",isbn:"978-1-80355-607-9",printIsbn:"978-1-80355-606-2",pdfIsbn:"978-1-80355-608-6",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,isSalesforceBook:!1,isNomenclature:!1,hash:"6cf0b844f6881c758c61cca10dc8b134",bookSignature:"Associate Prof. Gülşen Akın Evingür and Dr. Önder Pekcan",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11830.jpg",keywords:"Elasticity, Toughness, Modulus, Compression, Extension, Optical Properties, Swelling, Drying, Diffusion, Release, Transmission Loss, Sound Absorption Coefficient",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"April 5th 2022",dateEndSecondStepPublish:"June 15th 2022",dateEndThirdStepPublish:"August 14th 2022",dateEndFourthStepPublish:"November 2nd 2022",dateEndFifthStepPublish:"January 1st 2023",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"20 days",secondStepPassed:!0,areRegistrationsClosed:!1,currentStepOfPublishingProcess:3,editedByType:null,kuFlag:!1,biosketch:"Dr. Evingür is a researcher in polymer composites and a lecturer at a maritime university. She has edited 2 books and has had 5 chapters published in international books, and 3 international and 5 national projects, respectively.",coeditorOneBiosketch:"Prof. Pekcan received their Ph.D. from the University of Wyoming, United States of America, in 1974. He has more than 362 SCI articles, 26 chapters, and 10 projects and is a member Science Academy in Turkey.",coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"180256",title:"Associate Prof.",name:"Gülşen",middleName:null,surname:"Akın Evingür",slug:"gulsen-akin-evingur",fullName:"Gülşen Akın Evingür",profilePictureURL:"https://mts.intechopen.com/storage/users/180256/images/system/180256.jpeg",biography:"Gülşen Akın Evingür graduated from Physics Department at the Yıldız Technical University (YTU, İstanbul, Turkey) in 1996. She completed her Master of Science degree in 2002 at the same department. The titled of her thesis was 'Electrical Properties of Polystyrene”. She received her PhD from Physics Engineering at İstanbul Technical University in 2011. The title of the thesis was 'Phase Transitions in Composite Gels”. She worked as an Assistant Professor between 2011 and 2018, and she is currently working as an Assosciate Professor at Pîrî Reis University, Istanbul, Turkey. She has been engaged in various academic studies in the fields of composites and their mechanical, optical, electrical, and acoustic properties. She has authored more than 60 SCI articles, 92 proceedings in national and international journals, respectively. 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He became Associate Professor at Hacettepe University in 1979. \nHe visited ICTP Trieste, Italy as Visiting Scientist between June and August 1980. Between 1980 and 1981 he was a Visiting Scientist at the Technical University of Gdansk, Poland. \nHe worked as Visiting Professor at the Department of Chemistry, University of Toronto, Canada between 1981 and 1988. \nHe was appointed as full Professor at the Department of Physics, Istanbul Technical University, Turkey and worked there between 1988 and 2005. \nHe became an Elected Member of the Turkish Academy of Sciences (TÜBA) in January 1995. \nHe became the Dean of School of Arts and Sciences at the Istanbul Technical University in 1997. \nHe received the Science Award from the Scientific and Technological Research Council of Turkey (TÜBİTAK) in 1998. 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1. Introduction
The treatment of ascending aorta diseases is usually performed by traditional surgery with median sternotomy and cardiopulmonary bypass. However, in some patients, the operative risk of this approach may be prohibitive. In these instances, endovascular approaches to the ascending aorta may be an alternative.
Endovascular approaches are being increasingly utilized to treat a variety of thoracic aortic diseases including aneurysms, pseudoaneurysms, dissections, penetrating aortic ulcers, traumatic aortic rupture, coarctation and abdominal aortic aneurysms [1].
Ascending aorta has several limitations for the endovascular approach, such as its larger diameter and the presence of the aortic valve and the coronary arteries.
2. Access and technique
The endovascular procedure is usually performed through the femoral arteries [1-3]. Sometimes this access is impossible or not recommended because of small size vessels, obstruction, calcification, dissection or extreme tortuosity.
The feasibility of endovascular treatment depends on many anatomic factors, including the diameter and the disease state of the access vessels [4,5]. Stenosis, calcifications, tortuosity, small size or dissection in both femoral and iliac arteries can make introduction of large sheath hazardous or impossible.
Endoprosthesis deployment in the ascending aorta usually requires large diameter and long sheath. There is always possibility of damaging the aortic valve, since the nose of the commercially available devices is designed for descending and/or abdominal aorta. The vascular prosthesis should be large enough to oversize by 15-20 % the aortic diameter and short in length to fit between the coronary arteries and the brachiocephalic trunk. This length usually measures 8 cm or less. The endovascular technique would have several advantages over the open surgical alternatives if the right tools for the procedure were available. Current thoracic aortic stent-grafts are too long, while abdominal aortic stent-grafts are too short and narrow. Moreover, abdominal aortic delivery systems are too short to traverse the long and tortuous path from the femoral artery to the ascending aorta.
Several different approaches have been presented and published over the last years as an attempt to solve very dramatic situations stretching the limits of the current technology [6-8].
The technique should be carefully planned. Rapid pacing and adenosine are useful to lower blood pressure and allow precise deployment. A rigid (Landerquist or super stiff) and long (260 cm) guidewire is usually placed in the left ventricle to give adequate support near the coronary arteries. This is similar to what we use when performing transcatheter aortic valve implantation (TAVI). One important tip is to perform a “wide J-shape” at the end of the rigid guidewire in order to prevent left ventricule perforation and, consequently, cardiac tamponade or left ventricular pseudoaneurysm.
Similar to other endovascular procedures, besides careful planning, patient selection and technical expertise are crucial to obtain satisfactory results. In this setting multidetector computed tomography (MDCT) plays an important role in selecting the patients suitable for the procedure and allows a careful and detailed step by step preoperative planning.
We have recently published a series of five clinical cases and described the technique in which the axillary artery was used to deliver the endograft for the treatment of different thoracic aortic diseases [9]. We also demonstrated the possibility of concomitant treatment of ascending aorta disease and coronary stent implantation [10,11].
Transcarotid is another alternative access and, recently, transapical approach through a small left thoracotomy has been described.
3. Clinical cases
In this part we will discuss clinical cases of endovascular treatment of ascending aortic diseases showing different approaches and techniques.
3.1. Clinical case 1
A 32-year-old female presenting cardiogenic shock (Figures 1-2).
Figure 1.
Aortogram showing bovine trunk and a pseudoaneurysm in the anterolateral wall of the ascending aorta 1 cm above the ostium of the right coronary artery.
Figure 2.
Final aortogram of emergency endovascular correction of a pseudoaneurysm through transfemoral implantation of a Cook endoprosthesis. The shorter device we had available was a 8 cm length endoprosthesis. In order to preserve flow in the brachiocephalic trunk and left carotid artery (bovine trunk) we had to use a chimney (snorkel) technique in this two vessels arch using two Viabahns to preserve flow.
3.2. Clinical case 2
A 57-year-old female underwent coronary artery bypass graft in another hospital with left internal mammary artery to the left anterior descending and saphenous vein graft to the right coronary artery. The patient developed mediastinitis and had 7 reinterventions resulting in acute bleeding through the sternum. She was sent to our hospital in cardiogenic shock and manual compression of the bleeding site in the sternum. Previous computed tomography showed ruptured pseudoaneurysm at the proximal anastomosis of saphenous vein graft to the (Figures 3-6).
Figure 3.
Emergency endovascular deployment of two abdominal extension cuffs (Gore-Tex) in the ascending aorta between the coronary ostium and the brachiocephalic trunk through the left axillary artery. The bleeding stopped immediately and the patient became stable.
Figure 4.
Right coronary angiography demonstrating severe stenosis. Once saphenous vein graft to the right coronary artery had occluded by the endoprosthesis, the native right coronary artery had to be treated.
Figure 5.
Deployment of three stents in the right coronary artery.
Figure 6.
Computed tomography showing the two extension cuffs in the ascending aorta and the three stents in the right coronary artery.
3.3. Clinical case 3
A 74-year-old male with previous coronary artery bypass graft presented with iatrogenic ascending aortic pseudoaneurysm that occurred during angiography. The patient was at very high risk for surgical treatment, therefore an (Figures 7-9).
Figure 7.
A) Coronary angiogram showing left main bifurcation with severe stenosis and circumflex with severe stenosis extending to large marginal branch. (B) Aortic angiogram demonstrating ascending aorta dilatation and image suggesting dissection at the saphenous vein graft ostium.
Figure 8.
Computerized tomographic angiography showing a 3.4-cm pseudoaneurysm with partial thrombosis in ascending aorta and surrounding intramural hematoma.
Figure 9.
A) Intravascular ultrasound of left main artery evidencing significant stenosis due to calcified plaque. (B) Intravascular ultrasound measurements confirming the presence of a large left main artery and significant plaque burden. (C) Virtual histology showing predominantly fibrous plaque and superficial calcium arch.
Figure 10.
Coronary angiogram showing final result in right anterior oblique projection (A) and spider view (B).
Figure 11.
(A) Ascending aorta angiogram before endoprosthesis deployment showing a large pseudoaneurysm (yellow arrow). (B) Ascending aorta angiogram after endoprosthesis deployment evidencing sealed pseudoaneurysm and a type 1 endoleak (red arrow).
Figure 12.
Computerized tomographic angiography showing final result after endoprosthesis deployment.
Both procedures were successfully performed and the patient was discharged without (Figures 10-12). At 6 months and 1 year clinical follow-up the patient had no symptoms as well as no other adverse cardiovascular events.
4. Target diseases
There are several pathologies of the ascending aorta that can be potentially addressed by the endovascular approach. Pseudoaneurysms or sacular aneurysms in the mid-ascending aorta are adequate for this technique because they usually appear with a sufficient proximal and distal landing zone. On the other hand, fusiform aneurysms have the limitation of lacking a sufficient landing zone in many cases.
Thoracic endovascular stent grafting has revolutionized the treatment of distal [type B] acute aortic dissection. Endovascular surgeons are now seeking the ways to improve the treatment of type A dissection by offering endovascular techniques to replace conventional surgical therapy. Less invasive endovascular therapy, obviates the need for sternotomy and cardiopulmonary bypass, reduces perioperative morbidity, and offers an alternative solution for those patients not eligible for conventional intervention due to co-morbidity or severe complications of the disease.
Thoracic stent grafting in the ascending aorta presents specific challenges and the role of uncovered stents is unclear in this situation. The majority of patients with acute type A aortic dissection has the intimal tear originated in the sinotubular junction. More than 90% of patients with this disease does not have sufficient proximal or distal landing zone required for secure fixation. Therefore, the site of the intimal tear as well as aortic valve insufficiency and aortic diameter >38mm are major factors limiting the use of endovascular therapy for acute type A dissection. Current available stents in use to treat type B aortic dissection do not address anatomical constraints present in type A aortic dissection in the majority of cases, hence the development of new devices is required.
5. Technical limitations
Endovascular approach of the ascending aorta has several limitations and is still in its beginning phase.
The diameter of the ascending aorta is usually larger than the rest of the aorta and the proximity with the aortic valve and the presence of the coronary arteries pose special challenges.
The length of the delivery system, which is designed for the abdominal aorta, does not allow to reach the ascending aorta through the groin.
Finally, the length of the endoprosthesis itself for descending aorta may be too long to be positioned between the coronary ostia and the brachiocephalic trunk.
6. Conclusions
Despite the fantastic progress in this field and the clear advantages of endovascular approaches for the ascending aorta in some clinical situations, one must bear in mind the high level of risk that these procedures entail.
Long-term data are not available to establish the safety and durability of stent-graft repairs. The cases described represent an off-label use of this technology and should be considered with the above mentioned limitations in mind.
The surgeon will need special skills in open aortic surgery and catheter based interventions to be able to plan the procedure carefully, to properly deliver the devices and to manage the potential serious complications [12].
Challenges in endograft design are the development of branched endografts and of pathology-specific endografts [13]. However, the unique composition of the proximal thoracic aorta and the associated mechanical properties have to be taken into account and make this effort by far more complex than initially expected. Moreover, there is a need for reducing the stent-graft devices profile as well as for increasing conformability and trackability.
We believe that future advances with devices specifically designed for the treatment of ascending aorta diseases will allow this technique to be incorporated into routine medical practice.
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Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil
Federal University of Rio Grande do Sul Hospital, Porto Alegre, RS, Brazil
West Paraná State University, Cascavel, PR, Brazil
Mãe de Deus Hospital, Cascavel, PR, Brazil
Cardiovascular Surgery Institute of West Paraná, Cascavel, PR, Brazil
'},{corresp:null,contributorFullName:"Alexandre do Canto Zago",address:null,affiliation:'
Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil
Federal University of Rio Grande do Sul Hospital, Porto Alegre, RS, Brazil
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1. Introduction
The current trends in photovoltaic (PV) deployments worldwide are a clear indication that PV energy will play a big role in the near future energy mix. For example, the global solar photovoltaic (PV) capacity is projected to increase by 37.5% from 2019 to 2030 (i.e from 593.9 GW in 2019 to 1,582.9 in 2030) [1]. This rapid growth in grid penetration of PV calls for more accurate methods to forecast the performance and reliability of PV. Additionally, PV projects policy and investment decisions rely on PV performance and reliability forecasts. Therefore, to reduce the risks of PV investments, more reliable methods to forecast the performance and reliability of PV power are a prerequisite.
Different methods have been proposed for PV power forecasting. These methods can be classified as: physical, heuristic, statistical and machine learning methods [2, 3]. Each method might have different conceptual design, implementation, application and accuracy. In this chapter, the application and accuracy of the different methods are assessed using measured PV module power and weather data.
PV power forecasting can range from different temporal horizons depending on ones need. At a moment there is no standard classification criterion of the temporal horizon. General classification can be made as: very short term (Intra-hour: 15 minutes to 2 hours ahead), short-term (hour ahead: 1 to 6 hours ahead, day ahead: 1–3 days ahead), Medium-term (week to months ahead), long-term (one to several years) and lifetime forecast (until PV expected lifetime).
Achieving high-accuracy forecasts at each of these temporal horizons is influenced by different variables such as: solar radiation models, PV performance models, data availability, data quality and forecasting methods. The accuracy highly deteriorates with increasing forecasting temporal horizon. This is because, more input parameters such as: seasonal variations, soiling effects, degradation and many other performance reducing effects need to be taken into consideration [4]. These factors are not easy to evaluate precisely, therefore, they are simply approximated which increase the uncertainties in long-term and lifetime PV forecast.
The main influencing factor of PV production is the amount of global solar irradiation incident on the PV panels. As shown in Figure 1, there is a quasi-linear correlation of power and irradiance. This property means that the accuracy of power prediction is highly determined by the accuracy of the solar irradiation forecast.
Figure 1.
Measured irradiance versus measured power. Data corresponds to six month measurements of irradiance and module power in Gran Canaria (Spain).
In this chapter, the different methods used in PV power forecasting are presented. The chapter is organised as follows: In Section 1, a brief introduction on power forecasting is presented. In Section 2, different PV forecasting methods are presented, for some methods a practical example of their application and their accuracy are evaluated using real measured PV module power. Section 3, is dedicated to lifetime PV power forecasting. In this section, several effects affecting lifetime PV power forecasting are stated and a more elaborative discussion of the degradation effect on lifetime PV power forecasting is presented. Lastly, in Section 4, a summary of the different aspects within the chapter is presented.
2. PV power forecasting methods
Different methods: physical, heuristic, statistical and machine learning are commonly used in PV power forecasting [2, 3]. The methods are based on two main approaches to generate the PV power forecasting. One is the physical approach, which requires prior knowledge of PV material properties and the metadata of a PV system, together with the need of weather data. The second ones is the data-driven approach, which requires operational data to train/calibrate coefficients of the models which are then used to generate the predictions. This means that, a data-driven approach can only be applied after a given PV module or system has been exposed and enough data is available to train/calibrate the models. On-contrary, a physical approach can be applied even when the PV system is not yet commissioned. Hence PV power forecasting methods based on a physical approach are the mostly used methods by PV stakeholders to evaluate the economic viability of PV projects during the initial phases.
Figure 2 illustrates the required inputs and general steps to generate PV power forecast by the two approaches. What is clear is that both approaches require weather data (mainly solar irradiation) as input. Therefore, solar irradiation forecast is highly essential step for PV power forecast using both approaches. Unlike data-driven approach, physical approach is based on physical assumptions and therefore, knowledge of the physical parameters influencing PV generation is required.
Figure 2.
Schematic of data-driven and physical approaches for PV power prediction. Data-driven approach requires historical data (in green) to train and physical approach requires physical parameters as inputs.
2.1 Physical method
Physical models calculate the PV power using the equivalent electrical circuits [5]. The equivalent circuit model developed for a single cell can be used to derive equivalent circuit models for a PV module as well as a PV system [6]. They are the commonly applied models in the PV power forecasting commercial software packages (such as PVSyst [7] PVWATTS [8]).
2.1.1 PV cell and module equivalent circuit models
To build the physical model one need to know the basic photo-to-voltage theory. The diode model is used to develop the PV cell model to calculate the PV output power. The diode model can be characterized as: one-, two- and three-diode models [5, 7] (see Figure 3). The choice of the model selection depend on charge carrier recombination mechanisms one need to take into consideration. Because of its simplest, the one-diode model is the most commonly used to model the operating principles of a PV cell. The one-diode model can consist of three, four or five parameters (see Table 1) .
Figure 3.
a, four parameters (4-p) one-diode model. b, five parameters (5-p) one-diode model. c, two-diode model (seven parameters) and d, three diode model (nine parameters).
Includes both series and shunt shunt resistive losses
Two-diode model
IPH, I01, I02, n1, n2, Rs, Rsh
Two diodes to represent junction recombination Relevant at low irradiance operation of a PV cell
Three-diode model
IPH, I01, I02, I03, n1, n2, n3, Rs, Rsh
Takes into account grain boundaries and leakage current
Table 1.
Parameters and characteristics of different diode models.
Photocurrent.
Reverse saturation current, the subscripts (1, 2 and 3) represents the diode number respectively.
diode ideality factor the subscripts (1, 2 and 3) represents the diode number respectively.
Series resistance.
Shunt resistance.
The three parameter (3-P) one-diode model is only used to demonstrate the basic working principles of a PV cell but not as a representative of the real operating condition. To simulate the actual working conditions of a PV cell, the five parameter (5-P) one-diode model is commonly used because it takes into account both the series and shunt resistive losses. It is expressed as [9, 10]:
I=IPH−I0expV+RsInVt−1−V+RsIRshE1
where I, Io and IPH are the generated solar cell current, reverse saturation current and photo-generated current respectively. V, Rs and Rsh are the solar cell voltage, series resistance and shunt resistance respectively. n is the ideality factor of the diode, and Vt is the thermal voltage.
To derive the equivalent circuit model for a PV module, the basic assumption that a PV module comprises of identical PV cells in series is usually taken [6]. This assumption implies that under similar conditions (irradiance and temperature), all the PV cells should generate equal current and voltage. According to [6], the PV module equivalent model is derived from a PV cell diode model (Eq. (1)) as:
IM=IPH−I0expVM+RsNsIMnNsVt−1−VM+RsNsIMNsRshE2
where Ns is the total number of cells in series IM and VM are the current and voltage of a PV module respectively.
The photo-generated current IPH has a direct relation with solar irradiance and operating solar cell temperature (see Eq. (3) It can be evaluated as [10]:
IPH=ISCGGSTC+kiTc−TSTCE3
where G is the given irradiance level, Tc is the cell temperature. GSTC and TSTC are the irradiance and temperature at standard test conditions (STC) respectively. ki is the temperature coefficient of the current in (A0C) and ISC is the short-circuit current at STC.
The reverse saturation current (I0) can be evaluated as a function of short-circuit current (ISC), open-circuit voltage (Voc), temperature and energy bandgap of a semiconductor (Eg) as:
Io=TcTSTC3.ISCexpEgo/Vto−Eg/VtexpVoc/nNsVto−1E4
where Vto is the thermal voltage at STC and Ego is the energy bandgap at T = 0 K.
Readers are referred to [6, 10, 11, 12] for extended knowledge on how to derive and evaluate the different PV cell and module model parameters. The functions are also implement is freely available pvlib simulation packages [13].
2.1.2 Temperature dependence of I-V characteristics
Addition to solar irradiation, the I-V curve characteristics also depend on the operating temperatures of the cell Tc. According to IEC60891 standards [14], the temperature and irradiance correction of I-V characteristics are given as:
Isc=ISC−STC.GGSTC.(1+αscTc−TSTCE5
VOC=VOC−STC.(1+βocTc−TSTC+n.Ns.Vt.lnGGSTCE6
where αsc and βoc are the temperature coefficients for short-circuit current and open-circuit voltage respectively. n, Ns and Vt are the ideality factor, total number of cells and thermal voltage respectively.
Figure 4 shows the effect of irradiance and module temperature on Isc and Voc. The irradiance has a greater impact on the short-circuit current and the temperature has a greater impact on the open circuit voltage.
Figure 4.
Effect of irradiance and cell temperature on I-V characteristics. (I-V curve simulated using Pvlib one-diode model).
According to module mounting (e.g Open rack, close to the roof, insulated rack) and module construction (e.g glass–glass or glass-backsheet), the cell temperature might be some degrees hotter than module temperature [15]. In [15] the cell temperature (Tc) is derived from the module temperature measured at the surface of the module(Tm) and the irradiance G by a simple relations as:
Tc=Tm+GGSTC.ΔTE7
where ΔT is the temperature difference between the cell and the module back surface at an irradiance level of 1000 W/m2. In [15], ΔT was found to be around 3 0C for open-rack mount, 1 0C roof mount and 0 0C for insulated back.
The module temperature is calculated from solar irradiation, ambient temperatures and/or wind speed using different methods [16]. The commonly used models are: Standard NOCT model (Eq. (8)), the Faiman model (Eq. (9)) [17] and the Kings model also known as Sandia model (Eq. (10)) [15].
Tm=Tamb+NOCT−TNOCTGNOCT.GE8
Tm=Tamb+GU0+U1.WSE9
Tm=Tamb+G.expa+b.WSE10
where NOCT is the nominal operating cell temperature at given conditions (TNOCT = 20 0C, GNOCT = 800 W/m2, wind speed (WS) = 1 m/s), NOCT is in the range of 40–500C [9]. Tamb0C is ambient temperature, GW/m2 is the incident solar irradiance on the module, and WSm/s is the wind speed. U0W/0Cm2 and U1Ws/0Cm2 are the coefficients describing the effect of the radiation on the module temperature and the cooling by the wind, [18] respectively. a and b are parameters that depend on the module construction, materials and on the mounting configuration of the module [15].
2.1.3 Example of PV power prediction using physical method
In this example, we demonstrate a practical application of the described physical model to predict four days PV module power using measured irradiance. The predicted power is then compared with real measured power of the PV module. The properties, electrical and thermal parameters of the PV module are presented in Table 2. The module is exposed in Gran Canaria, Spain at tilt angle of 230 and azimuth angle of 1690. The module is installed as open rack-fixed configuration. The power and module temperatures are recorded every 5 minutes. The module temperature is recorded using a Pt100 sensor attached at center-back of the module. In addition, weather data (ambient temperature, wind speed and global horizontal and in plane of array solar irradiation) are also recorded with a 1 minute resolution.
Temperature coefficient of short-circuit current (α)
2.39 [mA/K]
Temperature coefficient of open-circuit voltage (β)
−161 [mV/K]
Normal operating cell temperature TNOCT
48 [0C]
Table 2.
Module properties and manufacturer datasheet electrical and thermal parameters.
The values are measured at STC (i.e. 1000 W/m2 irradiance, 1.5 air mass and at 25°C temperature).
The PV module specific parameters and the weather variables (irradiance and cell temperature) are used as input variable in Pvlib to simulate the five parameter (5-P) one-diode model (Eq. (2)). Figure 5 shows the simulated I-V curves at different irradiance and temperature levels for a period of four days. 15 minutes aggregated data of temperature and irradiance are used hence the curves are evaluated every 15 minutes. From each I-V curve the power at maximum power point can be computed using:
Figure 5.
I-V curves of simulated solar module at different incident irradiance and temperature levels. Irradiance range from 0 w/m2 to 1168 w/m2 and module temperature range from 10 0C to 50.3 0C.
Pmpp=Impp×VmppE11
In this example, the uncertainty of power prediction due to temperature models are evaluated. The three commonly used temperature models presented in (Eqs. (8), (9) & (10)) are used to model the module temperature. The parameters of the models are: a = −3.87 & b = −0.0594 for Kings model and U0=25.6 & U1=25.6 for Faiman model. These parameters are extracted from literature values in [15, 18] with small modifications to minimize the uncertainty.
To evaluate the uncertainty in prediction, the normalized root mean square error (NRMSE) (Eq. (12)) is used. Additionally, the normalized mean bias error (NMBE) (Eq. (13)) is also evaluated as a metric to capture the average bias in the prediction (i.e, to check whether the predictions are overestimated or underestimated).
NRMSE=100×1N∑j=1Npj−mj2m¯E12
NMBE=100×1N∑j=1Npj−mjm¯,m¯≠0E13
Where p is the predicted data, m measured data, m¯ is the mean of the measured data.
Figure 6 shows the plot of measured and modeled temperature (a), measured and predicted power (b). The uncertainty in temperature models as well as the corresponding uncertainty in power predictions are presented in (c). It is clearly visible that, for each temperature model, the prediction is different and hence the uncertainty value. Generally, the Kings model showed the best performance both in temperature modeling as well as power prediction depending on the NRMSE and R2 values. The standard NOCT model showed the least performance which is not surprising since the model doesn’t take into account the impact of wind speed. All the models overestimate the module temperature which correlates with the underestimation of the predicted power (see Figure 6c). It should be noted that, although the Kings model showed good predictions based on this example, it is not enough to guarantee that this will always be the case when the model is applied on different dataset. This is because the accuracy of the temperature models has been found to be influenced by; geographical locations, model design and mounting conditions [18].
Figure 6.
a, Measured module (black) and ambient (yellow) temperatures, modeled temperature with standard NOCT (green), Faiman (blue) and Kings (red) models. b, Measured (black) and predicted power with module temperature calculated using NOCT (green), Faiman (blue) and Kings (red) models. c, Evaluated NRMSE and NMBE for module temperature modeling (blue) and power prediction (orange). d, Measured versus predicted power with module temperature calculated using NOCT, Faiman and Kings models.
2.2 Data-driven methods
Data-driven methods can be categorized into: data-driven heuristic methods, statistical methods and machine learning methods.
2.2.1 Data-driven heuristic methods
The physical models described in Section 2.1 have one big drawback that they require too many input variables which are not usually directly available. In this case, heuristic models are proposed to reduce the number of required inputs. They are heuristic models because they are not developed from physical assumptions/theories. Therefore, they have no physical dependencies/interpretations. They are classified as data-driven models because they are derived from correlation between weather and power output data. In [19], several heuristic models are presented and compared. They are developed on similar principles of generating power forecast from irradiance and module temperature but only differs in the numbers of fitting model parameters. The basic advantage of heuristic models is their simplicity to derive the model parameters from PV power historic data. Here we present the two- (Eq. (14)) and three- (Eq. (15)) parameter models described in [19, 20] respectively.
Where Pmpp, is the generated power at maximum power point by a PV module, G is the simulated or measured irradiance Tc is the cell temperature evaluated using equation (Eq. (7)), a, b, c, x and y are the models fitting parameters γ is the temperature coefficient of power in (%/0C). PSTC and GSTC=1000w/m2 are the power and the irradiance at STC.
2.2.2 Example of PV power prediction by heuristic methods
In this example, the described heuristic models are applied to predict the power of the same PV module described in subSection 2.1.3. To calibrate the models, six days historical data (25/March −31/March) are used. The extracted parameters are presented in Table 3. To demonstrate the usefulness of temperature correction term, the second term in (Eq. (15)) is removed and the model is re-calibrated.
Extracted model parameters of the heuristic models.
Tcorr is the temperature correction.
In Table 3 3-parameter model corresponds to calibration without a temperature term and 3-parameter-Tcorr model corresponds to calibration with a temperature correction term.
Figure 7a shows a plot of the four days measured power and the predict power using 2-parameters and the 3-parameters model. Figure 7b shows the uncertainty in model calibration and the corresponding uncertainty in prediction using the different models. According to the NRMSE and NMBE values, the 3-parameter model with temperature correction term showed the best performance. From the same figure it can also be concluded that it is important to include a temperature correction in power prediction since the same model showed the least performance when applied without a temperature correction.
Figure 7.
a, Measured power (black), predicted power using 2-parameter model (blue) and with 3-parameter model without (green) and with (red) temperature correction. b, Evaluated NRMSE and NMBE for the models during calibration (blue) and prediction (orange).
2.2.3 Statistical and Machine learning methods
Like heuristic models, statistical and machine learning (ML) methods are also based on historical data to generate PV power forecast. While heuristic models focus on an in-deep formulation of mathematical operations, statistical models require selecting a model that considers previous knowledge of the system. ML methods require the selection of a predictive algorithm by relying on its empirical capabilities. Statistical models aim to “inference” the outcome of a model, while ML approaches aim to find generalizable predictive patterns [21]. Both statistical and ML methods are data-driven approaches that rely on the availability and accuracy of existing operational data to generate the forecasting. Usually, the larger the historical data, the better the PV system can be understood in terms of operational behaviour under different weather conditions and hence the better the forecasting accuracy.
The list of published methods is extensive and a case-to-case benchmark is usually needed. Statistical methods such as Naive method, ARIMA (Autoregressive Integrated Moving Average), SARIMA (seasonal ARIMA), Ordinary Least Squares (OLS) or Facebook Prophet (FbP) are typically applied for PV forecasting with and without regressors [22, 23, 24]. Below is a basic description of the commonly applied statistical methods. A detailed description of each method can be found on the respective cited reference.
Autoregressive Integrated Moving Average (ARIMA): This method is composed of three main elements: the auto-regression order (p), differencing order (d) and moving average order (q). The statistical formulation of this method can be found in [25].
Seasonal ARIMA (SARIMA): This model is an extension of the ARIMA approach, which adds the seasonal behaviour of the dataset analysed. This feature is of interest for PV applications due to the high seasonality on daily and annual basis observed in PV systems [26].
Ordinary Least Squares (OLS): This method analyses the system by fitting linear relationships between one or more input variables, and by minimizing the sum of square errors of a continuous or at least interval outcome variable (actual versus predicted values) [27].
Facebook Prophet (FbP): This methodology has been developed to allows non-experts in data science to adapt and configure the model to their needs. The FbP method is based on a decomposable time series model including trend, seasonality, holidays (not important for our application), and an error term. It is also possible to define the type of evolution: linear or logistic. For PV forecasting, it facilitates the modelling for short- and long-term by enabling features such as time resolutions and temporal seasonalities [28].
More advanced methods, the so-called machine learning (ML) methods, can provide better results [29, 30], however, in most cases they require more computational efforts. Some examples of machine learning methods used in PV power forecasting include [2, 29, 30, 31]: k-nearest neighbors (k-NN), artificial neural networks (ANN), support vector machine (SVM), random forests (RF) and light gradient boost machines (LightGBM). The basic description of these methods is presented below with the references for a detailed description.
k-Nearest Neighbors (k-NN): Is classified as one of the simplest and straight-forward ML method. The K-NN algorithm compares the current state’s Euclidean distances with training samples in feature space to select the “k” nearest neighbours used in predictions. Detailed description and application in PV power forecasting are presented in [32, 33]
Artificial Neural Networks (ANN): Inspired by biological neural networks, this algorithm is composed by neurons (mathematical units) and weights (the link between mathematical units). Using gradient-based optimization techniques, the ANNs learn a specific task (e.g., prediction) by the optimization of the “weights”. This method is widely used in PV power forecasting [2] described in [29, 34, 35]
Support Vector Machine (SVM): The method separates the data linearly and transforms it into a higher dimensional feature space through a specific kernel function. The linear separation is performed with the so-called “hyperplanes”. The SVMs can be used for regression as well as classification tasks. In [32] the model is presented and applied for short-term PV power forecasting.
Random Forests (RF): The RF algorithm is based on an ensemble learning. A set of decision/regression trees is created and the final result is voted. For a regression problem, a set of regression trees are trained and the forecast will equal to the mean of individual regression tree results [36].
Light gradient boosting machine (LightGBM): This algorithm is an advanced gradient boosting decision tree (GBDT), which combines two techniques to find more effectively (higher accuracy and high processing speed) the optimal split point in the GBDT: (1) Gradient-based one-side sampling (GOSS), to reduce the number of data instances, and (2) exclusive feature bundling (EFB) to reduce the feature space. In [30, 37], the method is presented and applied for PV power prediction.
ML algorithms can be classified as supervised and unsupervised. A supervised ML algorithm uses labelled training data. It is related to a standard fitting procedure to find the unknown function/relationship between the input and output variables. The unsupervised ML algorithm uses unlabelled training data to find the data patterns (e.g., in the samples’ clustering). For PV power prediction, the supervised algorithms are commonly used, due to weather forecasting availability. In general, the procedure to run a ML algorithm can be composed of the following stages:
Data collection: the available historical data (weather and PV operational) are collected and filtered. The collection of weather forecasting data is also considered.
Feature selection: identification of the most relevant variables with regard to the PV power output selected for further analysis.
Data augmentation: in this stage, the enhancement of the initial dataset is expected by typically applying mathematical operations (e.g., physical relationships) to one or more relevant input variables.
Dataset split: the input dataset is divided into a training and testing dataset. Also, a validation dataset is recommended. This task is typically applied over the sorted or random timestamps.
Accuracy improvement: statistical indicators such as the MBE, RMSE or R2 are used to quantify the accuracy of any forecasting model. Cross-validation techniques are recommended.
A simple ANN network architecture is shown in Figure 8, where the layers of a multilayer perceptron (MLP) for PV power forecasting are presented. The input data for training can be the historical weather and PV power output, while for testing and forecasting, expected weather variables are the input to the expected PV power output in the future.
Figure 8.
Multilayer perceptron (MLP) for PV power forecasting, where the input layer includes at least irradiance and temperature, while the output layer comprises the PV energy yield.
2.2.3.1 Example of PV power prediction by statistical and machine learning approaches
The statistical and ML approaches are applied to the same dataset used for physical and heuristic models. To train the models, the regressors selected are limited to the plane-of-array irradiance and PV module temperature, while the target variable is the PV output power. Most of these models are already implemented in open-source software packages (i.e., statsmodels [38], prophet [28], sklearn [39] and lightgbm [39]) and executed with Python scripts. Setting parameters for each statistical and machine learning method are given in Table 4.
Setup parameters for different statistical and machine learning approaches.
Statsmodels.
Prophet.
Sklearn.
Lightgbm.
Figure 9a shows a plot of the four-day measured power and the predicted power using statistical and machine learning models. Figure 9b shows the uncertainty in model calibration and the corresponding uncertainty in prediction using the different models. According to the NRMSE, LightGBM model shows the best performance, followed by the SVM and Facebook Prophet.
Figure 9.
A, Measured power (black), predicted power using statistical and machine learning approaches. B, Evaluated NRMSE and NMBE of statistical and machine learning approaches at calibration and predictions.
Generally, comparing the uncertainty values among the physical, heuristic, statistical and machine learning methods (see Figures 6c, 7b and 9b), physical method has the worst performance in comparison to other methods. This is not surprising given that physical models generate prediction without preliminary performance data, unlike the counterpart models that base their predictions from historical data. Also, the different assumptions and the too many input parameters increase the uncertainty range in physical models.
3. Lifetime PV power forecasting
To begin with, it is important to understand how the lifetime of a PV module is defined. Unlike other electrotechnical devices where the term lifetime is clearly defined [40], the definition of a PV module “lifetime” is somewhat complex. This is because, despite the catastrophic events (such as fire) it is unlikely that a PV module drops its power generation to zero. However, even though a PV module is still generating power, its output might be too low to be economically viable to continue its operation. Therefore, in general terms PV lifetime is defined in economical rather than technical terms.
For economical viability of PV projects, most PV module manufacturers guarantee a power reduction of less than 20% within 25–30 years of operation. The 20% power reduction is usually referenced at standard test conditions (STC) (modules tested under 250C temperatures, 1000 W/m2,irradiance and air mass 1.5). Therefore, in this context the lifetime of a PV modules is defined as the time required for a PV module to loss it’s STC power by 20% .
The actual performance of a PV module throughout its lifetime is very uncertain and difficult to accurately forecast. This is because many factors can influence the performance of a PV module. Some of these factors may include: solar resource, the quality of the PV components and the long-term variations in system performance (degradation). All these factors increases the uncertainty in PV lifetime forecast. The Internation Energy Agency- Photovoltaic Power Systems Programme (IEA-PVPS) -task 13 report [41] provides a detailed overview of the uncertainties in lifetime yield predictions. To improve the accuracy and to achieve reliable lifetime PV forecast, all these effects must be explored separately. Here, we asses the effect of degradation on lifetime PV power forecasting.
3.1 Effect of degradation on lifetime PV power forecasting
Degradation is defined as the gradual and non-reversible decrease in PV performance over time. Degradation is a crucial influencing factor to be taken into account during lifetime PV power forecast. This is because over time the PV components are ageing and deteriorating in their normal operation. Understanding how PV degrades is a very widely studied topic in the PV community but it is also among the not well understood topics. This can be explained by the numerous factors influencing PV degradation. These factors include: PV technology, bill of materials (BoM), climatic conditions [4], transportation, installation and operational conditions. More-so, since new materials are being proposed frequently, it increases the complexity to correlate the factors that influence PV degradation. Usually, different materials have different degradation kinetics and are influenced by different stress factors differently.
In lifetime power prediction models, degradation effect is included in a number of ways. For example, according to the survey carried out among PVPS-task 13 experts regarding degradation effects in lifetime energy yield prediction, the following assumptions are taken [41]: (a). A variable degradation during the first five year of operation and a fixed degradation from 5 to 30 years of operation. A degradation of 1–2% is assumed in the first year, 0.7% to 0.5% to year 5 and0.3% to 0.5% up to year 30. (b). Initial degradation of 0.3% to 1.0% in the first years to include the effects of initial degradation modes such as light induced degradation (LID). (c). Constant degradation over the years with the exception of the first year to take into account technology specific behaviour.
Generally, a constant degradation rate with linear performance loss is considered (see Eq. (16)). However, some authors [42, 43, 44], have evaluated and modeled the non-linearity in degradation rates and performance. For example in [43] a non-linear power degradation model (Eq. (17)) was proposed and applied in [44] with a time-dependent degradation rates to predict PV performance lifetime.
Pt=Pmpp.1−k.tE16
Pt=Pmpp×1−exp−Bk.tμE17
Where Pmpp is the power calculated using either the physical model or the heuristic models described in the previous sections. k [%/year] is the degradation rate B and μ are model and shape parameters respectively.
3.2 Example of lifetime PV power forecasting
In this example, the effect of degradation rate on lifetime power forecast is presented. Using, 30 years of historical weather data (global irradiance, ambient temperature and wind speed from ERA 5 reanalysis [45]), three different degradation scenarios are presented and their impact on lifetime power and energy yield prediction. The first scenario is using a non-linear performance degradation with a shape parameter (μ=1.2), the second scenario is using a linear performance degradation and the third scenario is using a non-linear model with a different shape parameter (μ=0.2). It should be noted that in all the three cases, a constant degradation rate of 0.8%/year corresponding to a lifetime of 25 years (to have a − 20% power loss) is used. In all the cases Pmpp is calculated using a 3-parameter heuristic model (Eq. (15)). Although it is not usually the case, the prediction without degradation effect and its impact on lifetime energy prediction is also shown in this example.
Figure 10 shows the PV lifetime power and yield predictions using different scenarios. It can be seen that depending on the degradation scenario, the lifetime yield is significantly different. In numbers, when compared to the usually used linear scenario, a relative difference of over 5.0% is evaluated in respect with the non-linear scenarios.
Figure 10.
A, Lifetime power prediction using different degradation scenarios: non-linear with shape parameter = 1.2 (red), linear (green) and non-linear with shape parameter = 0.2 (blue) as well as a no degradation scenario (cyan). B, Corresponding lifetime yield for all the respective scenarios.
4. Conclusions
PV power forecasting is important to stabilize the electrical grids, financing PV projects and also to plan operational and maintenance activities. In this regard, different methods are proposed to forecast the PV power generation. In this chapter, the different methods used in PV power forecasting are presented, applied and their accuracy in PV forecasting is evaluated using measured PV module and weather data. The degradation effect on lifetime PV power forecasting is also assessed using two main scenarios; linear degradation scenario and non-linear degradation scenario. The key observations in the chapter are:
The uncertainties in PV module temperature modelling affect the forecasting accuracy. In the chapter, three temperature models: Standard NOCT, Faiman and King’s are compared, the King model showed the best performance among the three models. The standard NOCT model, that neglects the impact of wind speed displayed huge uncertainty.
Data-driven models outperforms physical models in prediction accuracy. This can be explained by the fact that physical models are derived from to many assumptions and that they need too many input parameters that are usually approximated.
For lifetime PV power forecasting, a relative difference of over 5% is evaluated between the linear and non-linear degradation scenarios.
Conflict of interest
The authors declare no conflict of interest.
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When benchmarked, it is shown that physical method showed the highest uncertainties compared to other methods. In the chapter, the effect of degradation on lifetime PV power and energy forecast is also assessed using linear and non-linear degradation scenarios. 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A. ‘Modeling Outdoor Service Lifetime Prediction of PV Modules: Effects of Combined Climatic583Stressors on PV Module Power Degradation’, IEEE Journal of Photovoltaics, 2019; 9, 1105–1112. doi:10.1109/JPHOTOV.2019.291619'},{id:"B44",body:'Kaaya, I., Lindig, S., Weiss, K.-A., Virtuani, A., Ortin, M. S. d. C., & Moser, D., Photovoltaic lifetime forecast model based on degradation patterns.Progress in Photovoltaics: Research and Applications, 2020; 28, 979–992. doi:10.1002/pip.3280'},{id:"B45",body:'Copernicus Climate Change Service (C3S) (2017): ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate. Copernicus Climate Change Service Climate Data Store (CDS), [Internet] Available from: https://cds.climate.copernicus.eu/cdsapp#!/home, [Accessed: 16-November -2020]'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Ismail Kaaya",address:"ismail.kaaya@ise.fraunhofer.de",affiliation:'
Faculty of Electrical Engineering, University of Ljubljana, Slovenia
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These Terms and Conditions outline the rules and regulations pertaining to the use of IntechOpen’s website www.intechopen.com and all the subdomains owned by IntechOpen located at 5 Princes Gate Court, London, SW7 2QJ, United Kingdom.
',metaTitle:"Terms and Conditions",metaDescription:"These terms and conditions outline the rules and regulations for the use of IntechOpen Website at https://intechopen.com and all its subdomains owned by Intech Limited located at 7th floor, 10 Lower Thames Street, London, EC3R 6AF, UK.",metaKeywords:null,canonicalURL:"/page/terms-and-conditions",contentRaw:'[{"type":"htmlEditorComponent","content":"
1. Terms
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By accessing the website at www.intechopen.com you are agreeing to be bound by these Terms of Service, all applicable laws and regulations, and agree that you are responsible for compliance with any applicable local laws. Use and/or access to this site is based on full agreement and compliance of these Terms. All materials contained on this website are protected by applicable copyright and trademark laws.
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The following terminology applies to these Terms and Conditions, Privacy Statement, Disclaimer Notice, and any or all Agreements:
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“Client”, “Customer”, “You” and “Your” refers to you, the person accessing this website and accepting the Company’s Terms and Conditions;
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“The Company”, “Ourselves”, “We”, “Our” and “Us”, refers to our Company, IntechOpen;
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“Party”, “Parties”, or “Us”, refers to both the Client and ourselves, or either the Client or ourselves.
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All Terms refer to the offer, acceptance, and consideration of payment necessary to provide assistance to the Client in the most appropriate manner, whether by formal meetings of a fixed duration, or by any other agreed means, for the express purpose of meeting the Client’s needs in respect of provision of the Company’s stated services/products, and in accordance with, and subject to, the prevailing laws of the United Kingdom.
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Any use of the above terminology, or other words in the singular, plural, capitalization and/or he/she or they, are taken as interchangeable.
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2. License
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Unless otherwise stated, IntechOpen and/or its licensors own the intellectual property rights for all materials on www.intechopen.com. All intellectual property rights are reserved. You may view, download, share, link and print pages from www.intechopen.com for your own personal use, subject to the restrictions set out in these Terms and Conditions.
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3. Cookies
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We employ the use of cookies. By using the IntechOpen website you consent to the use of cookies in accordance with IntechOpen’s Privacy Policy. Most modern day interactive websites use cookies to enable the retrieval of user details for each visit. On our site, cookies are predominantly used to enable functionality and ease of use for those visiting the site.
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4. Limitations
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In no circumstances shall IntechOpen or its suppliers be liable for any damages (including, without limitation, damages for loss of data or profit, or due to business interruption) arising out of the use, or inability to use, the materials on IntechOpen's websites, even if IntechOpen or an IntechOpen authorized representative has been notified orally or in writing of the possibility of such damage. Some jurisdictions do not allow limitations on implied warranties, or limitations of liability for consequential or incidental damages; consequently, these limitations may not apply to you.
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5. Accuracy of Materials
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Intechopen.com website content and services are provided on an "AS IS" and an "AS AVAILABLE" basis. Material appearing on www.intechopen.com could include minor technical, typographical, or photographic errors. IntechOpen may make changes to any material contained on its website at any time without notice.
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6. Links
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IntechOpen has no formal affiliation to any external sites that link to www.intechopen.com, unless otherwise specifically stated. As such, it is not responsible for content that appears on any such sites. The inclusion of any link to IntechOpen does not imply endorsement by IntechOpen. Use of any such linked website is done solely at the user's own discretion.
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We reserve the right of ownership over our entire website www.intechopen.com, and all contents. By using our services, you agree to remove all links to our website immediately upon request. We also reserve the right to amend these Terms and Conditions and our linking policy at any time. By continuing to link to our website, you agree to be bound to, and abide by, these linking Terms and Conditions.
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If you find any link on our website, or any linked website, objectionable for any reason, please Contact Us. We will consider all requests to remove links but will have no obligation to do so.
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7. Frames
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Without prior approval and express written permission, you may not create frames around our web pages or use other techniques that alter in any way the visual presentation or appearance of our website.
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8. Modifications
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IntechOpen may revise its Terms of Service for its website at any time without notice. By using this website, you are agreeing to be bound by the current version of all Terms at the time of use.
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9. Governing Law
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These Terms and Conditions are governed by and construed in accordance with the laws of the United Kingdom and you irrevocably submit to the exclusive jurisdiction of the courts in London, United Kingdom.
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Croatian version of Terms and Conditions available here
By accessing the website at www.intechopen.com you are agreeing to be bound by these Terms of Service, all applicable laws and regulations, and agree that you are responsible for compliance with any applicable local laws. Use and/or access to this site is based on full agreement and compliance of these Terms. All materials contained on this website are protected by applicable copyright and trademark laws.
\n\n
The following terminology applies to these Terms and Conditions, Privacy Statement, Disclaimer Notice, and any or all Agreements:
\n\n
“Client”, “Customer”, “You” and “Your” refers to you, the person accessing this website and accepting the Company’s Terms and Conditions;
\n\n
“The Company”, “Ourselves”, “We”, “Our” and “Us”, refers to our Company, IntechOpen;
\n\n
“Party”, “Parties”, or “Us”, refers to both the Client and ourselves, or either the Client or ourselves.
\n\n
All Terms refer to the offer, acceptance, and consideration of payment necessary to provide assistance to the Client in the most appropriate manner, whether by formal meetings of a fixed duration, or by any other agreed means, for the express purpose of meeting the Client’s needs in respect of provision of the Company’s stated services/products, and in accordance with, and subject to, the prevailing laws of the United Kingdom.
\n\n
Any use of the above terminology, or other words in the singular, plural, capitalization and/or he/she or they, are taken as interchangeable.
\n\n
2. License
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Unless otherwise stated, IntechOpen and/or its licensors own the intellectual property rights for all materials on www.intechopen.com. All intellectual property rights are reserved. You may view, download, share, link and print pages from www.intechopen.com for your own personal use, subject to the restrictions set out in these Terms and Conditions.
\n\n
3. Cookies
\n\n
We employ the use of cookies. By using the IntechOpen website you consent to the use of cookies in accordance with IntechOpen’s Privacy Policy. Most modern day interactive websites use cookies to enable the retrieval of user details for each visit. On our site, cookies are predominantly used to enable functionality and ease of use for those visiting the site.
\n\n
4. Limitations
\n\n
In no circumstances shall IntechOpen or its suppliers be liable for any damages (including, without limitation, damages for loss of data or profit, or due to business interruption) arising out of the use, or inability to use, the materials on IntechOpen's websites, even if IntechOpen or an IntechOpen authorized representative has been notified orally or in writing of the possibility of such damage. Some jurisdictions do not allow limitations on implied warranties, or limitations of liability for consequential or incidental damages; consequently, these limitations may not apply to you.
\n\n
5. Accuracy of Materials
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Intechopen.com website content and services are provided on an "AS IS" and an "AS AVAILABLE" basis. Material appearing on www.intechopen.com could include minor technical, typographical, or photographic errors. IntechOpen may make changes to any material contained on its website at any time without notice.
\n\n
6. Links
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IntechOpen has no formal affiliation to any external sites that link to www.intechopen.com, unless otherwise specifically stated. As such, it is not responsible for content that appears on any such sites. The inclusion of any link to IntechOpen does not imply endorsement by IntechOpen. Use of any such linked website is done solely at the user's own discretion.
\n\n
We reserve the right of ownership over our entire website www.intechopen.com, and all contents. By using our services, you agree to remove all links to our website immediately upon request. We also reserve the right to amend these Terms and Conditions and our linking policy at any time. By continuing to link to our website, you agree to be bound to, and abide by, these linking Terms and Conditions.
\n\n
If you find any link on our website, or any linked website, objectionable for any reason, please Contact Us. We will consider all requests to remove links but will have no obligation to do so.
\n\n
7. Frames
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Without prior approval and express written permission, you may not create frames around our web pages or use other techniques that alter in any way the visual presentation or appearance of our website.
\n\n
8. Modifications
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IntechOpen may revise its Terms of Service for its website at any time without notice. By using this website, you are agreeing to be bound by the current version of all Terms at the time of use.
\n\n
9. Governing Law
\n\n
These Terms and Conditions are governed by and construed in accordance with the laws of the United Kingdom and you irrevocably submit to the exclusive jurisdiction of the courts in London, United Kingdom.
\n\n
Croatian version of Terms and Conditions available here
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is one of the most well-known and useful plants in the world. Almost all the parts of this plant, that are, fruit, leaves, flower bud, trunk, and pseudo-stem, can be utilized. This chapter deals with the fiber extracted from the pseudo-stem of the banana plant. It discusses the production of banana pseudo-stem fiber, which includes plantation and harvesting; extraction of banana pseudo-stem fiber; retting; and degumming of the fiber. It also deals with the characteristics of the banana pseudo-stem fiber, such as morphological, physical and mechanical, durability, degradability, thermal, chemical, and antibacterial properties. Several potential applications of this fiber are also mentioned, such as the use of this fiber to fabricate rope, place mats, paper cardboard, string thread, tea bags, high-quality textile materials, absorbent, polymer/fiber composites, etc.",book:{id:"7544",slug:"banana-nutrition-function-and-processing-kinetics",title:"Banana Nutrition",fullTitle:"Banana Nutrition - Function and Processing Kinetics"},signatures:"Asmanto Subagyo and Achmad Chafidz",authors:[{id:"257742",title:"M.Sc.",name:"Achmad",middleName:null,surname:"Chafidz",slug:"achmad-chafidz",fullName:"Achmad Chafidz"},{id:"268400",title:"Mr.",name:"Asmanto",middleName:null,surname:"Subagyo",slug:"asmanto-subagyo",fullName:"Asmanto Subagyo"}]},{id:"40180",title:"Plant Tissue Culture: Current Status and Opportunities",slug:"plant-tissue-culture-current-status-and-opportunities",totalDownloads:66452,totalCrossrefCites:43,totalDimensionsCites:89,abstract:null,book:{id:"3568",slug:"recent-advances-in-plant-in-vitro-culture",title:"Recent Advances in Plant in vitro Culture",fullTitle:"Recent Advances in Plant in vitro Culture"},signatures:"Altaf Hussain, Iqbal Ahmed Qarshi, Hummera Nazir and Ikram Ullah",authors:[{id:"147617",title:"Dr.",name:"Altaf",middleName:null,surname:"Hussain",slug:"altaf-hussain",fullName:"Altaf Hussain"}]},{id:"66996",title:"Ethiopian Common Medicinal Plants: Their Parts and Uses in Traditional Medicine - Ecology and Quality Control",slug:"ethiopian-common-medicinal-plants-their-parts-and-uses-in-traditional-medicine-ecology-and-quality-c",totalDownloads:4059,totalCrossrefCites:6,totalDimensionsCites:10,abstract:"The main purpose of this review is to document medicinal plants used for traditional treatments with their parts, use, ecology, and quality control. Accordingly, 80 medicinal plant species were reviewed; leaves and roots are the main parts of the plants used for preparation of traditional medicines. The local practitioners provided various traditional medications to their patients’ diseases such as stomachaches, asthma, dysentery, malaria, evil eyes, cancer, skin diseases, and headaches. The uses of medicinal plants for human and animal treatments are practiced from time immemorial. Stream/riverbanks, cultivated lands, disturbed sites, bushlands, forested areas and their margins, woodlands, grasslands, and home gardens are major habitats of medicinal plants. Generally, medicinal plants used for traditional medicine play a significant role in the healthcare of the majority of the people in Ethiopia. The major threats to medicinal plants are habitat destruction, urbanization, agricultural expansion, investment, road construction, and deforestation. Because of these, medicinal plants are being declined and lost with their habitats. Community- and research-based conservation mechanisms could be an appropriate approach for mitigating the problems pertinent to the loss of medicinal plants and their habitats and for documenting medicinal plants. Chromatography; electrophoretic, macroscopic, and microscopic techniques; and pharmaceutical practice are mainly used for quality control of herbal medicines.",book:{id:"8502",slug:"plant-science-structure-anatomy-and-physiology-in-plants-cultured-in-vivo-and-in-vitro",title:"Plant Science",fullTitle:"Plant Science - Structure, Anatomy and Physiology in Plants Cultured in Vivo and in Vitro"},signatures:"Admasu Moges and Yohannes Moges",authors:[{id:"249746",title:"Ph.D.",name:"Admasu",middleName:null,surname:"Moges",slug:"admasu-moges",fullName:"Admasu Moges"},{id:"297761",title:"MSc.",name:"Yohannes",middleName:null,surname:"Moges",slug:"yohannes-moges",fullName:"Yohannes Moges"}]},{id:"70658",title:"Factors Affecting Yield of Crops",slug:"factors-affecting-yield-of-crops",totalDownloads:4044,totalCrossrefCites:25,totalDimensionsCites:40,abstract:"A good understanding of dynamics involved in food production is critical for the improvement of food security. It has been demonstrated that an increase in crop yields significantly reduces poverty. Yield, the mass of harvest crop product in a specific area, is influenced by several factors. These factors are grouped in three basic categories known as technological (agricultural practices, managerial decision, etc.), biological (diseases, insects, pests, weeds) and environmental (climatic condition, soil fertility, topography, water quality, etc.). These factors account for yield differences from one region to another worldwide. The current chapter will discuss each of these three basic factors as well as providing some recommendations for overcoming them. In addition, it will provide the importance of climate-smart agriculture in the increase of crop yields while facilitating the achievement of crop production in safe environment. This goes in line with the second goal of 2030 Agenda for Sustainable Development of United Nations in transforming our world formulated as end hunger, achieve food security, improve nutrition and promote sustainable agriculture.",book:{id:"8153",slug:"agronomy-climate-change-food-security",title:"Agronomy",fullTitle:"Agronomy - Climate Change & Food Security"},signatures:"Tandzi Ngoune Liliane and Mutengwa Shelton Charles",authors:[{id:"313819",title:"Dr.",name:"Liliane",middleName:null,surname:"Tandzi",slug:"liliane-tandzi",fullName:"Liliane Tandzi"},{id:"314316",title:"Prof.",name:"Charles Shelton",middleName:null,surname:"Mutengwa",slug:"charles-shelton-mutengwa",fullName:"Charles Shelton Mutengwa"}]},{id:"59402",title:"Robotic Harvesting of Fruiting Vegetables: A Simulation Approach in V-REP, ROS and MATLAB",slug:"robotic-harvesting-of-fruiting-vegetables-a-simulation-approach-in-v-rep-ros-and-matlab",totalDownloads:2797,totalCrossrefCites:7,totalDimensionsCites:8,abstract:"In modern agriculture, there is a high demand to move from tedious manual harvesting to a continuously automated operation. This chapter reports on designing a simulation and control platform in V-REP, ROS, and MATLAB for experimenting with sensors and manipulators in robotic harvesting of sweet pepper. The objective was to provide a completely simulated environment for improvement of visual servoing task through easy testing and debugging of control algorithms with zero damage risk to the real robot and to the actual equipment. A simulated workspace, including an exact replica of different robot manipulators, sensing mechanisms, and sweet pepper plant, and fruit system was created in V-REP. Image moment method visual servoing with eye-in-hand configuration was implemented in MATLAB, and was tested on four robotic platforms including Fanuc LR Mate 200iD, NOVABOT, multiple linear actuators, and multiple SCARA arms. Data from simulation experiments were used as inputs of the control algorithm in MATLAB, whose outputs were sent back to the simulated workspace and to the actual robots. ROS was used for exchanging data between the simulated environment and the real workspace via its publish-and-subscribe architecture. Results provided a framework for experimenting with different sensing and acting scenarios, and verified the performance functionality of the simulator.",book:{id:"6265",slug:"automation-in-agriculture-securing-food-supplies-for-future-generations",title:"Automation in Agriculture",fullTitle:"Automation in Agriculture - Securing Food Supplies for Future Generations"},signatures:"Redmond R. Shamshiri, Ibrahim A. Hameed, Manoj Karkee and\nCornelia Weltzien",authors:[{id:"182449",title:"Prof.",name:"Ibrahim",middleName:"A.",surname:"Hameed",slug:"ibrahim-hameed",fullName:"Ibrahim Hameed"},{id:"203413",title:"Dr.",name:"Redmond R.",middleName:null,surname:"Shamshiri",slug:"redmond-r.-shamshiri",fullName:"Redmond R. Shamshiri"},{id:"241193",title:"Dr.",name:"Manoj",middleName:null,surname:"Karkee",slug:"manoj-karkee",fullName:"Manoj Karkee"},{id:"241194",title:"Dr.",name:"Cornelia",middleName:null,surname:"Weltzien",slug:"cornelia-weltzien",fullName:"Cornelia Weltzien"}]}],onlineFirstChaptersFilter:{topicId:"5",limit:6,offset:0},onlineFirstChaptersCollection:[{id:"82522",title:"Macauba (Acrocomia aculeata): Biology, Oil Processing, and Technological Potential",slug:"macauba-acrocomia-aculeata-biology-oil-processing-and-technological-potential",totalDownloads:0,totalDimensionsCites:0,doi:"10.5772/intechopen.105540",abstract:"The global production of vegetable oil has increased since the beginning of the century, reaching a peak of 209 million tons in 2020/2021 and is projected to continue to increase due to population growth and the impact of the biodiesel industry. In this context, palm oil and soybean oil have stood out. However, both palm oil and soybean oil production chains are not fully sustainable, leading to socioeconomic and environmental challenges, which have driven the search for new raw materials with sustainability potential. Macauba [Acrocomia aculeata (Jacq.) Lodd. Ex Mart.] is an oleaginous palm distributed mainly in Central and South America, and most of the Brazilian territory. It is one of the species with greater potential for economic exploitation due to its high oil productivity and use of by-products from oil extraction and processing. This chapter addresses the most up-to-date information in biology, oil production, and oil processing from fruit to oil applications.",book:{id:"11627",title:"Oilseed Crops - Biology, Production and Processing",coverURL:"https://cdn.intechopen.com/books/images_new/11627.jpg"},signatures:"Odalys García Cabrera, Larissa Magalhães Grimaldi, Renato Grimaldi and Ana Paula Badan Ribeiro"},{id:"81567",title:"Basic Animal Breeding Methods",slug:"basic-animal-breeding-methods",totalDownloads:1,totalDimensionsCites:0,doi:"10.5772/intechopen.104136",abstract:"In the era of genomic selection, basic animal breeding methods are still playing a very important role in animal selection and their improvement. Animal Breeding involves the selective breeding of domestic animals with the intention to improve desirable and heritable qualities in the next generation. An animal’s overall performance is mostly influenced by genetic potential acquired from its parents, as well as the environment, which includes nutrition, health, management, and other factors. This chapter covers a brief outline of traditional breeding methods for the selection of animals and their improvement.",book:{id:"10898",title:"Animal Husbandry",coverURL:"https://cdn.intechopen.com/books/images_new/10898.jpg"},signatures:"Mohan Singh Thakur"},{id:"82262",title:"Quality of Postharvest Degreened Citrus Fruit",slug:"quality-of-postharvest-degreened-citrus-fruit",totalDownloads:4,totalDimensionsCites:0,doi:"10.5772/intechopen.105119",abstract:"External color is a key factor that defines external citrus fruit quality. Degreening with exogenous ethylene exposure is a widely used postharvest treatment applied to promote external citrus fruit color development, mainly with those cultivars that reach internal maturity while their external peel color is still green. Ethylene plays a crucial role in the color change of citrus fruit because it induces two simultaneous, but independent, processes—chlorophyll degradation and carotenoid synthesis. However, it is important to know, in addition to the effect on skin color development, whether this treatment can negatively affect other fruit quality parameters. This chapter addresses the influence of postharvest degreening treatment on the physicochemical, nutritional, and sensory quality of citrus fruit.",book:{id:"11629",title:"Advances in Citrus Production and Research",coverURL:"https://cdn.intechopen.com/books/images_new/11629.jpg"},signatures:"Julia Morales, Lourdes Cervera, Pilar Navarro and Alejandra Salvador"},{id:"82508",title:"Introductory Chapter: Global Cotton Research Development Trends for the Past Five Years - Key Directions",slug:"introductory-chapter-global-cotton-research-development-trends-for-the-past-five-years-key-direction",totalDownloads:0,totalDimensionsCites:0,doi:"10.5772/intechopen.105748",abstract:null,book:{id:"11362",title:"Cotton",coverURL:"https://cdn.intechopen.com/books/images_new/11362.jpg"},signatures:"Ibrokhim Y. Abdurakhmonov"},{id:"82512",title:"Antimicrobial Residues in Meat and Meat Products",slug:"antimicrobial-residues-in-meat-and-meat-products",totalDownloads:1,totalDimensionsCites:0,doi:"10.5772/intechopen.105784",abstract:"The presence of antimicrobial residues (AMRs) in meat is considered a serious threat to public health in the twenty-first century. This work aims at addressing the problem of AMRs in meat regarding their sources, negative effects, detection tests, and prevention and control practices. The health risks associated with such residues include direct toxicity, drug allergy, hypersensitive reactions, and the development of antibiotic-resistant bacteria. Moreover, disturbance of gut microbiota and bone marrow disorders are also direct consequences of continuous exposure to small quantities of antimicrobial residues (AMRs). Due to long-term exposure to antibiotic residues during gestation, various congenital anomalies were also seen in newborn children. Carcinogenic impacts and mutagenic effects are other negative impacts of antibiotic residues on the food. Different practices are known to introduce AMRs into meat and meat products, such as misuse of chemotherapeutic medications, violating withdrawal periods, even with the proper administration of anti-infective agents, and usage of antibiotics as growth promoters and feed additives. The prevention of this problem requires multi-sector cooperation to restrict the improper use of antimicrobial drugs, standardize the rationale usage, and development of alternative chemicals or biologics for the purposes of preservation of meat products and as growth promoters for food-producing animals.",book:{id:"11638",title:"Meat Science and Nutrition - Recent Advances and Innovative Processing Technologies",coverURL:"https://cdn.intechopen.com/books/images_new/11638.jpg"},signatures:"Dhary Alewy Almashhadany, Hero I. Mohammed, Thaera Abdulwahid M. Muslat, Rzgar F. Rashid, Rawaz R. Hassan and Abdullah O. Hassan"},{id:"81769",title:"Biological Control of Agricultural Insect Pests",slug:"biological-control-of-agricultural-insect-pests",totalDownloads:4,totalDimensionsCites:0,doi:"10.5772/intechopen.104464",abstract:"Pests are highly responsible for heavy crop losses and reduced food supplies, poorer quality of agricultural products, economic hardship for growers and processor. Generally, chemical control methods are practiced for their control which is neither always economical nor effective and may have associated unwanted health, safety and environmental risks. However, to meet the challenge of feeding to the ever increasing human population, an efficient, economical and environment friendly disease control methods are requisites. In this regard, biological control may be an effective means of reducing or mitigating the pests and pest effects through the use of natural enemies. Biological control is an environmentally sound which involves the use of beneficial microorganism to control plant pathogens and diseases they cause. Therefore, in this chapter we will provide a comprehensive account of this environmental friendly approach for effectively management of plant diseases. This chapter will also accentuate the development of biological control agents for practical applications and the underlying mechanism. The contents in the chapter will be beneficial and advantageous to all those working in academia or industry related to crop protection.",book:{id:"11015",title:"Insecticides",coverURL:"https://cdn.intechopen.com/books/images_new/11015.jpg"},signatures:"Mrinalini Kumari, Atul Srivastava, Shyam Babu Sah and Subhashini Sini"}],onlineFirstChaptersTotal:357},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:90,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:104,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:32,numberOfPublishedChapters:319,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:12,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:141,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:133,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:113,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:107,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:5,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:16,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}},{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}}]},series:{item:{id:"24",title:"Sustainable Development",doi:"10.5772/intechopen.100361",issn:null,scope:"
\r\n\tTransforming our World: the 2030 Agenda for Sustainable Development endorsed by United Nations and 193 Member States, came into effect on Jan 1, 2016, to guide decision making and actions to the year 2030 and beyond. Central to this Agenda are 17 Goals, 169 associated targets and over 230 indicators that are reviewed annually. The vision envisaged in the implementation of the SDGs is centered on the five Ps: People, Planet, Prosperity, Peace and Partnership. This call for renewed focused efforts ensure we have a safe and healthy planet for current and future generations.
\r\n
\r\n\t
\r\n
\r\n\tThis Series focuses on covering research and applied research involving the five Ps through the following topics:
\r\n
\r\n\t
\r\n
\r\n\t1. Sustainable Economy and Fair Society that relates to SDG 1 on No Poverty, SDG 2 on Zero Hunger, SDG 8 on Decent Work and Economic Growth, SDG 10 on Reduced Inequalities, SDG 12 on Responsible Consumption and Production, and SDG 17 Partnership for the Goals
\r\n
\r\n\t
\r\n
\r\n\t2. Health and Wellbeing focusing on SDG 3 on Good Health and Wellbeing and SDG 6 on Clean Water and Sanitation
\r\n
\r\n\t
\r\n
\r\n\t3. Inclusivity and Social Equality involving SDG 4 on Quality Education, SDG 5 on Gender Equality, and SDG 16 on Peace, Justice and Strong Institutions
\r\n
\r\n\t
\r\n
\r\n\t4. Climate Change and Environmental Sustainability comprising SDG 13 on Climate Action, SDG 14 on Life Below Water, and SDG 15 on Life on Land
\r\n
\r\n\t
\r\n
\r\n\t5. Urban Planning and Environmental Management embracing SDG 7 on Affordable Clean Energy, SDG 9 on Industry, Innovation and Infrastructure, and SDG 11 on Sustainable Cities and Communities.
\r\n
\r\n\t
\r\n
\r\n\tThe series also seeks to support the use of cross cutting SDGs, as many of the goals listed above, targets and indicators are all interconnected to impact our lives and the decisions we make on a daily basis, making them impossible to tie to a single topic.
",coverUrl:"https://cdn.intechopen.com/series/covers/24.jpg",latestPublicationDate:"July 5th, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:0,editor:{id:"262440",title:"Prof.",name:"Usha",middleName:null,surname:"Iyer-Raniga",slug:"usha-iyer-raniga",fullName:"Usha Iyer-Raniga",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRYSXQA4/Profile_Picture_2022-02-28T13:55:36.jpeg",biography:"Usha Iyer-Raniga is a professor in the School of Property and Construction Management at RMIT University. Usha co-leads the One Planet Network’s Sustainable Buildings and Construction Programme (SBC), a United Nations 10 Year Framework of Programmes on Sustainable Consumption and Production (UN 10FYP SCP) aligned with Sustainable Development Goal 12. The work also directly impacts SDG 11 on Sustainable Cities and Communities. She completed her undergraduate degree as an architect before obtaining her Masters degree from Canada and her Doctorate in Australia. Usha has been a keynote speaker as well as an invited speaker at national and international conferences, seminars and workshops. Her teaching experience includes teaching in Asian countries. She has advised Austrade, APEC, national, state and local governments. She serves as a reviewer and a member of the scientific committee for national and international refereed journals and refereed conferences. She is on the editorial board for refereed journals and has worked on Special Issues. Usha has served and continues to serve on the Boards of several not-for-profit organisations and she has also served as panel judge for a number of awards including the Premiers Sustainability Award in Victoria and the International Green Gown Awards. Usha has published over 100 publications, including research and consulting reports. Her publications cover a wide range of scientific and technical research publications that include edited books, book chapters, refereed journals, refereed conference papers and reports for local, state and federal government clients. She has also produced podcasts for various organisations and participated in media interviews. She has received state, national and international funding worth over USD $25 million. Usha has been awarded the Quarterly Franklin Membership by London Journals Press (UK). Her biography has been included in the Marquis Who's Who in the World® 2018, 2016 (33rd Edition), along with approximately 55,000 of the most accomplished men and women from around the world, including luminaries as U.N. Secretary-General Ban Ki-moon. In 2017, Usha was awarded the Marquis Who’s Who Lifetime Achiever Award.",institutionString:null,institution:{name:"RMIT University",institutionURL:null,country:{name:"Australia"}}},editorTwo:null,editorThree:null},subseries:{paginationCount:11,paginationItems:[{id:"91",title:"Sustainable Economy and Fair Society",coverUrl:"https://cdn.intechopen.com/series_topics/covers/91.jpg",editor:{id:"181603",title:"Dr.",name:"Antonella",middleName:null,surname:"Petrillo",slug:"antonella-petrillo",fullName:"Antonella Petrillo",profilePictureURL:"https://mts.intechopen.com/storage/users/181603/images/system/181603.jpg",biography:"Antonella Petrillo is a Professor at the Department of Engineering of the University of Naples “Parthenope”, Italy. She received her Ph.D. in Mechanical Engineering from the University of Cassino. Her research interests include multi-criteria decision analysis, industrial plant, logistics, manufacturing and safety. 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He worked as a post-doctoral fellow at the Public Health Research Institute (PHRI), Newark, NJ for four years before accepting a three-year faculty position at Brigham Young University-Hawaii. Dr. Engohang-Ndong is a tenured faculty member with the academic rank of Full Professor at Kent State University, Ohio, where he teaches a wide range of biological science courses and pursues his research in medical and environmental microbiology. 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He has an excellent track record in the herpesvirus field, and his group is engaged in clinical research in the field of Epstein-Barr virus diseases. He is the editor of the online Encyclopedia of Environment and he coordinates the Universal Health Coverage education program for the BioHealth Computing Schools of the European Institute of Science.",institutionString:null,institution:{name:"Grenoble Alpes University",country:{name:"France"}}},{id:"131400",title:"Prof.",name:"Alfonso J.",middleName:null,surname:"Rodriguez-Morales",slug:"alfonso-j.-rodriguez-morales",fullName:"Alfonso J. Rodriguez-Morales",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/131400/images/system/131400.png",biography:"Dr. Rodriguez-Morales is an expert in tropical and emerging diseases, particularly zoonotic and vector-borne diseases (especially arboviral diseases). He is the president of the Travel Medicine Committee of the Pan-American Infectious Diseases Association (API), as well as the president of the Colombian Association of Infectious Diseases (ACIN). He is a member of the Committee on Tropical Medicine, Zoonoses, and Travel Medicine of ACIN. He is a vice-president of the Latin American Society for Travel Medicine (SLAMVI) and a Member of the Council of the International Society for Infectious Diseases (ISID). Since 2014, he has been recognized as a Senior Researcher, at the Ministry of Science of Colombia. He is a professor at the Faculty of Medicine of the Fundacion Universitaria Autonoma de las Americas, in Pereira, Risaralda, Colombia. He is an External Professor, Master in Research on Tropical Medicine and International Health, Universitat de Barcelona, Spain. He is also a professor at the Master in Clinical Epidemiology and Biostatistics, Universidad Científica del Sur, Lima, Peru. In 2021 he has been awarded the “Raul Isturiz Award” Medal of the API. Also, in 2021, he was awarded with the “Jose Felix Patiño” Asclepius Staff Medal of the Colombian Medical College, due to his scientific contributions to COVID-19 during the pandemic. He is currently the Editor in Chief of the journal Travel Medicine and Infectious Diseases. His Scopus H index is 47 (Google Scholar H index, 68).",institutionString:"Institución Universitaria Visión de las Américas, Colombia",institution:null},{id:"332819",title:"Dr.",name:"Chukwudi Michael",middleName:"Michael",surname:"Egbuche",slug:"chukwudi-michael-egbuche",fullName:"Chukwudi Michael Egbuche",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/332819/images/14624_n.jpg",biography:"I an Dr. Chukwudi Michael Egbuche. I am a Senior Lecturer in the Department of Parasitology and Entomology, Nnamdi Azikiwe University, Awka.",institutionString:null,institution:{name:"Nnamdi Azikiwe University",country:{name:"Nigeria"}}},{id:"284232",title:"Mr.",name:"Nikunj",middleName:"U",surname:"Tandel",slug:"nikunj-tandel",fullName:"Nikunj Tandel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/284232/images/8275_n.jpg",biography:'Mr. Nikunj Tandel has completed his Master\'s degree in Biotechnology from VIT University, India in the year of 2012. He is having 8 years of research experience especially in the field of malaria epidemiology, immunology, and nanoparticle-based drug delivery system against the infectious diseases, autoimmune disorders and cancer. He has worked for the NIH funded-International Center of Excellence in Malaria Research project "Center for the study of complex malaria in India (CSCMi)" in collaboration with New York University. The preliminary objectives of the study are to understand and develop the evidence-based tools and interventions for the control and prevention of malaria in different sites of the INDIA. Alongside, with the help of next-generation genomics study, the team has studied the antimalarial drug resistance in India. Further, he has extended his research in the development of Humanized mice for the study of liver-stage malaria and identification of molecular marker(s) for the Artemisinin resistance. At present, his research focuses on understanding the role of B cells in the activation of CD8+ T cells in malaria. Received the CSIR-SRF (Senior Research Fellow) award-2018, FIMSA (Federation of Immunological Societies of Asia-Oceania) Travel Bursary award to attend the IUIS-IIS-FIMSA Immunology course-2019',institutionString:"Nirma University",institution:{name:"Nirma University",country:{name:"India"}}},{id:"334383",title:"Ph.D.",name:"Simone",middleName:"Ulrich",surname:"Ulrich Picoli",slug:"simone-ulrich-picoli",fullName:"Simone Ulrich Picoli",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/334383/images/15919_n.jpg",biography:"Graduated in Pharmacy from Universidade Luterana do Brasil (1999), Master in Agricultural and Environmental Microbiology from Federal University of Rio Grande do Sul (2002), Specialization in Clinical Microbiology from Universidade de São Paulo, USP (2007) and PhD in Sciences in Gastroenterology and Hepatology (2012). She is currently an Adjunct Professor at Feevale University in Medicine and Biomedicine courses and a permanent professor of the Academic Master\\'s Degree in Virology. She has experience in the field of Microbiology, with an emphasis on Bacteriology, working mainly on the following topics: bacteriophages, bacterial resistance, clinical microbiology and food microbiology.",institutionString:null,institution:{name:"Universidade Feevale",country:{name:"Brazil"}}},{id:"229220",title:"Dr.",name:"Amjad",middleName:"Islam",surname:"Aqib",slug:"amjad-aqib",fullName:"Amjad Aqib",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/229220/images/system/229220.png",biography:"Dr. Amjad Islam Aqib obtained a DVM and MSc (Hons) from University of Agriculture Faisalabad (UAF), Pakistan, and a PhD from the University of Veterinary and Animal Sciences Lahore, Pakistan. Dr. Aqib joined the Department of Clinical Medicine and Surgery at UAF for one year as an assistant professor where he developed a research laboratory designated for pathogenic bacteria. Since 2018, he has been Assistant Professor/Officer in-charge, Department of Medicine, Manager Research Operations and Development-ORIC, and President One Health Club at Cholistan University of Veterinary and Animal Sciences, Bahawalpur, Pakistan. He has nearly 100 publications to his credit. His research interests include epidemiological patterns and molecular analysis of antimicrobial resistance and modulation and vaccine development against animal pathogens of public health concern.",institutionString:"Cholistan University of Veterinary and Animal Sciences",institution:null},{id:"62900",title:"Prof.",name:"Fethi",middleName:null,surname:"Derbel",slug:"fethi-derbel",fullName:"Fethi Derbel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/62900/images/system/62900.jpeg",biography:"Professor Fethi Derbel was born in 1960 in Tunisia. He received his medical degree from the Sousse Faculty of Medicine at Sousse, University of Sousse, Tunisia. He completed his surgical residency in General Surgery at the University Hospital Farhat Hached of Sousse and was a member of the Unit of Liver Transplantation in the University of Rennes, France. He then worked in the Department of Surgery at the Sahloul University Hospital in Sousse. Professor Derbel is presently working at the Clinique les Oliviers, Sousse, Tunisia. His hospital activities are mostly concerned with laparoscopic, colorectal, pancreatic, hepatobiliary, and gastric surgery. He is also very interested in hernia surgery and performs ventral hernia repairs and inguinal hernia repairs. He has been a member of the GREPA and Tunisian Hernia Society (THS). During his residency, he managed patients suffering from diabetic foot, and he was very interested in this pathology. For this reason, he decided to coordinate a book project dealing with the diabetic foot. Professor Derbel has published many articles in journals and collaborates intensively with IntechOpen Access Publisher as an editor.",institutionString:"Clinique les Oliviers",institution:null},{id:"300144",title:"Dr.",name:"Meriem",middleName:null,surname:"Braiki",slug:"meriem-braiki",fullName:"Meriem Braiki",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/300144/images/system/300144.jpg",biography:"Dr. Meriem Braiki is a specialist in pediatric surgeon from Tunisia. She was born in 1985. She received her medical degree from the University of Medicine at Sousse, Tunisia. She achieved her surgical residency training periods in Pediatric Surgery departments at University Hospitals in Monastir, Tunis and France.\r\nShe is currently working at the Pediatric surgery department, Sidi Bouzid Hospital, Tunisia. Her hospital activities are mostly concerned with laparoscopic, parietal, urological and digestive surgery. She has published several articles in diffrent journals.",institutionString:"Sidi Bouzid Regional Hospital",institution:null},{id:"229481",title:"Dr.",name:"Erika M.",middleName:"Martins",surname:"de Carvalho",slug:"erika-m.-de-carvalho",fullName:"Erika M. de Carvalho",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/229481/images/6397_n.jpg",biography:null,institutionString:null,institution:{name:"Oswaldo Cruz Foundation",country:{name:"Brazil"}}},{id:"186537",title:"Prof.",name:"Tonay",middleName:null,surname:"Inceboz",slug:"tonay-inceboz",fullName:"Tonay Inceboz",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/186537/images/system/186537.jfif",biography:"I was graduated from Ege University of Medical Faculty (Turkey) in 1988 and completed his Med. PhD degree in Medical Parasitology at the same university. I became an Associate Professor in 2008 and Professor in 2014. I am currently working as a Professor at the Department of Medical Parasitology at Dokuz Eylul University, Izmir, Turkey.\n\nI have given many lectures, presentations in different academic meetings. I have more than 60 articles in peer-reviewed journals, 18 book chapters, 1 book editorship.\n\nMy research interests are Echinococcus granulosus, Echinococcus multilocularis (diagnosis, life cycle, in vitro and in vivo cultivation), and Trichomonas vaginalis (diagnosis, PCR, and in vitro cultivation).",institutionString:"Dokuz Eylül University",institution:{name:"Dokuz Eylül University",country:{name:"Turkey"}}},{id:"71812",title:"Prof.",name:"Hanem Fathy",middleName:"Fathy",surname:"Khater",slug:"hanem-fathy-khater",fullName:"Hanem Fathy Khater",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/71812/images/1167_n.jpg",biography:"Prof. Khater is a Professor of Parasitology at Benha University, Egypt. She studied for her doctoral degree, at the Department of Entomology, College of Agriculture, Food and Natural Resources, University of Missouri, Columbia, USA. She has completed her Ph.D. degrees in Parasitology in Egypt, from where she got the award for “the best scientific Ph.D. dissertation”. She worked at the School of Biological Sciences, Bristol, England, the UK in controlling insects of medical and veterinary importance as a grant from Newton Mosharafa, the British Council. Her research is focused on searching of pesticides against mosquitoes, house flies, lice, green bottle fly, camel nasal botfly, soft and hard ticks, mites, and the diamondback moth as well as control of several parasites using safe and natural materials to avoid drug resistances and environmental contamination.",institutionString:null,institution:{name:"Banha University",country:{name:"Egypt"}}},{id:"99780",title:"Prof.",name:"Omolade",middleName:"Olayinka",surname:"Okwa",slug:"omolade-okwa",fullName:"Omolade Okwa",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/99780/images/system/99780.jpg",biography:"Omolade Olayinka Okwa is presently a Professor of Parasitology at Lagos State University, Nigeria. She has a PhD in Parasitology (1997), an MSc in Cellular Parasitology (1992), and a BSc (Hons) Zoology (1990) all from the University of Ibadan, Nigeria. She teaches parasitology at the undergraduate and postgraduate levels. She was a recipient of a Commonwealth fellowship supported by British Council tenable at the Centre for Entomology and Parasitology (CAEP), Keele University, United Kingdom between 2004 and 2005. She was awarded an Honorary Visiting Research Fellow at the same university from 2005 to 2007. \nShe has been an external examiner to the Department of Veterinary Microbiology and Parasitology, University of Ibadan, MSc programme between 2010 and 2012. She is a member of the Nigerian Society of Experimental Biology (NISEB), Parasitology and Public Health Society of Nigeria (PPSN), Science Association of Nigeria (SAN), Zoological Society of Nigeria (ZSN), and is Vice Chairperson of the Organisation of Women in Science (OWSG), LASU chapter. She served as Head of Department of Zoology and Environmental Biology, Lagos State University from 2007 to 2010 and 2014 to 2016. She is a reviewer for several local and international journals such as Unilag Journal of Science, Libyan Journal of Medicine, Journal of Medicine and Medical Sciences, and Annual Research and Review in Science. \nShe has authored 45 scientific research publications in local and international journals, 8 scientific reviews, 4 books, and 3 book chapters, which includes the books “Malaria Parasites” and “Malaria” which are IntechOpen access publications.",institutionString:"Lagos State University",institution:{name:"Lagos State University",country:{name:"Nigeria"}}},{id:"273100",title:"Dr.",name:"Vijay",middleName:null,surname:"Gayam",slug:"vijay-gayam",fullName:"Vijay Gayam",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/273100/images/system/273100.jpeg",biography:"Dr. Vijay Bhaskar Reddy Gayam is currently practicing as an internist at Interfaith Medical Center in Brooklyn, New York, USA. He is also a Clinical Assistant Professor at the SUNY Downstate University Hospital and Adjunct Professor of Medicine at the American University of Antigua. He is a holder of an M.B.B.S. degree bestowed to him by Osmania Medical College and received his M.D. at Interfaith Medical Center. His career goals thus far have heavily focused on direct patient care, medical education, and clinical research. He currently serves in two leadership capacities; Assistant Program Director of Medicine at Interfaith Medical Center and as a Councilor for the American\r\nFederation for Medical Research. As a true academician and researcher, he has more than 50 papers indexed in international peer-reviewed journals. He has also presented numerous papers in multiple national and international scientific conferences. His areas of research interest include general internal medicine, gastroenterology and hepatology. He serves as an editor, editorial board member and reviewer for multiple international journals. His research on Hepatitis C has been very successful and has led to multiple research awards, including the 'Equity in Prevention and Treatment Award” from the New York Department of Health Viral Hepatitis Symposium (2018) and the 'Presidential Poster Award” awarded to him by the American College of Gastroenterology (2018). He was also awarded 'Outstanding Clinician in General Medicine” by Venus International Foundation for his extensive research expertise and services, perform over and above the standard expected in the advancement of healthcare, patient safety and quality of care.",institutionString:"Interfaith Medical Center",institution:{name:"Interfaith Medical Center",country:{name:"United States of America"}}},{id:"93517",title:"Dr.",name:"Clement",middleName:"Adebajo",surname:"Meseko",slug:"clement-meseko",fullName:"Clement Meseko",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/93517/images/system/93517.jpg",biography:"Dr. Clement Meseko obtained DVM and PhD degree in Veterinary Medicine and Virology respectively. He has worked for over 20 years in both private and public sectors including the academia, contributing to knowledge and control of infectious disease. Through the application of epidemiological skill, classical and molecular virological skills, he investigates viruses of economic and public health importance for the mitigation of the negative impact on people, animal and the environment in the context of Onehealth. \r\nDr. Meseko’s field experience on animal and zoonotic diseases and pathogen dynamics at the human-animal interface over the years shaped his carrier in research and scientific inquiries. He has been part of the investigation of Highly Pathogenic Avian Influenza incursions in sub Saharan Africa and monitors swine Influenza (Pandemic influenza Virus) agro-ecology and potential for interspecies transmission. He has authored and reviewed a number of journal articles and book chapters.",institutionString:"National Veterinary Research Institute",institution:{name:"National Veterinary Research Institute",country:{name:"Nigeria"}}},{id:"158026",title:"Prof.",name:"Shailendra K.",middleName:null,surname:"Saxena",slug:"shailendra-k.-saxena",fullName:"Shailendra K. Saxena",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRET3QAO/Profile_Picture_2022-05-10T10:10:26.jpeg",biography:"Professor Dr. Shailendra K. Saxena is a vice dean and professor at King George's Medical University, Lucknow, India. His research interests involve understanding the molecular mechanisms of host defense during human viral infections and developing new predictive, preventive, and therapeutic strategies for them using Japanese encephalitis virus (JEV), HIV, and emerging viruses as a model via stem cell and cell culture technologies. His research work has been published in various high-impact factor journals (Science, PNAS, Nature Medicine) with a high number of citations. He has received many awards and honors in India and abroad including various Young Scientist Awards, BBSRC India Partnering Award, and Dr. JC Bose National Award of Department of Biotechnology, Min. of Science and Technology, Govt. of India. Dr. Saxena is a fellow of various international societies/academies including the Royal College of Pathologists, United Kingdom; Royal Society of Medicine, London; Royal Society of Biology, United Kingdom; Royal Society of Chemistry, London; and Academy of Translational Medicine Professionals, Austria. He was named a Global Leader in Science by The Scientist. He is also an international opinion leader/expert in vaccination for Japanese encephalitis by IPIC (UK).",institutionString:"King George's Medical University",institution:{name:"King George's Medical University",country:{name:"India"}}},{id:"94928",title:"Dr.",name:"Takuo",middleName:null,surname:"Mizukami",slug:"takuo-mizukami",fullName:"Takuo Mizukami",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/94928/images/6402_n.jpg",biography:null,institutionString:null,institution:{name:"National Institute of Infectious Diseases",country:{name:"Japan"}}},{id:"233433",title:"Dr.",name:"Yulia",middleName:null,surname:"Desheva",slug:"yulia-desheva",fullName:"Yulia Desheva",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/233433/images/system/233433.png",biography:"Dr. Yulia Desheva is a leading researcher at the Institute of Experimental Medicine, St. Petersburg, Russia. She is a professor in the Stomatology Faculty, St. Petersburg State University. She has expertise in the development and evaluation of a wide range of live mucosal vaccines against influenza and bacterial complications. Her research interests include immunity against influenza and COVID-19 and the development of immunization schemes for high-risk individuals.",institutionString:'Federal State Budgetary Scientific Institution "Institute of Experimental Medicine"',institution:null},{id:"238958",title:"Mr.",name:"Atamjit",middleName:null,surname:"Singh",slug:"atamjit-singh",fullName:"Atamjit Singh",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/238958/images/6575_n.jpg",biography:null,institutionString:null,institution:null},{id:"333753",title:"Dr.",name:"Rais",middleName:null,surname:"Ahmed",slug:"rais-ahmed",fullName:"Rais Ahmed",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/333753/images/20168_n.jpg",biography:null,institutionString:null,institution:null},{id:"252058",title:"M.Sc.",name:"Juan",middleName:null,surname:"Sulca",slug:"juan-sulca",fullName:"Juan Sulca",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/252058/images/12834_n.jpg",biography:null,institutionString:null,institution:null},{id:"191392",title:"Dr.",name:"Marimuthu",middleName:null,surname:"Govindarajan",slug:"marimuthu-govindarajan",fullName:"Marimuthu Govindarajan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/191392/images/5828_n.jpg",biography:"Dr. M. Govindarajan completed his BSc degree in Zoology at Government Arts College (Autonomous), Kumbakonam, and MSc, MPhil, and PhD degrees at Annamalai University, Annamalai Nagar, Tamil Nadu, India. He is serving as an assistant professor at the Department of Zoology, Annamalai University. His research interests include isolation, identification, and characterization of biologically active molecules from plants and microbes. He has identified more than 20 pure compounds with high mosquitocidal activity and also conducted high-quality research on photochemistry and nanosynthesis. He has published more than 150 studies in journals with impact factor and 2 books in Lambert Academic Publishing, Germany. He serves as an editorial board member in various national and international scientific journals.",institutionString:null,institution:null},{id:"274660",title:"Dr.",name:"Damodar",middleName:null,surname:"Paudel",slug:"damodar-paudel",fullName:"Damodar Paudel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/274660/images/8176_n.jpg",biography:"I am DrDamodar Paudel,currently working as consultant Physician in Nepal police Hospital.",institutionString:null,institution:null},{id:"241562",title:"Dr.",name:"Melvin",middleName:null,surname:"Sanicas",slug:"melvin-sanicas",fullName:"Melvin Sanicas",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241562/images/6699_n.jpg",biography:null,institutionString:null,institution:null},{id:"337446",title:"Dr.",name:"Maria",middleName:null,surname:"Zavala-Colon",slug:"maria-zavala-colon",fullName:"Maria Zavala-Colon",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Puerto Rico, Medical Sciences Campus",country:{name:"United States of America"}}},{id:"338856",title:"Mrs.",name:"Nur Alvira",middleName:null,surname:"Pascawati",slug:"nur-alvira-pascawati",fullName:"Nur Alvira Pascawati",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Universitas Respati Yogyakarta",country:{name:"Indonesia"}}},{id:"441116",title:"Dr.",name:"Jovanka M.",middleName:null,surname:"Voyich",slug:"jovanka-m.-voyich",fullName:"Jovanka M. Voyich",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Montana State University",country:{name:"United States of America"}}},{id:"330412",title:"Dr.",name:"Muhammad",middleName:null,surname:"Farhab",slug:"muhammad-farhab",fullName:"Muhammad Farhab",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Agriculture Faisalabad",country:{name:"Pakistan"}}},{id:"349495",title:"Dr.",name:"Muhammad",middleName:null,surname:"Ijaz",slug:"muhammad-ijaz",fullName:"Muhammad Ijaz",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Veterinary and Animal Sciences",country:{name:"Pakistan"}}}]}},subseries:{item:{id:"95",type:"subseries",title:"Urban Planning and Environmental Management",keywords:"Circular economy, Contingency planning and response to disasters, Ecosystem services, Integrated urban water management, Nature-based solutions, Sustainable urban development, Urban green spaces",scope:"
\r\n\tIf we aim to prosper as a society and as a species, there is no alternative to sustainability-oriented development and growth. Sustainable development is no longer a choice but a necessity for us all. Ecosystems and preserving ecosystem services and inclusive urban development present promising solutions to environmental problems. Contextually, the emphasis on studying these fields will enable us to identify and define the critical factors for territorial success in the upcoming decades to be considered by the main-actors, decision and policy makers, technicians, and public in general.
\r\n
\r\n\tHolistic urban planning and environmental management are therefore crucial spheres that will define sustainable trajectories for our urbanizing planet. This urban and environmental planning topic aims to attract contributions that address sustainable urban development challenges and solutions, including integrated urban water management, planning for the urban circular economy, monitoring of risks, contingency planning and response to disasters, among several other challenges and solutions.
",coverUrl:"https://cdn.intechopen.com/series_topics/covers/95.jpg",hasOnlineFirst:!1,hasPublishedBooks:!1,annualVolume:11979,editor:{id:"181079",title:"Dr.",name:"Christoph",middleName:null,surname:"Lüthi",slug:"christoph-luthi",fullName:"Christoph Lüthi",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRHSqQAO/Profile_Picture_2022-04-12T15:51:33.png",biography:"Dr. Christoph Lüthi is an urban infrastructure planner with over 25 years of experience in planning and design of urban infrastructure in middle and low-income countries. He holds a Master’s Degree in Urban Development Planning from the University College of London (UCL), and a Ph.D. in Urban Planning & Engineering from TU Berlin. He has conducted applied research on urban planning and infrastructure issues in over 20 countries in Africa and Asia. In 2005 he joined Eawag-Sandec as Leader of the Strategic Environmental Sanitation Planning Group. Since 2015 he heads the research department Sanitation, Water and Solid Waste for Development (Sandec) at the Swiss Federal Institute of Aquatic Research and Technology (Eawag).",institutionString:"Swiss Federal Institute of Aquatic Science and Technology, Switzerland",institution:null},editorTwo:{id:"290571",title:"Dr.",name:"Rui Alexandre",middleName:null,surname:"Castanho",slug:"rui-alexandre-castanho",fullName:"Rui Alexandre Castanho",profilePictureURL:"https://mts.intechopen.com/storage/users/290571/images/system/290571.jpg",biography:"Rui Alexandre Castanho has a master\\'s degree in Planning, Audit, and Control in Urban Green Spaces and an international Ph.D. in Sustainable Planning in Borderlands. Currently, he is a professor at WSB University, Poland, and a visiting professor at the University of Johannesburg, South Africa. Dr. Castanho is a post-doc researcher on the GREAT Project, University of Azores, Ponta Delgada, Portugal. He collaborates with the Environmental Resources Analysis Research Group (ARAM), University of Extremadura (UEx), Spain; VALORIZA - Research Center for the Enhancement of Endogenous Resources, Polytechnic Institute of Portalegre (IPP), Portugal; Centre for Tourism Research, Development and Innovation (CITUR), Madeira, Portugal; and AQUAGEO Research Group, University of Campinas (UNICAMP), Brazil.",institutionString:"University of Johannesburg, South Africa and WSB University, Poland",institution:{name:"University of Johannesburg",institutionURL:null,country:{name:"South Africa"}}},editorThree:null,series:{id:"24",title:"Sustainable Development",doi:"10.5772/intechopen.100361",issn:null},editorialBoard:[{id:"181486",title:"Dr.",name:"Claudia",middleName:null,surname:"Trillo",slug:"claudia-trillo",fullName:"Claudia Trillo",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSAZHQA4/Profile_Picture_2022-03-14T08:26:43.jpg",institutionString:null,institution:{name:"University of Salford",institutionURL:null,country:{name:"United Kingdom"}}},{id:"308328",title:"Dr.",name:"Dávid",middleName:null,surname:"Földes",slug:"david-foldes",fullName:"Dávid Földes",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00002nXXGKQA4/Profile_Picture_2022-03-11T08:25:45.jpg",institutionString:null,institution:{name:"Budapest University of Technology and Economics",institutionURL:null,country:{name:"Hungary"}}},{id:"282172",title:"Dr.",name:"Ivan",middleName:null,surname:"Oropeza-Perez",slug:"ivan-oropeza-perez",fullName:"Ivan Oropeza-Perez",profilePictureURL:"https://mts.intechopen.com/storage/users/282172/images/system/282172.jpg",institutionString:"Universidad de las Américas Puebla",institution:{name:"Universidad de las Américas Puebla",institutionURL:null,country:{name:"Mexico"}}}]},onlineFirstChapters:{paginationCount:14,paginationItems:[{id:"82457",title:"Canine Hearing Management",doi:"10.5772/intechopen.105515",signatures:"Peter M. 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