**4. Applications of heat sinks in real life**

Today, we can observe all levels of technological devices in everyday life, such as smartphones, gaming consoles, PCs, and laptops, etc. Companies changed many parameters to achieve the best performance of devices, including decreasing thickness, enhancing GPU and CPU numbers, and changing the appearance [109].

Based on the last researches, with the increasing number of microprocessors, the heat generation is intensified; thus, heat dissipation becomes a critical issue for thermal researchers. The manufacturer's primary purpose is to achieve the best performance of devices by minimizing device and heat sink size and increasing the heat transfer rate utilizing the different items such as changing the geometrical parameters and working fluids.

In this section, some commercial electronic cooling systems and their operating principles are discussed. The purpose of this section is to establish a link between the scientific and commercial platforms. All effective parameters discussed in previous sections are required to fabricate optimum microchannel/micro pin-fin heat sink.

*Effective Parameters on Increasing Efficiency of Microscale Heat Sinks and Application… DOI: http://dx.doi.org/10.5772/intechopen.96467*


2.2 Membrane or plate absorbers

A membrane or plate absorber is an air impervious material fixed at its edges at a certain distance from a rigid surface, so as to leave a tight cavity between both (Figure 4). When the stiffness of the material is negligible in relation to the tensions that hold it, it is said to be a membrane; when its stiffness must be considered, the material is said to be a plate.

The best performance of the plate is expected to occur at its resonance frequency and can be calculated as

$$f\_r = \frac{60}{\sqrt{md}}\tag{5}$$

In any case, the value of α coefficient of these plates is usually not more than 0.50. Since the surface mass of the panels has a practical minimum, these absorbers are useful only at low frequencies. Increasing the mass m of the panel or the distance d lowers the resonance frequency. If the cavity is filled with a soft material, the operating range broadens in terms of frequencies, but in turn it lowers the

The acoustic resonators consist of a cavity that communicates with the outside by a narrower conduit or neck, and of such dimensions they can dissipate energy in

The resonance frequency of a Helmholtz-type resonator can be calculated as follows:

; and l is the effective length of the neck of the resonator, in m: le =

ð6Þ

; V the inside volume of the

a certain frequency, that is, its resonance frequency (Figure 5).

where S being the neck area of the resonator, in m2

lreal + 1.7 r; r the radius of the neck, in m.

Outline of a Helmholtz acoustic resonator (adapted from [1]).

maximum value of α of the plate.

2.3 Resonator absorbers

Typical acoustic absorbent panel.

How Do Acoustic Materials Work?

DOI: http://dx.doi.org/10.5772/intechopen.82380

Figure 4.

resonator, in m3

Figure 5.

9

where m is the surface mass of the plate, in kg/m2 ; and d is the distance from the panel.

How Do Acoustic Materials Work? DOI: http://dx.doi.org/10.5772/intechopen.82380

#### Figure 4. Typical acoustic absorbent panel.

In any case, the value of α coefficient of these plates is usually not more than 0.50. Since the surface mass of the panels has a practical minimum, these absorbers are useful only at low frequencies. Increasing the mass m of the panel or the distance d lowers the resonance frequency. If the cavity is filled with a soft material, the operating range broadens in terms of frequencies, but in turn it lowers the maximum value of α of the plate.

#### 2.3 Resonator absorbers

The acoustic resonators consist of a cavity that communicates with the outside by a narrower conduit or neck, and of such dimensions they can dissipate energy in a certain frequency, that is, its resonance frequency (Figure 5).

The resonance frequency of a Helmholtz-type resonator can be calculated as follows:

$$f\_{\rm res} = \frac{c}{2\,\pi} \cdot \sqrt{\frac{S}{VI\_e}}\tag{6}$$

where S being the neck area of the resonator, in m2 ; V the inside volume of the resonator, in m3 ; and l is the effective length of the neck of the resonator, in m: le = lreal + 1.7 r; r the radius of the neck, in m.

Figure 5. Outline of a Helmholtz acoustic resonator (adapted from [1]).

Design and Faculty of Engineering) that demonstrated good acoustic performance and very low manufacturing cost, in addition to allowing the recycling of a very

Acoustic absorbers made with weaving wastes. Up: suspended mounting. Down: experimental absorption

A membrane or plate absorber is an air impervious material fixed at its edges at a

The best performance of the plate is expected to occur at its resonance frequency

ð5Þ

; and d is the distance from the

certain distance from a rigid surface, so as to leave a tight cavity between both (Figure 4). When the stiffness of the material is negligible in relation to the tensions that hold it, it is said to be a membrane; when its stiffness must be

common waste in Uruguay [3].

coefficient for different ways of mounting.

and can be calculated as

panel.

8

Figure 3.

Acoustics of Materials

2.2 Membrane or plate absorbers

considered, the material is said to be a plate.

where m is the surface mass of the plate, in kg/m2

#### Acoustics of Materials

As these devices are very selective, they are generally used only when a reverberation has to be eliminated at a well-defined frequency or, in any case, in a fairly narrow frequency range. If some sound absorbent material is placed inside the cavity and especially close to the neck, the range of frequencies absorbed by the resonator can be expanded somewhat, but the efficiency in the frequency of better performance will decrease.

surface, most of the energy will be reflected, and only a small portion will be transmitted to the wall, as its acoustic impedance is undoubtedly much greater than that of air. When passing from the wall again to the air, most of the energy

transmitted to the air. Then, the transmitted wave will have a smaller amplitude than the wave that would result if the propagation media has not changed

The transmission coefficient τ of a material is then the relation between the

τ ¼ Et Ei

R ¼ 10 log 1

A single wall in acoustics is formed by only one foil. If it is a macroscopically homogeneous wall, its acoustic insulation will depend on several of its mechanical

is heavy and tight to the passage of air but only weakly rigid. A more rigorous analysis allows recognizing several zones with different behavior, as shown in Figure 7: the design zones are those controlled by mass or by coincidence; the zones controlled by stiffness or resonance refer to a poor and irregular acoustic

Intuitively, a simple and homogeneous wall offers good sound insulation when it

(8)

τ (9)

will be again reflected inside the wall, and only a small portion will be

The acoustic reduction index R is defined with basis on τ:

transmitted energy and the incident energy:

How Do Acoustic Materials Work?

DOI: http://dx.doi.org/10.5772/intechopen.82380

3.2 Acoustic performance of a single wall

(Figure 6).

properties.

performance.

Figure 6.

11

Destinations of the acoustic energy that reaches a wall.

The construction of Helmholtz resonators is usually done by panels with circular or linear perforations, where the total area of the resonator is the sum of the areas of all the holes.


**3.1 Site investigation**

Field investigation was formulated to recognise, manage, and remediate the oilcontaminated sand. As the first step, the concentration and type of contamination present ought to be determined in the Greater Al-Burqan oil field. The data from the investigation will be utilised to plan future rehabilitation works. As such, the aim of this survey can be summarised as:

The selected approached was based upon the concepts of soil survey. The main parameters measured were the depth of contamination (by site measurement), TPH level (using gravimetric method), colour of the soil (using Munsell colour chart), and the texture of the soil. The site investigation categorises the oil-contaminated soil into four layers, namely, liquid oil, tar mat, oily soil, soot and clean soil with no contamination. For remediation the bulk of the contaminated soil to be dealt with has oily soil characteristics; also in some areas the oily soil placed under the liquid oil requires to be treated. In addition, all of these areas may contain unexploded ordnance (UXO). Any method for remediation of the tar mat and soot would need to take into account that they occur over an extensive area and form a thin layer on the soil surface.

#### **3.2 Preliminary survey**

It is evident from the field data in **Figure 2** that these layers of contaminated oil can be segregated based on their colour and property consistency. Typically, weathered crude oil is black, oily soil is dark brown to black with a moderate to slightly hard consistency, while the colour of tar mat is black with a hard stability. The depth of these oil lakes is 70 cm below the surface; therefore, the crude oil has penetrated the soil to different depths subject to the condition and characteristics of the soil belowground (**Figure 2**).

**157**

investigation.

**Figure 2.**

*3.3.1 Soil sampling*

**3.3 Characterisation of oil lakes**

conducted at the University of Portsmouth, UK.

meanwhile the sludge will be taken by the KOC for reuse.

*The Assessment Strategy for Selecting and Evaluating Geoenvironmental Remediation*

A thick oily sludge deposit has covered the affected areas with a thicker layer beneath, and oil has seeped through aided by gravity and rainfall [6, 7]. Besides this, the quality of oil has deteriorated caused by prolonged exposure to the extreme weather. The volatile hydrocarbons within the oil structure have been lost, and the oil has endured changes in its chemical and physical properties causing its sale not as lucrative. The nature of layers and the manner they are arranged provide indication of the category of contamination, the three contamination categories in **Figure 2**. Different sorts of oil-contaminated soils might not be differentiated using only the analytical results. For example, field investigation is vital to classify these contaminations, as they require various remediation methods to remediate or rehabilitate the contaminated site or options of land use; furthermore, varying physical characteristics are noticed during the

*The layers in the oil lake at the Burgan oil field in the State of Kuwait (13 Jan 2012).*

It is vital to evaluate the characteristics of Kuwait's oil-contaminated sands such as organic and inorganic material contents and soil particle size distributions in order to select the appropriate treatment method. All analytical methods were

The soil samples were collected from the Burgan oil field in the south of the Kuwaiti desert. The sample was collected from the edge of lake no. 105, in September 2011 (**Figure 3**). Firstly, the KOC and Ministry of Defence (MOD) checked for unexploded bombs and landmines, and then a hand shovel was used to remove about 3 cm of oily sludge from the soil surface. Then, after which heavily oil-contaminated soil (concentration of oil 35%) was collected at a depth of approximately 30 cm below the surface level of oily sludge. Subsequently, the samples were placed into plastic containers after being excavated from the soil with a shovel. This project has been focused on the contaminated layer below the oil sludge layer,

*DOI: http://dx.doi.org/10.5772/intechopen.88166*

*The Assessment Strategy for Selecting and Evaluating Geoenvironmental Remediation DOI: http://dx.doi.org/10.5772/intechopen.88166*

**Figure 2.** *The layers in the oil lake at the Burgan oil field in the State of Kuwait (13 Jan 2012).*

A thick oily sludge deposit has covered the affected areas with a thicker layer beneath, and oil has seeped through aided by gravity and rainfall [6, 7]. Besides this, the quality of oil has deteriorated caused by prolonged exposure to the extreme weather. The volatile hydrocarbons within the oil structure have been lost, and the oil has endured changes in its chemical and physical properties causing its sale not as lucrative. The nature of layers and the manner they are arranged provide indication of the category of contamination, the three contamination categories in **Figure 2**. Different sorts of oil-contaminated soils might not be differentiated using only the analytical results. For example, field investigation is vital to classify these contaminations, as they require various remediation methods to remediate or rehabilitate the contaminated site or options of land use; furthermore, varying physical characteristics are noticed during the investigation.

#### **3.3 Characterisation of oil lakes**

It is vital to evaluate the characteristics of Kuwait's oil-contaminated sands such as organic and inorganic material contents and soil particle size distributions in order to select the appropriate treatment method. All analytical methods were conducted at the University of Portsmouth, UK.

#### *3.3.1 Soil sampling*

The soil samples were collected from the Burgan oil field in the south of the Kuwaiti desert. The sample was collected from the edge of lake no. 105, in September 2011 (**Figure 3**). Firstly, the KOC and Ministry of Defence (MOD) checked for unexploded bombs and landmines, and then a hand shovel was used to remove about 3 cm of oily sludge from the soil surface. Then, after which heavily oil-contaminated soil (concentration of oil 35%) was collected at a depth of approximately 30 cm below the surface level of oily sludge. Subsequently, the samples were placed into plastic containers after being excavated from the soil with a shovel. This project has been focused on the contaminated layer below the oil sludge layer, meanwhile the sludge will be taken by the KOC for reuse.

*Geopolymers and Other Geosynthetics*

**3. Methods**

**3.1 Site investigation**

this survey can be summarised as:

• Classify the types of damage.

• Assess the level of contamination in the affected soil.

and treatment walls, or in mining applications [28–30]. Geomembranes have been employed and evaluated for the first time in the soil remediation area, as a layer for biopile method in the composite liner barrier system used for treating hydrocarboncontaminated soil [31]. Geomembranes have been used for a variety of applications during remediation of heavily contaminated sand with polychlorinated biphenyls (PCBs), hydrocarbons, and metal [32, 33]. They proposed to construct geomembranes in the landfill site as the base barrier system to filter and treat the contaminants from the spring thaw. This technique is required when contaminated soil or landfill needs to be isolated from the surrounding groundwater or ecological system to prevent the release of hazardous gases or liquids. Furthermore, Various advantage can be obtained by using geomembrane such as protecting people from contacting with hazards or reduce the impact of discharge water through the contaminated land

which allow to decrease in leachate of these hazardous to the groundwater.

Field investigation was formulated to recognise, manage, and remediate the oilcontaminated sand. As the first step, the concentration and type of contamination present ought to be determined in the Greater Al-Burqan oil field. The data from the investigation will be utilised to plan future rehabilitation works. As such, the aim of

• Provide information to assist in future land use planning and to determine remediation options using House of Quality (HOQ ) analysis system.

The selected approached was based upon the concepts of soil survey. The main parameters measured were the depth of contamination (by site measurement), TPH level (using gravimetric method), colour of the soil (using Munsell colour chart), and the texture of the soil. The site investigation categorises the oil-contaminated soil into four layers, namely, liquid oil, tar mat, oily soil, soot and clean soil with no contamination. For remediation the bulk of the contaminated soil to be dealt with has oily soil characteristics; also in some areas the oily soil placed under the liquid oil requires to be treated. In addition, all of these areas may contain unexploded ordnance (UXO). Any method for remediation of the tar mat and soot would need to take into account that they occur over an extensive area and form a thin layer on

It is evident from the field data in **Figure 2** that these layers of contaminated oil can be segregated based on their colour and property consistency. Typically, weathered crude oil is black, oily soil is dark brown to black with a moderate to slightly hard consistency, while the colour of tar mat is black with a hard stability. The depth of these oil lakes is 70 cm below the surface; therefore, the crude oil has penetrated the soil to different depths subject to the condition and characteristics of

**156**

the soil surface.

**3.2 Preliminary survey**

the soil belowground (**Figure 2**).

**Figure 3.** *Sample collection from Burgan Lake No. 105.*

**Figure 4.** *Particle size distribution on wet basis.*

### *3.3.2 Properties of oil lakes*

Soil properties are classified into two categories: chemical properties and physical properties. The soil samples were taken from the Burgan oil field to classify and analyse the physical properties according to the soil layers. The constant head permeability test were used to measure the soil permeability. These samples were considered as moderately permeable soil, with an average permeability rate of 0.064 mm/s having been recorded. Due to the presence of oil on the top layer, it prevented water from penetrating. Furthermore, mechanical sieve analysis (**Figure 4**) was carried out based on British Standards (BS 1377: Part 2:1990) [34] for wet sample to eliminate coarse fraction as well as to ensure homogeneity for the oil-contaminated soil. Chemical properties were investigated by determining TPH and measuring concentration of metal contents in Kuwaiti oil-contaminated sand.

As illustrated in **Table 2**, the results indicate the presence of some ions such as Ba, Cr, Fe, Ni, Pb, Cd, and Ag, as well as high concentration of TPH. The present preliminary study showed that with the high average value of electrical

**159**

*The Assessment Strategy for Selecting and Evaluating Geoenvironmental Remediation*

**Parameters Concentration of metals (mg/kg) KEPA limit (mg/kg)**

Barium (Ba) 0.78 10 Chromium (Cr) 0.52 5 Iron (Fe) 8.10 5 Nickel (Ni) 0.43 10 Lead (Pb) 0.70 5 Cadmium (Cd) 1.1 1 Silver Ag 0 5 TPH 367,234 10,000 pH 7.8 5.5–7.5

conductivity (EC) (2455 μS/cm), the existence of ions could be caused by using seawater to extinguish fires from oil well. Moreover, pH for the contaminated sample was found slight higher than the permissible level as shown in **Table 2**. For the purpose of selecting the most appropriate treatment method, the chemical and physical properties of the lake as well as unexploded ordnance, weathered soil, and

In carrying out decision-making process, a number of techniques could be utilised, for example, pairwise comparison chart, decision matrix, force field analysis, cost-benefit analysis, and HOQ. HOQ is used in this study among various available methods. This decision-making tool is considered as a simple decision mechanism with potential of implementation at various stages of advertising and product manufacturing. Function deployment instrument is required to develop the specifications into the product and organise customer requirements as well as enhance procedure of work. This decision-making tool needs to assign weight to each specification also; upon outlining personnel responsible for decision-making ought to outline weighted symbols among the progressions that constitute the interlink between the proposed processes and specifications and non-weighted symbols between the processes themselves. Towards the end, the sum of the product of the specified weight by the equivalent symbol weight is determined by calculating the accumulated score for each process. Moreover, HOQ provides assistance to engineers in focusing on specified needs and deciding on the best sequence in the case that the process goes ahead. Because of this, HOQ was employed for this project to incorporate the requirements of UNCC and KNFP and choose the best technique of

remediation method for which the remediation process will proceed.

Within this research, relationships were established between pre-set objectives outlined by UNCC and KNFP and various methods of remediation chosen by the team. In contrast to the numerical evaluation matrix, HOQ utilises symbols to demonstrate the relationship between objectives and alternatives in addition to the connection between alternatives themselves. In an effort to compare the proposed

*DOI: http://dx.doi.org/10.5772/intechopen.88166*

local conditions are taken into consideration.

*The concentration of metals during the washing of Kuwait oil residual [36].*

**Table 2.**

**4. Determination of remediation options**

**4.1 Establishing house of quality**

*The Assessment Strategy for Selecting and Evaluating Geoenvironmental Remediation DOI: http://dx.doi.org/10.5772/intechopen.88166*

**Table 2.**

*Geopolymers and Other Geosynthetics*

**158**

**Figure 4.**

**Figure 3.**

*Particle size distribution on wet basis.*

*Sample collection from Burgan Lake No. 105.*

Soil properties are classified into two categories: chemical properties and physical properties. The soil samples were taken from the Burgan oil field to classify and analyse the physical properties according to the soil layers. The constant head permeability test were used to measure the soil permeability. These samples were considered as moderately permeable soil, with an average permeability rate of 0.064 mm/s having been recorded. Due to the presence of oil on the top layer, it prevented water from penetrating. Furthermore, mechanical sieve analysis (**Figure 4**) was carried out based on British Standards (BS 1377: Part 2:1990) [34] for wet sample to eliminate coarse fraction as well as to ensure homogeneity for the oil-contaminated soil. Chemical properties were investigated by determining TPH and measuring concentration of metal contents in Kuwaiti oil-contaminated sand. As illustrated in **Table 2**, the results indicate the presence of some ions such as Ba, Cr, Fe, Ni, Pb, Cd, and Ag, as well as high concentration of TPH. The present preliminary study showed that with the high average value of electrical

*3.3.2 Properties of oil lakes*

*The concentration of metals during the washing of Kuwait oil residual [36].*

conductivity (EC) (2455 μS/cm), the existence of ions could be caused by using seawater to extinguish fires from oil well. Moreover, pH for the contaminated sample was found slight higher than the permissible level as shown in **Table 2**. For the purpose of selecting the most appropriate treatment method, the chemical and physical properties of the lake as well as unexploded ordnance, weathered soil, and local conditions are taken into consideration.


**7. Challenges to postnatal care in sub-Sahara Africa**

Several challenges to PNC in sub-Saharan Africa have been documented [23]. One of the key challenges associated with postnatal care in SSA is a lack of access to basic healthcare services [23]. Many women in the region live in remote, rural areas that lack adequate healthcare facilities [35, 38]. Healthcare facilities in the region are often understaffed, and the staff available may not be adequately trained to provide the care that is needed [18]. The facilities also lack basic medical supplies such as drugs and equipment, which can make it difficult for healthcare workers to provide the care that is needed, and for women to receive appropriate care during the postnatal period [18, 38].

Even when postnatal care services are available, a variety of sociocultural barriers prevent mothers and their newborns from accessing these services. These include household barriers related to the educational level and health literacy of mothers and other family members, the influence of sociocultural beliefs and practices, and

women's autonomy and decision-making power [39]. Specific examples of sociocultural barriers to postnatal care utilization include cultural beliefs about the 40 days following childbirth, during which mothers and babies should stay indoors [40], misconceptions about the importance of postnatal care, and a lack of knowledge about postnatal care and its benefits [35].

At the individual level, a woman's education and literacy level, occupation, income and access to information influence her access and utilization of PNC. At the health system level, the cost of health services, transportation, accessibility, travel time and distance to healthcare facilities all influence utilization of PNC. The functionality and standard of health systems at the community level, the standard of care provided at the facility and concerns about the quality of care, including the attitudes of health workers, their skills, resources, workload and effect on the quality of care, and the cultural acceptability of services all influence women's decision to seek PNC [28, 35].

Socioeconomic characteristics such as place of residence, cultural attitudes, gender norms, women's autonomy and empowerment, wealth or poverty levels in the community, levels of education at the community level, population density, and the government's contribution to healthcare spending, and gross national income per capita are contextual variables that affect the utilization of postnatal care [25, 29]. The extent of poverty, socioeconomic development and infrastructure, including poor roads and transportation, can also make it difficult for women to reach healthcare facilities, even if they are available [18, 35, 38].

Utilization of postnatal care is correlated with several of these factors predictably and consistently. For instance, it is well known that women are more likely to use maternal healthcare if they reside in regions or countries with higher socioeconomic or educational standards. it is also well known that women who reside in rural regions are less likely to use postnatal care and other maternal healthcare services. Many women in the region live in poverty and do not have the resources, information, autonomy and decision-making power to access care during the postnatal period. Discrimination and other social factors also make it difficult for women to access and receive the care they need [18, 35, 38].

Contextual factors such as cultural beliefs and practices, also hinder the delivery of quality postnatal care in SSA [18, 35]. Many women in the region follow traditional birthing practices, which can make it difficult for healthcare workers to provide the care that is needed [35]. Additionally, there may be cultural taboos surrounding certain aspects of postnatal care, such as institutional childbirth, which can make it difficult for women to access PNC [33].

One of the strongest predictors of postnatal care use is women's empowerment and autonomy [41]. The evidence suggests, that living in communities where the majority of women are empowered, that is, educated and financially independent, is advantageous for access and utilization of postnatal care in sub-Saharan Africa [29]. Furthermore, women who reside in places where their freedom is curtailed, due to certain cultural beliefs or social restrictions on women and/or insecure places such as conflict situations are less likely to seek postnatal care after childbirth. Relational factors have also been shown to be significant contextual determinants of postnatal care use. These elements mostly stem from regular contacts or relationships that women have with males. These relationships may have an impact on whether or not women choose to receive maternity care. Furthermore, household size, the number of children per household, and small family norms also influence the decision to access postnatal care [41].


*2.1. Analysis Procedure*

The method of this study is divided into four steps. First, the data for the annual maximum daily rainfall in the Kyushu region are collected. Next, the spatial correlation of rainfall data between the two stations is investigated. Following this, the annual maximum daily rainfall is normalized using quantiles, corresponding to the non-exceedance probability of 50% and 90% of the generalized extreme value (GEV) distribution. The second and third steps are performed to apply the station-year method. Then, the normalized rainfall data are combined and converted to the RP.

#### *2.2. Data Collection*

The annual maximum value for daily rainfall at 23 meteorological observatories operated by the Japan Meteorological Agency (JMA) in Kyushu until 2020 was used to estimate the RP of daily rainfall because long-term records are available (Figure 1 and Table 1). The longest record is 143 years at Nagasaki, the shortest is 59 years at Fukue, and the average for the 23 sites is 98 years. The highest elevation at the rainfall station is 677.5 m at Unzen, the lowest is 2.5 m at Fukuoka, and the average is 20.6 m. The distance between the stations is 25.0 km at the shortest and 441.8 km at the longest.

In addition, the annual maximum value for daily rainfall until 2020 from the Automated Meteorological Data Acquisition System (AMeDAS) Izumi, Morotsuka, AsoOtohime, and Asakura operated by the JMA was used to validate our method (Figure 1). These stations were selected because they have short-period records (approximately 40 years) and observed extreme rainfall events that triggered floods and landslides (Table 2) [12,32–34]. Specifically, a rainfall event that triggered a deep-seated landslide and debris flow was observed at AMeDAS Izumi in July 1997. At AMeDAS Morotsuka, a rainfall event that triggered deep-seated landslides was observed in September 2005. At AMeDAS Aso-Otohime, rainfall events that triggered floods and debris flow were observed in July 1990 and July 2012, respectively. At AMeDAS Asakura, an extreme rainfall event that triggered shallow landslides and debris flow including driftwood was observed in July 2017.

**Figure 1.** Location of rainfall stations. White circles and triangles indicate meteorological observatories and AMeDAS, respectively.

**Table 1.** Specifications of meteorological observatories in Kyushu.

**Table 1.** *Cont.*

**Table 2.** Specifications of AMeDAS.

#### *2.3. Investigation of Spatial Correlation of Annual Maximum Value for Daily Rainfall*

The Kendall rank correlation coefficient (Kendall's τ) [35] of the annual maximum daily rainfall between two stations was investigated because the station-year method assumes the spatial independence of stations [17]. In previous studies [17,28,30,36], spatial independence was investigated using Pearson's correlation coefficient, but Kendall's τ was used in this study since the annual maximum daily rainfall between two stations was not assumed to be Gaussian distribution.

Kendall's τ is a non-parametric method for testing the dependence between two variables based on an ordinal association between two measured quantities. Kendall's τ is given as:

$$\tau = \frac{\sum\_{i$$

where *sign* (·) is the sign function. To conduct this investigation, common period data from all stations between 1981 and 2010 were used.

#### *2.4. Normalization of Daily Rainfall Data*

Quantiles corresponding to the non-exceedance probability of 50% and 90% were used to normalize the daily rainfall data; in other words, the values corresponding to the RP of 2 and 10 years (2- and 10-year values). These indices were less variable since they were calculated by interpolation at each station and were considered suitable for normalization. Suzuki and Kikuchihara [24] also used 2- and 10-year values to normalize the daily rainfall data. The data for the annual maximum daily rainfall were normalized following Suzuki and Kikuchihara [24]:

$$y\_T = \frac{\mathbf{x}\_j - \mathbf{x}\_2}{\mathbf{x}\_{10} - \mathbf{x}\_2} \tag{2}$$

where *yT* is normalized daily rainfall and *xj* is the annual maximum daily rainfall. *x2* and *x10* are the 2- and 10-year values, respectively.

## *2.5. Extreme Value Analysis*

The 2- and 10-year values were calculated using parameters in the GEV distribution [22], estimated by the L-moment method [21]. The data for the annual maximum daily rainfall between 1981 and 2010 were used due to the need to unify the period for calculating the GEV parameters. The GEV cumulative distribution function and the quantile of the GEV corresponding to the non-exceedance probability are given as, respectively:

$$F(\mathbf{x}) = \exp\left\{-\left(1 - k\frac{\mathbf{x} - \mathbf{c}}{a}\right)^{\frac{1}{k}}\right\} \text{ for } k \neq 0 \tag{3}$$

$$\mathbf{x}\_p = \mathbf{c} + \frac{a}{k} [1 - \{-\ln(p)\}^k] \tag{4}$$

where *k* is the shape parameter, *c* is the scale parameter, *a* is the location parameter, and *p* is the non-exceedance probability. The parameters of the GEV are given as:

$$\begin{cases} \begin{array}{l} k = 7.8590d + 2.9554d^2 \\ a = \frac{k\lambda\_2}{\left(1 - 2^{-k}\right)\Gamma(1+k)} \\ c = \lambda\_1 - \frac{a}{k} \{1 - \Gamma(1+k)\} \end{array} \end{cases} \tag{5}$$

$$d = \frac{2\lambda\_2}{\lambda\_3 + 3\lambda\_2} - \frac{\ln(2)}{\ln(3)}\tag{6}$$

where *λ*1–3 are sample L-moments and Γ the gamma function. *λ*1–3 are given as:

$$\begin{cases} \lambda\_1 = \beta\_0 = \frac{1}{N} \sum\_{j=1}^{N} \mathbf{x}\_{(j)}\\ \lambda\_2 = \beta\_1 = \frac{1}{N(N-1)} \sum\_{j=1}^{N} (j-1)\mathbf{x}\_{(j)}\\ \lambda\_3 = \beta\_2 = \frac{1}{N(N-1)(N-2)} \sum\_{j=1}^{N} (j-1)(j-2)\mathbf{x}\_{(j)} \end{cases} \tag{7}$$

where *x*(*j*) is the *j*-th value from the smallest when the sample is arranged in increasing order.

The standard least-squares criterion (SLSC) [37,38] was used to evaluate the goodness of fit between the observed rainfall and the probability distribution. The SLSC compares the goodness of fit across distributions, and their smaller values imply better fits [37]. In this study, we judged a good fit when the SLSC value was below 0.04 in accordance with the JMA [39]. The SLSC value is given as:

$$SLSC = \frac{\sqrt{\frac{1}{N} \sum\_{j=1}^{N} \left\{ s \left( x\_{(j)} \right) - s^\* \left( p\_{(j)} \right) \right\}^2}}{|s\_{0.99} - s\_{0.01}|} \tag{8}$$

$$s(x\_{(j)}) = -\ln\left[\left\{ \left(1 - k \frac{x\_{(j)} - c}{a} \right)^{\frac{1}{k}} \right\} \right] \tag{9}$$

$$s^\*\left(p\_{(j)}\right) = -\ln\left[-\ln\left\{p\_{(j)}\right\}\right] \tag{10}$$

where *s*(*x*(*j*)) is the standardized variate by GEV parameters and *s\**(*p*(*j*)) is the standardized variate corresponding to the non-exceedance probability calculated by the plotting position formula [40]. *s*0.99 and *s*0.01 are the standardized variates corresponding to the

non-exceedance probability of 1% and 99%, respectively. The plotting position formula is given as:

$$p\_{(j)} = F\left(x\_{(j)}\right) = \frac{j - \alpha}{N + 1 - 2\alpha} \tag{11}$$

where *α* is a constant; we used Cunnane's formula (*α* = 0.4) [41] to give *pi* following the JMA [39].

#### **3. Results and Discussion**

#### *3.1. Spatial Correlation of Annual Maximum Value Daily Rainfall between 1981 and 2010*

Figure 2 shows Kendall's τ of the annual maximum daily rainfall between two stations plotted against distance. As shown in Figure 2, Kendall's τ of the annual maximum daily rainfall between two stations tended to decrease as the distance between the two stations increased. In general, the correlation of rainfall decreases [17,28,30,36]; for example, Kuzuha et al. [34] examined the spatial correlation structure of precipitation using rainfall data at AMeDAS in Japan and showed that Pearson's correlation coefficient of daily rainfall decreased exponentially as the distance between the two stations increased. Hence, our result agreed with previous studies.

Furthermore, Kendall's τ of the annual maximum daily rainfall between two stations was 0.57 at its maximum and was generally small (Figure 2). Overeem et al. [17] examined the spatial dependence to apply the station-year method by using Pearson's correlation coefficient of daily rainfall between the two stations. They assumed the spatial dependence even if the Pearson's correlation coefficient was around 0.60 [17]. Although the Kendall's τ and Pearson's correlation coefficient were not simply comparable, we believed that the data for the annual maximum daily rainfall at 23 stations were spatially independent considering a previous study [17].

**Figure 2.** Kendall's τ of annual maximum daily rainfall between stations plotted against distance.


**2. Focused studies**

#### **2.1 Indialite/cordierite glass ceramics**

#### *2.1.1 Indialite Q-factor improved by Ni-substitution*

Cordierite (Mg2Al4Si5O18) has two polymorphs: cordierite and indialite, as shown in **Figure 1(a)** and **(c)**, respectively [30, 31]. Cordierite is of low symmetry form: orthorhombic crystal system *Cccm* (No. 66), which has Si4Al2O18 six-membered tetrahedron rings with ordered SiO4 and AlO4 tetrahedra as shown in **Figure 1(b)**. On the other hand, indialite is of high symmetry form: hexagonal crystal system *P*6/*mcc* (No. 192), which has disordered Si4Al2O18 equilateral hexagonal rings as shown in **Figure 1(c)**.

Cordierite shows a lower *ε*r of 6.19 which depends on the silicates and a near-zero *TCf* of −24 ppm/°C [32] as compared to other silicates as shown in **Figure 2(a)**. Based on these properties, Terada et al. carried out initiative research on these microwave dielectrics [16]. They reported an excellent *Qf* by substituting Ni for Mg as shown in **Figure 2(b)**. The *Qf* was improved from 40 × 103 GHz to 100 × 103 GHz by Ni substitution of *x* = 0.1 in (Mg1−*x*Ni*x*)2Al4Si5O18. The Ni substitution did not change the *ε*r value

**5**

*Dielectric Losses of Microwave Ceramics Based on Crystal Structure*

considerably, but the *TCf* was degraded from −24 to −30 ppm/°C [16]. For *x* > 0.1, the

*Crystal structure of Ni-substituted cordierite: (Mg1−xNix)2Al4Si5O18 with composition x = 0 (a), 0.05 (b), 0.1*

*Schematic representation of cordierite (a), six-membered tetrahedron ring with ordered SiO4 and AlO4 (b)*

*Cordierite with near zero ppm/°C deviated from other compounds (a). Ni-substituted cordierite Qf (b), volume of AlO4 and SiO4 (c) and covalencies of Si-O and Al-O as a function of composition x (d).*

Terada et al. also analysed the crystal structure by the Rietveld method [33] to clarify the origin of the improved *Qf* value. The X-ray powder diffraction (XRPD) pattern was obtained by a multi-detector system (MDS) [34] in the synchrotron radiation "Photon Factory" of the National Laboratory for High Energy Physics in Tsukuba, Japan. **Figure 3(a)–(d)** shows the crystal structures of Ni-substituted cordierite (Mg1−*x*Ni*x*)2Al4Si5O18 with *x* = 0, 0.05, 0.1 and 0.15. The crystal structure showed a tendency to deform to indialite with high symmetry on the hexagonal ring composed of corner-sharing of (Si, Al)O4 tetrahedra in the *a*-*b* plane. Ni-substituted cordierite (Mg1−*x*Ni*x*)2Al4Si5O18 with composition *x* = 0.1

properties were affected by the formation of the secondary phase of NiAl2O4.

*DOI: http://dx.doi.org/10.5772/intechopen.82483*

**Figure 1.**

**Figure 2.**

**Figure 3.**

*(c) and 0.15 (d).*

*and indialite (c).*

*Dielectric Losses of Microwave Ceramics Based on Crystal Structure DOI: http://dx.doi.org/10.5772/intechopen.82483*

**Figure 1.**

*Electromagnetic Materials and Devices*

nondistortion and without strain.

defects, inclusions, density and distortion from stress.

coefficient of the dielectric constant). Among the dielectric properties, the most essential property is *Q* , the inversion of the dielectric loss (tan*δ*); thus *Q* = 1/ tan*δ*. The dielectric losses of microwave dielectrics should be small. So, most of the microwave dielectrics are paraelectrics with inversion symmetry *i*, while most of the electronic materials are ferroelectrics with spontaneous polarity showing substantial dielectric losses [11–13]. The microwave dielectrics attract attention as a high potential material, which have an over-well-proportional rigid crystal structure with symmetry. That is, the structure should be without electric defects,

Under the influence of an electric field, four types of polarisation mechanisms can occur in dielectric ceramics, that is, interfacial, dipolar, ionic and electronic. In general, the microwave dielectric properties such as *ε*r and *Q* are mostly influenced by ionic or electronic polarisation. The dielectric polarisation generates the dielectric losses in the presence of an electromagnetic wave. When the frequency is increased to millimetre-wave values, the dielectric losses may be increased or decreased depending on the polarisation mechanism. There are two kinds of losses: those depending on crystal structure and losses due to external factors. It was believed that the intrinsic losses are due to the ordering/disordering, symmetry and phonon vibration, while extrinsic losses are due to factors such as grain size,

In this chapter, the origins of high *Q* are discussed based on the intrinsic factors related to the crystal structure, such as symmetry, compositional ordering and compositional density. Although it has previously been believed that ordering based on the order-disorder phase transition brings high *Q* [14], the authors propose that it is primarily a high symmetry that leads to high *Q* [15]. The following focused studies relate to specific examples; indialite with high symmetry showing higher *Q* than cordierite with an ordered structure [16–18]; pseudo tungsten-bronze solid solutions without phase transition showing high *Q* based on the compositional ordering [19–21]; complex perovskite compounds with order-disorder transitions depending on density and grain size [22, 23] and complex perovskites with composition deviated from the stoichiometric depending on the compositional density showing

Cordierite (Mg2Al4Si5O18) has two polymorphs: cordierite and indialite, as shown in **Figure 1(a)** and **(c)**, respectively [30, 31]. Cordierite is of low symmetry form: orthorhombic crystal system *Cccm* (No. 66), which has Si4Al2O18 six-membered tetrahedron rings with ordered SiO4 and AlO4 tetrahedra as shown in **Figure 1(b)**. On the other hand, indialite is of high symmetry form: hexagonal crystal system *P*6/*mcc* (No. 192), which has disordered Si4Al2O18 equilateral hexagonal rings as

Cordierite shows a lower *ε*r of 6.19 which depends on the silicates and a near-zero

tution of *x* = 0.1 in (Mg1−*x*Ni*x*)2Al4Si5O18. The Ni substitution did not change the *ε*r value

GHz to 100 × 103

GHz by Ni substi-

*TCf* of −24 ppm/°C [32] as compared to other silicates as shown in **Figure 2(a)**. Based on these properties, Terada et al. carried out initiative research on these microwave dielectrics [16]. They reported an excellent *Qf* by substituting Ni for Mg as shown

**4**

a high *Q* [24–29].

**2. Focused studies**

shown in **Figure 1(c)**.

**2.1 Indialite/cordierite glass ceramics**

*2.1.1 Indialite Q-factor improved by Ni-substitution*

in **Figure 2(b)**. The *Qf* was improved from 40 × 103

*Schematic representation of cordierite (a), six-membered tetrahedron ring with ordered SiO4 and AlO4 (b) and indialite (c).*

**Figure 2.**

*Cordierite with near zero ppm/°C deviated from other compounds (a). Ni-substituted cordierite Qf (b), volume of AlO4 and SiO4 (c) and covalencies of Si-O and Al-O as a function of composition x (d).*

#### **Figure 3.**

*Crystal structure of Ni-substituted cordierite: (Mg1−xNix)2Al4Si5O18 with composition x = 0 (a), 0.05 (b), 0.1 (c) and 0.15 (d).*

considerably, but the *TCf* was degraded from −24 to −30 ppm/°C [16]. For *x* > 0.1, the properties were affected by the formation of the secondary phase of NiAl2O4.

Terada et al. also analysed the crystal structure by the Rietveld method [33] to clarify the origin of the improved *Qf* value. The X-ray powder diffraction (XRPD) pattern was obtained by a multi-detector system (MDS) [34] in the synchrotron radiation "Photon Factory" of the National Laboratory for High Energy Physics in Tsukuba, Japan. **Figure 3(a)–(d)** shows the crystal structures of Ni-substituted cordierite (Mg1−*x*Ni*x*)2Al4Si5O18 with *x* = 0, 0.05, 0.1 and 0.15. The crystal structure showed a tendency to deform to indialite with high symmetry on the hexagonal ring composed of corner-sharing of (Si, Al)O4 tetrahedra in the *a*-*b* plane. Ni-substituted cordierite (Mg1−*x*Ni*x*)2Al4Si5O18 with composition *x* = 0.1

(**Figure 3(c)**) was obviously closer to equilateral hexagonal rings compared to (Mg0. 95Ni0.05)2Al4Si5O18 (**Figure 3(b)**) and Mg2Al4Si5O18 (**Figure 3(a)**).

The transformation from cordierite to indialite, represented by the ratio of disordering between the SiO4 and AlO4 tetrahedra, is based on the volumes and covalencies of the SiO4 and AlO4 tetrahedra [35]. The volume was calculated using atomic coordinates obtained by Rietveld crystal structural analysis as shown above. The covalency (*fc*) of the cation-oxygen bond was estimated from the following equation [36].

$$f\_c = a\mathbf{s}^M\tag{1}$$

The empirical constants *a* and *M* depending on the inner-shell electron number 10 are 0.54 v.u. and 1.64, respectively [37], where *s* is the bond length obtaining from the following equation:

$$\mathfrak{s} = \left(\mathbb{R}/R\mathbb{1}\right)^{-N} \tag{2}$$

where, *R* is defined as the bond length, and *R*1 and N are the measured parameter reliant on the cation site and each cation-anion pair, respectively.

**Figure 2(c)** and **(d)** depicts the calculated volume and covalency of SiO4 and AlO4 octahedra, respectively. These figures show the phase changing from cordierite to indialite as substitution of Ni in the Mg site. In the cordierite Mg2Al4Si5O18 (**Figure 1(a)**), Si/Al ions in the tetrahedra are ordered. Therefore, the volume and covalency of tetrahedra are different values, but the values are becoming similar to the substitution of Ni in the Mg site. This is due to the disordering of Si/Al ion phase transition in the cordierite (**Figure 1(a)**) to indialite (**Figure 1(c)**). In the indialite, the disordered Si4Al2O18 equilateral hexagonal rings with 6-ford axis are the main framework as analysed by the Rietveld method as shown in **Figure 3(d)**. The improvement of *Qf* as shown in **Figure 2(b)** should be based on the disordering due to high symmetry instead of an ordering of SiO4 and AlO4 tetrahedra by orderdisorder transition. It is one example of high symmetry bringing a higher *Q* than ordering by the order-disorder transition [18].

#### *2.1.2 Indialite glass ceramics with high Q*

As described in the previous section, the *Qf* value of indialite derived by substituting Ni for Mg was improved to three times that of cordierite. Based on the new knowledge, Ohsato et al. proposed the synthesis of indialite with superior microwave dielectric properties [17]. The indialite, being a high-temperature form, could not be synthesised by the solid-state reaction because the order-disorder phase transition is hindered by the incongruent melting to form mullite and liquid. On the other hand, indialite is an intermediate phase during the crystallisation process from glass with a cordierite composition to cordierite, as shown in **Figure 4**.

Therefore, fabrication of indialite glass ceramics has been attempted [17, 39]. Although the indialite is a metastable phase transforming to cordierite at higher temperatures, it is a relatively stable phase which occurs in nature formed by the crystallisation of natural glass. As this occurrence is in India, the mineral was named indialite. Another phase of *μ*-cordierite precipitating in the early stage of the crystallisation of cordierite glass is *β*-quartz solid solutions. The naming of *μ*-cordierite is not correct because of the different crystal structure, so the name that should be used is *β*-quartz solid solutions [38].

The cordierite composition was melted at 1550°C and was cast into a cylindrical rod with the diameter *φ* = 10 mm and *l* = 30 mm in a graphite mould. In order to avoid fracture due to internal strain, the cast glass rod was annealed at 760°C below

**7**

**Figure 5.**

*sensitive test plate (b) and (c).*

*Dielectric Losses of Microwave Ceramics Based on Crystal Structure*

the glass transition point of 778°C [38]. The 10 mm diameter glass rod was cut to form a resonator with a height of 6 mm. The glass pellets were crystallised at temperatures in the range 1200–1470°C/10 and 20 h. The crystallised pellets had two problems: deformation by the formation of glass phase and cracking by anisotropic crystal growth from the surface (**Figure 5(a)**) [39]. **Figure 5(b)** and **(c)** shows photographs taken by a polarising microscope of a thin section of the crystallised samples. The needle-like crystals grown from the surface had an orientation with *c*-axis elongation. The microwave dielectric properties of the sample with cracking

*Polymorphism of cordierite: indialite is the high temperature/high symmetry form, and cordierite is the low temperature/low symmetry form. In addition, indialite is an intermediate phase during crystallisation from*

**Figure 6(a)** shows the volume of indialite/cordierite examined by the Rietveld method [40], which is estimated with two phases such as indialite and cordierite. Hereabout, the residual % is compared to that of cordierite. At 1200°C, the precipitated phase of indialite was about 96.7%. The volume of indialite reduced as the temperature and to 17.1% (82.9% for cordierite) at 1400°C. **Figure 6(b)** and **(c)** shows the microwave dielectric properties of indialite/cordierite glass ceramics and

with Ni using the conventional solid-state reaction as previously described (**Figure 2(b)**) and is feasible for millimetre-wave dielectrics. The *Qf* values decreased as crystallisation temperature. In comparison with the amount of indialite as shown in **Figure 6(a)**

*Cracking of crystallised pellets (a) and anisotropic crystal growth of the pellets under the crossed polars with a*

GHz at 1300°C/20 h [17]. This is

GHz obtained by substitution

*DOI: http://dx.doi.org/10.5772/intechopen.82483*

**Figure 4.**

*glass to cordierite.*

had a wide scattering range of the data [17, 39].

remarkably high *Qf* value of more than 200 × 103

much better than the highest *Qf* value of 100 × 103

*Dielectric Losses of Microwave Ceramics Based on Crystal Structure DOI: http://dx.doi.org/10.5772/intechopen.82483*

**Figure 4.**

*Electromagnetic Materials and Devices*

from the following equation:

(**Figure 3(c)**) was obviously closer to equilateral hexagonal rings compared to (Mg0.

The transformation from cordierite to indialite, represented by the ratio of disordering between the SiO4 and AlO4 tetrahedra, is based on the volumes and covalencies of the SiO4 and AlO4 tetrahedra [35]. The volume was calculated using atomic coordinates obtained by Rietveld crystal structural analysis as shown above. The covalency (*fc*) of the cation-oxygen bond was estimated from the following equation [36].

*fc* = *asM* (1)

The empirical constants *a* and *M* depending on the inner-shell electron number 10 are 0.54 v.u. and 1.64, respectively [37], where *s* is the bond length obtaining

*s* = (*R*/*R*1)−*N* (2)

where, *R* is defined as the bond length, and *R*1 and N are the measured param-

**Figure 2(c)** and **(d)** depicts the calculated volume and covalency of SiO4 and AlO4 octahedra, respectively. These figures show the phase changing from cordierite to indialite as substitution of Ni in the Mg site. In the cordierite Mg2Al4Si5O18 (**Figure 1(a)**), Si/Al ions in the tetrahedra are ordered. Therefore, the volume and covalency of tetrahedra are different values, but the values are becoming similar to the substitution of Ni in the Mg site. This is due to the disordering of Si/Al ion phase transition in the cordierite (**Figure 1(a)**) to indialite (**Figure 1(c)**). In the indialite, the disordered Si4Al2O18 equilateral hexagonal rings with 6-ford axis are the main framework as analysed by the Rietveld method as shown in **Figure 3(d)**. The improvement of *Qf* as shown in **Figure 2(b)** should be based on the disordering due to high symmetry instead of an ordering of SiO4 and AlO4 tetrahedra by orderdisorder transition. It is one example of high symmetry bringing a higher *Q* than

As described in the previous section, the *Qf* value of indialite derived by substituting Ni for Mg was improved to three times that of cordierite. Based on the new knowledge, Ohsato et al. proposed the synthesis of indialite with superior microwave dielectric properties [17]. The indialite, being a high-temperature form, could not be synthesised by the solid-state reaction because the order-disorder phase transition is hindered by the incongruent melting to form mullite and liquid. On the other hand, indialite is an intermediate phase during the crystallisation process from glass with a

Therefore, fabrication of indialite glass ceramics has been attempted [17, 39]. Although the indialite is a metastable phase transforming to cordierite at higher temperatures, it is a relatively stable phase which occurs in nature formed by the crystallisation of natural glass. As this occurrence is in India, the mineral was named indialite. Another phase of *μ*-cordierite precipitating in the early stage of the crystallisation of cordierite glass is *β*-quartz solid solutions. The naming of *μ*-cordierite is not correct because of the different crystal structure, so the name that should be used is *β*-quartz solid solutions [38]. The cordierite composition was melted at 1550°C and was cast into a cylindrical rod with the diameter *φ* = 10 mm and *l* = 30 mm in a graphite mould. In order to avoid fracture due to internal strain, the cast glass rod was annealed at 760°C below

eter reliant on the cation site and each cation-anion pair, respectively.

ordering by the order-disorder transition [18].

cordierite composition to cordierite, as shown in **Figure 4**.

*2.1.2 Indialite glass ceramics with high Q*

95Ni0.05)2Al4Si5O18 (**Figure 3(b)**) and Mg2Al4Si5O18 (**Figure 3(a)**).

**6**

*Polymorphism of cordierite: indialite is the high temperature/high symmetry form, and cordierite is the low temperature/low symmetry form. In addition, indialite is an intermediate phase during crystallisation from glass to cordierite.*

the glass transition point of 778°C [38]. The 10 mm diameter glass rod was cut to form a resonator with a height of 6 mm. The glass pellets were crystallised at temperatures in the range 1200–1470°C/10 and 20 h. The crystallised pellets had two problems: deformation by the formation of glass phase and cracking by anisotropic crystal growth from the surface (**Figure 5(a)**) [39]. **Figure 5(b)** and **(c)** shows photographs taken by a polarising microscope of a thin section of the crystallised samples. The needle-like crystals grown from the surface had an orientation with *c*-axis elongation. The microwave dielectric properties of the sample with cracking had a wide scattering range of the data [17, 39].

**Figure 6(a)** shows the volume of indialite/cordierite examined by the Rietveld method [40], which is estimated with two phases such as indialite and cordierite. Hereabout, the residual % is compared to that of cordierite. At 1200°C, the precipitated phase of indialite was about 96.7%. The volume of indialite reduced as the temperature and to 17.1% (82.9% for cordierite) at 1400°C. **Figure 6(b)** and **(c)** shows the microwave dielectric properties of indialite/cordierite glass ceramics and remarkably high *Qf* value of more than 200 × 103 GHz at 1300°C/20 h [17]. This is much better than the highest *Qf* value of 100 × 103 GHz obtained by substitution with Ni using the conventional solid-state reaction as previously described (**Figure 2(b)**) and is feasible for millimetre-wave dielectrics. The *Qf* values decreased as crystallisation temperature. In comparison with the amount of indialite as shown in **Figure 6(a)**

**Figure 5.**

*Cracking of crystallised pellets (a) and anisotropic crystal growth of the pellets under the crossed polars with a sensitive test plate (b) and (c).*

**Figure 6.**

*Amount of indialite (a) and microwave dielectric properties of crystallised indialite at 1200–1440°C for 10 (b) and 20 (c) hours.*

[39] and its *Qf* values as shown in **Figure 6(b)** and **(c)** [17], it is clear that the indialite glass ceramics present a higher *Qf* than that of cordierite. The *ε*r was the lowest among the silicates, about 4.7 as shown in **Figure 6(b)** and **(c)**, and the *TCf* was −27 ppm/°C as shown in **Figure 6(b)**. Therefore, from these figures, indialite shows a higher *Qf* than cordierite. This *TCf* value of −27 ppm/°C is better than that of other silicates having a low *TCf* of approximately −60 ppm/°C [39].

#### *2.1.3 Conclusions for indialite/cordierite glass ceramics*

### **2.2 Pseudo tungsten-bronze solid solutions: compositional ordering bringing high** *Q*

#### *2.2.1 Introduction*

The pseudo tungsten-bronze solid solutions Ba6−3*xR*8+2*x*Ti18O54 (*R* = rare earth) located on the tie-line of BaTiO3-*R*2Ti3O9 are shown in **Figure 7(a)** and have been utilised in mobile phones because of their high dielectric constant of 80–90 [20, 21]. This solid solution was first reported by Varfolomeev et al. [41], based on Nd and Sm systems. The composition ranges 0.0 < < *x* < < 0.7 for *R* = Nd and 0.3 < < *x* < < 0.7 for

**9**

**Figure 8.**

**Figure 7.**

*Dielectric Losses of Microwave Ceramics Based on Crystal Structure*

Sm [42] were reported by Ohsato et al. [19] and Negas et al. [43]. The composition range of the solid solutions becomes narrower with the decrease in the ionic radius of

*A part of the BaO-R2O3-TiO2 ternary phase diagram with pseudo tungsten-bronze type solid solutions (a). Oscillation photograph along c-axis of pseudo tungsten-bronze type solid solutions (b). Electron density map (Fourier map) of the fundamental structure superimposed on a superstructure framework (c) and TiO6 tilting octahedra along the c-axis on the super-lattice (d) deduced from the splitting of oxygen in the fundamental structure (c), and the splitting of oxygen atoms based on the tilting of octahedra as shown in left side figure of the fundamental lattice (d). Right side schematic figure: super structure produced by tilting octahedral (d).*

the *R*-ion, and Ga and Eu form only BaO·*R*2O3·4TiO2 composition [44].

*Qf (a), εr (b) and TCf (c) of Sm, Nd, Pr and La system as a function of composition x.*

*DOI: http://dx.doi.org/10.5772/intechopen.82483*

#### *Dielectric Losses of Microwave Ceramics Based on Crystal Structure DOI: http://dx.doi.org/10.5772/intechopen.82483*

#### **Figure 7.**

*Electromagnetic Materials and Devices*

**Figure 6.**

*and 20 (c) hours.*

[39] and its *Qf* values as shown in **Figure 6(b)** and **(c)** [17], it is clear that the indialite glass ceramics present a higher *Qf* than that of cordierite. The *ε*r was the lowest among the silicates, about 4.7 as shown in **Figure 6(b)** and **(c)**, and the *TCf* was −27 ppm/°C as shown in **Figure 6(b)**. Therefore, from these figures, indialite shows a higher *Qf* than cordierite. This *TCf* value of −27 ppm/°C is better than that

*Amount of indialite (a) and microwave dielectric properties of crystallised indialite at 1200–1440°C for 10 (b)*

• Indialite/cordierite glass ceramics are one of the examples of high symmetry bringing a higher *Q* than ordering by order-disorder transition. Indialite glass ceramics with disordered high symmetry have higher *Qf* properties than

• Cordierite with substituted Ni for Mg synthesised by solid-state reaction

Rietveld crystal structure analysis showed that the cordierite was transformed

• A novel idea from glass ceramics suggested the fabrication of indialite as an intermediate phase. Glass ceramics crystallised at 1200°C were almost com-

• Indialite/cordierite crystallised from cordierite glass at 1300°C/20 h showed

The pseudo tungsten-bronze solid solutions Ba6−3*xR*8+2*x*Ti18O54 (*R* = rare earth) located on the tie-line of BaTiO3-*R*2Ti3O9 are shown in **Figure 7(a)** and have been utilised in mobile phones because of their high dielectric constant of 80–90 [20, 21]. This solid solution was first reported by Varfolomeev et al. [41], based on Nd and Sm systems. The composition ranges 0.0 < < *x* < < 0.7 for *R* = Nd and 0.3 < < *x* < < 0.7 for

lised at 1400°C were cordierite at 82.9% with a lower *Qf* of 80 × 103

good microwave dielectric properties of *ε*r = 4.7, *Qf* > 200 × 103

**2.2 Pseudo tungsten-bronze solid solutions: compositional ordering**

to 100 × 103

GHz (**Figure 2(b)**).

GHz, and those crystal-

GHz.

GHz and

of other silicates having a low *TCf* of approximately −60 ppm/°C [39].

*2.1.3 Conclusions for indialite/cordierite glass ceramics*

cordierite with ordered low symmetry.

exhibited an improved *Qf* from 40 × 103

*TCf* = −27 ppm/°C (**Figure 6**) [17, 39].

pletely indialite at 96.7% with a high *Qf* of 150 × 103

to indialite [16].

(**Figure 6**) [17, 39].

**bringing high** *Q*

*2.2.1 Introduction*

**8**

*A part of the BaO-R2O3-TiO2 ternary phase diagram with pseudo tungsten-bronze type solid solutions (a). Oscillation photograph along c-axis of pseudo tungsten-bronze type solid solutions (b). Electron density map (Fourier map) of the fundamental structure superimposed on a superstructure framework (c) and TiO6 tilting octahedra along the c-axis on the super-lattice (d) deduced from the splitting of oxygen in the fundamental structure (c), and the splitting of oxygen atoms based on the tilting of octahedra as shown in left side figure of the fundamental lattice (d). Right side schematic figure: super structure produced by tilting octahedral (d).*

Sm [42] were reported by Ohsato et al. [19] and Negas et al. [43]. The composition range of the solid solutions becomes narrower with the decrease in the ionic radius of the *R*-ion, and Ga and Eu form only BaO·*R*2O3·4TiO2 composition [44].

**Figure 8.** *Qf (a), εr (b) and TCf (c) of Sm, Nd, Pr and La system as a function of composition x.*

Ohsato et al. and Negas et al. reported the microwave dielectric properties for the Sm, Nd, Pr and La systems as a function of composition *x* as shown in the **Figure 8(a)** [20, 43, 45] and Fukuda et al. reported the Pr system [46]. On the solid solutions, the composition with *x* = 2/3 was found by Ohsato et al. [42], at which the *Qf* value becomes the highest due to the ordering in the rhombic and pentagonal sites. The dielectric constants *ε*r and *TCf* (**Figure 8(b)** and **(c)**) are decreased as a function of the composition *x* and are affected by volume and tilting angle of the TiO6 octahedra and the polarizabilities of *R* and Ba ions [20]. The Clausius-Mosotti equation determined the temperature coefficient of the dielectric constant *TCε*r as a function of the ratio of the mean radii (*r*a*/r*b) of *A*- and *B*-site ions by Valant et al. [47]. Hither, ra/rb is connected to the tilting of the TiO6 octahedra. In this study, on the system without order-disorder phase transition that is without symmetry change, it is discussed that the ordering especially compositional ordering brings high *Qf*.


*The Use of Artificial Intelligence to Bridge Multiple Narratives in Disaster Response DOI: http://dx.doi.org/10.5772/intechopen.108196*

number of texts still creates confusion, as many studies and mission statements do not focus specifically on architectural practices in disaster response. Instead, such clustering has a general approach, making it challenging to select studies or tools to answer questions about spatial or constructive problems. Therefore, we will use the SOM as a first filter to narrow the focus to specific tasks (architectural practices). The SOM algorithm allows us to have an organized view of the data; that is to say, we can make a selection based on a specific approach. Thus, it will enable us to concentrate on the information relevant to the focus of this experiment.

The filtering will be performed on both datasets beginning with the mission statements of humanitarian organizations. For this purpose, the numerical vectors representing the texts from humanitarian organizations were fed as inputs in a 10x10 SOM grid. The algorithm started with an initial distribution of random weights, and over

#### **Figure 3.**

*The SOM HO is a SOM grid of 10×10 data from the humanitarian organization data.*

1 million epochs eventually settled into a map of stable zones or clusters. The output layer can be visualized as a smooth changing spectrum where each SOM node has its coordinates and an associated n-dimensional vector or Best Matching Unit (BMU). For visualization purposes, a color assigned to the weight value (n-dimensional vector) of each BMU and a list of keywords are displayed together. The keywords are the most common terms used in the texts that are clustered in each SOM node. The size of the word represents the number of occasions the word appeared in the group of text. **Figure 3** shows the consistency in clustering, such as similar keywords being positioned close to each other. This trained SOM grid can be considered a common ground for the mission statements of humanitarian organizations, which will be called SOM HO.

#### **Figure 4.**

*Shows the activated cells of the SOM HO, where humanitarian organizations that share a common interest in academic writing are found.*

#### *The Use of Artificial Intelligence to Bridge Multiple Narratives in Disaster Response DOI: http://dx.doi.org/10.5772/intechopen.108196*

Although this SOM HO represents data in an organized manner, it also contains data on humanitarian organizations that may not be relevant to this thesis's focus. As our focus is on architectural practices for natural disaster response, we will perform filtering of the humanitarian organization's data based on similar interests shared with academic publications. When a new dataset is fed to a trained SOM, each unique data point measures its Euclidean distance to each BMU of the trained SOM. The closer the distance, the better the data point fits that node. When feeding the data on academic literature into the trained SOM HO, this data activates a specific number of cells. Hence, the data we will take for creating our final common ground comes only from the cells activated by the academic publication data. **Figure 4** shows the activated cells of the SOM HO, where humanitarian organizations that share a common interest in academic writing are found.

After collecting all the activated cells, 1081 humanitarian organizations out of the original list of 1930 were filtered. Some of the selected humanitarian organizations are: The International NGO Safety Organization, Nansen International Office for Refugees, Peoplesafe, Rise Against Hunger, SeedChange, and Association of Assistance Solidarity Supportiveness of Refugees and Asylum Seekers.

A similar filtering process was performed on the data on academic writing––initially 8364. First, the encoded abstracts were fed into a new 2D SOM algorithm of 10×10, where each text was clustered based on the similarity of content (the training procedure was like that in the case of humanitarian organization mission statements)—creating a SOM of academic publications, which we will call SOM AP. The filtering constraint was defined by a sample of publications that will be taken as exemplary text. Those samples of academic publications concentrate on architectural practices for disaster response. Such selected academic publications were fed into the trained SOM AP and found some cells with similar approaches. Out of the 8364 abstracts, 835 were selected. **Figure 5** shows the Map of Events AP and the selected cells matching the selected publications.

#### **Figure 5.**

*SOM 10×10 of abstracts and the selected cells based on specific literature. Resulting in the final filtering of 835 abstracts.*

#### *Avantgarde Reliability Implications in Civil Engineering*

#### **Figure 6.**

*Common Ground, a SOM of 10x10 trained with joined data from filtered humanitarian organizations (1082) and academic writing from the field of disaster response (835).*

The filtered dataset of humanitarian organizations (1082) was joined with the filtered dataset of academic publications (835), creating a new dataset of 1917 texts. These data were fed as input in a new SOM grid of 10×10 that, after a million iterations, settled into a map of clustered texts or what we will call a final Common Ground (**Figure 6**). Such a Common Ground joins two discourses regarding disaster response in an organized manner that serves as a ground for articulating informed decisions, which will emerge out of specific requirements and interests.


Author details

Serban C. Moldoveanu1 \* and Victor David2

\*Address all correspondence to<EMAIL_ADDRESS>
© 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.


*4.5. Salicin*

Salicin is the main component of the willow bark extract, commonly used in traditional medicine for its anti-inflammatory and antipyretic e ffects [115]. Salicin is metabolized to salicylic acid in vivo; therefore, it is also known as "nature's aspirin". Previous studies showed that salicin exerts protective effects on EC, both inhibiting angiogenesis [116] and reducing ROS production [117]. Song et al. demonstrated that the treatment of HRECs with salicin led to a reduction in HMGB1 release and the prevention of cellular apoptosis. Moreover, the authors demonstrated that salicin can suppress the production of IL-1β and its related cytokines, such as TNF-κ and IL-6, responsible for retinal toxicity. Salicin is also able to block the release of the adhesion molecules ICAM-1 and VCAM-1 and the NF-κB inflammatory pathway [118].

#### *4.6. Ethyl Pyruvate (EP)*

Ethyl pyruvate is a pyruvate derivative with the addition of an aliphatic ester group. EP is considered to be safer and more e ffective than pyruvate in inhibiting ROS and inflammation [119]. EP is a strong HMGB1 inhibitor. Treatment with EP promotes stable vascular growth and blocks retinal pathological neovascularization by preventing the overexpression of HMGB1. Moreover, EP can inhibit the expression of IL-6, TNF-κ, and NF-kB, exerting a protective role in chronic inflammatory diseases such as DR [120].