**2. Methodology and experimental details**

The deposition of the coatings was performed using a multi-target sputtering magnetron. This specific type of system allows to perform the multilayer deposition process in situ. This equipment uses four (4) magnetrons (Torus - 4 ", 10 cm Kurt J. Lesker) with diameters of 10 cm, three (3) radio frequency sources (13.56 MHz, RFX 600A), and three (3) direct current sources (MDX 500, Advanced Energy). In addition, the pressure during the deposition process in monitored by a control and measurement system (Baraton, MKS), which has four (4) gas flow controllers, a radiation heating system (Athena 500), which has a maximum temperature of 400°C, and a planetary type rotary sampling system. For TiN, TiCrN, TiCN, BCN and CrAlN coatings, titanium (Ti), titanium carbide (TiC) and Chromium-Aluminum (Cr-Al) targets were used, respectively; each cathode with an approximate purity of 99.99%. Two (2) different type of substrate were used, silicon with preferential crystallographic orientation (100) and AISI 1045 and H13 steel substrates respectively. The silicon substrates were subjected to a surface cleaning process in an ultrasonic system and the steel substrates were prepared superficially using sandpaper (SiC) and finally polished in a metallographic polisher. Before starting the deposition process, a vacuum with a pressure of 1.4x10−4 mbar was applied. The TiCN coating was deposited using a working pressure of 1.4x10−2 mbar in a gas mixture of 50 sccm (Ar) and 16 sccm (N2) at 250°C, and a r.f power density of 5 W/cm2 was applied on the TiC target. For BCN coating, it was deposited by a pressure of 7.4x10−3 mbar in a gas mixture of 44 sccm (Ar) and 6 sccm (N2) at 250°C, and a r.f power density of 7 W/cm2 was applied on the BC target. For the CrAlN coating was deposited using a working pressure of 6.4x10−3 mbar was used a gas mixture of 50 sccm (Ar) and 5.5 sccm (N2) at 250°C, and the r.f power density applied to the Cr target and Al target was 2.5 W/cm2 and 4.5 W/cm2, respectively [8]. The deposition of the Si3N4 based coatings was performed by magnetron sputtering with an r.f source (13.56 MHz) on silicon (100) and AISI 316 stainless steel substrates. A cathode (Si3N4) with a purity of 99.9% approximately. During the deposition a power of 550 W was used and a bias voltage of −20 V was applied, the distance between substrates-targets was approximately 7 cm, and the deposition process was carried out at a temperature of 200°C inside the chamber. In addition, the substrate holder rotated at a

#### *Analysis of the Tribological Evolution of Nitride-Based Coatings DOI: http://dx.doi.org/10.5772/intechopen.100629*

speed of 60 RPM during the entire deposition process with a working pressure of 5.1x103 mbar. The coating obtained a thickness of approximately 2.5 μm.

The structural analysis of TiCN, BCN and CrAlN coatings was analyzed by X-ray diffraction (PANalytical X 'pert proTM), using a Cu Kα radiation source with a wavelength of λ = 1.5405 Å. By X-ray photo-electron spectroscopy (XPS) using a SAGE HR100 (SPECSTM) equipment with a monochromatic source (Mg Kα 1253.6 eV), CasaXPS V2.3.15 software was used to determine the chemical composition of the obtained coatings. The morphological study of the coatings was carried out by atomic force microscopy (AFM) with an Asylum Research MFP-3D® device and with a scanning prove image processor (SPIP®), the grain size and roughness of the coatings obtained were calculated. The mechanical study was carried out by nanoindentation using an Ubi1-HysitronTM device, which uses a Berkovich diamond tip at variable loads. From this test, load–displacement curves were obtained as a function of penetration for the coatings. Based on these curves, hardness and elastic modulus were determined using the Oliver-Phar method.


**6. Conclusions**

Irrigation areas have a complex history (spatially and temporally) with respect to the timing of irrigation development, water use e fficiency, drainage, climate, and crop type, among other factors. This complexity can lead to a lack of transparency, obscure interpretation, and require additional resources and skills to represent it appropriated within a modelling framework. Any new methodology has to ensure that it does not add to the complexity of prior approaches and it must be widely applicable. Our development of the transfer function and integrated modelling was cognisant of these considerations. It attempted to simulate the transmission of the unsaturated zone with as few parameters as possible. It implemented a flexible method that can be applied in other semi-arid environments where there is a deep vadose zone. It makes use of existing datasets and attempts to use all of these datasets more thoroughly to calibrate and constrain the model outputs.

This paper described an integrated methodology for the estimation of groundwater returns to streams from irrigation areas with deep vadose zones and perched water tables. This methodology was implemented for an irrigation district in south-eastern Australia, adjacent to the lower reaches of the River Murray. An existing surface water balance model and the spatial distribution of the historical irrigation development were used to assess irrigation accessions to the unsaturated zone. A recently developed unsaturated zone model [22,23] estimated the groundwater recharge from the irrigation accession. Two di fferent implementations of a groundwater model using these data were compared to a pre-existing groundwater model by using recharge calibrated using groundwater responses. In the first implementation, the newly estimated recharge values replaced the recharge values in the pre-existing model, that is, the saturated zone properties were the same. In the second implementation, a new set ofsaturatedzoneparameterswerecalibrated.

 The study has shown that

Further work is required to optimize the calibration methodology, standardise scripting approaches, and test the method in other vadose zone settings.

**Author Contributions:** Conceptualization, G.W.; data curation, D.C., T.L., and K.B.; formal analysis, D.C., T.L., G.W., and T.S.; funding acquisition, D.C. and J.W.; investigation, D.C., T.L., G.W., and T.S.; methodology, D.C., T.L., and G.W.; project administration, D.C.; software, T.L., G.W., and T.S.; supervision, G.W. and J.W.; writing—original draft, D.C.; writing—review and editing, G.W. and J.W. All authors have read and agreed to the published version of the manuscript.

**Funding:** The authors would like to thank the Murray-Darling Basin Authority (MDBA) and the Department of Environment and Water, South Australia (DEW), for funding support.

**Acknowledgments:** The authors would like to thank and acknowledge Emmanuel Xevi for his support throughout this work program, and Hugh Middlemis, Ray Evans, and Prathapur for technical advice.

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


**9. Effective screening for NPC?**

Some efforts have been done oriented for early detection in relatives of patients considered at high risk for NPC, according to their viral DNA and anti-EBV profile (Baizig et al,2011;Liu et al,2011). Sophisticated endoscopic technique have been also tested to detect easily early stages of NPC (Lin et al,2011, Lin et al,2011).


**1. Introduction**

From the problem conception in terms of a functional demand to finding a solution, the designing and making of the product, its manufacturing and marketing, to taking social responsibility, technological innovation is a complex problem-solving process, requiring the cooperation of a chain of different industries and experts. For example, the semiconductor industry was formed around 1960 and consists of an aggregate of companies engaged in the design and fabrication of semiconductors and semiconductor devices, such as transistors and integrated circuits (IC). It is in turn the driving force behind the wider electronics industry for such products as power electronics, consumer electronics, and e-commerce. The semiconductor market segments include networks and communications devices. A need for technological innovation in the IC manufacturing industry may be driven by demands or orders from the users, the IC design, the IC fabrication and manufacturing, IC packaging and testing, logistical supports to deliver products to customers, and user services. The size of silicon wafers determines how many IC can be produced per wafer in

IC manufacturing. The semiconductor industry has been continuously striving to increase the wafer size. Although foundries used to produce 3-inch wafers, today's common wafer size is 12 inches. Increasing wafer size is not a trivial process. In fact, silicon wafer manufacturing technologies have to be re-designed and re-engineered in order to increase the wafer size. Innovation is a common daily demand of all companies involved [1].

The CDIO theory proposed by Crawley and colleagues [2, 3] for improving engineering education pinpoints 4 critical phases in technological innovation, whether it is a new product, procedure, or system [4]:

#### **1.1 Conceptualization**

The need for a certain functional requirement of a product/system that is meaningful and valuable to the society is sensed, such as how to reduce CO2 emissions from the production process. This raises a design problem, the next phase of innovation.

### **1.2 Design**

The initial functional requirement is abstract in nature. It must be developed into the tangible possible designs of a physical device or system. Based on the design of the current product, the industrial designer may sketch several different designs of how the product/system will look like when new functional requirements are added. The most promising design is chosen, given current technological constraints, and a prototype is constructed.

#### **1.3 Implementation**

A prototype is merely a conceptual design presented in 3D. How the prototype of a conceived new product or system can be physically realized must be studied and implemented by a team of RD workers and engineers. That means various components of the product will be made, assembled, and contextualized into a real product that can work in a specified context.


**1. Introduction**

Detailed knowledge of the characteristics of probability models is desirable (if not essential) if data are to be modeled properly. In studying these properties, many authors have considered orderings within probability distribution families, according to diverse measuring criteria. The usual approach taken by researchers in this field is to evaluate or measure one or more theoretical characteristics of a given distribution and to study the effect produced by the value of its parameters on this measurement. In actuarial science, stochastic orders are widely used in order to make risk comparisons [1].

Some parametric distributions can be ordered according to the evaluation made of a given property, merely by comparing some of its parameters. Although most related orders are actually preorders, each one presents interesting applications. Many studies have been conducted in this area, and the following are particularly significant: Lehmann (1955) [2], which is of seminal importance; Arnold (1987) [3], who compared random variables according to stochastic ordering in a particular Lorenz order; Shaked and Shanthikumar (2006) [1], on stochastic orders; Nanda and Shaked (2001) [4], on reversed hazard rate orders; Ramos-Romero and Sordo-Díaz (2001) [5], on the likelihood ratio order; and Gupta and Aziz (2010) [6], on convex orders.

In this paper, we study the relationship between the skewness of some parametric distributions and the value of one of their parameters. The first question to be addressed is that of measuring the skewness. In this respect, Oja (1981) [7] introduced a set of axioms to be verified by any measurement of skewness considered. These axioms were established for indexes of skewness with one main constraint: that the skewness of a distribution should be evaluated by a single real number. This point is discussed below.

Many authors have proposed and obtained different descriptive elements to measure skewness (see, for instance, [8–13]). Ref [10] suggested a measurement of skewness corresponding to the (unique) mode, *M*, given by the following index:

$$\gamma\_M\left(F\right) = 1 - 2F\left(M\right). \tag{1}$$

Ref [10] applied this index to ordering the gamma, log-logistic, lognormal and Weibull families of distributions by their skewness, taking into account the feasible values of their respective parameters. Index (1), which is proven to satisfy those axioms derived from Oja (1981) [7], is also recommended in [14] as a (very) good index of skewness. However, notice that (1) only compares the probability weight on the left side of a central point (the mode) with the value 1/2, but it does not account for how the weights are distributed to each side of the centre.

García et al. (2015) [15] introduced some further elements to be incorporated into the list of skewness measurements of a probability distribution. According to these authors, given a unimodal probability distribution *F* (*x*), its skewness is considered to be a local function of a given distance, *z*, from the mode, *M*. For such a distance, and given the interval [*M* − *z*, *M* + *z*], the aggregate skewness function, *νF* (*z*), compares the probability weight of *F* at either side of the interval:

$$\nu\_{\mathbb{F}}\left(z\right) = \Pr\left(X > M + z\right) - \Pr\left(X < M - z\right),\tag{2}$$

where *z* ≥ 0. Thus, the (maximum) right skewness of the distribution *F* and its (minimum) left skewness are respectively given by

$$S^{+}\left(F\right) = \max\_{z \ge 0} \nu\_F\left(z\right), \quad S^{-}\left(F\right) = \min\_{z \ge 0} \nu\_F\left(z\right). \tag{3}$$

The distances, *zp* and *zn*, where these extreme values are achieved, are termed the critical distances to the mode. As the skewness function is bounded inside the interval [−1, 1] and *νF* (∞) = 0, the bivariate index *S*− (*F*), *S*+ (*F*) belongs to [−1, 0] × [0, 1]. A given distribution function *F* such that *νF* (*z*) ≥ 0 for all *z* ≥ 0 is said to be only skewed to the right; and if *νF* (*z*) ≤ 0 for all *z* ≥ 0, it is said to be only skewed to the left.

The relationship *F* <*c G* (*F c*−precedes *G*) means that *G*−1 [*F* (*x*)] is a convex function. For a continuous distribution *F*, the bivariate measurement of skewness *S*− (*F*), *S*+ (*F*) verifies the following properties, where *aF* + *b* and −*F* mean the distributions of the corresponding transformation of a random variable that is *F*−distributed:

These properties can be considered as a vectorial interpretation of the axioms given by Oja (1981) [7].

As it is easily proven that *νF* (0) = *γM* (*F*), we can establish that (2) and (3) give considerably clearer and more complete information than (1) about the skewness of any distribution function.

Most families of continuous distributions are only skewed to the right (or only to the left), while doubles-sign skewness is abundant within the discrete families, as shown in [15]. Nevertheless, the joint use of the function (2) and the bivariate index (3) makes it possible to improve the ordering of the skewness-based distribution discussed in [10], as can be seen in the following example.

**Example 1.** *Assume the following random variable X* ∈ [−2, ∞) *with PDF given by:*

$$f'(x) = \begin{cases} \frac{1}{4}x + \frac{1}{2}, & -2 \le x < 0; \\\\ \frac{1}{2}\exp\left(-x\right), & x \ge 0. \end{cases}$$

*Assume also the PDF, g* (*y*) *of Y* = −*X. Then, γM* (*F*) = 0 = *γM* (*G*). *That is, according to coefficient γM* (·)*, both distributions have the same null skewness, although they do not even have a symmetric support set.*

*However, using expression (2), we find that*

$$\nu\_F(z) = \begin{cases} \frac{1}{2} \exp\left(-z\right) - \frac{1}{8} \left(z - 2\right)^2, & 0 \le z < 2; \\\\ \frac{1}{2} \exp\left(-z\right), & z \ge 2; \end{cases}$$

*and νG* (*z*) = −*νF* (*z*), *for all z* ≥ 0*. These functions are plotted in Figure 1, where it can be seen that νF* (*z*) ≥ *νG* (*z*) *for all z* ≥ 0*, S*+ (*F*) = −*S*− (*G*) = 0 *and S*− (*F*) = 0 = *S*+ (*G*)*. Clearly, the information about skewness obtained from the aggregate skewness function ν* (*z*) *and the indices S*+ (·) *and S*− (·) *is considerably more comprehensive than that obtained from γM* (·)*.*

**Figure 1.** Skewness functions *νF*(*z*) and *νG*(*z*) in Example 1.