**5. References**

22 Echocardiography – In Specific Diseases

issue may appear. This should also be clearly pointed out by the post-pump echocardiographic examination. In this case, further surgery for the mitral valve might not

The main problem with residual mitral regurgitation arises in patients with moderate insufficiency. In loose teams, the surgeon tends to underestimates the importance of the mitral regurgitation. Because the echocardiography is a semi quantitative method, in every day practice the surgeon will go easier to the second run pump only if the echocardiographer was able to 100% accurately identify the preoperative lesions, compared to the intraoperative findings. Therefore, an excellent preoperative assessment will bond the team, creating a trustful relationship between the surgeon and the echocardiographer.

The evaluation of the outcome after mitral repair has been done mainly using the Colour Doppler mapping. When the residual regurgitation is absent or of mild degree, the result is judged as adequate. Generally, superior degrees of residual regurgitation, naturally correlated with the coaptation and geometrical analysis, indicate the need for a second pump run. In selected cases (e.g. old patients or significant comorbidities), moderate or more than moderate residual regurgitation might be accepted when the risk of a second pump run to correct the valvular problem exceeds the potential benefit for the patient.

The annuloplasty ring is used in almost all operated patients. From an echocardiographic technical perspective this might determine difficulties in the postoperative evaluation of the repaired mitral valve when performing the distal esophageal long axis views. Usually, the presence of the annuloplasty ring might 'hide' the mitral valve leaflets by posterior shadowing immediately after surgery. To overcome this problem a valid solution could be

The standardization of the preoperative, intraoperative and postoperative echocardiographic examination is crucial for the skilled dialogue with the surgical team and for the results. As the surgery of degenerative mitral insufficiency is somehow standardised, echocardiography should also be as standardised as possible. The use of a specific pattern of lesion confers better and tailored surgical planning, adapted to each given case. Use of different types of patterns such as mitral valve flail, undulating valve or marginal prolapse

Alongside specific patterns of lesions, a crucial point in surgical repair is the evaluation of the mitral geometry. Apart from the three dimensional echo, the main tool in assessing the

The echocardiographer must consider the mitral valve as an eight-element anatomical structure, and separately assess each segment. The preoperative exam has to be done based on a structural echocardiographic algorithm and finally expressed as a prolapsing score.

In dedicated centres, the mitral valve repair for degenerative disease is possible with more than 95% rate of success. The use of the triangle of coaptation, coaptation length and

the evaluation from the deep transgastric short-axis and long axis views.

mitral valve geometry by 2 D echocardiography, is the coaptation triangle.

By using this strategy, the mitral valve repair is feasible, with excellent results.

be needed, but only ventricular assistance.

**3. Conclusion**

facilitates the dialog with the surgeon.

**2**

Gani Bajraktari

*Republic of Kosovo*

**Echocardiography in Severe Aortic Stenosis**

Aortic stenosis (AS) is the most frequent valvular heart disease in west developed and developing countries, with prevalence between 0.02% in adults under 44 years and 3-9% in elderly over 80 years. Patients with this disease may remain asymptomatic for years, particularly in elderly with naturally limited exercise. If the patients remain untreated after they become symptomatic, the mortality at 10 years follow-up is 80-90%. Based on the etiology, mainly are three types of AS: 1) Calcific AS, which is most frequent type in adults of advanced age (2–7% of the population), 2) Congenital, which dominates in the younger

Patient history and physical examination remain important in the diagnosis of AS. For the proper patient management, the evidence of the symptoms characteristic for AS: exertional shortness of breath, angina, dizziness, or syncope. Further diagnostic right direction is

Aortic valve replacement (AVR) is the only effective treatment for severe aortic AS. It is performed either isolated or concomitantly with coronary artery by-pass graft operation, which take place in almost 50% of patients with AS. The overall mortality of isolated AVR is 3-5% in patients below 70 years and 5-15% in elderly. After successful AVR, symptoms and quality of live improves significantly. The long term 10 years survival after successful AVR is very satisfied and it resulted till 75%. The most important factors that may affect the survival are old age, high NYHA functional class, associated aortic regurgitation,

Echocardiography is the key diagnostic tool, not only to confirm the presence of AS, but also to assesses the degree of valve calcification, LV function and wall thickness. Today,

The severity of AS is provided with a very high sensitivity and specificity by Doppler echocardiography. A valve area 1.0 cm2 in a patient with AS is considered severe. The indexing of aortic valve area to body surface area is more powerful parameter, and a cut-off value of 0.6 cm2/m2 is considered severe AS. However, valve area detected by Doppler

patients, and 3) Rheumatic AS, which is becoming rare in developed countries.

The disappearance of the second aortic sound is specific to severe AS.

concomitant coronary aortic by-pass graft and atrial fibrillation.

echocardiography provides prognostic information in patients with AS.

**2. Echocardiography in aortic stenosis patients**

**1. Introduction**

characteristic systolic murmur.

*Service of Cardiology, University Clinical Centre of Kosova, Prishtina*


*2.3. Advances in TWH Research*

The research goals in the National *Total Worker Health*® Agenda focus on advancing and conducting etiologic, surveillance, and intervention research activities that build the evidence base [12]. Though the research base has grown, the field will benefit from further exploration of current and new research areas to solidify the evidence base and advance the field [19]. This led the Office for TWH to develop the intramural research program and coordinate research-related activities, both intramurally and extramurally, targeting priority topics and working populations [6]. At NIOSH, researchers are engaged in varied TWH activities such as conducting research, participating on the TWH steering committee, providing support activities, presenting at seminars and in webinars, publishing peer-reviewed papers, and engaging in collaborative stakeholder and partnership research efforts.


Preface

The advent and widespread application of computational intelligence in recent years, specifically artificial intelligence, machine learning, deep learning, data mining, and data analytics, has led to increased interest in sophisticated and optimized searching and data-matching problems, especially in information retrieval and computational biology.

This book explores how we search for resources in our minds and in the world and discusses the recent advancement and applications of quantum search, harmony search, cognitive search, genetic search, combinatorial search, and parallel random search algorithms in solving tough optimization difficulties that arise in advanced cross-domain engineering technologies.

This book presents state-of-the-art technical contributions based on the most sought-after search algorithms in various domains, including computation intelligence, medicine, e-Commerce, and many others. It consists of a selection of the best contributions from researchers, academicians, and experts on search optimization who present their latest findings and discuss the past, present, and future of this exciting field.

I would like to convey our appreciation to all contributing authors for their excellent chapters. I owe special thanks to Author Service Manager Mr. Josip Knapić and Commissioning Editor Ms. Jelena Germuth at IntechOpen for their kind support and great efforts in bringing the book to fruition. In addition, I also appreciate all those who assisted me throughout the publication process.

> **Dinesh G. Harkut** Head and Associate Professor, Department of Computer Science and Engineering, Prof Ram Meghe College of Engineering and Management, Amravati, M.S., India


**2. Materials and Methods**

## *2.1. Location of the Experiments and Plant Material*

The study was conducted at the Centre for Agricultural Genomics and Biotechnology, Faculty of the Agricultural and Food Science and Environmental Management, University of Debrecen, Hungary. This study was carried out on 29 potato (*Solanum tuberosum* L.) genotypes including 27 breeding lines (C2, C3, C4, C5, C6, C8, C9, C10, C11, C12, C14, C17, C19, C20, C21, C22, C26, C28, C30, C32, C35, C37, C41, C42, C57, C58 and C63) and two drought-tolerant referent lines (C103 and C107). Selection of referent genotypes was based on their response to drought conditions in previous in vivo (field and greenhouse) experiments, in which they were proven to be drought-tolerant. C103 parental line belongs to the mid-early maturity group; its tuber is round oval, its skin is red, and the tuber flesh is yellow.  They are tolerant to late blight (*Phytophthora infestans*). All of the breeding lines tested originated from crossings of referent lines. In vitro culture establishment of breeding lines was initiated from seeds; they were surface sterilized by 3% NaOCl for 4 min, then they were washed with sterile distilled water three times. The seeds were placed onto medium containing Murashige and Skoog [44] (MS) salts and vitamins, 3% sucrose (VWR Chemicals, Radnor, PA, USA) and 0.7% agar-agar (Sigma-Aldrich, A1296, St. Louis, MO, USA) and incubated in dark at 24 ◦C for 5 days. Then, they were transferred to a growing room (at 22/15 ± 2 ◦C day/night temperature and 16 h daily illumination by 65 μmol m−2 s−1 PPF). In vitro shoot cultures of both referent lines and breeding lines were subcultured every 4 weeks on the same MS medium mentioned above.

## *2.2. Explant, Treatments and Culture Conditions*

Single nodal cuttings, each containing an axillary bud from 4-week-old in vitro shoot cultures, were used as explant, and the shoot apex and basal part of the shoots were discarded. Explants were placed onto the basal MS medium supplemented with 3% sucrose and 0.7% agar and with polyethylene glycol (Alfa Aesar, Haverhill, MA, USA) with different molecular weights either in concentrations of 2.5, 5.0 and 7.5% or 5.0, 7.5 and 10% for PEG 600 and PEG 6000, respectively. Moreover, 0.1, 0.2 and 0.3 M D-mannitol (Merck, Burlington, VT, USA) were also applied in order to induce the osmotic stress, while the control medium was free of osmotic agent. Twenty explants per jar (450 mL, cylindrical shaped) were placed onto 50 mL of medium. Treatments consisted of five repetitions (five jars); thus, a total of 100 plantlets were observed. Experiments were repeated twice. Shoot cultures were grown under controlled conditions, in a culture room at 22/15 ± 2 ◦C day/night temperature and 16 h daily illumination by 65 μmol m−2 s−1 PPF for 4 weeks. At the end of experiments, the rate of survival (SR) was observed and the shoot length (SL) and the number and length of roots (RN and RL, respectively) on surviving explants were measured. Before analysis, results were expressed as percentages of the results obtained on the medium without stress agents (SI, stress index; [45]), to compare the responses to the different levels of osmotic stress in the breeding lines and referent lines.

SISL = Shoot length of treated shoots (mm)/shoot length of control shoots (mm) × 100

SIRL = The longest root length of treated shoots (mm)/the longest root length of control shoots (mm) × 100

SIRN = Root number of treated shoots/root number of control shoots × 100

SISR = Survival rate of treated shoots (per jar)/survival rate of control shoots (per jar) × 100

## *2.3. Statistical Analysis*

SI calculated from morphological data (shoot and root parameters) were analyzed statistically by ANOVA followed by LSD and Tukey-B tests, using SPSS Statistics 27.0 (IBM, New York, NY, USA).

#### **3. Results**

Osmotic stress tolerance of 27 breeding lines and 2 drought-tolerant referent genotypes and their responses were compared to each other after SI values were calculated. We found that all morpho-physiological parameters were affected by treatments (Table 1), and differences were found between genotypes. Significant interactions between genotypes and type and concentration of osmotic agent were also detected.

**Table 1.** The main effect of osmotic agents calculated from SI values for morpho-physiological traits on the average of all potato breeding lines at different osmotic stress levels.

Levels for D-mannitol: 1: 0.1 M, 2: 0.2 M, 3: 0.3 M; for PEG 600: 1: 2.5%, 2: 5%, 3: 7.5%; for PEG 6000: 1: 5%, 2: 7.5%, 3: 10%.

#### *3.1. Effect of Osmotic Stress Induced by PEG 6000 on In Vitro Shoot Cultures*

3.1.1. Changes in the SI of Survival Rate (SISR)

SI values of survival rates (SISR) were significantly decreased or most frequently not affected by 5% PEG 6000 (Table 2, Figure S1a). Some breeding lines showed significantly lower SISR values than the referent lines. The raised level (7.5%) of PEG 6000 decreased significantly the SISR values very rarely, while other responses were similar to those observed at the level of 5% PEG (Table 2, Figure S1b). Increasing the PEG 6000 level to 10% resulted in significantly decreased SISR values for seven lines (C2, C17, C20, C28, C41, C42 and C57) compared to those observed at 7.5% (Table 2, Figure S1c). Although the C103 referent line showed the best SISR results along with two breeding lines (C8, C30), very similar results were found in eight breeding lines. However, the SISR value of the C107 referent line was significantly reduced, resulting in a number of breeding lines ahead of it.

**Table 2.** Survival rate SI (SISR) values of potato genotypes under osmotic stress induced by PEG 6000 added to the medium at levels of 5%, 7.5% and 10%.

Symbols mark significant differences from the C107 referent line (-), or from the C103 referent line () according to LSD test; the small letters indicate significantly different means between the breeding lines within a treatment, and the capital letters indicate significantly different means between treatment levels within a breeding line according to Tukey-B test.

3.1.2. Changes in the SI of Shoot Length (SISL)

Each breeding line and both referent lines responded with decreased shoot length to the presence of PEG 6000 at all concentrations (Table 3, Figure S2a–c). In general, inhibition of shoot growth increased with increasing concentration of PEG 6000 to 7.5%, but higher concentrations did not result in further significant decrease. The best result (SISL 76.8) was obtained in C9 breeding line, although SISL values of some breeding lines were very similar (C2, C22, C30 and C41) (Table 3, Figure S2a). At raised PEG 6000 concentration (7.5%), five breeding lines (C2, C9, C12, C30 and C32) showed significantly higher SISL values

compared to both referent lines (Table 3, Figure S2b). At the highest level of PEG 6000 (10%), only one breeding line (C22) reached significantly higher SISL values compared to both referent lines, but most of the breeding lines performed better than the C107 referent line (Table 3, Figure S2c).

Symbols mark significant differences from the C107 referent line (-), or from the C103 referent line () according to LSD test; the small letters indicate significantly different means between the breeding lines within a treatment, and the capital letters indicate significantly different means between treatment levels within a breeding line according to Tukey-B test.


*2.3. Main Parameters of HPTO Unit*

As previously mentioned in [14], there are several important parameters affecting the performance of the HPTO unit such as piston diameter, the volume capacity of HPA, displacement of HM, etc. Table 1 provides the important parameters of the HPTO unit that are summarized from Equations (3)–(16). For some parameters, the higher and lower values can reduce the performance of the HPTO unit. To investigate this problem, a detailed study regarding the influence of these parameters on the overall performance of WECs is performed in the present study.

**Table 1.** Important parameters of the HPTO unit.

In the present study, the complete simulation investigation of HPTO unit parameters was implemented in the MATLAB/Simulink software (Version: 2018b). The detailed methodology of the present study is described in the following subsections.

#### **3. Simulation Investigation of HPTO Unit Parameters**

#### *3.1. Simulation Set-up of WEC with HPTO Unit*

The MATLAB/Simulink software was used in this study to model a complete WEC system shown in Figure 1. The WECs consists of two main parts, the WEC model and the HPTO model. The WEC model was developed using a mathematical function block based on Equations (1)–(5). The wave elevation data, *ηW* and *FHPTO* are the inputs, while the *xp* is the output of the WEC model. According to Equations (1) and (2), the hydrodynamics parameters of the WEC device such as *krad*, *kres*, *hex*, added mass coefficient (*kadd*) and impulse response function (*kimp*) are required to develop the considered WEC model. The hydrodynamic parameters of the WEC model from the previous study in [20] were used, since a similar WEC concept was considered in this study. In [20], these hydrodynamic parameters were obtained from the frequency domain analysis study that was carried out using ANSYS/AQWA software. The obtained hydrodynamic parameters are presented in Figure 2.

**Figure 2.** Hydrodynamic parameters of WEC device. (**A**) Excitation force coefficient, (**B**) Added mass coefficient, (**C**) Radiation damping coefficient, and (**D**) Impulse response function.

Meanwhile, the Simscape SimHydraulic toolbox in MATLAB/Simulink was used to develop the HPTO unit model. The snapshot of the developed model in MATLAB/Simulink is depicted in Figure 3. As illustrated in the figure, *xp* is the input of the HPTO unit. Using *xp* signal, the linear velocity of the piston, . *xp* is obtained using a first-order lag-based linear displacement to linear velocity converter. The double-acting hydraulic cylinder (DAC) component was used as HA. In the Simscape SimHydraulic toolbox, the DAC component is constructed based on the translational hydro-mechanical converter and translational hard stop blocks. The rod motion is limited with the mechanical translational hard stop block. The ideal force sensor block was connected to the HA rod to measure the *FHPTO*. The pressure and flow rate sensor blocks were also connected to the HA to measure the dynamic pressure and flow rate of HA. To account for the friction loss along the pipe length and the fluid compressibility, the hydraulic pipeline blocks were used to connect some of the components, as illustrated in Figure 3. The thermodynamic transformation in the HPA was assumed to be isentropic, which is reasonable considering the cycle time in the device. Furthermore, for ease of control, a simple rotational damper with varying damping coefficients was used to represent the electric generator unit. In this way, the resistive torque imposed by the electric generator can be manipulated by varying the value of the damping coefficient (*dG*). The generated electric power from the electric generator can be calculated using Equation (16). In this study, the initial parameters of the HPTO unit were manual tuned. However, these parameters are not optimal yet. The detailed specifications of each component in the developed HPTO model are provided in Table 2. Finally, the developed HPTO unit model in MATLAB/Simulink was then experimentally validated using an actual HPTO test rig in the dry lab environment.

**Figure 3.** A complete model of WEC with HPTO unit in MATLAB/Simulink. (**A**) WEC device model, and (**B**) HPTO unit model.


**2. Sisal fibre-toughened polymer composites**

Along with the different natural fibre-based composites, sisal fibre-supported composite produces predominant effect potency with reasonable ductile and flexural property. It can be used in applications where high-effect strength is required. Presently, sisal fibre utilizes different types of polymers such as thermoset-, thermoplastic- and bio-degradable polymeric-based resources and their different properties have been reported in the literature.

Ramesh et al. [6] researched the mechanical properties of sisal, jute and glass fibre-supported polyester composites and seen that the expansion of glass fibre into jute fibre composite brought about greatest elasticity. Similarly, in the case of jute and sisal combination composites were having maximum flexural force and most extreme effect power was obtained from the sisal fibre composite. Properties of elasticity, flexural strength and compressive strength of epoxybased sisal-glass-based composites are reported [8, 21] and sisal normal fibre composites are created amid and with no silica by joining fully biodegradable sisal fibres in addition to the polymer framework. The outcomes depicted that the elasticity and pliable modulus of composites with silica are 1.5 and 1.08 occasions more prominent than those of composites without silica individually. The sway strength of composite with sand is 1.36 and 1.8 occasions as compared to the composites without silica what's more, pure polyester, individually. The effect of sisal fibre on the properties of the polymers has been reviewed [9].

Utilizations of sisal fibre and plant fibres show huge commitment in vehicle applications because of its qualities, for example high solidness with light weight per unit region, simple to reuse, 30–40% lighter than glass fibre, decreased fuel utilization, minimal expense, no wear of panels or any part of tiles.


**3. Developed Model Results, Performance, and Validation**

The results produced by the developed models are presented in this section, and their capability to use empirical data to estimate the dynamic modulus of asphalt mixtures is evaluated.

#### *3.1. Model Performance*

The performance is first compared with the existing predictive models; modified Witczak, Hirsch, and Alkhateeb models selected from the literature are presented in Equations (10)–(13), respectively [13,14,16]

$$\log|E^{\*}| = -0.349 + 0.754(|C\_{b}^{\*}|^{-0.0052})(6.65 - 0.032\rho\_{200} - 0.0027(\rho\_{200})^{2} + \\\\ \log|V\_{b}^{\*}|^{-2})$$

$$0.011\rho\_{4} - 0.0001(\rho\_{4})^{2} + 0.006\rho\_{3/8} - 0.00014(\rho\_{3/8})^{2} - 0.08V\_{a} - 1.06(\frac{V\_{beff}}{V\_{beff} + V\_{a}}) $$

$$+ \frac{2.558 + 0.032V\_{a} + 0.713(\frac{V\_{beff}}{V\_{brf} + V\_{a}}) + 0.0124\rho\_{3/8} - 0.0001(\rho\_{3/8})^{2} - 0.0098\rho\_{3/4}}{1 + \exp(-0.7814 - 0.5785\log|G\_{b}^{\*}| + 0.8834\log\delta\_{b})} \tag{10}$$

where |*E*∗| is dynamic modulus in psi; |*G*∗| is the binder shear modulus in psi; *δb* is the binder phase angle in degrees; *ρ*3/4 is the cumulative percent aggregate retained on the 3/4 sieve (19 mm); *ρ*3/8 is the cumulative percent aggregate retained on the 3/8 sieve (9.5 mm); *ρ*4 is the cumulative percent aggregate retained on the No. 4 sieve (4.75 mm); *ρ*200 is the percent aggregate passing the No. 200 sieve (0.075 mm); *Va* is the percent air void in the mix; *Vbef f* is the effective asphalt content; VMA is the percent of voids in the mineral aggregate, and VFA is the percent voids filled with asphalt,

$$|E\_{\rm m}^\*| = P\_{\varepsilon} \left( 4, 200, 000(1 - \frac{VMA}{100}) + 3 \vert G^\* \vert\_{\delta} (\frac{VFA \times VMA}{10, 000}) \right) + \frac{(1 - P\_{\varepsilon})}{\frac{1 - VMA}{4, 200, 000} + \frac{VMA}{3 \vert G\_{\rm b}^\* \vert (VFA)}} \tag{11}$$

where

$$P\_{\mathcal{E}} = \frac{(20 + 3|G\_b^\*| (VFA) / (VMA))^{0.58}}{650 + (3|G^\*|\_b (VFA) / (VMA))^{0.58}} \tag{12}$$

and |*E*∗|*m* is dynamic modulus of HMA in psi; *Pc* is the aggregate contact volume; VMA is the percentage of mineral aggregate voids in compacted mixture; and VFA is the percentage of voids filled with asphalt in the compacted mixture,

$$|E\_m^\*| = 3(\frac{100 - VMA}{100})(\frac{90 + 1.45 \frac{[G^\*]\_b}{VMA})^{0.66}}{1100 + (0.13 \frac{[G\_g^\*]}{VMA})^{0.66}})|G\_g^\*|\tag{13}$$

where |*E*∗ *m*|, |*G*∗ *b* |, and |*G*∗ *g* | (the complex shear modulus of binder in the glassy state, assumed to be 109 Pa.) are in Pa. Equation (14) shows the best reduced third-order (linear) regression model (PCR) fitting the measured response:

$$\begin{aligned} \mathcal{Y} &= c\_0 + c\_1 p c\_1 + c\_2 p c\_2 + c\_3 p c\_3 + c\_4 p c\_4 + c\_5 p c\_5 \\ &+ c\_6 p c\_1 p c\_2 + c\_7 p c\_1 p c\_3 + c\_8 p c\_1 p c\_4 + c\_9 p c\_1 p c\_5 \\ &+ c\_{10} p c\_2 p c\_3 + c\_{11} p c\_2 p c\_4 + c\_{12} p c\_2 p c\_5 + c\_{13} p c\_3 p c\_4 \\ &+ c\_{14} p c\_3 p c\_5 + c\_{15} p c\_4 p c\_5 + c\_{16} p c\_1 p c\_2 p c\_3 + c\_{17} p c\_1 p c\_2 p c\_4 \\ &+ c\_{18} p c\_1 p c\_2 p c\_5 + c\_{19} p c\_1 p c\_3 p c\_4 + c\_{20} p c\_1 p c\_3 p c\_5 + c\_{21} p c\_2 p c\_3 p c\_4 \\ &+ c\_{22} p c\_1 p c\_4 p c\_5 + c\_{23} p c\_2 p c\_4 p c\_5 + c\_{24} p c\_3 p c\_4 p c\_5 \end{aligned} \tag{14}$$

where, *c*0 = 6.59; *c*1 = 2.58; *c*2 = 4.4; *c*3 = −0.36; *c*4 = 0.49; *c*5 = 1.93; *c*6 = −0.33; *c*7 = −0.77; *c*8 = −1.69; *c*9 = 0.15; *c*10 = −1.65; *c*11 = −4.68; *c*12 = 4.81; *c*13 = 0.7; *c*14 = −0.85; *c*15 = −1.58; *c*16 = −0.17; *c*17 = −0.79; *c*18 = 1.83; *c*19 = 0.04; *c*20 = 0.18; *c*21 = 0.42; *c*22 = 0.05; *c*23 = 0.32; *c*24 = 0.06. The trained three-layer ANN (PCNN) presented in Equation (9) contains the following connection weights and biases:

$$\mathbf{W}^{T} = \begin{bmatrix} -0.511 & 0.134 & 0.654 & -1.064 & -0.267 \\ -0.315 & -0.147 & -0.267 & 0.177 & -1.047 \\ -0.660 & -1.266 & 0.759 & -1.248 & -0.331 \\ -0.075 & 0.022 & 0.208 & 0.015 & 0.167 \\ -0.074 & 0.022 & 0.206 & 0.015 & 0.167 \\ 0.103 & -0.177 & 1.253 & -1.045 & 0.558 \\ 0.078 & -0.020 & -0.231 & -0.014 & -0.172 \\ 0.238 & 0.070 & -0.885 & 0.848 & 0.943 \\ 0.123 & 0.456 & -0.387 & 1.547 & -0.017 \\ -0.079 & 0.020 & 0.213 & 0.014 & 0.173 \end{bmatrix}$$

$$\mathbf{W}\_{H} = \begin{bmatrix} 0.869 \\ -0.886 \\ -0.866 \\ 0.632 \\ -0.291 \\ -0.859 \\ 0.299 \\ 0.299 \\ 0.007 \\ 0.007 \\ 0.007 \\ -0.373 \\ -0.373 \\ -0.007 \end{bmatrix}, \quad \mathbf{B}\_{0} = \begin{bmatrix} 0.162 \\ 0.150 \\ 0.250 \\ 0.070 \\ 0.070 \\ 0.071 \\ -0.373 \\ -0.007 \end{bmatrix}$$

Figure 6 presents the performance of the developed models in terms of measured values of dynamic modulus versus the fitted dynamic modulus values. The measured and fitted values are fairly close to the line of equality, indicating that the fitted values are highly correlated with the measured ones.

**Figure 6.** Measured values of dynamic modulus versus fitted values by PCR and PCNN.

Comparisons of PCR and PCNN performance to that of the existing predictive models are conducted based on three statistics: average difference (AD), average absolute difference (AAD), and correlation between measured and fitted values of response (*rfit*). A summary of the definitions of these statistical components and their formulas is presented in Table 5. In the formulas presented in Table 5, *yi* is the *i*th measured response, *y*ˆ*i* is the *i*th fitted response, and *n* is the number of data points.

The results of the comparison are presented in Table 6. According to the values of *rfit* in Table 6, the estimated dynamic modulus values obtained form PCR and PCNN models are highly correlated

with measured values according to the values, showing that the both PCR and PCNN performed well in terms of modeling the response variable.

**Table 5.** Statistics which are used to compare model performance.

Although the corresponding values of *rfit* for modified Witczak, Hirsch, and Alkhateeb models are 0.93, 0.95, and 0.95, respectively, the average difference and average absolute difference with respect to the measured response are significantly higher than those of PCR and PCNN. This means that the fitted values by the modified Witczak, Hirsch, and Alkhateeb models are not close as those fitted by PCR and PCNN to the response value. In other words, *rfit*, which reflects the correlation between response and estimated response (if one goes up the other one goes up), could be biased, and in this situations other statistics (AD, and AAD) could be used to evaluate the goodness of fit. The dynamic modulus measured and predicted values are presented in Figure 7 for four asphalt mixtures. According to the presented master curves the current study (PCNN model) provides the closest values of *E*∗ to the measurements for all of three test temperatures, while, the conventional models either overestimate or underestimate the response variable.

**Figure 7.** Comparing the measured and predicted (current study and conventional models) dynamic modulus. Results are presented at three temperatures. Green color indicates 0.4 ◦C, Orange color indicates 17.1 ◦C, and Purple color indicates 33.8 ◦C.

A graphical comparison of the PCR and PCNN performance and that of the existing models is presented in the following section.

#### *3.2. Receiver Operating Characteristic Analysis (ROC)*

A receiver operating characteristic (ROC) graph is a technique for visualizing, organizing, and selecting classifiers based on their performance. ROC graphs are widely used in medical decision-making as well as in machine learning and data-mining research [35]. True ROC curves plot the false positive rate (probability of false alarm) on the *x*-axis and the true positive rate (probability of detection) on the *y*-axis. A classifier is said to perform well if the ROC curve climbs rapidly towards the upper left-hand corner. The more the curve deviates from *y* = *x* behavior, the more accurate the prediction is [36]. We can borrow from the concept of ROC curve to obtain a measurement of fit for the competing models and a ROC graph for this study is presented in Figure 8 for this study. As described in Equation (15), the *x*-axis indicates the standardized residuals ordered from the lowest to the highest (*e*∗∗ *i* ). Residuals are sorted in ascending order and divided by the largest one that belongs to the Hirsch model.

$$\left|e\_{i}^{\ast \ast}\right| = \frac{\left|e\_{i}^{\ast}\right|}{\left|e\_{i\_{M\mathcal{A}\mathcal{A}}}^{\ast}\right|}\tag{15}$$

**Figure 8.** ROC curves for the developed and the existing predictive models for the dynamic modulus. The curves indicate the goodness of the fit provided by the models with regard to a pre-specified residual. The PCNN model showed the highest performance (farthest from the *y* = *x* line), and the Hirsch model showed the lowest (closest to the *y* = *x* line).

The *y*-axis indicates the fraction of points whose standardized residuals are less than *e*∗∗ *i* [37]. Although the curves obtained for all of the models are monotonically non-decreasing and climb towards the upper left-hand corner (the desired situation shows that the predictive models perform well), PCNN and PCR curves were the highest, proving their better performance to be better than that of existing predictive models.

A convenient global measure of the goodness-of-the-fit is the Area Under the Curve (AUC). To compare classifiers, it is more desirable to reduce ROC performance, to a single scalar value representing expected performance. Since the AUC is a portion of the area of the unit square, its value will always lie between 0 and 1. The AUC values for the PCNN, PCR, Alkhateeb, modified Witczak, and Hirsch models are 0.9864, 0.9717, 0.8746, 0.7609, and 0.6320, respectively. One can use the ROC and AUC analysis results and rank the predictive models according to their performances. In this study, the PCNN model reflected the highest performance in predicting the dynamic modulus value, while the Hirsch model ranked the lowest among all the models.

#### *3.3. Model Validation*

The current regression model is presented in the following general form as

$$y\_i = f\_i(\mathbf{Z}\_i, \boldsymbol{\theta}) + \boldsymbol{\varepsilon}\_i^\*. \tag{16}$$

In the above equation *fi* is the *i*th expectation function, *θ* is the vector of parameters, and *e*∗ *i* is a random deviation of *yi* from *fi*. This term is assumed to be independent and normally distributed with a mean of zero and unknown variance *σ*2 for *i* = 1, ···, *n*, where *n* is the number of input vectors. If the above assumptions are violated, the results of the analysis could be misleading or erroneous. These assumptions can be testified by examining residuals as defined by

$$e\_i^\* = \mathcal{Y}\_i - \mathcal{Y}\_i. \tag{17}$$

The assumption of independency holds when the residuals plot does not reflect a trivial pattern. The normality assumption is assessed by creating a normal probability plot of the residuals. When the error has a normal distribution, this plot will appear as a straight line [10]. These assumptions were checked for PCR and PCNN, as presented in Figure 9. The assumption of equal variances does not appear to be violated because there are no trivial pattern in this plot. Figure 9 presents the normal probability of the residuals in which it can be seen that the data points are close to the straight line and the normality assumption is validated.

**Figure 9.** *Cont.*

**Figure 9.** Checking the assumptions of independency using residual plot for PCR (**left**) and PCNN (**right**) models and the normality using normal probability plot of the residuals for PCR (**left**) and PCNN (**right**) models.

#### **4. Application of the Framework: Flexible Pavement Design and Optimization**

The above framework is used along with an optimization algorithm to answer the following two central questions:

One can see that the first item corresponds to the optimal design problem while the second one corresponds to the so-called inverse design.

Since it was shown through multiple statistical measurements that PCNN had the best prediction capability, this model is used in the following section to solve the optimization problems. The ANN used in PCNN is essentially an interconnected nonlinear function, and this necessitates the application of a global optimizer. Moreover, the effective variable space enters the problem as a series of constraints and further restricts the available algorithms. The optimal design problem is formulated as follows:

$$\begin{aligned} \text{maximize} \quad & |E^\*| = F\_{ANN}(\mathbf{x})\\ \text{with respect to} \quad & \mathbf{x} = (\mathbf{x}\_1, \dots, \mathbf{x}\_{14})\\ \text{subject to} \quad & (\mathbf{x} - \mathbf{v})^T A (\mathbf{x} - \mathbf{v}) \le \mathbf{1},\\ & (\mathbf{x}\_{pca} - \mathbf{v}')^T A' (\mathbf{x}\_{pca} - \mathbf{v}') \le \mathbf{1}, \end{aligned} \tag{18}$$

where the vector of fourteen variables is **x**, and (**x** − **v**) *TA*(**x** − **v**) ≤ **1** are the enclosing ellipsoid constraint equations for the original and PCA-based variables. A penalty function approach is used to convert the above constrained problem to an unconstrained one [38]. In this case, when the penalty function is active, it decreases (increases) the objective function when the problem is one of maximization (minimization), and the degree of penalty is based on the closeness of the solution to the corresponding constraint.

Since the inverse design problem aims at finding the specification of a predefined goal, it is defined as a minimization problem as follows:

$$\begin{aligned} \text{minimize} \quad &error = ||E^\*|| - |E\_0^\*|| \\ \text{with respect to} \quad & \mathbf{x} = (\mathbf{x}\_1, \dots, \mathbf{x}\_{14}) \\ \text{subject to} \quad & (\mathbf{x} - \mathbf{v})^T A (\mathbf{x} - \mathbf{v}) \le \mathbf{1}, \\ & (\mathbf{x}\_{pca} - \mathbf{v}')^T A' (\mathbf{x}\_{pca} - \mathbf{v}') \le \mathbf{1}, \end{aligned} \tag{19}$$

where |*E*∗ 0 | is the desired (goal) dynamic modulus. Although a similar penalization method can also be used to address the constraints in this case, for the above problem the constraints will penalize the objective function when they are active.

Reliable solution of the above problems requires the application of a gradient-free optimization algorithm. Gradient-based optimization algorithms are not applicable in this case because of the network-based nature of the ANNs. Evolutionary-based algorithms are potentially easy-to-use algorithms in the above problems. Novel algorithms have been used to solve complex optimization problems in recent years [39,40], and in this case, Mean-Variance Mapping Optimization (MVMO), an in-house optimization algorithm based on the work by Elrich et al. [41,42], is used. The constraints are handled using the approach described in Aslani et al. [43], in which, the convergence rate of a constrained MVMO was compared to the already-developed methodologies using benchmark structural problems. Authors in [22] indicated that a constrained MVMO is capable accurately identifying an optimal value with a minimum number of simulations. It should be noted that the choice of optimization algorithm is not the principal focus of this study.

Figure 10 (left) depicts the convergence achieved for the first design problem by the constrained MVMO algorithm. The initial data points are random making it heavily penalized, and then the objective function increases as the algorithms evolves. Exploration-exploitation behavior is achieved using adaptive strategies in the course of optimization for MVMO. *δ* = 0.05 is used as the threshold in Figure 4. Solving the maximization problem resulted in |*E*∗ *max*|= 53,703 MPa. The optimal design parameters are presented in the first column of Table 7.

To find the maximum amount of dynamic modulus one could design for without low temperature failure in the asphalt binder, the maximization problem was solved one more time with an additional constraint of *G*∗*sinδ* ≤ 5000, resulting in |*E*∗ *max*|= 36,307 MPa. Corresponding design parameters are presented in the second column of Table 7 as the optimal design 2.

Figure 10 (right) shows the convergence of the algorithm for the inverse design problem after starting randomly from three different initial points, with the algorithm is terminated when the error reaches about 10−9. A pre-specified |*E*∗ 0 | of 20,417 MPa is considered and the inverse problem of finding the corresponding design parameters is solved. Because of non-linearity of the function, the problem has no unique solution. Three of the possible solutions are presented as designs 1 to 3 in Table 7.

Finally, the five sets of design parameters are compared with current design specification, with the results shown in Table 7. The percentage of aggregate passing by each sieve size is within the acceptable range of the gradation specification. Gradation charts are presented in Figure 11. The obtained percentages of air voids are 4%, which is the target value in the design specification. The obtained values for VMA are slightly less than 14% for a nominal maximum aggregate size (NMAS) of 12.5 mm because the VMA values of the nine mixtures used to train the PCNN are slightly less that 14% (see Table 1). The acceptable range for VFA varies with the amount of traffic load measured in million Equivalent Single Axle Loads (ESALs) as follows:

The VFAs obtained for all of the five sets of design are satisfied for all of the traffic categories.

**Figure 10.** Convergence for the optimal design problem (**left**) and inverse (**right**) design of dynamic modulus.

**Table 7.** Th Corresponding Design Parameters Obtained from Solving Optimal Design and Inverse Design Problems.

**Figure 11.** Aggregate gradation graphs with 12.5 mm NMAS particle size distribution obtained from PCNN.

#### **5. Conclusions**

This study used the HMA dynamic modulus data and focused to evaluate the quality of predictor variables to be used in a procedure of model development. Correlation analysis is performed to identify cross-correlated input variables, and correlated inputs are replaced by orthogonal pseudo-inputs (PCs) obtained using PCA. Two separate models are developed using multivariate regression and ANN (called PCR and PCNN, respectively). Extrapolation in empirical modeling is addressed by adding the constraint of an n-dimensional enclosing ellipsoid to the modeling problem. Performances of the proposed models were compared to existing predictive models using both statistical analysis and ROC analysis. The models developed satisfactorily estimated the dynamic modulus value, with PCNN indicating remarkably better performance when fitted to the test data than the existing predictive models from the literature. These PCA-based approaches are thus highly recommended as precise modeling strategies in this application. Moreover, these methodologies appear to be capable of modeling other material properties and future investigation in this regard is recommended. To determine this framework's application in pavement design, two optimization problems including optimal design and inverse design have been presented and solved using a mean-variance mapping optimization algorithm. The results for the two problems are in a good agreement with the HMA mix design specification and thus could be a reasonable starting point in solving real-life design problems. Although, the developed models as well as obtained optimal design parameters are based on the empirical database created in this study, the suggested framework has the capability of being re-trained and adjusted to fit new data. For obtaining more reliable and applicable results, a larger empirical database would be required.

**Author Contributions:** Conceptualization, P.G. and M.A.; methodology, P.G., M.A., and D.K.R.; software, P.G. and M.A.; validation, P.G., M.A., D.K.R., and R.C.W. formal analysis, P.G., M.A., D.K.R., and R.C.W.; investigation, P.G., M.A., D.K.R., and R.C.W.; resources, P.G., M.A., D.K.R., and R.C.W.; data curation, P.G., M.A., D.K.R., and R.C.W.; writing–original draft preparation, P.G., M.A., D.K.R., and R.C.W.; writing–review and editing, P.G., M.A., D.K.R., and R.C.W.; visualization, P.G., M.A., D.K.R., and R.C.W.; supervision, P.G., M.A., D.K.R., and R.C.W.; project administration, R.D.W.

**Funding:** This research received no external funding.

**Acknowledgments:** The authors would like to thank anonymous reviewers for their fruitful comments on the manuscript.

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


D. **STRIV-Hi technique**

**S**pon**T**aneous **R**espiration using **I**ntra **V**enous anesthesia & High flow nasal oxygenation. By using this way of anesthesia, we apply the concept of spontaneous ventilation according stepwise Target – Controlled Infusion (TCI Marsh technique) of Propofol applying the formula CP – Ce = 1.

Cp = the expected Propofol concentration at the plasma.

Ce = the predicted Propofol effect site concentration.

## **Benefits For all: (Patient, Surgeon, Anesthetist).**

The use of STRIV-Hi technique decreases stress of endotracheal tube on cardio vascular system (CVS), respiratory system. Eventually it decreases post intubation complications. Because of the surgeon & the anesthetist are working at the same site there will be a high risk of affection on the ventilation, oxygenation and loss of airway.

It provides (wider field) for the surgeon & at the same time it can facilitate oxygenation during the induction of anesthesia in cases of severe laryngotracheal

**135**

**Figure 8.** *HFNC in use.*

**Figure 7.**

*Hunsaker Mon-Jet catheter.*

*Airway Management in Head and Neck Pathology DOI: http://dx.doi.org/10.5772/intechopen.94498*

stenosis which cannot be intubated conventionally. Also, it facilitates dynamic airway assessment & prevents risk of barotrauma (compared to jet ventilation).

Duration of surgery: Operation less than 30 minutes so it is not suitable for junior staff both anesthetist and operating surgeon. It leads to CO2 accumulation. It cannot be used in case of complete nasal obstruction, bleeding inside the airway & in case of Infection: opened abscess or COVID 19 patients. Also, not used in morbid

1. Difficult laryngeal exposure because of underlying anatomy which affect on the performance of micro-laryngoscope using rigid laryngoscopy/bronchoscopy. We

may expect a "difficult exposure" per-orally in the following conditions:

**3.4 Limitations of HFNC in MLS surgery**

obesity and predicted difficult airway.

A. Limited neck extension

C. Retrognathia

**3.5 In case of difficult exposure of the larynx**

B. Big tongue/difficult palatal visualization

**Figure 6.** *Jet ventilation probe.*

*Airway Management in Head and Neck Pathology DOI: http://dx.doi.org/10.5772/intechopen.94498*

**Figure 7.** *Hunsaker Mon-Jet catheter.*

*Special Considerations in Human Airway Management*

the perfection of the surgical technique.

catheter with Twin-stream jet ventilator.

of up to 60–80 L/min.

D. **STRIV-Hi technique**

C. **Apneic technique using high flow nasal cannula (HFNC)**

technique) of Propofol applying the formula CP – Ce = 1. Cp = the expected Propofol concentration at the plasma. Ce = the predicted Propofol effect site concentration. **Benefits For all: (Patient, Surgeon, Anesthetist).**

Tracheal jet ventilation is better than supra-glottic jet ventilation [7], because tracheal jet ventilation helps the operating surgeon with less movement of the vocal cords. This is very important while performing phono-microsurgery. Also, it allows for better end-tidal CO2 monitoring. On the other hand, Jet ventilation (supra-glottic, as opposed to subglottic) is safest when used proximally. However, movement of the vocal cords due to jet air will effect on

3.Tracheal Jet ventilation using plastic catheter inside the trachea: for example: Hunsaker Mon-Jet infra-glottic ventilation catheter (**Figure 7**) or tri-lumen

In our center we use this technique in 95% of the benign laryngeal lesion's surgery (**Figure 8**). High flow nasal cannula (HFNC) has been shown beneficial in pre-oxygenation, oxygenation after extubation & in the treatment of respiratory failure and heart failure. Recently HFNC proved to provide a very good oxygenation & ventilation for the patients who performing various upper airway surgeries without the need for the jet ventilation or endotracheal intubation. Delivery of O2 via (HFNC) is an exciting & emerging therapy in acute adult medical practice. Humidified and worm O2 is delivered at flow rate

**S**pon**T**aneous **R**espiration using **I**ntra **V**enous anesthesia & High flow nasal oxygenation. By using this way of anesthesia, we apply the concept of spontaneous ventilation according stepwise Target – Controlled Infusion (TCI Marsh

The use of STRIV-Hi technique decreases stress of endotracheal tube on cardio vascular system (CVS), respiratory system. Eventually it decreases post intubation complications. Because of the surgeon & the anesthetist are working at the same site there will be a high risk of affection on the ventilation, oxygenation and loss of airway. It provides (wider field) for the surgeon & at the same time it can facilitate oxygenation during the induction of anesthesia in cases of severe laryngotracheal

**134**

**Figure 6.**

*Jet ventilation probe.*

**Figure 8.** *HFNC in use.*

stenosis which cannot be intubated conventionally. Also, it facilitates dynamic airway assessment & prevents risk of barotrauma (compared to jet ventilation).


**Preface to "Using Total Worker Health**-**R to Advance Worker Health and Safety"**

It is now recognized that workplace aspects (scheduling, shift work, physically demanding work, chemical exposure) not only increase the risk of injury and illness, but also impact health behaviors (smoking, physical activity) and health outcomes (sleep disorders and fatigue, obesity, musculoskeletal disorders). In turn, ill health and chronic conditions can affect performance at work, increasing risk for injury, absenteeism, and reduced productivity. In the past few decades, programs that expand the traditional focus of occupational safety and health to consider nontraditional work-related sources of health and well-being have been shown to be more effective than programs that separately address these issues. This Total Worker Health-R approach has been recognized by the National Institute for Occupational Safety and Health (NIOSH) as a method for protecting the safety and health of workers, while also advancing the overall well-being of these workers by addressing work conditions.

This Special Issue is devoted to "Advancing Worker Health and Safety". It includes basic and applied research relevant to programs, policies, and practices that promote holistic approaches to worker well-being. The volume begins with an overview of the NIOSH Total Worker Health program, describing the history of the program and identifying new challenges with the rapidly changing nature of work. The international importance of this topic is demonstrated by research addressing working populations in the United States, Australia, Korea, Canada, and countries in the European Union who are employed in worksites as varied as manufacturing, retail, healthcare, accounting, construction, and mining.

Several articles address new workers to the workplace. These focus on providing training and encouraging young workers to communicate about health and safety, the importance of preventing injuries to reduce retention, and how supervisor support and clear work organization and policies can help reduce stress among junior employees. Other articles describe the evaluation of interventions among construction workers, young workers, and homecare workers. Required components for the successful development, implementation, and adoption of interventions promoting worker safety and well-being are identified (e.g., the elimination of hazards, leadership support, participatory approaches in the design and delivery of programs, and evaluation metrics). Not surprisingly, implementation differs across industries and by organization size.

This compendium presents work from an international collection of scholars exploring the relationship between workplace factors and worker safety, health, and well-being. It provides guidance for improving the organization and design of work environments, innovative strategies for promoting worker well-being, and novel methods for exposing underlying occupational causes of chronic disease.

We would like to thank all of the authors that took the time to contribute to this Special Issue, and the editors and reviewers for their assistance.

**Diane S. Rohlman, Kevin M. Kelly**

*Special Issue Editors*

International Journal of *Environmental Research and Public Health*


**A Case Study on the Application of the Analytic Hierarchy Process (AHP) to Assess Agri-Environmental Measures of the Rural Development Programme (RDP 2007–2013) in Slovenia**

Monica Huehner, Črtomir Rozman and Karmen Pažek

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/63924

#### **Abstract**

[26] Bureau International des Poids et Mesures. JCGM 200:2012 International vocabulary of metrology - Basic and general concepts and associated terms, 2008 version with minor corrections. 3rd ed. Sèvres: BIPM; 2012. Retrieved from http://www.bipm.org/utils/

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[28] Burtis C, Ashwood E. Tietz Textbook of Clinical Chemistry. 3rd ed. Philadelphia (PA):

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[31] Clinical and Laboratory Standards Institute. EP7-A2 Interference Testing in Clinical

[32] Clinical and Laboratory Standards Institute. EP9-A3 Measurement Procedure Com‐ parison and Bias Estimation Using Patient Samples. 3rd ed. Philadelphia (PA): CLSI;

[33] Clinical and Laboratory Standards Institute. EP17-A2 Valuation of Detection Capability for Clinical Laboratory Measurement Procedures. 2nd ed. Philadelphia (PA): CLSI;

[34] Clinical and Laboratory Standards Institute. EP15-A3User Verification of Precision and

[35] Clinical and Laboratory Standards Institute. EP5-A3 Evaluation of Precision of Quantitative Measurement Procedures. 3rd ed. Philadelphia (PA): CLSI; 2014.

[36] Cornbleet P, Gochman N. Incorrect least-squares regression coefficients in method-

[37] Clinical and Laboratory Standards Institute. EP21-A Estimation of Total Analytical

[38] Westgard J. A method evaluation decision chart (MEDx chart) for judging method

[39] Clinical and Laboratory Standards Institute. Retrieved fromml: http://clsi.org/ Ac‐

Error for Clinical Laboratory Methods. Philadelphia (PA): CLSI; 2003.

common/documents/jcgm/JCGM\_200\_2012.pdf. Accessed: May 1, 2016.

4th ed. Philadelphia (PA): WB Saunders Co; 1996.

36 Applications and Theory of Analytic Hierarchy Process - Decision Making for Strategic Decisions

Chemistry. 2nd ed. Philadelphia (PA): CLSI; 2005.

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comparison analysis. Clin Chem 1979; 25(3):432-438.

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cessed: February 26, 2016.

WB Saunders Co; 1999.

2013.

2012.

ments.htm/ Accessed: May 1, 2016.

The reform of the Common Agricultural Policy (CAP) in 2003 focused mainly on the economic and environmental challenges. The Rural Development Programme 2007– 2013, hereafter RDP, being implemented in Slovenia is therefore aiming at promoting proposed activities that help to improve the rural areas. Agri-environmental meas‐ ures (AEMs) encourage farmers to make an environmental commitment for a period of at least 5 years aiming at preserving the environment and maintaining the country‐ side. Because of practising environmental friendly production methods, the farmers might be encountered with more costs and reduction of yield. Therefore, payments are made as compensation. Concentrating only on one of the four pillars of the RDP, "Improvement of environment and the countryside", this paper attempts to assess the Slovenian agri-environmental measures with the help of the multicriteria decision analysis, that is, analytic hierarchy process (AHP) and its supporting software Expert Choice™. In the presented case study, three main criteria and their attributes were determined. With the help of experts (questionnaires), data were collected, which made the assessment possible. The results show that organic fruit, vine and horticultural production are seen as the most important AEM. This is specific for the Republic of Slovenia because of its large amount of area designated as least favoured areas (LFA) that are not suitable for arable farming.

**Keywords:** agri-environmental measures, rural development programme, multicrite‐ ria decision method, analytic hierarchy process, Expert Choice™

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### **1. Introduction**

For a long time, agriculture was marked by intensive production practices, which meant that the massive use of mineral fertilisers and pesticides was indispensable to achieve high yields. This led to the deterioration of the environmental conditions [1]. The pollution of soils and ground water was the result, biodiversity dwindled [2]. The Slovene agriculture is not an exception. The resources in Slovenia are already limited because of unfavourable natural conditions. More than 75% of the Slovene territory lies in the less favoured areas (LFA), where agricultural practice is limited by natural factors. More than half of the Slovene territory is covered by forests. Also, 90% of the entire Slovene territory is classified as rural, with 57.2% of its inhabitants living there [3]. Of the 57.2% inhabitants in the rural areas, only 6% are engag‐ ed in agriculture [4]. Agriculture has a potential of making a great contribution to the econom‐ ic development in the rural areas. The emphasis in today's agricultural policies, therefore, has to be on sustainable agricultural production, which can be practised and maintained when the following three main features are fulfiled [3,5]:

Sustainable agricultural production is strongly linked to the environmental aspect. Many countries or regions in the world have therefore adopted environmental programmes to slowly suit the changing agricultural production methods and to counter climatic change. While a couple of years ago environmental protection was a fashion, today, it has become a strong and indispensable philosophy being followed in all aspects of life. The research on the role of environmental attitudes towards the participation in the next generation of agriculture conservation programmes was already going on in the USA in 1999 [6]. Ho et al. [7] point the importance of the Environmental Technology Centre of the Murdoch University in Australia with training and research programmes on renewable energy in the context of environmentally sound technologies. Zbinden and Lee [8] state that since 1997, Costa Rica's Payments for Environmental Services Programme has provided payments to more than 4400 farmers and forest owners for reforestation, forest conservation and sustainable forest management activities. The idea of a Danube River Basin environmental programme was born in Sofia in 1991, and the programme was started in 1992 as described by Nachtnebel [9]. Nachtnebel [9] points out that the Danube River Basin environmental programme provides for joint actions of the 10 Danubian countries to assist integrated environmental management in the basin. Environmental programmes are not only limited to the agriculture but are also found in the industrial sector. Abaza [10] argued that the structural adjustment programmes of the World Bank in the 1990s, packages of economic reforms specifically designed to enhance the recovery of economies in crises, were urged to address environmental issues. Abaza elaborates further that efficient management of natural resources is essential for sustainable development and poverty alleviation. To promote and support sustainable agriculture in its member states, the European Union introduced a Common Agricultural Policy (CAP) [11]. Within the scope of A Case Study on the Application of the Analytic Hierarchy Process (AHP) to Assess Agri-Environmental Measures of the Rural Development Programme (RDP 2007–2013) in Slovenia http://dx.doi.org/10.5772/63924 39

**1. Introduction**

**•** Social acceptability

**•** Economic feasibility

**•** Environmental reliability

following three main features are fulfiled [3,5]:

For a long time, agriculture was marked by intensive production practices, which meant that the massive use of mineral fertilisers and pesticides was indispensable to achieve high yields. This led to the deterioration of the environmental conditions [1]. The pollution of soils and ground water was the result, biodiversity dwindled [2]. The Slovene agriculture is not an exception. The resources in Slovenia are already limited because of unfavourable natural conditions. More than 75% of the Slovene territory lies in the less favoured areas (LFA), where agricultural practice is limited by natural factors. More than half of the Slovene territory is covered by forests. Also, 90% of the entire Slovene territory is classified as rural, with 57.2% of its inhabitants living there [3]. Of the 57.2% inhabitants in the rural areas, only 6% are engag‐ ed in agriculture [4]. Agriculture has a potential of making a great contribution to the econom‐ ic development in the rural areas. The emphasis in today's agricultural policies, therefore, has to be on sustainable agricultural production, which can be practised and maintained when the

38 Applications and Theory of Analytic Hierarchy Process - Decision Making for Strategic Decisions

Sustainable agricultural production is strongly linked to the environmental aspect. Many countries or regions in the world have therefore adopted environmental programmes to slowly suit the changing agricultural production methods and to counter climatic change. While a couple of years ago environmental protection was a fashion, today, it has become a strong and indispensable philosophy being followed in all aspects of life. The research on the role of environmental attitudes towards the participation in the next generation of agriculture conservation programmes was already going on in the USA in 1999 [6]. Ho et al. [7] point the importance of the Environmental Technology Centre of the Murdoch University in Australia with training and research programmes on renewable energy in the context of environmentally sound technologies. Zbinden and Lee [8] state that since 1997, Costa Rica's Payments for Environmental Services Programme has provided payments to more than 4400 farmers and forest owners for reforestation, forest conservation and sustainable forest management activities. The idea of a Danube River Basin environmental programme was born in Sofia in 1991, and the programme was started in 1992 as described by Nachtnebel [9]. Nachtnebel [9] points out that the Danube River Basin environmental programme provides for joint actions of the 10 Danubian countries to assist integrated environmental management in the basin. Environmental programmes are not only limited to the agriculture but are also found in the industrial sector. Abaza [10] argued that the structural adjustment programmes of the World Bank in the 1990s, packages of economic reforms specifically designed to enhance the recovery of economies in crises, were urged to address environmental issues. Abaza elaborates further that efficient management of natural resources is essential for sustainable development and poverty alleviation. To promote and support sustainable agriculture in its member states, the European Union introduced a Common Agricultural Policy (CAP) [11]. Within the scope of

CAP and to be able to successfully integrate the environmental aspect, agri-environmental measures (hereafter AEM) were compiled as part of the second pillar of the Slovenian rural development programme (RDP) aiming at improving the environment and the rural areas. AEM are now compulsory for all EU member states. Each EU member state has its own RDP especially compiled to suit their circumstances and special conditions. Petersen [12] from the European Environment Agency gathered information on the countries preparing to access the European Union using questionnaires and information from the responsible national minis‐ tries. He used this data for his exposition with the main focus on agri-environmental pro‐ grammes of the candidate countries. AEM enable payments to farmers who voluntarily take up environmental commitment for at least five years. In these 5 years, they commit themselves to use environmental friendly production methods (RDP 2007–2013). The emphasis is on the right balance between competitive agricultural production and the respect of nature and the environment. Furthermore, awareness of sustainable production with focus on regenerative use of the available natural resources has to be roused [13]. AEM also ensures agricultural production that suits the needs of consumers and protects their health. Through these measures, the standard of living in the countryside is expected to be improved.

An ex ante evaluation carried out by the Biotechnical Faculty of the University of Ljubljana together with the Danish Orbicon in September 2006 using the Strengths, Weaknesses, Opportunities, Threats (SWOT) analysis was to help reveal loopholes and faults in the draft for the RDP. In the evaluation, the need for a hierarchical structure of the objectives was mentioned several times in order to clearly determine the main objective and the subobjectives [14].

Cunder analysed the role of rural development policy in environmental and land management in Slovenia based on a desk research [15–17] using the legislative documents like the Common Agricultural Policy Reform from the EU and the document on the analysis of the accession of Slovenia and its agriculture into the EU in 2004. No modelling was done in all these cases. At this point, in the decision-making process, the analyst should consider a multicriteria (objec‐ tive) decision analysis approach (hereinafter MCDA), which combines different mathemati‐ cally based methods—the most commonly known approaches are the utility theory and the analytical hierarchical process [18–20]. Multicriteria analysis (MCA), also known as multicri‐ teria decision analysis (MCDA), is an umbrella term for a number of decision-making techni‐ ques. As the name implies, MCDA makes it possible to tackle "problems" with many different conflicting criteria. According to the Department for Communities and Local Government in London [21], their role is to deal with the difficulties that human decision-makers have been shown to have in handling large amounts of complex information in a consistent way. In agriculture, decisions to be made are complex, mostly consisting not only of a single criterion but multiple criteria as in the implementation of the AEM. Thus, many criteria determine or influence the optimum decision. For such complex decision-making procedures, the tradi‐ tional mathematical programming, especially linear programming, is therefore not adequate for modelling them [22]. Also, just determining strengths, weaknesses, opportunities and threats does not analyse the problem being assessed thoroughly enough. The relations and interactions of the criteria are not determined.

Besides for making decisions, some scientists [23–25] have used MCDA methods also as assessment tools. Ferrarini et al. [26] used MCA to assess and compare municipal performance in environmental quality and sustainability in the province of Reggio Emilia in Italy. Gómez-Limón et al. [27] also made use of MCA to analyse input usage in agriculture and the way it affects the environment. Hellstrand [28] found MCA useful to survey the sustainability effects of increasing concentrate intensity in Swedish milk production. Solomon and Hughey [29] proposed a MCA decision support tool for international environmental policy issues on the example of emissions control in the international aviation sector. Crete Tsoutsos et al. [30] showed how sustainable energy planning can be done by MCA. The analysis of air pollution [31] and soil pollution [32] in an urban area in Serbia was done by Nikolić et al. using MCA. For improving strategic environmental assessment of water programmes in Brazil, MCA was also taken by Garfì et al. [58]. In Malaysia, Al-Hadu et al. [33] showed how useful MCA is for environmental management. Payraudeau and Gregoire [34] modelled pesticides transfer to surface water with MCA.

One of the most common methods of MCA is analytical hierarchical process (AHP). AHP has found its use in a few branches of the agricultural field for more than two decades, though not extensively. It has since been very attractive and useful for water management engineers. Pillai and Rasu [35] used this method for ranking irrigation management alternatives in an Indian region in order to increase the effectiveness of the irrigation system, which was underutilised. Tiwari et al. [36] used AHP to develop a framework for environmental-economic decision making that includes the environmental and economic sustainability criteria, and local people's preferences in the context of a lowland irrigated agriculture system in Thailand. The method was also relevant for Ni and Li [37], who used it for the assessment of soil erosion in terms of land use structure changes. Tran et al. [38] used AHP to prioritize future renewals of irrigation and drainage assets in the La Khe irrigation scheme in North Vietnam; Srdjevic and Medeiros [39] also demonstrated the use of AHP for the assessment of water management plans. Braunschweig and Becker [40] showed how AHP could be used in international agriculture to choose research priorities. Pažek et al. [18] used AHP for evaluation of business alternatives on organic farms. Liu et al. [5] made an assessment of how sustainable a high-yield agro ecosystem in Huantai County, China, was. In Iran, Rezaei-Moghaddam and Karami [41] used AHP for the evaluation of sustainable agricultural development models. Ziolkowska [42] used AHP in combination with cost-effectiveness analysis for the evaluation of the AEM and analysis of the economic aspects to support the decision-making process of the Polish gov‐ ernment. In the same year, Ziolkowska [43] also combined AHP and linear programming to estimate the importance of AEM with respect to the environmental objectives and calculate an objective orientated budget allocation for AEM. Ziolowska also used the AHP to investigate/ evaluate the importance of AEM from the regional perspective in Poland. Mortazavi et al. [44] showed how AHP can successfully be used for prioritizing agricultural research projects. Vindis et al. [45] also used AHP to perform a further evaluation of energy crops for biogas production.

The aim of this paper is therefore a further attempt to show how multicriteria decision models can be successfully applied in the assessment of agricultural problems, the main focus being on the assessment of AEM in Slovenia. The paper is organised as follows: first, we present the methodology and describe the AHP model, and this is followed by the main results. The main findings conclude this article.


Randomized 337 76 83 85 93

Table 5 provides the distribution of patients at baseline according to ulcer size. Ten percent (10%) of patients had ulcers of size 0.30 cm; 12.5% had ulcers of size greater than 0.30 but at most 0.40 cm; 17.8% had ulcers of size greater than 0.40 cm but at most 0.50 cm; 27.2% had ulcers of size between 0.50 cm and 1.00 cm; 17.8% had ulcers 1.00 cm in size; and 14.7% had

Table 6 provides the distribution of patients in the Placebo group by baseline ulcer size whose ulcers had healed by 4 weeks. Seventy-one percent (71%) of Placebo patients with ulcers of size 0.30 cm healed; 78% of Placebo patients with ulcers of size greater than 0.30 but at most 0.40 cm healed; 45% of Placebo patients with ulcers of size greater than 0.40 cm but at most 0.50 cm healed; 41% of Placebo patients with ulcers between 0.50 cm and 1.00 cm in size healed; 30% of Placebo patients with ulcers 1.00 cm in size healed; and 25% of Placebo patients with ulcers of size greater than 1.00 cm but at most 3.00 cm healed. Table 6

Week 1 304 67 80 73 84 Week 2 235 46 63 60 66 Week 4 174 41 47 47 39 ≥ 1 week 315 71 82 75 87

Total 0 mg 400 mg 800 mg 1600 mg

those of the interim analysis in terms of estimates of treatment effects.

Table 3. Number of Patients by Treatment Group (Mid Study Analysis)

**3.1.2 Distribution of patients according to ulcer size**

ulcers of size greater than 1.00 cm but at most 3.00 cm.

**3. Results and discussion**

consisting of many centers.

patients with the largest ulcers.


1. Introduction

#### 1.1 Conventional hot glass embossing process

Hot embossing process is one of the common replication technologies for the replication of microstructures in micro optic systems, such as micro-lens array, micro-prism array, photoresist columns on a glass substrate, etc. The microreplication process is known as a process replicated from a microstructured master. With the development of microsystem technology, low cost is one of the important requirements for manufacturing optical components in a micro range. Besides injection molding process, hot embossing has been favored because of its operation as well as the convenience in producing mold tools. Especially, it is the best choice to create micro components with complex geometry and high aspect ratio. In addition, hot embossing is also suitable for mass volume fabrication with high quality.

Material is the main component in molding process, also hot embossing process. Most applicable materials for hot embossing are thermoplastic polymers. Nevertheless, alternative molding materials like glass, metals, or ceramics will also be used [1]. Glass is a material commonly used in optical microsystem. Compared to optical polymers, optical glass has higher transparency, higher scratch, and humid resistance. Another advantage of the optical glass is that its thermal expansion coefficient is smaller than that of the optical polymers. This reduces significantly the

deviation between the design and the final optical components. Moreover, the refractive index of the optical glass is much higher than that of optical polymers (a range from 1.5 to over 2.0 compared to a range from 1.3 to 1.7). With the higher refractive index, optical glass could bend the light rays to focus in a smaller range, which is suitable to optical lenses. With the above advantages, optical glass has been favored for high-precision applications.

The hot glass embossing process is divided into four major steps (Figure 1). The process starts with heating the sample to the molding temperature, which is usually above transition temperature Tg or the annealing temperature At of the material, followed by an isothermal molding by embossing with speed- and/or forcecontrolled, the cooling of the molded part to the de-molding temperature, and finally de-molding of the component. Heating and embossing stages are usually performed in vacuum environment to protect molds from oxidation, while the cooling stage is usually supported by high-pressure nitrogen. Compared to injection molding, hot embossing process has more advantages, such as shorter flow distances and lower velocity, which decreases shear stress of material significantly. As a result, the decrease of shear stress of material as filling into micro cavities should reduce the residual stress of the embossed parts. In addition, hot embossing process could be the best choice for manufacturing microstructures, which are hardly performed by injection molding. Since the molding temperature is set constantly, the conventional hot glass embossing process is isothermal. The temperature setting of this process is shown in Figure 2.

1.2 Ultrasonic vibration-assisted hot glass embossing process

Temperature setting for the ultrasonic vibration-assisted glass hot embossing process.

Steps for the ultrasonic vibration-assisted hot glass embossing process [2].

Ultrasonic Vibration-Assisted Hot Glass Embossing Process

DOI: http://dx.doi.org/10.5772/intechopen.86546

2. Components for ultrasonic vibration-assisted hot glass

In general, an ultrasonic vibration-assisted hot glass embossing process has three main components: heating furnace, compression tester, and ultrasonic vibration

embossing process

17

Figure 3.

Figure 4.

Ultrasonic vibration technology has been widely applied in various industrial processes, such as machining, welding, and forming. Recently, this technology has been also utilized for processes working at high temperature like hot upsetting and hot embossing. The steps of an ultrasonic hot glass embossing process are like those of the traditional one, except the embossing stage. During the embossing stage, an ultrasonic source is located on the top of the mold to generate high-frequency vibrations (Figure 3). The high energy of ultrasonic vibration rapidly increases the temperature at the contact area between the glass and the mold. Because of this principle, the temperature distribution between the traditional process and the ultrasonic process is different. In the conventional process, the temperature distribution inside the glass during the embossing stage is identical, whereas in the ultrasonic process the localized heat-affected zones are concentrated on the contact area of the mold and the glass. Therefore, this ultrasonic embossing method is not an isothermal process. Temperature setting of this process is shown in Figure 4.

Figure 1.

Steps for the conventional hot glass embossing process [2].

Figure 2. Temperature setting for the conventional glass hot embossing process.

Ultrasonic Vibration-Assisted Hot Glass Embossing Process DOI: http://dx.doi.org/10.5772/intechopen.86546

Figure 3.

deviation between the design and the final optical components. Moreover, the refractive index of the optical glass is much higher than that of optical polymers (a range from 1.5 to over 2.0 compared to a range from 1.3 to 1.7). With the higher refractive index, optical glass could bend the light rays to focus in a smaller range, which is suitable to optical lenses. With the above advantages, optical glass has been

The hot glass embossing process is divided into four major steps (Figure 1). The process starts with heating the sample to the molding temperature, which is usually above transition temperature Tg or the annealing temperature At of the material, followed by an isothermal molding by embossing with speed- and/or forcecontrolled, the cooling of the molded part to the de-molding temperature, and finally de-molding of the component. Heating and embossing stages are usually performed in vacuum environment to protect molds from oxidation, while the cooling stage is usually supported by high-pressure nitrogen. Compared to injection molding, hot embossing process has more advantages, such as shorter flow distances and lower velocity, which decreases shear stress of material significantly. As a result, the decrease of shear stress of material as filling into micro cavities should reduce the residual stress of the embossed parts. In addition, hot embossing process could be the best choice for manufacturing microstructures, which are hardly performed by injection molding. Since the molding temperature is set constantly, the conventional hot glass embossing process is isothermal. The temperature setting

favored for high-precision applications.

Noise and Vibration Control - From Theory to Practice

of this process is shown in Figure 2.

Steps for the conventional hot glass embossing process [2].

Temperature setting for the conventional glass hot embossing process.

Figure 1.

Figure 2.

16

Steps for the ultrasonic vibration-assisted hot glass embossing process [2].

Figure 4.

Temperature setting for the ultrasonic vibration-assisted glass hot embossing process.

#### 1.2 Ultrasonic vibration-assisted hot glass embossing process

Ultrasonic vibration technology has been widely applied in various industrial processes, such as machining, welding, and forming. Recently, this technology has been also utilized for processes working at high temperature like hot upsetting and hot embossing. The steps of an ultrasonic hot glass embossing process are like those of the traditional one, except the embossing stage. During the embossing stage, an ultrasonic source is located on the top of the mold to generate high-frequency vibrations (Figure 3). The high energy of ultrasonic vibration rapidly increases the temperature at the contact area between the glass and the mold. Because of this principle, the temperature distribution between the traditional process and the ultrasonic process is different. In the conventional process, the temperature distribution inside the glass during the embossing stage is identical, whereas in the ultrasonic process the localized heat-affected zones are concentrated on the contact area of the mold and the glass. Therefore, this ultrasonic embossing method is not an isothermal process. Temperature setting of this process is shown in Figure 4.


**3.1. From photography to photogrammetry**

By themselves, photographic images, whether analogue or digital, do not have metric properties. That is, we cannot infer direct measures of the objects displayed in it, or obtain the distances that lie between them. We can visualize, interpret, or analyse, but not exploit them metrically. This is because images are built on the base of the conical perspective, rather than an orthogonal projection. As a consequence scale in images is variable. Therefore, performing accurate measurements on images must be done by taking some precautions.

Photogrammetry deals with obtaining metric information (two‐dimensional or three‐dimen‐ sional) from photographic images, allowing crime scene 2D or 3D reconstruction while preserving strictly their geometric characteristics [8]. The basic principle for photogrammetry is the geometric relationship that takes place between the points of the object (3D element in space), the corresponding image points (2D), and the point of view (placed in the centre of the camera lens). In essence, it is noted that each point of the object, the photographic image, and the viewpoint verified the colinearity condition, that is, they define a straight line (**Figure 4**).

In the photogrammetric workflow, we can distinguish the acquisition phase and the phase of restitution. In the first one, from a three‐dimensional real object, the image is built. The restitution process is just the opposite: from the image will attempt to reconstruct the object, that is the mapping or virtual representation.

**Figure 4.** Basis of colinearity condition.

**3. Geomatics and Forensics: overview**

8 Forensic Analysis - From Death to Justice

This section will describe the main geomatic technologies currently applied in forensic analysis. Since the early 1960s, we are witnessing a quick evolution of sensors and algorithms in geomatic science. That evolution was supported by the electronic advances such as electro‐ magnetic distance and angular measurement, computer miniaturization, increase of computer power or imaging semiconductor circuits. Nowadays, the drive is now reinforced by the

telecommunications advances. **Figure 3** shows a quick summary of this evolution.

**Figure 3.** Chronological chart showing geomatic advances related to forensic science.

By themselves, photographic images, whether analogue or digital, do not have metric properties. That is, we cannot infer direct measures of the objects displayed in it, or obtain the distances that lie between them. We can visualize, interpret, or analyse, but not exploit them metrically. This is because images are built on the base of the conical perspective, rather than an orthogonal projection. As a consequence scale in images is variable. Therefore, performing

Photogrammetry deals with obtaining metric information (two‐dimensional or three‐dimen‐ sional) from photographic images, allowing crime scene 2D or 3D reconstruction while preserving strictly their geometric characteristics [8]. The basic principle for photogrammetry is the geometric relationship that takes place between the points of the object (3D element in space), the corresponding image points (2D), and the point of view (placed in the centre of the

accurate measurements on images must be done by taking some precautions.

**3.1. From photography to photogrammetry**

For the cartographic restitution of a photograph, it must be solved three problems of geometric nature. First, the position of the viewpoint with respect to the photograph (or digital image) must be identified. This implies that the internal geometric characteristics of the camera (essentially its focal length, distortions and deformations) should be known. This process is known as *internal orientation*.

Second, we must determine the location of the photograph viewpoint regarding the object. This process is called *external orientation* if the location of the object is referred to a ground coordinate system, or *relative orientation* when not working with a real reference system. When documentation of indoor scenes is done, the Cartesian reference system is characterized by the fact that the Z axis tends to coincide with the vertical direction and *XZ* (or *YZ*) plane usually matches to some of the façades or walls object (if there is possible, on the contrary it could be arbitrary). The positioning of each photograph in space is determined by the three spatial coordinates (*X*, *Y*, *Z*) and the three spatial angles (ϖ, ϕ, χ) of the viewpoint.

Finally, the reconstruction of the object is performed through the intersection between different correspondent and perspective lines (collinearity condition) by using at least two photographs. In geometric terms, this means a difficult task, especially in those cases where a full automation is required. To solve this problem, photogrammetry, without prior knowl‐ edge of the object, acts similar to the behaviour of human vision, in which images are obtained from two converging intersections.