\r\n\tHomeostasis is brought about by a natural resistance to change when already in the optimal conditions, and equilibrium is maintained by many regulatory mechanisms. All homeostatic control mechanisms have at least three interdependent components for the variable to be regulated: a receptor, a control center, and an effector. The receptor is the sensing component that monitors and responds to changes in the environment, either external or internal. Receptors include thermoreceptors and mechanoreceptors. Control centers include the respiratory center and the renin-angiotensin system. An effector is a target acted on to bring about the change back to the normal state. At the cellular level, receptors include nuclear receptors that bring about changes in gene expression through up-regulation or down-regulation and act in negative feedback mechanisms. An example of this is in the control of bile acids in the liver.
\r\n\tSome centers, such as the renin-angiotensin system, control more than one variable. When the receptor senses a stimulus, it reacts by sending action potentials to a control center. The control center sets the maintenance range—the acceptable upper and lower limits—for the particular variable, such as temperature. The control center responds to the signal by determining an appropriate response and sending signals to an effector, which can be one or more muscles, an organ, or a gland. When the signal is received and acted on, negative feedback is provided to the receptor that stops the need for further signaling.
\r\n\tThe cannabinoid receptor type 1 (CB1), located at the presynaptic neuron, is a receptor that can stop stressful neurotransmitter release to the postsynaptic neuron; it is activated by endocannabinoids (ECs) such as anandamide (N-arachidonoylethanolamide; AEA) and 2-arachidonoylglycerol (2-AG) via a retrograde signaling process in which these compounds are synthesized by and released from postsynaptic neurons, and travel back to the presynaptic terminal to bind to the CB1 receptor for modulation of neurotransmitter release to obtain homeostasis.
\r\n\tThe polyunsaturated fatty acids (PUFAs) are lipid derivatives of omega-3 (docosahexaenoic acid, DHA, and eicosapentaenoic acid, EPA) or of omega-6 (arachidonic acid, ARA) and are synthesized from membrane phospholipids and used as a precursor for endocannabinoids (ECs) mediate significant effects in the fine-tuning adjustment of body homeostasis.
\r\n\t
\r\n\tThe aim of this book is to discuss further various aspects of homeostasis, information that we hope to be useful to scientists, clinicians, and the wider public alike.
While it is essential for every researcher to obtain data that is highly accurate, complete, representative and comparable, it is known that missing values, outliers and censored values are common characteristics of a water quality data-set. Random and systematic errors at various stages of a monitoring program tend to produce erroneous values, which complicates statistical analysis. For example, the central tendency statistics, particularly the mean and standard deviation, are distorted by a single grossly inaccurate data point. An error, which is initially identified and is later incorporated into a decision making tool, like a water quality index (WQI) or a model, could subsequently lead to costly consequences to humans and the environment.
\nChecking for erroneous and anomalous data points should be routine, and an initial stage of any data analysis study. However, distinguishing between a data-point and an error requires experience. For example, outliers may actually be results which might require statistical attention before a decision can be made to either discard or retain them. Human judgement, based on knowledge, experience and intuition thus continue to be important in assessing the integrity and validity of a given data-set. It is therefore essential for water resources practitioners to be knowledgeable regarding the identification and treatment of errors and anomalies in water quality data before undertaking an in-depth analysis.
\nOn the other hand, although the advent of computers and various software have made it easy to analyse large amounts of data, lack of basic statistical knowledge could result in the application of an inappropriate technique. This could ultimately lead to wrong conclusions that are costly to humans and the environment [1]. Such necessitate the need for some basic understanding of data characteristics and statistics methods that are commonly applied in the water quality sector. This chapter, discusses common anomalies and errors in water quality data-sets, methods of their identification and treatment. Knowledge reviewed could assist with building appropriate and validated data-sets which might suit the statistical method under consideration for data analysis and/or modelling.
\nReferring to water quality studies, an error can be defined as a value that does not represent the true concentration of a variable such as turbidity. These may arise from both human and technical error during sample collection, preparation, analysis and recording of results [2]. Erroneous values can be recorded even where an organisation has a clearly defined monitoring protocol. If invalid values are subsequently combined with valid data, the integrity of the latter is also impaired [1]. Incorporating erroneous values into a management tool like a WQI or model, could result in wrong conclusions that might be costly to the environment or humans.
\nData validation is a rigorous process of reviewing the quality of data. It assists in determining errors and anomalies that might need attention during analysis. Validation is crucial especially where a study depends on secondary data as it increases confidence in the integrity of the obtained data. Without such confidence, further data manipulation is fruitless [3]. Though data validation is usually performed by a quality control personnel in most organisations, it is important for any water resource practitioner to understand the common characteristics that may affect in-depth analysis of a water quality data-sets.
\nAmong the common methods of assessing the integrity of a data-set is visual scan. This approach assists to identify values that are distinct and, which might require attention during statistical analysis and model building. The ability to visually assess the integrity of data depends on both the monitoring objectives and experience [4]. Transcription errors, erroneous values (e.g. a pH value of greater than 14, or a negative reading) and inaccurate sample information (e.g. units of mg/L for specific conductivity data) are common errors that can be easily noted by a visual scan. A major source of transcription errors is during data entry or when converting data from one format to another [5, 6]. This is common when data is transferred from a manually recorded spreadsheet to a computer oriented format. The incorrect positioning of a decimal point during data entry is also a common transcription error [7, 8].
\nA report by [7] suggested that transcription errors can be reduced by minimising the number of times that data is copied before a final report is compiled. [9] recommended the read-aloud technique as an effective way of reducing transcription errors. Data is printed and read-aloud by one individual, while the second individual simultaneously compares the spoken values with the ones on the original sheet. Even though the double data-entry method has been described as an effective method of reducing transcription errors, its main limitation is of being laborious [9-11]. [12], however, recommended slow and careful entry of results as an effective approach of reducing transcription errors.
\nWhile it might be easy to detect some of the erroneous values by a general visual scan, more subtle errors, for example outliers, may only be ascertained by statistical methods [13]. Censored values, missing values, seasonality, serial correlation and outliers are common characteristics in data-sets that need identification and treatment [14]. The following sections review the common characteristics in water quality data namely; outliers, missing values and censored values. Methods of their identification and treatment are discussed.
\nThe presence of values that are far smaller or larger than the usual results is a common feature of water quality data. An outlier is defined as a value that has a low probability of originating from the same statistical distribution as the rest of observations in a data-set [15]. Outlying values should be examined to ascertain if they are possibly erroneous. If erroneous, the value can be discarded or corrected, where possible. Extreme values may arise from an imprecise measurement tool, sample contamination, incorrect laboratory analysis technique, mistakes made during data transfer, incorrect statistical distribution assumption or a novel phenomenon, [15, 16]. Since many ecological phenomena (e.g., floods, storms) are known to produce extreme values, their removal assumes that the phenomenon did not occur when actually it did. A decision must thus be made as to whether an outlying datum is an occasional value and an appropriate member of the data-set or whether it should be amended, or excluded from subsequent statistical analyses as it might introduce bias [1].
\nAn outlying value should only be objectively rejected as erroneous after a statistical test indicates that it is not real or when it is desired to make the statistical testing more sensitive [17]. In figure 1, for example, simple inspection might mean that the two spikes are erroneous, but in-depth analysis might correlate the spikes to very poor water quality for those two days, which would make the two observations valid. The model, however, does not pick the extreme values, which negatively affects the R2 value, and ultimately the accuracy and usefulness of the model in predicting polymer dosage.
\nData inspection during validation and treatment
Both observational (graphical) and statistical techniques have been applied to identify outliers. Among the common observational methods are the box-plots, time series, histogram, ranked data plots and normal probability plots [18, 19]. These methods basically detect an outlier value by quantifying how far it lies from the other values. This could be the difference between the outlier and the mean of all points, between the outlier and the next closest value or between the outlier and the mean of the remaining values [20].
\nThe box-plot, a graphical representation of data dispersion, is considered to be a simple observation method for screening outliers. It has been recommended as a primary exploratory tool of identifying outlying values in large data-sets (15). Since the technique basically uses the median value and not the mean, it poses a greater advantage by allowing data analysis disregarding its distribution. [21] and [22] categorised potential outliers using the box-plot as:
\ndata points between 1.5 and 3 times the Inter Quantile Range (IQR) above the 75th percentile or between 1.5 and 3 times the IQR below the 25th percentile, and
data points that exceed 3 times the IQR above the 75th percentile or exceed 3 times the IQR below the 25th percentile.
The limitation of a box plot is that it is basically a descriptive method that does not allow for hypothesis testing, and thus cannot determine the significance of a potential outlier [15].
\nThe probability plot method identifies outliers as values that do not closely fit a normal distribution curve. The points located along the probability plot line represent ‘normal’,observation, while those at the upper or lower extreme of the line, indicates the suspected outliers as depicted in Figure 2.
\nNormal probability plot showing outliers
The approach assumes that if an extreme value is removed, the resulting population becomes normally distributed [21]. If, however, the data still does not appear normally distributed after the removal of outlying values, a researcher might have to consider normalising it by transformation techniques, such as using logarithms [21, 23]. However, it should be highlighted that data transformation tends to shrink large values (see the two extreme values in Figure 1, before transformation), thus suppressing their effect which might be of interest for further analysis [23, 24]. Data should thus not be simply transformed for the sole purpose of eliminating or reducing the impact of outliers. Furthermore, since some data transformation techniques require non-negative values only (e.g. square root function) and a value greater than zero (e.g. logarithm function), transformation should not be considered as an automatic way of reducing the effect of outliers [23].
\nSince observational methods might fail to identify some of the subtle outliers, statistical tests may be performed to identify a data point as an outlier. However a decision still has to be made on whether to exclude or retain an outlying data point. The section below describes the common statistical test for identifying outliers.
\nThe Grubb’s test, also known as the Studentised Deviate test, compares outlying data points with the average and standard deviation of a data-set [25-27]. Before applying the Grubbs test, one should firstly verify that the data can be reasonably approximated by a normal distribution. The test detects and removes one outlier at a time until all are removed. The test is two sided as shown in the two equations below.
\nTo test whether the maximum value is an outlier, the test:
To test whether the minimum value is an outlier, the test is:
Where \n
s=standard deviation of the whole data set
\nX=mean
\nThe main limitation of Grubbs test is of being invalid when data assumes non-normal distribution [28]. Multiple iterations of data also tends to change the probabilities of detection. Grubbs test is only recommended for sample sizes of not more than six, since it frequently tags most of the points as outliers. It suffers from masking, which is failure to identify more than one outlier in a data-set [28, 29]. For instance, for a data-set consisting of the following points; 3, 5, 7, 13, 15, 150, 153, the identification of 153 (maximum value) as an outlier might fail because it is not extreme with respect to the next highest value (150). However, it is clear that both values (150 and 153) are much higher than the rest of the data-set and could jointly be considered as outliers.
\nDixon’s test is considered an effective technique of identifying an outlier in a data-set containing not more than 25 values [21, 30]. It is based on the ratio of the ranges of a potential outlier to the range of the whole data set as shown in equation 1 [31]. The observations are arranged in ascending order and if the distance between the potential outlier to its nearest value (Qgap) is large enough, relative to the range of all values (Qrange), the value is considered an outlier.
\nThe calculated Qexp value is then compared to a critical Q-value (Qcrit) found in tables. If Qexp is greater than the suspect value, the suspected value can be characterised as an outlier. Since the Dixon test is based on ordered statistics, it tends to counter-act the normality assumption [15]. The test assumes that if the suspected outlier is removed, the data becomes normally distributed. However, Dixon’s test also suffers the masking effect when the population contains more than one outlier.
\n[32] recommended the use of multivariate techniques like Jackknife distance and Mahalanobis distance [33, 34]. The strength of multivariate methods is on their ability to incorporation of the correlation or covariance between variables thus making them more correct as compared to univariate methods. [34] introduced the chi-square plot, which draws the empirical distribution function of the robust Mahalanobis distances against the chi-square distribution. A value that is out of distribution tail indicates that it is an outlier [33].
\nFor an on-going study, an outlier can be ascertained by re-analysis of the sample, if still available and valid. [28] and [2] advised the practise of triplicate sampling as an effective method of verifying the unexpected results. When conducting a long-term study, researchers might consider re-sampling when almost similar conditions prevail again. Nevertheless, this option might not be feasible when carrying out a retrospective study since it generally depend on secondary data from past events.
\nFor data intended for trend analysis, studies have recommended the application of nonparametric techniques such as the Seasonal Kendal test where transformation techniques do not yield symmetric data [19]. Should a parametric test be preferred on a data-set that includes outliers, practitioners may evaluate the influence of outliers by performing the test twice, once using the full data-set (including the outliers) and again on the reduced data-set (excluding the outliers). If the conclusions are essentially the same, then the suspect datum may be retained, failing which a nonparametric test is recommended.
\nWhile most statistical methods presumes a complete data-set for analysis, missing values are frequently encountered problems in water quality studies [35, 36]. Handling missing values can be a challenge as it requires a careful examination of the data to identify the type and pattern of missingness, and also have a clear understanding of the most appropriate imputation method. Gaps in water quality data-sets may arise due to several reasons, among which are imperfect data entry, equipment error, loss of sample before analysis and incorrect measurements [37]. Missing values complicate data analysis, cause loss of statistical efficiency and reduces statistical estimation power [37-39]. For data intended for time-series analysis and model building, gaps become a significant obstacle since both generally require continuous data [40, 41]. Any estimation of missing values should be done in a manner that minimise the introduction of more bias in order to preserve the structure of original data-set [41, 42].
\nThe best way to estimate missing values is to repeat the experiment and produce a complete data-set. This option is however, not feasible when conducting a retrospective study since it depend on historical data. Where it is not possible to re-sample, a model or non-model techniques may be applied to estimate missing values [43].
\nIf the proportion of missing values is relatively small, listwise deletion has been recommended. This approach, which is considered the easiest and simplest, discards the entire case where any of the variables are missing. Its major advantage is that it produce a complete data-set, which in turn allows for the use of standard analysis techniques [44]. The method also does not require special computational techniques. However, as the proportion of missing data increases, deletion tends to introduce biasness and inaccuracies in subsequent analyses. This tends to reduce the power of significance test and is more pronounced particularly if the pattern of missing data is not completely random. Furthermore, listwise deletion also decreases the sample size which tends to reduce the ability to detect a true association. For example, suppose a data-set with 1,000 samples and 20 variables and each of the variables has missing data for 5% of the cases, then, one could expect to have complete data for only about 360 individuals, thus discarding the other 640.
\nOn the other hand, pairwise deletion removes incomplete cases on an analysis-by-analysis basis, such that any given case may contribute to some analyses but not to others [44]. This approach is considered an improvement over listwise deletion because it minimises the number of cases discarded in any given analysis. However, it also tend to produce bias if the data is not completely random.
\nSeveral studies have applied imputation techniques to estimate missing values. A common assumption with these methods is that data should be missing randomly [45]. The most common and easiest imputation technique is replacing the missing values with an arithmetic mean for the rest of the data [35, 41]. This is recommended where the frequency distribution of a variable is reasonably symmetric, or has been made so by data transformation methods. The advantage of arithmetic mean imputation is generation of unbiased estimates if the data is completely random because the mean lands on the regression line. Even though the insertion of mean value does not add information, it tends to improve subsequent analysis. However, while simple to execute, this method does not take into consideration the subjects patterns of scores across all the other variables. It changes the distribution of the original data by narrowing the variance [46]. If the data assumes an asymmetric distribution, the median has been recommended as a more representative estimate of the central tendency and should be used instead of the mean.
\n[47], recommended model-based substitution techniques as more flexible and less ad hoc approach of estimating missing values as compared to non-model methods. A simple modelling technique is to regress the previous observations into an equation which estimates missing values [35, 48]. The time-series auto-regressive model has been described as an improvement and more accurate method of estimating missing values [25]. Unlike the arithmetic mean and median replacement methods, regression imputation techniques estimates missing values of a given variable using data of other parameters. This tends to reduce the variance problem, which is common with the arithmetic mean imputation and median replacement methods [41, 49].
\nOn the other hand, the maximum likelihood technique uses all the available complete and incomplete data to identify the parameter values that have the highest probability of producing the sample data [44]. It runs a series of data iterations by replacing different values for the unknown parameters and converges to a single set of parameters with the highest probability of matching the observed data [41]. The method has been recommended as it tends to give efficient estimates with correct standard errors. However, just like other imputation methods, the maximum likelihood estimates can be heavily biased if the sample size is small. In addition, the technique requires a specialised software which may be expensive, challenging to use and time consuming.
\nSome studies have considered the relationship between parameters as an effective approach of estimating missing values [50]. For instance, missing conductivity values can be calculated from the total dissolved solids value (TDS) by a simple linear regression where p-value and r-value are known to exist and the missing value lies between the two variables. Equation 2, where
The constant,
As of late, research has explored the application of artificial intelligence (AI) techniques to handle missing values in the water quality sector. Among the major AI techniques that have been applied is the Artificial Neural Networks (ANN) and Hybrid Evolutionary Algorithms (HEA) (48, 53, 54). Nevertheless, it should also be highlighted that all techniques for estimating missing values invariably affect the results. This is more pronounced when missing values characterise a significant proportion of the data being analysed. A research should thus consider the sample size when choosing the most appropriate imputation method.
\nThe integrity of water quality data can also be assessed by checking whether the results are inline with known scientific facts. To ascertain that, a researcher must have some scientific knowledge regarding the characteristics of water quality variables. Below are some scientific facts that can be used to assess data integrity [1].
\nPresence of nitrate in the absence of dissolved oxygen may indicate an error since nitrate is rapidly reduced in the absence of oxygen. The dissolved oxygen meter might have malfunctioned or oxygen might have escaped from the sample before analysis.
Component parts of a water-quality variable must not be greater than the total variable. For example:
Total phosphorus ≥Total dissolved phosphorus>Ortho-phosphate.
Total Kjeldahl nitrogen ≥Total dissolved Kjeldahl nitrogen>ammonia.
Total organic carbon ≥Dissolved organic carbon.
Species in a water body should be described correctly with regards to original pH of the water sample. For example, carbonate species will normally exist as HCO3\n -while CO3\n 2-cannot co-exist with H2CO3.\n
A common problem faced by researchers analysing environmental data is the presence of observations reported to have non-detectable levels of a contaminant. Data which are either less than the lower detection limit, or greater than the upper detection limit of the analytical method applied are normally artificially curtailed at the end of a distribution, and are termed “censored values” [14]. Multiple censored results may be recorded when the laboratory has changed levels of detection, possibly as a result of an instrument having gained more accuracy, or the laboratory protocol having established new limits. If the values are below the detection limit, they are abbreviated as BDL, and when above the limit, as ADL [55, 56].
\nVarious methods of treating censored values have been developed to reduce the complication generally brought about by censored values [57]. The application of an incorrect method may introduce bias especially when estimating the mean and variance of data distribution [58]. This may consequently distort the regression coefficients and their standard errors, and further reduce the hypothesis testing power. A researcher must thus decide on the most appropriate method to analyse censored values. One might reason that since these values are extra ordinarly small, they are not important and discard them while some might be tempted to remove them inorder to ease statistical analysis. Deletion has however been described as the worst practise as it tends to introduce a strong upward bias of the central tendency which lead to inaccurate interpretation of data [19, 59-62].
\nThe relatively easiest and most common method of handling censored values is to replace them with a real number value so that they conform to the rest of data. The United State Environmental Agency suggested substitution if censored data is less than 15% of the total data-set (63, 64). [8], BDL, for example x < 1.1, were multiplied by the factor 0.75 to give 0.825. ADL values, for example 500 < x, were recorded as one magnitude higher than the limit values to give 501. [65] recommended substituting with \n
A second approach for handling censored values is the maximum likelihood estimation (MLE). It is recommended for a large data-set which assumes normality and contains censored results [38, 65, 70, 71]. This approach basically uses the statistical properties of non-censored portion of the data-set, and an iterative process to determine the means and variance. The MLE technique generates an equation that calculates mean and standard deviation from values assumed to represent both the detects and non-detect results [69]. The equation can be used to estimate values that can replace censored values. However, the technique is reportedly ineffective for a small data-set that has fewer than 50 BDLs [69].
\nWhen data assumes an independent distribution and contain censored values, non-parametric methods like the Kaplan-Meir method, can be considered for analysis [59]. The Kaplan-Meir method creates an estimate of the population mean and standard deviation, which is adjusted for data censoring, based on the fitted distribution model. Just like any non-parametric techniques for analysing censored data, the Kaplan-Meier is only applicable to right-censored results (i.e. greater than) [72]. To use Kaplan-Meier on left-censored values, the censored values must be converted to right-censored by flipping them over to the largest observed value [65, 71, 72]. To ease the process, [73] have developed a computer program that does the conversion. [71], however, found the Kaplan-Meier method to be effective when summarising a data-set containing up to 70% of censored results.
\nIn between the parametric and non-parametric methods is a robust technique called Regression on Order Statistics (ROS) [38]. It treats BDLs based on the probability plot of detects. The technique is applicable where the response variable (concentration) is a linear function of the explanatory variable (the normal quartiles) and if the error variance of the model is constant. It also assumes that all censoring thresholds are left-censored and is effective for a data-set which contains up to 80% censored values [59]. The ROS technique uses data plots on a modelling distribution to predict censored values. [59] and [68] evaluated ROS as a reliable method for summarising multiply censored data. Helsel and Cohn (38) also described ROS as a better estimator of the mean and standard deviation as compared to MLE, when the sample size is less than 50 and contains censored values.
\nThe success of an analysis of water quality data primarily depends on the selection of the right statistical method which considers common data characteristics such as normality, seasonality, outliers, missing values, censoring, etc., [74]. If the data assumes an understandable and describable distribution, parametric methods can be used [14]. However, non-parametric techniques are slowly replacing parametric techniques mainly because the latter are sensitive to common water characteristics like outliers, missing values and censored value [75].
\nThe increase in various computer programs has made it easy to detect and treat erroneous data. Computers now provide flexibility and speedy methods of data analysis, tabulation, graph preparation or running models, among others. Various software such as Microsoft Excel, Minitab, Stata and MATLAB have become indispensable tools for analysing environmental data. These software perform various computations associated with checking assumptions about statistical distributions, error detection and their treatment. However, the major problem encountered by researchers, is lack of guidance regarding selection of the most appropriate software. Computer-aided statistical analysis should be undertaken with some understanding of the techniques being used. For example, some statistical software packages might replace missing values with the means of the variable, or prompt the user for case-wise deletion of analytical data, both of which might be considered undesirable [52].
\nLately, machine learning algorithms like the artificial neural networks (ANNs) [67, 76-78], and genetic algorithms (GA) [76, 79] have gained momentum in water quality monitoring studies. [41] pointed out that these technique generally yields the best parameter estimates in the data set with the least amount of missing data. Nevertheless, as the percentage of missing data increases, the performance of ANN which is generally measured by the errors in the parameter estimates, decreases and may reach performance levels similar to those obtained by the general substitution methods. However, in all cases the effectiveness of these methods lies on the user’s ability to manipulate and display data correctly.
\nThis chapter discussed the common data characteristics which tend to affect statistical analysis. It is recommended that practitioners should explore for outliers, missing values and censored values in a data-set before undertaking in-depth analysis. Although an analyst might not be able to establish the causal of such characteristic, eliminate or overcome some of the errors, having knowledge of their existence assists in establishing some level of confidence in drawing meaningful conclusions. It is recommended that water quality monitoring programs should strive to collect data of high quality. Common methods of ascertaining data quality are practising duplicate samples, using blanks or reference samples, and running performance audits. If a researcher is not sure of how to treat a characteristic of interest, a non-parametric method like Seasonal Kendal test could provide a better alternative since it is insensitive to common water quality data characteristics like outliers.
\nNowadays, residues are wrongfully disposed of and underutilized, becoming an increasingly alarming problem for the environment and the population’s well-being. One of the primary sources of waste is the food industry. It is estimated that about 1600 Mton of food residues are produced annually, and about 500 Mton are entirely derived from fruits [1]. The consumption of natural fruit juices has been increasing recently, mainly due to health concerns in the population. A shift toward a healthier and more natural lifestyle implies a reduction in the intake of soft drinks that could contain a high concentration of sugars, artificial colorants, and artificial sweeteners with possible adverse effects on the human body [2]. Orange juice holds most of the market share due to its vitamin content and general health benefits. As with other citrus fruits, the majority of the fruit is discarded during the juice-making process. The residues include peels, seeds, and remnant pulp, which represent almost 50% of the total weight of the fruit [3].
Over the years, research has been made to develop ways to use organic waste as a source of chemical substances and energy. There are many studies regarding the obtention of multiple products from citrus peels [1, 4, 5, 6]. Some of these added-value products include pectin, essential oils, bioethanol, biogas, and polyphenolic compounds. These products can serve as feedstocks for other industrial processes or as final products by themselves, so the possibilities for selling them are very extensive.
Nonetheless, pectin has been one of the main chemical substances retrieved from citrus residues with organoleptic characteristics that depend highly on the processing steps and conditions used for its production. Moreover, due to the multiple value-added products obtained from citrus residues, it is appealing to investigate the possibility of integrating all these processes under the biorefinery concept, which encompasses a series of steps aimed to transform, refine, purify, or separate different kinds of biological assets into other products [7].
This chapter intends to compile relevant information regarding the production of pectin from citrus residues and thus, determine the most efficient methods that result in the best quality and yields of the final product. Using information collected in the last ten years and reported in relevant scientific databases (Scopus, Springer Link, Wiley, Taylor & Francis, and ACS), a description of the processing alternatives for pectin production was made. Additionally, the gathered information was used to propose the most convenient alternatives and process conditions for its obtention. Finally, the possibility of integrating pectin production into a whole citrus residues biorefinery was discussed, including novel valorization pathways that could increase the process’s economic, environmental, and social sustainability.
In the last few years, studies on developing new routes for utilizing organic citrus residues have mainly focused on pectin production. Pectin is primarily found as a component of the cell wall of plants that gives them resistance and flexibility due to its content of galacturonic acid, partially esterified with methyl ester or acetyl groups [8]. In general, the process begins by collecting citrus residues. The raw material is then washed, dried, and grinded before bioactive compound extraction. During the extraction of bio-compounds, essential oils, polyphenols, and flavonoids are removed to improve pectin’s quality. After this step, pectin is retrieved from biomass by breaking down the polymer and “dissolving it” into the liquid phase. The solid phase residue contains other structural carbohydrates that could be further valorized. The liquid, rich in galacturonic acid units, is then submitted to a separation step (“precipitation”), where it is washed with alcohols or organic solvents that cause pectin to agglomerate. These solvents also eliminate remnant bioactive compounds that can alter the final pectin’s organoleptic properties. Finally, solvents are evaporated from the jellified pectin to obtain the product of interest. Figure 1 shows a diagram representing each one of the processing steps to obtain pectin.
Process block diagram representing the unit operations to produce pectin from orange residues.
As seen in Figure 1, the process begins by washing the material to eliminate excess dirt. After that, citrus residues are prepared for further processing by drying, which guarantees their storage for long periods. The material’s drying process is usually carried out at temperatures around 40–60°C and drying times up to 2 days. However, the highest drying temperature reported is 95°C [9], which reduces the drying time but could cause the degradation of bioactive compounds. Also, it is desired to achieve low humidity (approximately 10%) as a way to extend the storage time of the raw material and to achieve a small particle size (< 1 mm) that generates a higher contact surface and a better performance during extraction [9].
It is important to remove certain bioactive compounds such as essential oils and flavonoids, besides some sugars interfering with the pectin’s final quality. The purpose of removing these compounds is to improve pectin’s esterification degree, galacturonic acid content and guarantee its physicochemical characteristics. At this stage, the principal compound of interest is the essential oil coming from the flavedo of the citrus peel. The essential oils from citrus fruits are conformed mostly by terpenes, which are organic substances responsible for the vegetal material’s organoleptic properties. With terpenes removal, unpleasant flavors are avoided, which improves the quality of the final product [10].
Multiple methods such as vapor explosion, hydrodistillation, steam distillation, and in some cases solvent extraction can be implemented to perform essential oil extraction. The most common method used is steam distillation. In this method, the organic material is placed in a container where steam can pass and reach the sample uniformly. On the other hand, hydrodistillation works by placing the residue in direct contact with boiling water. The essential oils are retrieved once the water vapor rich in terpenes and terpenoids is condensed in both cases. Nonetheless, hydrodistillation can present agglomerations due to the direct contact of the submerged material with the liquid, which interferes with steam access to specific system zones. Another extraction method is Solid–Liquid Extraction, which can be done with various polar and non-polar solvents to retrieve the bioactive compounds selectively. However, SLE can also be assisted by heat, agitation, ultrasound, or microwaves, increasing the yields of the desired compounds.
In Figures 2 and 3, the yields of essential oils and the limonene content reported using different extraction methods for orange residues are shown in relationship with the pectin process. In Figure 2, the highest essential oil yields were obtained using Solid–Liquid Extraction with acetone (~2.2%) [1]. Nonetheless, the Solid–Liquid Extraction with acetone would require further separation of the polar and non-polar compounds due to the polarity of the solvent. For steam distillation, yields of 0.7% [11] and 0.84% [9] were obtained, which are slightly lower than those obtained by Hilali et al. with hydrodistillation and solar hydrodistillation ~1% [12]. Differences observed in yields for steam distillation could be attributed to the distribution of the sample in the system and how steam interacts with the residue. It is possible to increase steam distillation yields by increasing the pressure in the system (steam explosion) or performing double hydrodistillation [10, 13]. In the case of hydrodistillation, similar yields were obtained independently if the process is carried out with solar energy or not. As seen in Figure 3, the limonene content in the essential oils of orange residues is between 90% and 95% [9, 10, 12].
Essential oil recovery.
Content of limonene in the essential oils extracted.
Once essential oils and other bioactive compounds are removed, the extraction of pectin can be carried out. The first option is to use the liquid phase from hydrodistillation, rich in pectic substances released during heating in direct contact with water. Since pectin is heat-sensible and water-soluble, this option is attractive to perform both essential oils removal and pectin extraction. Hilali et al. reported a yield of ~12% for conventional hydrodistillation and ~ 8.3% for solar hydrodistillation [12]. Even though similar yields were obtained for essential oils using hydrodistillation, the way the heat is applied to the system may affect how much of the pectin is dissolved, resulting in lower yields. Similar behavior can be observed when pectin is retrieved from microwave-assisted hydrodistillation, with yields of around 15% [14].
The most common way to extract pectin from citrus residues is to employ acid hydrolysis, which consists of breaking down the bonds of pectin to obtain galacturonic acid units at high temperatures (from 80–116°C) and low pH values (1–3) with the help of dilute inorganic or organic acids. These hydrolysis reactions can also be assisted by agitation, which enhances the rate of depolymerization of pectin. Figure 4 shows the best yields reported in recent literature for the pectin extraction process using different acids and processing conditions. From the inorganic acids in Figure 4, the highest yields were obtained using sulfuric acid (30.5%) [15], phosphoric acid (29.4%) [16], and hydrochloric acid with (~25%) [11]. It is important to note that the hydrolysis performed with sulfuric acid was completed at shorter times and higher temperatures (10 min and 116°C) [15] than the ones done with phosphoric acid (120 min, and 95°C) [16].
Yield of pectin obtained from acid hydrolysis of citrus residues (Orange peel, *lemon peel, **lime peel) using sulfuric acid, phosphoric acid, hydrochloric acid, and citric acid.
Moreover, the similar yields of pectin obtained from citrus residues using hydrochloric acid with different processing times [11, 17, 18] allow us to hypothesize that longer times could only cause a slight increase in the yield of pectin when temperatures are higher than 95°C at low pH values (1.6–1.8). On the contrary, lower temperatures (around 80°C) with hydrochloric acid reduce pectin yields. As seen in Figure 4, pectin yields decreased down to 16–20% [1, 19]. On the other hand, the hydrolysis of citrus residues using organic acids is mainly done with citric acid. The highest pectin yield reported using citric acid is 32.6% (160 min, at 90°C, and pH 2) [11], attributed to the long hydrolysis time. In Figure 4, it is possible to see that a short time of hydrolysis with citric acid results in lower yields. Once again, the use of temperatures around 80°C decreases pectin yields considerably, a behavior that was also observed when using inorganic acids. In the work of Rodsamran et al., microwave-assisted acid hydrolysis of lime residues was performed, with yields of ~16% and ~ 10% of pectin, for hydrochloric acid and citric acid, respectively [18]; once again, the yields obtained with the inorganic acid resulted higher. The implementation of microwave-assisted hydrolysis has the benefit of implementing shorter process times (~5 min) but has the disadvantage of altering the final color of pectin, making it more brownish than the desired one for commercial pectin [18].
The reported data in Figure 4 shows that the use of strong acids results in a better hydrolysis performance than organic acids due to their affinity for Ca2+ ions, which are responsible for stabilizing pectin chains [18]. However, it has been evidenced that the use of strong acids could be problematic since it causes the loss of some volatile compounds, environmental impacts such as the acidification of rain and water sources [20], and the degradation of valuable remnant substances that could have been further valorized due to their over hydrolysis. Conversely, the use of citric acid may cause lower environmental impacts than those resulting from the use of inorganic acids in the process. In addition, citric acid has been reported to cause less harsh depolymerization of pectin [18]. Also, it is easier to handle its traces during food formulations in comparison to inorganic acids.
The liquid phase that results from the hydrolysis, rich in galacturonic acid, is then retrieved and mixed with alcohols such as ethanol, methanol, 1-propanol, or its isomer isopropanol to separate pectin due to its insolubility in this type of solvents [21]. Most of the authors highlight the use of ethanol, acidified ethanol, or acetone to precipitate citrus pectin. Precipitation of pectin with ethanol is mainly done at 20–25°C, leaving the samples overnight (18 - 24 h) [17, 18, 22]. Depending on the degree of purification desired, different concentrations of ethanol can be used. At least one wash with ethanol at 96% (v/v) is made after pectin extraction. What is more, there are some cases in which the sample is washed three times or more with ethanol at different concentrations (50%, 70%, and 96%), not only to separate pectin but also to remove sugars, polyphenols, and essential oils that remain [1, 8, 9, 10, 16, 17, 22, 23]. The removal of these undesired substances helps to obtain pectin in its whitened form. In addition, ethanol could be ideal since it avoids the precipitation of other non-desired compounds [24] and can absorb water from the pectin. Ethanol could also be beneficial for the process since it can be further recovered and reused.
Moreover, since pectin requires acidic conditions for its precipitation, it is necessary to use acidified ethanol (0.5% HCl) when pectin is obtained from hot water extraction [10], as happens when doing hydrodistillation. It is also possible to remove other remnant substances from pectin and increase the organoleptic characteristic of the final product by using a final wash with acetone. For example, Rodsamran et al. used three ethanol washes and a final acetone wash to guarantee almost a complete removal of bioactive compounds and increase the purity of pectin [18].
At this point, some authors report the use of centrifugation to facilitate the separation of pectin from the solvents once they had made effect. Centrifugation has been carried out at low temperatures (4–10°C) using speeds from 4000 rpm to 9000 rpm in a time range of 10 to 20 min [9, 11, 12, 13, 22, 23]. After pectin is fully separated, it can be dried at low temperatures that guarantee the thermal stability of the polymer. It is possible to used use vacuum drying at 40°C for short periods of time (1-2 h) [1, 11, 16, 19] or convection drying at 50–55°C for 16 to 24 h [8, 9, 12, 15, 17, 18, 22, 23, 25]. It is important to highlight that pectin yields are primarily affected by other process stages, not by the drying step. However, to guarantee pectin’s quality, it is recommended to avoid the exposure of the material to high temperatures for long periods.
To evaluate the final quality of the obtained pectin after purification, the galacturonic acid content and the degree of esterification are the two main characteristics that should always be considered. The galacturonic acid content reveals how much of the retrieved sample contains the primary units to form the polymer. The degree of esterification describes how many carboxyl groups of the galacturonic acid in pectin are esterified with methanol which influences the gelling capacity of pectin. Consequently, both properties help to define the most suitable applications for the extracted pectin.
As can be seen in Figures 5 and 6, the highest content of galacturonic acid (~90%) and esterification degree (71–85.6%) was reported by Rodsamran et al. using hydrochloric acid and citric acid in the hydrolysis of lime peels [18]. The standalone result for the esterification degree of orange pectin obtained with phosphoric acid is also high (83.6%) [16] and suggests the necessity of further investigation of the use of this acid in the process. In orange peels, even though broad ranges of galacturonic acid content (50–75%) were reported for hydrochloric acid and citric acid, the esterification degree reported maintained a value around 65–70%. The low galacturonic acid content reported in some cases could be attributed to how the sample was washed to remove remnant phytochemicals and sugars and to the prolonged effect of temperature at low pH values. The decrease in the pH at high temperatures over long periods causes an increment in the degree of dissociation of the carboxylic acid groups [24], leading to the degradation of pectin into substances of lower molecular weight, which ethanol cannot precipitate [26].
Galacturonic acid content of pectin obtained from acid hydrolysis of citrus residues (Orange peel and *lime peel) using hydrochloric acid and citric acid.
Degree of esterification of pectin obtained from acid hydrolysis of citrus residues (Orange peel and *lime peel) using phosphoric acid, hydrochloric acid, and citric acid.
It is possible to infer that orange pectin would have similar gelling properties no matter if it were obtained using either citric acid or hydrochloric acid at different process conditions. Since the galacturonic acid content reported in Figure 5 is always higher than 50% and the esterification degree higher than 65%, it is possible to say that the obtained citrus pectin can be considered as high-methoxyl pectin [27, 28]. This kind of pectin forms its structure based on hydrogen bonds between hydroxyl groups, where sugars, thanks to their highly hydrophilic effect, allow the bonding between polymer chains. High-methoxyl pectin can achieve jellification in few minutes at temperatures around 95°C, suggesting the possibility of using citrus pectin in various food products. On the contrary, low-methoxyl pectin requires metallic cations (Ca2+ or Mg2+) that bond between themselves and the anionic structure of pectin to form gels due to its low degree of esterification [14].
Figure 7 shows a process diagram that suggests the most appropriate process conditions to obtain citrus pectin. In the first place, the raw material must be adequately dried to assure its preservation and milled to increase the contact surface which yields during essential oils extraction and hydrolysis. Secondly, steam distillation is preferable for essential oils extraction since it would selectively retrieve these valuable substances without affecting the material. Contrary to this, during hydrodistillation, the material is in direct contact with hot water, which causes its partial hydrolysis and the degradation of pectic substances, resulting in lower pectin yields; additionally, the use of hydrodistillation would require the acidification of ethanol during precipitation. Thirdly, the acid hydrolysis of pectin can be carried out either with hydrochloric acid or citric acid since the final pectin would always have high-methoxyl properties. Nonetheless, process conditions that tend to increase yields and galacturonic acid percentage should be employed. It is necessary to perform a careful separation and purification during the final steps to assure high yields and purity of pectin. The last stage of pectin production will always require ethanol at 96% (v/v) for its precipitation and several washes with ethanol and acetone that remove sugars and bioactive compounds. After that, centrifugation is used to assure proper separation from the solvent (that can be later evaporated and reused) and vacuum drying to avoid the degradation of the final product. It is important to highlight that it is possible to obtain additional valuable products from the bioactive compounds extracted through steam distillation and the solids retrieved after hydrolysis rich in lignocellulose.
The production process of citrus pectin and suggested operational conditions.
It is useful to study how different processes can be integrated with the existent pectin production process under the biorefinery concept to improve the integral sustainability of the valorization of citrus residues. This means that the sustainable use of citrus residues implies the maximization of possible products and energy obtained from this feedstock. For that, it is crucial to consider a logical order in which the different compounds are extracted or produced, as the presence of some of them can impact the quality of other compounds later in the process, which relates to the concept of biomass cascading applied to the biorefinery design process [29, 30]. Additionally, other reagents used along the steps should be carefully selected and studied as they may impact the desired product itself, cause environmental issues, or affect the economic viability of the whole process. Finally, the technical aspects of each step should always be considered to guarantee the quality and yield of the different products.
In this context, citrus residues constitute the primary raw material derived from biomass, and the different processes discussed earlier help to separate it and transform said reagents into chemical substances that can be used as final bioproducts. Nonetheless, there are opportunities to produce more value-added products by integrating the pectin production process with several configurations of other technologies, which are summarized in Figure 8. For example, Hilali et al. proposed an orange peel biorefinery that obtains essential oils and pectin but extracts additional value from the solar hydrodistillation process by retrieving partially solubilized polyphenols (flavanones) such as Narirutin and Hesperidin [12]. In another work, Budarin et al. proposed the use of microwave-assisted steam distillation (using only the water present in the peel) and microwave-assisted hydrothermal treatment to obtain essential oils, pectin but also hydroxymethylfurfural and 5-chloromethyl furfural (CMF) which can be used as platform chemicals to produce herbicides, insecticides, pharmaceuticals, monomers, solvents and fuels [31]. Ortiz-Sanchez et al. proposed the anaerobic digestion of the solid residue obtained after acid hydrolysis to produce biogas with a high methane content [9], and also the use of hydrolyzed pectin in a fermentation process with fungi (
Alternatives for the integration of the pectin production process under the biorefinery concept.
The biorefinery concept can be associated with several relevant terms such as bioeconomy, circular economy, and industrial symbiosis. Many countries have started promoting policies and programs regarding the bioeconomy as a sustainable development strategy [7]. Circular economy and industrial symbiosis have also gained popularity among the policymakers and stakeholders of different companies. Generally speaking, these three concepts can be summarized as approaches that include the use of biomass-derived feedstocks obtained from various processes from different industries and that contribute to closing down the cycle of industrial processes by using one industry’s residues as the feedstocks for another. Not only the value-added products are being produced, but a significant quantity of residues could be used as raw material, a material that would typically end up in a landfill with no further treatment. With this in mind, it is clear why incorporating the processes described above under the biorefinery concept results in a relevant field of study for the valorization of citrus residues and the sustainability of pectin production.
More studies must be performed to determine the feasibility of integrating the possible biorefinery configurations shown in Figure 8, the most convenient processing scale [4], and their sustainability. It would be interesting to include not only technical but also environmental, economic, and social aspects into the evaluation of the sustainability of biorefineries from citrus residues by performing an Early-Stage assessment, a methodology that allows the evaluation of multiple biorefinery pathways without the need for vast amounts of data [35, 36, 37, 38]. However, the integrated biorefinery’s isolated technical, economic, and environmental viability analysis is not enough. It is also essential to demonstrate the sustainability of those bio-based products to promote the deployment of a circular bio-based economy [39] because using residues as feedstocks does not necessarily mean that a process is sustainable. Additionally, in terms of industrial symbiosis, several strategic alliances could be built by selling some of the obtained added-value products to companies that use them as feedstocks. For example, essential oils and polyphenols are mainly used in cosmetics, toiletries, and fragrances due to their essence and benefits for the skin. Also, the market has seen a shift toward organic and natural products, increasing the popularity of essential oils both in pure form and as additives in skin care and hair products. Other products formulated using biorefinery products are jellies, jams, and frozen foods using pectin. In addition, pectin is widely used in the pharmaceutical industry to reduce blood cholesterol levels and treat gastrointestinal disorders [40]. Other applications include paper substitutes, foams, and plasticizers. Knowing this, the potential benefits of the biorefinery increase, as it would not only align with the current strategies for developing a greener industry, but other companies would also benefit from the possible sustainable-produced chemical substances, materials, and energy derived from the pectin production process.
After studying the different options available for pectin extraction, some key findings were made. First, it is crucial to remove essential oils and bioactive compounds beforehand, as they can interfere with the yield and quality of pectin. Citrus essential oil is most commonly removed by steam distillation. However, hydrodistillation and Solid–Liquid Extraction have been shown as an alternative. One advantage of hydrodistillation is that it can also partially extract pectin while the essential oil is retrieved, thus reducing time and resources. Pectin is mainly obtained through acid hydrolysis using different solvents. Hydrochloric acid and citric acid have shown better yields than other solvents, and both result in the obtention of high-methoxyl pectin with rapid jellification. However, when considering an industrial approach, the environmental and safety hazards should be revised; because of this, citric acid represents a better option. It is essential to perform a careful separation and purification of pectin with ethanol and acetone to achieve the appropriate organoleptic properties of citrus pectin. Finally, when considering a biorefinery approach, other valorization alternatives such as the recuperation of flavonoids, the use of sugar-rich hydrolysates to produce ethanol, organic acids, and cellulose, the anaerobic digestion to produce biogas and liquid digestate, and the possibility to use citrus residues directly as fertilizers, are presented as novel possibilities to improve the pectin production process under the biorefinery concept.
Part of this research was financed by The Sustainable Development Goals (SDG) Center for Latin America and the Caribbean, based at Universidad de Los Andes (Bogotá, Colombia), in relationship with the call for the financing of teaching and research projects related to the scope of the sustainable development goals. Also, this research was also financed by the Product and Processs Design Group (PPDG) and the Research Vice-Chancellor Office of Universidad de Los Andes.
The authors declare no conflict of interest.
We believe financial barriers should not prevent researchers from publishing their findings. With the need to make scientific research more publicly available and support the benefits of Open Access, more and more institutions and funders are dedicating resources to assist faculty members and researchers cover Open Access Publishing Fees (OAPFs). In addition, IntechOpen provides several further options presented below, all of which are available to researchers, and could secure the financing of your Open Access publication.
",metaTitle:"Waiver Policy",metaDescription:"We feel that financial barriers should never prevent researchers from publishing their research. With the need to make scientific research more publically available and support the benefits of Open Access, more institutions and funders have dedicated funds to assist their faculty members and researchers cover the APCs associated with publishing in Open Access. Below we have outlined several options available to secure financing for your Open Access publication.",metaKeywords:null,canonicalURL:"/page/waiver-policy",contentRaw:'[{"type":"htmlEditorComponent","content":"At IntechOpen, the majority of OAPFs are paid by an Author’s institution or funding agency - Institutions (73%) vs. Authors (23%).
\\n\\nThe first step in obtaining funds for your Open Access publication begins with your institution or library. IntechOpen’s publishing standards align with most institutional funding programs. Our advice is to petition your institution for help in financing your Open Access publication.
\\n\\nHowever, as Open Access becomes a more commonly used publishing option for the dissemination of scientific and scholarly content, in addition to institutions, there are a growing number of funders who allow the use of grants for covering OA publication costs, or have established separate funds for the same purpose.
\\n\\nPlease consult our Open Access Funding page to explore some of these funding opportunities and learn more about how you could finance your IntechOpen publication. Keep in mind that this list is not definitive, and while we are constantly updating and informing our Authors of new funding opportunities, we recommend that you always check with your institution first.
\\n\\nFor Authors who are unable to obtain funding from their institution or research funding bodies and still need help in covering publication costs, IntechOpen offers the possibility of applying for a Waiver.
\\n\\nOur mission is to support Authors in publishing their research and making an impact within the scientific community. Currently, 14% of Authors receive full waivers and 6% receive partial waivers.
\\n\\nWhile providing support and advice to all our international Authors, waiver priority will be given to those Authors who reside in countries that are classified by the World Bank as low-income economies. In this way, we can help ensure that the scientific work being carried out can make an impact within the worldwide scientific community, no matter where an Author might live.
\\n\\nThe application process is open after your submitted manuscript has been accepted for publication. To apply, please fill out a Waiver Request Form and send it to your Author Service Manager. If you have an official letter from your university or institution showing that funds for your OA publication are unavailable, please attach that as well. The Waiver Request will normally be addressed within one week from the application date. All chapters that receive waivers or partial waivers will be designated as such online.
\\n\\nDownload Waiver Request Form
\\n\\nFeel free to contact us at funders@intechopen.com if you have any questions about Funding options or our Waiver program. If you have already begun the process and require further assistance, please contact your Author Service Manager, who is there to assist you!
\\n\\nNote: All data represented above was collected by IntechOpen from 2013 to 2017.
\\n"}]'},components:[{type:"htmlEditorComponent",content:'At IntechOpen, the majority of OAPFs are paid by an Author’s institution or funding agency - Institutions (73%) vs. Authors (23%).
\n\nThe first step in obtaining funds for your Open Access publication begins with your institution or library. IntechOpen’s publishing standards align with most institutional funding programs. Our advice is to petition your institution for help in financing your Open Access publication.
\n\nHowever, as Open Access becomes a more commonly used publishing option for the dissemination of scientific and scholarly content, in addition to institutions, there are a growing number of funders who allow the use of grants for covering OA publication costs, or have established separate funds for the same purpose.
\n\nPlease consult our Open Access Funding page to explore some of these funding opportunities and learn more about how you could finance your IntechOpen publication. Keep in mind that this list is not definitive, and while we are constantly updating and informing our Authors of new funding opportunities, we recommend that you always check with your institution first.
\n\nFor Authors who are unable to obtain funding from their institution or research funding bodies and still need help in covering publication costs, IntechOpen offers the possibility of applying for a Waiver.
\n\nOur mission is to support Authors in publishing their research and making an impact within the scientific community. Currently, 14% of Authors receive full waivers and 6% receive partial waivers.
\n\nWhile providing support and advice to all our international Authors, waiver priority will be given to those Authors who reside in countries that are classified by the World Bank as low-income economies. In this way, we can help ensure that the scientific work being carried out can make an impact within the worldwide scientific community, no matter where an Author might live.
\n\nThe application process is open after your submitted manuscript has been accepted for publication. To apply, please fill out a Waiver Request Form and send it to your Author Service Manager. If you have an official letter from your university or institution showing that funds for your OA publication are unavailable, please attach that as well. The Waiver Request will normally be addressed within one week from the application date. All chapters that receive waivers or partial waivers will be designated as such online.
\n\nDownload Waiver Request Form
\n\nFeel free to contact us at funders@intechopen.com if you have any questions about Funding options or our Waiver program. If you have already begun the process and require further assistance, please contact your Author Service Manager, who is there to assist you!
\n\nNote: All data represented above was collected by IntechOpen from 2013 to 2017.
\n'}]},successStories:{items:[]},authorsAndEditors:{filterParams:{},profiles:[{id:"6700",title:"Dr.",name:"Abbass A.",middleName:null,surname:"Hashim",slug:"abbass-a.-hashim",fullName:"Abbass A. Hashim",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/6700/images/1864_n.jpg",biography:"Currently I am carrying out research in several areas of interest, mainly covering work on chemical and bio-sensors, semiconductor thin film device fabrication and characterisation.\nAt the moment I have very strong interest in radiation environmental pollution and bacteriology treatment. The teams of researchers are working very hard to bring novel results in this field. I am also a member of the team in charge for the supervision of Ph.D. students in the fields of development of silicon based planar waveguide sensor devices, study of inelastic electron tunnelling in planar tunnelling nanostructures for sensing applications and development of organotellurium(IV) compounds for semiconductor applications. I am a specialist in data analysis techniques and nanosurface structure. I have served as the editor for many books, been a member of the editorial board in science journals, have published many papers and hold many patents.",institutionString:null,institution:{name:"Sheffield Hallam University",country:{name:"United Kingdom"}}},{id:"54525",title:"Prof.",name:"Abdul Latif",middleName:null,surname:"Ahmad",slug:"abdul-latif-ahmad",fullName:"Abdul Latif Ahmad",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"20567",title:"Prof.",name:"Ado",middleName:null,surname:"Jorio",slug:"ado-jorio",fullName:"Ado Jorio",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Universidade Federal de Minas Gerais",country:{name:"Brazil"}}},{id:"47940",title:"Dr.",name:"Alberto",middleName:null,surname:"Mantovani",slug:"alberto-mantovani",fullName:"Alberto Mantovani",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"12392",title:"Mr.",name:"Alex",middleName:null,surname:"Lazinica",slug:"alex-lazinica",fullName:"Alex Lazinica",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/12392/images/7282_n.png",biography:"Alex Lazinica is the founder and CEO of IntechOpen. After obtaining a Master's degree in Mechanical Engineering, he continued his PhD studies in Robotics at the Vienna University of Technology. Here he worked as a robotic researcher with the university's Intelligent Manufacturing Systems Group as well as a guest researcher at various European universities, including the Swiss Federal Institute of Technology Lausanne (EPFL). During this time he published more than 20 scientific papers, gave presentations, served as a reviewer for major robotic journals and conferences and most importantly he co-founded and built the International Journal of Advanced Robotic Systems- world's first Open Access journal in the field of robotics. Starting this journal was a pivotal point in his career, since it was a pathway to founding IntechOpen - Open Access publisher focused on addressing academic researchers needs. Alex is a personification of IntechOpen key values being trusted, open and entrepreneurial. Today his focus is on defining the growth and development strategy for the company.",institutionString:null,institution:{name:"TU Wien",country:{name:"Austria"}}},{id:"19816",title:"Prof.",name:"Alexander",middleName:null,surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/19816/images/1607_n.jpg",biography:"Alexander I. Kokorin: born: 1947, Moscow; DSc., PhD; Principal Research Fellow (Research Professor) of Department of Kinetics and Catalysis, N. Semenov Institute of Chemical Physics, Russian Academy of Sciences, Moscow.\r\nArea of research interests: physical chemistry of complex-organized molecular and nanosized systems, including polymer-metal complexes; the surface of doped oxide semiconductors. He is an expert in structural, absorptive, catalytic and photocatalytic properties, in structural organization and dynamic features of ionic liquids, in magnetic interactions between paramagnetic centers. The author or co-author of 3 books, over 200 articles and reviews in scientific journals and books. He is an actual member of the International EPR/ESR Society, European Society on Quantum Solar Energy Conversion, Moscow House of Scientists, of the Board of Moscow Physical Society.",institutionString:null,institution:{name:"Semenov Institute of Chemical Physics",country:{name:"Russia"}}},{id:"62389",title:"PhD.",name:"Ali Demir",middleName:null,surname:"Sezer",slug:"ali-demir-sezer",fullName:"Ali Demir Sezer",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/62389/images/3413_n.jpg",biography:"Dr. Ali Demir Sezer has a Ph.D. from Pharmaceutical Biotechnology at the Faculty of Pharmacy, University of Marmara (Turkey). He is the member of many Pharmaceutical Associations and acts as a reviewer of scientific journals and European projects under different research areas such as: drug delivery systems, nanotechnology and pharmaceutical biotechnology. Dr. Sezer is the author of many scientific publications in peer-reviewed journals and poster communications. Focus of his research activity is drug delivery, physico-chemical characterization and biological evaluation of biopolymers micro and nanoparticles as modified drug delivery system, and colloidal drug carriers (liposomes, nanoparticles etc.).",institutionString:null,institution:{name:"Marmara University",country:{name:"Turkey"}}},{id:"61051",title:"Prof.",name:"Andrea",middleName:null,surname:"Natale",slug:"andrea-natale",fullName:"Andrea Natale",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"100762",title:"Prof.",name:"Andrea",middleName:null,surname:"Natale",slug:"andrea-natale",fullName:"Andrea Natale",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"St David's Medical Center",country:{name:"United States of America"}}},{id:"107416",title:"Dr.",name:"Andrea",middleName:null,surname:"Natale",slug:"andrea-natale",fullName:"Andrea Natale",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Texas Cardiac Arrhythmia",country:{name:"United States of America"}}},{id:"64434",title:"Dr.",name:"Angkoon",middleName:null,surname:"Phinyomark",slug:"angkoon-phinyomark",fullName:"Angkoon Phinyomark",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/64434/images/2619_n.jpg",biography:"My name is Angkoon Phinyomark. I received a B.Eng. degree in Computer Engineering with First Class Honors in 2008 from Prince of Songkla University, Songkhla, Thailand, where I received a Ph.D. degree in Electrical Engineering. My research interests are primarily in the area of biomedical signal processing and classification notably EMG (electromyography signal), EOG (electrooculography signal), and EEG (electroencephalography signal), image analysis notably breast cancer analysis and optical coherence tomography, and rehabilitation engineering. I became a student member of IEEE in 2008. During October 2011-March 2012, I had worked at School of Computer Science and Electronic Engineering, University of Essex, Colchester, Essex, United Kingdom. In addition, during a B.Eng. I had been a visiting research student at Faculty of Computer Science, University of Murcia, Murcia, Spain for three months.\n\nI have published over 40 papers during 5 years in refereed journals, books, and conference proceedings in the areas of electro-physiological signals processing and classification, notably EMG and EOG signals, fractal analysis, wavelet analysis, texture analysis, feature extraction and machine learning algorithms, and assistive and rehabilitative devices. I have several computer programming language certificates, i.e. Sun Certified Programmer for the Java 2 Platform 1.4 (SCJP), Microsoft Certified Professional Developer, Web Developer (MCPD), Microsoft Certified Technology Specialist, .NET Framework 2.0 Web (MCTS). I am a Reviewer for several refereed journals and international conferences, such as IEEE Transactions on Biomedical Engineering, IEEE Transactions on Industrial Electronics, Optic Letters, Measurement Science Review, and also a member of the International Advisory Committee for 2012 IEEE Business Engineering and Industrial Applications and 2012 IEEE Symposium on Business, Engineering and Industrial Applications.",institutionString:null,institution:{name:"Joseph Fourier University",country:{name:"France"}}},{id:"55578",title:"Dr.",name:"Antonio",middleName:null,surname:"Jurado-Navas",slug:"antonio-jurado-navas",fullName:"Antonio Jurado-Navas",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",biography:"Antonio Jurado-Navas received the M.S. degree (2002) and the Ph.D. degree (2009) in Telecommunication Engineering, both from the University of Málaga (Spain). He first worked as a consultant at Vodafone-Spain. From 2004 to 2011, he was a Research Assistant with the Communications Engineering Department at the University of Málaga. In 2011, he became an Assistant Professor in the same department. From 2012 to 2015, he was with Ericsson Spain, where he was working on geo-location\ntools for third generation mobile networks. Since 2015, he is a Marie-Curie fellow at the Denmark Technical University. His current research interests include the areas of mobile communication systems and channel modeling in addition to atmospheric optical communications, adaptive optics and statistics",institutionString:null,institution:{name:"University of Malaga",country:{name:"Spain"}}}],filtersByRegion:[{group:"region",caption:"North America",value:1,count:6654},{group:"region",caption:"Middle and South America",value:2,count:5944},{group:"region",caption:"Africa",value:3,count:2452},{group:"region",caption:"Asia",value:4,count:12681},{group:"region",caption:"Australia and Oceania",value:5,count:1014},{group:"region",caption:"Europe",value:6,count:17700}],offset:12,limit:12,total:133951},chapterEmbeded:{data:{}},editorApplication:{success:null,errors:{}},ofsBooks:{filterParams:{hasNoEditors:"1",sort:"dateEndThirdStepPublish"},books:[{type:"book",id:"11254",title:"Optical Coherence Tomography",subtitle:null,isOpenForSubmission:!0,hash:"a958c09ceaab1fc44c1dd0a817f48c92",slug:null,bookSignature:"",coverURL:"https://cdn.intechopen.com/books/images_new/11254.jpg",editedByType:null,editors:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"11616",title:"Foraging",subtitle:null,isOpenForSubmission:!0,hash:"955b60bb658c8d1a09dd4efc9bf6674b",slug:null,bookSignature:"",coverURL:"https://cdn.intechopen.com/books/images_new/11616.jpg",editedByType:null,editors:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"11632",title:"Updated Research on Bacteriophages",subtitle:null,isOpenForSubmission:!0,hash:"d34dfa0d5d10511184f97ddaeef9936b",slug:null,bookSignature:"",coverURL:"https://cdn.intechopen.com/books/images_new/11632.jpg",editedByType:null,editors:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"11697",title:"Scoliosis",subtitle:null,isOpenForSubmission:!0,hash:"fa052443744b8f6ba5a87091e373bafe",slug:null,bookSignature:"",coverURL:"https://cdn.intechopen.com/books/images_new/11697.jpg",editedByType:null,editors:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"11699",title:"Neonatal Surgery",subtitle:null,isOpenForSubmission:!0,hash:"e52adaee8e54f51c2ba4972daeb410f7",slug:null,bookSignature:"",coverURL:"https://cdn.intechopen.com/books/images_new/11699.jpg",editedByType:null,editors:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"11730",title:"Midwifery",subtitle:null,isOpenForSubmission:!0,hash:"95389fcd878d0e929234c441744ba398",slug:null,bookSignature:"",coverURL:"https://cdn.intechopen.com/books/images_new/11730.jpg",editedByType:null,editors:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"11843",title:"Abortion Access",subtitle:null,isOpenForSubmission:!0,hash:"e07ed1706ed2bf6ad56aa7399d9edf1a",slug:null,bookSignature:"",coverURL:"https://cdn.intechopen.com/books/images_new/11843.jpg",editedByType:null,editors:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"11850",title:"Systemic Sclerosis",subtitle:null,isOpenForSubmission:!0,hash:"df3f380c5949c8d8c977631cac330f67",slug:null,bookSignature:"",coverURL:"https://cdn.intechopen.com/books/images_new/11850.jpg",editedByType:null,editors:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"11858",title:"Terahertz Radiation",subtitle:null,isOpenForSubmission:!0,hash:"f08ee0bf20cd8b5fa772b4752081f2fe",slug:null,bookSignature:"",coverURL:"https://cdn.intechopen.com/books/images_new/11858.jpg",editedByType:null,editors:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"11770",title:"Feminism",subtitle:null,isOpenForSubmission:!0,hash:"008be465c708a6fde48c8468757a40af",slug:null,bookSignature:"",coverURL:"https://cdn.intechopen.com/books/images_new/11770.jpg",editedByType:null,editors:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"11773",title:"Archaeology - Challenges and Updates",subtitle:null,isOpenForSubmission:!0,hash:"17d91462fa926279f65164ac0d5641cd",slug:null,bookSignature:"",coverURL:"https://cdn.intechopen.com/books/images_new/11773.jpg",editedByType:null,editors:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"11776",title:"Fashion Industry",subtitle:null,isOpenForSubmission:!0,hash:"e8d53d1029a7bccf825aa55d43fecc68",slug:null,bookSignature:"",coverURL:"https://cdn.intechopen.com/books/images_new/11776.jpg",editedByType:null,editors:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}],filtersByTopic:[{group:"topic",caption:"Agricultural and Biological Sciences",value:5,count:30},{group:"topic",caption:"Biochemistry, Genetics and Molecular Biology",value:6,count:7},{group:"topic",caption:"Business, Management and Economics",value:7,count:4},{group:"topic",caption:"Chemistry",value:8,count:14},{group:"topic",caption:"Computer and Information Science",value:9,count:10},{group:"topic",caption:"Earth and Planetary Sciences",value:10,count:11},{group:"topic",caption:"Engineering",value:11,count:24},{group:"topic",caption:"Environmental Sciences",value:12,count:5},{group:"topic",caption:"Immunology and Microbiology",value:13,count:7},{group:"topic",caption:"Materials Science",value:14,count:9},{group:"topic",caption:"Mathematics",value:15,count:5},{group:"topic",caption:"Medicine",value:16,count:83},{group:"topic",caption:"Neuroscience",value:18,count:5},{group:"topic",caption:"Pharmacology, Toxicology and Pharmaceutical Science",value:19,count:6},{group:"topic",caption:"Physics",value:20,count:1},{group:"topic",caption:"Psychology",value:21,count:4},{group:"topic",caption:"Robotics",value:22,count:2},{group:"topic",caption:"Social Sciences",value:23,count:25},{group:"topic",caption:"Technology",value:24,count:1}],offset:12,limit:12,total:253},popularBooks:{featuredBooks:[],offset:0,limit:12,total:null},hotBookTopics:{hotBooks:[],offset:0,limit:12,total:null},publish:{},publishingProposal:{success:null,errors:{}},books:{featuredBooks:[{type:"book",id:"10858",title:"MOOC (Massive Open Online Courses)",subtitle:null,isOpenForSubmission:!1,hash:"d32f86793bc72dde32532f509b1ec5b0",slug:"mooc-massive-open-online-courses-",bookSignature:"Dragan Cvetković",coverURL:"https://cdn.intechopen.com/books/images_new/10858.jpg",publishedDate:"June 23rd 2022",numberOfDownloads:1677,editors:[{id:"101330",title:"Dr.",name:"Dragan",middleName:"Mladen",surname:"Cvetković",slug:"dragan-cvetkovic",fullName:"Dragan Cvetković"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"10195",title:"Serotonin and the CNS",subtitle:"New Developments in Pharmacology and Therapeutics",isOpenForSubmission:!1,hash:"7ed9d96da98233a885bd2869a8056c36",slug:"serotonin-and-the-cns-new-developments-in-pharmacology-and-therapeutics",bookSignature:"Berend Olivier",coverURL:"https://cdn.intechopen.com/books/images_new/10195.jpg",publishedDate:"June 23rd 2022",numberOfDownloads:1337,editors:[{id:"71579",title:"Prof.",name:"Berend",middleName:null,surname:"Olivier",slug:"berend-olivier",fullName:"Berend Olivier"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"10755",title:"Corporate Governance",subtitle:"Recent Advances and Perspectives",isOpenForSubmission:!1,hash:"ffe06d1d5c4bf0fc2e63511825fe1257",slug:"corporate-governance-recent-advances-and-perspectives",bookSignature:"Okechukwu Lawrence Emeagwali and Feyza Bhatti",coverURL:"https://cdn.intechopen.com/books/images_new/10755.jpg",publishedDate:"June 23rd 2022",numberOfDownloads:1309,editors:[{id:"196317",title:"Associate Prof.",name:"Okechukwu Lawrence",middleName:null,surname:"Emeagwali",slug:"okechukwu-lawrence-emeagwali",fullName:"Okechukwu Lawrence Emeagwali"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"11120",title:"Environmental Impact and Remediation of Heavy Metals",subtitle:null,isOpenForSubmission:!1,hash:"9e77514288e7394f1e6cd13481af3509",slug:"environmental-impact-and-remediation-of-heavy-metals",bookSignature:"Hosam M. Saleh and Amal I. Hassan",coverURL:"https://cdn.intechopen.com/books/images_new/11120.jpg",publishedDate:"June 23rd 2022",numberOfDownloads:847,editors:[{id:"144691",title:"Prof.",name:"Hosam M.",middleName:null,surname:"Saleh",slug:"hosam-m.-saleh",fullName:"Hosam M. Saleh"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"10901",title:"Grapes and Wine",subtitle:null,isOpenForSubmission:!1,hash:"5d7f2aa74874444bc6986e613ccebd7c",slug:"grapes-and-wine",bookSignature:"Antonio Morata, Iris Loira and Carmen González",coverURL:"https://cdn.intechopen.com/books/images_new/10901.jpg",publishedDate:"June 15th 2022",numberOfDownloads:2273,editors:[{id:"180952",title:"Prof.",name:"Antonio",middleName:null,surname:"Morata",slug:"antonio-morata",fullName:"Antonio Morata"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"11080",title:"Engineering Principles",subtitle:"Welding and Residual Stresses",isOpenForSubmission:!1,hash:"6c07a13a113bce94174b40096f30fb5e",slug:"engineering-principles-welding-and-residual-stresses",bookSignature:"Kavian Omar Cooke and Ronaldo Câmara Cozza",coverURL:"https://cdn.intechopen.com/books/images_new/11080.jpg",publishedDate:"June 23rd 2022",numberOfDownloads:591,editors:[{id:"138778",title:"Dr.",name:"Kavian",middleName:"Omar",surname:"Cooke",slug:"kavian-cooke",fullName:"Kavian Cooke"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"11332",title:"Essential Oils",subtitle:"Advances in Extractions and Biological Applications",isOpenForSubmission:!1,hash:"742e6cae3a35686f975edc8d7f9afa94",slug:"essential-oils-advances-in-extractions-and-biological-applications",bookSignature:"Mozaniel Santana de Oliveira and Eloisa Helena de Aguiar Andrade",coverURL:"https://cdn.intechopen.com/books/images_new/11332.jpg",publishedDate:"June 23rd 2022",numberOfDownloads:515,editors:[{id:"195290",title:"Ph.D.",name:"Mozaniel",middleName:null,surname:"Santana De Oliveira",slug:"mozaniel-santana-de-oliveira",fullName:"Mozaniel Santana De Oliveira"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"11029",title:"Hepatitis B",subtitle:null,isOpenForSubmission:!1,hash:"609701f502efc3538c112ff47a2c2119",slug:"hepatitis-b",bookSignature:"Luis Rodrigo",coverURL:"https://cdn.intechopen.com/books/images_new/11029.jpg",publishedDate:"June 23rd 2022",numberOfDownloads:413,editors:[{id:"73208",title:"Prof.",name:"Luis",middleName:null,surname:"Rodrigo",slug:"luis-rodrigo",fullName:"Luis Rodrigo"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"9537",title:"Human Rights in the Contemporary World",subtitle:null,isOpenForSubmission:!1,hash:"54f05b93812fd434f3962956d6413a6b",slug:"human-rights-in-the-contemporary-world",bookSignature:"Trudy Corrigan",coverURL:"https://cdn.intechopen.com/books/images_new/9537.jpg",publishedDate:"June 8th 2022",numberOfDownloads:2194,editors:[{id:"197557",title:"Dr.",name:"Trudy",middleName:null,surname:"Corrigan",slug:"trudy-corrigan",fullName:"Trudy Corrigan"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"11371",title:"Cerebral Circulation",subtitle:"Updates on Models, Diagnostics and Treatments of Related Diseases",isOpenForSubmission:!1,hash:"e2d3335445d2852d0b906bb9750e939f",slug:"cerebral-circulation-updates-on-models-diagnostics-and-treatments-of-related-diseases",bookSignature:"Alba Scerrati, Luca Ricciardi and Flavia Dones",coverURL:"https://cdn.intechopen.com/books/images_new/11371.jpg",publishedDate:"June 23rd 2022",numberOfDownloads:341,editors:[{id:"182614",title:"Dr.",name:"Alba",middleName:null,surname:"Scerrati",slug:"alba-scerrati",fullName:"Alba Scerrati"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}}],latestBooks:[{type:"book",id:"10755",title:"Corporate Governance",subtitle:"Recent Advances and Perspectives",isOpenForSubmission:!1,hash:"ffe06d1d5c4bf0fc2e63511825fe1257",slug:"corporate-governance-recent-advances-and-perspectives",bookSignature:"Okechukwu Lawrence Emeagwali and Feyza Bhatti",coverURL:"https://cdn.intechopen.com/books/images_new/10755.jpg",editedByType:"Edited by",publishedDate:"June 23rd 2022",editors:[{id:"196317",title:"Associate Prof.",name:"Okechukwu Lawrence",middleName:null,surname:"Emeagwali",slug:"okechukwu-lawrence-emeagwali",fullName:"Okechukwu Lawrence Emeagwali"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"11029",title:"Hepatitis B",subtitle:null,isOpenForSubmission:!1,hash:"609701f502efc3538c112ff47a2c2119",slug:"hepatitis-b",bookSignature:"Luis Rodrigo",coverURL:"https://cdn.intechopen.com/books/images_new/11029.jpg",editedByType:"Edited by",publishedDate:"June 23rd 2022",editors:[{id:"73208",title:"Prof.",name:"Luis",middleName:null,surname:"Rodrigo",slug:"luis-rodrigo",fullName:"Luis Rodrigo"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"10774",title:"Model Organisms in Plant Genetics",subtitle:null,isOpenForSubmission:!1,hash:"f6624b58571ac10c9b636c5d85ec5e54",slug:"model-organisms-in-plant-genetics",bookSignature:"Ibrokhim Y. Abdurakhmonov",coverURL:"https://cdn.intechopen.com/books/images_new/10774.jpg",editedByType:"Edited by",publishedDate:"June 23rd 2022",editors:[{id:"213344",title:"Prof.",name:"Ibrokhim Y.",middleName:null,surname:"Abdurakhmonov",slug:"ibrokhim-y.-abdurakhmonov",fullName:"Ibrokhim Y. Abdurakhmonov"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"11332",title:"Essential Oils",subtitle:"Advances in Extractions and Biological Applications",isOpenForSubmission:!1,hash:"742e6cae3a35686f975edc8d7f9afa94",slug:"essential-oils-advances-in-extractions-and-biological-applications",bookSignature:"Mozaniel Santana de Oliveira and Eloisa Helena de Aguiar Andrade",coverURL:"https://cdn.intechopen.com/books/images_new/11332.jpg",editedByType:"Edited by",publishedDate:"June 23rd 2022",editors:[{id:"195290",title:"Ph.D.",name:"Mozaniel",middleName:null,surname:"Santana De Oliveira",slug:"mozaniel-santana-de-oliveira",fullName:"Mozaniel Santana De Oliveira"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"11080",title:"Engineering Principles",subtitle:"Welding and Residual Stresses",isOpenForSubmission:!1,hash:"6c07a13a113bce94174b40096f30fb5e",slug:"engineering-principles-welding-and-residual-stresses",bookSignature:"Kavian Omar Cooke and Ronaldo Câmara Cozza",coverURL:"https://cdn.intechopen.com/books/images_new/11080.jpg",editedByType:"Edited by",publishedDate:"June 23rd 2022",editors:[{id:"138778",title:"Dr.",name:"Kavian",middleName:"Omar",surname:"Cooke",slug:"kavian-cooke",fullName:"Kavian Cooke"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"10839",title:"Protein Detection",subtitle:null,isOpenForSubmission:!1,hash:"2f1c0e4e0207fc45c936e7d22a5369c4",slug:"protein-detection",bookSignature:"Yusuf Tutar and Lütfi Tutar",coverURL:"https://cdn.intechopen.com/books/images_new/10839.jpg",editedByType:"Edited by",publishedDate:"June 23rd 2022",editors:[{id:"158492",title:"Prof.",name:"Yusuf",middleName:null,surname:"Tutar",slug:"yusuf-tutar",fullName:"Yusuf Tutar"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"10858",title:"MOOC (Massive Open Online Courses)",subtitle:null,isOpenForSubmission:!1,hash:"d32f86793bc72dde32532f509b1ec5b0",slug:"mooc-massive-open-online-courses-",bookSignature:"Dragan Cvetković",coverURL:"https://cdn.intechopen.com/books/images_new/10858.jpg",editedByType:"Edited by",publishedDate:"June 23rd 2022",editors:[{id:"101330",title:"Dr.",name:"Dragan",middleName:"Mladen",surname:"Cvetković",slug:"dragan-cvetkovic",fullName:"Dragan Cvetković"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"11371",title:"Cerebral Circulation",subtitle:"Updates on Models, Diagnostics and Treatments of Related Diseases",isOpenForSubmission:!1,hash:"e2d3335445d2852d0b906bb9750e939f",slug:"cerebral-circulation-updates-on-models-diagnostics-and-treatments-of-related-diseases",bookSignature:"Alba Scerrati, Luca Ricciardi and Flavia Dones",coverURL:"https://cdn.intechopen.com/books/images_new/11371.jpg",editedByType:"Edited by",publishedDate:"June 23rd 2022",editors:[{id:"182614",title:"Dr.",name:"Alba",middleName:null,surname:"Scerrati",slug:"alba-scerrati",fullName:"Alba Scerrati"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"11120",title:"Environmental Impact and Remediation of Heavy Metals",subtitle:null,isOpenForSubmission:!1,hash:"9e77514288e7394f1e6cd13481af3509",slug:"environmental-impact-and-remediation-of-heavy-metals",bookSignature:"Hosam M. Saleh and Amal I. Hassan",coverURL:"https://cdn.intechopen.com/books/images_new/11120.jpg",editedByType:"Edited by",publishedDate:"June 23rd 2022",editors:[{id:"144691",title:"Prof.",name:"Hosam M.",middleName:null,surname:"Saleh",slug:"hosam-m.-saleh",fullName:"Hosam M. Saleh"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"10696",title:"Applications of Calorimetry",subtitle:null,isOpenForSubmission:!1,hash:"8c87f7e2199db33b5dd7181f56973a97",slug:"applications-of-calorimetry",bookSignature:"José Luis Rivera Armenta and Cynthia Graciela Flores Hernández",coverURL:"https://cdn.intechopen.com/books/images_new/10696.jpg",editedByType:"Edited by",publishedDate:"June 23rd 2022",editors:[{id:"107855",title:"Dr.",name:"Jose Luis",middleName:null,surname:"Rivera Armenta",slug:"jose-luis-rivera-armenta",fullName:"Jose Luis Rivera Armenta"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},subject:{topic:{id:"603",title:"Information System",slug:"numerical-analysis-and-scientific-computing-information-system",parent:{id:"95",title:"Numerical Analysis and Scientific Computing",slug:"numerical-analysis-and-scientific-computing"},numberOfBooks:1,numberOfSeries:0,numberOfAuthorsAndEditors:21,numberOfWosCitations:20,numberOfCrossrefCitations:11,numberOfDimensionsCitations:21,videoUrl:null,fallbackUrl:null,description:null},booksByTopicFilter:{topicId:"603",sort:"-publishedDate",limit:12,offset:0},booksByTopicCollection:[{type:"book",id:"3070",title:"Decision Support Systems",subtitle:null,isOpenForSubmission:!1,hash:"8a394eca4d225bb90196753ada8ff296",slug:"decision-support-systems_2012",bookSignature:"Chiang Jao",coverURL:"https://cdn.intechopen.com/books/images_new/3070.jpg",editedByType:"Edited by",editors:[{id:"5577",title:"Prof.",name:"Chiang",middleName:null,surname:"Jao",slug:"chiang-jao",fullName:"Chiang Jao"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}],booksByTopicTotal:1,seriesByTopicCollection:[],seriesByTopicTotal:0,mostCitedChapters:[{id:"40005",doi:"10.5772/51756",title:"Decision Support Systems in Medicine - Anesthesia, Critical Care and Intensive Care Medicine",slug:"decision-support-systems-in-medicine-anesthesia-critical-care-and-intensive-care-medicine",totalDownloads:3159,totalCrossrefCites:1,totalDimensionsCites:5,abstract:null,book:{id:"3070",slug:"decision-support-systems_2012",title:"Decision Support Systems",fullTitle:"Decision Support Systems"},signatures:"Thomas M. Hemmerling, Fabrizio Cirillo and Shantale Cyr",authors:[{id:"26787",title:"Prof.",name:"Thomas",middleName:"M",surname:"Hemmerling",slug:"thomas-hemmerling",fullName:"Thomas Hemmerling"}]},{id:"40007",doi:"10.5772/51306",title:"Towards Developing a Decision Support System for Electricity Load Forecast",slug:"towards-developing-a-decision-support-system-for-electricity-load-forecast",totalDownloads:2175,totalCrossrefCites:3,totalDimensionsCites:5,abstract:null,book:{id:"3070",slug:"decision-support-systems_2012",title:"Decision Support Systems",fullTitle:"Decision Support Systems"},signatures:"Connor Wright, Christine W. Chan and Paul Laforge",authors:[{id:"26220",title:"Dr.",name:"Christine",middleName:null,surname:"Chan",slug:"christine-chan",fullName:"Christine Chan"}]},{id:"40083",doi:"10.5772/51974",title:"Optimal Control of Integrated Production – Forecasting System",slug:"optimal-control-of-integrated-production-forecasting-system",totalDownloads:1989,totalCrossrefCites:3,totalDimensionsCites:4,abstract:null,book:{id:"3070",slug:"decision-support-systems_2012",title:"Decision Support Systems",fullTitle:"Decision Support Systems"},signatures:"R. Hedjar, L. Tadj and C. Abid",authors:[{id:"152482",title:"Dr.",name:"Ramdane",middleName:null,surname:"Hedjar",slug:"ramdane-hedjar",fullName:"Ramdane Hedjar"}]},{id:"37614",doi:"10.5772/50801",title:"DairyMGT: A Suite of Decision Support Systems in Dairy Farm Management",slug:"dairymgt-a-suite-of-decision-support-systems-in-dairy-farm-management",totalDownloads:3676,totalCrossrefCites:1,totalDimensionsCites:3,abstract:null,book:{id:"3070",slug:"decision-support-systems_2012",title:"Decision Support Systems",fullTitle:"Decision Support Systems"},signatures:"Victor E. Cabrera",authors:[{id:"152490",title:"Dr.",name:"Victor",middleName:"E",surname:"Cabrera",slug:"victor-cabrera",fullName:"Victor Cabrera"}]},{id:"38073",doi:"10.5772/51222",title:"Comparison of Multicriteria Analysis Techniques for Environmental Decision Making on Industrial Location",slug:"comparison-of-multicriteria-analysis-techniques-for-environmental-decision-making-on-industrial-loca",totalDownloads:2380,totalCrossrefCites:1,totalDimensionsCites:2,abstract:null,book:{id:"3070",slug:"decision-support-systems_2012",title:"Decision Support Systems",fullTitle:"Decision Support Systems"},signatures:"M.T. Lamelas, O. Marinoni, J. de la Riva and A. Hoppe",authors:[{id:"39458",title:"Dr.",name:"Juan",middleName:null,surname:"de la Riva",slug:"juan-de-la-riva",fullName:"Juan de la Riva"},{id:"152774",title:"Dr.",name:"María Teresa",middleName:null,surname:"Lamelas",slug:"maria-teresa-lamelas",fullName:"María Teresa Lamelas"},{id:"153073",title:"Dr.",name:"Oswald",middleName:null,surname:"Marinoni",slug:"oswald-marinoni",fullName:"Oswald Marinoni"},{id:"153074",title:"Prof.",name:"Andreas",middleName:null,surname:"Hoppe",slug:"andreas-hoppe",fullName:"Andreas Hoppe"}]}],mostDownloadedChaptersLast30Days:[{id:"40010",title:"Semi-Automatic Semantic Data Classification Expert System to Produce Thematic Maps",slug:"semi-automatic-semantic-data-classification-expert-system-to-produce-thematic-maps",totalDownloads:2071,totalCrossrefCites:0,totalDimensionsCites:0,abstract:null,book:{id:"3070",slug:"decision-support-systems_2012",title:"Decision Support Systems",fullTitle:"Decision Support Systems"},signatures:"Luciene Stamato Delazari, André Luiz Alencar de Mendonça, João Vitor Meza Bravo, Mônica Cristina de Castro, Pâmela Andressa Lunelli, Marcio Augusto Reolon Schmidt and Maria Engracinda dos Santos Ferreira",authors:[{id:"6016",title:"Mrs.",name:"Luciene",middleName:null,surname:"Delazari",slug:"luciene-delazari",fullName:"Luciene Delazari"}]},{id:"38959",title:"Whether Moving Suicide Prevention Toward Social Networking: A Decision Support Process with XREAP Tool",slug:"whether-moving-suicide-prevention-toward-social-networking-a-decision-support-process-with-xreap-too",totalDownloads:1834,totalCrossrefCites:0,totalDimensionsCites:0,abstract:null,book:{id:"3070",slug:"decision-support-systems_2012",title:"Decision Support Systems",fullTitle:"Decision Support Systems"},signatures:"Po-Hsun Cheng, Heng-Shuen Chen, Wen-Chen Chiang and Hsin- Ciang Chang",authors:[{id:"3422",title:"Dr.",name:"Heng-Shuen",middleName:null,surname:"Chen",slug:"heng-shuen-chen",fullName:"Heng-Shuen Chen"},{id:"7762",title:"Dr.",name:"Po-Hsun",middleName:null,surname:"Cheng",slug:"po-hsun-cheng",fullName:"Po-Hsun Cheng"},{id:"152898",title:"Mr.",name:"Hsin-Ciang",middleName:null,surname:"Chang",slug:"hsin-ciang-chang",fullName:"Hsin-Ciang Chang"},{id:"152899",title:"Mr.",name:"Wen-Chen",middleName:null,surname:"Chiang",slug:"wen-chen-chiang",fullName:"Wen-Chen Chiang"}]},{id:"37614",title:"DairyMGT: A Suite of Decision Support Systems in Dairy Farm Management",slug:"dairymgt-a-suite-of-decision-support-systems-in-dairy-farm-management",totalDownloads:3676,totalCrossrefCites:1,totalDimensionsCites:3,abstract:null,book:{id:"3070",slug:"decision-support-systems_2012",title:"Decision Support Systems",fullTitle:"Decision Support Systems"},signatures:"Victor E. Cabrera",authors:[{id:"152490",title:"Dr.",name:"Victor",middleName:"E",surname:"Cabrera",slug:"victor-cabrera",fullName:"Victor Cabrera"}]},{id:"38073",title:"Comparison of Multicriteria Analysis Techniques for Environmental Decision Making on Industrial Location",slug:"comparison-of-multicriteria-analysis-techniques-for-environmental-decision-making-on-industrial-loca",totalDownloads:2380,totalCrossrefCites:1,totalDimensionsCites:2,abstract:null,book:{id:"3070",slug:"decision-support-systems_2012",title:"Decision Support Systems",fullTitle:"Decision Support Systems"},signatures:"M.T. Lamelas, O. Marinoni, J. de la Riva and A. Hoppe",authors:[{id:"39458",title:"Dr.",name:"Juan",middleName:null,surname:"de la Riva",slug:"juan-de-la-riva",fullName:"Juan de la Riva"},{id:"152774",title:"Dr.",name:"María Teresa",middleName:null,surname:"Lamelas",slug:"maria-teresa-lamelas",fullName:"María Teresa Lamelas"},{id:"153073",title:"Dr.",name:"Oswald",middleName:null,surname:"Marinoni",slug:"oswald-marinoni",fullName:"Oswald Marinoni"},{id:"153074",title:"Prof.",name:"Andreas",middleName:null,surname:"Hoppe",slug:"andreas-hoppe",fullName:"Andreas Hoppe"}]},{id:"40007",title:"Towards Developing a Decision Support System for Electricity Load Forecast",slug:"towards-developing-a-decision-support-system-for-electricity-load-forecast",totalDownloads:2175,totalCrossrefCites:3,totalDimensionsCites:5,abstract:null,book:{id:"3070",slug:"decision-support-systems_2012",title:"Decision Support Systems",fullTitle:"Decision Support Systems"},signatures:"Connor Wright, Christine W. Chan and Paul Laforge",authors:[{id:"26220",title:"Dr.",name:"Christine",middleName:null,surname:"Chan",slug:"christine-chan",fullName:"Christine Chan"}]}],onlineFirstChaptersFilter:{topicId:"603",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:0,limit:8,total:null},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:89,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:104,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:31,numberOfPublishedChapters:314,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:11,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:141,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:129,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:113,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:105,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:18,numberOfOpenTopics:2,numberOfUpcomingTopics:1,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:5,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:14,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}},{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}}]},series:{item:{id:"14",title:"Artificial Intelligence",doi:"10.5772/intechopen.79920",issn:"2633-1403",scope:"Artificial Intelligence (AI) is a rapidly developing multidisciplinary research area that aims to solve increasingly complex problems. In today's highly integrated world, AI promises to become a robust and powerful means for obtaining solutions to previously unsolvable problems. This Series is intended for researchers and students alike interested in this fascinating field and its many applications.",coverUrl:"https://cdn.intechopen.com/series/covers/14.jpg",latestPublicationDate:"June 11th, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:9,editor:{id:"218714",title:"Prof.",name:"Andries",middleName:null,surname:"Engelbrecht",slug:"andries-engelbrecht",fullName:"Andries Engelbrecht",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRNR8QAO/Profile_Picture_1622640468300",biography:"Andries Engelbrecht received the Masters and PhD degrees in Computer Science from the University of Stellenbosch, South Africa, in 1994 and 1999 respectively. He is currently appointed as the Voigt Chair in Data Science in the Department of Industrial Engineering, with a joint appointment as Professor in the Computer Science Division, Stellenbosch University. Prior to his appointment at Stellenbosch University, he has been at the University of Pretoria, Department of Computer Science (1998-2018), where he was appointed as South Africa Research Chair in Artifical Intelligence (2007-2018), the head of the Department of Computer Science (2008-2017), and Director of the Institute for Big Data and Data Science (2017-2018). In addition to a number of research articles, he has written two books, Computational Intelligence: An Introduction and Fundamentals of Computational Swarm Intelligence.",institutionString:null,institution:{name:"Stellenbosch University",institutionURL:null,country:{name:"South Africa"}}},editorTwo:null,editorThree:null},subseries:{paginationCount:6,paginationItems:[{id:"22",title:"Applied Intelligence",coverUrl:"https://cdn.intechopen.com/series_topics/covers/22.jpg",isOpenForSubmission:!0,editor:{id:"27170",title:"Prof.",name:"Carlos",middleName:"M.",surname:"Travieso-Gonzalez",slug:"carlos-travieso-gonzalez",fullName:"Carlos Travieso-Gonzalez",profilePictureURL:"https://mts.intechopen.com/storage/users/27170/images/system/27170.jpeg",biography:"Carlos M. Travieso-González received his MSc degree in Telecommunication Engineering at Polytechnic University of Catalonia (UPC), Spain in 1997, and his Ph.D. degree in 2002 at the University of Las Palmas de Gran Canaria (ULPGC-Spain). He is a full professor of signal processing and pattern recognition and is head of the Signals and Communications Department at ULPGC, teaching from 2001 on subjects on signal processing and learning theory. His research lines are biometrics, biomedical signals and images, data mining, classification system, signal and image processing, machine learning, and environmental intelligence. He has researched in 52 international and Spanish research projects, some of them as head researcher. He is co-author of 4 books, co-editor of 27 proceedings books, guest editor for 8 JCR-ISI international journals, and up to 24 book chapters. He has over 450 papers published in international journals and conferences (81 of them indexed on JCR – ISI - Web of Science). He has published seven patents in the Spanish Patent and Trademark Office. He has been a supervisor on 8 Ph.D. theses (11 more are under supervision), and 130 master theses. He is the founder of The IEEE IWOBI conference series and the president of its Steering Committee, as well as the founder of both the InnoEducaTIC and APPIS conference series. He is an evaluator of project proposals for the European Union (H2020), Medical Research Council (MRC, UK), Spanish Government (ANECA, Spain), Research National Agency (ANR, France), DAAD (Germany), Argentinian Government, and the Colombian Institutions. He has been a reviewer in different indexed international journals (<70) and conferences (<250) since 2001. He has been a member of the IASTED Technical Committee on Image Processing from 2007 and a member of the IASTED Technical Committee on Artificial Intelligence and Expert Systems from 2011. \n\nHe has held the general chair position for the following: ACM-APPIS (2020, 2021), IEEE-IWOBI (2019, 2020 and 2020), A PPIS (2018, 2019), IEEE-IWOBI (2014, 2015, 2017, 2018), InnoEducaTIC (2014, 2017), IEEE-INES (2013), NoLISP (2011), JRBP (2012), and IEEE-ICCST (2005)\n\nHe is an associate editor of the Computational Intelligence and Neuroscience Journal (Hindawi – Q2 JCR-ISI). He was vice dean from 2004 to 2010 in the Higher Technical School of Telecommunication Engineers at ULPGC and the vice dean of Graduate and Postgraduate Studies from March 2013 to November 2017. He won the “Catedra Telefonica” Awards in Modality of Knowledge Transfer, 2017, 2018, and 2019 editions, and awards in Modality of COVID Research in 2020.\n\nPublic References:\nResearcher ID http://www.researcherid.com/rid/N-5967-2014\nORCID https://orcid.org/0000-0002-4621-2768 \nScopus Author ID https://www.scopus.com/authid/detail.uri?authorId=6602376272\nScholar Google https://scholar.google.es/citations?user=G1ks9nIAAAAJ&hl=en \nResearchGate https://www.researchgate.net/profile/Carlos_Travieso",institutionString:null,institution:{name:"University of Las Palmas de Gran Canaria",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"23",title:"Computational Neuroscience",coverUrl:"https://cdn.intechopen.com/series_topics/covers/23.jpg",isOpenForSubmission:!0,editor:{id:"14004",title:"Dr.",name:"Magnus",middleName:null,surname:"Johnsson",slug:"magnus-johnsson",fullName:"Magnus Johnsson",profilePictureURL:"https://mts.intechopen.com/storage/users/14004/images/system/14004.png",biography:"Dr Magnus Johnsson is a cross-disciplinary scientist, lecturer, scientific editor and AI/machine learning consultant from Sweden. \n\nHe is currently at Malmö University in Sweden, but also held positions at Lund University in Sweden and at Moscow Engineering Physics Institute. \nHe holds editorial positions at several international scientific journals and has served as a scientific editor for books and special journal issues. \nHis research interests are wide and include, but are not limited to, autonomous systems, computer modeling, artificial neural networks, artificial intelligence, cognitive neuroscience, cognitive robotics, cognitive architectures, cognitive aids and the philosophy of mind. \n\nDr. Johnsson has experience from working in the industry and he has a keen interest in the application of neural networks and artificial intelligence to fields like industry, finance, and medicine. \n\nWeb page: www.magnusjohnsson.se",institutionString:null,institution:{name:"Malmö University",institutionURL:null,country:{name:"Sweden"}}},editorTwo:null,editorThree:null},{id:"24",title:"Computer Vision",coverUrl:"https://cdn.intechopen.com/series_topics/covers/24.jpg",isOpenForSubmission:!0,editor:{id:"294154",title:"Prof.",name:"George",middleName:null,surname:"Papakostas",slug:"george-papakostas",fullName:"George Papakostas",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002hYaGbQAK/Profile_Picture_1624519712088",biography:"George A. Papakostas has received a diploma in Electrical and Computer Engineering in 1999 and the M.Sc. and Ph.D. degrees in Electrical and Computer Engineering in 2002 and 2007, respectively, from the Democritus University of Thrace (DUTH), Greece. Dr. Papakostas serves as a Tenured Full Professor at the Department of Computer Science, International Hellenic University, Greece. Dr. Papakostas has 10 years of experience in large-scale systems design as a senior software engineer and technical manager, and 20 years of research experience in the field of Artificial Intelligence. Currently, he is the Head of the “Visual Computing” division of HUman-MAchines INteraction Laboratory (HUMAIN-Lab) and the Director of the MPhil program “Advanced Technologies in Informatics and Computers” hosted by the Department of Computer Science, International Hellenic University. He has (co)authored more than 150 publications in indexed journals, international conferences and book chapters, 1 book (in Greek), 3 edited books, and 5 journal special issues. His publications have more than 2100 citations with h-index 27 (GoogleScholar). His research interests include computer/machine vision, machine learning, pattern recognition, computational intelligence. \nDr. Papakostas served as a reviewer in numerous journals, as a program\ncommittee member in international conferences and he is a member of the IAENG, MIR Labs, EUCogIII, INSTICC and the Technical Chamber of Greece (TEE).",institutionString:null,institution:{name:"International Hellenic University",institutionURL:null,country:{name:"Greece"}}},editorTwo:null,editorThree:null},{id:"25",title:"Evolutionary Computation",coverUrl:"https://cdn.intechopen.com/series_topics/covers/25.jpg",isOpenForSubmission:!0,editor:{id:"136112",title:"Dr.",name:"Sebastian",middleName:null,surname:"Ventura Soto",slug:"sebastian-ventura-soto",fullName:"Sebastian Ventura Soto",profilePictureURL:"https://mts.intechopen.com/storage/users/136112/images/system/136112.png",biography:"Sebastian Ventura is a Spanish researcher, a full professor with the Department of Computer Science and Numerical Analysis, University of Córdoba. Dr Ventura also holds the positions of Affiliated Professor at Virginia Commonwealth University (Richmond, USA) and Distinguished Adjunct Professor at King Abdulaziz University (Jeddah, Saudi Arabia). Additionally, he is deputy director of the Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) and heads the Knowledge Discovery and Intelligent Systems Research Laboratory. He has published more than ten books and over 300 articles in journals and scientific conferences. Currently, his work has received over 18,000 citations according to Google Scholar, including more than 2200 citations in 2020. In the last five years, he has published more than 60 papers in international journals indexed in the JCR (around 70% of them belonging to first quartile journals) and he has edited some Springer books “Supervised Descriptive Pattern Mining” (2018), “Multiple Instance Learning - Foundations and Algorithms” (2016), and “Pattern Mining with Evolutionary Algorithms” (2016). He has also been involved in more than 20 research projects supported by the Spanish and Andalusian governments and the European Union. He currently belongs to the editorial board of PeerJ Computer Science, Information Fusion and Engineering Applications of Artificial Intelligence journals, being also associate editor of Applied Computational Intelligence and Soft Computing and IEEE Transactions on Cybernetics. Finally, he is editor-in-chief of Progress in Artificial Intelligence. He is a Senior Member of the IEEE Computer, the IEEE Computational Intelligence, and the IEEE Systems, Man, and Cybernetics Societies, and the Association of Computing Machinery (ACM). Finally, his main research interests include data science, computational intelligence, and their applications.",institutionString:null,institution:{name:"University of Córdoba",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"26",title:"Machine Learning and Data Mining",coverUrl:"https://cdn.intechopen.com/series_topics/covers/26.jpg",isOpenForSubmission:!0,editor:{id:"24555",title:"Dr.",name:"Marco Antonio",middleName:null,surname:"Aceves Fernandez",slug:"marco-antonio-aceves-fernandez",fullName:"Marco Antonio Aceves Fernandez",profilePictureURL:"https://mts.intechopen.com/storage/users/24555/images/system/24555.jpg",biography:"Dr. Marco Antonio Aceves Fernandez obtained his B.Sc. (Eng.) in Telematics from the Universidad de Colima, Mexico. He obtained both his M.Sc. and Ph.D. from the University of Liverpool, England, in the field of Intelligent Systems. He is a full professor at the Universidad Autonoma de Queretaro, Mexico, and a member of the National System of Researchers (SNI) since 2009. Dr. Aceves Fernandez has published more than 80 research papers as well as a number of book chapters and congress papers. He has contributed in more than 20 funded research projects, both academic and industrial, in the area of artificial intelligence, ranging from environmental, biomedical, automotive, aviation, consumer, and robotics to other applications. He is also a honorary president at the National Association of Embedded Systems (AMESE), a senior member of the IEEE, and a board member of many institutions. His research interests include intelligent and embedded systems.",institutionString:"Universidad Autonoma de Queretaro",institution:{name:"Autonomous University of Queretaro",institutionURL:null,country:{name:"Mexico"}}},editorTwo:null,editorThree:null},{id:"27",title:"Multi-Agent Systems",coverUrl:"https://cdn.intechopen.com/series_topics/covers/27.jpg",isOpenForSubmission:!0,editor:{id:"148497",title:"Dr.",name:"Mehmet",middleName:"Emin",surname:"Aydin",slug:"mehmet-aydin",fullName:"Mehmet Aydin",profilePictureURL:"https://mts.intechopen.com/storage/users/148497/images/system/148497.jpg",biography:"Dr. Mehmet Emin Aydin is a Senior Lecturer with the Department of Computer Science and Creative Technology, the University of the West of England, Bristol, UK. His research interests include swarm intelligence, parallel and distributed metaheuristics, machine learning, intelligent agents and multi-agent systems, resource planning, scheduling and optimization, combinatorial optimization. Dr. Aydin is currently a Fellow of Higher Education Academy, UK, a member of EPSRC College, a senior member of IEEE and a senior member of ACM. In addition to being a member of advisory committees of many international conferences, he is an Editorial Board Member of various peer-reviewed international journals. He has served as guest editor for a number of special issues of peer-reviewed international journals.",institutionString:null,institution:{name:"University of the West of England",institutionURL:null,country:{name:"United Kingdom"}}},editorTwo:null,editorThree:null}]},overviewPageOFChapters:{paginationCount:19,paginationItems:[{id:"82196",title:"Multi-Features Assisted Age Invariant Face Recognition and Retrieval Using CNN with Scale Invariant Heat Kernel Signature",doi:"10.5772/intechopen.104944",signatures:"Kamarajugadda Kishore Kumar and Movva Pavani",slug:"multi-features-assisted-age-invariant-face-recognition-and-retrieval-using-cnn-with-scale-invariant-",totalDownloads:6,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Pattern Recognition - New Insights",coverURL:"https://cdn.intechopen.com/books/images_new/11442.jpg",subseries:{id:"26",title:"Machine Learning and Data Mining"}}},{id:"82063",title:"Evaluating Similarities and Differences between Machine Learning and Traditional Statistical Modeling in Healthcare Analytics",doi:"10.5772/intechopen.105116",signatures:"Michele Bennett, Ewa J. Kleczyk, Karin Hayes and Rajesh Mehta",slug:"evaluating-similarities-and-differences-between-machine-learning-and-traditional-statistical-modelin",totalDownloads:6,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Machine Learning and Data Mining - Annual Volume 2022",coverURL:"https://cdn.intechopen.com/books/images_new/11422.jpg",subseries:{id:"26",title:"Machine Learning and Data Mining"}}},{id:"81791",title:"Self-Supervised Contrastive Representation Learning in Computer Vision",doi:"10.5772/intechopen.104785",signatures:"Yalin Bastanlar and Semih Orhan",slug:"self-supervised-contrastive-representation-learning-in-computer-vision",totalDownloads:25,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Pattern Recognition - New Insights",coverURL:"https://cdn.intechopen.com/books/images_new/11442.jpg",subseries:{id:"26",title:"Machine Learning and Data Mining"}}},{id:"79345",title:"Application of Jump Diffusion Models in Insurance Claim Estimation",doi:"10.5772/intechopen.99853",signatures:"Leonard Mushunje, Chiedza Elvina Mashiri, Edina Chandiwana and Maxwell Mashasha",slug:"application-of-jump-diffusion-models-in-insurance-claim-estimation-1",totalDownloads:8,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Data Clustering",coverURL:"https://cdn.intechopen.com/books/images_new/10820.jpg",subseries:{id:"26",title:"Machine Learning and Data Mining"}}}]},overviewPagePublishedBooks:{paginationCount:9,paginationItems:[{type:"book",id:"7723",title:"Artificial Intelligence",subtitle:"Applications in Medicine and Biology",coverURL:"https://cdn.intechopen.com/books/images_new/7723.jpg",slug:"artificial-intelligence-applications-in-medicine-and-biology",publishedDate:"July 31st 2019",editedByType:"Edited by",bookSignature:"Marco Antonio Aceves-Fernandez",hash:"a3852659e727f95c98c740ed98146011",volumeInSeries:1,fullTitle:"Artificial Intelligence - Applications in Medicine and Biology",editors:[{id:"24555",title:"Dr.",name:"Marco Antonio",middleName:null,surname:"Aceves Fernandez",slug:"marco-antonio-aceves-fernandez",fullName:"Marco Antonio Aceves Fernandez",profilePictureURL:"https://mts.intechopen.com/storage/users/24555/images/system/24555.jpg",biography:"Dr. Marco Antonio Aceves Fernandez obtained his B.Sc. (Eng.) in Telematics from the Universidad de Colima, Mexico. He obtained both his M.Sc. and Ph.D. from the University of Liverpool, England, in the field of Intelligent Systems. He is a full professor at the Universidad Autonoma de Queretaro, Mexico, and a member of the National System of Researchers (SNI) since 2009. Dr. Aceves Fernandez has published more than 80 research papers as well as a number of book chapters and congress papers. He has contributed in more than 20 funded research projects, both academic and industrial, in the area of artificial intelligence, ranging from environmental, biomedical, automotive, aviation, consumer, and robotics to other applications. He is also a honorary president at the National Association of Embedded Systems (AMESE), a senior member of the IEEE, and a board member of many institutions. His research interests include intelligent and embedded systems.",institutionString:"Universidad Autonoma de Queretaro",institution:{name:"Autonomous University of Queretaro",institutionURL:null,country:{name:"Mexico"}}}]},{type:"book",id:"7726",title:"Swarm Intelligence",subtitle:"Recent Advances, New Perspectives and Applications",coverURL:"https://cdn.intechopen.com/books/images_new/7726.jpg",slug:"swarm-intelligence-recent-advances-new-perspectives-and-applications",publishedDate:"December 4th 2019",editedByType:"Edited by",bookSignature:"Javier Del Ser, Esther Villar and Eneko Osaba",hash:"e7ea7e74ce7a7a8e5359629e07c68d31",volumeInSeries:2,fullTitle:"Swarm Intelligence - Recent Advances, New Perspectives and Applications",editors:[{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:null}]},{type:"book",id:"7656",title:"Fuzzy Logic",subtitle:null,coverURL:"https://cdn.intechopen.com/books/images_new/7656.jpg",slug:"fuzzy-logic",publishedDate:"February 5th 2020",editedByType:"Edited by",bookSignature:"Constantin Volosencu",hash:"54f092d4ffe0abf5e4172a80025019bc",volumeInSeries:3,fullTitle:"Fuzzy Logic",editors:[{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:"Polytechnic University of Timişoara",institution:{name:"Polytechnic University of Timişoara",institutionURL:null,country:{name:"Romania"}}}]},{type:"book",id:"9963",title:"Advances and Applications in Deep Learning",subtitle:null,coverURL:"https://cdn.intechopen.com/books/images_new/9963.jpg",slug:"advances-and-applications-in-deep-learning",publishedDate:"December 9th 2020",editedByType:"Edited by",bookSignature:"Marco Antonio Aceves-Fernandez",hash:"0d51ba46f22e55cb89140f60d86a071e",volumeInSeries:4,fullTitle:"Advances and Applications in Deep Learning",editors:[{id:"24555",title:"Dr.",name:"Marco Antonio",middleName:null,surname:"Aceves Fernandez",slug:"marco-antonio-aceves-fernandez",fullName:"Marco Antonio Aceves Fernandez",profilePictureURL:"https://mts.intechopen.com/storage/users/24555/images/system/24555.jpg",biography:"Dr. Marco Antonio Aceves Fernandez obtained his B.Sc. (Eng.) in Telematics from the Universidad de Colima, Mexico. He obtained both his M.Sc. and Ph.D. from the University of Liverpool, England, in the field of Intelligent Systems. He is a full professor at the Universidad Autonoma de Queretaro, Mexico, and a member of the National System of Researchers (SNI) since 2009. Dr. Aceves Fernandez has published more than 80 research papers as well as a number of book chapters and congress papers. He has contributed in more than 20 funded research projects, both academic and industrial, in the area of artificial intelligence, ranging from environmental, biomedical, automotive, aviation, consumer, and robotics to other applications. He is also a honorary president at the National Association of Embedded Systems (AMESE), a senior member of the IEEE, and a board member of many institutions. His research interests include intelligent and embedded systems.",institutionString:"Universidad Autonoma de Queretaro",institution:{name:"Autonomous University of Queretaro",institutionURL:null,country:{name:"Mexico"}}}]}]},openForSubmissionBooks:{paginationCount:3,paginationItems:[{id:"11601",title:"Econometrics - Recent Advances and Applications",coverURL:"https://cdn.intechopen.com/books/images_new/11601.jpg",hash:"bc8ab49e2cf436c217a49ca8c12a22eb",secondStepPassed:!0,currentStepOfPublishingProcess:3,submissionDeadline:"May 13th 2022",isOpenForSubmission:!0,editors:[{id:"452331",title:"Dr.",name:"Brian",surname:"Sloboda",slug:"brian-sloboda",fullName:"Brian Sloboda"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null},{id:"12141",title:"Leadership - Advancing Great Leadership Practices and Good Leaders",coverURL:"https://cdn.intechopen.com/books/images_new/12141.jpg",hash:"85f77453916f1d80d80d88ee4fd2f2d1",secondStepPassed:!1,currentStepOfPublishingProcess:2,submissionDeadline:"July 1st 2022",isOpenForSubmission:!0,editors:[{id:"420133",title:"Dr.",name:"Joseph",surname:"Crawford",slug:"joseph-crawford",fullName:"Joseph Crawford"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null},{id:"12139",title:"Global Market and Trade",coverURL:"https://cdn.intechopen.com/books/images_new/12139.jpg",hash:"fa34af07c3a9657fa670404202f8cba5",secondStepPassed:!1,currentStepOfPublishingProcess:2,submissionDeadline:"July 21st 2022",isOpenForSubmission:!0,editors:[{id:"243649",title:"Dr.Ing.",name:"Ireneusz",surname:"Miciuła",slug:"ireneusz-miciula",fullName:"Ireneusz Miciuła"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null}]},onlineFirstChapters:{},subseriesFiltersForOFChapters:[],publishedBooks:{},subseriesFiltersForPublishedBooks:[],publicationYearFilters:[],authors:{}},subseries:{item:{},onlineFirstChapters:{},publishedBooks:{},testimonialsList:[]},submityourwork:{pteSeriesList:[],lsSeriesList:[],hsSeriesList:[],sshSeriesList:[],subseriesList:[],annualVolumeBook:{},thematicCollection:[],selectedSeries:null,selectedSubseries:null},seriesLanding:{item:null},libraryRecommendation:{success:null,errors:{},institutions:[]},route:{name:"profile.detail",path:"/profiles/102109",hash:"",query:{},params:{id:"102109"},fullPath:"/profiles/102109",meta:{},from:{name:null,path:"/",hash:"",query:{},params:{},fullPath:"/",meta:{}}}},function(){var e;(e=document.currentScript||document.scripts[document.scripts.length-1]).parentNode.removeChild(e)}()