Details of satellite dataset for North Sinai (acquired via https://earthexplorer.usgs.gov/).
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These books synthesize perspectives of renowned scientists from the world’s most prestigious institutions - from Fukushima Renewable Energy Institute in Japan to Stanford University in the United States, including Columbia University (US), University of Sidney (AU), University of Miami (USA), Cardiff University (UK), and many others.
\\n\\nThis collaboration embodied the true essence of Open Access by simplifying the approach to OA publishing for Academic editors and authors who contributed their research and allowed the new research to be made available free and open to anyone anywhere in the world.
\\n\\nTo celebrate the 50 books published, we have gathered them at one location - just one click away, so that you can easily browse the subjects of your interest, download the content directly, share it or read online.
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IntechOpen and Knowledge Unlatched formed a partnership to support researchers working in engineering sciences by enabling an easier approach to publishing Open Access content. Using the Knowledge Unlatched crowdfunding model to raise the publishing costs through libraries around the world, Open Access Publishing Fee (OAPF) was not required from the authors.
\n\nInitially, the partnership supported engineering research, but it soon grew to include physical and life sciences, attracting more researchers to the advantages of Open Access publishing.
\n\n\n\nThese books synthesize perspectives of renowned scientists from the world’s most prestigious institutions - from Fukushima Renewable Energy Institute in Japan to Stanford University in the United States, including Columbia University (US), University of Sidney (AU), University of Miami (USA), Cardiff University (UK), and many others.
\n\nThis collaboration embodied the true essence of Open Access by simplifying the approach to OA publishing for Academic editors and authors who contributed their research and allowed the new research to be made available free and open to anyone anywhere in the world.
\n\nTo celebrate the 50 books published, we have gathered them at one location - just one click away, so that you can easily browse the subjects of your interest, download the content directly, share it or read online.
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This highly infectious disease spread quickly across the globe, mutating into a number of variants that have made containment extremely difficult. It is clear that this global pandemic will leave in its wake extensive social, economic and health impacts for many years to come and we are only just beginning to recognise the extent of its legacy.
During the outbreak, citizens around the world experienced significant restrictions in terms of their social and economic activities in the form of quarantining at home for prolonged periods of time so that social interaction (and thus, the ability of the virus to transmit between people) is limited. Behavioural guidelines to help prevent infection and slow the spread of disease have mandated the wearing of face coverings in confined spaces and recommended the adoption of a wide range of hygienic practices (for example frequent hand washing, cleansing surfaces more regularly and using hand sanitizer when hand washing was not possible). These measures have signified large-scale changes in behaviour that are psychologically burdensome for individuals to successfully achieve [1]. However, human behaviour plays a decisive role in in shaping the progression and spread of COVID-19 [2] and therefore it is a matter of urgency that behavioural scientists understand the psychological drivers that underpin such behaviour to help swiftly implement interventions to promote behavioural changes on a population level that are necessary to stem the spread of the virus and protect vulnerable groups from contagion [1, 3].
The Capability, Opportunity, Motivation-Behaviour (COM-B) model of behaviour change (Figure 1) [4] is widely used in behavioural science research to explore influences on behaviour. This model proposes that a person must have sufficient psychological and physical capability (strength, knowledge, skills, etc.), physical and social opportunity (time, social cues, etc.) as well as reflective and automatic motivation (intentions, planning, emotion regulation, etc.) to enact a given behaviour. Michie, West and Harvey [5] argue that each of these factors could contribute to lower levels of adherence than are needed to enact behaviours that prevent the spread of the COVID-19 virus. The COM-B model is at the centre of the Behaviour Change Wheel (BCW), which is a tool kit for designing tailored behaviour change interventions (BCIs) [6]. Thus, once a behavioural ‘diagnosis’ has been conducted utilising the components of the COM-B model, suitable targets for intervention can then be identified [1]. These targets will be the components of the COM-B that are most likely to influence a particular behaviour and can be developed into BCIs to improve adherence to protective health behaviours.
The COM-B model.
In this chapter, we apply the COM-B model to two key sets of COVID-19 transmission-related protective behaviours: ‘hygienic practices’ (including frequent hand washing and wearing a face covering) and ‘social distancing practices’ (involving staying at home where possible, keeping a 2-metre distance from others in public and not gathering in large groups). These behaviours are key in reducing transmission of the virus and it is likely that such measures will remain in place for some time in most countries, to some extent [7, 8]. Indeed, despite the inception of widespread vaccination programmes across the globe, maintaining protective behaviours will ensure the continued reduction in the spread of infection to mitigate low vaccination uptake rates, difficulties in vaccine supply and variants immune to the vaccine. It is vital therefore that behavioural scientists understand the psychological factors influencing such behaviours in the context of the COVID-19 pandemic within a theoretical framework to feed into efforts to promote continued adherence to essential protective behaviours.
To inform BCIs, an understanding of the drivers that underpin protective behaviours are required, along with a deeper exploration that addresses the nuances in how people might understand, accept and adhere to such a set of behaviours. As yet, there is a dearth of evidence relating to how protective behavioural practices could be adopted on a population-wide level [4] and so it is important to assess behavior under the current adverse circumstances. Protective behaviours are largely under the volitional control of individuals, in that one can choose whether or not to follow the suggested practices. Further, whilst wearing a face covering and washing or sanitising hands in specified situations represents a fairly clear set of actions, the actions required to achieve ‘social distancing’ successfully are arguably more complex and nuanced. Some social distancing behaviours rely on the individual themselves committing to and enacting the behaviour (e.g., staying at home) and others require the reciprocal observance of others (e.g., gathering in groups, close contact greetings). We also know that social isolation could have a negative impact on health and well-being, which impacts upon decisions about adherence to behaviours [9].
Whilst there is a wide and good-quality literature on the enactment of hygiene behaviour, especially handwashing [10], we know little about these behaviours in the current context where the drivers of behaviour and nature of the threat may be entirely different from usual circumstances.
The term ‘social distancing’ has been coined during the pandemic and is complex and nuanced. Although large-scale population surveys have shown that social distancing practices have been sustained as the pandemic unfolded and citizens generally support these measures (e.g., [11, 12, 13]), there is evidence that motivation to comply over time may be threatened by other psychological factors. For example, as psychological resources are cumulatively depleted over time with lengthy and repeated lockdowns [11]; as competing drivers of behaviour begin to take priority (e.g., the inherent drive for social connection) [14]; as confidence in the government reduces [15]; and ‘moral’ judgements impact upon decision making [16] adherence to social distancing practices may diminish.
Indeed, evidence suggests that the extent to which different groups of individuals have been willing and able to comply with these important protective behaviours is mixed. Population surveys have found that 1 in 4 individuals struggle to follow social distancing guidelines, due to difficulties in meeting up with family or friends outside because of bad weather or feeling worn out by the pandemic [11, 17]. For other groups in society, it is likely that enacting social distancing behaviours is difficult for other, more practical, reasons. For example, individuals who do not have access to a garden, those who share private spaces with other families, or those who are required to work outside the home may not have the opportunity to comply and are inevitably at increased risk of exposure and infection [18]. These ‘structural’ factors are likely to be more impactful on the ability to comply with social distancing in groups who are already disadvantaged and who are faring worse due to the pandemic – reflecting the ‘slow burn of inequality’ exposed by epidemics, described by Marmot [19].
Exploring protective behaviours in relation to the COM-B is useful for understanding the conditions that must be in place for these behaviours to be successfully enacted and therefore developing BCIs that promote adherence. We conducted this investigation using data from a large-scale survey of UK citizens.
The COVID-19 Psychological Research Consortium (C19PRC) Study (www.sheffield.ac.uk/psychology-consortium-covid19) is a longitudinal study mapping changes in behaviour and mental health over time from the very early days of the COVID-19 outbreak. The C19PRC study has collected data from 2025 participants in five waves over 12 months (March 2020–March 2021) from the four UK Nations, with comparable data sets from Ireland, Italy, Spain, and Saudi Arabia. A multitude of detailed demographic, health, behavioural and psychosocial measures have been collected, including socio-demographic characteristics, health status, depression, anxiety, traumatic stress, somatic symptoms, loneliness, resilience as well as health behaviours and lifestyle habits (see McBride et al. for full methodology [20, 21]). We modelled the complex relationships between the social, physical and mental health of our sample and conducted extended behavioural analyses on protective behaviours and the COM-B model [17, 22, 23, 24, 25, 26].
Participants self-reported motivation, capability and opportunity to enact protective behaviours in the C19PRC survey. Items were adapted from a preliminary version of the COM-B self-evaluation questionnaire and other guidelines (COM-B-Qv1) [4, 6] and respondents indicated the extent to which seventeen statements were true for them during the COVID-19 pandemic on a 5–point scale (labelled: strongly agree, agree, neither agree nor disagree, disagree, strongly disagree). Three items measured psychological capability: e.g., “I knew about why it was important and had a clear idea about how the virus was transmitted”. Two items measured physical opportunity: e.g., “It was easy for me to do it” and four items measured social opportunity: e.g., “I had support from others”. Five items measured reflective motivation: e.g., “I intended to do it” and three items measured automatic motivation: e.g., “I would feel bad if I didn’t do it”.
Analysis of the C19PRC data revealed three main themes in relation to protective behaviours. First, we identified specific components of the COM-B model that drive different types of protective behaviours. Second, we identified specific demographic groups that have particular difficulties with such behaviours. And third, there are significant emotional drivers that influence adherence to protective behaviours.
The first set of behaviours explored in Wave 1 during the first lockdown in the UK (March 2020) were five self-reported hygienic practices: Touching eyes or mouth, washing hands with soap and water more often, using hand sanitising gel if soap and water were not available, using disinfectants to wash surfaces in the home more frequently and covering nose and mouth with a tissue or sleeve when coughing or sneezing. Response scales were ‘No’, ‘Occasionally’ and ‘Whenever possible’.
After controlling for demographic variables (age, gender, ethnicity, income, etc.), psychological capability, social opportunity and reflective motivation predicted hygienic practices most and reflective motivation had the largest influence [20]. This means that adults who knew why hygienic practices were effective in reducing the transmission of the virus, who had social support, and had made plans to carry out hygienic practices were more likely to successfully carry out these protective health behaviours. Notably, we observed that older age and higher levels of household income were associated with more engagement with hygienic practices. Hygienic practices were practiced less by males (compared to females) and those living in suburban areas (compared to those living in more rural areas).
For social distancing behaviours, participants in Wave 2 (April 2020) self-reported which behaviours in the past week they had engaged in, out of seven social distancing practices; e.g., “Stayed at least 2 metres (6ft) away from other people when in”, “Met up with friends or extended family (outside of your home)”; “Engaged in close contact greetings with people outside of your family (e.g., shaking hands, hugging)”; “Gathered in a group of more than two people in a park and other public space”. These behaviours represented clear violations of or adherence to social distancing guidelines in the first UK lockdown (responses were: Not at all, 1–2 days a week, 3–4 days a week, Most days, Every day).
Here, a different picture emerged. Of the COM-B components, only Psychological Capability exhibited a direct and positive association with adherence to social distancing [21]. Older adults and city dwellers were more likely to report higher levels of psychological capability and women were more likely to report increased motivation for social distancing. As with hygienic practices, those with higher levels of education and income were more likely to practice social distancing.
We explored adherence to social distancing further using a list experiment, embedded in Wave 4 of the C-19PRC survey (December 2020). This method allows researchers to measure responses to sensitive items that may normally invoke untrue or inaccurate answers due to social desirability concerns. The C19PRC survey list experiment used four control states and included a fifth sensitive item, as follows:
“We would now like to ask you how willing you are to break rules or conventions. Please look at the following list of common rules and indicate how many of these you have done in the last 6 months:
I have driven a car at more than 100 miles an hour.
I have travelled illegally to North Korea.
I have sometimes not paid my bills on time.
I have borrowed something from a friend and forgotten to return it.
I have socialised in another household during lockdown (
One-quarter of our sample revealed that they had violated government guidelines by socialising in another household during lockdown. An examination of whether any particular social or psychological factors were associated with agreement to the sensitive item, we found that the only statistically significant predictor was anxiety related to COVID-19. This anxiety was in response to the question ‘How anxious are you about the coronavirus COVID-19 pandemic?’; participants were provided with a ‘slider’ (electronic visual analogue scale) to indicate their degree of anxiety with ‘0’ and ‘100’ at the left- and right-hand extremes, respectively, and 10-point increments. This produced continuous scores ranging from 0 to 100 with higher scores reflecting higher levels of COVID-19-related anxiety. This factor was negatively correlated with agreement to the sensitive item - indicating that experience of COVID-related anxiety was strongly associated with a tendency to follow the lockdown rules.
Previous research has found that emotions are an important influencing factor in the behavioural responses to pandemics; in particular, worry has been found to motivate action to control danger [27]. Liao et al. [28] conducted a multi-wave longitudinal survey study in Hong Kong during the influenza A(H7N9) pandemic and reported that worry about infection from the virus was positively associated with the enactment of protective behaviours (e.g., avoiding crowds, rescheduling travel plans). The authors reported that, as worry about the virus changed over time, so did protective behaviours, implying a causal link between worry and engaging in protective behaviours. Other evidence from the Swine Flu pandemic also illustrates how emotional status mediates behavioural responses; Jones and Salathe [29] reported that self-reported anxiety over the epidemic mediated the likelihood that US citizens engaged in protective behaviours such as social distancing. Exploring emotional factors that might mediate protective behavioural responses during the current pandemic, may help enormously with the design of BCIs to promote the enactment of essential protective behaviours such as social distancing.
The findings of the C19PRC Study in relation to the COM-B have clear implications for the design of BCIs to promote protective behaviours at a population level. For hygienic practices, interventions should focus on increasing and maintaining motivation to act and should contain behaviour change techniques (BCTs) that focus on self-regulatory processes involving planning and goal setting. We have suggested utilising implementation intentions, a specific planning technique found to help successfully bridge the ‘intention-behaviour’ gap [30, 31]. Further, to make it feasible that individuals are able to enact such techniques independently (e.g., during the lockdown), we suggest utilising the compendium of self-enactment BCTs [32] in intervention design (self-regulatory techniques #5 - #18 are especially relevant for hygienic practices). Our data show that groups in particular need of targeting for interventions to increase hygienic practices are males and those living in cities and suburbs.
For social distancing, interventions should focus on increasing psychological capability and include BCTs that bolster knowledge around social distancing and why it is important, to enable citizens to develop psychological skills in enacting and maintaining these behaviours. For increasing psychological capability, it is important that it is clear why social distancing is important and how social contact transmits the virus; as well as specifying the situations in which social distancing should be enacted and exactly how to do that. BCIs would help people to overcome physical or psychological barriers to action (or inaction) and should be specifically tailored to those sociodemographic groups who display particular difficulties in enacting social distancing, namely, younger people and those living in cities. For those with lower incomes and lower levels of education, who may struggle with social distancing for more practical reasons, wider functions of intervention from the BCW would need to be employed, whereby economic and social policy would assist in overcoming practical or structural barriers to enable these groups to follow guidelines (e.g., if working from home is not possible, ensuring COVID-safe workspaces where social distancing is achievable and implementing paid time off for isolation). It is important that individuals who feel anxious about COVID-19 are supported in managing their anxiety levels.
This chapter has explored psychological and demographic influences on citizens’ ability to enact protective behaviours during the COVID-19 pandemic. We have discussed how enacting social distancing and hygienic practices are influenced by different components of the COM-B model and made recommendations for intervention. Behavioural scientists face the challenge of urgently developing interventions that help citizens to maintain adherence to protective behaviours to control the spread of the COVID-19 virus.
Coastal zones are now experiencing increased natural and human disruptions, such as sea-level rise, coastal erosion, and resource overexploitation, to name a few. Coastal erosion affects almost 80% of the world’s beaches, with rates ranging from 1.0 cm year−1 to 30 m year−1, posing a major threat to several coastal regions [1]. According to [2], increased knowledge of many driving forces is affecting the health of global coastal ecosystems has expedited efforts to evaluate, monitor, and reduce coastal stressors to understand the spatial distribution of erosion risks, predict their growth tendency, and support mechanism research on erosion and its solutions.
Shoreline extraction and change detection rates at different times are critical for coastal zone monitoring. The coastline, defined by [3] as the position of the land-water interface at a single point in time, is a highly dynamic characteristic that serves as a predictor of coastal erosion and accretion. Shoreline changes occur on a variety of time scales, ranging from geological to short-term catastrophic events. Waves, winds, tides, sea-level rise, frequent storms, geomorphic processes of erosion and accretion, and human activities are all factors that affect these changes [4].
Several international studies looked at quantitative and qualitative analysis of shoreline spatiotemporal fluctuations [5, 6, 7, 8, 9, 10, 11, 12, 13].
Alternatively, efforts were made to estimate the potential position of the shoreline to reduce the impact of the upcoming erosion activity. Moreover, for future predictions of shoreline spatial change, extensive and reliable information regarding historical and present coastline position is required. In a GIS framework, shoreline prediction models are simple to implement. With the help of historical data, several statistical models for example the Average of Rates (AOR), Least Median of Squares (LMS), Linear Regression Rate (LRR), End Point Rate (EPR) model, and Jackknife model (JK) were used to evaluate shoreline prediction [12, 14, 15, 16, 17, 18, 19].
Few encouraging investigations have been conducted along Egypt’s North Sinai shore [20] used an aerial picture taken in 1955 and a topographic map analyzed in 1992 to describe the shoreline alteration along Sinai’s northern coast. Despite this, the magnitude of shoreline variations was not quantified in their analysis due to the inability of the analyzed maps’ surveying methodologies to calculate it. Moreover [21] used a hydrographic survey to investigate the impact of the El Arish power station (located west of the El Arish valley coast) on the surrounding area on Sinai’s Mediterranean coast. The authors discovered a 5.5 m/year coastline retreat east of the El Arish power plant breakwater [22] used topographic maps from 1973 with satellite pictures from 1984 and 1996 to tutor the coastal changes over the western half of the North Sinai coast (i.e., from El Tinah Bay to El Bardawil Lake). They also calculated how the area of El Bardawil Lake changed throughout time. They discovered that the extent of El Bardawil Lake changed dramatically from 1973 to 1984, losing an average of 128 km2, then slowing to a loss of 5 km2 from 1984 to 1996. El Banna et al. (2009) used the same method to track changes in the shoreline along the North Sinai coast for 15 years (from El Bardawil Lake to Rafah) by studying TM and ETM true color Landsat pictures from 1986 to 2001. The accretion and erosion rates were calculated to be +0.076 km2 year−1 and 0.123 km2 year−1, respectively.
To sum up, the extensive literature survey undertaken in the North Sinai coast area demonstrated that the current published data related to the area needs to be improved and renovated. Furthermore, it cannot determine coastline change rates with high-precision approaches. The current study uses GIS and DSAS geospatial approaches to examine shoreline changes along the North Sinai coastline from 1989 to 2016. Furthermore, the current research aims to: (1) apply three different semi-automated shoreline extraction methods, including Histogram threshold of band ratio, Histogram threshold of band 5, and Tasseled Cap Transformation (TCT); (2) plot and measure shoreline accretion/erosion rates using several statistical methods functionalized in DSAS, including NSM, LRR, EPR, and LMS; and (3) develop a decision-support algorithm that can vigorously support in elaborating shoreline accretion/erosion rates; (4) using the EPR model, outline a futuristic decision plotting based on the North Sinai shoreline forecast in the years 2025, 2035, and 2050.
Sinai’s coastal area is considered an essential part of Egypt’s Mediterranean Coast [22]. It is a geographical connecting point between Asia and Africa, with the Gulf of Suez and the Suez Canal on the west, the Gulf of Aqaba and the Egyptian-Israeli border on the east, and the Mediterranean Sea on the north (Figure 1). The latitudes and longitudes are (28°–31°N) and (32° 30/–34° 30/ E) respectively. The northern Sinai coast stretches for about 220 km along the Mediterranean Sea, extending from Port Said in the west to Rafah in the east, from the Egyptian border [23]. The current study area is split into three subzones based on the vulnerability of coastal areas as well as the availability of data from the field and remotely sensed data. Zone I contain El Tinah Plain Bay, which stretches 38.5 km from Port Said in the west to El Bardawil Lake in the east, (Figure 1a). Zone I is characterized by some features such as Lagoons, vegetation cover, and fish ponds. Zone II includes El Bardawil Lake (Figure 1b). This lake covers approximately 60% of Sinai’s northern coast. It has a total area of over 700 km2 and is approximately 72.5 km long, 22 km wide-ranging, and 2 m deep. The Mediterranean Sea is isolated from the Lake by shallow sand barriers that range in width from 300 to 1000 m, and are overtopped by storm waves in the winter. It has three inlets joining it to the Mediterranean Sea, two of which are manmade (no. 1 and 2) and one of which is natural (El Zaranek inlet), [24]. Zone III includes the El Arish Valley shore, which almost forms a 37 km west-to-east intersection between the El Arish power plant and Rafah, (Figure 1c).
False-color composite images of the study area and shoreline digitization in different periods from (1989–2016) for the three zones (a, b, c) respectively, after [
The intensity and direction of wave action along Egypt’s Mediterranean coast are inextricably linked to significant pressure systems over the Mediterranean and North Atlantic [25]. Wave heights reach 1.16 m and average 0.4 m during the spring and summer, with the prevailing wave direction being NW. Prevailing wave direction is come mainly from N, NNW, and NW in Winter. The maximum wave height is 4.25 m, with an average wave height of 0.51 m and a period of 6.5 sec.
Wave data was analyzed previously by [26, 27, 28] along Egypt’s Mediterranean coast show that waves from the northwest predominate (81%), with small components from the northeast (14%) and southwest (5%). The prevailing wave direction is the main key of the eastward-flowing alongshore current. Reversed alongshore currents are generated by waves incoming from the N, NNE, and NE (Figure 2). The main alongshore current path (62–65%) on the North Sinai coast is from west to east, stimulated by waves from the NNW and NW, according to preceding measured data. However, west trending alongshore currents (24–29%) result from the remaining wave components from the N, NNE, and NE, particularly during March and April due to easterly winds. Furthermore, with a range of 31 cm, the tide along Sinai’s Mediterranean coast is micro-tidal and semi-diurnal. The average high water level is 20 cm, and the average low water level is −11 cm [30].
Wave rose for study area, and wave induced currents’ directions (modified from El Banna et al., 2009), after [
This study used multi-temporal satellite data from Landsat TM, ETM, and OLI/TIRS that cover our coast from 1989 to 2016. Even so, thanks to the shortage of cloud-free imagery during the selected period, satellite images could not be obtained at regular intervals. The polynomial geo-rectification method is used to ortho-rectify the selected satellite images, as it is afterward used to track changes in the shoreline along the Sinai Peninsula’s northern coast Satellite images’ data are described in detail in Table 1. Data acquired for North Sinai coastline surveying from El Tinah bay to El Arish valley was supplied by the Egyptian Institute of Oceanography and Fisheries in 2010.
Satellite data | Path/Row | Year of acquisition | Resolution (pixel size) | Zone |
---|---|---|---|---|
Landsat 4 - TM | 176/38 | 1989 | 30 m | I |
Landsat 5 - TM | 175/38 | 1989 | 30 m | II and III |
Landsat 5 - TM | 176/38 | 1998 | 30 m | I |
Landsat 5 - TM | 175/38 | 1998 | 30 m | II and III |
Landsat 7 - ETM | 176/38 | 2003 | 30 m | I |
Landsat 5 - TM | 175/38 | 2003 | 30 m | II and III |
Landsat 7 - ETM | 176/38 | 2010 | 30 m | I |
Landsat 7 - ETM | 175/38 | 2010 | 30 m | II and III |
Landsat 8 - OLI/TIRS | 176/38 | 2016 | 15 m | I |
Landsat 8 - OLI/TIRS | 175/38 | 2016 | 15 m | II and III |
Related abbreviations: | ||||
TM | : Thematic Mapper; | |||
ETM | : Enhanced Thematic Mapper; | |||
OLI | : Operational Land Imager; | |||
TIRS | : Thermal Infrared Sensor. |
Details of satellite dataset for North Sinai (acquired via https://earthexplorer.usgs.gov/).
Image processing carried out in this study were strip filling, georeferencing, and radiometric correction. Firstly, gap filling was applied to image 2010 for all its bands using modeling done by [31] in Arc GIS 10.2.2 using the python algorithm, see Figure 3.
Landsat image for zone I in 2010; (a) before gap filling; (b) after gap filling.
Ground Control Points (GPCs) are used to implement the geometric correction process (i.e. more than 40 GCPs are identified on the images), [10, 32]. The geometric correction is accomplished utilizing ENVI 5.3 software to reduce distortions caused by scale variation, angle, and lens distortion.
The image is projected to actual coordinate Universal Transverse Mercator (UTM), WGS-84 datum. After georeferencing, (RMSE) was found to be less than 0.5 pixels, indicating that the images were geometrically well-matched. After that, a radiometric correction is applied using the ENVI software’s radiometric, which combines the sun and view angle effects, as well as sensor calibration and atmospheric correction. Eventually, all georeferenced images are processed in ArcGIS to get the coastline digitized.
Shorelines are the high water line as surveyed by GPS units in kinematic mode [33]. Meanwhile, automatic coastline demarcation from low resolution satellite images is a complicated job due to the unclear boundary between water and land in saturated zone [34]. Three semi-automatic delineation approaches are first tried for Landsat images ETM 2010 in this study to identify the best digitization methodology that gives the least error with the related field data in 2010 (Figure 4).
Methodology framework to extract shoreline.
Since water absorbs the majority of radiation in the near-infrared and mid-infrared regions of the spectrum, its reflectance in these wavelengths is nearly zero; nevertheless, the reflectance is higher in these areas for land cover than water bodies. As a result, the coastline can be derived from a single band image. As a consequence, getting the binary image is becoming simple by estimating the histogram threshold for one of the infrared bands (i.e. Band 5) of the TM or ETM imagery [35]. Another method is to use the histogram threshold of band ratio technique, which produces a binary image by combining the two conditions of Band (2)/Band (4)
Moreover, the Tasseled Cap Transformation technique (TCT) is also used to extract shorelines. The coefficients for TCT of Landsat data are determined from [37]. TCT reconstitutes the spectral information of the six ETM bands into three primary perspective elements using coefficients deduced from sampling known land cover spectral features. The moistness component is used to distinguish land from water among the three main view elements (brightness, greenness, and wetness). In this wetness index band, the land-water configuration is clearly visible, and a binary image could be easily acquired. Finally, in 2010, each technique’s raster binary image is transformed into a vector image, which can then be used to extract the coastline border.
To identify the best technique, a comparison is made in Arc GIS 10.2.2 for the three regions between the derived shorelines (e.g., by TCT) and the observed shorelines in 2010 (Figure 5). This comparison is assessed using DSAS tools, which developed by the United States Geological Survey (USGS). The original purpose of this extension is to compute change rate in coastline’ positions. Using DSAS is summarized in the following steps: After extraction of shoreline, the baseline is created; creating transects; calculating the distances between coastline and baseline for transects; finally, the shoreline change rate is calculated [38]. Accordingly, the deviation between the derived and observed shoreline is computed using 1800 perpendicular to the baseline transects. These transects are accurately cast at intervals of 20 m (Figure 5a–c).
Comparison of digitized shorelines based on field data from 2010 and the corresponding Landsat imagery (e.g., using TCT) for (a) Zone (I); (b) Zone (II); (c) Zone (III).
Figure 6 depicts a validation process between data from a field investigation and extracted shoreline from satellite image obtained in 2010. It is based on the coupling of DSAS software and Arc GIS 10.2.2. The residuals between the measured and computerized shorelines in 2010 at each transect line from 1 to 1800 were estimated using both the histogram threshold of band 5, histogram threshold of band ratio, and TCT, as shown in Figure 6a–c. It is noticed that data are reasonably correlated (Figure 6a1, b1, and c1).
Validation process between the shoreline monitored in the field and the shoreline detected by imagery 2010 for the different zones based on NRMSE of ; (a,a1) histogram threshold of the band (5); (b,b1) histogram threshold of band ratio; (c,c1) TCT.
The normalized root means square error (NRMSE) is considered to find the best method that precisely extract the coastline. TCT technique is proved to be better in shoreline delimitation using low resolution satellite imagery (medium resolution). It achieved the least NRMSE for all zones, Figure 6a1, b1, and c1. As a result, the TCT technique was used to demarcate shorelines in 1989, 1998, 2003, and 2016 (see sectors a1, b1, b2, c1, and c2 in Figure 1a–c).
Changes in the shoreline locations are calculated using different four analysis methods (i.e., EPR, LRR, LMS, and NSM). The End Point Rate (EPR) is easily determined by dividing the length (in m.) between two coastlines by number of the years (Eq. (1) and Figure 7). This method is widely used by different coastal researchers and is widely used in shoreline movement rate calculations [39, 40, 41, 42].
Detecting changes of zone I; (a, b) Satellite images (TM) and OLI/TIRS of band (5) for the year1989 and 2016; (c, d) TCT’s equivalent binary images from 1989 and 2016; (e) The Change detection image for the period 1989 to 2016; (f) Vector map showing the erosion/accretion pattern showed in vector map for the period 1989 to 2016.
where:
L1 and L2 are the distances between the baseline (benchmark) and the shoreline, while t1 and t2 are the dates of the two shoreline locations.
Linear Rate Regression (LRR) is the second method for calculating change rates. For a specific transect, this method entails fitting a least-squares regression line to multiple shoreline location points, (Figure 8). R-squared (Eq. (2)), R2 > 0.87 has been held as the threshold of certainty in our research, considering a confidence interval (LCI) of 95%. R2 at each transect line are calculated as follows:
Explanatory example of NSM, EPR, LRR, and LMS computation; (a) Map of multi-temporal shoreline locations west and east El Bardawil inlet (1); (b) transect line’ details (x) and coastline intersection; (c) Time series of shoreline distances from the baseline along the transect line (x).
where:
L: observed distance between the reference line(baseline) for a coastline’ data point;
Lp: forecast value based on the best-fit linear regression equation;
L−: Average of the observed shoreline data points; and.
N: number of dates.
The sample data are used to calculate an average offset in the linear regression method, and the formula for the line is deduced by reducing this value so that the source points are as near to the regression line as possible. In the least median of squares method (LMS), instead of using the average, the median value of the squared residuals is used to identify the optimal equation for the line (Figure 8).
The net spacing (in meters) between the past and present shoreline locations for each transect is recognized as the Net Shoreline Movement (NSM) (i.e., 1989 and 2016). It represents a distance rather than a rate (Figure 8b).
EPR, LRR, LMS, and NSM have negative values, implying landward decline of the shoreline, whereas positive values indicate landward advancement. The erosion/accretion rates measured along the North Sinai coast are divided into seven categories (Table 2) [43].
Category | shoreline change’ rate (m/year) | classification of shoreline |
---|---|---|
1 | > -2 | Very high erosion |
2 | > -1 and < -2 | High erosion |
3 | > 0 and < -1 | Moderate erosion |
4 | 0 | Stable |
5 | > 0 and < +1 | Moderate accretion |
6 | > +1 and < +2 | High accretion |
7 | > +2 | Very high accretion |
The classification of Shoreline according to EPR, LRR, and LMS.
A long-term process of two-dimensional shoreline change detection has been extensively investigated along the coastal line of different three zones over a 27-year period (1989–2016). This procedure is conducted through different steps. Firstly, binary images from 1989 and 2016 are derived for each zone using TCT techniques to separate land and water. This step masks the land cover with all of its categories. Second, the binary images are converted from raster to vector (feature class) using ArcGIS10.2.2 software, with two main polygon attributes: water and land. Finally, the two polygon layers are superimposed to assess shoreline erosion/accretion trend from 1989 to 2016, (Figure 7f).
As a result, Figure 7a and b show satellite TM and OLI/TIRS images of the band (5) for zone I in 1989 and 2016, respectively, while Figure 7c and d show their classified binary images The post-classification change detection image (Figure 7e) on the other hand, shows severe erosion in El Tinah Bay’s western part. This erosion is the result of the combined effects of the coast’s stormy climate and the restriction of sediment movement from the Nile Delta as a result of the construction of both the jetties at Suez Canal entrance and seawalls at eastern canal. Besides that, a portion of the incident wave’s energy is shifted into the adjacent beach due to the construction of this seawall. Consequently, the shifted energy, soil disconnection has occurred in the western part of El Tinah Bay. Based on the hydrodynamic processes on the North Sinai coast, alongshore currents induced sediments to move from west to east, resulting a highly sensitive eroded area (Figure 7f, f1).
In 2013, a natural opening nearly in the middle of El Mallaha Lagoon was formed as a result of this erosion. Furthermore, as a result of hindering the sediment path by inlet (2) jetties and groins at east of the inlet, part of the transported sediments has settled nearly in the middle of El Tinah Bay shoreline. In 2015, the artificial inlet (2) was completely blocked due to sediment restrictions.
The eastern part of the Bay, on the other hand, appears to be relatively stable. This part’s shoreline is almost straight, with no major merged parts to erode or major embayments to receive sediments, hence, the shore zone is nearly stable; Only a few small pockets of accumulation have been noticed. To quantify the dynamical changes in the zone I coastline from 1989 to 2016, an asymmetrical difference vector map is created from binary images in ArcGIS10.2.2 and is then classified into two categories: erosion and accretion pattern, (Figure 7f). The change detection clearly shows a cumulative accretion of +3.442 km2 and a rate of +0.127 km2/year, while the cumulative erosion is −5.409 km2 and a rate of −0.2 km2/year over a period of 27 years (Table 3).
Study zone | Research | Date | Data source | The used technique | Area (km2) | Rate (km2/year) | ||||
---|---|---|---|---|---|---|---|---|---|---|
Loss | Gain | Net | Loss | Gain | Net | |||||
I | (Azab & Noor 2003) | 1973-1996 | Topographic maps | Manually by FCC | -3.22 | 2.42 | -0.81 | -0.14 | 0.105 | -0.035 |
Present study | 1989-2016 | Satellite imagery | TCT | -5.41 | 3.44 | -1.97 | -0.2 | 0.127 | -0.073 | |
II | (Azab & Noor 2003) | 1973-1996 | Topographic maps | Manually by FCC | -4.12 | 2.19 | -1.93 | -0.179 | 0.095 | -0.084 |
(El Banna et al. 2009) | 1986-2001 | Satellite imagery | Histogram threshold | -3.03 | 1.52 | -1.52 | -0.202 | 0.101 | -0.101 | |
Present study | 1989-2016 | Satellite imagery | TCT | -6.95 | 4.37 | -2.57 | -0.257 | 0.162 | -0.095 | |
III | (El Banna et al. 2009) | 1986-2001 | Satellite imagery | Histogram threshold | -0.63 | 0.77 | 0.138 | -0.042 | 0.051 | 0.0092 |
Present study | 1989-2016 | Satellite imagery | TCT | -1.61 | 1.947 | 0.339 | -0.059 | 0.072 | 0.0126 | |
Overall | Present study | 1989-2016 | Satellite imagery | TCT | -24.9 | 16.6 | -8.31 | -0.925 | 0.61 | -0.308 |
Calculated area of erosion(-ve), accretion(+ve), and net balance surfaces along the North Sinai coast.
The defined trend of the two-dimensional shoreline change rate (km2/year) along the coastline, extracted using TCT technique is noticed to be rather coherent with other earlier studies when compared to the other two remote sensing techniques. This is evident when the current results have been compared with the previous results in researches of [22, 23] as shown in
Mean change rates (km2/year) along the North Sinai shoreline.
The digitized shorelines have been used in the ArcGIS extension Digital Shoreline Analysis System (DSAS) to calculate the rate of shoreline change in vector format over a specific period of time [44]. DSAS is a statistical software applied in coastal research to compute rate of change from historical GIS-based shoreline positions [45].
In this study, DSAS is utilized to calculate shoreline change rate from different historical shoreline locations along the North Sinai coast in 1989, 1998, 2003, 2010, and 2016. The method for determining shoreline change rates begins with the creation of a personal geodatabase for the extracted shoreline positions in ArcCatalog 10.2.2. Each shoreline has attributes that include date, length, ID, shape, and uncertainty. Each image’s acquisition date is entered in the date column, whereas the length, ID, and shape are easily obtained. Uncertainties are also measured (Table 2) and recorded in the uncertainty column as integers. The five shoreline positions are then appended to one shapefile. Thereafter, speculative baseline is formed from the shoreline. Three different methods are available in DSAS to delineate baseline: (1) constructing a baseline along the shoreline at a particular distance; (2) utilizing a previously established baseline; (3) buffering method. The last method is the most consistent and accurate technique for baseline demarcation because it uses the same sinuosity shape as the nearby shoreline, so it was selected for the current study [12].
The baseline is then created at a buffering distance of 1000 meters offshore from the nearest shoreline.
These attributes provide DSAS with information about the sequence of transects as well as the baseline’s position in relation to the shoreline (onshore or offshore). Transects have been set orthogonally from the benchmark (baseline) along the coastline of various years in 100 m intervals for the three different zones. Finally, the shoreline change rates are statistically computed using the various techniques (i.e., EPR, LRR, LMS, and NSM). As shown in (Figure 10a–c), a qualitative analysis is performed to determine the related erosion/accretion transects using the NSM model. The field that connects the table of NSM statistical results to the transect feature class is the field that they have in common. Where the values in the transect-ID field of the NSM results table are equal to the object identifier field (Object ID) in the transect feature class. After completing the joining process, the symbology of the transect feature class can be adjusted to classify transects into two categories: erosion (green transects) and accretion (orange transects), (Figure 10). Most beaches in zones I, II, and III are susceptible to accretion and retreat (1989–2016), according to the delineation of erosion and accretion transects.
Qualitative analysis of erosion/accretion transects using NSM, conducted in DSAS, in the years of 1989, 1998, 2003, 2010, and 2016 for (a) zone (I); (b) zone (II); (c) zone (III).
Furthermore, the results reveal that, 49.61% (191 transects), 73.52% (533 transects), and 72.24% (255 transects) of the coastline corresponding to 19.1, 53.3, and 25.65 km are experiencing erosion for zone I, II, and III respectively. On ht. eother hand, 50.39%, 26.48%, and 27.76% of the coastline with lengths of 19.4, 19.2, and 9.85 km are suffering accretion (
The rates of shoreline change are computed annually for each zone during the period from 1989 to 2016 using the statistical outcomes of EPR, LRR, and LMS (Figure 11). The findings of this study are summarized in
Shoreline change rates by EPR, LRR, and LMS (m/year) for Zones, (a) I; (b) II; and (c) III during the period from 1989 to 2016. Shoreline change rates by EPR, LRR, and LMS (m/year) for Zones, (a) I; (b) II; and (c) III during the period from 1989 to 2016.
Comparison of shoreline change rates (m/year) calculated by, (a) EPR vs LRR; (b) EPR vs LMS; (c) LRR vs LMS for the overall North Sinai coast.
Geospatial techniques and DSAS models were utilized to assess the shoreline morphodynamic changes along the North Sinai shoreline between 1989 and 2016 via multi-temporal satellite images. The semi-automatic shoreline extraction method (Tasseled Cap Transformation technique, TCT) was accustomed to digitalize the shoreline positions in 1989, 1998, 2003, and 2016. Extreme variance in the spatial scale characterizes the study area where the highest obtained coastal erosion/accretion kinematics for El Arish valley coast, El Bardawil Lake, and El Tinah Bay are −1.61/+1.95 km2, −6.95/+4.37 km2, and − 5.41/+3.44 km2, respectively.
Moreover, the construction of eastern jetty of the Suez Canal extremely lowered sediments inputs to El Tinah Bay, which highlighted the erosion of the western segment by wave hydrodynamics and the eastwards alongshore currents. Contrary, the eastern part of El Tinah Bay has demonstrated a nearly constant shoreline throughout the study period. Instantaneously, protection jetties of El Bardawil inlet 1, El Bardawil inlet 2, and El Arish Harbor have intermittent long-shore sand movement resulting in a continuous erosion at the downdrift side and an accretion at their updrift side. The institution of El Arish power plant has substantially decreased the sedimentary routine and created destructive impacts on coastal dynamics in the west of El Arish harbor.
In the meantime, the forthcoming speculation of the North Sinai coastline variations is predicted using the End Point Rate (EPR) model for the near future of years 2025, 2035, and 2050 after model validation based on 2010 data. A well-matching between the historical and futuristic trends of shoreline is obtained which means the calculation is almost succeeding the same accretion and erosion patterns. Study results in this chapter prove that medium-resolution satellite images, geospatial features of the GIS, and digital shoreline analysis system (DSAS) successfully assessed the coastal morphodynamic changes and shoreline detection of the North Sinai coast and could be used for other coastal areas based on the data quality and availability. Additionally, the results of this study deliver a high-reliable tool to the decision-makers and the coastal managers to support their decision when developing sustainable coastal management plans for North Sinai coast.
The author would like to thank the editor and the reviewers for their constructive comments for enhancing the chapter quality.
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\n\nPlease complete the publishing proposal form. The completed form should serve as an overview of your future Compacts, Monograph or Edited Book. Once submitted, your publishing proposal will be sent for evaluation, and a notice of acceptance or rejection will be sent within 10 to 30 working days from the date of submission.
\n\n2. SUBMIT YOUR MANUSCRIPT
\n\nAfter approval, you will proceed in submitting your full-length manuscript. 50-130 pages for compacts, 130-500 for Monographs & Edited Books.Your full-length manuscript must follow IntechOpen's Author Guidelines and comply with our publishing rules. Once the manuscript is submitted, but before it is forwarded for peer review, it will be screened for plagiarism.
\n\n3. PEER REVIEW RESULTS
\n\nExternal reviewers will evaluate your manuscript and provide you with their feedback. You may be asked to revise your draft, or parts of your draft, provide additional information and make any other necessary changes according to their comments and suggestions.
\n\n4. ACCEPTANCE AND PRICE QUOTE
\n\nIf the manuscript is formally accepted after peer review you will receive a formal Notice of Acceptance, and a price quote.
\n\nThe Open Access Publishing Fee of your IntechOpen Compacts, Monograph or Edited Book depends on the volume of the publication and includes: project management, editorial and peer review services, technical editing, language copyediting, cover design and book layout, book promotion and ISBN assignment.
\n\nWe will send you your price quote and after it has been accepted (by both the author and the publisher), both parties will sign a Statement of Work binding them to adhere to the agreed upon terms.
\n\nAt this step you will also be asked to accept the Copyright Agreement.
\n\n5. LANGUAGE COPYEDITING, TECHNICAL EDITING AND TYPESET PROOF
\n\nYour manuscript will be sent to Straive, a leader in content solution services, for language copyediting. You will then receive a typeset proof formatted in XML and available online in HTML and PDF to proofread and check for completeness. The first typeset proof of your manuscript is usually available 10 days after its original submission.
\n\nAfter we receive your proof corrections and a final typeset of the manuscript is approved, your manuscript is sent to our in house DTP department for technical formatting and online publication preparation.
\n\nAdditionally, you will be asked to provide a profile picture (face or chest-up portrait photograph) and a short summary of the book which is required for the book cover design.
\n\n6. INVOICE PAYMENT
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\n\nIntechOpen will help you complete your payment safely and securely, keeping your personal, professional and financial information safe.
\n\n7. ONLINE PUBLICATION, PRINT AND DELIVERY OF THE BOOK
\n\nIntechOpen authors can choose whether to publish their book online only or opt for online and print editions. IntechOpen Compacts, Monographs and Edited Books will be published on www.intechopen.com. If ordered, print copies are delivered by DHL within 12 to 15 working days.
\n\nIf you feel that IntechOpen Compacts, Monographs or Edited Books are the right publishing format for your work, please fill out the publishing proposal form. For any specific queries related to the publishing process, or IntechOpen Compacts, Monographs & Edited Books in general, please contact us at book.department@intechopen.com
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If its use complies with the five specifications including synthesis pathway, crystallographic phase, purity, amount and innocuousness, all other parameters are not defined and were hardly documented. However, in the last 3 years, two studies have deeply characterized food-grade TiO2 and converged to the fact that the size distribution of food-grade TiO2 spans over the nanoparticle range (<100 nm) and the surface is not pure TiO2 but covered by phosphate and eventually silicon species or aluminium species, which modify the surface chemistry of these particles. Until now, this material was considered as safe. However, the toxicological studies later to the last re-evaluation by the European Food Safety Agency reveal some concerns due to the ability of TiO2 particles to alter the intestinal barrier. This reinforces the idea to go on reinforcing the risk assessment about food-grade TiO2.",book:{id:"6407",slug:"application-of-titanium-dioxide",title:"Application of Titanium Dioxide",fullTitle:"Application of Titanium Dioxide"},signatures:"Marie-Hélène Ropers, Hélène Terrisse, Muriel Mercier-Bonin and\nBernard Humbert",authors:[{id:"203603",title:"Dr.",name:"Marie-Hélène",middleName:null,surname:"Ropers",slug:"marie-helene-ropers",fullName:"Marie-Hélène Ropers"},{id:"206434",title:"Dr.",name:"Hélène",middleName:null,surname:"Terrisse",slug:"helene-terrisse",fullName:"Hélène Terrisse"},{id:"206435",title:"Dr.",name:"Muriel",middleName:null,surname:"Mercier-Bonin",slug:"muriel-mercier-bonin",fullName:"Muriel Mercier-Bonin"},{id:"206436",title:"Prof.",name:"Bernard",middleName:null,surname:"Humbert",slug:"bernard-humbert",fullName:"Bernard Humbert"}]},{id:"51808",doi:"10.5772/64654",title:"Plasma-Enhanced Chemical Vapor Deposition: Where we are and the Outlook for the Future",slug:"plasma-enhanced-chemical-vapor-deposition-where-we-are-and-the-outlook-for-the-future",totalDownloads:7761,totalCrossrefCites:8,totalDimensionsCites:29,abstract:"Chemical vapor deposition (CVD) is a technique for the fabrication of thin films of polymeric materials, which has successfully overcome some of the issues faced by wet chemical fabrication and other deposition methods. There are many hybrid techniques, which arise from CVD and are constantly evolving in order to modify the properties of the fabricated thin films. Amongst them, plasma enhanced chemical vapor deposition (PECVD) is a technique that can extend the applicability of the method for various precursors, reactive organic and inorganic materials as well as inert materials. Organic/inorganic monomers, which are used as precursors in the PECVD technique, undergo disintegration and radical polymerization while exposed to a high-energy plasma stream, followed by thin film deposition. In this chapter, we have provided a summary of the history, various characteristics as well as the main applications of PECVD. By demonstrating the advantages and disadvantages of PECVD, we have provided a comparison of this technique with other techniques. PECVD, like any other techniques, still suffers from some restrictions, such as selection of appropriate monomers, or suitable inlet instrument. 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Although typical biological treatments of water offer some advantages such as low cost and operability, many investigations referring to the removal of pesticides have suggested that in many cases they have low effectiveness due to the limited biodegradability of many agrochemicals. In recent years, research for new techniques for water detoxification to avoid these disadvantages has led to processes that involve light, which are called advanced oxidation processes (AOPs). Among the different semiconductor (SC) materials tested as potential photocatalysts, titanium dioxide (TiO2) is the most popular because of its photochemical stability, commercial availability, non-toxic nature and low cost, high photoactivity, ease of preparation in the laboratory, possibility of doping with metals and non-metals and coating on solid support. Thus, in the present review, we provide an overview of the recent research being developed to photodegrade pesticide residues in water using TiO2 as photocatalyst.",book:{id:"6407",slug:"application-of-titanium-dioxide",title:"Application of Titanium Dioxide",fullTitle:"Application of Titanium Dioxide"},signatures:"Nuria Vela, Gabriel Pérez-Lucas, José Fenoll and Simón Navarro",authors:[{id:"202983",title:"Dr.",name:"Simón",middleName:null,surname:"Navarro",slug:"simon-navarro",fullName:"Simón Navarro"},{id:"202988",title:"Dr.",name:"Nuria",middleName:null,surname:"Vela",slug:"nuria-vela",fullName:"Nuria Vela"},{id:"202989",title:"Dr.",name:"José",middleName:null,surname:"Fenoll",slug:"jose-fenoll",fullName:"José Fenoll"},{id:"206059",title:"Dr.",name:"Gabriel",middleName:null,surname:"Pérez-Lucas",slug:"gabriel-perez-lucas",fullName:"Gabriel Pérez-Lucas"}]}],mostDownloadedChaptersLast30Days:[{id:"55440",title:"Solubility Products and Solubility Concepts",slug:"solubility-products-and-solubility-concepts",totalDownloads:3084,totalCrossrefCites:6,totalDimensionsCites:7,abstract:"The chapter refers to a general concept of solubility product Ksp of sparingly soluble hydroxides and different salts and calculation of solubility of some hydroxides, oxides, and different salts in aqueous media. A (criticized) conventional approach, based on stoichiometry of a reaction notation and the solubility product of a precipitate, is compared with the unconventional/correct approach based on charge and concentration balances and a detailed physicochemical knowledge on the system considered, and calculations realized according to generalized approach to electrolytic systems (GATES) principles. 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The PA industry is spread out worldwide in Europe, Asia and America, including countries that operate phosphate rock (PR) mines and produce PA, phosphatic fertilizers and phosphate-based products.",book:{id:"5595",slug:"phosphoric-acid-industry-problems-and-solutions",title:"Phosphoric Acid Industry",fullTitle:"Phosphoric Acid Industry - Problems and Solutions"},signatures:"Benjamín Valdez Salas, Michael Schorr Wiener and Juan Ricardo\nSalinas Martinez",authors:[{id:"16436",title:"Dr.",name:"Michael",middleName:null,surname:"Schorr",slug:"michael-schorr",fullName:"Michael Schorr"}]},{id:"62941",title:"Inorganic Coordination Chemistry: Where We Stand in Cancer Treatment?",slug:"inorganic-coordination-chemistry-where-we-stand-in-cancer-treatment-",totalDownloads:2147,totalCrossrefCites:5,totalDimensionsCites:10,abstract:"Metals have unique characteristics such as variable coordination modes, redox activity, and reactivity being indispensable for several biochemical processes in cells. Due to their reactivity, their concentration is tightly regulated inside the cells, and abnormal concentrations are associated with many disorders, such as cancer. As such metal complexes turned out to be very attractive as potential anticancer agents. The discovery of cisplatin was a crucial moment, which prompted the interest in Pt(II) and other metal complexes as potential anticancer agents. This chapter highlights the state of the art on metal complexes in cancer therapy, highlighting their uptake mechanisms, biological targets, toxicity, and drug resistance. Finally, based on the importance of selective target of cancer cells, drug delivery systems will also be discussed.",book:{id:"7549",slug:"basic-concepts-viewed-from-frontier-in-inorganic-coordination-chemistry",title:"Basic Concepts Viewed from Frontier in Inorganic Coordination Chemistry",fullTitle:"Basic Concepts Viewed from Frontier in Inorganic Coordination Chemistry"},signatures:"Pedro Pedrosa, Andreia Carvalho, Pedro V. Baptista and Alexandra R. Fernandes",authors:[{id:"253664",title:"Prof.",name:"Alexandra R",middleName:null,surname:"Fernandes",slug:"alexandra-r-fernandes",fullName:"Alexandra R Fernandes"}]},{id:"57464",title:"General Aspects of the Cobalt Chemistry",slug:"general-aspects-of-the-cobalt-chemistry",totalDownloads:2295,totalCrossrefCites:1,totalDimensionsCites:2,abstract:"This chapter aims to collect and summarize the chemical properties of cobalt and some new cobalt compounds. It deals with the progress of cobalt chemistry. Cobalt has been substantial in both chemical reactions and within many compounds. Some of them are heterocyclic reactions, cobalt-based catalyst and cobalamin. Also, it discusses variety of applications of cobalt in a wide range of areas and toxicity of cobalt. The studies carried out in this area so far have enabled and will be continued to be responsible for producing unknown and difficult reactions. 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Radiotherapy and Nuclear Medicine Technology has always been my aspiration and my life. As years passed I accumulated a tremendous amount of skills and knowledge in Radiotherapy and Nuclear Medicine, Conventional Radiology, Radiation Protection, Bioinformatics Technology, PACS, Image processing, clinically and lecturing that will enable me to provide a valuable service to the community as a Researcher and Consultant in this field. My method of translating this into day to day in clinical practice is non-exhaustible and my habit of exchanging knowledge and expertise with others in those fields is the code and secret of success.",institutionString:null,institution:{name:"Majmaah University",country:{name:"Saudi Arabia"}}},{id:"313277",title:"Dr.",name:"Bartłomiej",middleName:null,surname:"Płaczek",slug:"bartlomiej-placzek",fullName:"Bartłomiej Płaczek",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/313277/images/system/313277.jpg",biography:"Bartłomiej Płaczek, MSc (2002), Ph.D. (2005), Habilitation (2016), is a professor at the University of Silesia, Institute of Computer Science, Poland, and an expert from the National Centre for Research and Development. His research interests include sensor networks, smart sensors, intelligent systems, and image processing with applications in healthcare and medicine. He is the author or co-author of more than seventy papers in peer-reviewed journals and conferences as well as the co-author of several books. He serves as a reviewer for many scientific journals, international conferences, and research foundations. Since 2010, Dr. Placzek has been a reviewer of grants and projects (including EU projects) in the field of information technologies.",institutionString:"University of Silesia",institution:{name:"University of Silesia",country:{name:"Poland"}}},{id:"35000",title:"Prof.",name:"Ulrich H.P",middleName:"H.P.",surname:"Fischer",slug:"ulrich-h.p-fischer",fullName:"Ulrich H.P Fischer",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/35000/images/3052_n.jpg",biography:"Academic and Professional Background\nUlrich H. P. has Diploma and PhD degrees in Physics from the Free University Berlin, Germany. He has been working on research positions in the Heinrich-Hertz-Institute in Germany. Several international research projects has been performed with European partners from France, Netherlands, Norway and the UK. He is currently Professor of Communications Systems at the Harz University of Applied Sciences, Germany.\n\nPublications and Publishing\nHe has edited one book, a special interest book about ‘Optoelectronic Packaging’ (VDE, Berlin, Germany), and has published over 100 papers and is owner of several international patents for WDM over POF key elements.\n\nKey Research and Consulting Interests\nUlrich’s research activity has always been related to Spectroscopy and Optical Communications Technology. Specific current interests include the validation of complex instruments, and the application of VR technology to the development and testing of measurement systems. He has been reviewer for several publications of the Optical Society of America\\'s including Photonics Technology Letters and Applied Optics.\n\nPersonal Interests\nThese include motor cycling in a very relaxed manner and performing martial arts.",institutionString:null,institution:{name:"Charité",country:{name:"Germany"}}},{id:"341622",title:"Ph.D.",name:"Eduardo",middleName:null,surname:"Rojas Alvarez",slug:"eduardo-rojas-alvarez",fullName:"Eduardo Rojas Alvarez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/341622/images/15892_n.jpg",biography:null,institutionString:null,institution:{name:"University of Cuenca",country:{name:"Ecuador"}}},{id:"215610",title:"Prof.",name:"Muhammad",middleName:null,surname:"Sarfraz",slug:"muhammad-sarfraz",fullName:"Muhammad Sarfraz",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/215610/images/system/215610.jpeg",biography:"Muhammad Sarfraz is a professor in the Department of Information Science, Kuwait University. His research interests include computer graphics, computer vision, image processing, machine learning, pattern recognition, soft computing, data science, intelligent systems, information technology, and information systems. Prof. Sarfraz has been a keynote/invited speaker on various platforms around the globe. He has advised various students for their MSc and Ph.D. theses. He has published more than 400 publications as books, journal articles, and conference papers. He is a member of various professional societies and a chair and member of the International Advisory Committees and Organizing Committees of various international conferences. Prof. Sarfraz is also an editor-in-chief and editor of various international journals.",institutionString:"Kuwait University",institution:{name:"Kuwait University",country:{name:"Kuwait"}}},{id:"32650",title:"Prof.",name:"Lukas",middleName:"Willem",surname:"Snyman",slug:"lukas-snyman",fullName:"Lukas Snyman",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/32650/images/4136_n.jpg",biography:"Lukas Willem Snyman received his basic education at primary and high schools in South Africa, Eastern Cape. He enrolled at today's Nelson Metropolitan University and graduated from this university with a BSc in Physics and Mathematics, B.Sc Honors in Physics, MSc in Semiconductor Physics, and a Ph.D. in Semiconductor Physics in 1987. After his studies, he chose an academic career and devoted his energy to the teaching of physics to first, second, and third-year students. After positions as a lecturer at the University of Port Elizabeth, he accepted a position as Associate Professor at the University of Pretoria, South Africa.\r\n\r\nIn 1992, he motivates the concept of 'television and computer-based education” as means to reach large student numbers with only the best of teaching expertise and publishes an article on the concept in the SA Journal of Higher Education of 1993 (and later in 2003). The University of Pretoria subsequently approved a series of test projects on the concept with outreach to Mamelodi and Eerste Rust in 1993. In 1994, the University established a 'Unit for Telematic Education ' as a support section for multiple faculties at the University of Pretoria. In subsequent years, the concept of 'telematic education” subsequently becomes well established in academic circles in South Africa, grew in popularity, and is adopted by many universities and colleges throughout South Africa as a medium of enhancing education and training, as a method to reaching out to far out communities, and as a means to enhance study from the home environment.\r\n\r\nProfessor Snyman in subsequent years pursued research in semiconductor physics, semiconductor devices, microelectronics, and optoelectronics.\r\n\r\nIn 2000 he joined the TUT as a full professor. Here served for a period as head of the Department of Electronic Engineering. Here he makes contributions to solar energy development, microwave and optoelectronic device development, silicon photonics, as well as contributions to new mobile telecommunication systems and network planning in SA.\r\n\r\nCurrently, he teaches electronics and telecommunications at the TUT to audiences ranging from first-year students to Ph.D. level.\r\n\r\nFor his research in the field of 'Silicon Photonics” since 1990, he has published (as author and co-author) about thirty internationally reviewed articles in scientific journals, contributed to more than forty international conferences, about 25 South African provisional patents (as inventor and co-inventor), 8 PCT international patent applications until now. Of these, two USA patents applications, two European Patents, two Korean patents, and ten SA patents have been granted. A further 4 USA patents, 5 European patents, 3 Korean patents, 3 Chinese patents, and 3 Japanese patents are currently under consideration.\r\n\r\nRecently he has also published an extensive scholarly chapter in an internet open access book on 'Integrating Microphotonic Systems and MOEMS into standard Silicon CMOS Integrated circuitry”.\r\n\r\nFurthermore, Professor Snyman recently steered a new initiative at the TUT by introducing a 'Laboratory for Innovative Electronic Systems ' at the Department of Electrical Engineering. The model of this laboratory or center is to primarily combine outputs as achieved by high-level research with lower-level system development and entrepreneurship in a technical university environment. Students are allocated to projects at different levels with PhDs and Master students allocated to the generation of new knowledge and new technologies, while students at the diploma and Baccalaureus level are allocated to electronic systems development with a direct and a near application for application in industry or the commercial and public sectors in South Africa.\r\n\r\nProfessor Snyman received the WIRSAM Award of 1983 and the WIRSAM Award in 1985 in South Africa for best research papers by a young scientist at two international conferences on electron microscopy in South Africa. He subsequently received the SA Microelectronics Award for the best dissertation emanating from studies executed at a South African university in the field of Physics and Microelectronics in South Africa in 1987. In October of 2011, Professor Snyman received the prestigious Institutional Award for 'Innovator of the Year” for 2010 at the Tshwane University of Technology, South Africa. This award was based on the number of patents recognized and granted by local and international institutions as well as for his contributions concerning innovation at the TUT.",institutionString:null,institution:{name:"University of South Africa",country:{name:"South Africa"}}},{id:"317279",title:"Mr.",name:"Ali",middleName:"Usama",surname:"Syed",slug:"ali-syed",fullName:"Ali Syed",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/317279/images/16024_n.png",biography:"A creative, talented, and innovative young professional who is dedicated, well organized, and capable research fellow with two years of experience in graduate-level research, published in engineering journals and book, with related expertise in Bio-robotics, equally passionate about the aesthetics of the mechanical and electronic system, obtained expertise in the use of MS Office, MATLAB, SolidWorks, LabVIEW, Proteus, Fusion 360, having a grasp on python, C++ and assembly language, possess proven ability in acquiring research grants, previous appointments with social and educational societies with experience in administration, current affiliations with IEEE and Web of Science, a confident presenter at conferences and teacher in classrooms, able to explain complex information to audiences of all levels.",institutionString:null,institution:{name:"Air University",country:{name:"Pakistan"}}},{id:"75526",title:"Ph.D.",name:"Zihni Onur",middleName:null,surname:"Uygun",slug:"zihni-onur-uygun",fullName:"Zihni Onur Uygun",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/75526/images/12_n.jpg",biography:"My undergraduate education and my Master of Science educations at Ege University and at Çanakkale Onsekiz Mart University have given me a firm foundation in Biochemistry, Analytical Chemistry, Biosensors, Bioelectronics, Physical Chemistry and Medicine. After obtaining my degree as a MSc in analytical chemistry, I started working as a research assistant in Ege University Medical Faculty in 2014. In parallel, I enrolled to the MSc program at the Department of Medical Biochemistry at Ege University to gain deeper knowledge on medical and biochemical sciences as well as clinical chemistry in 2014. In my PhD I deeply researched on biosensors and bioelectronics and finished in 2020. Now I have eleven SCI-Expanded Index published papers, 6 international book chapters, referee assignments for different SCIE journals, one international patent pending, several international awards, projects and bursaries. In parallel to my research assistant position at Ege University Medical Faculty, Department of Medical Biochemistry, in April 2016, I also founded a Start-Up Company (Denosens Biotechnology LTD) by the support of The Scientific and Technological Research Council of Turkey. Currently, I am also working as a CEO in Denosens Biotechnology. The main purposes of the company, which carries out R&D as a research center, are to develop new generation biosensors and sensors for both point-of-care diagnostics; such as glucose, lactate, cholesterol and cancer biomarker detections. My specific experimental and instrumental skills are Biochemistry, Biosensor, Analytical Chemistry, Electrochemistry, Mobile phone based point-of-care diagnostic device, POCTs and Patient interface designs, HPLC, Tandem Mass Spectrometry, Spectrophotometry, ELISA.",institutionString:null,institution:{name:"Ege University",country:{name:"Turkey"}}},{id:"267434",title:"Dr.",name:"Rohit",middleName:null,surname:"Raja",slug:"rohit-raja",fullName:"Rohit Raja",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/267434/images/system/267434.jpg",biography:"Dr. Rohit Raja received Ph.D. in Computer Science and Engineering from Dr. CVRAMAN University in 2016. His main research interest includes Face recognition and Identification, Digital Image Processing, Signal Processing, and Networking. Presently he is working as Associate Professor in IT Department, Guru Ghasidas Vishwavidyalaya (A Central University), Bilaspur (CG), India. He has authored several Journal and Conference Papers. He has good Academics & Research experience in various areas of CSE and IT. He has filed and successfully published 27 Patents. He has received many time invitations to be a Guest at IEEE Conferences. He has published 100 research papers in various International/National Journals (including IEEE, Springer, etc.) and Proceedings of the reputed International/ National Conferences (including Springer and IEEE). He has been nominated to the board of editors/reviewers of many peer-reviewed and refereed Journals (including IEEE, Springer).",institutionString:"Guru Ghasidas Vishwavidyalaya",institution:{name:"Guru Ghasidas Vishwavidyalaya",country:{name:"India"}}},{id:"246502",title:"Dr.",name:"Jaya T.",middleName:"T",surname:"Varkey",slug:"jaya-t.-varkey",fullName:"Jaya T. Varkey",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/246502/images/11160_n.jpg",biography:"Jaya T. Varkey, PhD, graduated with a degree in Chemistry from Cochin University of Science and Technology, Kerala, India. She obtained a PhD in Chemistry from the School of Chemical Sciences, Mahatma Gandhi University, Kerala, India, and completed a post-doctoral fellowship at the University of Minnesota, USA. She is a research guide at Mahatma Gandhi University and Associate Professor in Chemistry, St. Teresa’s College, Kochi, Kerala, India.\nDr. Varkey received a National Young Scientist award from the Indian Science Congress (1995), a UGC Research award (2016–2018), an Indian National Science Academy (INSA) Visiting Scientist award (2018–2019), and a Best Innovative Faculty award from the All India Association for Christian Higher Education (AIACHE) (2019). She Hashas received the Sr. Mary Cecil prize for best research paper three times. She was also awarded a start-up to develop a tea bag water filter. \nDr. Varkey has published two international books and twenty-seven international journal publications. She is an editorial board member for five international journals.",institutionString:"St. Teresa’s College",institution:null},{id:"250668",title:"Dr.",name:"Ali",middleName:null,surname:"Nabipour Chakoli",slug:"ali-nabipour-chakoli",fullName:"Ali Nabipour Chakoli",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/250668/images/system/250668.jpg",biography:"Academic Qualification:\r\n•\tPhD in Materials Physics and Chemistry, From: Sep. 2006, to: Sep. 2010, School of Materials Science and Engineering, Harbin Institute of Technology, Thesis: Structure and Shape Memory Effect of Functionalized MWCNTs/poly (L-lactide-co-ε-caprolactone) Nanocomposites. Supervisor: Prof. Wei Cai,\r\n•\tM.Sc in Applied Physics, From: 1996, to: 1998, Faculty of Physics & Nuclear Science, Amirkabir Uni. of Technology, Tehran, Iran, Thesis: Determination of Boron in Micro alloy Steels with solid state nuclear track detectors by neutron induced auto radiography, Supervisors: Dr. M. Hosseini Ashrafi and Dr. A. Hosseini.\r\n•\tB.Sc. in Applied Physics, From: 1991, to: 1996, Faculty of Physics & Nuclear Science, Amirkabir Uni. of Technology, Tehran, Iran, Thesis: Design of shielding for Am-Be neutron sources for In Vivo neutron activation analysis, Supervisor: Dr. M. Hosseini Ashrafi.\r\n\r\nResearch Experiences:\r\n1.\tNanomaterials, Carbon Nanotubes, Graphene: Synthesis, Functionalization and Characterization,\r\n2.\tMWCNTs/Polymer Composites: Fabrication and Characterization, \r\n3.\tShape Memory Polymers, Biodegradable Polymers, ORC, Collagen,\r\n4.\tMaterials Analysis and Characterizations: TEM, SEM, XPS, FT-IR, Raman, DSC, DMA, TGA, XRD, GPC, Fluoroscopy, \r\n5.\tInteraction of Radiation with Mater, Nuclear Safety and Security, NDT(RT),\r\n6.\tRadiation Detectors, Calibration (SSDL),\r\n7.\tCompleted IAEA e-learning Courses:\r\nNuclear Security (15 Modules),\r\nNuclear Safety:\r\nTSA 2: Regulatory Protection in Occupational Exposure,\r\nTips & Tricks: Radiation Protection in Radiography,\r\nSafety and Quality in Radiotherapy,\r\nCourse on Sealed Radioactive Sources,\r\nCourse on Fundamentals of Environmental Remediation,\r\nCourse on Planning for Environmental Remediation,\r\nKnowledge Management Orientation Course,\r\nFood Irradiation - Technology, Applications and Good Practices,\r\nEmployment:\r\nFrom 2010 to now: Academic staff, Nuclear Science and Technology Research Institute, Kargar Shomali, Tehran, Iran, P.O. Box: 14395-836.\r\nFrom 1997 to 2006: Expert of Materials Analysis and Characterization. Research Center of Agriculture and Medicine. Rajaeeshahr, Karaj, Iran, P. O. Box: 31585-498.",institutionString:"Atomic Energy Organization of Iran",institution:{name:"Atomic Energy Organization of Iran",country:{name:"Iran"}}},{id:"248279",title:"Dr.",name:"Monika",middleName:"Elzbieta",surname:"Machoy",slug:"monika-machoy",fullName:"Monika Machoy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/248279/images/system/248279.jpeg",biography:"Monika Elżbieta Machoy, MD, graduated with distinction from the Faculty of Medicine and Dentistry at the Pomeranian Medical University in 2009, defended her PhD thesis with summa cum laude in 2016 and is currently employed as a researcher at the Department of Orthodontics of the Pomeranian Medical University. She expanded her professional knowledge during a one-year scholarship program at the Ernst Moritz Arndt University in Greifswald, Germany and during a three-year internship at the Technical University in Dresden, Germany. She has been a speaker at numerous orthodontic conferences, among others, American Association of Orthodontics, European Orthodontic Symposium and numerous conferences of the Polish Orthodontic Society. She conducts research focusing on the effect of orthodontic treatment on dental and periodontal tissues and the causes of pain in orthodontic patients.",institutionString:"Pomeranian Medical University",institution:{name:"Pomeranian Medical University",country:{name:"Poland"}}},{id:"252743",title:"Prof.",name:"Aswini",middleName:"Kumar",surname:"Kar",slug:"aswini-kar",fullName:"Aswini Kar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/252743/images/10381_n.jpg",biography:"uploaded in cv",institutionString:null,institution:{name:"KIIT University",country:{name:"India"}}},{id:"204256",title:"Dr.",name:"Anil",middleName:"Kumar",surname:"Kumar Sahu",slug:"anil-kumar-sahu",fullName:"Anil Kumar Sahu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/204256/images/14201_n.jpg",biography:"I have nearly 11 years of research and teaching experience. I have done my master degree from University Institute of Pharmacy, Pt. Ravi Shankar Shukla University, Raipur, Chhattisgarh India. I have published 16 review and research articles in international and national journals and published 4 chapters in IntechOpen, the world’s leading publisher of Open access books. I have presented many papers at national and international conferences. I have received research award from Indian Drug Manufacturers Association in year 2015. My research interest extends from novel lymphatic drug delivery systems, oral delivery system for herbal bioactive to formulation optimization.",institutionString:null,institution:{name:"Chhattisgarh Swami Vivekanand Technical University",country:{name:"India"}}},{id:"253468",title:"Dr.",name:"Mariusz",middleName:null,surname:"Marzec",slug:"mariusz-marzec",fullName:"Mariusz Marzec",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/253468/images/system/253468.png",biography:"An assistant professor at Department of Biomedical Computer Systems, at Institute of Computer Science, Silesian University in Katowice. Scientific interests: computer analysis and processing of images, biomedical images, databases and programming languages. He is an author and co-author of scientific publications covering analysis and processing of biomedical images and development of database systems.",institutionString:"University of Silesia",institution:{name:"University of Silesia",country:{name:"Poland"}}},{id:"212432",title:"Prof.",name:"Hadi",middleName:null,surname:"Mohammadi",slug:"hadi-mohammadi",fullName:"Hadi Mohammadi",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/212432/images/system/212432.jpeg",biography:"Dr. Hadi Mohammadi is a biomedical engineer with hands-on experience in the design and development of many engineering structures and medical devices through various projects that he has been involved in over the past twenty years. Dr. Mohammadi received his BSc. and MSc. degrees in Mechanical Engineering from Sharif University of Technology, Tehran, Iran, and his PhD. degree in Biomedical Engineering (biomaterials) from the University of Western Ontario. He was a postdoctoral trainee for almost four years at University of Calgary and Harvard Medical School. He is an industry innovator having created the technology to produce lifelike synthetic platforms that can be used for the simulation of almost all cardiovascular reconstructive surgeries. He’s been heavily involved in the design and development of cardiovascular devices and technology for the past 10 years. He is currently an Assistant Professor with the University of British Colombia, Canada.",institutionString:"University of British Columbia",institution:{name:"University of British Columbia",country:{name:"Canada"}}},{id:"254463",title:"Prof.",name:"Haisheng",middleName:null,surname:"Yang",slug:"haisheng-yang",fullName:"Haisheng Yang",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/254463/images/system/254463.jpeg",biography:"Haisheng Yang, Ph.D., Professor and Director of the Department of Biomedical Engineering, College of Life Science and Bioengineering, Beijing University of Technology. He received his Ph.D. degree in Mechanics/Biomechanics from Harbin Institute of Technology (jointly with University of California, Berkeley). Afterwards, he worked as a Postdoctoral Research Associate in the Purdue Musculoskeletal Biology and Mechanics Lab at the Department of Basic Medical Sciences, Purdue University, USA. He also conducted research in the Research Centre of Shriners Hospitals for Children-Canada at McGill University, Canada. Dr. Yang has over 10 years research experience in orthopaedic biomechanics and mechanobiology of bone adaptation and regeneration. He earned an award from Beijing Overseas Talents Aggregation program in 2017 and serves as Beijing Distinguished Professor.",institutionString:null,institution:{name:"Beijing University of Technology",country:{name:"China"}}},{id:"89721",title:"Dr.",name:"Mehmet",middleName:"Cuneyt",surname:"Ozmen",slug:"mehmet-ozmen",fullName:"Mehmet Ozmen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/89721/images/7289_n.jpg",biography:null,institutionString:null,institution:{name:"Gazi University",country:{name:"Turkey"}}},{id:"265335",title:"Mr.",name:"Stefan",middleName:"Radnev",surname:"Stefanov",slug:"stefan-stefanov",fullName:"Stefan Stefanov",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/265335/images/7562_n.jpg",biography:null,institutionString:null,institution:{name:"Medical University Plovdiv",country:{name:"Bulgaria"}}},{id:"242893",title:"Ph.D. Student",name:"Joaquim",middleName:null,surname:"De Moura",slug:"joaquim-de-moura",fullName:"Joaquim De Moura",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/242893/images/7133_n.jpg",biography:"Joaquim de Moura received his degree in Computer Engineering in 2014 from the University of A Coruña (Spain). In 2016, he received his M.Sc degree in Computer Engineering from the same university. He is currently pursuing his Ph.D degree in Computer Science in a collaborative project between ophthalmology centers in Galicia and the University of A Coruña. His research interests include computer vision, machine learning algorithms and analysis and medical imaging processing of various kinds.",institutionString:null,institution:{name:"University of A Coruña",country:{name:"Spain"}}},{id:"294334",title:"B.Sc.",name:"Marc",middleName:null,surname:"Bruggeman",slug:"marc-bruggeman",fullName:"Marc Bruggeman",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/294334/images/8242_n.jpg",biography:"Chemical engineer graduate, with a passion for material science and specific interest in polymers - their near infinite applications intrigue me. \n\nI plan to continue my scientific career in the field of polymeric biomaterials as I am fascinated by intelligent, bioactive and biomimetic materials for use in both consumer and medical applications.",institutionString:null,institution:null},{id:"255757",title:"Dr.",name:"Igor",middleName:"Victorovich",surname:"Lakhno",slug:"igor-lakhno",fullName:"Igor Lakhno",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/255757/images/system/255757.jpg",biography:"Igor Victorovich Lakhno was born in 1971 in Kharkiv (Ukraine). \nMD – 1994, Kharkiv National Medical Univesity.\nOb&Gyn; – 1997, master courses in Kharkiv Medical Academy of Postgraduate Education.\nPh.D. – 1999, Kharkiv National Medical Univesity.\nDSC – 2019, PL Shupik National Academy of Postgraduate Education \nProfessor – 2021, Department of Obstetrics and Gynecology of VN Karazin Kharkiv National University\nHead of Department – 2021, Department of Perinatology, Obstetrics and gynecology of Kharkiv Medical Academy of Postgraduate Education\nIgor Lakhno has been graduated from international training courses on reproductive medicine and family planning held at Debrecen University (Hungary) in 1997. Since 1998 Lakhno Igor has worked as an associate professor in the department of obstetrics and gynecology of VN Karazin National University and an associate professor of the perinatology, obstetrics, and gynecology department of Kharkiv Medical Academy of Postgraduate Education. Since June 2019 he’s been a professor in the department of obstetrics and gynecology of VN Karazin National University and a professor of the perinatology, obstetrics, and gynecology department. He’s affiliated with Kharkiv Medical Academy of Postgraduate Education as a Head of Department from November 2021. Igor Lakhno has participated in several international projects on fetal non-invasive electrocardiography (with Dr. J. A. Behar (Technion), Prof. D. Hoyer (Jena University), and José Alejandro Díaz Méndez (National Institute of Astrophysics, Optics, and Electronics, Mexico). He’s an author of about 200 printed works and there are 31 of them in Scopus or Web of Science databases. Igor Lakhno is a member of the Editorial Board of Reproductive Health of Woman, Emergency Medicine, and Technology Transfer Innovative Solutions in Medicine (Estonia). He is a medical Editor of “Z turbotoyu pro zhinku”. Igor Lakhno is a reviewer of the Journal of Obstetrics and Gynaecology (Taylor and Francis), British Journal of Obstetrics and Gynecology (Wiley), Informatics in Medicine Unlocked (Elsevier), The Journal of Obstetrics and Gynecology Research (Wiley), Endocrine, Metabolic & Immune Disorders-Drug Targets (Bentham Open), The Open Biomedical Engineering Journal (Bentham Open), etc. He’s defended a dissertation for a DSc degree “Pre-eclampsia: prediction, prevention, and treatment”. Three years ago Igor Lakhno has participated in a training course on innovative technologies in medical education at Lublin Medical University (Poland). Lakhno Igor has participated as a speaker in several international conferences and congresses (International Conference on Biological Oscillations April 10th-14th 2016, Lancaster, UK, The 9th conference of the European Study Group on Cardiovascular Oscillations). His main scientific interests: are obstetrics, women’s health, fetal medicine, and cardiovascular medicine. \nIgor Lakhno is a consultant at Kharkiv municipal perinatal center. He’s graduated from training courses on endoscopy in gynecology. He has 28 years of practical experience in the field.",institutionString:null,institution:null},{id:"244950",title:"Dr.",name:"Salvatore",middleName:null,surname:"Di Lauro",slug:"salvatore-di-lauro",fullName:"Salvatore Di Lauro",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0030O00002bSF1HQAW/ProfilePicture%202021-12-20%2014%3A54%3A14.482",biography:"Name:\n\tSALVATORE DI LAURO\nAddress:\n\tHospital Clínico Universitario Valladolid\nAvda Ramón y Cajal 3\n47005, Valladolid\nSpain\nPhone number: \nFax\nE-mail:\n\t+34 983420000 ext 292\n+34 983420084\nsadilauro@live.it\nDate and place of Birth:\nID Number\nMedical Licence \nLanguages\t09-05-1985. Villaricca (Italy)\n\nY1281863H\n474707061\nItalian (native language)\nSpanish (read, written, spoken)\nEnglish (read, written, spoken)\nPortuguese (read, spoken)\nFrench (read)\n\t\t\nCurrent position (title and company)\tDate (Year)\nVitreo-Retinal consultant in ophthalmology. Hospital Clinico Universitario Valladolid. Sacyl. National Health System.\nVitreo-Retinal consultant in ophthalmology. Instituto Oftalmologico Recoletas. Red Hospitalaria Recoletas. Private practise.\t2017-today\n\n2019-today\n\t\n\t\nEducation (High school, university and postgraduate training > 3 months)\tDate (Year)\nDegree in Medicine and Surgery. University of Neaples 'Federico II”\nResident in Opthalmology. Hospital Clinico Universitario Valladolid\nMaster in Vitreo-Retina. IOBA. University of Valladolid\nFellow of the European Board of Ophthalmology. Paris\nMaster in Research in Ophthalmology. University of Valladolid\t2003-2009\n2012-2016\n2016-2017\n2016\n2012-2013\n\t\nEmployments (company and positions)\tDate (Year)\nResident in Ophthalmology. Hospital Clinico Universitario Valladolid. Sacyl.\nFellow in Vitreo-Retina. IOBA. University of Valladolid\nVitreo-Retinal consultant in ophthalmology. Hospital Clinico Universitario Valladolid. Sacyl. National Health System.\nVitreo-Retinal consultant in ophthalmology. Instituto Oftalmologico Recoletas. Red Hospitalaria Recoletas. \n\t2012-2016\n2016-2017\n2017-today\n\n2019-Today\n\n\n\t\nClinical Research Experience (tasks and role)\tDate (Year)\nAssociated investigator\n\n' FIS PI20/00740: DESARROLLO DE UNA CALCULADORA DE RIESGO DE\nAPARICION DE RETINOPATIA DIABETICA BASADA EN TECNICAS DE IMAGEN MULTIMODAL EN PACIENTES DIABETICOS TIPO 1. Grant by: Ministerio de Ciencia e Innovacion \n\n' (BIO/VA23/14) Estudio clínico multicéntrico y prospectivo para validar dos\nbiomarcadores ubicados en los genes p53 y MDM2 en la predicción de los resultados funcionales de la cirugía del desprendimiento de retina regmatógeno. Grant by: Gerencia Regional de Salud de la Junta de Castilla y León.\n' Estudio multicéntrico, aleatorizado, con enmascaramiento doble, en 2 grupos\nparalelos y de 52 semanas de duración para comparar la eficacia, seguridad e inmunogenicidad de SOK583A1 respecto a Eylea® en pacientes con degeneración macular neovascular asociada a la edad' (CSOK583A12301; N.EUDRA: 2019-004838-41; FASE III). Grant by Hexal AG\n\n' Estudio de fase III, aleatorizado, doble ciego, con grupos paralelos, multicéntrico para comparar la eficacia y la seguridad de QL1205 frente a Lucentis® en pacientes con degeneración macular neovascular asociada a la edad. (EUDRACT: 2018-004486-13). Grant by Qilu Pharmaceutical Co\n\n' Estudio NEUTON: Ensayo clinico en fase IV para evaluar la eficacia de aflibercept en pacientes Naive con Edema MacUlar secundario a Oclusion de Vena CenTral de la Retina (OVCR) en regimen de tratamientO iNdividualizado Treat and Extend (TAE)”, (2014-000975-21). Grant by Fundacion Retinaplus\n\n' Evaluación de la seguridad y bioactividad de anillos de tensión capsular en conejo. Proyecto Procusens. Grant by AJL, S.A.\n\n'Estudio epidemiológico, prospectivo, multicéntrico y abierto\\npara valorar la frecuencia de la conjuntivitis adenovírica diagnosticada mediante el test AdenoPlus®\\nTest en pacientes enfermos de conjuntivitis aguda”\\n. National, multicenter study. Grant by: NICOX.\n\nEuropean multicentric trial: 'Evaluation of clinical outcomes following the use of Systane Hydration in patients with dry eye”. Study Phase 4. Grant by: Alcon Labs'\n\nVLPs Injection and Activation in a Rabbit Model of Uveal Melanoma. Grant by Aura Bioscience\n\nUpdating and characterization of a rabbit model of uveal melanoma. Grant by Aura Bioscience\n\nEnsayo clínico en fase IV para evaluar las variantes genéticas de la vía del VEGF como biomarcadores de eficacia del tratamiento con aflibercept en pacientes con degeneración macular asociada a la edad (DMAE) neovascular. Estudio BIOIMAGE. IMO-AFLI-2013-01\n\nEstudio In-Eye:Ensayo clínico en fase IV, abierto, aleatorizado, de 2 brazos,\nmulticçentrico y de 12 meses de duración, para evaluar la eficacia y seguridad de un régimen de PRN flexible individualizado de 'esperar y extender' versus un régimen PRN según criterios de estabilización mediante evaluaciones mensuales de inyecciones intravítreas de ranibizumab 0,5 mg en pacientes naive con neovascularización coriodea secunaria a la degeneración macular relacionada con la edad. CP: CRFB002AES03T\n\nTREND: Estudio Fase IIIb multicéntrico, randomizado, de 12 meses de\nseguimiento con evaluador de la agudeza visual enmascarado, para evaluar la eficacia y la seguridad de ranibizumab 0.5mg en un régimen de tratar y extender comparado con un régimen mensual, en pacientes con degeneración macular neovascular asociada a la edad. CP: CRFB002A2411 Código Eudra CT:\n2013-002626-23\n\n\n\nPublications\t\n\n2021\n\n\n\n\n2015\n\n\n\n\n2021\n\n\n\n\n\n2021\n\n\n\n\n2015\n\n\n\n\n2015\n\n\n2014\n\n\n\n\n2015-16\n\n\n\n2015\n\n\n2014\n\n\n2014\n\n\n\n\n2014\n\n\n\n\n\n\n\n2014\n\nJose Carlos Pastor; Jimena Rojas; Salvador Pastor-Idoate; Salvatore Di Lauro; Lucia Gonzalez-Buendia; Santiago Delgado-Tirado. Proliferative vitreoretinopathy: A new concept of disease pathogenesis and practical\nconsequences. Progress in Retinal and Eye Research. 51, pp. 125 - 155. 03/2016. DOI: 10.1016/j.preteyeres.2015.07.005\n\n\nLabrador-Velandia S; Alonso-Alonso ML; Di Lauro S; García-Gutierrez MT; Srivastava GK; Pastor JC; Fernandez-Bueno I. Mesenchymal stem cells provide paracrine neuroprotective resources that delay degeneration of co-cultured organotypic neuroretinal cultures.Experimental Eye Research. 185, 17/05/2019. DOI: 10.1016/j.exer.2019.05.011\n\nSalvatore Di Lauro; Maria Teresa Garcia Gutierrez; Ivan Fernandez Bueno. Quantification of pigment epithelium-derived factor (PEDF) in an ex vivo coculture of retinal pigment epithelium cells and neuroretina.\nJournal of Allbiosolution. 2019. ISSN 2605-3535\n\nSonia Labrador Velandia; Salvatore Di Lauro; Alonso-Alonso ML; Tabera Bartolomé S; Srivastava GK; Pastor JC; Fernandez-Bueno I. Biocompatibility of intravitreal injection of human mesenchymal stem cells in immunocompetent rabbits. Graefe's archive for clinical and experimental ophthalmology. 256 - 1, pp. 125 - 134. 01/2018. DOI: 10.1007/s00417-017-3842-3\n\n\nSalvatore Di Lauro, David Rodriguez-Crespo, Manuel J Gayoso, Maria T Garcia-Gutierrez, J Carlos Pastor, Girish K Srivastava, Ivan Fernandez-Bueno. A novel coculture model of porcine central neuroretina explants and retinal pigment epithelium cells. Molecular Vision. 2016 - 22, pp. 243 - 253. 01/2016.\n\nSalvatore Di Lauro. Classifications for Proliferative Vitreoretinopathy ({PVR}): An Analysis of Their Use in Publications over the Last 15 Years. Journal of Ophthalmology. 2016, pp. 1 - 6. 01/2016. DOI: 10.1155/2016/7807596\n\nSalvatore Di Lauro; Rosa Maria Coco; Rosa Maria Sanabria; Enrique Rodriguez de la Rua; Jose Carlos Pastor. Loss of Visual Acuity after Successful Surgery for Macula-On Rhegmatogenous Retinal Detachment in a Prospective Multicentre Study. Journal of Ophthalmology. 2015:821864, 2015. DOI: 10.1155/2015/821864\n\nIvan Fernandez-Bueno; Salvatore Di Lauro; Ivan Alvarez; Jose Carlos Lopez; Maria Teresa Garcia-Gutierrez; Itziar Fernandez; Eva Larra; Jose Carlos Pastor. Safety and Biocompatibility of a New High-Density Polyethylene-Based\nSpherical Integrated Porous Orbital Implant: An Experimental Study in Rabbits. Journal of Ophthalmology. 2015:904096, 2015. DOI: 10.1155/2015/904096\n\nPastor JC; Pastor-Idoate S; Rodríguez-Hernandez I; Rojas J; Fernandez I; Gonzalez-Buendia L; Di Lauro S; Gonzalez-Sarmiento R. Genetics of PVR and RD. Ophthalmologica. 232 - Suppl 1, pp. 28 - 29. 2014\n\nRodriguez-Crespo D; Di Lauro S; Singh AK; Garcia-Gutierrez MT; Garrosa M; Pastor JC; Fernandez-Bueno I; Srivastava GK. Triple-layered mixed co-culture model of RPE cells with neuroretina for evaluating the neuroprotective effects of adipose-MSCs. Cell Tissue Res. 358 - 3, pp. 705 - 716. 2014.\nDOI: 10.1007/s00441-014-1987-5\n\nCarlo De Werra; Salvatore Condurro; Salvatore Tramontano; Mario Perone; Ivana Donzelli; Salvatore Di Lauro; Massimo Di Giuseppe; Rosa Di Micco; Annalisa Pascariello; Antonio Pastore; Giorgio Diamantis; Giuseppe Galloro. Hydatid disease of the liver: thirty years of surgical experience.Chirurgia italiana. 59 - 5, pp. 611 - 636.\n(Italia): 2007. ISSN 0009-4773\n\nChapters in books\n\t\n' Salvador Pastor Idoate; Salvatore Di Lauro; Jose Carlos Pastor Jimeno. PVR: Pathogenesis, Histopathology and Classification. Proliferative Vitreoretinopathy with Small Gauge Vitrectomy. Springer, 2018. ISBN 978-3-319-78445-8\nDOI: 10.1007/978-3-319-78446-5_2. \n\n' Salvatore Di Lauro; Maria Isabel Lopez Galvez. Quistes vítreos en una mujer joven. Problemas diagnósticos en patología retinocoroidea. Sociedad Española de Retina-Vitreo. 2018.\n\n' Salvatore Di Lauro; Salvador Pastor Idoate; Jose Carlos Pastor Jimeno. iOCT in PVR management. OCT Applications in Opthalmology. pp. 1 - 8. INTECH, 2018. DOI: 10.5772/intechopen.78774.\n\n' Rosa Coco Martin; Salvatore Di Lauro; Salvador Pastor Idoate; Jose Carlos Pastor. amponadores, manipuladores y tinciones en la cirugía del traumatismo ocular.Trauma Ocular. Ponencia de la SEO 2018..\n\n' LOPEZ GALVEZ; DI LAURO; CRESPO. OCT angiografia y complicaciones retinianas de la diabetes. PONENCIA SEO 2021, CAPITULO 20. (España): 2021.\n\n' Múltiples desprendimientos neurosensoriales bilaterales en paciente joven. Enfermedades Degenerativas De Retina Y Coroides. SERV 04/2016. \n' González-Buendía L; Di Lauro S; Pastor-Idoate S; Pastor Jimeno JC. Vitreorretinopatía proliferante (VRP) e inflamación: LA INFLAMACIÓN in «INMUNOMODULADORES Y ANTIINFLAMATORIOS: MÁS ALLÁ DE LOS CORTICOIDES. 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Dr. Wang was awarded two research project grants focused on multimodal optical coherence tomography imaging and deep learning in cataract and retinal disease, from the National Natural Science Foundation of China. He has published around 30 peer-reviewed journal papers and four book chapters and co-edited one book.",institutionString:null,institution:null},{id:"7227",title:"Dr.",name:"Hiroaki",middleName:null,surname:"Matsui",slug:"hiroaki-matsui",fullName:"Hiroaki Matsui",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Tokyo",country:{name:"Japan"}}},{id:"312999",title:"Dr.",name:"Bernard O.",middleName:null,surname:"Asimeng",slug:"bernard-o.-asimeng",fullName:"Bernard O. 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