3.8.1. Emulsion Activity Index (EAI)

Figure 2A shows the EAI values of cricket and mealworm meals and their hydrolysates (control and generated from pressure-treated insect meals). Higher EAI values mean that the dispersed fat droplets are smaller in size and that proteins (or peptides) have more ability to absorb at the oil–water interface [88]. Globally, cricket ingredients (11.86–13.32 m2 /g) had higher EAI values compared to mealworms (3.01–6.50 m2 /g) which could be due to the difference in the hydrophobicity of the proteins and the smaller size of cricket proteins, which would allow faster diffusion at the oil–water interface [23,57,89]. Chatsuwan et al. [90] obtained EAI values ranging from 29.23 to 36.69 m2 /g for *P. succinta* and *C. rosea*, respectively. Regardless of the insect species, the lower EAI values in our study can be attributed to the drying method used to produce commercial insect meals, which modifies protein solubility [19] and emulsifying properties [91]. The EAI (*p* > 0.05) of the cricket hydrolysate control was not significantly different from the meal. This tendency was also observed by Hall et al. for some enzymatic hydrolysis conditions of cricket using Alcalase® (E/S of 0.5%; hydrolysis time of 90 min and E/S of 1.5%; hydrolysis time of 30 min) compared to unhydrolyzed proteins [3]. Conversely, the EAI value of the mealworm control hydrolysate was reduced compared to the meal. Hall et al. also obtained a decreased EAI after enzymatic hydrolysis of tropical banded cricket with similar enzymatic digestion conditions (E/S of 3%; hydrolysis time of 90 min) [3]. This reduction of EAI was possibly caused by the enzymatic hydrolysis parameters which would have reduced the interfacial activity of proteins due to excessive protein degradation [57,92]. However, several studies have shown that the enzymatic hydrolysis of several proteins using Alcalase® improved their EAI values [23,38,88]. This has been explained by the enzyme's specificity in cutting aromatic residues to reveal hydrophobic peptides, which facilitates the formation of emulsions [73,93]. In addition, the increased peptide solubility after enzymatic hydrolysis promotes peptide absorption at the oil–water interface [23]. Applying HHP treatment prior to enzymatic hydrolysis had no impact on EAI values for cricket, since the degree of hydrolysis was similar. However, HPP treatment of the mealworm meal increased its EAI compared to the control hydrolysate, possibly due to modification of the peptide hydrophobicity [89] caused by different enzyme cut sites linked to the structural modifications of proteins after HHP. Despite this improvement of EAI, the value is still lower than unhydrolyzed mealworm meal.

Figure 2B shows the EAI of insect ingredients (meals and hydrolysates) at different pHs (4.0, 5.5 and 7.0), regardless of insect and concentration of the aqueous phase. The insect meals had significantly (*p* < 0.05) lower EAIs at pH 4.0 than at pH 5.5 and 7.0, near the pI of commercial insect meals (3.85 and 3.95 respectively, for cricket and mealworm meals) [60]. Measuring at the pI produced the lowest EAI (Table 3), consequently, diffusion and adsorption of protein at the oil–water interface were delayed [94]. This reduction of EAI at the pI was also observed for chickpea and whey protein isolate [95,96]. For hydrolysates (control and HHP-treated), the EAI decreased slightly with increasing pH (4.0–7.0) due, once again, to solubility modification. Pacheco-Aguilar et al. [88] obtained similar EAI values for fish (*Mercuccius productus*) protein hydrolysate at pHs 4.0 and 7.0, but higher values at pH 10.0 and a different degree of hydrolysis (DH 10–20%).


**1. Introduction**

Waste generation through human activities has become a global issue because it has a direct influence on the environment, society, and economy, i.e. overall sustainability [1]. Waste can be any material by-product of human and industrial activity that has no residual value. Both the government and industry see opportunities to

reduce one of the sources of global warming through the implementation of waste management strategies. In recent years, there has been increasing scientific evidence that human activity is harming the environment with the excessive production of waste that results in pollution [2]. For this reason, waste management is a fundamental aspect of sustainability within the construction industry. Because by controlling the waste generation, it is possible to reduce the number of pollutants being released into the environment. Effective waste management will help in the reduction of landfill waste as well as minimize the costs associated with the construction of the project [3]. The global market is largely driven by the growing construction activity and the tendency of governments in different regions of the world to strive for sustainability. The growing demand for sustainable and recycled building materials for commercial construction projects continues to drive the construction waste market. It is estimated that the global solid waste market will reach \$2.01 billion by 2021 [4] and is expected to touch \$3.40 billion by 2050, an increase of about 59% [5] as shown in **Figure 1**.

**Figure 1.** *Global annual waste by 2050 [5].*

### *Circular Economy - Recent Advances in Sustainable Construction Waste Management DOI: http://dx.doi.org/10.5772/intechopen.105050*

Moreover, 50–80% of construction and demolition waste can be recycled or reused thus indicating that mismanagement of construction waste can result in the loss of valuable economic resources [6]. Therefore, through the successful implementation of a waste management plan, project managers can often reduce the amount of waste being created within the project and the amount sent to landfills [7]. Both the environment agencies and academics globally believe that through sustainable waste management plans, construction organizations develop an understanding of the quantity of waste generated. This will enable everyone within the company from the top down to be focused and aligned with the same goals such as reuse, recycling, or reducing construction waste [8].

Waste could become a potential source of profit and a financial benefit. Because of reducing the amount of waste going to landfills, there will be a reduction in the costs of the project due to lower landfill taxes. It is also possible to recover costs by selling waste such as scrap metal, the contractor can offset construction costs, which can make them more competitive. Based on the amount of waste generated, its composition, and its treatment, the solid waste treatment and disposal estimated in 2016 was equivalent to about 1.6 billion tons of carbon dioxide (CO2) greenhouse gas emissions, or 5% of world emissions [5]. This is mainly due to landfills and non-landfill gas collection systems' unavailability. Food waste is responsible for almost 50% of emissions. Solid waste emissions are projected to increase to the equivalent of 2.38 billion tons of CO2 per year by 2050 if no progress is made in this area [5].

In many countries, solid waste management is usually under local responsibility, with almost 70% of countries setting up institutions responsible for regulatory oversight of waste policy development [5]. Nearly two-thirds of the countries have adopted specific solid waste management laws and regulations, the implementation of which varies considerably. In addition to regulatory oversight, and tax transfers, it is not uncommon for the central government to be directly involved in waste disposal services, and about 70% of waste disposal services are directly overseen by local governments [5]. At least half of the services, from primary waste collection to treatment to disposal, are provided by government agencies, and about one-third through public-private partnerships. However, successful financing with the private sector and operational partnerships are usually successful only under certain conditions, with appropriate incentives and implementation mechanisms, therefore, are not always the ideal solution.

Financing solid waste management systems is a major challenge, especially because of ongoing operating costs rather than capital investment; operating costs must be forecast [9]. According to The World Bank [5] in high-income countries, the operating costs of integrated waste management, including collection, transportation, treatment, and disposal, typically exceed \$100 per ton. Low-income countries (Sub-Saharan Africa) spend less on waste management at about \$35/ton, but in these countries, the costs are much harder to recover. Waste management is labor-intensive, with transportation costs of \$20–50 per ton. The cost-effectiveness of removal services varies greatly depending on the level of revenue. In low-income countries, costs for users range from an average of \$35 a year to \$170 a year, and full or near-total cost recovery is generally limited to high-income countries [5]. User payment models can be fixed or variable depending on the type of user invoice. Communities typically cover about 50% of the investment costs of waste disposal systems, with the remainder coming primarily from national private sector subsidies. Globally, the production of waste varies from country to country. **Figure 2** highlights the country-wise production of waste in which Spain generates the maximum percentage of waste i.e., 70%.

**Figure 2.** *Country wise waste production [10].*

Hao et al. [11] argue that for too long waste management has focused on the construction practices and processes but overlooks the critical human attitudes. For a labor-intensive industry, how people perceive the importance of waste management is a definitive decider over its success, in particular how the Project Manager conveys this message. Mousavi et al. [12] make note that the success of waste reduction is down to the project manager's ability to value waste reduction as importantly as construction materials ordered. This, therefore, illustrates how attitudes and values within construction are just as critical as legislation. Therefore, the government should focus more on educating the Project Managers. Furthermore, the implementation of sustainable waste management can be bureaucratic and costly. One of these relevant pieces of legislation that construction projects within the UK have had to adhere to has been the Waste Management Plans Regulations 2008 [13], which meant obliged projects over £300,000 were required to plan how much waste the project was to produce and where it would go [14]. For some within the industry, this was considered a burden and bureaucratic and too often left out of the design stage but the design stage is a critical stage whereby waste can often be eliminated and opportunities should be taken. As already stressed on the economic opportunities from the circular economy, **Figure 3** illustrates further the circular economy opportunities by 2030 [15].

The project team along with the Project Manager can now structure the waste management plan on the dependencies within each project, which can often be unique and unpredictable. However, it should be noted that the lack of government enforcement has been a barrier to sustainable waste management, this current

*Circular Economy - Recent Advances in Sustainable Construction Waste Management DOI: http://dx.doi.org/10.5772/intechopen.105050*

### **Figure 3.**

*Circular business model for 2030 vision [15].*

**Figure 4.** *Circular economy principles.*

government is trying to remove this. Over the last few years, the importance of sustainability within the construction industry has grown to become with it now being a critical aspect of many companies' policies and ideals. Sustainable Waste Management is something that companies are now looking to promote within the company such as 'zero waste' to improve their public image [16]. Though some may state these theories of zero waste are unrealistic, Kannan [17] arguably makes note that sustainable procurement in the future will become ever-increasingly important and therefore companies who promote themselves as green may take advantage of these future projects. As well as future procurement rules, construction waste management should be looked at as a cost-savings initiative as well as sustainability. The contractors should not just look at improving productivity or compressing schedules but instead first look into using their sustainable waste management as a way of making huge cost savings. In other words, the contractors and associated project managers should look at waste as not just a problem but as an asset to the project. Therefore, considering the principles of circular economy as shown in **Figure 4**, it is a closed-looped system in which raw materials, components, and products lose minimum value with maximized utilization of renewable energy resources. The circular economy will contribute to a more sustainable world as the focus of both concepts is people, planet, resources, and economy.


**Figure 9.**

*Nanopicks formed in the (a) interdendritic zone and in the (b) dendrites branches.*

irradiated surface will be stable and will tend to grow as a result of the sputtering yield variation promoted by the local incidence angle change.

**Figure 9(a) and (b)** shows MFA images of the dendritic (**Figure 9b**) and interdendritic (**Figure 9a**) regions of the irradiation-affected area, both figures show the formation of peaks, cones, and other types of geometries and all of them have peaks that tend to have a conical shape. Another important factor detected is that the sputtering originated by chemical composition is of lower intensity than the geometrical sputtering, induced by incidence angle local variations of the ion beam.


*5.2.2. MHC genotyping using DNA samples of wild animals*

3.0. To discover novel Mafa-class I sequences, we perform the de novo assembly set to detect >85% matches using the trimmed and MID-binned sequences after converting the outputs to ace files for the Sequencher Ver. 5.01 DNA sequence assembly software (Gene Code Co., Ann Arbor, MI). We then use the defined consensus sequence obtained from the de novo assembly as a reference sequence to identify and map the correct allele sequences. Using this process, we genotyped a set of 400 unrelated animals by the Sanger sequencing method and high resolution pyrosequencing and identified 190 different alleles, 28 at Mafa-A, 54 at Mafa-B, 12 at Mafa-I, 11 at Mafa-E, 7 at Mafa-F, 34 at Mafa-DRB, 13 at Mafa-DQA1, 13 at Mafa-DQB1, 9

**Figure 11.** A schematic workflow of the allele assignment process using the SeaBass software.

On the basis of our large-scale project to genotype the MHC of 5000 Filipino cynomolgus macaques by NGS, we so far have detected 15 different types of Mafa haplotypes (HT1~HT15) in 45 homozygous animals. These Mafa homozygous animals provided the basis to efficiently estimate other Mafa haplotypes. For example, we estimated a variety of Mafa-A, Mafa-B/I, Mafa-E, and Mafa-class II (Mafa-DRB, Mafa-DQA1, Mafa-DQB1, Mafa-DPA1, and Mafa-DPB1) haplotypes by comparing the homozygous animals with heterozygous animals that

at Mafa-DPA1, and 9 at Mafa-DPB1 alleles [35, 59].

98 Next Generation Sequencing - Advances, Applications and Challenges

At this time in the development of MHC genotyping by NGS, it is difficult to apply the RNAsequencing mapping method to accurately genotype the MHC of wild animals using known allele sequences as references. This is because the present allele information is relatively poor for most of them (Table 5). Therefore, MHC genotyping of wild animals or poorly studied species by NGS is based on *de novo* assembly of DNA sequences. In this case, the definition of "real alleles" and "artifact alleles" is important because NGS errors such as monostretch sequences are frequently observed in the assembled consensus sequences. Some of the allele assignment approaches based on *de novo* assembly that have been published include the allele validation threshold (AVT) method [61], clustering method [62–64], and the relative sequenc‐ ing depth modeling methods [65]. These methods suppose that the contigs that have a sequence depth greater than the threshold level are the "real alleles," and they are determined by

**Figure 13.** Application of Mafa homozygous and heterozygous animals for nonclinical trials of induced pluripotent stem (iPS) cells.

statistical calculation of the threshold using the sequence depth values of all contigs obtained in *de novo* assembly. Therefore, the detection of exact or "real" alleles depends largely on the setting of the threshold level and the quality of the sequence reads [65]. To enable the correct setting of the threshold level, it is important to use primers that can amplify all alleles of the target locus or loci without allelic imbalance. Furthermore, additional considerations such as repeating independent NGS experiments at least three times and detecting identical allele sequences in at least two animals are necessary to distinguish between real and artifactual alleles.


*2.4. Statistical Analyses*

General linear mixed-e ffect models for the analysis of variance (ANOVA) were used using R statistical software and the *lmerTest* package to check for the e ffects of treatments and years, after verifying the normality and homoscedasticity of errors. In the case of fruit number, data were modelled in a generalized linear mixed-e ffect model (*lmerTest* package) using Poisson distribution. In all models, treatments (systems) and years were used as fixed factors, and blocks and years as random ones. Data were presented separated by year because of the significant year e ffect and interaction between year and treatment in most of the cases. Pairwise comparisons for all variables were computed by estimating the 95% confidence interval of the di fference between the least squares means (Equation (2)):

$$\text{CI(difference)} = (\mathbf{x}\_1 - \mathbf{x}\_2) \pm 1.96\sqrt{\left(SE\_{x1}\right)^2 + \left(SE\_{x2}\right)^2} \tag{2}$$

where (*x*1) is the mean of the first value, (*x*2) is the mean of the second value, (*SEx*1) is the standard error of (*x*1), and (*SEx*2) is the standard error of (*x*2).

If the resulting 95% confidence interval (CI) of the di fference between values did not cross the zero value, the null hypothesis that the compared values are similar was rejected.

All data in the manuscript were reported in the original scale as least square means with their corresponding standard errors. Results of all analysis of variance/deviance in terms of *p*-values are presented in Table S1.


Mainstreaming, 11, 97 MARKet and ALlocation model (MARKAL), 29, 30, 41, 102–105 Market regulation, 73–76, 84, 86 Market regulators, 104 Michael, Mike, 114 MLP, *see* Multi-Level Perspective Modelling, 98, 101, 102, 104, 111 Modelling families, 31 Models, 29, 31–37, 39–43 Monodisciplinarity, 72 Monodisciplinary, 11, 12, 14, 15 Multidisciplinarity, 93, 112, 114 Multidisciplinary, 10–11 Multi-Level Perspective (MLP), 60 Multi-stakeholder engagement, 12

### **N**

Narrative scenarios, 25, 32, 37 National Centre for Energy Systems Integration (CESI), 24–30, 26n2, 32, 33, 36–39, 41, 42, 107, 108 Natural monopoly problem, 74 Natural science, vii Non-Delivered Electricity (NDE), 84 Non-political, 41 Non-technical tasks, 40 Non-technological drivers of demand, 35 Norway, 50–67, 92 Norwegian Declaration of Soria Maria, 52 Norwegian Research Council, 50, 53, 54, 114 Norwegian University of Science and Technology's (NTNU) Energy Transition Initiative (NETI), 66, 67

### **O**

Objective, 79 Ontology, 13 Oslo Centre for Research on Environmentally Friendly Energy (CREE), 53 Outliers, 79 Outsourcing, 61

### **P**

Participant observation, 73 Performative, 104 *Planning Complex Infrastructure Under Uncertainty*, 39 Planning of infrastructures, 40 Plausible scenarios, 38 Policy decisions, 41 Power Systems Engineering, 72, 85 Prediction, 28, 36–39 Price, 72–87 Pricing reliability, 78–84 Public interest, 73–75, 84–87

**Q** Qualitative, 80, 81, 94, 97, 98, 108, 109

### **R**

Rational consumers, 78–79 Regulators, 73, 74, 81 Relevant scenarios, 38 Reliability costs, 104 Research Centres for Environment-Friendly Energy Research, *see* Forskningssentre for miljøvennlig energy (FME) Research Council of Norway, 50, 52–54, 56 Research Councils UK (RCUK), 24 Resource burdens, 6

Responsible Research and Innovation (RRI), 55, 56 Reward structures, 60 RRI, *see* Responsible Research and Innovation

### **S**

Sandpit funding model, 98 Scales of interdisciplinarity, 94 Scenario-building, 37 Scenarios, 26, 34, 37–39, 43 Science and Technology Studies (STS), vii–ix, 50, 63, 67, 93, 94, 96, 99, 105, 109, 110, 114, 115 Science Studies, 51, 109–111 Scientifc paradigms, 114 SHAPE ENERGY, 3n3, 8 Shaping Interdisciplinary Practices in Europe (SHAPE-ID), 3 Social acceptance, 63 Social practice theory, 60 Social Sciences and Humanities (SSH), viii, ix, 3–6, 3n3, 11–15, 27, 28, 30, 32, 34–36, 38–43, 50–53, 55–65, 74, 85, 86, 92, 94, 97, 98, 115 Social Science FMEs, 53, 59 *Sociology of Interdisciplinarity*, viii, ix, 12–15, 92–115 Sociotechnical, ix, 4–7, 9, 13 SSH, *see* Social Sciences and Humanities Star, Susan Leigh Storylines, 34, 37, 38 Strategic Challenges in International Climate and Energy Policy (CICEP), 53 Subjective, 79, 80, 86, 108, 109 Survey, 72, 78, 79, 81, 83 Systems-oriented perspectives, 59 Systems theory, 24

### **T**

Tampere University of Technology, 78 Techno-economic approach, 34 Temporality, 80 TIP, *see* Transformative Innovation Policy Transdisciplinarity, 93, 112, 114 Transdisciplinary, 10–12 Transformative Innovation Policy (TIP), 56 Transition Pathways, 24, 26, 26n2, 29, 36

UKERC Energy 2050 project, 29 Uncertainty, 30, 33, 36, 42 United Kingdom (UK), 14, 15, 24–44, 92, 93, 97, 98, 102–104, 113 US electricity industry, 74 User studies, 63, 105

### **V**

Value of Lost Load, 78

### **W**

**U** UCL Energy Systems Team, 29, 103 UK Energy Research Centre (UKERC), 24, 26, 28–31, 36, 93 Whole systems, 2, 15, 24–44, 98 Willingness to Accept (WTA), 80 Willingness to Pay (WTP), 80, 82 Winskel, Mark, 93, 96, 113


*AAA improved outcomes?* Vasc Endovasc Surg 2009 **Part 3**

**Anesthestic and Radiation Management in Aortic Surgery**

134 Aneurysmal Disease of the Thoracic and Abdominal Aorta

Vogel TR, Dombrovskiy V, Haser P, Graham A. *Has the implementation of EVAR for ruptured*

**7**

*Japan*

**Ultrasound-Guided Peripheral Nerve Block in**

**Vascular Surgery – An Alternative Choice for**

Abdominal aortic aneurysm, with its incidence approaching approximately 8% in the population, accounts for more than 8,000 deaths annually in the United States. It is a multifactorial disease associated with atherosclerosis. Age, male gender, smoking, and the family history of abdominal aortic aneurysm are thought to be the risk factors for the disease (Ashton et al., 2002, Fleming et al., 2005). The natural history of abdominal aortic aneurysm is characterized by the progressive enlargement of the aneurysm, which

The aorta wall consists of a thin intima, a thick tunica media, and an adventitia, composed of endothelium for a thin intima, smooth muscle layers for a thick tunica media, and a connective tissue including the vasa vasorum and nervi vascularis for the adventitia, respectively. The wall of aorta is particularly vulnerable to an injury owing to the continuous exposure to high pulsate blood pressure and shear stress. Once degenerated which most commonly occurs by atherosclerosis, cystic medial necrosis, a condition in which the collagen and elastic fibers in the tunica media of the aorta are degenerated and replaced by the mucoid connective tissues, will immediately take place. It will subsequently result in the circumferential weakness, dilatation, and as a consequence, the development of aneurysm. The other etiological conditions that lead to the development of aneurysm include some hereditary basis such as Marfan's and Ehlers-Dnlos syndromes, the infection including syphilis and tuberculosis, and vasculitis associated with autoimmune diseases such as Takayasu's arteritis and rheumatoid arthritis (White et

The prognosis of abdominal aortic aneurysm is strongly correlated with the size of aneurysm, and the risk of rupture is 1-2% for 5 years for an aneurysm less than 5cm in diameter, whereas it is more than 20-40% for those more than 5cm in diameter. Hence, an elective operation is indicated if the diameter of aneurysm is >5.5cm (Sakalihasan et al.,

ultimately leads to the rupture and the subsequent death of the patient.

**1. Introduction**

al., 1993, Shah et al., 1997).

2005).

**the Anesthetic Considerations for**

**Neuroaxial Anesthesia Techniques**

Yusuke Asakura1 and Hideki Takagi2

*1Department of Anesthesiology, 2Department of Orthopedic Surgery, Nagoya Kyoritsu Hospital, Nagoya,*


*2.4. Characterization of the Synthesized Polymer and its Composites with Gum Arabic*

The UV/visible spectrophotometer (UV752PC) (Citi Scientific Supply, Ltd. Karachi, Pakistan) was employed to identify the electronic transitions from lower to higher energy levels in the UV-visible range of radiation. To detect functional groups and the interaction of PPy and PPy/GA composites, we employed Fourier-transform infrared (FTIR) spectroscopy (model 783 PerkinElmer Inc., Waltham, MA, USA). The crystallinity of the conducting polymers (CPs) was measured using X-ray diffractometry. The XRD spectra of the synthesized material were taken by using {Cu Kα radiations (λ = 1.5405 A◦) JEOL JDX-3532, X-ray diffractometer, JEOL Ltd., Tokyo, Japan}. Scanning electron microscopy (SEM JSM-IT-100 JEOL Ltd., Tokyo, Japan) helped to scan the surface of the synthesized materials. The thermogravimetric analysis of the synthesized materials was carried out at TGA SDT Q600 (PerkinElmer Inc., Waltham, MA, USA). The electrochemical properties such as cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), and galvanostatic charge–discharge (GCD) were performed on the electrochemical workstation (PGSTAT302, Metrohm AUTOLAB B.V. Ltd., KM Utrecht, Netherlands). The CV tests were performed at the scan rate of 100 mV/s in the potential window of −0.4 to 0.8 V. The GCD tests were

conducted under current densities from 1 to 2.5 A/g by holding the cut-off voltage between −0.4 and 0.8 V.