Comparison of DC-plasma, DC-pulse plasma, PECVD, and RF-DC plasma nitriding processes.
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Barely three months into the new year and we are happy to announce a monumental milestone reached - 150 million downloads.
\n\nThis achievement solidifies IntechOpen’s place as a pioneer in Open Access publishing and the home to some of the most relevant scientific research available through Open Access.
\n\nWe are so proud to have worked with so many bright minds throughout the years who have helped us spread knowledge through the power of Open Access and we look forward to continuing to support some of the greatest thinkers of our day.
\n\nThank you for making IntechOpen your place of learning, sharing, and discovery, and here’s to 150 million more!
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The first chapter is devoted to the prediction of railways development in the nearest future. The second chapter discusses safety and security problems in general, precisely from the system point of view. In the third chapter, both the general approach and a particular case study of a critical incident with regard to railway safety are presented. In the fourth chapter, the question of railway infrastructure studies is presented, which is devoted to track superstructure. In the fifth chapter, the modern system for the technical condition monitoring of railway tracks is discussed. The compact on-board sensing device is presented. 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His general areas of interest are mechanical engineering and transport. His specialization is in vehicle dynamics. His specific area of research concerns railway vehicles. He is the author and coauthor of more than 200 publications and research elaborations, among which the most important and internationally known concern modeling rail vehicle dynamics, including multibody systems, computer methods, and curved track motion; stability in a curved track; dynamics in transition curves; and simulations of vehicle dynamics in general. These publications are subject to more than 150 international citations all over the world. 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They address the research question: Does a new post-renaissance normal now prevail? The research progresses a multivariate design that they developed to a case study approach as proliferation of private railway operators as a consequence of renaissance has constrained access to formerly public information. The study examined four countries, Brazil, Russia, India, and China, and one region, the Gulf Cooperation Council States, whose railways have advanced substantially in recent years through implementation of high speed, heavy haul, heavy intermodal, and urban rail. It also examined the migration of countries from the previously identified Fortuitous and Insecure railways clusters to the Enlightened, Progressive, and Assertive clusters. It found advances in institutional learning with respect to design of interventions to achieve renaissance, ownership and funding, market structure, as well as networking and expanding strategic horizons. 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She is also actively involved in dissemination activities, in particular the visibility of women in STEM, as a member of the Association for Computing Machinery\\'s Council on Women in Computing (ACM-W) Europe.",institutionString:"University of A Coruña",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"1",institution:{name:"University of A Coruña",institutionURL:null,country:{name:"Spain"}}}],coeditorOne:{id:"323123",title:"Dr.",name:"David",middleName:null,surname:"Cabrero",slug:"david-cabrero",fullName:"David Cabrero",profilePictureURL:"https://mts.intechopen.com/storage/users/323123/images/system/323123.jpg",biography:"Dr. David Cabrereo is an associate professor at the University of A Coruña, Spain, where he leads the Models and Applications of Distributed Systems (MADS) research group. He is currently responsible for the mandatory course on Human-Computer Interaction for CS undergraduate students. He has been working in distributed systems for more than a decade but has combined this with his interests in accessibility and open source. 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Iron-nitrogen or Fe-N binary system is essential in the steel design in a similar manner to the iron-carbon system [1]. Since the maximum solubility limit of nitrogen solutes is only 0.1 mass% or 0.3 at%, most of the previous studies concentrated on the solubility of nitrogen into γ-phase at a higher temperature than 1000 K [2]. Under those research circumstances, three important items in the material science were pointed out as findings to be noticed. Nitrogen works as a γ-phase stabilizer so that phase transformation temperature from α/α′- to γ-phase decreases with increasing the nitrogen content, [N] [3]. Mechanical properties significantly improve by themselves also with increasing [N] [4]. Thirdly, a crystalline structure of nitrogen super-saturated iron or Fe (N) resembles with that of iron nitride; for example, α′-Fe (N) with the nitrogen content of 11 at% has the same crystalline structure of α″-Fe16N2 and γ-Fe (N) with 20 at% nitrogen solute content corresponds to γ′-Fe4N as surveyed in the textbook [5]. The first two items stimulated further researches for high-nitrogen steels (HNS) as discussed in [6]. The third item leads to the chemical vapor deposition of Fe16N2 thin films on the template substrate [7].
An pressurized electro-slag remelting (ESR) method has become a standard approach to fabricate an ingot of HNS [8]. Relatively high amount of dissociated nitrogen atoms from N2 gas can diffuse into the depth of γ-phase matrix at 1263 K. This solution nitriding method induced the nitrogen atoms even into high chromium content steels including austenitic stainless steels [9]. Through this process, the nickel resource can be saved by 69 kg only by the addition of nitrogen atoms by 1 kg. The nitrogen works to stabilize the austenitic phase even with less nickel content. These nickel-free HNS have coarse grains, resulting in embrittlement, difficulty in welding, and insufficient stability in working. Although many trials have been made to improve the nitrogen solute content higher than 1 mass%, most of the studies experienced engineering difficulties to find a new alloying effect on the increase of strength and corrosion resistance [10].
In parallel with research on HNS, ion- and radical nitriding processes were developed with the use of the direct current (DC)-plasma and DC-pulse plasma technologies [11]. The TH was held to be higher than 900 K in them; CrN was synthesized as a precipitate in the matrix together with iron nitrides such as Fe2N and γ′-Fe4N [12]. Hence, the stainless steels and Fe-Cr alloys were hardened by fine precipitation of CrN; however, the chromium content in the matrix was reduced by CrN-precipitation reaction to lower the original corrosion resistance [13]. In addition, this high temperature plasma nitriding was mainly governed by the nitrogen diffusion process; the nitrogen solute content exponentially decreases from the maximum nitrogen solid solubility of 0.1 mass% at the surface down to 0 toward the nitriding front-end [14]. Furthermore, when the TH was higher than 1000 K, the chromium also diffuses to form a multi-stripe pattern with layered structure of {- (Cr-rich) - (CrN-rich) -} during nitriding [15]. Most of engineers and companies related to plasma nitriding believe that chemical reaction of chromium with nitrogen should drive the nitrided layer formation and hardening.
British research group [16] first found the nitrogen super-saturated lattices in the austenitic stainless steels by low temperature plasma nitriding. When using the same DC or DC-pulse plasmas, the nitriding process is characterized mainly by CrN precipitation into matrix when TH is higher than 800 K. On the other hand, an original γ-lattice expands to form a peak shift from the original peaks of austenitic stainless steels in X-ray diffraction (XRD) analysis when TH < 800 K. This finding does not mean a formation of new phase, so called by S-phase, but implies that nitrogen super-saturation accompanies with the γ-lattice expansion and that the crystalline structure of this Fe (N) is essentially different from that of original austenitic stainless steel matrix. In addition, various new engineering is expected to start from this nitrogen super-saturated Fe (N) [17].
In the present chapter, this low temperature nitriding (LTN) with nitrogen super-saturation is reconsidered by developing a new tool to drive LTN in the AISI304 stainless steels. First, Radio-frequency(RF)-DC plasma nitriding system is introduced with comments on the essential difference from other plasma nitriding processes such as DC- and DC-pulse plasmas. Quantitative plasma diagnosis equipment is stated to describe the nitrogen-hydrogen plasmas. In particular, the effect of hydrogen content in the mixture gas on the nitriding process is analyzed to determine the optimum condition. A hollow cathode device is proposed to intensify the ion and electron densities.
An austenitic stainless steel type AISI304 specimen is employed for plasma nitriding at 673 K for 14.4 ks by 60 Pa. Each fundamental process in this low temperature inner nitriding is analyzed by XRD, scanning electron microscopy (SEM)-electron dispersive X-ray spectroscopy (EDX), and electron back-scattering diffraction (EBSD). The γ-lattice expansion is analyzed as a peak shift in the XRD diagram. The nitrogen super-saturation is described by SEM-EDX; the elastic distortion is directly calculated by the lattice strain. The phase transformation, the plastic straining as well as the microstructure refinement are analyzed by EBSD. The nitrogen diffusion path is mainly estimated by the grain boundary diffusion process. These processes are mutually related to form a synergetic loop to drive this low temperature inner nitriding. When this loop is sustained during nitriding, the nitriding front-end advances homogeneously into the depth of stainless steel matrix. Once this loop is shut down at any point, the inner nitriding localizes by itself only to form a heterogeneous microstructure.
High-density RF-DC plasma nitriding system is introduced together with comments on the quantitative plasma diagnosis of nitrogen-hydrogen plasmas and on the hollow cathode device to intensify the ion and electron densities.
DC-plasma and DC-pulse plasma [18, 19, 20] have been utilized for nitriding of stainless steel parts, tools and dies at higher hold temperature than 800 K. plasma enhanced chemical vapor deposition (PECVD) has been utilized for nitriding at lower temperature than 800 K. Table 1 compares the difference in their capacity, inner nitriding behavior, and characteristics together with the present high-density RF-DC plasma nitriding. The former two approaches, widely utilized in the market, harden the stainless steels by CrN-precipitation and form thicker nitrided layer than 100 μm after 36 ks or 10 hours. PECVD nitriding works in the low-pressure of 1 to 2 Pa to form a moderate nitrided layer with the thickness of 20–30 μm. Inner nitriding process both in PECVD [21] and RF-DC plasma [22] is governed by nitrogen super-saturation without CrN precipitation. In the following, a detail of RF-DC plasma generation as well as a hollow cathode device is stated together with plasma diagnosis equipment in the present system.
Comparison of DC-plasma, DC-pulse plasma, PECVD, and RF-DC plasma nitriding processes.
A high-density plasma nitriding system [23, 24, 25] consisted of the vacuum chamber, the evacuation system, the DC-RF generators working in the frequency of 2 MHz, the gas supply of N2 and H2, and the heating unit located under the cathode plate as depicted in Figure 1. The nitriding parameters as well as controlling procedure are specified on the panels. All through the nitriding process, the measured pressure, temperature as well as gas pressure is automatically controlled by the process computer. Through the telecommunication, time history of RF- and DC-voltages and currents are also monitored on the panel to recognize the temporal status of RF-DC plasmas.
RF-DC plasma nitriding system. (1) Vacuum chamber, (2) RF-generator, (3) control-panel, (4) RF- and DC-power suppliers, (5) evacuation units, and (6) carrier gas supply.
Figure 2 illustrates an actual experimental setup for plasma nitriding. The thermocouple is inserted into this cathode plate to monitor the TH. In the vacuum chamber, the specimen is placed inside a hollow cathode setup on the cathode plate, which is electrically connected with DC generator. This hollow cathode setup includes a rectangle-shaped tube with the size of 40 × 20 × 70 mm3 and the thickness of 2 mm. To be explained later, the specimen is located at the position with the highest nitrogen ion density, far half from the mixture gas inlet, as shown in Figure 2.
Experimental setup for RF-DC high-density plasma nitriding.
In the standard plasma diagnosis, two methods are often employed to quantitatively describe the nitrogen-hydrogen mixture plasma state; that is, emissive light optical spectroscopy (EOS) and Langmuir probe (LP). A typical setup for EOS is illustrated in Figure 3 as precisely stated in the previous studies [26, 27, 28].
EOS for plasma diagnosis to describe the active species in the ignited plasmas.
Emissive light from plasmas is detected through the optically transparent silica window on the chamber in Figure 1 and analyzed to deduce the spectrum of species in the plasmas. Figure 4 shows the effect of hydrogen content in the gas mixture on the measured spectra together with the spectra for hydrogen plasmas.
Measured spectra for the hydrogen-nitrogen plasmas by EOS.
As seen in the spectra for hydrogen plasmas inserted in Figure 4, hydrogen peak intensity (Hα) at 656 nm increases monotonically with hydrogen content [H]. Pure nitrogen plasma mainly consists of an excited nitrogen molecule (N2*) and a nitrogen molecule ion (N2+) besides for a nitrogen molecule. When [H] = 83%, the whole population of nitrogen species including the NH-radicals significantly decreases. On the other hand, when [H] = 17%, {N2*, N2+, and NH} have high-intensity without peaks for the molecular nitrogen in the range of short wavelength (<300 nm). Although the active species such as N2(A3 ∑u+, ϑ) or N2(X1 ∑g+, ϑ) cannot be observed directly from the EOS spectra, their transitions can be investigated to describe the reaction model on the active species; for example, N2(C3 ∏u → B3 ∏ g*) peaks observed at 337, 358, and 370 nm are related to the N2* and N2+(B2 ∑u+ → X2 ∑g+) at 391 and 427 nm, related to the N2+. These second positive and the first negative bands of nitrogen play an important role in the generation of atomic nitrogen by reaction with N2 and N2+ in parallel with the formation of NH-radicals as detected at 336 nm.
The LP was also utilized in the diagnosis to describe the effect of [H] on the generated plasmas. Figure 5 depicts how to measure the ion and electron densities by using LP.
Experimental setups of the LP for measurement of ion and electron densities.
Through the direct measurement of I-V curves at the probe tip, the electron resistivity as well as the ion and electron densities are analyzed to describe the plasma state. In particular, the electron resistivity is proportional to the enhancement of plasma chemical reaction. Figure 6shows a variation of measured resistivity in the plasmas with increasing the hydrogen content in the mixture gas.
Variation of the measured resistivity in the plasmas with increasing the hydrogen content in the mixture gas.
The measured resistivity has maximum around [H] = 20–30%; a hot spot is formed in the plasmas where the chemical reaction is most enhanced for nitriding by directly controlling the hydrogen content [29].
The hollow cathode device is utilized to intensify the ion density in the nitrogen-hydrogen plasmas. The LP is employed to directly measure the ion density in the hollow. The LP-tip was inserted into the hollow along the X-axis in every 2 cm. In each position, the tip was fixed at the center of hollow. Figure 7 depicts the measured ion density distribution along the X-axis.
Measured ion density by the LP along the X-axis in the hollow.
The ion density increases monotonically with X in Figure 7; in particular, a hot spot with higher ion density than 1.5 × 1018 m−3 is located in the latter half of hollow. This is common to the hollow device effect where the ionization is enhanced at the vicinity of outlet in the hollow [30].
XRD (Rigaku SmartLab) with monochromatic Cu-Kα radiation (λ = 0.1542 nm) and Bragg–Brentano geometry, 40 kV, and 30 mA was utilized for analysis. The 2θ range was set between 30 and 90° with the scanning speed of 10 mm/min and the step angle of 0.02°. EDX device and software were utilized to make element mapping over a specified depth for nitrogen, chromium, iron, and carbon. Its spatial resolution was at most 5 μm. EBSD was utilized with the accelerating voltage of 20 kV, the working distance of 20 mm, the magnification (×2000), and the resolution of 0.1 μm. The inverse pole figure (IPF) was determined for each constituent grain to describe the change in microstructure through the nitriding. In addition, the kernel average misorientation (KAM) and the phase mapping were also measured to explain the plastic straining and phase transformation processes, respectively.
The micro-hardness testing apparatus (Mitsutoyo HM-200) was used by applying the load of 50 g or 0.5 N for hardness measurement on the cross-section in every 10 μm. The matrix hardness of AISI304 was 400 HV.
An austenitic stainless steel type AISI304 was employed as a specimen for high-density plasma nitriding at 673 K for 14.4 ks by 60 Pa with use of the hollow cathode device. Essential processes in this low temperature plasma nitriding are described by chemical analyses.
An austenitic stainless steel type AISI304 was selected for plasma nitriding at lower TH than 723 K. Table 2 summarizes the experimental results in the literature.
Previous studies on the low temperature plasma nitriding of AISI304 stainless steels.
Although detail information is not written in a few papers, relatively high-nitrogen surface content and formation of a nitrided layer with the thickness of 10–20 μm are common to those previous studies [31, 32, 33, 34, 35]. More precise analysis and discussion are needed to investigate the essential processes, governing the inner nitriding behavior at a lower temperature than 700 K. High-density RF-DC plasma systems [36, 37, 38, 39, 40] provides a new way to further analyze this low temperature plasma nitriding by experiments.
In the present study, AISI304 stainless steel plate with the size of 40 × 20 × 2 mm3 was employed as a specimen for RF-DC high-density plasma nitriding at 673 K for 14.4 ks by 60 Pa for the nitrogen and hydrogen mixture gas with the flow rate ratio of 160–30 ml/min. The pre-sputtering only with the use of nitrogen gas was first performed for 1.8 ks to clean the surface condition of the specimen before nitriding. Table 3 lists the whole plasma nitriding condition in this experiment.
RF-DC high-density plasma nitriding conditions.
The nitrided specimen was halved to prepare the test-pieces for SEM-EDX analyses. Figure 8 depicts the cross-sectional SEM image as well as the nitrogen mapping from the surface to the depth of nitrided specimen. The nitrogen content is measured at the surface to be 9 mass% or 26 at%. The nitrided layer thickness reaches to be 66.5 μm.
Cross-section of the plasma nitrided at 673 K for 14.4 ks. (a) SEM cross-sectional image and (b) nitrogen mapping from the surface to the depth.
Compared with Table 2, both the nitrogen solute content at the surface and the nitrided layer thickness become the highest by using this plasma nitriding at 673 K for 14.4 ks. Formation of uniform nitrogen super-saturated layer reveals that inner nitriding advances homogeneously into the depth of matrix.
A nitrogen super-saturated lattice is expected to expand by itself; for example, the previous studies in Table 2 reported a γ-lattice expansion by this nitrogen super-saturation. Figure 9 compares the analyzed XRD diagrams before and after the plasma nitriding. The original austenitic phase is characterized by three peaks for γ (111), γ (200), and γ (220) detected at 2θ = 43.4, 50.82, and 74.5°, respectively.
Comparison of analyzed XRD diagrams before and after plasma nitriding at 673 K.
Through the plasma nitriding process, all the austenitic lattices were elastically distorted to the expanded austenite (γN); for example, the original γ-peaks to AISI304 shifted to the lower 2θ, from 43.4 to 41.1°, from 50.82 to 47.94°, and from 74.5 to 70.08°, respectively. In parallel with these γN phases, the expanded martensitic peaks are also detected at 2θ = 43.7–63.5°. Negligibly small peak was detected at 2θ = 37.5° in trace level, which corresponds to the chromium nitrides. The γ-lattice expansion by this shift in XRD induces the tensile lattice strain by 5.4% for the peak shift from γ (111) to γN (111). This strain slightly increases to be 5.6% for the shift of γ (200) to γN (200) and 5.4% for γ (220) to γN (220), respectively. This elastic distortion in the nitrogen super-saturated lattices just corresponds to the previous report in [41]. The grains housing these elastically distorted lattices are forced to deform plastically to compensate for strain incompatibility between the nitrogen unsaturated and the super-saturated lattices in grains.
EDX as well as the micro-Vickers testing are utilized to investigate the nitrogen content and hardness depth profiles. As depicted in Figure 10, the hardness becomes maximum at the surface by 1550 HV and gradually reduces down to 1300 HV in the depth of d < 40 μm. From d = 40–66.5 μm, this hardness gradually decreases to the matrix hardness of 400 HV. A nitriding front-end is defined by the position in depth where the measured hardness coincides with the substrate hardness; the nitrided layer thickness (E) after nitriding for 14.4 ks is 66.5 μm. The nitrogen content is kept constant to be 15–17 at% in the depth of 5 < d < 60 μm except for the vicinities of surface and nitriding front-end. In the high temperature nitriding, the nitrogen content exponentially decays from the maximum nitrogen solubility limit of 0.1 mass% at the surface and goes to zero at the nitriding front-end. The nitrogen solute content depth profile in Figure 10 is far from the common knowledge on the inner nitriding process in the high temperature nitriding.
Nitrogen content and hardness depth profiles from the surface to the depth.
EBSD provides a tool to describe the interrelation among the phase transformation, the plastic straining, and the microstructure evolution. The measured phase mapping and KAM distribution on the cross-section of the nitrided specimen are shown in Figure 11a and b, respectively. After [42], the measured cross-sectional KAM profile can be identified as an equivalent plastic strain distribution.
EBSD analysis on the cross-section of the nitrided AISI304 specimen at 673 K for 14.4 ks. (a) KAM distribution, (b) phase mapping, and (c) IPF depth profile.
In Figure 11a, the expanded γ-phase and transformed α′-phase finely distribute in the depth of d < 40 μm and form the two-phase microstructure. This homogeneous two-phase structure abruptly changes to a heterogeneous one where α′-phase sparsely distributes in the γ-phase matrix. This autonomous phase mapping change coincides with the onset of hardness reduction in Figure 10. This is because the volume fraction of extended γ-phase and transformed α′-phase zones begins to reduce from d = 40 μm in Figure 11a.
In Figure 11b, the layer of d < 40 μm is plastically strained in all since most of grains house the expanded lattice zones and plastically distort to compensate for the strain incompatibility in each grain. Just as seen in Figure 11a, this homogeneous plastic straining also changes by itself and localizes to specified grains. In fact, plastic strains localize in each grain below d = 40 μm. That is, uniform phase transformation and plastic straining change themselves across this critical depth by their localization to grains. The neighboring lattices to elastically distorted ones by phase transformation are forced to make plastic distortion. The transformed α′-phase zones in Figure 11a correspond to the highly strained zones in Figure 11b.
Phase transformation and plastic straining in the above reflects the microstructure change by the nitrogen super-saturation. Figure 11c depicts the IPF distribution on the cross-section of the nitrided specimen. Each grain with a specified crystallographic orientation is represented by a different color. In correspondence to Figure 11a and b, the layer of d < 40 mm has refined microstructure with the average grains size of 0.1 μm, just near the spatial resolution of EBSD. This gray color for this layer in Figure 11c implies that each grain in this layer is homogeneously refined to have random crystallographic orientation. Just as observed in Figure 11a and b, this homogeneous microstructure changes by itself to heterogeneous one at d = 40 μm; the average grain size comes near to the original grain size before nitriding. To be noticed, the crystallographic orientations with different colors from original one or with graded colors are seen in most of the grains below d = 40 μm. The plastically strained grains are partially decomposed into several or tens of subgrains with different crystallographic orientations.
LTN of austenitic stainless steels is essentially different from the conventional plasma nitriding at higher temperatures. No nitrides are formed in the matrix so that no change in the original chromium content proves less change in the original corrosion resistance of stainless steels. The surface layer is hardened by nitrogen solid-solution where the γ-phase is expanded by nitrogen super-saturation with the occupation of octahedral vacancy sites by nitrogen solutes. Owing to fine grain size in the homogeneously nitrided layer, higher strength is expected to this high-nitrogen stainless steel surface. In addition, the fine-grained two-phase structure has a role to improve the trade-off-balancing between strength and fracture toughness and to increase the fatigue life [2]. How to extend this homogeneously nitrided layer toward the nitriding front-end must be an engineering issue to be discussed further.
Inner nitriding mechanism in this low temperature plasma nitriding of austenitic stainless steels is discussed with importance on the difference between the homogeneous and heterogeneous nitriding processes.
LTN mechanism is described by a synergetic loop as explained by Figure 12. Nitrogen solute, penetrating from the surface under high-nitrogen flux, occupies with an octahedral vacancy sites in the fcc-structured lattice as suggested by [43]. Under this nitrogen super-saturation, the γ-lattice expands, and elastically distorts to drive the γ to α′ phase transformation. The whole γ-lattices neighboring to expanding γ-lattices and transformed α′-lattices, are plastically strained to compensate for the strain incompatibility between two zones. Original grain is distorted and decomposed into fine subgrains by this plastic straining. More nitrogen solutes diffuse to the depth of unsaturated matrix through the refined grain boundaries. Evolution of the nitrided layer accompanies with this loop.
Synergetic loop of processing steps to drive the low temperature inner nitriding of stainless steels.
When this synergetic loop is sustained during the plasma nitriding, every unit process uniformly advances from the surface to the depth of matrix. As seen in Figure 11, the original coarse-grained AISI304 matrix is surface-modified to have fine-grained, two-phase microstructure from the surface to the depth of 40 μm. This homogeneous nitriding is shut down at the critical depth of 40 μm for the nitrided AISI304 at 673 K for 14.4 ks in Figure 11; the above loop only takes place locally below this critical depth. There is no change in the synergetic loop across this criticality. When the loop works uniformly in the matrix, the nitriding advances homogeneously, while it does heterogeneously when the loop localizes in the selected grains. In other words, this autonomous change from heterogeneous nitriding to homogeneous nitriding is driven by the nitrogen super-saturation process into grains. When the nitrogen super-saturated γ-phase zones are closely neighboring to each other in the specified grain, the whole related grains are homogeneously nitrided and refined by the synergetic loop in Figure 12. On the other hand, when each super-saturated γ-phase zones are isolated from each other, every process in the loop works only inside of each grain.
Let us first describe the localization of phase transformation, plastic straining, and micro-refinement below the critical depth in Figure 11. The phase mapping, the plastic strain distribution as well as the IPF mapping for the grain-A at d = 100 μm in Figure 11 are analyzed and shown in Figure 13. Since a grain boundary works as a nitrogen diffusion path, most of the γ-phase zones at its vicinity transform to α′-phase. As pointed by an arrow-a in Figure 13a and b, a series of α′-phase zones are aligned in the alternate order of {- (α′-phase zone) − (highly plastic-strained zone) − (α’-phase) -}. In correspondence to this alignment, an original (001) orientation rotates by the plastic straining as shown in the graded colors in Figure 11c. The transformed zones have (111) orientation as pointed by arrow-b. This local change in phase mapping, plastic straining, and crystallographic orientation distribution in the inside of grain-A proves that the heterogeneous nitriding process is driven by this localization in each grain of matrix below the critical depth.
Localized steps around the grain-a in the heterogeneous nitriding process. (a) Local phase mapping, (b) local plastic straining, and (c) local IPF mapping.
Figure 13 also suggests that each transformed band, pointed by the arrow-a, has a unit size of 0.3–0.5 μm in common and that these bands are isolated by highly strained γ-phase zones. With the enhancement of the nitrogen flux from the surface or with an increase of the nitrogen diffusion path density, those isolated zones overlap with each other to change the heterogeneous nitriding to the homogeneous nitriding. In other words, homogeneous nitriding mode prevails in the low temperature plasma nitriding process with higher activation of nitrogen flux from plasmas or with reduction of the initial grain size to a comparable level of transformed units in Figure 13.
An initial grain size of AISI304 sheet is controlled to decrease by intense rolling with the reduction in thickness by 90 % to demonstrate this mode change from heterogeneous nitriding to homogeneous nitriding. Figure 14 depicts the phase mapping, the plastic straining, and the microstructure refinement on the cross-section of rolled AISI304 before nitriding. Although crystallographic textures are formed along the rolling direction as shown by the arrow-a in Figure 14, the average grain size is uniformly reduced down to 1.5 μm.
Microstructure of intensely rolled AISI304 sheet with the reduction of thickness by 90%. Average grain size is 1.5 μm.
Under the same processing conditions, this fine-grained AISI304 specimen is nitrided at 673 K for 14.4 ks. The nitriding front-end is analyzed to be E = 60 μm, nearly the same as shown in Figure 11. Figure 15 shows the phase mapping, the plastic strain distribution, and the microstructure after nitriding. The heterogeneous microstructure observed above the nitriding front-end as well as the textures by rolling in Figure 14, completely disappears to form two-phase and fine-grained homogeneous nitrided layer. Although the initial fully martensitic phase turns to be γ – α’ two-phase; this two-phase fine microstructure is continuously formed across the nitriding front-end. This homogenization of the heterogeneous microstructure reveals that nitrogen super-saturation process advances homogeneously into the depth of stainless steel matrix under the synergetic loop once the grain size of the matrix is comparable to the nitrogen super-saturated unit size.
Microstructure after nitriding the fine-grained AISI304 at 673 K for 14.4 ks.
LTN of stainless steels is essentially governed by the homogeneous nitrogen super-saturation. When the synergetic loop is sustained during the nitriding, the nitrided layer has two-phase, fine-grained microstructure. Once the nitrogen super-saturation process is localized into the specified grains, the homogeneous nitriding changes itself to heterogeneous nitriding. Refinement of the initial grain size into a comparable size of nitrogen saturated γ-lattice units homogenizes the heterogeneously nitrided layer.
Low temperature plasma nitriding provides a processing tool for the surface treatment of the stainless steels to have a fine two-phase microstructure with the average grain size of 0.1 μm. This homogeneously nitrided layer has higher surface hardness than 1500 HV and higher nitrogen content than 15 at%. Different from the conventional nitriding, (1) no nitrides are formed as a precipitate in the matrix, (2) stainless steel matrix is nitrogen super-saturated to have a nitrogen content plateau of 15–17 at% toward the nitriding front-end, (3) phase transformation and plastic straining take place together with γ-lattice expansion, and (4) original coarse grains are refined. This homogeneous nitriding follows the heterogeneous nitriding process where γ-lattice expands locally in the specified grains with phase transformation to α′-phase and plastic straining. When nitriding the fine-grained stainless steels, their surfaces are homogeneously nitrided to have fine, a two-phase microstructure with high hardness, strength, and corrosion resistance.
The author would like to express his gratitude to Mr. Abdelrahman Farghali (SIT) and late Mr. Y. Sugita (YS-Electric Industry, Co. Ltd.) for their help in experiments. This study was financially supported in part by the Abe-Initiative in Japan Government and the METI-program on the supporting industries, Japan, respectively.
No conflicts of interest were declared.
In real decision-making problems, the evaluation of a decision is usually nondeterministic, because each problem concerns future activities and is evaluated in terms of future results. A significant portion of the parameters determining the decision conditions and assessment of the results may change, for example, raw material prices, product prices, currency exchange rates, and the sales potential of a given product.
The paper [1] presents a method of modeling decisions under risk in the form of a multi-criteria optimization problem. In this chapter, this approach is developed to apply multi-criteria optimization to supporting decision-making under risk.
As pointed out in [1], when making a decision, the decision maker must take into account both the choice of decisions and the risk’s conditions that may occur in his/her environment. Depending on the degree of knowledge of the decision-making situation (features of the problems being solved and the nature of the environment), decisions can be made in a situation of certainty, uncertainty, or risk. This chapter extends the previous work to represent three types of decision-making under risk. The first type of decision-making: decisions are made under conditions of certainty when the decision maker has accurate and reliable information on which to base his/her actions. The effects of the actions can be predicted with high accuracy. The second type of decision-making: a decision maker has a situation of uncertainty when he can determine what factors will affect the decision-making situation, but he cannot determine the probabilities of their occurrence and therefore also the risks’ impacts of the decisions taken. The other type of decision-making is a decision in risk conditions that applies to situations where the decision maker can determine what factors will affect the decision situation and determine the probabilities of their occurrence. The decision maker, using his experience and information from the environment, can determine with known or estimated probability the effects of decisions as well as the circumstances surrounding them.
Decision-making under risk is a process in which the results of actions taken by the decision maker are uncertain due to the potential of unforeseen circumstances, factors interfering with these circumstances, or disruptive factors, for example, ambient conditions, called scenarios. These, in turn, are caused by factors independent of the decision maker and have a significant impact on the results of the decision. Examples of scenarios can be: good or bad weather in the future; decline, stabilization, or rising stock values on the stock exchange in the future; and different price values and order volumes for a company operating in the future. Each such variant is a scenario. At the same time, each scenario clearly defines the implementation of results for individual decisions. Only the past is known from experience; we observe the present and try to predict the future. Such predictions are related to the construction of probable scenarios based on statistical analysis of the past data in order to find indications about the future and to anticipate it as accurately as possible. The decision maker is not able to determine with certainty which actions will lead to a result, but he can calculate the probability that a given result will occur. Specific scenarios correspond to the appropriate implementation of the assessment function. For each scenario, we are interested in the best evaluation value [1, 2].
As pointed out in [1], the theory of decision-making under risk refers to utility function and two-criterion techniques (Markowitz-type models). The utility function of the decision maker ensures complete order. If it is known, then the optimal decision is one that maximizes the expected utility [3, 4, 5].
This chapter shows an extension of [1] on how the decision problem under risk can be modeled with the multi-criteria optimization, that is, simultaneous minimization of a vector evaluation function whose particular coordinates represent the result of the decision when the given scenario is under risk occurred. The traditional approach [1] to solving a multi-criteria optimization problem requires the introduction of a single scalar objective function valuating individual y vectors and hence the decision vectors x. The solution of the decision problem is then reduced to determining the solution of the optimal single-criteria optimization problem. This approach implies the assumption that the preference relationship can be described using the utility function, u. The major difficulty in solving multi-criteria decision problems is due to the inability to determine a single aggregate quality indicator a priori, while the utility function is just such an indicator. Multi-criteria optimization techniques allow you to solve such a problem without using utility function models. This provides to the interactive multi-criteria techniques for decision support under risk. There are tools of the interactive analysis to define decision support process. They depend on additional preference information gained interactively from the decision maker, allowing simultaneously the decision maker to learn the problem during the process with possible evolvement of the preferences. The effective decision support is using the reference point method. Using the multi-criteria optimization approach, there is no need to identify the utility function of the decision maker. This approach is good for any decision maker who makes decisions under risk conditions (in a cost problem where less is better) for which less is better. This is consistent with first-order stochastic dominance.
The chapter is organized as follows:
Section 2 presents a modeling approach of decision under risk.
Section 3 defines a symmetrically effective decision that resolves the decision problem under risk conditions.
Section 4 discusses the technique of generating symmetrically effective decisions and the method of supporting the decision maker.
Section 5 gives an example of the application of the proposed decision support approach to a discrete problem.
Section 6 provides a conclusion of this chapter.
This section discusses how multi-criteria optimization methods can be used to model decisions under risk. The problem of multi-criteria optimization in the decision space and in the assessment of decision space is formulated.
Decision-making under risk is modeled by introducing scenarios, which represent possible states of the environment. Scenarios are factors that influence the outcome of a decision but are beyond the influence of the decision maker. For example, the risk factors can be raw material prices, product prices, currency exchange rates, deposit rates, and demand (e.g., sales opportunities for a given product) which may change. There may also be a catastrophic event changing the situation, for example, closing of the sales or supply market (e.g., due to embargo), customer insolvency, loss of license, etc.
The scenarios representing the risk factors are presented according to their probability distribution. If we assume that the probability of each scenario is a rational number, then by repeating relevant scenarios, it is possible to approach a situation where the probability of each scenario is the same, for example, selection between random variables
is equivalent to the problem of choosing between two lotteries
The number of occurrences of a specific scenario corresponds to the probability assigned to it. The specific set of scenarios
They are given as:
the feasible decision set
the set of scenarios
the decision assessment function,
The problem of decision under risk is modeled in the form of some kind of multi-criteria optimization problem:
This is a special problem of multi-criteria optimization in the sense that all assessment functions are expressed in the same units. This differs from the standard multi-criteria optimization problem, where evaluation functions can be expressed in different units. In the case of modeling decisions in risk conditions, individual assessments, although generated by different functions, are all expressed on the same scale, which allows comparison of their values.
There are as many assessment functions in a multi-criteria problem as there are scenarios. Each scenario has a different assessment function. You want to have the best score for all scenarios.
In the problem of multi-criteria optimization, all values for all scenarios are taken into account and by not looking at the values in each scenario (and not looking at individual coordinates). The result of the decision is the grade vector. You want to have the best score for all scenarios. One grading vector that gives the best score for all scenarios is sought.
The function,
There is a transformation
This leads to a multi-criteria model in the assessment space:
where grades are directly specified as individual variables.
Each vector
This chapter extended the way of defining a symmetrically effective decision compared to the work [1]. This chapter provides the basic definition of a symmetrically effective decision. It is a decision that is a solution to a specific multi-criteria optimization problem, a problem used to support decisions under risk. Decision assessments must meet an additional condition—the condition of anonymity of preference relationships.
The model of the decision problem under risk in the form of a multi-criteria optimization problem imposes additional properties of preference relations and, consequently, limits the choice of decisions to an appropriate subset of the entire set of effective decisions. In the problem of making decisions under risk, minimizing all assessments is equally important.
Decision problems under risk are, when the decision is based on minimization of a vector outcome with various realizations under several scenarios. The preference model leads to the Pareto efficiency with respect to the realizations under scenarios understood as multiple criteria. The case of equally probable scenarios leads to the concept of symmetric optimization (efficiency) of multi-criteria corresponding to realizations under scenarios. The solution should have the feature of anonymity: no distinction is made between results that differ in their orientation coordinates. This solution of the problem, called a symmetrically efficient decision, is an efficient decision that possesses an additional property, that is, that of preference relation anonymity.
The vector of evaluation
The set of
Dominance structure in
where
The set
Nondominated solutions.
A decision
In the problem with homogeneous and equally important assessments, the relation of the decision maker preferences should be impartial due to individual assessment functions. That is, for a given set of evaluation functions, only the distribution of the values achieved by these functions for a given decision is important, and it is not important which function it took. This requirement is formulated mathematically as a property of the anonymity of preference relationships. The risk assessment vector should meet the property of anonymity.
The relation is called an
We look at the whole with the help of an anonymous preference relationship, and all scenarios are considered rather than on individual results for given scenarios. Anonymous preference relationship is a superstructure over a preference relationship—an additional condition of anonymity is added.
A nondominated vector satisfying the anonymity property is called
The domination structure of symmetric dominance depends on the location of an evaluation vector,
Symmetric dominance structure in
The relation of symmetric domination can be expressed as the domination of evaluation vectors with coordinates ordered in no decreasing order. This can be formalized with the map
The evaluation vector
The relation of symmetrical domination
For the problem of decisions under risk expressed in the form of a multi-criteria optimization problem, the solution is a set of symmetrically effective decisions.
This chapter extended the way of defining a symmetrically effective decision compared to the work [1]. This chapter discusses how to support decisions under risk. It is an interactive IT system that processes relevant data for a given decision situation and assists the decision maker in recognizing the decision problem in the sense of understanding his own preferences. The decision maker’s role is paramount. The system is not a substitute for the decision maker at any stage of decision-making. Such a system is to support, not replace in the final selection of the decision maker.
In multi-criteria decision problems, the relation of preferences is not known a priori, and therefore the final choice of solution can be made only by the decision maker. Given the numerous set of solutions, this selection is made using the appropriate interactive information system—the decision support system. Such a system processes important data for a given decision situation but also supports the decision maker in recognizing the decision problem in the sense of understanding his own preferences. The decision maker’s role is paramount. The system is not a substitute for the decision maker at any stage of decision-making. Decision support system is to support, not replace in the final selection of the decision maker. In the problem of multi-criteria optimization, you cannot impose an optimal solution on the decision maker, you should support it—give the decision maker the opportunity to review such solutions that give the best results—symmetrically nondominated solutions. The decision maker chooses the decision by looking at the symmetrically nondominated set. This system enables a controlled review of the set of symmetrically efficient solutions. On the basis of the values of certain control parameters given by the decision maker, the system presents various solutions that are symmetrically efficient for analysis.
Solutions of a symmetrically efficient multi-criteria problem can be determined by solving the optimization of a multi-criteria problem:
with the scalarizing function
If the relation fulfills the condition of anonymity, the efficient solution generated by this scalarization is also a symmetrically efficient solution to the multi-criteria problem (1).
Symmetrically efficient decisions for a multiple criteria problem (1) are obtained by solving a special problem in multi-criteria optimization, that is, a problem with coordinates of the vector of evaluation arranged in a no decreasing order. This problem is as follows:
where
An efficient solution of multi-criteria optimization problem (9) is a symmetrically efficient solution of the multi-criteria problem (1).
The method of determining individual symmetrically efficient decisions involves the solution of a parametric scalarization of a multi-criterion problem. This is a problem of single objective optimization using a specially created scalarizing function of two variables: the evaluation vector,
The parameter
To ensure the anonymity of the relationship, it is necessary and sufficient that the scalarizing function is symmetrical, that is,
for any permutation,
Complete and sufficient parameterization of the set of symmetrically efficient solutions can be achieved, using the method of the reference point for problem (9). In this method, aspiration levels are applied as control parameters. An aspiration level is a value of the evaluation function that satisfies the decision maker.
The scalarizing function defined in the method of the reference point is as follows:
where
This kind of scalarizing function is called
The solution to the multi-criteria optimization problem is a set of efficient solutions. The choice of solution should be made by the decision maker using an IT system. Such a system allows him to browse the entire set of solutions and make choices freely. The final choice of the solution among the set of efficient solutions can only take place based on the user’s preferences. A tool for searching the set of solutions is the function (12). The maximum of this function depends on the parameter,
calculations, that is, finding another symmetrically efficient solutions;
interaction with the system, that is, dialog with the decision maker, which is a source of additional information about his or her preferences.
The method of supporting decision selection is shown in Figure 4.
The method of supporting decision selection.
This method of supporting decision-making, which does not impose a rigid scenario for the analysis of the decision-making problem upon the decision maker, enables modification of his or her preferences during the analysis of the problem. The decision maker plays a key role in the decision-making process.
The problem of selecting a decision is shown in order to illustrate the method of supporting a decision under risk [12]. The costs of 10 alternatives in three scenarios are presented in Table 1. The probabilities of particular scenarios are as follows: P1 = 0.3, P2 = 0.6, and P3 = 0.1.
Decision | |||
---|---|---|---|
Decision | 59 | 65 | 75 |
Decision | 50 | 58 | 71 |
Decision | 68 | 72 | 60 |
Decision | 69 | 72 | 62 |
Decision | 53 | 60 | 63 |
Decision | 51 | 59 | 65 |
Decision | 68 | 71 | 77 |
Decision | 56 | 57 | 75 |
Decision | 62 | 58 | 80 |
Decision | 62 | 55 | 70 |
Scenarios of 10 decisions.
The decision maker’s problem is to select one of 10 decisions with three possible future scenarios. Since the configuration of conditions that will apply during the decision is unknown, this problem is a selection decision under risk [13].
The problem of decision-making under risk is modeled as a multi-criteria optimization problem:
where the results of particular decisions are the following vectors:
in which particular coordinates of evaluation vectors occur with probabilities:
The problem consists in selecting a decision for which the evaluation vector has the minimum value in the sense of symmetrical dominance.
The repeating of relevant scenarios results in a situation in which the probability of each scenario is the same and, that is,
In order to compare the vectors in the sense of symmetrical dominance, the coordinates of vectors are ordered in no decreasing order and the results are the following evaluation vectors for each decision:
The set of symmetrically nondominated vectors is as follows:
The method of the reference point for the problem with coordinates of the evaluation vector arranged in no decreasing order is used to determine the solution of the problem (13). The decision maker controls the selection of an investment project through the levels of aspiration by specifying the desired values of the aspiration vector for each scenario:
The multiple-criteria analysis is presented in Table 2.
Iteration | |
---|---|
1. Aspiration level Solution | Decision |
2. Aspiration level Solution | Decision |
3. Aspiration level Solution | Decision |
4. Aspiration level Solution | Decision |
5. Aspiration level Solution | Decision |
Interactive analysis of the search for a decision.
Source: own calculations.
At the beginning of the selection, the decision maker identifies the aspiration levels as the best values that can be achieved separately for each scenario, and in subsequent iterations, he or she changes the aspiration levels depending on his or her preferences.
In the first iteration, the decision maker determines the preferences as an aspiration level equal to the vector
The final selection of a specific solution depends on the decision maker’s preferences. The example given here shows that the method enables the decision maker to discover his or her decision-making capabilities in the course of interactive analysis and obtain a satisfactory solution.
In the decision-making process, risk plays a significant role, influencing the final result of the decision. The decision maker should be able to analyze them when making decisions. Using his experience and information from the environment, he should make such decisions that will not bring unnecessary threat (risk) to the effects of the decision. Despite the use of objectified tools optimizing decision-making processes in the choice of solution, ultimately the decision maker takes responsibility for the decisions taken.
The chapter presents a method for the decision made under risky situations. The risk is introduced to the model with a set of scenarios with specified probabilities. The choice is made by solving the problem of multi-criteria optimization. This provides a systematic procedure to help a decision maker choose the most desirable and satisfactory decision under risk situations. Therefore, using this way, a decision can be made according to the decision maker’s preference. This method is characterized by:
The use of reference point method, that is, the concepts of aspiration levels and minimization of the achievement function to organize interaction with the decision maker.
The assumption that the decision maker’s preferences are not fully formed changes during the decision-making process, while the main problem of the decision support system is to support the decision maker’s learning rather than the final act of choice.
The method gives a whole set of solutions symmetrically effective decisions and allows the decision maker a free choice. This procedure does not replace the decision maker in making decisions. The whole decision-making process is controlled by the decision maker.
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Currently, he is a professor of Orthodontics. He holds a Certificate of Advanced Study type A in Technology of Biomaterials used in Dentistry (1995); Certificate of Advanced Study type B in Dento-Facial Orthopaedics (1997) from the Faculty of Dental Surgery, University Denis Diderot-Paris VII, France; Diploma of Advanced Study (DESA) in Biocompatibility of Biomaterials from the Faculty of Medicine and Pharmacy of Casablanca (2002); Certificate of Clinical Occlusodontics from the Faculty of Dentistry of Casablanca (2004); University Diploma of Biostatistics and Perceptual Health Measurement from the Faculty of Medicine and Pharmacy of Casablanca (2011); and a University Diploma of Pedagogy of Odontological Sciences from the Faculty of Dentistry of Casablanca (2013). 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He is an academic staff member of the Department of Reproduction and Artificial Insemination, Selçuk University, Turkey. He manages several studies on sperms and embryos and is an editorial board member for several international journals. His studies include sperm cryobiology, in vitro fertilization, and embryo production in animals.",institutionString:"Selçuk University, Faculty of Veterinary Medicine",institution:null},{id:"90846",title:"Prof.",name:"Yusuf",middleName:null,surname:"Bozkurt",slug:"yusuf-bozkurt",fullName:"Yusuf Bozkurt",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/90846/images/system/90846.jpg",biography:"Yusuf Bozkurt has a BSc, MSc, and Ph.D. from Ankara University, Turkey. He is currently a Professor of Biotechnology of Reproduction in the field of Aquaculture, İskenderun Technical University, Turkey. His research interests include reproductive biology and biotechnology with an emphasis on cryo-conservation. He is on the editorial board of several international peer-reviewed journals and has published many papers. Additionally, he has participated in many international and national congresses, seminars, and workshops with oral and poster presentations. He is an active member of many local and international organizations.",institutionString:"İskenderun Technical University",institution:{name:"İskenderun Technical University",country:{name:"Turkey"}}},{id:"61139",title:"Dr.",name:"Sergey",middleName:null,surname:"Tkachev",slug:"sergey-tkachev",fullName:"Sergey Tkachev",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/61139/images/system/61139.png",biography:"Dr. Sergey Tkachev is a senior research scientist at the Institute of Fundamental Medicine and Biology, Kazan Federal University, Russia, and at the Institute of Chemical Biology and Fundamental Medicine SB RAS, Novosibirsk, Russia. He received his Ph.D. in Molecular Biology with his thesis “Genetic variability of the tick-borne encephalitis virus in natural foci of Novosibirsk city and its suburbs.” His primary field is molecular virology with research emphasis on vector-borne viruses, especially tick-borne encephalitis virus, Kemerovo virus and Omsk hemorrhagic fever virus, rabies virus, molecular genetics, biology, and epidemiology of virus pathogens.",institutionString:"Russian Academy of Sciences",institution:{name:"Russian Academy of Sciences",country:{name:"Russia"}}},{id:"310962",title:"Dr.",name:"Amlan",middleName:"Kumar",surname:"Patra",slug:"amlan-patra",fullName:"Amlan Patra",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/310962/images/system/310962.jpg",biography:"Amlan K. Patra, FRSB, obtained a Ph.D. in Animal Nutrition from Indian Veterinary Research Institute, India, in 2002. He is currently an associate professor at West Bengal University of Animal and Fishery Sciences. He has more than twenty years of research and teaching experience. He held previous positions at the American Institute for Goat Research, The Ohio State University, Columbus, USA, and Free University of Berlin, Germany. His research focuses on animal nutrition, particularly ruminants and poultry nutrition, gastrointestinal electrophysiology, meta-analysis and modeling in nutrition, and livestock–environment interaction. He has authored around 175 articles in journals, book chapters, and proceedings. Dr. Patra serves on the editorial boards of several reputed journals.",institutionString:null,institution:{name:"West Bengal University of Animal and Fishery Sciences",country:{name:"India"}}},{id:"53998",title:"Prof.",name:"László",middleName:null,surname:"Babinszky",slug:"laszlo-babinszky",fullName:"László Babinszky",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/53998/images/system/53998.png",biography:"László Babinszky is Professor Emeritus, Department of Animal Nutrition Physiology, University of Debrecen, Hungary. He has also worked in the Department of Animal Nutrition, University of Wageningen, Netherlands; the Institute for Livestock Feeding and Nutrition (IVVO), Lelystad, Netherlands; the Agricultural University of Vienna (BOKU); the Institute for Animal Breeding and Nutrition, Austria; and the Oscar Kellner Research Institute for Animal Nutrition, Rostock, Germany. In 1992, Dr. Babinszky obtained a Ph.D. in Animal Nutrition from the University of Wageningen. His main research areas are swine and poultry nutrition. He has authored more than 300 publications (papers, book chapters) and edited four books and fourteen international conference proceedings.",institutionString:"University of Debrecen",institution:{name:"University of Debrecen",country:{name:"Hungary"}}},{id:"201830",title:"Dr.",name:"Fernando",middleName:"Sanchez",surname:"Davila",slug:"fernando-davila",fullName:"Fernando Davila",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/201830/images/5017_n.jpg",biography:"I am a professor at UANL since 1988. My research lines are the development of reproductive techniques in small ruminants. We also conducted research on sexual and social behavior in males.\nI am Mexican and study my professional career as an engineer in agriculture and animal science at UANL. Then take a masters degree in science in Germany (Animal breeding). Take a doctorate in animal science at the UANL.",institutionString:null,institution:{name:"Universidad Autónoma de Nuevo León",country:{name:"Mexico"}}},{id:"309250",title:"Dr.",name:"Miguel",middleName:null,surname:"Quaresma",slug:"miguel-quaresma",fullName:"Miguel Quaresma",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/309250/images/9059_n.jpg",biography:"Miguel Nuno Pinheiro Quaresma was born on May 26, 1974 in Dili, Timor Island. He is married with two children: a boy and a girl, and he is a resident in Vila Real, Portugal. He graduated in Veterinary Medicine in August 1998 and obtained his Ph.D. degree in Veterinary Sciences -Clinical Area in February 2015, both from the University of Trás-os-Montes e Alto Douro. He is currently enrolled in the Alternative Residency of the European College of Animal Reproduction. He works as a Senior Clinician at the Veterinary Teaching Hospital of UTAD (HVUTAD) with a role in clinical activity in the area of livestock and equine species as well as to support teaching and research in related areas. He teaches as an Invited Professor in Reproduction Medicine I and II of the Master\\'s in Veterinary Medicine degree at UTAD. Currently, he holds the position of Chairman of the Portuguese Buiatrics Association. He is a member of the Consultive Group on Production Animals of the OMV. He has 19 publications in indexed international journals (ISIS), as well as over 60 publications and oral presentations in both Portuguese and international journals and congresses.",institutionString:"University of Trás-os-Montes and Alto Douro",institution:{name:"University of Trás-os-Montes and Alto Douro",country:{name:"Portugal"}}},{id:"38652",title:"Prof.",name:"Rita",middleName:null,surname:"Payan-Carreira",slug:"rita-payan-carreira",fullName:"Rita Payan-Carreira",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRiFPQA0/Profile_Picture_1614601496313",biography:"Rita Payan Carreira earned her Veterinary Degree from the Faculty of Veterinary Medicine in Lisbon, Portugal, in 1985. She obtained her Ph.D. in Veterinary Sciences from the University of Trás-os-Montes e Alto Douro, Portugal. After almost 32 years of teaching at the University of Trás-os-Montes and Alto Douro, she recently moved to the University of Évora, Department of Veterinary Medicine, where she teaches in the field of Animal Reproduction and Clinics. Her primary research areas include the molecular markers of the endometrial cycle and the embryo–maternal interaction, including oxidative stress and the reproductive physiology and disorders of sexual development, besides the molecular determinants of male and female fertility. She often supervises students preparing their master's or doctoral theses. She is also a frequent referee for various journals.",institutionString:null,institution:{name:"University of Évora",country:{name:"Portugal"}}},{id:"283019",title:"Dr.",name:"Oudessa",middleName:null,surname:"Kerro Dego",slug:"oudessa-kerro-dego",fullName:"Oudessa Kerro Dego",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/283019/images/system/283019.png",biography:"Dr. Kerro Dego is a veterinary microbiologist with training in veterinary medicine, microbiology, and anatomic pathology. Dr. Kerro Dego is an assistant professor of dairy health in the department of animal science, the University of Tennessee, Institute of Agriculture, Knoxville, Tennessee. He received his D.V.M. (1997), M.S. (2002), and Ph.D. (2008) degrees in Veterinary Medicine, Animal Pathology and Veterinary Microbiology from College of Veterinary Medicine, Addis Ababa University, Ethiopia; College of Veterinary Medicine, Utrecht University, the Netherlands and Western College of Veterinary Medicine, University of Saskatchewan, Canada respectively. He did his Postdoctoral training in microbial pathogenesis (2009 - 2015) in the Department of Animal Science, the University of Tennessee, Institute of Agriculture, Knoxville, Tennessee. Dr. Kerro Dego’s research focuses on the prevention and control of infectious diseases of farm animals, particularly mastitis, improving dairy food safety, and mitigation of antimicrobial resistance. Dr. Kerro Dego has extensive experience in studying the pathogenesis of bacterial infections, identification of virulence factors, and vaccine development and efficacy testing against major bacterial mastitis pathogens. Dr. Kerro Dego conducted numerous controlled experimental and field vaccine efficacy studies, vaccination, and evaluation of immunological responses in several species of animals, including rodents (mice) and large animals (bovine and ovine).",institutionString:"University of Tennessee at Knoxville",institution:{name:"University of Tennessee at Knoxville",country:{name:"United States of America"}}},{id:"251314",title:"Dr.",name:"Juan Carlos",middleName:null,surname:"Gardón Poggi",slug:"juan-carlos-gardon-poggi",fullName:"Juan Carlos Gardón Poggi",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/251314/images/system/251314.jpeg",biography:"Juan Carlos Gardón Poggi received University degree from the Faculty of Agrarian Science in Argentina, in 1983. Also he received Masters Degree and PhD from Córdoba University, Spain. He is currently a Professor at the Catholic University of Valencia San Vicente Mártir, at the Department of Medicine and Animal Surgery. He teaches diverse courses in the field of Animal Reproduction and he is the Director of the Veterinary Farm. He also participates in academic postgraduate activities at the Veterinary Faculty of Murcia University, Spain. His research areas include animal physiology, physiology and biotechnology of reproduction either in males or females, the study of gametes under in vitro conditions and the use of ultrasound as a complement to physiological studies and development of applied biotechnologies. Routinely, he supervises students preparing their doctoral, master thesis or final degree projects.",institutionString:null,institution:{name:"Valencia Catholic University Saint Vincent Martyr",country:{name:"Spain"}}},{id:"309529",title:"Dr.",name:"Albert",middleName:null,surname:"Rizvanov",slug:"albert-rizvanov",fullName:"Albert Rizvanov",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/309529/images/9189_n.jpg",biography:'Albert A. Rizvanov is a Professor and Director of the Center for Precision and Regenerative Medicine at the Institute of Fundamental Medicine and Biology, Kazan Federal University (KFU), Russia. He is the Head of the Center of Excellence “Regenerative Medicine” and Vice-Director of Strategic Academic Unit \\"Translational 7P Medicine\\". Albert completed his Ph.D. at the University of Nevada, Reno, USA and Dr.Sci. at KFU. He is a corresponding member of the Tatarstan Academy of Sciences, Russian Federation. Albert is an author of more than 300 peer-reviewed journal articles and 22 patents. He has supervised 11 Ph.D. and 2 Dr.Sci. dissertations. Albert is the Head of the Dissertation Committee on Biochemistry, Microbiology, and Genetics at KFU.\nORCID https://orcid.org/0000-0002-9427-5739\nWebsite https://kpfu.ru/Albert.Rizvanov?p_lang=2',institutionString:"Kazan Federal University",institution:{name:"Kazan Federal University",country:{name:"Russia"}}},{id:"210551",title:"Dr.",name:"Arbab",middleName:null,surname:"Sikandar",slug:"arbab-sikandar",fullName:"Arbab Sikandar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/210551/images/system/210551.jpg",biography:"Dr. Arbab Sikandar, PhD, M. Phil, DVM was born on April 05, 1981. He is currently working at the College of Veterinary & Animal Sciences as an Assistant Professor. He previously worked as a lecturer at the same University. \nHe is a Member/Secretory of Ethics committee (No. CVAS-9377 dated 18-04-18), Member of the QEC committee CVAS, Jhang (Regr/Gen/69/873, dated 26-10-2017), Member, Board of studies of Department of Basic Sciences (No. CVAS. 2851 Dated. 12-04-13, and No. CVAS, 9024 dated 20/11/17), Member of Academic Committee, CVAS, Jhang (No. CVAS/2004, Dated, 25-08-12), Member of the technical committee (No. CVAS/ 4085, dated 20,03, 2010 till 2016).\n\nDr. Arbab Sikandar contributed in five days hands-on-training on Histopathology at the Department of Pathology, UVAS from 12-16 June 2017. He received a Certificate of appreciation for contributions for Popularization of Science and Technology in the Society on 17-11-15. He was the resource person in the lecture series- ‘scientific writing’ at the Department of Anatomy and Histology, UVAS, Lahore on 29th October 2015. He won a full fellowship as a principal candidate for the year 2015 in the field of Agriculture, EICA, Egypt with ref. to the Notification No. 12(11) ACS/Egypt/2014 from 10 July 2015 to 25th September 2015.; he received a grant of Rs. 55000/- as research incentives from Director, Advanced Studies and Research, UVAS, Lahore upon publications of research papers in IF Journals (DR/215, dated 19-5-2014.. He obtained his PhD by winning a HEC Pakistan indigenous Scholarship, ‘Ph.D. fellowship for 5000 scholars – Phase II’ (2av1-147), 17-6/HEC/HRD/IS-II/12, November 15, 2012. \n\nDr. Sikandar is a member of numerous societies: Registered Veterinary Medical Practitioner (life member) and Registered Veterinary Medical Faculty of Pakistan Veterinary Medical Council. The Registration code of PVMC is RVMP/4298 and RVMF/ 0102.; Life member of the University of Veterinary and Animal Sciences, Lahore, Alumni Association with S# 664, dated: 6-4-12. ; Member 'Vets Care Organization Pakistan” with Reference No. VCO-605-149, dated 05-04-06. :Member 'Vet Crescent” (Society of Animal Health and Production), UVAS, Lahore.",institutionString:"University of Veterinary & Animal Science",institution:{name:"University of Veterinary and Animal Sciences",country:{name:"Pakistan"}}},{id:"311663",title:"Dr.",name:"Prasanna",middleName:null,surname:"Pal",slug:"prasanna-pal",fullName:"Prasanna Pal",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/311663/images/13261_n.jpg",biography:null,institutionString:null,institution:{name:"National Dairy Research Institute",country:{name:"India"}}},{id:"202192",title:"Dr.",name:"Catrin",middleName:null,surname:"Rutland",slug:"catrin-rutland",fullName:"Catrin Rutland",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/202192/images/system/202192.png",biography:"Catrin Rutland is an Associate Professor of Anatomy and Developmental Genetics at the University of Nottingham, UK. She obtained a BSc from the University of Derby, England, a master’s degree from Technische Universität München, Germany, and a Ph.D. from the University of Nottingham. She undertook a post-doctoral research fellowship in the School of Medicine before accepting tenure in Veterinary Medicine and Science. Dr. Rutland also obtained an MMedSci (Medical Education) and a Postgraduate Certificate in Higher Education (PGCHE). She is the author of more than sixty peer-reviewed journal articles, twelve books/book chapters, and more than 100 research abstracts in cardiovascular biology and oncology. She is a board member of the European Association of Veterinary Anatomists, Fellow of the Anatomical Society, and Senior Fellow of the Higher Education Academy. Dr. Rutland has also written popular science books for the public. https://orcid.org/0000-0002-2009-4898. www.nottingham.ac.uk/vet/people/catrin.rutland",institutionString:null,institution:{name:"University of Nottingham",country:{name:"United Kingdom"}}},{id:"283315",title:"Prof.",name:"Samir",middleName:null,surname:"El-Gendy",slug:"samir-el-gendy",fullName:"Samir El-Gendy",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRduYQAS/Profile_Picture_1606215849748",biography:"Samir El-Gendy is a Professor of anatomy and embryology at the faculty of veterinary medicine, Alexandria University, Egypt. Samir obtained his PhD in veterinary science in 2007 from the faculty of veterinary medicine, Alexandria University and has been a professor since 2017. Samir is an author on 24 articles at Scopus and 12 articles within local journals and 2 books/book chapters. His research focuses on applied anatomy, imaging techniques and computed tomography. Samir worked as a member of different local projects on E-learning and he is a board member of the African Association of Veterinary Anatomists and of anatomy societies and as an associated author at local and international journals. Orcid: https://orcid.org/0000-0002-6180-389X",institutionString:null,institution:{name:"Alexandria University",country:{name:"Egypt"}}},{id:"246149",title:"Dr.",name:"Valentina",middleName:null,surname:"Kubale",slug:"valentina-kubale",fullName:"Valentina Kubale",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/246149/images/system/246149.jpg",biography:"Valentina Kubale is Associate Professor of Veterinary Medicine at the Veterinary Faculty, University of Ljubljana, Slovenia. Since graduating from the Veterinary faculty she obtained her PhD in 2007, performed collaboration with the Department of Pharmacology, University of Copenhagen, Denmark. She continued as a post-doctoral fellow at the University of Copenhagen with a Lundbeck foundation fellowship. She is the editor of three books and author/coauthor of 23 articles in peer-reviewed scientific journals, 16 book chapters, and 68 communications at scientific congresses. Since 2008 she has been the Editor Assistant for the Slovenian Veterinary Research journal. She is a member of Slovenian Biochemical Society, The Endocrine Society, European Association of Veterinary Anatomists and Society for Laboratory Animals, where she is board member.",institutionString:"University of Ljubljana",institution:{name:"University of Ljubljana",country:{name:"Slovenia"}}},{id:"258334",title:"Dr.",name:"Carlos Eduardo",middleName:null,surname:"Fonseca-Alves",slug:"carlos-eduardo-fonseca-alves",fullName:"Carlos Eduardo Fonseca-Alves",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/258334/images/system/258334.jpg",biography:"Dr. Fonseca-Alves earned his DVM from Federal University of Goias – UFG in 2008. He completed an internship in small animal internal medicine at UPIS university in 2011, earned his MSc in 2013 and PhD in 2015 both in Veterinary Medicine at Sao Paulo State University – UNESP. Dr. Fonseca-Alves currently serves as an Assistant Professor at Paulista University – UNIP teaching small animal internal medicine.",institutionString:null,institution:{name:"Universidade Paulista",country:{name:"Brazil"}}},{id:"245306",title:"Dr.",name:"María Luz",middleName:null,surname:"Garcia Pardo",slug:"maria-luz-garcia-pardo",fullName:"María Luz Garcia Pardo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/245306/images/system/245306.png",biography:"María de la Luz García Pardo is an agricultural engineer from Universitat Politècnica de València, Spain. She has a Ph.D. in Animal Genetics. Currently, she is a lecturer at the Agrofood Technology Department of Miguel Hernández University, Spain. Her research is focused on genetics and reproduction in rabbits. The major goal of her research is the genetics of litter size through novel methods such as selection by the environmental sensibility of litter size, with forays into the field of animal welfare by analysing the impact on the susceptibility to diseases and stress of the does. Details of her publications can be found at https://orcid.org/0000-0001-9504-8290.",institutionString:null,institution:{name:"Miguel Hernandez University",country:{name:"Spain"}}},{id:"350704",title:"M.Sc.",name:"Camila",middleName:"Silva Costa",surname:"Ferreira",slug:"camila-ferreira",fullName:"Camila Ferreira",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/350704/images/17280_n.jpg",biography:"Graduated in Veterinary Medicine at the Fluminense Federal University, specialist in Equine Reproduction at the Brazilian Veterinary Institute (IBVET) and Master in Clinical Veterinary Medicine and Animal Reproduction at the Fluminense Federal University. She has experience in analyzing zootechnical indices in dairy cattle and organizing events related to Veterinary Medicine through extension grants. I have experience in the field of diagnostic imaging and animal reproduction in veterinary medicine through monitoring and scientific initiation scholarships. I worked at the Equus Central Reproduction Equine located in Santo Antônio de Jesus – BA in the 2016/2017 breeding season. I am currently a doctoral student with a scholarship from CAPES of the Postgraduate Program in Veterinary Medicine (Pathology and Clinical Sciences) at the Federal Rural University of Rio de Janeiro (UFRRJ) with a research project with an emphasis on equine endometritis.",institutionString:null,institution:null},{id:"41319",title:"Prof.",name:"Lung-Kwang",middleName:null,surname:"Pan",slug:"lung-kwang-pan",fullName:"Lung-Kwang Pan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/41319/images/84_n.jpg",biography:null,institutionString:null,institution:null},{id:"125292",title:"Dr.",name:"Katy",middleName:null,surname:"Satué Ambrojo",slug:"katy-satue-ambrojo",fullName:"Katy Satué Ambrojo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/125292/images/system/125292.jpeg",biography:"Katy Satué Ambrojo received her Veterinary Medicine degree, Master degree in Equine Technology and doctorate in Veterinary Medicine from the Faculty of Veterinary, CEU-Cardenal Herrera University in Valencia, Spain.Dr. Satué is accredited as a Private University Doctor Professor, Doctor Assistant, and Contracted Doctor by AVAP (Agència Valenciana d'Avaluació i Prospectiva) and currently, as a full professor by ANECA (since January 2022). To date, Katy has taught 22 years in the Department of Animal Medicine and Surgery at the CEU-Cardenal Herrera University in undergraduate courses in Veterinary Medicine (General Pathology, integrated into the Applied Basis of Veterinary Medicine module of the 2nd year, Clinical Equine I of 3rd year, and Equine Clinic II of 4th year). Dr. Satué research activity is in the field of Endocrinology, Hematology, Biochemistry, and Immunology in the Spanish Purebred mare. She has directed 5 Doctoral Theses and 5 Diplomas of Advanced Studies, and participated in 11 research projects as a collaborating researcher. She has written 2 books and 14 book chapters in international publishers related to the area, and 68 scientific publications in international journals. Dr. Satué has attended 63 congresses, participating with 132 communications in international congresses and 19 in national congresses related to the area. Dr. Satué is a scientific reviewer for various prestigious international journals such as Animals, American Journal of Obstetrics and Gynecology, Veterinary Clinical Pathology, Journal of Equine Veterinary Science, Reproduction in Domestic Animals, Research Veterinary Science, Brazilian Journal of Medical and Biological Research, Livestock Production Science and Theriogenology, among others. Since 2014 she has been responsible for the Clinical Analysis Laboratory of the CEU-Cardenal Herrera University Veterinary Clinical Hospital.",institutionString:null,institution:null},{id:"201721",title:"Dr.",name:"Beatrice",middleName:null,surname:"Funiciello",slug:"beatrice-funiciello",fullName:"Beatrice Funiciello",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/201721/images/11089_n.jpg",biography:"Graduated from the University of Milan in 2011, my post-graduate education included CertAVP modules mainly on equines (dermatology and internal medicine) and a few on small animal (dermatology and anaesthesia) at the University of Liverpool. After a general CertAVP (2015) I gained the designated Certificate in Veterinary Dermatology (2017) after taking the synoptic examination and then applied for the RCVS ADvanced Practitioner status. After that, I completed the Postgraduate Diploma in Veterinary Professional Studies at the University of Liverpool (2018). My main area of work is cross-species veterinary dermatology.",institutionString:null,institution:null},{id:"291226",title:"Dr.",name:"Monica",middleName:null,surname:"Cassel",slug:"monica-cassel",fullName:"Monica Cassel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/291226/images/8232_n.jpg",biography:'Degree in Biological Sciences at the Federal University of Mato Grosso with scholarship for Scientific Initiation by FAPEMAT (2008/1) and CNPq (2008/2-2009/2): Project \\"Histological evidence of reproductive activity in lizards of the Manso region, Chapada dos Guimarães, Mato Grosso, Brazil\\". Master\\\'s degree in Ecology and Biodiversity Conservation at Federal University of Mato Grosso with a scholarship by CAPES/REUNI program: Project \\"Reproductive biology of Melanorivulus punctatus\\". PhD\\\'s degree in Science (Cell and Tissue Biology Area) \n at University of Sao Paulo with scholarship granted by FAPESP; Project \\"Development of morphofunctional changes in ovary of Astyanax altiparanae Garutti & Britski, 2000 (Teleostei, Characidae)\\". She has experience in Reproduction of vertebrates and Morphology, with emphasis in Cellular Biology and Histology. She is currently a teacher in the medium / technical level courses at IFMT-Alta Floresta, as well as in the Bachelor\\\'s degree in Animal Science and in the Bachelor\\\'s degree in Business.',institutionString:null,institution:null},{id:"442807",title:"Dr.",name:"Busani",middleName:null,surname:"Moyo",slug:"busani-moyo",fullName:"Busani Moyo",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Gwanda State University",country:{name:"Zimbabwe"}}},{id:"439435",title:"Dr.",name:"Feda S.",middleName:null,surname:"Aljaser",slug:"feda-s.-aljaser",fullName:"Feda S. Aljaser",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"King Saud University",country:{name:"Saudi Arabia"}}},{id:"423023",title:"Dr.",name:"Yosra",middleName:null,surname:"Soltan",slug:"yosra-soltan",fullName:"Yosra Soltan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Alexandria University",country:{name:"Egypt"}}},{id:"349788",title:"Dr.",name:"Florencia Nery",middleName:null,surname:"Sompie",slug:"florencia-nery-sompie",fullName:"Florencia Nery Sompie",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Sam Ratulangi University",country:{name:"Indonesia"}}},{id:"428600",title:"MSc.",name:"Adriana",middleName:null,surname:"García-Alarcón",slug:"adriana-garcia-alarcon",fullName:"Adriana García-Alarcón",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"National Autonomous University of Mexico",country:{name:"Mexico"}}},{id:"428599",title:"MSc.",name:"Gabino",middleName:null,surname:"De La Rosa-Cruz",slug:"gabino-de-la-rosa-cruz",fullName:"Gabino De La Rosa-Cruz",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"National Autonomous University of Mexico",country:{name:"Mexico"}}},{id:"428601",title:"MSc.",name:"Juan Carlos",middleName:null,surname:"Campuzano-Caballero",slug:"juan-carlos-campuzano-caballero",fullName:"Juan Carlos Campuzano-Caballero",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"National Autonomous University of Mexico",country:{name:"Mexico"}}}]}},subseries:{item:{id:"95",type:"subseries",title:"Urban Planning and Environmental Management",keywords:"Circular Economy, Contingency Planning and Response to Disasters, Ecosystem Services, Integrated Urban Water Management, Nature-based Solutions, Sustainable Urban Development, Urban Green Spaces",scope:"