Information of the datasets used in the research.
\r\n\tb. The growth of digital environments which can educate and empower as well as exploit and destroy (mobile learning, STEM education, tablets, etc.).
\r\n\tc. Social, racial, class, and gender-based discriminations that restrict the developmental potential and the prosperity perspectives
\r\n\td. Health hazards and illnesses such as the laters COVID-19 pandemic.
\r\n\te. Armed conflicts with casualties and displacements of populations seeking refuge
\r\n\tf. Lack of physical spaces that will support and nourish development and learning, etc.
\r\n\tEducation in the post-modern era strives to address the above issues and develop policies, curricula, methodologies, and strategies to contribute to an environmentally and socially sustainable future. It embraces multiple perspectives and worldviews and seeks to touch on inequalities and discriminations in favor of equity. In this direction, children’s s agency lies at the heart of democratic approaches. Educational processes adopt forms of interactions that actualize learning as “becoming” and place it in a continuum between past, present, and future. This book intends to feature innovative approaches that employ transformative elements (targets, methods, materials, ideas, etc.) and embrace the concept of child development as “becoming” in an ever-changing and challenging world.
\r\n\r\n\tWe invite authors to contribute original research or research review papers that present innovative approaches addressing personal and social transformation. All aspects of early childhood education will be considered, including research methodology for the early years.
",isbn:"978-1-80355-949-0",printIsbn:"978-1-80355-948-3",pdfIsbn:"978-1-80355-950-6",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,isSalesforceBook:!1,isNomenclature:!1,hash:"351c41dca5c8c997f15e758f2e035178",bookSignature:"Dr. Maria Ampartzaki and Associate Prof. Michail Kalogiannakis",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11281.jpg",keywords:"Early Childhood Education, Preschool, STEAM, Environmental Sustainability, Social Sciences, Social Sustainability, ICT, Digital Devices, Education for Equity, Gender Issues, Post-modern Epistemology, Social Constructivism",numberOfDownloads:45,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"November 16th 2021",dateEndSecondStepPublish:"December 14th 2021",dateEndThirdStepPublish:"February 12th 2022",dateEndFourthStepPublish:"May 3rd 2022",dateEndFifthStepPublish:"July 2nd 2022",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"7 months",secondStepPassed:!0,areRegistrationsClosed:!0,currentStepOfPublishingProcess:5,editedByType:null,kuFlag:!1,biosketch:"Dr. Maria Ampartzaki is an Assistant Professor in Early Childhood Education in the Department of Preschool Education at the University of Crete. Her research interests include ICT in education, science education in the early years, inquiry-based and art-based learning, teachers’ professional development, action research, and the Pedagogy of Multiliteracies, among others. She has run and participated in several funded and non-funded projects on the teaching of Science, Social Sciences, and ICT in education.",coeditorOneBiosketch:"Michail Kalogiannakis is an Associate Professor of the Department of Preschool\r\nEducation, University of Crete in Greece. He graduated from the Physics Department\r\nof the University of Crete and continued his post-graduate studies at the University\r\nParis-7 and University Paris-5 and received his Ph.D. degree at the University Paris 5.\r\nHis research interests include science education in early childhood, science teaching\r\nand learning, e-learning, the use of ICT in science education, and games simulations.",coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"422488",title:"Dr.",name:"Maria",middleName:null,surname:"Ampartzaki",slug:"maria-ampartzaki",fullName:"Maria Ampartzaki",profilePictureURL:"https://mts.intechopen.com/storage/users/422488/images/system/422488.jpg",biography:"Dr Maria Ampartzaki is an Assistant Professor in Early Childhood Education in the Department of Preschool Education at the University of Crete. Her research interests include ICT in education, science education in the early years, inquiry-based and art-based learning, teachers’ professional development, action research, and the Pedagogy of Multiliteracies, among others. She has run and participated in several funded and non-funded projects on the teaching of Science, Social Sciences, and ICT in education. 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He graduated from the Physics Department of the University of Crete and continued his post-graduate studies at the University Paris 7-Denis Diderot (D.E.A. in Didactic of Physics), University Paris 5-René Descartes-Sorbonne (D.E.A. in Science Education) and received his Ph.D. degree at the University Paris 5-René Descartes-Sorbonne (PhD in Science Education). His research interests include science education in early childhood, science teaching and learning, e-learning, the use of ICT in science education, games simulations, and mobile learning. 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Haphazard infrastructural project execution that includes disregard in prioritizing city (or cities) selection is also a factor hampering sustainable development practices. Development projects that rely on selected organizations, which in turn rely on human judgment, can lead to unrealistic criteria evaluation, causing delays in project execution [1]. However, the fact remains that continued infrastructure development is unavoidable, especially since urban cities constantly need to evolve and grow to keep up with the times [2].
The selection of a city (or a group of cities) is one of the most important steps for sustainable development. The selection criteria must ensure that the city (cities) has high priority for development and is (are) in line with the needs of the local citizens. Moreover, timely selection requires effective planning and analysis and must consider multiple conflicting and disproportionate factors (such as those that have critical socioeconomic and environmental implications to different stakeholders). Urban planning application using remote sensing (RS) and geographical information systems (GIS) is one of the many areas that can be explored for city selection. Such applications would not only eliminate human bias but would also be able to make more objective decisions based on data.
Remote sensing can be applied in different aspects of urban planning such as (but not limited to) urban traffic analysis, urban environment analysis (air and water pollutions), and urban expansion. With recent developments in remote sensing technologies, remote sensing data can be exploited for urban studies. One example is the classification of land use based on high spatial and spectral resolution data such as orthomosaic and elevation images. Multidimensional spatiotemporal data can now be reliably obtained by sensors in different scale ranges and with flexible repetition rates [3].
Medium- to high-resolution satellite imagery can be used by urban planners and land managers to monitor land conditions to support decision-making for sustainable urban development. Remote sensors are able to provide voluminous amounts of data, which can be exploited to produce/update GIS maps or for detection changes in urban land covers. High-resolution satellite sensors available on IKONOS, for example, can collect diverse geospatial data for studying vegetation. The sensors can sense 4 m resolution multispectral and 1 m resolution panchromatic, Quickbird imageries with 2.4 m resolution multispectral and 61 cm resolution panchromatic, and Worldview-4 imageries with 1.24 m resolution multispectral and 31 cm panchromatic. Medium-resolution satellite sensors, available on Landsat-8, Sentinel-2, and SPOT, are also valuable data sources for urban and vegetation change detection from various time periods during the same season, which further supports analyzing any past changes. Analysis of such data can then be used for decision-making and planning for further development of a particular urban area [4].
Remote sensing data can be integrated with other spatial data to perform various types of full-fledged assessments. GIS techniques can be utilized to integrate the required spatial data and analytic data from various sources, such as field survey data, topographic maps, aerial photographs, and also archived data. The data can be represented as location (i.e., latitude and longitudes) or even as tabular attributes. GIS techniques play a substantial role in the data integration process of multilayer spatial information along with statistical information in various developmental scenarios [5].
Multi-criteria decision-making (MCDM) is concerned with making a decision by evaluating multiple conflicting criteria. It embodies various methods and procedures where the gist is the formal incorporation of multiple conflicting criteria in the analytical process [6]. In the context of GIS, this refers to the spatial decision-making process based on GIS data with geolocation tags. Spatial decision-making techniques have been used to solve many GIS problems such as locating solar plants, urban planning, and project construction optimization [7]. Advanced MCDM methods include simple additive weighting (SAW) [8], analytic hierarchy process (AHP) [9], and TOPSIS [10]. Fuzzy set theory and random set theory are also MCDM techniques that incorporate sophisticated algorithms to resolve uncertainty in data [11, 12, 13, 14].
TOPSIS is a MCDM technique that deals with real-world problems. It basically ranks criteria on the basis of the shortest distance from the positive ideal solution (PIS) and the farthest distance from the negative ideal solution (NIS) [15]. The work in [2] illustrates the application of a GIS-based MCDM tool for urban infrastructural planning. Awasthi et al. [16] presented a fuzzy TOPSIS method for selecting the best location for an urban distribution center in Canada. Uysal and Tosun [17] proposed a fuzzy TOPSIS-based maintenance management system using 17 criteria categorized under 5 contending parameters. The criteria were deduced from questionnaire feedbacks and interviews administered to company maintenance managers. In addition, Momeni et al. [18] presented a fuzzy TOPSIS-based method for maintenance strategy selection. Baysal et al. [19] developed a two-stage fuzzy method to determine the best sub-municipal projects among a set of proposed projects. The method simplifies the selection process and provides an objective decision outcome for stakeholders. Shelton and Medina [20] presented an integrated method to prioritize transportation projects in Wilmington Area, USA, based on multi-criteria decision support systems, AHP and TOPSIS methods. The process optimally selects the important routes that best serve the interest of the general public.
Based on the literature, TOPSIS has been successfully applied in many fields, producing reasonably accurate results. This study proposes an automated TOPSIS-based solution for prioritizing urban projects based on criteria that meet sustainable development. Specifically, this work addresses the following questions on the value of remote sensing (and GIS) to urban planning:
Which remotely sensed dataset(s) is (are) useful for urban planning?
Which criteria can be derived from remotely sensed data?
What are the major factors that need to be considered in urban developments?
From these questions, this study further looks at the automated prioritization of urban projects based on criteria that meet sustainable development practices. The specific objectives are (i) to identify factors that play major roles in urban development and (ii) to develop a geospatial solution based on TOPSIS for prioritizing projects for urban development.
This study focuses on Libya, a country in the Maghreb region of North Africa (Figure 1). Libya borders the Mediterranean Sea to the north and Egypt to the east. Along the southeast of Libya is Sudan, Chad. To the south is Niger. Algeria and Tunisia constitute the western border. Libya is the 17th largest nation in the world with a landmass of over 1,759,540 km2. The study area in the northern part of Libya covers six districts, namely, Darnah, Al Jabal Al Akhdar, Benghazi, Al Marj, Al Qubbah, and Al Hizam Al Akhdar (Figure 1). Libya is geographically bounded between 20°00′00″ E and 23°30′00″ E and 31°00′00″ N and 33°00′00″ N. The climate in Libya is categorized by hot and dry summers with high temperatures. The mean annual temperature in the coastal region ranges from 14.2°C (Shahat) to 21.0°C (Tripoli Airport) and at stations in the interior region (inland) between 21.3°C (Al Qaryat) and (Ghat) 23.4°C (1945–2009). Libya is one of the driest countries in the world with mean annual rainfall along the Libyan coast ranging between 140 and 550 mm and rarely exceeding 50 mm in the interior regions (1945–2010). December and January are the wettest months with 6 months (October–March) receiving 87.1% of the total annual precipitation. The majority of rainfall occurs in the winter season with the rainy season beginning in September-October and ends in March-April [21].
The data used in this study include Landsat satellite imagery acquired in the year 2017 with 15 m resolution panchromatic and 30 m resolution multispectral, Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) downloaded from USGS data archive with 30 m resolution, population density map obtained from GHSL with 250 m resolution, road network map from Diva-GIS, and MODIS satellite imagery from where the land surface temperature with 0.25° resolution was derived. Other data include rainfall data at 0.25°, net primary productivity (NPP) at 0.1°, NDVI at 0.1°, and air quality (CO, NO2) at 0.25° resolution (Table 1). Details of the Landsat data are presented in Table 2. Seven set of images with overlapping areas were acquired between 4 February 2017 and 1 March 2017. In addition, the highest cloud cover was 1.03%, which does not pose a problem for land use information extraction from the study area. Since the images have overlapping areas, they were preprocessed and mosaicked to create one seamless image of the area for effective and efficient processing (Figure 1d).
Data | Source | Resolution |
---|---|---|
Landsat satellite imagery | USGS | 30 m |
DEM | USGS | 30 m |
Population density | GHSL | 250 m |
Road network | Diva GIS | / |
Land surface temperature | MODIS | 0.25° |
Rainfall | MODIS | 0.25° |
Net primary productivity | MODIS | 0.1° |
NDVI | MODIS | 0.1° |
Air quality (CO, NO2) | MODIS | 0.25° |
Information of the datasets used in the research.
Image ID | Acquisition date | Raw | Path | Cloud cover (%) |
---|---|---|---|---|
Landsat 8 OLI 1 | 1 March 2017 | 38 | 182 | 0.77 |
Landsat 8 OLI 2 | 13 February 2017 | 39 | 182 | 1.03 |
Landsat 8 OLI 3 | 4 February 2017 | 38 | 183 | 0.00 |
Landsat 8 OLI 4 | 4 February 2017 | 37 | 183 | 0.11 |
Landsat 8 OLI 5 | 4 February 2017 | 39 | 183 | 0.13 |
Landsat 8 OLI 6 | 19 February 2017 | 37 | 184 | 1.00 |
Landsat 8 OLI 7 | 19 February 2017 | 38 | 184 | 0.00 |
Information of the Landsat images.
(a) Map of Libya with northern part highlighted, (b) the location of the urban area, (c) the road networks in the study area, and (d) mosaicked Landsat images.
Four preprocessing steps were performed on the Landsat satellite images: (i) Pan-sharpening using a fusion of the panchromatic and multispectral bands for the enhancement of the spatial resolution of multispectral band; (ii) atmospheric correction, which is applied to correct the atmospheric distortion by retrieving surface reflectance and engage topographic correction as well as adjacency effect correction; (iii) radiometric correction, which converts radiance values to the pure surface reflectance to enhance image capability and contrast; and (iv) mosaicking to create one seamless image coverage of the area for effective and efficient processing [22]. The MODIS source data was preprocessed using MODIS Conversion Tool Kit (MCTK). Note that the spatial resolution of the MODIS dataset varied according to the source. However, during the preprocessing, they were resampled to 30 m to match the DEM and Landsat resolutions.
In this study, we considered 17 critical urban conditioning factors for selecting the most suitable city or cities for sustainable urban development. The factors are grouped into five main categories: (i) topography, (ii) land use and infrastructure, (iii) demography and climate, (iv) vegetation, and (v) air quality.
Topography is a very important consideration for urban development projects [23]. For this study, altitude and slope are the two main factors related to topography. Altitude is important for citing facility because it affects the living conditions as well as breathing behavior. The collected DEM shows that the study area is between −4 and 865 m above mean sea level (Figure 2a). The slope factor, which ranges from 0 to 14° (can be classified as almost flat), was also generated (Figure 2b). Such data is important when estimating cost. For example, any increase in slope will increase the cost of facility installation and maintenance since moving workers, transport vehicles, and machineries will be more difficult (i.e., up and down a slope). Low slope areas also may incur undesirable cost, in the instance of weather anomalies such as dust/sand storms.
Elevation criteria: (a) altitude, (b) slope, (c) land use, (d) distance to the primary routes, (e) distance to the secondary routes, (f) distance to the trails, (g) distance to the Benghazi city, (h) percent of urban areas, (i) population density, (j) LST, (k) rainfall, (l) NDVI, (m) NPP product, (n) CO concentrations, (o) NO2 concentrations.
Land use and infrastructure are also important considerations for urban projects. Land use information can show human activity patterns, whereas infrastructure can indicate development status in a particular city. The land use of the study area was derived from Landsat images and refined based on Google maps (Figure 2c). In this study we applied the SVM classifier, which was based on object-based image analysis (OBIA) using the ENVI 5.3 software. Training sites for the SVM were collected form all land use classes by stratified random method (i.e., at least 80 sample points for each class) [24]. The area contains five main land use types: (i) irrigated crops, (ii) vegetation, (iii) artificial areas, (iv) bare lands, and (v) waterbodies. Most parts of the study area were bare land (desert), which were predominantly located in the middle and southern parts of the study area. The northern part mostly comprised of irrigated crops and artificial areas, specifically man-made features and urban areas.
Road networks play an important role in the country’s economy, serving the people by linking main cities to industrial and commercial sites. In this study, three types of roads, namely, main routes, secondary routes, and trail routes (Figure 2d–f), were considered as factors for city selection. The northern part of the city is supported by main routes (Figure 2d). These roads mainly link other cities to Benghazi, which support Benghazi city itself. Main routes span a significant number of kilometers within the study area. The study area also contains several kilometers of secondary routes (Figure 2e). Unlike main routes, secondary routes are found in most parts of the study area and in different cities including Benghazi. Secondary routes mainly support transportation of goods and are used for civil construction projects. Finally, trail routes support the rural areas, mainly for transportation of agricultural produce to the markets. The class of roads plays a vital role in selecting a city for development according to the available budget. Cities that can support more people will normally be prioritized for development projects.
Another important factor related to infrastructure is the percentage of built-up areas in a particular city. This is important because cities with many built-up areas indicate little or no space for new projects. On the contrary, cities with fewer built-up areas mean that they are more suitable for new developmental projects. The Normalized Difference Built-up Index (NDBI), which is a quantity of the intensity of urban area from satellite images, was used in this study [25]. The NDBI was initially established regarding the ratio of bands 4 and 5 of TM sensor. However, the NDBI can be adopted on Landsat-8 data or even any multispectral sensor data [26]. It basically extracts the urban areas where there is an upper reflectance in the short-wave infrared band associated to the near-infrared band. The accuracy of built-up areas extracted using NDBI is reported to be around 93% [27, 28]. We calculate the NDBI (Eq. (1)) based on the work in [27]:
Besides infrastructure factors, distance to the city is also a critical factor. Preferably, a city’s location should be as near as possible to the capital or large cities such as Benghazi. This is because it facilitates ease of access to better business opportunities and education. Therefore, in this work, the distance to Benghazi city is one of the important parameters. Specifically, the desired distance to the city should range from 0 to 600 km (Figure 2g) so that cities such as Darnah and Al Qubbah (which are as far as 400 km away from Benghazi) are also covered.
An increase in a city’s population often leads to an increase in urbanization. Moreover, if the population increase is rapid, urbanization often happens randomly. This can be a major problem for most cities in a developing country. However, proper planning and effective decision-making can mitigate this problem. In this study, the population density (Figure 2i) was analyzed. The analysis results indicate that the northern part (mostly around Benghazi city) is most populated with a density of 669 people per 250 m cell of raster data.
Climate is a factor that also influences the selection of cities for developmental projects. Land surface temperature (LST) and rainfall are two factors considered in this work (Figures 2j and 3k). The LST map shows that the southern part of the study area (mostly desert with no vegetation) has higher surface temperature compared to the northern parts. Another observation from the map reveals that Benghazi has slightly higher temperature than other urbanized areas. In arid regions, people often prefer to settle in areas with low temperature. The average day-night temperature ranges from 11° to 20° Centigrade. High temperatures are also observed in the west-southern part, whereas the lowest temperature is found in the northern part of Al Jabal Al Akhdar cities.
Flowchart of the methodology implemented in this study.
Rainfall, which is another climate factor, is also considered in deciding the location of settlement and development. This is because rainfall frequency and intensity affect the dryness of the cities, the local climate system, as well as agriculture activities. Figure 2k presents the rainfall intensity of the study area for year 2016 where minimum and maximum rainfall intensities were 48 and 1119 mm per month, respectively. The central part of the area has less amount of rainfall compared to other areas.
Normalized difference vegetation index (NDVI) is an indicator derived from remote sensing satellite data. It is mostly used to monitor vegetation cover over any area on the planet. It serves as a good indicator for vegetation cover of the study area. The presence of abundant vegetation is able to lower the local temperature as well as reduces the negative effects of noise and air pollutants. In the study area, the northern part has higher NDVI compared to the south (Figure 2l).
Larger amounts of vegetation can indicate higher vegetation productivity. Having higher vegetation productivity helps asses the net primary productivity (Figure 2m). Plant productivity plays a major role in the global carbon cycle by absorbing some of the carbon dioxide released through coal, oil, and other fossil-fuel burning. Large NPP values are found in the southern part of the study area.
Air quality directly affects the environment and consequently people’s health. In this work, we have considered the CO and NO2 (Figure 2n and o) air quality indicators. In 2016, higher CO and NO2 levels were measured in the southern part of Benghazi. Benghazi city also recorded high levels of these gasses for the year under investigation. The air quality data was extracted from the MODIS source with a resolution of 0.25°. We utilized the ENVI 5.3 software to process the MODIS imagery. However, in order to prepare unprocessed MODIS satellite images for analysis, they must firstly be converted into ENVI format. This was done using the MODIS Conversion Tool Kit.
This section describes the modeling process, specifically the application of TOPSIS for scheduling and prioritizing the cities for urban development (Figure 3). First, a medium-resolution Landsat-8 satellite image from the study area was acquired and preprocessed. Then, the image was segmented using a multiresolution segmentation algorithm and classified into several classes using object-based image classification. The multiresolution algorithm has three main parameters, namely, scale, shape, and compactness. Since these parameters are data and application dependent, in this study, we had to select them empirically via trial and error. This meant that the best values were determined via visual examination of the segmentation results. After the segmentation process, several attributes were selected and used as class predictors in the classification algorithm. From the spectral attributes, the five bands of the Landsat-8 image were selected. For spatial attributes, shape index, roundness, compactness, and density were used [29, 30]. In the classification step, the support vector machine (SVM) algorithm was used. Although the SVM is a relatively simple binary classifier, it has very good generalization capabilities if properly trained [31, 32].
Several other digital data such as DEM and population density were also obtained from various online sources. The factors used as described in the previous section are widely reported in the literature for selecting urban projects or relevant projects. Fuzzy overlay (FO) analysis was carried out to determine the importance of each parameter to achieving the goal of the study. The SVM classifier was further applied to refine the results obtained from the FO model. Finally, the cities were sorted according to their importance by applying the TOPSIS model on the results of the SVM.
Fuzzy overlay analysis is based on the fuzzy set theory that relies on membership relationship of events to define specific sets or classes [33]. Operationally, FO is similar to overlay analysis but differs in the reclassified values and results from the combination of multiple criteria. It involves problem definition, partitioning into sub-models and determining the significant layers. FO transforms the data to a common scale and defines the likelihood of the data belonging to a specific class, for example, slope values being transformed into the probability of fitting into the favorable suitability set based on a scale of 0 to 1, expressed in terms of membership [34]. Input raster are not weighted in FO since the transformed values indicate the possibility of membership rather than using ratio scale as with weighted overlay and weighted sum. The equation using fuzzy Gaussian function can be given as [35]
The inputs
As previously mentioned, the results of FO are refined using the SVM, which develops a linear regression between suitability status and criteria factors. SVM aims to determine an optimal separating hyperplane (maximizing the margin width) between two classes in feature space [36]. The training points near the hyperplane are called support vectors and are utilized for classification once the decision line/surface is obtained. The separating hyperplane is found as follows:
where
where
Developed in [38, 39, 40], TOPSIS is a multi-criteria decision tool based on the intuition that a selected alternative has the shortest possible geometric distance from the PIS. In other words, the alternative has the longest geometric distance from the NIS [41]. The analysis compares a set of alternatives by assigning weightage to each criterion to compute the geometric distance between possible alternatives to determine the ideal alternative based on the assumption that the criteria uniformly increases or decreases. TOPSIS allows trade-offs between criteria; a poor result in one criterion can be compensated by a good result in another criterion. TOPSIS provides a more realistic model than non-compensatory methods by including or excluding alternative solutions using hard cutoffs. Consider
Step 1: Construct the normalized decision matrix calculated using Eq. (6):
Step 2: Construct the weighted normalized decision matrix using Eq. (7):
Step 3: The positive and negative ideal solutions are determined by
Step 4: Calculation of separation (positive and negative) measurement using Euclidean distance. Eq. (9) is used to calculate the distance.
Step 5: Closeness to the ideal solution is calculated using Eq. (10):
Step 6: Ranking alternatives based on closeness to the ideal solution. TOPSIS has been used in different circumstances (e.g., individual and grouping). By applying the TOPSIS model using the results of the FO as input, the cities were sorted according to their importance for proposed urban development projects.
According to [43], the most contributing factors to urban suitability are topography, land use and infrastructure, vegetation, demography and climate, and air quality. Therefore, these factors should be thoroughly analyzed to discover the most (and the least) suitable area for urbanization. Hence, in this work, 17 detailed factors were analyzed in order to rank each’s importance (via weight assignment) for the selection of a city (or cities) for sustainable urban development. Subsequently, a suitability map was generated based on the FO (Figure 4a) method. A continuous scale was used for suitability weightage, which ranges from 0 (less suitable) to 1 (highly suitable). From the generated map, areas indicated as most suitable are located in the northern parts, especially the areas surrounding Benghazi and the northern parts of Al Marj and Al Jabal Al Akhdar. Sole reliance on the generated map, however, does not help much in deciding city development prioritization. As a result, the map was further refined to make it much more distinct for decision-makers. To do this, the map was firstly reclassified into three categorical classes: (i) not suitable, (ii) less suitable, and (iii) highly suitable. This was done using the natural break classification method (Figure 4b) where several samples were selected from the not suitable and highly suitable areas (results of FO) to generate training and testing data. These datasets were then used to train a SVM to classify between the two classes. Table 3 presents the estimated factors and their coefficients. The result indicates that land use, distance to primary route, distance to capital city, rainfall, NPP, and NO2 have negative effects on the suitability level of the selection process. The remaining factors have positive effects. Among the positive factors, population density has the highest effects on the selection process.
Results of fuzzy overlay in (a) continuous scale and (b) categorical format.
Based on the estimated coefficients, the suitability map in Figure 5 was produced. It can be seen that the map reflects the same thing as in the previous suitability map. However, it is clearly more informative for decision-makers. Based on this, the cities were ranked according to their importance using TOPSIS method.
FO after refinement with SVM.
Table 4 presents the positive ideal, negative ideal, closeness coefficient, and TOPSIS rank for each of the cities being analyzed. According to the closeness coefficients, the ranking order for the cities is as follows:
Benghazi
Al Jabal Al Akhdar
Al Marj
Darnah
Al Hizam Al Akhdar
Al Qubbah
Evaluation criteria | SVM weight | Criteria code |
---|---|---|
Land use | −0.36 | C12 |
Percent built-up area | 0.97 | C1 |
NDVI | 2.15 | C10 |
Altitude | 0.60 | C15 |
Slope | 0.96 | C7 |
Distance to primary route | −2.35 | C4 |
Distance to secondary route | 0.74 | C6 |
Distance to trail lines | 0.27 | C8 |
Distance to capital city | −0.62 | C13 |
Population density | 4.88 | C3 |
Rainfall | −1.17 | C5 |
LST | 0.23 | C11 |
NPP | −0.10 | C2 |
Carbon monoxide | 1.23 | C14 |
Nitrogen dioxide | −1.18 | C9 |
List of criteria, estimated coefficient, and their code.
City | A+ | A− | Closeness coefficient | TOPSIS rank |
---|---|---|---|---|
Darnah | 2.58 | 1.41 | 0.35 | 4 |
Al Jabal Al Akhdar | 2.05 | 1.74 | 0.46 | 2 |
Benghazi | 0.98 | 3.40 | 0.77 | 1 |
Al Marj | 2.23 | 1.57 | 0.41 | 3 |
Al Qubbah | 3.13 | 1.08 | 0.25 | 6 |
Al Hizam Al Akhdar | 2.69 | 1.29 | 0.32 | 5 |
Ranking of cities on the basis of importance for urban development.
The ranking results were then used to generate a map for final decision-making (Figure 6). Cities with green and light green colors are suggested to be prioritized first for development. More details about the TOPSIS analysis can be found in the Appendices.
The map showing the cities’ ranks based on TOPSIS.
Based on the results, the importance of each group of factors was evaluated (Figure 7). The bar chart shows the importance of the standardized factor weights in each group. Demography and vegetation are the two most influential factors with positive contribution, followed by vegetation and topography. The other factors have negative contribution.
Importance degree of factor groups.
Recall that the refined suitability map was recategorized into the three classes of “not suitable,” “less suitable,” and “highly suitable,” These classes reflect the degree of urban development suitability in the study area. The categorized suitability map can be validated accurately through each class. The continuous refined is suitability map ranging from 0 to 1. The most suitable areas that range from 0.751 to 1 fall into high suitable class, while the moderate suitable areas for urban development were extracted from 0.401 to 0.751 from the continuous refined suitability map. Consequently, the least feasible areas were classified out of 0.001 to 0.40. The categorized suitability map was then validated based on the same randomly selected samples (Table 5). The SVM model accurately classified 1178 samples, which is about 78.5% of the total samples tested, which produced kappa index of o.67. The kappa index is calculated using Eq. (11) [44]:
Accuracy metric | Value |
---|---|
Correctly classified instances | 1178 (78.5%) |
Incorrectly classified instances | 322 (21.4%) |
Kappa statistic | 0.67 |
Overall accuracy assessment of SVM modeling.
where
AUC for the SVM.
Class | ROC area | PRC area |
---|---|---|
Not suitable | 0.934 | 0.884 |
Less suitable | 0.799 | 0.60 |
High suitable | 0.852 | 0.673 |
Average | 0.861 | 0.719 |
Accuracy assessment of SVM modeling based on ROC.
An automated geospatial solution for selecting and ranking cities in Libya for urban development is proposed in this chapter. The suitability map showed that most areas indicated to be suitable are in the northern part of Libya. The results indicate that land use, distance to primary route, distance to large city, rainfall, NPP, and NO2 have negative effects on the level of suitability for the selection process, whereas the other factors have positive effects with population density taking the lead. It is revealed that SVM model accurately classifies 1178 samples, about 78.5% of the total samples tested which produced kappa statistic of 0.67. The high-priority city was selected as Benghazi that is followed by Al Jabal Al Akhdar. The results suggest that demography and vegetation are the two most influential factors contributing to the selection of city for development in Libya. This study is limited to analysis of six cities; the procedure developed through this study can be extended to other cities. It is of the opinion that evaluated criteria can be adjusted according to the environment and the current development of the cities.
TOPSIS 1
City | Percent urban | NPP | Population | Primary route | Rainfall | Secondary route | Slope | Trail route | NO | NDVI | LST | land cover | Distance to Benghazi | CO | Altitude |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
C1 | C2 | C3 | C4 | C5 | C6 | C7 | C8 | C9 | C10 | C11 | C12 | C13 | C14 | C15 | |
Darnah | 0.37 | 3.68 | 131333.00 | 142935.00 | 680.50 | 356539.00 | 0.86 | 0.00 | 118.32 | −0.42 | 14.46 | 200 | 139404.00 | 81.09 | 226.11 |
Al Jabal Al Akhdar | 0.49 | 4.20 | 224171.00 | 196505.00 | 422.65 | 696167.00 | 0.82 | 221496.00 | 117.11 | −0.34 | 15.11 | 200 | 162514.00 | 79.77 | 341.61 |
Benghazi | 44.77 | 2.18 | 571466.00 | 52346.60 | 370.37 | 41068.40 | 0.36 | 27.63 | 148.66 | −0.10 | 17.19 | 30 | 541.96 | 78.36 | 55.55 |
Al Marj | 0.11 | 4.12 | 202732.00 | 188407.00 | 409.90 | 1328530.00 | 0.62 | 395212.00 | 120.24 | −0.30 | 15.86 | 200 | 147881.00 | 80.26 | 261.23 |
Al Qubbah | 0.00 | 5.83 | 47112.30 | 75610.90 | 461.25 | 906233.00 | 0.36 | 555787.00 | 127.56 | −0.52 | 16.51 | 200 | 441023.00 | 82.69 | 209.68 |
Al Hizam Al Akhdar | 0.28 | 4.31 | 164855.00 | 170954.00 | 598.51 | 1449880.00 | 0.39 | 346141.00 | 127.79 | −0.41 | 17.47 | 200 | 110122.00 | 80.76 | 122.89 |
Criteria sign range | −1 | 1 | 1 | 1 | 1 | 1 | −1 | 1 | −1 | 1 | −1 | −1 | −1 | −1 | −1 |
W(Lambda) | −0.36 | 0.98 | 2.15 | 0.60 | 0.96 | −2.35 | 0.74 | 0.27 | −0.62 | 4.88 | −1.17 | 0.23 | −0.10 | 1.23 | −1.18 |
Ideal | 0.00 | 5.83 | 571466.00 | 196505.00 | 680.50 | 1449880.00 | 0.36 | 555787.00 | 117.11 | −0.10 | 14.46 | 30.00 | 541.96 | 78.36 | 55.55 |
The worst | 44.77 | 2.18 | 47112.30 | 52346.60 | 370.37 | 41068.40 | 0.86 | 0.00 | 148.66 | −0.52 | 17.47 | 200.00 | 441023.00 | 82.69 | 341.61 |
TOPSIS 2
C1 | C2 | C3 | C4 | C5 | C6 | C7 | C8 | C9 | C10 | C11 | C12 | C13 | C14 | C15 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
44.40 | 3.68 | 131333.00 | 142935.00 | 680.50 | 356539.00 | 0.00 | 0.00 | 30.35 | −0.42 | 3.00 | 0.00 | 301619.00 | 1.60 | 115.49 | |
44.28 | 4.20 | 224171.00 | 196505.00 | 422.65 | 696167.00 | 0.04 | 221496.00 | 31.56 | −0.34 | 2.35 | 0.00 | 278509.00 | 2.92 | 0.00 | |
N= | 0.00 | 2.18 | 571466.00 | 52346.60 | 370.37 | 41068.40 | 0.50 | 27.63 | 0.00 | −0.10 | 0.28 | 170.00 | 440481.04 | 4.33 | 286.06 |
44.66 | 4.12 | 202732.00 | 188407.00 | 409.90 | 1328530.00 | 0.25 | 395212.00 | 28.42 | −0.30 | 1.60 | 0.00 | 293142.00 | 2.43 | 80.38 | |
44.77 | 5.83 | 47112.30 | 75610.90 | 461.25 | 906233.00 | 0.50 | 555787.00 | 21.11 | −0.52 | 0.95 | 0.00 | 0.00 | 0.00 | 131.93 | |
44.49 | 4.31 | 164855.00 | 170954.00 | 598.51 | 1449880.00 | 0.48 | 346141.00 | 20.87 | −0.41 | 0.00 | 0.00 | 330901.00 | 1.93 | 218.72 | |
Normal | 99.55 | 10.3 | 681594.73 | 363626.55 | 1232.02 | 2302576.25 | 0.88 | 796219.68 | 60.05 | 0.90 | 4.25 | 170 | 746988.97 | 6.28 | 408.49 |
TOPSIS 3
C1 | C2 | C3 | C4 | C5 | C6 | C7 | C8 | C9 | C10 | C11 | C12 | C13 | C14 | C15 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0.445 | 0.36 | 0.192 | 0.393 | 0.552 | 0.154 | 0 | 0 | 0.505 | −0.47 | 0.71 | 0 | 0.403 | 0.25 | 0.282 |
0.444 | 0.41 | 0.328 | 0.540 | 0.343 | 0.302 | 0.05 | 0.278 | 0.526 | −0.37 | 0.55 | 0 | 0.372 | 0.46 | 0 |
0 | 0.21 | 0.838 | 0.143 | 0.300 | 0.017 | 0.562 | 0 | 0 | −0.11 | 0.07 | 1 | 0.589 | 0.69 | 0.700 |
0.448 | 0.4 | 0.297 | 0.518 | 0.332 | 0.576 | 0.278 | 0.496 | 0.473 | −0.33 | 0.38 | 0 | 0.392 | 0.39 | 0.196 |
0.449 | 0.57 | 0.069 | 0.207 | 0.374 | 0.393 | 0.563 | 0.698 | 0.352 | −0.57 | 0.22 | 0 | 0 | 0 | 0.322 |
0.446 | 0.42 | 0.241 | 0.470 | 0.485 | 0.629 | 0.537 | 0.434 | 0.348 | −0.45 | 0 | 0 | 0.442 | 0.31 | 0.535 |
TOPSIS 4
C1 | C2 | C3 | C4 | C5 | C6 | C7 | C8 | C9 | C10 | C11 | C12 | C13 | C14 | C15 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
−0.164 | 0.35 | 0.415 | 0.236 | 0.533 | −0.364 | 0 | 0 | −0.318 | −2.28 | −0.83 | 0 | −0.044 | 0.31 | −0.333 | |
−0.163 | 0.4 | 0.709 | 0.325 | 0.331 | −0.711 | 0.037 | 0.075 | −0.331 | −1.8 | −0.65 | 0 | −0.040 | 0.57 | 0 | |
0 | 0.21 | 1.808 | 0.086 | 0.290 | −0.041 | 0.417 | 0 | 0 | −0.52 | −0.08 | 0.234 | −0.064 | 0.85 | −0.826 | |
−0.165 | 0.39 | 0.641 | 0.311 | 0.321 | −1.356 | 0.206 | 0.134 | −0.298 | −1.62 | −0.44 | 0 | −0.043 | 0.48 | −0.232 | |
−0.165 | 0.56 | 0.149 | 0.125 | 0.361 | −0.925 | 0.418 | 0.189 | −0.221 | −2.78 | −0.26 | 0 | 0 | 0 | −0.381 | |
−0.164 | 0.41 | 0.521 | 0.283 | 0.468 | −1.480 | 0.398 | 0.118 | −0.219 | −2.18 | 0 | 0 | −0.048 | 0.38 | −0.632 | |
Ideal | 0.00 | 0.56 | 1.81 | 0.33 | 0.53 | −0.04 | 0.42 | 0.19 | 0.00 | −0.52 | 0.00 | 0.23 | 0.00 | 0.85 | 0.00 |
The worst | −0.17 | 0.21 | 0.15 | 0.09 | 0.29 | −1.48 | 0.00 | 0.00 | −0.33 | −2.78 | −0.83 | 0.00 | −0.06 | 0.00 | −0.83 |
TOPSIS 5
C1 | C2 | C3 | C4 | C5 | C6 | C7 | C8 | C9 | C10 | C11 | C12 | C13 | C14 | C15 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0.16 | 0.20 | 1.39 | 0.09 | 0.00 | 0.32 | 0.42 | 0.19 | 0.32 | 1.76 | 0.83 | 0.23 | 0.04 | 0.54 | 0.33 |
0.16 | 0.16 | 1.10 | 0.00 | 0.20 | 0.67 | 0.38 | 0.11 | 0.33 | 1.28 | 0.65 | 0.23 | 0.04 | 0.28 | 0.00 |
0.00 | 0.35 | 0.00 | 0.24 | 0.24 | 0.00 | 0.00 | 0.19 | 0.00 | 0.00 | 0.08 | 0.00 | 0.06 | 0.00 | 0.83 |
0.17 | 0.16 | 1.17 | 0.01 | 0.21 | 1.32 | 0.21 | 0.05 | 0.30 | 1.10 | 0.44 | 0.23 | 0.04 | 0.37 | 0.23 |
0.17 | 0.00 | 1.66 | 0.20 | 0.17 | 0.88 | 0.00 | 0.00 | 0.22 | 2.26 | 0.26 | 0.23 | 0.00 | 0.85 | 0.38 |
0.16 | 0.15 | 1.29 | 0.04 | 0.06 | 1.44 | 0.02 | 0.07 | 0.22 | 1.66 | 0.00 | 0.23 | 0.05 | 0.47 | 0.63 |
For sustainable food production, it is an absolute requirement that nutrients removed with the harvest of crops are replaced to prevent nutrient depletion and soil degradation. Achievement and maintenance of high nutrient use efficiency (NUE) together with high crop productivity have become a major challenge in both developed and developing countries with an increasing growing population, depletion of natural resources, and deteriorating environmental conditions. This is occurring at the same time as society becomes ever more concerned about resource management practices and the environment, especially when it comes to nutrient management [1]. Fertilizer nutrients applied that are not taken up by the crop are also vulnerable to losses from leaching, erosion, and denitrification or volatilization in the case of N, or they could be temporarily immobilized in soil organic matter to be released at a later time, all of which impact apparent use efficiency [2].
Improving nutrient use efficiency (NUE) in plants is vital to enhance the yield and quality of crops, reduce nutrient input cost and improve soil, water, and air quality [3]. Higher NUE by plants could reduce fertilizer input costs, decrease the rate of nutrient losses, and enhance crop yields. Improving crop nutrient use efficiency ideally requires an understanding of the whole system, from the macro (agro-ecosystem) to the molecular level [4]. Nutrient uptake and their internal utilization efficiencies are the two central cores for improving crop NUE [5]. This can be achieved through optimizing agronomic strategies (soil-rhizosphere management) and breeding nutrient-efficient cultivars. Plant genetics and physiological mechanisms and their interaction with best agronomic practice are also a tool that can be used to increase efficiency of cropping systems [3]. Thus, it needs involvement of integrated nutrient management strategies that take into consideration improved fertilizer along with soil and crop management practices are necessary [6]. Sustainable nutrient management must be both efficient and effective to deliver anticipated economic, social, and environmental benefits.
Plants experience nutrient deficiency when soil nutrient availability is either an inherently low amount or low mobility of nutrients in the soil, or poor solubility of certain chemical forms of soil nutrients [7]. Of the various nutrients essential for plants, nitrogen (N), phosphorus (P), and potassium (K) are required in the largest quantities, and their deficiency severely limits crop yield [8]. The dynamic nature of N and P in soil-plant systems creates a unique and challenging environment with nitrate and phosphate contamination of surface and/or groundwater, which can be attributed in large part to low efficiency in plant nutrient uptake. The main challenge for improving P and K use efficiency at the farm level is to apply the existing knowledge in a practical manner [9]. Hence, the best management practice for N, P, and K must consider the specific characteristics of crops, cropping systems, environments, and soils is application of 4R nutrient stewardship. Therefore, this chapter tries to summarize the concept of NUE and recent strategies for enhancing use efficiency of N, P, and K. These approaches consider economic, social, and environmental dimensions essential to sustainable agricultural systems and afford a suitable context for specific NUE indicators.
The variations in defining nutrient efficient plants and methods used in calculating nutrient use efficiency make it difficult to compare results of different studies [10, 11, 12, 13]. Understanding the terminology and the context in which it is used is critical to prevent misinterpretation and misunderstanding and determination of NUE in crop plants is an important approach to evaluate the fate of applied chemical fertilizers and their role in improving crop yields. In order to develop a common framework for NUE, scientists started to formulate concepts and definitions that should serve as a basis for comparison and discussion of research. Nutrient use efficiency in its broadest sense indicates how effectively a plant is able to capture and utilize nutrients to produce biomass. It is simply a measure of how well plants use the available mineral nutrients [10]. The earlier definition of NUE by [14] is simply increment of yield per applied nutrient (Eq. (1)).
While the most recent and complicated one used in crop modeling formula is (Eq. (2)) [12].
where
Generally, nutrient use efficiency comprises both yield as a function of inputs and percentage of nutrient recovered respectively, contributing to yield and quality [15]. The NUE is based on (a) uptake efficiency (acquire from soil, influx rate into roots, influx kinetics, radial transport in roots are based on root parameters per weight or length, and uptake is also related to the amounts of the particular nutrient applied or present in soil), (b) incorporation efficiency (transports to shoot and leaves are based on shoot parameters), and (c) utilization efficiency (based on remobilization, whole plant, i.e., root and shoot parameters) [4].
Phosphorus use efficiency can be divided into (i) P acquisition efficiency [the capacity of a cultivar to extract P from soil] and (ii) P internal utilization efficiency [the capacity of a cultivar to transform the acquired P into biomass/grain yield] [16, 17, 18].
Phosphorus uptake or acquisition efficiency (PACE)
Uptake efficiency or the ability of the plant to extract the nutrient from the soil is calculated as [19] (Eq. (3)).
Phosphorus utilization efficiency (PUTE)
Phosphorus utilization efficiency is defined as a crop’s ability to convert the absorbed P into grain yield [19] (Eq. (4)) can be calculated as:
Utilization efficiency can also be calculates as suggested by [20], (Eqs. (5) and (6)) and expressed as follows:
Generally, if P supply is limited or in more acidic and calcareous soil, P acquisition could be more important than P utilization and high fertilizer application necessary in order to provide sufficient plant-available P. On the other hand, with adequate P supply, PUTE could be considered more important than PACE for crop P efficiency [17]. Therefore, the improvement of both PACE and PUTE in the given species under different P supply conditions in the different soil types seems to be the perfect breeding approach (Figure 1) [17].
Schematic representation of the possible mechanisms of P acquisition and utilization for better growth of modern crops grown in intensive cropping systems
Hence, Nutrient use efficiency = Uptake efficiency × Utilization efficiency. All unit dry weights are in g m−2 [19].
For nitrogen use efficiency in their various definitions and components (Figure 2) [21].
Illustration of nutrient use efficiency parameters exemplified by NUE in wheat. Key process contributing to the NUE trait: nitrogen uptake efficiency, NUpE; nitrogen utilization efficiency, NUtE; nitrogen harvest index, NHI (adopted form [
Apparent recovery efficiency is one of the more complex forms of nutrient use efficiency (NUE) expressions and is most commonly defined as the difference in nutrient uptake in above-ground parts of the plant between the fertilized and unfertilized crop relative to the quantity of nutrient applied. It is often the preferred NUE expression by scientists studying the nutrient response of the crop [22]. Reference [23] proposed that the balance method be used to assess fertilizer P efficiency (Eq. (7)). The balance method is described mathematically as:
Phosphorus use efficiency has become burning issues in recent times due to several reasons [24]. Unlike N, the amount of P is less-abundant, finite resource, less-available, and poor mobility in the soil, being one of the most inaccessible elements for plants. Its deficiency is a major constraint to agricultural production, and it affects an area of over 2 billion hectares worldwide that is on about 70% of the world’s arable land [25]. Remarkably, usually only about 10–30% of the P fertilizer applied in the first year is taken up by the roots, with a substantial part accumulated in the soil as residual P not readily available for plants [26]. This may be due to nature of P that can bound to calcium in alkaline soils and readily complexed to charged Al and Fe oxides and groups hydroxyls on clay surfaces in acidic soils [23]. In addition, agricultural phosphorus (P) run-off is a primary factor in the eutrophication of aquatic and marine ecosystems and has also led to blooms of toxic cyanobacteria [27] and can contain heavy metals such as cadmium that may accumulate in arable soils. Moreover, organic material present in the soil (e.g., from manure or crop residues) can also bind phosphate ions as well as phytate (inositol compounds). In order to avoid a future food-related crisis, phosphorus scarcity needs to be recognized and addressed in contemporary discussions on global environmental change and food security, alongside water, energy, and nitrogen [28].
Selection and breeding nutrient-efficient species or genotypes within a species are justified in terms of reduction in fertilizer input cost of crop production and also reduced risk of contamination of soil and water. Many plants have evolved morphological, physiological, biochemical, and molecular adaptive systems to cope with P-deficiency stress, such as altered root architecture to explore more soil volume and increased carboxylate exudation containing phosphatases, nucleases, and various organic acids [29]. These mechanisms and strategies are necessary to liberate or solubilize Pi from organic and other insoluble pools [30], enhance Pi uptake capacity [31], recycle internal Pi remobilize/retranslocate P from mature to young developing organs [32, 33], and reprioritize metabolic P utilization [34]. Under the current situation, farmers need P-efficient genotypes that perform better than other genotypes with equivalent P inputs. Therefore, selection/identification of cultivars that can absorb and use P efficiently is a promising strategy to cope with environments deficient in bio-available P. Due to the diverse functional and structural roles of P in plants, P-use efficiency (PUE) is a complex trait to dissect [24].
The root morphological factors such as length, thickness, surface area, and volume have profound effects on the plant’s ability to acquire and absorb nutrients in soil [35]. These parameters are influencing the ability of the roots to penetrate high density soil layers, to extremes tolerate temperature, moisture, toxicities, and deficiencies of elements. Additionally, they have the ability to modify the rhizosphere pH and the nutrient uptake kinetics. Efficient acquisition will depend first on root architecture in terms of transporters and exudates and often the presence of symbiotic associations such as mycorrhiza. Hence, improving early root establishment, high-affinity transporter systems, association of microorganisms (mycorrizha), proliferation of roots, and enhanced mechanisms for increasing bio-availability of nutrients and then enhancing NUE [5]. Improvement of transporters plays essential roles particularly in conjunction with effective root proliferation in contributing to nutrient use efficiency. The other important attribute for uptake efficiency is having adequate sinks to store acquired nutrients, which will prevent negative feedback regulation on the initial acquisition/assimilatory processes and should provide important remobilizable storage [5]. The second component of uptake efficiency is root physiological activity such as differing uptake kinetics, i.e., maximum net influx (Imax), affinity of the transporter
A recent study further showed that root tips also play an important role and, despite their small size, accounted for approximately 20% of the total seedling Pi uptake [37], mainly increasing organic acid exudation strategies [38]. Plants increase total soil exploration by increasing root length, increasing root branching, increasing specific root length (i.e., roots with smaller diameter), and modifying branching angle [39, 40, 41]. The findings of Bates and Lynch [39] suggested that increased root growth is associated with improved plant performance under low P by exploring a larger volume of soil. Consequently, root: shoot ratio increases significantly in low-P environments and is an excellent index for partitioning photosynthesized carbon between above- and below-ground plant parts. Root density and root: shoot ratio generally increased under P deficiency, thus favoring P acquisition by plants [29].
Genetic variation for root hair traits, particularly root hair length, can be exploited in breeding for improved P uptake efficiency and P fertilizer use efficiency in crops. Moreover, a deeper root with more aerenchyma tissues in the cortex of the roots can also be an important trait that contributes to efficient N uptake with lower carbon input in root growth [42]. This root architecture may also be efficient in the uptake of deep water and therefore help to increase drought resistance [43]. However, Miguel et al. [44] showed in field trials that shallow and hairy root traits are synergistic in their effects on Pi uptake by bean. However, modifying root growth in response to nutrient deficiency, it is a challenge and complex to identify key regulators that are sufficiently upstream and robust to be suitable for developing plants with optimized root systems for nutrient uptake [8].
Levels of fertilizer applications influence the total dry matter accumulation, thereby affecting the nutrient demand (uptake/utilization) [9]. Improved nutrient utilization efficiency from agrochemicals through PGPR and (or) AMF can contribute to the protection of water resources against agro-pollution and reduce the growing cost of fertilizers [10]. After inorganic phosphate (Pi) acquisition from rhizosphere, Pi should be efficiently transported to shoot for the requirement of plant growth by phosphate transporters (
Another promising area for improvement of crop NUE is to enhance the efficiency of nutrient remobilization from senescing organs to young, developing organs, particularly immature leaves, and developing seeds [47]. The senescence process, that is, the dying-off of vegetative plant parts during seed maturation, is at the core of the nutrient use efficiency issue, as the nutrients need to be remobilized from these parts and translocated into the developing seed [48]. Maximizing the effectiveness of P-remobilization from senescing organs could make an important contribution to the development of crops that can tolerate Pi deficiency, because senescing organs of most “modern” crop varieties exhibit low P-remobilization efficiencies of <50% [30]. An integral understanding of P remobilization would facilitate development of effective biotechnological strategies to improve crop PUE, thereby reducing the rate of depletion of nonrenewable rock P reserves [30, 47]. Therefore, mobilization and redistribution of P from the old tissues to the young tissues will also contribute to high P use efficiency. Better distribution of nutrients in parts of plant (root, shoot, and grain) reflects their use efficiency [11].
In the plant, uptake and utilization efficiency of nutrients are governed by different physiological mechanisms and their response to deficiency, tolerance, and toxicity of element(s) and climatic variables [49]. Efficient internal utilization of nutrient is generally attributed because of high photosynthetic activity per unit of nutrient (P) and more efficient P remobilization from older to young leaves [47]. Acid phosphatase contributes to the increased P utilization efficiency in bean through P remobilization from old leaves [50]. Therefore, improving higher total chlorophyll concentration [51], enhancing phosphorylase stimulation [52], and improving partitioning of carbon between glycolytic and pentose phosphate pathways [53] also provide an effective approach to improve phosphorus use efficiency and crop productivity simultaneously.
P-utilization efficient cultivars produce high yield per unit of absorbed P under P deficient conditions, since they have low internal P demand for normal metabolic activities and growth. Hence, they have low requirement for mineral P fertilizer inputs to produce reasonably high yield. Moreover, they remove less P from soil during growth and therefore the quantity of P removed along with the harvestable parts of the crop would obviously be less, consequently reducing the quantity of mineral P fertilizer inputs required for maintenance fertilization [54].
Agronomic practices can change soil physicochemical properties and biological characteristics. As a result, a number of agronomic practices have been proposed to enhance nutrient availability under diverse climatic conditions [55, 56]. The rhizosphere (root-soil interface) is the most important area for plant–soil-microorganism interactions and is the hub for controlling nutrient transformation and plant uptake [7]. This modification is paramount to increase nutrient availability and to minimize losses in surface runoff. Possible management strategies options for improving NUE through optimizing agronomic practice or rhizosphere modification [57] are the following:
The 4R Nutrient Stewardship framework promotes the application of nutrients using the right source (or product) at the right rate, right time, and right place. The framework was established to help convey how fertilizer application can be managed to ensure alignment with economic, social, and environmental goals [58]. Nutrient Stewardship defines the right source, rate, time, and place for fertilizer application as those producing the economic, social, and environmental outcomes desired by all stakeholders of the plant ecosystem (Figure 3). This 4R techniques applies (1) right rate—supplying growing crops with the right amount of nutrients for healthy growth and development based on experimentation under various environmental conditions; (2) right time—matching nutrient availability to with the timing of plant peak nutrient uptake and demand; (3) right placement adding nutrients to the soil at a place where crops can easily access them related to volume of roots.; (4) right source—applying the correct fertilizer and organic resources that provide growing crops with all nutrients required for good growth and maturity [58]. The 4R concept was established to help convey how fertilizer application can be managed to ensure alignment with economic, environmental, and social goals [22, 59].
The 4R nutrient stewardship concept (adopted from [
Soil testing remains one of the most powerful tools available for determining the nutrient supplying capacity of the soil, but to be useful for making appropriate fertilizer recommendations good calibration data is also necessary [2]. As P is less mobile, less soluble, and highly prone to soil fixation; effectiveness of applied P depends on the properties of soil being fertilized, fertilizer itself, and time and method of its application [60]. To enhance phosphorus use efficiency (PUE) of applied P fertilizer, time and method of its application are critically important, because different P application methods differ in PUE [61]. In highly sandy soils, P may need to be managed like N, by splitting applications and applying small amounts at sowing and topdressing later in the crop growth cycle [62]. Studies of Jing et al. [63] suggested that localized supply of superphosphate combined with ammonium-N (NH4+-N) significantly stimulated root proliferation, especially of fine roots, and thus improved maize growth in a calcareous soil. Further studies indicated that localized supply of P and NH4+-N at both seeding and later growth stages increased maize yield by 8–10%, P uptake by 39–48%, and localized increases in root density and length of 50% [64]. Rehim et al. [65] also reported that the fixation of broadcasted P is much greater than the fertilizer applied in bands because of less contact with P fixing ingredients. At higher P application, the adsorption of P increased because the plants readily utilize only 8–33% of applied P in the first growing season and remaining portion remained fixed that consequently resulted in higher Olsen P. So, at higher P application rates, plants used smaller proportion of fertilizer P that resulted in low PUE [61].
In principle, N deficiency increases root growth, resulting in longer axial roots (primary roots, seminal roots, and nodal roots), and this helps maize roots to explore a larger soil volume and thus increases the spatial N availability [66]; however, long-term N deficiency stunts root growth due to insufficient N. But also, root elongation can be inhibited if the N supply is too high. Excessive application of N-P fertilizers may lead to high concentrations of soluble nutrients in the root zone, which can also restrict root growth and rhizosphere efficiency [67], even small amounts of P lost can be a cause of the adverse effects of eutrophication of surface waters. Therefore, judicious application of fertilizer best management practices (BMP) [22] that includes the right rate [68], right time [69], right source, right place, and balanced fertilization (4RB) is the best management practice for achieving optimum nutrient efficiency [2, 22].
Cereal-legume intercropping is a crop production system utilized to improve productivity and sustainability under diverse environmental conditions. It can also improve nutrient use efficiency and crop productivity [7]. Intermingling of maize and faba-bean roots increased N acquisition by both crop species by about 20% compared with complete or partial separation of the root systems. Further studies indicate that N2 fixation can be improved by yield maximization in the intercropping system. The improved productivity observed in this production system has been associated with increased levels of available phosphorus (P) in the root rhizosphere. Hinsinger et al. [70] reported more stable yield, superior land resource utilization or conservation, and enhanced pest or weed control [71, 72, 73]. Furthermore, cereal-legume intercropping can also enhance the phosphatase enzyme activity and available P in the soil due to rhizosphere acidification by the legumes in the cropping system [74].
The possible mechanism that increases PUE in intercropping is the increased rhizosphere soil acid phosphatase (RS-APase) activity observed in intercropping due to the fact that large amounts of acid phosphatase are known to be released from their roots into the root rhizosphere. The (RS-APase) activity was significantly higher (26–46%) in the intercropping and occurred concomitant with a significant increase in available phosphorus (RS-Pavailable) in the rhizosphere on podzols in cool climate boreal ecosystem [75]. Another mechanism could be secreting H+ into the soil that acidifiies the rhizosphere [57, 76] and improves dissolution of phosphorus and then enhances P-availability [70]. Additional possible mechanism that improves of plant growth and P uptake in mixed planting was due to root interspecific complementation or facilitation. The complementarity between root morphological and physiological traits of neighboring plants underpins the interactive facilitation, which was the main underlying mechanism improving nutrient-use efficiency, particularly of P, in mixed cropping system [77, 78]. The complementary niches of maize and faba bean significantly reduce interspecific nutrient competition and thus improve nutrient-use efficiency [79]. The presence of maize increased the secretion of carboxylates from alfalfa roots, suggesting that the root interactions between maize and alfalfa are crucial for improving P-use efficiency and productivity in intercropping [80]. Subsequently, Sun et al. [76] reported that decreasing rhizosphere pH and increasing organic anion exudation played key roles in soil P mobilization of maize and alfalfa, with little contribution of acid phosphatase.
The mycorrhizal symbiosis particularly, arbuscular mycorrhizal fungi (AMF), is arguably the most important symbiosis on earth [81]. AMF colonize the roots of many agriculturally important food and bioenergy crops form (approximately 80–90% of all known land plant species) [81] and could serve as “biofertilizers and bioprotectors” in environmentally sustainable agriculture [82]. In AMF associations, two pathways for plant P uptake exist: the direct pathway (P uptake by roots) and the AM fungal pathway [83]. AMF facilitates the uptake and transfer of mineral nutrients, such as phosphorus, nitrogen, sulfur, potassium, calcium, copper, and zinc, from the soil to their host plants by means of the extraradical mycelium extending from colonized roots into the soil [84]. The contribution of AMF to P uptake reaches up to 77% under low P supply compared with only 49% under high P supply [85]. Furthermore, the commercial inoculum Mycobiol, consisting of Glomus spp.,
Various mechanisms have been suggested for the increase in the plant uptake of P. These include: exploration of larger soil volume; faster movement of P into mycorrhizal hyphae; and solubilization of soil phosphorus [88]. Exploration of larger soil volume by mycorrhizal plants is achieved by decreasing the distance that P ions must diffuse to plant roots and by increasing the surface area for absorption. Faster movement of P into mycorrhizal hyphae is achieved by increasing the affinity for P ions and by decreasing the threshold concentration required for absorption of P [88]. Solubilization of soil P is achieved by rhizospheric modifications through the release of organic acids, phosphatase enzymes, and some specialized metabolites such as siderophores [55].
The composition and amount of root exudates affect the composition of microbes in the rhizosphere and the structure of the rhizosphere microbiome, affecting plant growth and nutrient uptake [81]. For precision rhizosphere management, plant-microbe interactions must be finely tuned to improve P use efficiency by crops [57]. Figure 4 illustrates the main structural differences between AM (more for P absorption) and ectomycorrhizal (more for N and few for P absorption) associations of angiosperms or gymnosperms [81].
Phosphorus acquisition efficiency related traits of wheat and barley roots affected by arbuscular mycorrhizal symbiosis in comparison to a non-colonized counterpart (adopted from [
Among the soil bacterial communities, ectorhizospheric strains from Pseudomonas and Bacilli and endosymbiotic rhizobia have been described as effective phosphate solubilizers [90]. Phosphate-solubilizing bacteria (PSB) are also capable of making P available to plants from both inorganic sources and organic ones and increasing P-fertilizer-use efficiency by different mechanisms [91]. They are rhizobacteria that convert insoluble phosphates into soluble forms through acidification, chelation, exchange reactions, and the production of organic acids [92]. Therefore, combined application of AMF and P solubilizers [93] and N fixers are the best inoculants. AM fungi together with PSMs could be much more effective in supplementing soil P. Understanding AM-plant symbiosis, developing AM fungi that could be cultured in vitro, and developing P-solubilizing AM will help realize their potential as phosphate biofertilizer [94].
Soil pH is one of the most important chemical properties influencing nutrient solubility and hence availability to plants. Large amount of P applied as fertilizer enters in to the immobile pools through precipitation reaction (fixation) with highly reactive Al3+ and Fe3+ in acidic and Ca2+ in calcareous or normal soils [94]. Acidic, highly weathered, iron (Fe)-rich soils rapidly bind phosphates at mineral surfaces, limiting access to plant roots. Furthermore, applied Pi (inorganic P) is quickly fixed into insoluble inorganic or organic forms due to its high reactivity and microbial action [95].
Soil pH markedly limits plant growth and P chemistry in soils through its effect on P adsorption and through interactions that affect precipitation of P into solid forms in soil [62]. Consequently, about 80–90% soil P becomes unavailable depending on soil composition and pH [96], 50–70% of the total applied conventional fertilizers are lost to the environment. This level of loss in agricultural nutrients not only leads to the loss of valuable resources but also causes the severe reduction of yield [97]. The pH of a calcareous soil is reduced by the presence of gypsum (CaSO4·2H2O) due to the concentration of Ca2+, which would be expected to decrease the sorption of P, if followed by leaching to removed much of the soluble Na+ and Ca2+ [98]. Thus, adjusting soil pH and base saturation are methods to reduce the amount of P that is bound by Al, Fe, and Ca, further reducing the effects of Al toxicity to plants, which can inhibit uptake, and use of P by the plant (Figure 5) [23, 99].
Soil P availability as affected by soil pH (adopted from Havlin et al. 1999).
Lime acidic soil is widely used in agriculture to create and maintain a soil pH optimal for plant growth in acid soils. Lime reduces toxic effects of hydrogen, aluminum, and manganese, improves soil biological activities, cation exchange capacity (CEC), P, Ca, and Mg availability and soil structure, promotes N2 fixation, stimulates nitrification, and decreases availability of K, Mn, Zn, Fe, boron (B), and Cu [11]. An increase in soil pH, as a result of liming, was due to an increase in hydroxide ions, which increases microbial activity and communities, hence, increasing decomposition of soil organic matter and release of Fe and Al [100]. The decrease in Al-P and Fe-P could be due to their precipitation as insoluble Al(OH)3 and Fe(OH)3 after increased addition of liming material [101]. In addition, Al and Fe oxides become more negatively charged with an increase in pH contributing to an increase in available P [102].
Liming, gypsum application, or mixing of both is an effective practice to improve pH, improve Ca content, and control Al toxicity. Lime has very low mobility in soil, and when surface applied, it does not reduce the acidity of subsurface soil horizons. Contrary to lime, gypsum (CaSO4) has a greater downward movement, and when applied to the surface, it can still impact and reduce the acidity of the subsoil [4]. The pH of a calcareous soil is reduced by the presence of gypsum (CaSO4·2H2O) due to the concentration of Ca2+, which would be expected to decrease the sorption of P, if followed by leaching to removed much of the soluble Na+ and Ca2+. The uptake of nutrients by plants, content of nutrients in plants and in soil were substantially positively influenced by both the wood ash, especially by FGD gypsum [103]. Gypsum application can ameliorate saline-sodic soil, thereby increasing crop yield and NUE [104].
Apart from traditional methods, new techniques have been developed such as site-specific/real-time nitrogen management, slow release/controlled release fertilizer (SR/CRF), site-specific precision nutrient management, and urease/nitrification inhibitor. Those techniques play an important role in decreasing fertilizer loss and increasing NUE [105]. The remote sensing is quicker than the previous two methods, and it obtains continuous data rather than spot data, which is more advantageous. It is becoming the major means of obtaining data for precision farming. GIS (geographic information system) establishes the field management information system by processing, analyzing, and trimming the data of soil and crops [105]. Another approach to synchronize release of N from fertilizers with crop need is the use of N stabilizers and controlled release fertilizers. Nitrogen stabilizers (e.g., nitrapyrin, DCD [dicyandiamide], NBPT [n-butyl-thiophosphoric triamide]) inhibit nitrification or urease activity, thereby slowing the conversion of the fertilizer to nitrate. The most promising for widespread agricultural use are polymer-coated products, which can be designed to release nutrients in a controlled manner.
Agronomic management strategies such as precision P fertilization, polymer coated P-fertilizers, and recycling of P from domestic, agricultural, and industrial wastes can be helpful in improving P use at farm level [106]. Modern concepts for tactical N management should involve a combination of anticipatory (before planting) and responsive (during the growing season) decisions [9]. On soils with moderate P and K levels and little fixation, management must focus on balancing inputs and outputs at field and farm scales to maximize profit, avoid excessive accumulation, and minimize risk of P losses. Improving the internal, on-farm and field recycling is the most important K management issue worldwide. As for N, the primary determinants for REP and REK are the size of the crop sink, soil supply, soil characteristics, and fertilizer rate.
Control release fertilizers with polymer coatings are commonly applied to crops to increase efficiency of nutrients [96]. One way of improving the P availability to crop plant is by coating diammonium phosphate (DAP) with polymer that allows a steady but controlled discharge of phosphorus from the granules for crop plant uptake and improved P recovery percentage. Thus, by the use of polymer, availability of P to plant increased because it has high cation exchange capacity, which holds the divalent calcium (Ca+2) and trivalent cations iron and aluminum (Fe+3 and Al+3) and stop P fixation with these cations. Moreover, polymer absorbs water efficiently and holds more water and keeps P in available form that enhanced the plant growth and yield-contributing factors [97]. This is because polymer-coated diammonium phosphate (DAP) absorbs water many times of its original weight, which increases the availability of phosphorus for longer period of time [107] and creates a diffusion shell around the grain of DAP and directly reduces the fixation and precipitation by reducing the availability of calcium and magnesium (Ca+2/Mg+2) cations [108]. As the result of this mechanism, availability of phosphorus to plants increases and leads to more P uptake, and this uptake indirectly influences the other nutrient absorption by crop plants.
Considering the wide variety of soil types, cropping patterns, and farmers’ resources, several management practices are adopted to reduce the magnitude of soil fertility degradation. Integrated Plant Nutrient Management System (IPNMS) is defined as the package of practices for the manipulation of the plant growth environment to supply essential nutrients to a crop in an adequate amount and proportion for optimum production without degrading the natural resources [3]. Many authors have reported that combining organic and inorganic P can improve and sustain crop yields in low fertility soils [109, 110, 111]. Best management practices (BMPs) such as use of fertilizer and amendment (lime), proper crop rotations, increases in organic matter content, and control of erosion, insects, diseases, and weeds can significantly improve crop yields and optimize nutrient use efficiency [11]. Integrated use of organic manures and fertilizers not only improves efficiency of crops but also significantly increases the availability of P [112, 113].
Organic amendment improves the structure and fertility of the soil by adding nutrients and organic matter and consequently promotes soil microbial biomass and activity. Blockage of P sorption sites by organic acids, as well as complexation of exchangeable Al and Fe in the soil, is potential cause of this mobilization [114]. Organic materials can reduce P fixation by masking the fixation sites on the soil colloids and by forming organic complexes or chelates with Al, Fe, and Mn ions, thereby improving P uptake efficiency of crop plants. Decomposition of organic matter produces organic anions that interact with soil to reduce P sorption via (1) complexation/competition for soil P binding sites such as Fe and Al oxyhydroxides or (2) increased soil PH. Organic materials also increase agronomic efficiency by improving availability of P by promoting soil aggregation, increased soil PH, microbial biomass, and parameters controlling soil-P-sorption [115]. The integration of biochar FYM, poultry manure, and inorganic P sources increases in PUE under both wheat and maize crops, and there is a concomitant increase in crop yields compared with the unamended soil [112, 113]. This increase in PUE with biochar addition could also be the result of the additional nutrients made available by biochar [112]. Similarly, FYM applications increase soil P bioavailability more than applications of triple supper phosphate that enhance P Uptake Efficiency. FYM is also a source of other nutrients used by crops via mineralization, which promotes root development and root area interception and thus increases nutrient uptake including P uptake [116].
Rotating a legume with a cereal can enhance P acquisition by cereals through indirect feedback interactions [117]. A legume crop modifies the rhizosphere through biological and chemical processes, thereby increasing P uptake by the following cereal crop. As reported by [77], legumes are able to mobilize P that is not initially available to cereal species, thereby improving the availability of P for the following crop. The biological processes include the promotion of symbiotic mutualists such as nitrogen-fixing rhizobacteria and mycorrhizal fungi, while the chemical processes are acidification of the rhizosphere and secretion of organic anions [79].
Achievement and maintenance of high nutrient use efficiency (NUE) together with high crop productivity have become a major challenge in both developed and developing countries with an increasing growing population, depletion of natural resources, and deteriorating environmental conditions. Improving nutrient use efficiency (NUE) in plants is vital to enhance the yield and quality of crops, reduce nutrient input cost and improve soil, water, and air quality [3]. Higher NUE by plants could reduce fertilizer input costs, decrease the rate of nutrient losses, and enhance crop yields. Improving crop nutrient use efficiency ideally requires an understanding of the whole system, from the macro (agro-ecosystem) to the molecular level.
The development of nutrient-efficient crop varieties that can grow and yield better with low supply is a key to improving crop production. A prerequisite for nutrient use efficiency for any germplasm will be the optimization of agronomic practice for any given environment and season. Judicious application of fertilizer that includes the right rate, right time, right source, right place, and balanced fertilization (4RB) is the best management practice for achieving optimum nutrient efficiency. By the coordination of the acquisition, root-to-shoot translocation, utilization, and remobilization of internal Pi can be achieved through genetic breeding. Selection and breeding nutrient efficient species or genotypes within a species are justified in terms of reduction in fertilizer input cost of crop production and also reduced risk of contamination of soil and water. Overall NUE in plant is a function of capacity of soil to supply adequate levels of nutrients and ability of plant to acquire, transport in roots and shoot, and remobilize to other parts of the plant. Improvement in NUE will ultimately come from integrating a range of different approaches to develop a more efficient farming system. Use of nutrient efficient crop species or genotypes within species in combination with other improved crop production practices offers the best option for meeting the future food requirements of expanding world populations. Modern tools and resources available to plant scientists and the agronomy and breeding communities should aid further improvements in NUE and hence crop production. Therefore, integrated strategy that seeks to increase phosphorus use efficiency and simultaneously seeks to recover unavoidable phosphorus losses. The nutrient inputs in the intensive farming system should be optimized to achieve both high crop productivity and high nutrient use efficiency through maximizing root/rhizosphere efficiency in nutrient mobilization and acquisition.
The authors are highly thankful to researchers whose findings are included directly or indirectly in preparing this manuscript.
The authors declare no conflict of interest.
The authors received no direct funding for this research.
All data generated are included in this article reference’s part.
AMF | arbuscular mycorrhizal fungi |
BMP | best management practice |
DAP | diammonium phosphate |
FYM | farm-yard manure |
NUE | nutrient use efficiency |
PACE | phosphorus acquisition efficiency |
PSB | phosphate solubilizing bacteria |
PUE | phosphorus use efficiency |
PUTE | phosphorus utilization efficiency |
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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:"14",title:"Artificial Intelligence",doi:"10.5772/intechopen.79920",issn:"2633-1403",scope:"Artificial Intelligence (AI) is a rapidly developing multidisciplinary research area that aims to solve increasingly complex problems. In today's highly integrated world, AI promises to become a robust and powerful means for obtaining solutions to previously unsolvable problems. This Series is intended for researchers and students alike interested in this fascinating field and its many applications.",coverUrl:"https://cdn.intechopen.com/series/covers/14.jpg",latestPublicationDate:"June 11th, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:9,editor:{id:"218714",title:"Prof.",name:"Andries",middleName:null,surname:"Engelbrecht",slug:"andries-engelbrecht",fullName:"Andries Engelbrecht",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRNR8QAO/Profile_Picture_1622640468300",biography:"Andries Engelbrecht received the Masters and PhD degrees in Computer Science from the University of Stellenbosch, South Africa, in 1994 and 1999 respectively. He is currently appointed as the Voigt Chair in Data Science in the Department of Industrial Engineering, with a joint appointment as Professor in the Computer Science Division, Stellenbosch University. Prior to his appointment at Stellenbosch University, he has been at the University of Pretoria, Department of Computer Science (1998-2018), where he was appointed as South Africa Research Chair in Artifical Intelligence (2007-2018), the head of the Department of Computer Science (2008-2017), and Director of the Institute for Big Data and Data Science (2017-2018). 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He is a full professor of signal processing and pattern recognition and is head of the Signals and Communications Department at ULPGC, teaching from 2001 on subjects on signal processing and learning theory. His research lines are biometrics, biomedical signals and images, data mining, classification system, signal and image processing, machine learning, and environmental intelligence. He has researched in 52 international and Spanish research projects, some of them as head researcher. He is co-author of 4 books, co-editor of 27 proceedings books, guest editor for 8 JCR-ISI international journals, and up to 24 book chapters. He has over 450 papers published in international journals and conferences (81 of them indexed on JCR – ISI - Web of Science). He has published seven patents in the Spanish Patent and Trademark Office. He has been a supervisor on 8 Ph.D. theses (11 more are under supervision), and 130 master theses. He is the founder of The IEEE IWOBI conference series and the president of its Steering Committee, as well as the founder of both the InnoEducaTIC and APPIS conference series. He is an evaluator of project proposals for the European Union (H2020), Medical Research Council (MRC, UK), Spanish Government (ANECA, Spain), Research National Agency (ANR, France), DAAD (Germany), Argentinian Government, and the Colombian Institutions. He has been a reviewer in different indexed international journals (<70) and conferences (<250) since 2001. He has been a member of the IASTED Technical Committee on Image Processing from 2007 and a member of the IASTED Technical Committee on Artificial Intelligence and Expert Systems from 2011. \n\nHe has held the general chair position for the following: ACM-APPIS (2020, 2021), IEEE-IWOBI (2019, 2020 and 2020), A PPIS (2018, 2019), IEEE-IWOBI (2014, 2015, 2017, 2018), InnoEducaTIC (2014, 2017), IEEE-INES (2013), NoLISP (2011), JRBP (2012), and IEEE-ICCST (2005)\n\nHe is an associate editor of the Computational Intelligence and Neuroscience Journal (Hindawi – Q2 JCR-ISI). He was vice dean from 2004 to 2010 in the Higher Technical School of Telecommunication Engineers at ULPGC and the vice dean of Graduate and Postgraduate Studies from March 2013 to November 2017. He won the “Catedra Telefonica” Awards in Modality of Knowledge Transfer, 2017, 2018, and 2019 editions, and awards in Modality of COVID Research in 2020.\n\nPublic References:\nResearcher ID http://www.researcherid.com/rid/N-5967-2014\nORCID https://orcid.org/0000-0002-4621-2768 \nScopus Author ID https://www.scopus.com/authid/detail.uri?authorId=6602376272\nScholar Google https://scholar.google.es/citations?user=G1ks9nIAAAAJ&hl=en \nResearchGate https://www.researchgate.net/profile/Carlos_Travieso",institutionString:null,institution:{name:"University of Las Palmas de Gran Canaria",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"23",title:"Computational Neuroscience",coverUrl:"https://cdn.intechopen.com/series_topics/covers/23.jpg",isOpenForSubmission:!0,editor:{id:"14004",title:"Dr.",name:"Magnus",middleName:null,surname:"Johnsson",slug:"magnus-johnsson",fullName:"Magnus Johnsson",profilePictureURL:"https://mts.intechopen.com/storage/users/14004/images/system/14004.png",biography:"Dr Magnus Johnsson is a cross-disciplinary scientist, lecturer, scientific editor and AI/machine learning consultant from Sweden. \n\nHe is currently at Malmö University in Sweden, but also held positions at Lund University in Sweden and at Moscow Engineering Physics Institute. \nHe holds editorial positions at several international scientific journals and has served as a scientific editor for books and special journal issues. \nHis research interests are wide and include, but are not limited to, autonomous systems, computer modeling, artificial neural networks, artificial intelligence, cognitive neuroscience, cognitive robotics, cognitive architectures, cognitive aids and the philosophy of mind. \n\nDr. Johnsson has experience from working in the industry and he has a keen interest in the application of neural networks and artificial intelligence to fields like industry, finance, and medicine. \n\nWeb page: www.magnusjohnsson.se",institutionString:null,institution:{name:"Malmö University",institutionURL:null,country:{name:"Sweden"}}},editorTwo:null,editorThree:null},{id:"24",title:"Computer Vision",coverUrl:"https://cdn.intechopen.com/series_topics/covers/24.jpg",isOpenForSubmission:!0,editor:{id:"294154",title:"Prof.",name:"George",middleName:null,surname:"Papakostas",slug:"george-papakostas",fullName:"George Papakostas",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002hYaGbQAK/Profile_Picture_1624519712088",biography:"George A. 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He has (co)authored more than 150 publications in indexed journals, international conferences and book chapters, 1 book (in Greek), 3 edited books, and 5 journal special issues. His publications have more than 2100 citations with h-index 27 (GoogleScholar). His research interests include computer/machine vision, machine learning, pattern recognition, computational intelligence. \nDr. Papakostas served as a reviewer in numerous journals, as a program\ncommittee member in international conferences and he is a member of the IAENG, MIR Labs, EUCogIII, INSTICC and the Technical Chamber of Greece (TEE).",institutionString:null,institution:{name:"International Hellenic University",institutionURL:null,country:{name:"Greece"}}},editorTwo:null,editorThree:null},{id:"25",title:"Evolutionary Computation",coverUrl:"https://cdn.intechopen.com/series_topics/covers/25.jpg",isOpenForSubmission:!0,editor:{id:"136112",title:"Dr.",name:"Sebastian",middleName:null,surname:"Ventura Soto",slug:"sebastian-ventura-soto",fullName:"Sebastian Ventura Soto",profilePictureURL:"https://mts.intechopen.com/storage/users/136112/images/system/136112.png",biography:"Sebastian Ventura is a Spanish researcher, a full professor with the Department of Computer Science and Numerical Analysis, University of Córdoba. Dr Ventura also holds the positions of Affiliated Professor at Virginia Commonwealth University (Richmond, USA) and Distinguished Adjunct Professor at King Abdulaziz University (Jeddah, Saudi Arabia). Additionally, he is deputy director of the Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) and heads the Knowledge Discovery and Intelligent Systems Research Laboratory. He has published more than ten books and over 300 articles in journals and scientific conferences. Currently, his work has received over 18,000 citations according to Google Scholar, including more than 2200 citations in 2020. In the last five years, he has published more than 60 papers in international journals indexed in the JCR (around 70% of them belonging to first quartile journals) and he has edited some Springer books “Supervised Descriptive Pattern Mining” (2018), “Multiple Instance Learning - Foundations and Algorithms” (2016), and “Pattern Mining with Evolutionary Algorithms” (2016). He has also been involved in more than 20 research projects supported by the Spanish and Andalusian governments and the European Union. He currently belongs to the editorial board of PeerJ Computer Science, Information Fusion and Engineering Applications of Artificial Intelligence journals, being also associate editor of Applied Computational Intelligence and Soft Computing and IEEE Transactions on Cybernetics. Finally, he is editor-in-chief of Progress in Artificial Intelligence. He is a Senior Member of the IEEE Computer, the IEEE Computational Intelligence, and the IEEE Systems, Man, and Cybernetics Societies, and the Association of Computing Machinery (ACM). Finally, his main research interests include data science, computational intelligence, and their applications.",institutionString:null,institution:{name:"University of Córdoba",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"26",title:"Machine Learning and Data Mining",coverUrl:"https://cdn.intechopen.com/series_topics/covers/26.jpg",isOpenForSubmission:!0,editor:{id:"24555",title:"Dr.",name:"Marco Antonio",middleName:null,surname:"Aceves Fernandez",slug:"marco-antonio-aceves-fernandez",fullName:"Marco Antonio Aceves Fernandez",profilePictureURL:"https://mts.intechopen.com/storage/users/24555/images/system/24555.jpg",biography:"Dr. Marco Antonio Aceves Fernandez obtained his B.Sc. (Eng.) in Telematics from the Universidad de Colima, Mexico. 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(Eng.) in Telematics from the Universidad de Colima, Mexico. He obtained both his M.Sc. and Ph.D. from the University of Liverpool, England, in the field of Intelligent Systems. He is a full professor at the Universidad Autonoma de Queretaro, Mexico, and a member of the National System of Researchers (SNI) since 2009. Dr. Aceves Fernandez has published more than 80 research papers as well as a number of book chapters and congress papers. He has contributed in more than 20 funded research projects, both academic and industrial, in the area of artificial intelligence, ranging from environmental, biomedical, automotive, aviation, consumer, and robotics to other applications. He is also a honorary president at the National Association of Embedded Systems (AMESE), a senior member of the IEEE, and a board member of many institutions. His research interests include intelligent and embedded systems.",institutionString:"Universidad Autonoma de Queretaro",institution:{name:"Autonomous University of Queretaro",institutionURL:null,country:{name:"Mexico"}}}]},{type:"book",id:"7726",title:"Swarm Intelligence",subtitle:"Recent Advances, New Perspectives and Applications",coverURL:"https://cdn.intechopen.com/books/images_new/7726.jpg",slug:"swarm-intelligence-recent-advances-new-perspectives-and-applications",publishedDate:"December 4th 2019",editedByType:"Edited by",bookSignature:"Javier Del Ser, Esther Villar and Eneko Osaba",hash:"e7ea7e74ce7a7a8e5359629e07c68d31",volumeInSeries:2,fullTitle:"Swarm Intelligence - Recent Advances, New Perspectives and Applications",editors:[{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. 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He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. 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She is also Invisalign certified. She’s working as a Senior Lecturer in the Department of Orthodontics, SRM Dental College since November 2019. She is actively involved in teaching orthodontics to the undergraduates and the postgraduates. Her clinical research topics include new orthodontic brackets, fixed appliances and TADs. She’s published 4 articles in well renowned indexed journals and has a published patency of her own. Her private practice is currently limited to orthodontics and works as a consultant in various clinics.",institutionString:null,institution:{name:"SRM Dental College",country:{name:"India"}}},{id:"323731",title:"Prof.",name:"Deepak M.",middleName:"Macchindra",surname:"Vikhe",slug:"deepak-m.-vikhe",fullName:"Deepak M. Vikhe",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/323731/images/13613_n.jpg",biography:"Dr Deepak M.Vikhe .\n\n\t\n\tDr Deepak M.Vikhe , completed his Masters & PhD in Prosthodontics from Rural Dental College, Loni securing third rank in the Pravara Institute of Medical Sciences Deemed University. He was awarded Dr.G.C.DAS Memorial Award for Research on Implants at 39th IPS conference Dubai (U A E).He has two patents under his name. He has received Dr.Saraswati medal award for best research for implant study in 2017.He has received Fully funded scholarship to Spain ,university of Santiago de Compostela. He has completed fellowship in Implantlogy from Noble Biocare. \nHe has attended various conferences and CDE programmes and has national publications to his credit. His field of interest is in Implant supported prosthesis. Presently he is working as a associate professor in the Dept of Prosthodontics, Rural Dental College, Loni and maintains a successful private practice specialising in Implantology at Rahata.\n\nEmail: drdeepak_mvikhe@yahoo.com..................",institutionString:null,institution:{name:"Pravara Institute of Medical Sciences",country:{name:"India"}}},{id:"204110",title:"Dr.",name:"Ahmed A.",middleName:null,surname:"Madfa",slug:"ahmed-a.-madfa",fullName:"Ahmed A. Madfa",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/204110/images/system/204110.jpg",biography:"Dr. Madfa is currently Associate Professor of Endodontics at Thamar University and a visiting lecturer at Sana'a University and University of Sciences and Technology. He has more than 6 years of experience in teaching. His research interests include root canal morphology, functionally graded concept, dental biomaterials, epidemiology and dental education, biomimetic restoration, finite element analysis and endodontic regeneration. Dr. Madfa has numerous international publications, full articles, two patents, a book and a book chapter. Furthermore, he won 14 international scientific awards. Furthermore, he is involved in many academic activities ranging from editorial board member, reviewer for many international journals and postgraduate students' supervisor. Besides, I deliver many courses and training workshops at various scientific events. Dr. Madfa also regularly attends international conferences and holds administrative positions (Deputy Dean of the Faculty for Students’ & Academic Affairs and Deputy Head of Research Unit).",institutionString:"Thamar University",institution:null},{id:"210472",title:"Dr.",name:"Nermin",middleName:"Mohammed Ahmed",surname:"Yussif",slug:"nermin-yussif",fullName:"Nermin Yussif",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/210472/images/system/210472.jpg",biography:"Dr. Nermin Mohammed Ahmed Yussif is working at the Faculty of dentistry, University for October university for modern sciences and arts (MSA). Her areas of expertise include: periodontology, dental laserology, oral implantology, periodontal plastic surgeries, oral mesotherapy, nutrition, dental pharmacology. She is an editor and reviewer in numerous international journals.",institutionString:"MSA University",institution:null},{id:"204606",title:"Dr.",name:"Serdar",middleName:null,surname:"Gözler",slug:"serdar-gozler",fullName:"Serdar Gözler",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/204606/images/system/204606.jpeg",biography:"Dr. Serdar Gözler has completed his undergraduate studies at the Marmara University Faculty of Dentistry in 1978, followed by an assistantship in the Prosthesis Department of Dicle University Faculty of Dentistry. Starting his PhD work on non-resilient overdentures with Assoc. Prof. Hüsnü Yavuzyılmaz, he continued his studies with Prof. Dr. Gürbüz Öztürk of Istanbul University Faculty of Dentistry Department of Prosthodontics, this time on Gnatology. He attended training programs on occlusion, neurology, neurophysiology, EMG, radiology and biostatistics. In 1982, he presented his PhD thesis \\Gerber and Lauritzen Occlusion Analysis Techniques: Diagnosis Values,\\ at Istanbul University School of Dentistry, Department of Prosthodontics. As he was also working with Prof. Senih Çalıkkocaoğlu on The Physiology of Chewing at the same time, Gözler has written a chapter in Çalıkkocaoğlu\\'s book \\Complete Prostheses\\ entitled \\The Place of Neuromuscular Mechanism in Prosthetic Dentistry.\\ The book was published five times since by the Istanbul University Publications. Having presented in various conferences about occlusion analysis until 1998, Dr. Gözler has also decided to use the T-Scan II occlusion analysis method. Having been personally trained by Dr. Robert Kerstein on this method, Dr. Gözler has been lecturing on the T-Scan Occlusion Analysis Method in conferences both in Turkey and abroad. Dr. Gözler has various articles and presentations on Digital Occlusion Analysis methods. He is now Head of the TMD Clinic at Prosthodontic Department of Faculty of Dentistry , Istanbul Aydın University , Turkey.",institutionString:"Istanbul Aydin University",institution:{name:"Istanbul Aydın University",country:{name:"Turkey"}}},{id:"240870",title:"Ph.D.",name:"Alaa Eddin Omar",middleName:null,surname:"Al Ostwani",slug:"alaa-eddin-omar-al-ostwani",fullName:"Alaa Eddin Omar Al Ostwani",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/240870/images/system/240870.jpeg",biography:"Dr. Al Ostwani Alaa Eddin Omar received his Master in dentistry from Damascus University in 2010, and his Ph.D. in Pediatric Dentistry from Damascus University in 2014. Dr. Al Ostwani is an assistant professor and faculty member at IUST University since 2014. \nDuring his academic experience, he has received several awards including the scientific research award from the Union of Arab Universities, the Syrian gold medal and the international gold medal for invention and creativity. Dr. Al Ostwani is a Member of the International Association of Dental Traumatology and the Syrian Society for Research and Preventive Dentistry since 2017. He is also a Member of the Reviewer Board of International Journal of Dental Medicine (IJDM), and the Indian Journal of Conservative and Endodontics since 2016.",institutionString:"International University for Science and Technology.",institution:{name:"Islamic University of Science and Technology",country:{name:"India"}}},{id:"42847",title:"Dr.",name:"Belma",middleName:null,surname:"Işik Aslan",slug:"belma-isik-aslan",fullName:"Belma Işik Aslan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/42847/images/system/42847.jpg",biography:"Dr. Belma IşIk Aslan was born in 1976 in Ankara-TURKEY. After graduating from TED Ankara College in 1994, she attended to Gazi University, Faculty of Dentistry in Ankara. She completed her PhD in orthodontic education at Gazi University between 1999-2005. Dr. Işık Aslan stayed at the Providence Hospital Craniofacial Institude and Reconstructive Surgery in Michigan, USA for three months as an observer. She worked as a specialist doctor at Gazi University, Dentistry Faculty, Department of Orthodontics between 2005-2014. She was appointed as associate professor in January, 2014 and as professor in 2021. Dr. Işık Aslan still works as an instructor at the same faculty. She has published a total of 35 articles, 10 book chapters, 39 conference proceedings both internationally and nationally. Also she was the academic editor of the international book 'Current Advances in Orthodontics'. She is a member of the Turkish Orthodontic Society and Turkish Cleft Lip and Palate Society. She is married and has 2 children. Her knowledge of English is at an advanced level.",institutionString:"Gazi University Dentistry Faculty Department of Orthodontics",institution:null},{id:"178412",title:"Associate Prof.",name:"Guhan",middleName:null,surname:"Dergin",slug:"guhan-dergin",fullName:"Guhan Dergin",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/178412/images/6954_n.jpg",biography:"Assoc. Prof. Dr. Gühan Dergin was born in 1973 in Izmit. He graduated from Marmara University Faculty of Dentistry in 1999. He completed his specialty of OMFS surgery in Marmara University Faculty of Dentistry and obtained his PhD degree in 2006. In 2005, he was invited as a visiting doctor in the Oral and Maxillofacial Surgery Department of the University of North Carolina, USA, where he went on a scholarship. Dr. Dergin still continues his academic career as an associate professor in Marmara University Faculty of Dentistry. He has many articles in international and national scientific journals and chapters in books.",institutionString:null,institution:{name:"Marmara University",country:{name:"Turkey"}}},{id:"178414",title:"Prof.",name:"Yusuf",middleName:null,surname:"Emes",slug:"yusuf-emes",fullName:"Yusuf Emes",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/178414/images/6953_n.jpg",biography:"Born in Istanbul in 1974, Dr. Emes graduated from Istanbul University Faculty of Dentistry in 1997 and completed his PhD degree in Istanbul University faculty of Dentistry Department of Oral and Maxillofacial Surgery in 2005. He has papers published in international and national scientific journals, including research articles on implantology, oroantral fistulas, odontogenic cysts, and temporomandibular disorders. Dr. Emes is currently working as a full-time academic staff in Istanbul University faculty of Dentistry Department of Oral and Maxillofacial Surgery.",institutionString:null,institution:{name:"Istanbul University",country:{name:"Turkey"}}},{id:"192229",title:"Ph.D.",name:"Ana Luiza",middleName:null,surname:"De Carvalho Felippini",slug:"ana-luiza-de-carvalho-felippini",fullName:"Ana Luiza De Carvalho Felippini",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/192229/images/system/192229.jpg",biography:null,institutionString:"University of São Paulo",institution:{name:"University of Sao Paulo",country:{name:"Brazil"}}},{id:"256851",title:"Prof.",name:"Ayşe",middleName:null,surname:"Gülşen",slug:"ayse-gulsen",fullName:"Ayşe Gülşen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/256851/images/9696_n.jpg",biography:"Dr. Ayşe Gülşen graduated in 1990 from Faculty of Dentistry, University of Ankara and did a postgraduate program at University of Gazi. \nShe worked as an observer and research assistant in Craniofacial Surgery Departments in New York, Providence Hospital in Michigan and Chang Gung Memorial Hospital in Taiwan. \nShe works as Craniofacial Orthodontist in Department of Aesthetic, Plastic and Reconstructive Surgery, Faculty of Medicine, University of Gazi, Ankara Turkey since 2004.",institutionString:"Univeristy of Gazi",institution:null},{id:"255366",title:"Prof.",name:"Tosun",middleName:null,surname:"Tosun",slug:"tosun-tosun",fullName:"Tosun Tosun",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/255366/images/7347_n.jpg",biography:"Graduated at the Faculty of Dentistry, University of Istanbul, Turkey in 1989;\nVisitor Assistant at the University of Padua, Italy and Branemark Osseointegration Center of Treviso, Italy between 1993-94;\nPhD thesis on oral implantology in University of Istanbul and was awarded the academic title “Dr.med.dent.”, 1997;\nHe was awarded the academic title “Doç.Dr.” (Associated Professor) in 2003;\nProficiency in Botulinum Toxin Applications, Reading-UK in 2009;\nMastership, RWTH Certificate in Laser Therapy in Dentistry, AALZ-Aachen University, Germany 2009-11;\nMaster of Science (MSc) in Laser Dentistry, University of Genoa, Italy 2013-14.\n\nDr.Tosun worked as Research Assistant in the Department of Oral Implantology, Faculty of Dentistry, University of Istanbul between 1990-2002. \nHe worked part-time as Consultant surgeon in Harvard Medical International Hospitals and John Hopkins Medicine, Istanbul between years 2007-09.\u2028He was contract Professor in the Department of Surgical and Diagnostic Sciences (DI.S.C.), Medical School, University of Genova, Italy between years 2011-16. \nSince 2015 he is visiting Professor at Medical School, University of Plovdiv, Bulgaria. \nCurrently he is Associated Prof.Dr. at the Dental School, Oral Surgery Dept., Istanbul Aydin University and since 2003 he works in his own private clinic in Istanbul, Turkey.\u2028\nDr.Tosun is reviewer in journal ‘Laser in Medical Sciences’, reviewer in journal ‘Folia Medica\\', a Fellow of the International Team for Implantology, Clinical Lecturer of DGZI German Association of Oral Implantology, Expert Lecturer of Laser&Health Academy, Country Representative of World Federation for Laser Dentistry, member of European Federation of Periodontology, member of Academy of Laser Dentistry. Dr.Tosun presents papers in international and national congresses and has scientific publications in international and national journals. He speaks english, spanish, italian and french.",institutionString:null,institution:{name:"Istanbul Aydın University",country:{name:"Turkey"}}},{id:"171887",title:"Prof.",name:"Zühre",middleName:null,surname:"Akarslan",slug:"zuhre-akarslan",fullName:"Zühre Akarslan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/171887/images/system/171887.jpg",biography:"Zühre Akarslan was born in 1977 in Cyprus. She graduated from Gazi University Faculty of Dentistry, Ankara, Turkey in 2000. \r\nLater she received her Ph.D. degree from the Oral Diagnosis and Radiology Department; which was recently renamed as Oral and Dentomaxillofacial Radiology, from the same university. \r\nShe is working as a full-time Associate Professor and is a lecturer and an academic researcher. \r\nHer expertise areas are dental caries, cancer, dental fear and anxiety, gag reflex in dentistry, oral medicine, and dentomaxillofacial radiology.",institutionString:"Gazi University",institution:{name:"Gazi University",country:{name:"Turkey"}}},{id:"256417",title:"Associate Prof.",name:"Sanaz",middleName:null,surname:"Sadry",slug:"sanaz-sadry",fullName:"Sanaz Sadry",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/256417/images/8106_n.jpg",biography:null,institutionString:null,institution:null},{id:"272237",title:"Dr.",name:"Pinar",middleName:"Kiymet",surname:"Karataban",slug:"pinar-karataban",fullName:"Pinar Karataban",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/272237/images/8911_n.png",biography:"Assist.Prof.Dr.Pınar Kıymet Karataban, DDS PhD \n\nDr.Pınar Kıymet Karataban was born in Istanbul in 1975. After her graduation from Marmara University Faculty of Dentistry in 1998 she started her PhD in Paediatric Dentistry focused on children with special needs; mainly children with Cerebral Palsy. She finished her pHD thesis entitled \\'Investigation of occlusion via cast analysis and evaluation of dental caries prevalance, periodontal status and muscle dysfunctions in children with cerebral palsy” in 2008. She got her Assist. Proffessor degree in Istanbul Aydın University Paediatric Dentistry Department in 2015-2018. ın 2019 she started her new career in Bahcesehir University, Istanbul as Head of Department of Pediatric Dentistry. In 2020 she was accepted to BAU International University, Batumi as Professor of Pediatric Dentistry. She’s a lecturer in the same university meanwhile working part-time in private practice in Ege Dental Studio (https://www.egedisklinigi.com/) a multidisciplinary dental clinic in Istanbul. Her main interests are paleodontology, ancient and contemporary dentistry, oral microbiology, cerebral palsy and special care dentistry. She has national and international publications, scientific reports and is a member of IAPO (International Association for Paleodontology), IADH (International Association of Disability and Oral Health) and EAPD (European Association of Pediatric Dentistry).",institutionString:null,institution:null},{id:"202198",title:"Dr.",name:"Buket",middleName:null,surname:"Aybar",slug:"buket-aybar",fullName:"Buket Aybar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/202198/images/6955_n.jpg",biography:"Buket Aybar, DDS, PhD, was born in 1971. She graduated from Istanbul University, Faculty of Dentistry, in 1992 and completed her PhD degree on Oral and Maxillofacial Surgery in Istanbul University in 1997.\nDr. Aybar is currently a full-time professor in Istanbul University, Faculty of Dentistry Department of Oral and Maxillofacial Surgery. She has teaching responsibilities in graduate and postgraduate programs. Her clinical practice includes mainly dentoalveolar surgery.\nHer topics of interest are biomaterials science and cell culture studies. She has many articles in international and national scientific journals and chapters in books; she also has participated in several scientific projects supported by Istanbul University Research fund.",institutionString:null,institution:null},{id:"260116",title:"Dr.",name:"Mehmet",middleName:null,surname:"Yaltirik",slug:"mehmet-yaltirik",fullName:"Mehmet Yaltirik",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/260116/images/7413_n.jpg",biography:"Birth Date 25.09.1965\r\nBirth Place Adana- Turkey\r\nSex Male\r\nMarrial Status Bachelor\r\nDriving License Acquired\r\nMother Tongue Turkish\r\n\r\nAddress:\r\nWork:University of Istanbul,Faculty of Dentistry, Department of Oral Surgery and Oral Medicine 34093 Capa,Istanbul- TURKIYE",institutionString:null,institution:null},{id:"172009",title:"Dr.",name:"Fatma Deniz",middleName:null,surname:"Uzuner",slug:"fatma-deniz-uzuner",fullName:"Fatma Deniz Uzuner",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/172009/images/7122_n.jpg",biography:"Dr. Deniz Uzuner was born in 1969 in Kocaeli-TURKEY. After graduating from TED Ankara College in 1986, she attended the Hacettepe University, Faculty of Dentistry in Ankara. \nIn 1993 she attended the Gazi University, Faculty of Dentistry, Department of Orthodontics for her PhD education. After finishing the PhD education, she worked as orthodontist in Ankara Dental Hospital under the Turkish Government, Ministry of Health and in a special Orthodontic Clinic till 2011. Between 2011 and 2016, Dr. Deniz Uzuner worked as a specialist in the Department of Orthodontics, Faculty of Dentistry, Gazi University in Ankara/Turkey. In 2016, she was appointed associate professor. Dr. Deniz Uzuner has authored 23 Journal Papers, 3 Book Chapters and has had 39 oral/poster presentations. She is a member of the Turkish Orthodontic Society. Her knowledge of English is at an advanced level.",institutionString:null,institution:null},{id:"332914",title:"Dr.",name:"Muhammad Saad",middleName:null,surname:"Shaikh",slug:"muhammad-saad-shaikh",fullName:"Muhammad Saad Shaikh",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Jinnah Sindh Medical University",country:{name:"Pakistan"}}},{id:"315775",title:"Dr.",name:"Feng",middleName:null,surname:"Luo",slug:"feng-luo",fullName:"Feng Luo",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Sichuan University",country:{name:"China"}}},{id:"423519",title:"Dr.",name:"Sizakele",middleName:null,surname:"Ngwenya",slug:"sizakele-ngwenya",fullName:"Sizakele Ngwenya",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of the Witwatersrand",country:{name:"South Africa"}}},{id:"419270",title:"Dr.",name:"Ann",middleName:null,surname:"Chianchitlert",slug:"ann-chianchitlert",fullName:"Ann Chianchitlert",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Walailak University",country:{name:"Thailand"}}},{id:"419271",title:"Dr.",name:"Diane",middleName:null,surname:"Selvido",slug:"diane-selvido",fullName:"Diane Selvido",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Walailak University",country:{name:"Thailand"}}},{id:"419272",title:"Dr.",name:"Irin",middleName:null,surname:"Sirisoontorn",slug:"irin-sirisoontorn",fullName:"Irin Sirisoontorn",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Walailak University",country:{name:"Thailand"}}},{id:"355660",title:"Dr.",name:"Anitha",middleName:null,surname:"Mani",slug:"anitha-mani",fullName:"Anitha Mani",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"SRM Dental College",country:{name:"India"}}},{id:"355612",title:"Dr.",name:"Janani",middleName:null,surname:"Karthikeyan",slug:"janani-karthikeyan",fullName:"Janani Karthikeyan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"SRM Dental College",country:{name:"India"}}},{id:"334400",title:"Dr.",name:"Suvetha",middleName:null,surname:"Siva",slug:"suvetha-siva",fullName:"Suvetha Siva",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"SRM Dental College",country:{name:"India"}}}]}},subseries:{item:{id:"8",type:"subseries",title:"Bioinspired Technology and Biomechanics",keywords:"Bioinspired Systems, Biomechanics, Assistive Technology, Rehabilitation",scope:'Bioinspired technologies take advantage of understanding the actual biological system to provide solutions to problems in several areas. Recently, bioinspired systems have been successfully employing biomechanics to develop and improve assistive technology and rehabilitation devices. The research topic "Bioinspired Technology and Biomechanics" welcomes studies reporting recent advances in bioinspired technologies that contribute to individuals\' health, inclusion, and rehabilitation. Possible contributions can address (but are not limited to) the following research topics: Bioinspired design and control of exoskeletons, orthoses, and prostheses; Experimental evaluation of the effect of assistive devices (e.g., influence on gait, balance, and neuromuscular system); Bioinspired technologies for rehabilitation, including clinical studies reporting evaluations; Application of neuromuscular and biomechanical models to the development of bioinspired technology.',coverUrl:"https://cdn.intechopen.com/series_topics/covers/8.jpg",hasOnlineFirst:!1,hasPublishedBooks:!0,annualVolume:11404,editor:{id:"144937",title:"Prof.",name:"Adriano",middleName:"De Oliveira",surname:"Andrade",slug:"adriano-andrade",fullName:"Adriano Andrade",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRC8QQAW/Profile_Picture_1625219101815",biography:"Dr. Adriano de Oliveira Andrade graduated in Electrical Engineering at the Federal University of Goiás (Brazil) in 1997. He received his MSc and PhD in Biomedical Engineering respectively from the Federal University of Uberlândia (UFU, Brazil) in 2000 and from the University of Reading (UK) in 2005. He completed a one-year Post-Doctoral Fellowship awarded by the DFAIT (Foreign Affairs and International Trade Canada) at the Institute of Biomedical Engineering of the University of New Brunswick (Canada) in 2010. Currently, he is Professor in the Faculty of Electrical Engineering (UFU). He has authored and co-authored more than 200 peer-reviewed publications in Biomedical Engineering. He has been a researcher of The National Council for Scientific and Technological Development (CNPq-Brazil) since 2009. He has served as an ad-hoc consultant for CNPq, CAPES (Coordination for the Improvement of Higher Education Personnel), FINEP (Brazilian Innovation Agency), and other funding bodies on several occasions. He was the Secretary of the Brazilian Society of Biomedical Engineering (SBEB) from 2015 to 2016, President of SBEB (2017-2018) and Vice-President of SBEB (2019-2020). He was the head of the undergraduate program in Biomedical Engineering of the Federal University of Uberlândia (2015 - June/2019) and the head of the Centre for Innovation and Technology Assessment in Health (NIATS/UFU) since 2010. He is the head of the Postgraduate Program in Biomedical Engineering (UFU, July/2019 - to date). He was the secretary of the Parkinson's Disease Association of Uberlândia (2018-2019). Dr. Andrade's primary area of research is focused towards getting information from the neuromuscular system to understand its strategies of organization, adaptation and controlling in the context of motor neuron diseases. 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Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. Currently, proteomics relies mainly on mass spectrometry (MS) combined with electrophoretic (1 or 2-DE-MS) and/or chromatographic techniques (LC-MS/MS). MS is an excellent tool that has gained popularity in proteomics because of its ability to gather a complex body of information such as cataloging protein expression, identifying protein modification sites, and defining protein interactions. 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