches the highest stage of growth with maximum NDVI values (between 0.7 and 0.8). The dark areas in images represent pixels covered by clouds and water.

There is no predominance of one or two clusters in all producing regions if we consider all months of the crop season. As we can observe, both plant and ratoon sugarcane are grown throughout the state, and the five clusters appear in all months. There is a higher percentage of pixels in the clusters with higher NDVI during some months. However, in other months, the largest number of pixels is included in clusters with lower NDVI (**Figure 3**).

**Figure 4** has the temporal profile of clusters showing dynamics of crop planting and harvesting throughout the growing season. Analyzing the temporal profile of **Figure 4**, we can observe that in months from December to May, the NDVI values are higher and represent a larger percentage of pixels for clusters 2, 3 and 4 (from 20 to 40% of the pixels). For the months from August to November, the NDVI values are lower, representing higher percentages for clusters 0 and 1 (around 30% of the pixels). Each month features a sugarcane planting area at a certain stage of growth, appearing in clusters 0 or 1 (harvested or bare soil) and in clusters 2, 3 and 4 (in growth or ready to be harvested) (**Figure 3**).

**Figure 4.** Temporal profile of five NDVI clusters of sugarcane fields for the months from April 2004 to March 2005.

Although the k-means method is simpler and more widely used, their application in satellite image time series of low spatial resolution allows the regional study of crop, even with the difficulty in the analysis due to the possibility of spectral mixing in pixels.

## **3.2. k-Means was used with DTW distance**

months of August, September and October correspond to harvest season. In these months (**Figure 3F**), pixels in magenta and blue, with minimum NDVI values, correspond to clusters 0 and 1, respectively. Cluster 2 (green) corresponds to sugarcane intermediate stage of growth. These clusters can be validated in the MVC NDVI images. The black squares over the satellite images in the left correspond to the main sugarcane planting areas. Analyzing the MVC NDVI images in the northeastern region of São Paulo, the evolution of the sugarcane vegetative growth cycle can be seen (**Figure 3**). Planting begins in August represented in the images by pixels in shades of green and blue located in the northeastern region of the state. These colors represent low NDVI values (around 0.2) characterizing areas with exposed soil and sparse vegetation. Similar pattern also occurs in the months from September to November. From December, when sugarcane begins to grow up and acquire more biomass, these regions are shades of yellow, orange and red. Months from January to May show shades of dark red, when sugarcane reaches the highest stage of growth with maximum NDVI values (between

0.7 and 0.8). The dark areas in images represent pixels covered by clouds and water.

the largest number of pixels is included in clusters with lower NDVI (**Figure 3**).

2, 3 and 4 (in growth or ready to be harvested) (**Figure 3**).

30 Time Series Analysis and Applications

There is no predominance of one or two clusters in all producing regions if we consider all months of the crop season. As we can observe, both plant and ratoon sugarcane are grown throughout the state, and the five clusters appear in all months. There is a higher percentage of pixels in the clusters with higher NDVI during some months. However, in other months,

**Figure 4** has the temporal profile of clusters showing dynamics of crop planting and harvesting throughout the growing season. Analyzing the temporal profile of **Figure 4**, we can observe that in months from December to May, the NDVI values are higher and represent a larger percentage of pixels for clusters 2, 3 and 4 (from 20 to 40% of the pixels). For the months from August to November, the NDVI values are lower, representing higher percentages for clusters 0 and 1 (around 30% of the pixels). Each month features a sugarcane planting area at a certain stage of growth, appearing in clusters 0 or 1 (harvested or bare soil) and in clusters

**Figure 4.** Temporal profile of five NDVI clusters of sugarcane fields for the months from April 2004 to March 2005.

Results of the MVC NDVI image time series analysis about the period 2001–2010 for the state of São Paulo are presented hereafter. Maps and temporal profiles correspond to results of clusters (k-means with DTW distance function), pixels with NDVI values from year to year. In general, clusters that were identified as sugarcane may be (i) related to the type of planting carried out each year, for example, identifying areas of sugarcane ratoon (the sugarcane available for harvest after one or more cuts), sugarcane expansion (the sugarcane planted in new areas that will be harvested for the first time), sugarcane renewed (the year-and-half sugarcane plant that has undergone renovation during the previous crop year and will be available for harvest in the current crop year), sugarcane under renewing (the sugarcane area is not harvested due to renovation, not available for that specific crop year) and not defined area, and (ii) related to the quantity produced. Clusters, which were determined by clustering analysis, do not remain constant from year to year as the sugarcane planting is dynamic along the time series.

Thus, applying the k-means clustering analysis, we can verify sugarcane planting type from the years analyzed. Cluster 4 (red) indicates the maximum NDVI values in the month, corresponding to areas with higher biomass. Cluster 0 (magenta) shows the lower NDVI values, corresponding to bare soil. The k-means method showed more homogeneous temporal profiles (**Figure 5**). Low peaks in NDVI profiles during the months of December and January (**Figure 5**) match NDVI values related to clouds, because this period of year is the rainy season in the state.

Analyzing every year, we found that each cluster corresponds to different types of sugarcane planting (**Table 1**). For example, in crop season 2001–2002, 2003–2004, 2006–2007 and 2008–2009, cluster 2 (green; **Figure 6A**, **C**, **F** and **H**) corresponds to the type of sugarcane ratoon, and this cluster (29–47% of the pixels) is correlated (between R = 0.74 and R = 0.87) with the crop production (**Figure 7**). In crop seasons 2002–2003 and 2009–2010 (**Figure 6B** and **I**), sugarcane ratoon corresponds to cluster 1 (blue), with a correlation of R = 0.84 and R = 0.73 with the production and 36 and 33% of the sugarcane pixels (**Figure 7**). Crop season 2004–2005 (**Figure 6D**) corresponds to cluster 3 (yellow), with correlation

**Figure 5.** Temporal profiles of each cluster for each crop season in the period 2001–2002 to 2009–2010.

**Table 1.** Type of sugarcane planting in each crop season and pixels number percentage for each cluster by k-means with DTW. Agricultural Monitoring in Regional Scale Using Clustering on Satellite Image Time Series http://dx.doi.org/10.5772/intechopen.71148 33

**Figure 6.** k-Means clustering with DTW distance function for each crop season in the period 2001–2002 to 2009–2010. (A) Clustering 2001–2002; (B) Clustering 2002–2003; (C) Clustering 2003–2004; (D) Clustering 2004–2005; (E) Clustering 2005–2006; (F) Clustering 2006–2007; (G) Clustering 2007–2008; (H) Clustering 2008–2009; (I) Clustering 2009–2010.

**Cluster2001–2002**

0 1 2 3

Not defined

Renewed 15%

Not defined

Ratoon 32%

Expansion 35%

Under

Expansion 28%

Expansion 22%

Under renewing

19%

renewing 21%

13%

19%

4

Under

Not defined

Renewed 9%

Renewed 24%

Not defined

Renewed 14%

Ratoon 29%

Not defined 7%

Not defined 6%

20%

Type of sugarcane planting in each crop season and pixels number percentage for each cluster by k-means with DTW.

renewing 24%

**Table 1.**

15%

Ratoon 29%

Expansion 13%

Ratoon 41%

Under

Under

Ratoon 29%

Renewed 21%

Ratoon 47%

Expansion 18%

renewing 20%

renewing 18%

Renewed 17%

Ratoon 36%

Under

Not defined

Ratoon 21%

Expansion 20%

Not defined 7%

Under renewing

Ratoon 33%

32 Time Series Analysis and Applications

11%

renewing 27%

17%

Expansion 9%

Under

Expansion 7%

Expansion 4%

Renovated 3%

Not defined

Under renewing

Renewed 11%

Renewed 21%

14%

12%

renewing 18%

**2002–2003**

**2003–2004**

**2004–2005**

**2005–2006**

**2006–2007**

**2007–2008**

**2008–2009**

**2009–2010**

**Figure 7.** Graph of pixels' number percentage for each cluster regarding each crop seasons in the period 2001–2002 to 2009–2010. Correlation values of the clusters with the sugarcane production.

index R = 0.81 and 32% of the sugarcane pixels (**Figure 7**). In most crop seasons, sugarcane ratoon is strongly correlated with the sugarcane production. Only in crop seasons 2005–2006 and 2007–2008 (**Figure 6E** and **G**), the sugarcane expansion is correlated with crop production.

### **3.3. k-Means was used with DTW distance function of three dimensional (multivariate) time series database**

Dataset with more than 220,000 series in the state of São Paulo were clustered into five clusters (0–4) by k-means method with DTW distance function. Each cluster was formed according to the characteristics of NDVI, surface temperature and albedo extracted from AVHRR/NOAA images in the period 2001–2010. The identified areas were cluster 0 (magenta), which corresponds to water; cluster 1 (blue), which to the urban area and areas where the soil is exposed or have low vegetation and pasture; cluster 2 (green), which represents areas of agricultural crops; cluster 3 (yellow), which corresponds to sugarcane; and cluster 4 (red), which represents forest areas (**Figure 8A** and **B**).

NDVI was useful to separate vegetation areas from other targets, for example, forests present high values of NDVI during the whole season (have high concentration of vegetation and biomass), and these areas are normally shown by red-colored representative time series, in profile visualization (**Figure 9A**). On the other hand, albedo variable was useful to separate water areas from other targets, but was not enough to distinguish areas having different levels of vegetation cover (**Figure 9B**). The water represented by cluster 0 was well clustered, since the NDVI values and especially the albedo values were different from other clusters, as shown in the temporal Agricultural Monitoring in Regional Scale Using Clustering on Satellite Image Time Series http://dx.doi.org/10.5772/intechopen.71148 35

**Figure 8.** Geographic spatial of 2001–2002 (A) and 2009–2010 (B) of clustering results; yellow represents sugarcane.

profile of NDVI (**Figure 9A**) and albedo (**Figure 9B**). The albedo and NDVI values are lower (less than 0.1), since there is no presence of vegetation in the water or when there is minimal.

Clustering results for agricultural crops and grassland were less accurate, probably because different crops present similar NDVI values in some phenological phase during vegetative crop cycle, but are useful to separate agricultural from nonagricultural areas, such as water,

index R = 0.81 and 32% of the sugarcane pixels (**Figure 7**). In most crop seasons, sugarcane ratoon is strongly correlated with the sugarcane production. Only in crop seasons 2005–2006 and 2007–2008 (**Figure 6E** and **G**), the sugarcane expansion is correlated with

**Figure 7.** Graph of pixels' number percentage for each cluster regarding each crop seasons in the period 2001–2002 to

2009–2010. Correlation values of the clusters with the sugarcane production.

**3.3. k-Means was used with DTW distance function of three dimensional (multivariate)**

Dataset with more than 220,000 series in the state of São Paulo were clustered into five clusters (0–4) by k-means method with DTW distance function. Each cluster was formed according to the characteristics of NDVI, surface temperature and albedo extracted from AVHRR/NOAA images in the period 2001–2010. The identified areas were cluster 0 (magenta), which corresponds to water; cluster 1 (blue), which to the urban area and areas where the soil is exposed or have low vegetation and pasture; cluster 2 (green), which represents areas of agricultural crops; cluster 3 (yellow), which corresponds to sugarcane; and cluster 4 (red), which repre-

NDVI was useful to separate vegetation areas from other targets, for example, forests present high values of NDVI during the whole season (have high concentration of vegetation and biomass), and these areas are normally shown by red-colored representative time series, in profile visualization (**Figure 9A**). On the other hand, albedo variable was useful to separate water areas from other targets, but was not enough to distinguish areas having different levels of vegetation cover (**Figure 9B**). The water represented by cluster 0 was well clustered, since the NDVI values and especially the albedo values were different from other clusters, as shown in the temporal

crop production.

34 Time Series Analysis and Applications

**time series database**

sents forest areas (**Figure 8A** and **B**).

**Figure 9.** Profile visualization (2001–2010) of NDVI (A), albedo (B) and surface temperature (C) of clustering results.

urban areas and forest. Clustering of these areas was defined mainly by surface temperature, being higher for targets with lower canopy, such as urban areas and exposed soil, and lower for woodland (**Figure 9A** and **C**). For example, the forest areas represented by cluster 4, in **Figure 8A** and **B**, have high NDVI values (**Figure 9A**) and lower surface temperature values (**Figure 9C**), as they are very shady and dense vegetation coverage areas.

However, sugarcane fields were well clustered over the crop seasons because the sugarcane has a typical behavior (long seasonal cycle) than other crops. In **Figure 8A** and **B**, it is possible to observe the dynamic of this agricultural crop, represented by cluster 3 (yellow), throughout the decade in which in the crop years 2001–2002 the acreage was low, with higher production, and planted in the northeast area of the state, and in the end of the crop years 2009–2010, there was a significant increase in the planted area toward the western of the state. This technique of clustering in three dimensional (multivariate) time series database was efficient to perform temporal analysis of land use, indicating that this methodology can be used to identify and analyze the dynamics of land use and cover.


*3.7. Thermal Properties*

Table 7 shows the WCAP heat capacity and thermal conductivity test results. It is interesting to note that the heat capacity increases with the wood ash content. It can contribute to reducing the heat loss rate of building walls given its relatively low thermal conductivity when used as interior partition. Wood ash level P3 yields a heat capacity 7% higher than that of the control panel. Conversely, the thermal conductivity does not change importantly between 0% and 30% wood ash replacement levels.

**Table 7.** Average thermal properties and density of WCAP as a function of the WA content.

#### *3.8. Microstructure of Mortars*

According to the results shown in Figure 7, there are no clear differences in microstructure between the two samples. They both exhibit a low porosity and pore sizes smaller than 10 μm. The occurrence of spherical particles that have the shape of WA can be observed in Figure 7b as shown by the white arrows.

**Figure 7.** Scanning electron microscopy images of cement control (**a**) and cement + 30% WA (**b**).

#### **4. Discussion**

Although the investigated wood ash does not qualify as a pozzolan, it can be used in replacement of cement up to significant amounts without affecting the physical and mechanical properties of the wood-cement particleboards significantly. In previous studies, maximum wood ash proportions in the order of 15%–20% were reported [10,11]. Compared to the control sample (P0), WCAP prepared with 30% of wood ash in replacement of cement (P3) showed moderate mechanical properties reductions of 10% for bending MOR and 21% for screw-withdrawal resistance. The pH value increases with the hydration of the cement. A high alkaline solution promotes the reactivity of the silica present in the WA, which enhances the pozzolanic activity at the initial stage. Increased pH levels favor the formation of hydrous silica. This compound reacts with Ca2<sup>+</sup> ions and produces insoluble compounds, which are secondary cementitious products [10]. Moreover, WA can act as a filler in the mixtures.

The density of the samples is found to decrease as the WA replacement rate increases, due to the slightly lower density of the ash and, most importantly, the increased amount of water (Tables 4 and 7). As a result of the larger volume of capillary pores, the mechanical and physical properties including density decline. Indeed, water absorption increased significantly from 30% of WA in replacement. It can be explained by the lower amount of cement particles with increasing wood ash contents. Therefore, the hydration reaction was reduced, and the water evaporated quickly in a porous medium with high porosity due to the presence of the wood fibers.

A fraction of the ash of about 7% dissolves in water and contributes to the hydration process. The large surface area associated to the ash particles could also be a factor, as it acts to some degree as nucleation sites for cement hydration. Indeed, based upon SEM examination, no significant difference in the microstructure of a mixture of neat cement and a mixture containing 30% of WA in replacement was found, both exhibiting a dense and uniform microstructure.

The increase in the heat capacity of WCAP after replacement of cement by wood ash has shown that it has the potential to reduce the heat losses of building walls, given the improved insulation it provides. Indeed, WCAP has a low thermal conductivity, about three times lower than that of gypsum boards (0.32 W/m·K) [4]. This low thermal conductivity is mainly due to the higher WCAP porosity compared to that of gypsum because the thermal conductivity of empty voids is very low (about 0.025 W/m·K).

#### **5. Conclusions**

This project studied the physical, thermal, and mechanical properties of wood-cement particleboards incorporating wood ash. Wood ash was found to have an excellent potential for use as partial replacement to Portland cement. Based on the results generated in this study, the optimum replacement rate is about 30% by weight. At this replacement level, the engineering properties of WPCA were moderately reduced (bending MOR by 12%; bending MOE by 20%; screw-withdrawal resistance by 21%) compared to a neat wood-cement control sample. Beyond 30% in replacement, the mechanical and physical properties start to decrease at a significantly higher rate (bending MOR by 43%, bending MOE by 41%, and screw-withdrawal resistance by 60% at a 40% replacement rate). The use of wood ash improves the heat capacity of the WCAP by 11% compared to a neat wood-cement control sample.

The work reported herein is quite promising in view of producing eco-friendly wood cement panels with improved characteristics compared to those of standard gypsum boards. Future work should include the fire-resistance and acoustic properties measurement of this material. The formulation and the processing phases could also be further improved. Notably, the use of a paper surface layer should be studied to enhance the mechanical properties of the panel.

**Author Contributions:** Conceptualization, V.-A.V., A.C., B.B. and P.B.; Data curation, V.-A.V.; Formal analysis, V.-A.V.; Funding acquisition, P.B.; Investigation, V.-A.V., A.C., B.B., P.B. and J.D.; Methodology, V.-A.V., A.C., B.B., P.B. and J.D.; Project administration, A.C. and P.B.; Supervision, A.C. and B.B.; Validation, A.C., B.B., P.B. and J.D.; Writing—original draft, V.-A.V.; Writing—review & editing, A.C., B.B., P.B. and J.D.

**Funding:** This work is part of the research program of the Natural Sciences and Engineering Research Council of Canada (NSERC) Industrial Research Chair on Eco-Construction in Wood (CIRCERB) through programs IRC (IRCPJ 461745-12) and CRD (RDCPJ 445200-12).

**Acknowledgments:** The authors are also grateful to the industrial partners of the NSERC Industrial Chair on Eco-Responsible Wood Construction (CIRCERB) and the SSQ insurance company for providing wood ash from "La Cité Verte".

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


**1. Introduction**

The impact of integrating renewable energy sources from the environment solves one of the major problems regarding global warming and CO2 emissions. Fossil fuel resources, hydrocarbons, coal, and natural gas are all limited, cause pollution, and have a strong influence on carbon emissions, affecting the environment irreversibly [1].

Green, renewable energy sources, as an alternative to fossil fuels, are used by hybrid energy systems [2] based on high-efficiency technologies. For example, the solar concentrator that provides the energy needed to drive a Stirling engine [3], with a working cycle having the same name, created by the Scotsman Robert Stirling and patented in 1816 [4]. Thus, the Stirling engine appears as a promising option in the supply of energy compared to other traditional solutions based on renewable energy sources, with a reduced polluting effect similar to that of geothermal, solar, or biomass energy [5,6].

In the specialist literature, Zabalaga et al. presented in [7] an analysis in terms of energy efficiency and economic profitability of a hybrid energy system consisting of photovoltaic panels, a Stirling engine, and a battery system [8]. The dimensioning, using Homer software and, later, the simulation [9–11] of the starting and cooling sequences in MATLAB/Simulink T.M. 8.9 demonstrated a 69% reduction in greenhouse gas emissions, an 11% improvement in annual costs, and a 5% increase of energy efficiency compared to conventional diesel engine systems [12–14].

In [15], Islas et al. conducted a research study on the influence of several design variables and operating parameters over the performance of a 2 kW alpha-type Stirling engine, as well as methods for optimizing the major factors affecting engine efficiency. An important aspect is temperature and pressure control in the primary and secondary circuits of the Stirling engine.

Malali et al. discuss in [16] the effects of circumsolar radiation on the thermal efficiency of a Stirling engine–solar concentrator system, the mathematical model being realized in MATLAB. In the optimal case, the parameters thus obtained are used in the design of the hybrid system.

In [17], Zare and Saleh used the mathematical model of the Stirling engine and the Lyapunov method to predict the optimal operating conditions of a Stirling engine [18,19]. The starting sequences and the oscillations that may occur during operation were especially studied.

Buscemi et al. presented in [20] a hybrid energy system consisting of a solar concentrator and a 32 kW Stirling engine installed in Southern Italy. Their model is capable of predicting annual energy production, especially taking into account the level of dirt on the solar concentrator mirrors [21,22].

In [23], Arora et al. developed a genetic evolutionary algorithm in MATLAB for choosing the optimum values of the decision variables in order to obtain maximum power in terms of energy and heat, as well as an increased economic efficiency [24,25].

Based on the scientific literature, the novelty of this paper consists of the design and implementation of an automation and control system for the operation of a Stirling engine—this system being used so far only in other types of industrial installations.

Even though the use of programmable logic controllers (PLCs) and industrial automations is widely presented in literature, there are few to no cases where these are used in Stirling engine systems for the generation of both electrical and thermal energy, which increased the research team's interest in approaching this topic.

One particular element of this automation, in comparison to others from the industrial sector, is related to the specificity of the Stirling engine system, namely the adaptation to operating requirements, starting sequences, normal operation, control and valve actuation for loading and unloading the working fluid, operation of the fan and cooling pump, controlled shut-down, overload protection, actuation of the safety curtain of the primary exchanger, monitoring of working parameters, temperature, and speed.

Another aspect was to ensure the recording of the parameters, the global automation transmission through bidirectional ethernet connection, including the positioning of the solar concentrator on the maximum flux of solar thermal irradiation, according to the data collected by the Solys2 weather station, which determines the solar coordinates of the Stirling engine solar concentrator location.

In this context, the installation developed in the National Research and Development Institute for Cryogenic and Isotopic Technologies in Romania has integrated a Stirling engine working together with a solar concentrator with the purpose of cogeneration of electricity (10 kW electric) and thermal energy (25 kW thermal) at a low cost [26].

In this paper, we have designed, tested, and optimized the starting, operating, and stopping sequences of a Tedom-modified Stirling alpha V191 engine, representing the conversion unit of the Sunflower 35 solar concentrator [27], through an automation system. In the laboratory, we designed the electrical installation and the automation system, we implemented practically the automation system [28,29], and we programmed the PLCs in order to realize the starting sequences and the opening of the valves for both the cooling system of the compressor and the charging system with helium. The automation also provides the protection and alarm system, control of the protection curtain, and command and control of the electric generator, the heat exchanger, and fan control for the Stirling engine.

Programmable logic controllers (PLCs) emulate the electrical scheme and the computer equipment built around a processor and are capable of controlling, through digital and analog input/output modules, one or more pieces of industrial equipment based on dedicated software [30].

The specific objectives of the present study, based on the automation design, are the following:

The main objective and focus of this paper is to solve the starting, operating, and shut-down sequences in safe conditions, as well as monitor the working parameters. The performance indicators are given by the stabilization of the temperature of the heat exchanger towards the working temperature, of the engine speed, and of the working pressure according to the values given by the producer [27].

The main findings are as follows:

The solar concentrator using a Stirling engine offers a promising solution compared to other thermal installations that use solar energy, which is an unlimited renewable energy. The most-developed solar solutions currently used are the following:

Even though photovoltaic panels are a great development, the solar concentrators using a Stirling engine accomplish a higher conversion efficiency of solar energy into electricity—29.4% [31]. By comparison, solar collectors that have a parabolic trough or Fresnel-type system can achieve a solar-to-electricity conversion efficiency of only 18–22% [32]. We observe a significant advantage for the Stirling solar concentrators. The tower solar concentrators achieve a solar-to-electricity conversion efficiency of about 20–27% [32], closer to that of the Stirling solar concentrator, but with considerably higher execution costs.

Another revealing analysis is related to the cost of obtaining a kWh, as presented by Zayeda [33]. The Stirling solar concentrator has a promising cost of USD 0.2565/kWh, compared to USD 0.4/kWh for the solar tower concentrators, and USD 0.14–0.16/kWh for solar collectors with a parabolic trough or Fresnel system. The best cost is for the photovoltaic panels, at USD 0.06/kWh.

Awana has shown in [34] the advantages of solar concentrators over photovoltaic systems. That is, a 35.7% higher electricity production, but also a better capacity-use factor and a better solar-to-electric conversion efficiency.

It was also shown that Stirling solar concentrators have led to a significant reduction in the cost of electricity production and, in addition, that the use of Stirling solar concentrator systems working on cogeneration of electric and thermal energy achieved an efficiency of over 60%.

The article was structured as follows: in the first part, in the introduction, the main objectives of the work were stated, up -to-date information was presented relating to PLC automation and control strategies used in Stirling solar power systems and a comparative study was conducted regarding the conversion efficiency and the cost of the proposed system with respect to other solar solutions. The second chapter, "Structure of the Automation and Control system", presented two automation systems and identified the most suitable one, according to the equipment to be monitored/controlled. The third chapter, "Operating Principle", presented the electrical schemes of the main and secondary circuits of the Stirling engine, as well as the automation schemes of the proposed system. In chapter four, "Case Study", the experimental test stand was presented together with the data acquisition, control, and monitoring of the process parameters in tandem with the HMI interface. The results and discussions were presented in the fifth chapter and, finally, in the sixth chapter, the work was summarized and future perspectives were presented.


*4.6. Residual Correction*

A series of improvements in downscaling methods for satellite precipitation products have been achieved, from the original ER model proposed by Immerzeel et al. [13] and the MLR model proposed by Jia et al. [14] to the GWR approach proposed by Xu et al. [17] and Chen et al. [20]. Residual correction is a key step in both ER and MLR [17]. In this study, residual correction for GWR was analyzed in detail.

Using DS\_CIMERG as an example, the residual correction processes for GWR downscaling in July 2016 and September 2017 were analyzed, and the results are shown in Figure 13; Figure 14, respectively. Figure 13a shows the CIMERG data before downscaling. Figure 13b shows the estimated precipitation based on the low-resolution (10 km) regression coefficients (i.e., Predicted Precipitation (10 km) in Figure 2). Figure 13c shows the 10-km residuals obtained by subtracting CIMERG from the 10-km precipitation predictions. Figure 13d shows the 1-km residuals obtained after applying the spline interpolation technique to the 10-km residuals. Figure 13e shows the DS\_CIMERG precipitation estimates, which are based on the high-resolution (1 km) regression coefficients. Finally, Figure 13f presents the residual-corrected downscaling results obtained by summing the 1-km residuals and the 1-km precipitation estimates. As shown in this figure, the precipitation estimates (Figure 13b) obtained with the low-resolution parameters already exhibited high consistency with CIMERG (Figure 13a), and their residual values were generally small, ranging between -10 and 10 mm (Figure 13c), indicating that the low-resolution estimated precipitation data obtained via the GWR method were close to the initial precipitation data. In contrast, the high-resolution precipitation estimates obtained based on the 1-km regression coefficients (Figure 13e) not only were highly consistent with the initial precipitation distribution but also reflected the detailed structure of the precipitation distribution. The results shown in Figure 14 are similar to those in Figure 13, except that they correspond to a different month. Table 2 summarizes the detailed error statistics. The CC, RMSE, and MAE values after residual correction were worse than those before residual correction. Therefore, the addition of residual correction to the GWR model transferred the errors of the original IMERG precipitation data to the final downscaling outcomes, thereby decreasing the reliability of the outcomes, as the residuals were obtained by subtracting the 10-km precipitation predictions from the original IMERG precipitation data.

**Figure 13.** Comparison of the DS\_CIMERG product in July 2016 before and after residual correction. (**a**) CIMERG; (**b**) predicted precipitation (10 km); (**c**) residuals (10 km); (**d**) residuals (1 km); (**e**) DS\_CIMERG; (**f**) downscaled precipitation after residual correction.

**Figure 14.** Comparison of the DS\_CIMERG product in September 2017 before and after residual correction. (**a**) CIMERG; (**b**) predicted precipitation (10 km); (**c**) residuals (10 km); (**d**) residuals (1 km); (**e**) DS\_CIMERG; (**f**) downscaled precipitation after residual correction.

To further evaluate the results through additional validation tests, the data before and after residual correction from 2010 to 2018 were also processed and evaluated. The results were consistent with those presented in Table 2. The CC, RMSE, and MAE values respectively increased from 0.576, 26.92 mm, and 18.12 mm before residual correction to 0.59, 26.55 mm, and 17.63 mm after residual

correction. Therefore, the residual correction step is not necessary in GWR downscaling; this conclusion is consistent with the findings of Xu et al. [17].

**Table 2.** Comparison of evaluation metrics before and after residual correction.


**2. Chapter outline**

	- Breast Growth and Development
		- Interactions of normal breast tissue with bone
		- Breast carcinoma
	- Bone Microenvironment

Breast Cancer Metastases to Bone: Role of the Microenvironment 533

content per month (9). Lactation-induced fragility fractures have been reported as a result, but are not common (10). Of interest, other important molecular mediators for the developing of lactating mammary gland are receptor activator of nuclear factorB (RANK) and its ligand RANKL, which are better known for their key role in regulating the formation of osteoclasts. Expression of RANKL in the mammary epithelium is induced by hormones increased during pregnancy, such as prolactin, progesterone, and PTHrP, and mice lacking RANKL or RANK cannot form lobuloalveolar mammary gland structures, resulting in complete inability to develop a lactating mammary gland (11). Thus, normal breast tissue can interact with bone through a system of hormonal regulators that are important during lactation, and it expresses molecular machinery that employs the same mediators to perform locally distinct functions

Fig. 1. Physiological interactions between the functions of breast and bone. Lactation involves secretion of large amounts of calcium. Bone is a key participant in calcium

homeostasis. PTH is reduced during lactation while PTHrP production by the breast tissue is increased. Suckling stimulates prolactin secretion and inhibits GnRH production, both of which reduce estradiol levels, leading to bone resorption. Prolactin and PTHrP induce breast expression of RANKL, necessary for normal lactating mammary gland function. In the bone tissue, osteoblast-produced RANKL is key regulator of osteoclastogenesis.

Breast carcinomas may arise from the inner lining of the milk ducts or from the lobules, known, respectively, as ductal carcinomas or lobular carcinomas (12). Once a tumour exceeds 1-2 mm in diameter, it requires extensive vascularization in order to survive (13), but the speed of cancer growth often exceeds its capability to form normal vascular organization. Poor angiogenesis results in an under-vascularized microenvironment, which leads to hypoxia, acidic pH and nutrient depletion in the tumour (14). Some cancer cells may

(Figure 1).


*Edited by Rosario Pignatello and Teresa Musumeci*

This contribution book collects five among reviews and original articles from eminent experts working in the interdisciplinary area of biomaterial synthesis and application. From their direct and recent experience, the readers can access the novel and ongoing potentialities of different synthetic and engineered biomaterials. Contributions reflect the fundamental studies, with a particular attention to the physico-chemical mechanical characterization of biomaterials, along with biocompatibility studies and potential clinical use. After an introductory chapter on the question of storage stability for biomaterial-based devices and products and for polymeric nanomedicines, a first review deals with the use and commercial sources of hydroxyapatite in tissue engineering and other biomedical applications. A study follows on optical fiber laser marking on the properties of stainless steel in implant manufacturing. Two other reviews, respectively, focused on the approaches to prevent or treat the effects of calcification that occurs in vivo on biomaterial-based implants and on the encapsulation of pancreatic islet cells for the treatment of type I diabetes will be presented. Finally, an overview on the physical bases and application in biomaterial science of the spray-drying process will close the volume. This setting will allow to achieve a general view of how classical and novel biomaterials can be applied, along with the methodologies necessary to design, develop, and characterize them, without the restrictions necessarily imposed by industrial or profit concerns. Readers will be apprised about the methodologies used to develop biomaterials possessing the physical and biological properties needed for specific medical and clinical applications.

ISBN 978-1-78923-064-2

Biomaterials - Physics and Chemistry - New Edition

Published in London, UK © 2018 IntechOpen © Rixipix / iStock


**4.3. Diagnosis**

The Role of Endoscopy in Management of Infertility 17

fallopian tube removal, has become the preferred method of surgical treatment.

During treatment, physicians should examine patients at least weekly and sometimes daily. Serial beta-hCG measurements should be taken after treatment until the level is

There are a number of possible disorders that can cause a pelvic mass. Some are common, while others are quite unusual or even rare. The physical exam should include visualization and palpation of the abdomen. The next step is usually an imaging study, such as

There are several different types of ovarian cysts, the most common being functional cysts. Often, ovarian cysts do not cause symptoms. Women in the age group of thirty to sixty are more prone to having ovarian cysts.The follicular cysts are easily identified on vaginal sonography, usually measure a few millimeters to a few centimeters in size, and rarely become symptomatic. If they enlarge in size they may rupture, producing transient abdominal pain. Ovarian cysts can cause several other problems if they twist, bleed, or

Ovarian dermoid cyst, also known as mature teratoma, is a non cancerous ovarian tumor, which is more commonly found in young women. Although dermoids are non cancerous, in some rare cases, they might develop into cancerous growth. Dermoid cysts may contain substances such as nails and dental, cartilage like, and bonelike structures. These growths can develop in a woman during her reproductive years. Dermoids can range in size anywhere between two to ten centimeters. It is very difficult to identify the presence of these cysts inside the ovaries as they do not produce any symptoms. They can cause torsion, infection, rupture, and cancer. These dermoid cysts can be removed with either conventional surgery or laparoscopy. Ovarian dermoid cysts do not affect the fertility of the

Laparoscopy has similar tubal patency and future fertility rates as medical treatment.

**Follow-up and prognosis**

**Adnexal mass in infertility**

ultrasound, CAT scan, or MRI

**Differential diagnoses**

**Follicular cysts**

rupture

woman.

**Dermoid cyst**

undetectable.


**7. Conclusion**

This chapter has presented the WPT methodology for implantable medical devices. The common configuration of WPT and its principle were demonstrated, including magnetic field coupling and electric field coupling. Most of all, the recent emerging biomedical topics such as wireless-charging pacemakers, implantable optogenetic devices, and implantable bio-impedance sensors were demonstrated. Overall, this chapter thoroughly presented the electric principle of WPT and extended it to emerging biomedical applications.