Description of NIGNET stations used in this work.
\r\n\tIn this book the authors will provide complete introduction of Polymers chemistry. The book is mainly divided into three parts. The readers will learn about the basic introduction of general polymer chemistry in the first part of the book.
\r\n\tThe second part of the book starts with a chapter which includes kinetics of polymerization. Polymer weight determination, molecular weight distribution curve and determination of glass transition temperature. The final part of the book deals polymer degradation which includes types of degradation. The chapters of the present book consist of both tutorial and highly advanced material.
The ionosphere is important to our existence as it affects our radio communication systems, especially our satellite communication systems. Particularly, the ionosphere poses the greatest natural challenge for our global navigation satellite systems (GNSS) when it comes to precise position measurement by ground-based receivers. There are a couple of satellite navigation systems, e.g. the United States’ GPS (Global Positioning System), Russia’s GLONASS (Global Navigation Satellite System), European Union’s GALILEO, China’s BEIDOU (or COMPASS), etc. The GPS is the most common and the most popular of the GNSS systems, and so in this chapter, we will carefully use the two words interchangeably. The evolution of our navigation requirements into satellite-based systems is therefore adding a rapid stair to interest in ionospheric research. The greatest efforts in ionospheric research have been directed towards ionospheric modeling, and related studies that tend to understand how the ionosphere changes in time and space. Several ionospheric models have been developed (e.g. Ref. [1, 2, 3, 4, 5, 6, 7]).
To understand exactly how the ionosphere influences our satellite-based navigation systems, it is important to understand how satellite-based navigation systems work. A more detailed introduction to the GNSS is presented by Ref. [8], but the core ideas are briefly and elegantly presented here. A satellite-based navigation system basically consists of some satellites in space. The satellites know their positions in space through the help of ground-based control stations and some internal programming. Through on-board transmitters on the satellites, each satellite continuously transmits radio signals. Each radio signal contains information about the 3-D position in space of the satellite from which it is transmitted, and the time in which the signal is transmitted. GPS receivers on ground (like the ones you and I own in cell phones and other devices) can receive these signals and automatically be able to compute the receivers’ 3-D positions.
Exactly how does this happen? How does a GPS receiver know its position by merely receiving position and time stamped radio signals from the satellites? The GPS receivers use in-built computer programs to compute their own positions from the positions of the satellite they receive signals from. The computer programs are based on quite simple geometric calculations. The geometric calculations are based on the premise that if we know the exact 3-D positions of any three objects and the exact range to each of them, then we would be able to determine our own 3-D position. It is emphasized here that the positions of three objects are required because we are interested in 3-D positions. If we are interested in knowing our position in 2-D space, then we will require the positions of only two objects. In the case of the GPS, the interest is to know the 3-D position of the receiver as well as the time the signal is received (this makes 4-D), so we need four objects. A GPS receiver will therefore be able to compute its exact position and time if it receives signals from at least four satellites. From the satellite radio signals they receive, GPS receivers retrieve information on the 3-D positions and times of the satellites as well as the exact range to each of them.
As explained earlier, we know that each of the signals already contain information on the 3-D positions and times of the satellites, but how do the receivers know the ranges to the satellites? GPS receivers estimate ranges to the satellites by using the formula
The travel time is how long the radio signals have traveled between their transmission and their reception (That is the time difference between when the signals were transmitted from the satellite and when they were received). The signals already contain information on when they were transmitted from the satellite, and the receiver time is one of the four parameters the receiver will compute.
The computation in Eq. (1) is based on the assumption that radio signals (which are electromagnetic) travel at the constant speed of about c = 2.998 × 108 ms−1 in vacuum. And this is where the problem of the ionosphere comes in. The space between the satellites and receivers is not entirely vacuum; there is an intervening region containing ionized matter known as the ionosphere. Because of ionized matter contained in the ionosphere, electromagnetic waves (e.g. the transmitted radio signals) do not travel through the ionosphere at the constant speed of about c = 2.998 × 108 ms−1. The signals are delayed, and this delay is interpreted in Eq. (1) as part of the travel time. This introduces an error into the computed range (the computed range is greater than the actual or true range) which therefore subsequently manifests as an error in the computed receiver position.
An obviously intelligent thing to do is to remove this effect of the delay introduced by the ionosphere, but this is only possible if we know how much the delay is. To make the situation worse, the ionosphere is highly dynamic; it changes appreciably over space and time. We therefore need to know the extent of ionospheric ionization at any given time along the radio route so as to be able to correct for the effect of the ionosphere on the radio signal. This is where ionospheric models are useful. Ionospheric models can be used to now-cast (and even fore-cast) the extent of ionospheric ionization over space and time. And by so doing, ionospheric models are useful and usually applied in GPS error correction for single frequency receivers.
Single frequency receivers are GNSS receivers that can receive radio signals from the satellites in only one frequency. These are the most common types of GNSS receivers we see in everyday usage. They are cost-effective but incapable of estimating the ionospheric delay. On the other hand, there are dual or more frequency receivers which can receive GNSS radio signals at two or more frequencies. In the explanation that follows (on the Data and Methods section), dual or more frequency GNSS receivers are capable of estimating the ionospheric delays, and therefore capable of internally removing the effects of such delays. These types of receivers are mainly used for research and other specialized usages. It is from these types of receivers that data used in this chapter was obtained. There is general intuition that dual-frequency receivers are better than single frequency ones, but in highlighting the tradeoffs between the two, Ref. [9] explained that, asides cost effectiveness of the single frequency receivers, a single frequency receiver may actually outperform the more advanced dual-frequency receiver in terms of accuracy during the first 10 minutes or so, and also in places associated with frequent loss of lock on GNSS signals. Rather than using dual-frequency receivers, some applications therefore prefer using ionospheric models on single frequency receivers to correct for the effects of the ionosphere. The accuracy obtained from this practice however depends on the accuracy of the model used; more accurate models will give more accurate GNSS positions. The development of a regional GPS model of the ionosphere (with improved accuracy) is presented in this chapter. The modeling technique used is the method of computer neural networks.
Computer neural networks (also commonly referred to as neural networks or just NNs for short) have capability for machine learning as well as pattern recognition, and they have been demonstrated to be powerful tools for predictive modeling. NNs operate in a manner that is similar to the human brain; the networks are composed of simple elements operating in parallel and inspired by the biological nervous system. NNs can learn trends and patterns in particular data they are given and consequently be able to correctly predict unseen and future trends for the data. A neural network can be trained to perform a particular function by adjusting the value of connections (also called weights) between elements [7]. The true power and advantages of neural networks lies in the ability to represent both linear and non-linear relationships directly from the data being modeled. Traditional linear models are simply inadequate when it comes for true modeling data that contains non-linear characteristics [8]. Recent explosion of ionospheric data from the GNSS is spurring interest in using computer neural networks for ionospheric modeling. A number of works have shown that neural networks (NNs) are good candidates for ionospheric modeling [6, 7, 10, 11, 12, 13]. In this chapter, neural networks have been used to develop a regional model of the ionosphere over Nigeria. Predictions from the model have also been demonstrated to be more improved in terms of accuracy when compared to predictions from global ionospheric models like the IRI-Plas (International Reference Ionosphere—extended to the Plasmasphere) and the NeQuick.
Three main sets of data were used in this chapter, these include: (i) GPS data, (ii) sunspot number (SSN) data, and (iii) disturbance storm time (DST) data. The next section will dwell on GPS data which is of major interest in this chapter.
The GPS data used in this chapter were derived from dual-frequency receivers on the NIGNET (Nigerian Permanent GNSS Network, www.nignet.net). A brief description of how ionospheric information is usually obtained from dual-frequency GPS receivers is presented.
How are dual-frequency GPS receivers able to estimate ionospheric delays? The delays introduced on radio signals by the ionosphere are frequency-dependent; the lower frequency signals are more delayed while the higher frequency signals are less delayed. More precisely, the delay (t) is inversely proportional to the radio frequency (f) as shown in Eq. (2a) [14].
c = 2.998 × 108 ms−1 is the speed of electromagnetic waves in vacuum, and TEC is the Total Electron Content. TEC is a parameter of the ionosphere that represents the total number of free electrons contained in a 1 m squared column, along the path of the signal through the ionosphere. It is this parameter of the ionosphere that is modeled in this chapter. Eq. (2a) shows that the ionospheric delay is directly proportional to the TEC, therefore the radio signals are more delayed when they travel through a route in the ionosphere with more number of free electrons.
The proportionality expressed in Eq. (2a) forms the underlying principle for deriving ionospheric information (precisely TEC) using dual-frequency GPS receivers. This is because two radio signals (having frequencies, f1 and f2) transmitted at the same time from the same satellite will be delayed differently by the ionosphere so they arrive the same receiver at different times. The delays that will be experienced by the two radio signals are, respectively, given by Eq. (2b) and Eq. (2c).
Subtracting Eq. (2b) from Eq. (2c), we get the time delay between arrivals of the two signals as in Eq. (3a).
Dual-frequency GPS receivers compute the TEC using Eq. (3b) which is obtained by making TEC subject of the formula from Eq. (3a).
The TECs computed in this manner using the pseudo-range measurements alone are usually noisy; differential carrier phase measurements are used to obtain precise measures of the relative TECs, and a combination with the pseudo-range measurements provide the absolute slant TEC values (STECs) along the receiver-satellite path [15, 16, 17]. The computed TECs are referred to as slant, to distinguish them from the unique TEC that will be obtained for a particular location when the satellite is exactly overhead the location (that is, satellite elevation = 90°). This unique TEC is called the vertical TEC (VTEC). VTECs are usually derived from the STECs using Eq. (4).
where br and bs are, respectively, the receiver and satellite biases, S(E) is the mapping function defined by Eq. (5) [18].
z and E are, respectively, the zenith and elevation angles in degrees; RE and hs are, respectively, the mean Earth radius and the ionosphere (effective) height above the Earth surface in km. The value of hs used for this chapter is 350 km.
GPS Data obtained from the NIGNET are in RINEX (Receiver Independent Exchange) format. The RINEX format is the standard data interchange format for raw satellite navigation system data. RINEX format data obtained from the NIGNET were processed into VTEC data using software developed by Dr. Gopi Seemala (seemala.blogspot.in). The software works basically on the principles highlighted above, and as expressed in Refs. [15, 19].
GPS Data used were from the 14 stations illustrated in Figure 1 and in Table 1. All available data coving the periods from years 2011 to 2016 were used. To obtain instantaneous values of VTEC for a given location, VTEC values from the various satellites that are visible over the location at the time were averaged excluding those from satellites with elevation angles less than 25°. The reason for excluding data associated with low elevation angles is usually to minimize multipath errors. Multipath errors are errors associated with signals that bounce off (or reflected from) nearby buildings, trees, or other structures before they reach the receiver antenna. The problem with these signals is that the resulting range will be greater than the actual straight path range between the satellite and receiver, because the signal first has to bounce off other structures before they reach the receiver antenna. The multipath problem is typical of signals coming from low elevation satellites; the lower the satellite elevation angles (especially satellites close to the horizon), the more likely signals from them are to bouncing off other structures before they reach the receiver antenna. This problem is mostly the reason why research-class GNSS receivers are installed such that their antennas are raised above nearby structures/buildings (or away from the structures/buildings), and the antennas are built in such a shape that the receiving surface faces the sky. In this way, radio signals that are reflected from structures beneath the antenna do not get received by the antenna even when they hit the bottom surface of the antenna. The resulting VTEC data were further averaged in 1-hour intervals to reduce data and to lessen spikes on the data profiles.
Map of Nigeria showing locations of GPS stations used in this work.
Description of NIGNET stations used in this work.
The other two set of data used in this chapter are the DST and SSN data. The DST is a measure of the disturbances in the Earth’s magnetic field, it is an index often used to describe the level of geomagnetic activity during storms. On the other hand, the SSN is a count of the number of sunspots present on the surface of the Sun. It is a measure of the Sun’s activeness (the level of activity going on in the Sun), and is found to be cyclical, reaching its peak in about every 11 years.
The idea in formulating the input layer structure of a neural network is to consider parameters/factors that affect the output parameter (which is VTEC in this chapter). VTEC has been convincingly proven to be affected by both geomagnetic storm activity [20] and solar activity [21]. The practice in neural networks is to supply parameters like DST and SSN which are well established to affect VTEC as inputs during the training of a network that will predict VTEC. For this reason, the DST and SSN parameters corresponding to instances of the VTEC data used in this chapter were used as inputs during the training of the neural networks in this chapter.
DST indices were obtained from the World Data Center (WDC) for Geomagnetism (http://wdc.kugi.kyoto-u.ac.jp/dstdir/index.html), while data on SSN were obtained from the WDC-SILSO (Sunspot Index and Long-term Solar Observations, http://www.sidc.be/silso/datafiles), Royal Observatory of Belgium, Brussels.
The Levenberg-Marquardt back-propagation algorithm [22] as implemented in MATLAB was used in this chapter. A couple of other algorithms exist [23] but the Levenberg-Marquardt algorithm is admired for its speed and efficiency in learning [24, 25]. NNs typically have input layers, output layers, and intermediary hidden layers. Each layer could consist of one or more units or nodes (also called neurons).
As explained in the previous section, the idea in formulating the input layer structure of a neural network is to consider parameters/factors that affect the output parameter. In the previous section, the inclusion of DST and SSN as inputs was justified. Other factors that have been established to affect VTEC are time and space; VTEC is known to vary with time and space.
Particularly, VTEC changes with time in the forms of diurnal, seasonal, and long-term yearly variations [7]. For the neural networks to learn long-term yearly variations, the year for each of the GPS VTEC data was included as input for the training. To learn seasonal variations, the day of the year for each of the data was included, and to learn diurnal variations, the hour of day for each data was included.
Spatially, VTEC changes with longitude and latitude of the GPS receiver location, and so the longitudes and latitudes of the GPS receivers were included for each of the GPS VTEC data so that the networks will learn spatial variations of the VTEC. Geomagnetic longitudes and latitudes (rather than geographic longitudes and latitudes) were used since the ionospheric properties are based mainly on the interactions between the solar radiation and the Earth’s geomagnetic field [6, 26]. Conversion of geographic to geomagnetic coordinates was done using the Apex Coordinate Conversion Utility Software [27].
In summary, a total of the following seven input nodes were used for the neural network training:
Hour of Day (to learn diurnal variations of the VTEC)
Day of Year (to learn the seasonal variations)
Year (to learn the long-term yearly variations)
Longitude (to learn the spatial variations longitude-wise)
Latitude (to learn the spatial variations latitude-wise)
DST index (to learn variations of the VTEC with geomagnetic storm activity)
SSN (to learn variations of the VTEC with solar activity)
The output layer is clearly known to have one neuron which is the GPS-VTEC to be modeled, but deciding the number of neurons in the hidden layer is an intricate aspect of neural network trainings. This is an aspect that conspicuously affects the performance of the trained networks. The most credible practice to deciding an appropriate number of hidden layer neurons has been to train several networks that vary in the number of hidden layer neurons, and then selecting the best of them using a performance index.
In this chapter, 20 neural networks were simulated, varying the number of hidden layer neurons in integer steps from 1 to 20. The main performance index used is the root-mean-squared-errors (RMSEs). RMSEs were computed using the formula in Eq. (6).
where GPSVTECi and
The criteria for deciding the best network is to choose the one that gives the least RMSE on the test dataset. Testing of the networks was done using 15% dataset that was randomly selected from the entire data and which were not used for the training. Another randomly selected 15% of the data was used for validation during the training, and the remaining 70% was used for the actual training. Figure 2 illustrates outcomes of the RMSEs when different number of hidden layer neurons were used on the networks.
Plot of the RMSEs for varied number of hidden layer neurons.
Figure 2 shows that the network that gave the least RMSE is the network that has 6 hidden layer neurons. The RMSE for this network is 5.03 TECU. It is this network that has been adopted as the optimal network in this study. A detailed and elementary treatment on how to train neural networks using MATLAB is contained in a more elementary book [28].
Using the Neural Network model developed in this chapter, sample simulations were made to assess predictions from the model in terms of known ionospheric variation patterns.
Diurnal variations of the ionosphere are variations in the ionosphere that are observed as the Earth makes a complete rotation about its axis. That is, the changes that are observed within an entire day as we go from morning to night. Figure 3(a)–(d) are constructed to visualize diurnal variations in the ionosphere over the Nigerian region. The figures are, respectively, images of the VTEC over Nigeria for 05:00 UT (06:00 local Nigerian time, which is around local sunrise), 11:00 UT (12:00 local Nigerian time, which is around local midday), 17:00 UT (18:00 local Nigerian time, which is around local sunset), and 23:00 UT (24:00 local Nigerian time, which is around local midnight) of 1st July 2014. The day was arbitrarily chosen for this illustration. Local time in Nigeria is UT + 1. In the color scheme used for the figure (and for all other figures in this chapter), the blue colors indicate lower VTECs, the red colors indicate higher VTECs, and the green-yellow colors indicate moderate VTECs (see the associated color bars for exact VTEC values in each case).
VTEC maps over Nigeria for (a) 06:00 LT, (b) 12:00 LT, (c) 18:00 LT, and (d) 24:00 LT, of 1st July 2014.
Figure 3 shows that within a day the VTEC values are greatest around local midday. Since the Sun is the major source of ionospheric ionization, the level of ionospheric ionization (and hence VTEC value) is usually higher during the daytime (when the solar-zenith angle is low) than at nights (when the solar-zenith angle is high). The VTEC values are also relatively high around sunset because the ionizations produced by the Sunlight do not instantly disappear (it takes about 2 hours for the ionized particles to substantially recombine when the Sun goes below horizon).
Figure 3(a) and (c) also reveals the interplay between the Sun and the ionosphere during sunrise and sunset. At sunrise (Figure 3(a)), the VTECs are higher eastwards than westwards. This is because the Sun rises from the east. At sunset (Figure 3(c)), the VTECs are higher westwards than eastwards. This is because the Sun sets to the west.
Seasonal variations have to with variations that are observed as the Earth makes a complete revolution about the Sun. That is, the changes that are observed within an entire year as we go through the seasons. Figure 4(a)–(d) are constructed to illustrate seasonal variations over Nigeria during year 2012. The figures are, respectively, VTEC maps of the Nigerian region for 11:00 UT (local midday) of 20th March 2012 (the March equinox day), 21st June 2012 (the June solstice day), 22nd September 2012 (the September equinox day), and 21st December 2012 (the December solstice day). The year 2012 was arbitrarily chosen for the illustration.
VTEC maps over Nigeria for local midday of (a) 20th March, (b) 21st June, (c) 22nd September, and (d) 21st December, of year 2012.
Figure 4(a) and (c) illustrates that the VTECs are relatively high during the equinoxes. This is because Nigeria is located close to the equator, and as such receives much sunlight during the equinoxes. During the equinoxes, the solar-zenith angle is lower at the equator as compared to the solstices. It is also conspicuous that the VTEC values are high during the December solstice (Figure 4(d)), even higher than at the September equinox (Figure 4(c)). This is because Nigeria is mostly located on the geomagnetic southern hemisphere. The December solstice is the summer solstice in the southern hemisphere, and so the solar-zenith angle is lower in the southern hemisphere during this season than at other seasons.
The ionosphere has also been established to vary with the level of solar activity. As explained earlier, the sunspot number is a good measure of the level of solar activity. The solar activity is known to have a time series cycle of about 11-years during which the activity level goes from peak to peak or trough to trough.
Figure 5(a)–(d) was constructed to illustrate how the ionosphere over Nigeria varies with the solar activity. The figures are, respectively, the local midday VTECs over Nigeria for the same day (1st July) of years 2011, 2012, 2013, and 2014.
VTEC maps over Nigeria for local midday of 1st July, year (a) 2011, (b) 2012, (c) 2013, and (d) 2014.
A look at the solar activity cycle shows that the solar activity level was on the rise from year 2011 to year 2014. Comparing with the VTEC values for those years (Figure 5(a)–(d)), it is observed that the VTEC values are also on the increase; the VTECs are least during year 2011 (Figure 5(a)) which also has the least solar activity level, and greatest during year 2014 (Figure 5(d)) which also has the greatest solar activity level. Figure 5(a)–(d) clearly indicates that the neural network was able to learn/capture the long-term variations associated with the solar activity.
Two of the most popular global ionospheric models (the IRI-Plas and the NeQuick) have been selected to make a comparative assessment of the neural network model developed in this chapter.
The IRI-Plas is the IRI (International Reference Ionosphere) extended to the plasmasphere [29]. The IRI model [3] has been widely accepted as a defector standard for specifying ionospheric parameters across the globe. The IRI-Plas model (rather than the IRI model) is selected for use in this chapter because TEC computed by the IRI-Plas model involves electron density integrations up to the GPS satellite altitudes of about 20,200 km, whereas for the IRI model, it only gets up to a maximum of 2000 km. Since, this chapter concentrates on TEC derived from the GPS, a more comprehensive comparison is therefore obtained using the IRI-Plas model rather than the IRI model. The IRI-Plas model has also been proposed for extension of the IRI model to the plasmasphere [30]. The most recent version of the IRI-Plas (the IRI-Plas 2017) was used for this comparison. The windows executable program of the IRI-Plas used was obtained from the website of the IZMIRAN Institute (http://ftp.izmiran.ru/pub/izmiran/SPIM/).
The NeQuick [31, 32, 33] is another popular global ionospheric model which has been severally compared with GNSS TEC measurements and shown to be a good representation of the ionosphere. The NeQuick is admired because of its improved performance in predicting the topside ionosphere, and consequently versions of the IRI model from 2007 and later have included the topside formulation of the NeQuick, and has adopted it as the most mature of the different proposals to compute the topside part of the IRI electron density profile [33, 34]. The NeQuick includes routines that compute the electron density along any ray-path from ground to GPS satellite altitudes of about 20,200 km, and so also makes for a comprehensive comparison with observations from the GPS. The latest version of the NeQuick (the NeQuick-2, which is currently recommended by the ITU [35] is the one used for this comparison. The NeQuick-2 used in this chapter is the windows executable program created from the FORTRAN source code, and was obtained from the Ionosphere Radio propagation Unit of the T/ICT4D Laboratory (https://t-ict4d.ictp.it/nequick2/source-code).
For the purpose of visual illustration, the diurnal VTEC profiles from GPS observations for four selected days, over the OSGF station, are illustrated in Figure 6(a)–(d) alongside corresponding VTEC predictions from the NeQuick, the IRI-Plas model, and the neural network (NN) model developed in this chapter. Figure 6(a)–(d), respectively, represents diurnal VTEC profiles over the OSGF station for 20th March 2012 (the March equinox day), 21st June 2012 (the June solstice day), 22nd September 2012 (the September equinox day), and 21st December 2012 (the December solstice day).
Diurnal VTEC plots over the OSGF stations for days of (a) March Equinox, (b) June Solstice, (c) September Equinox, and (d) December Solstice, in year 2012.
Figure 6 clearly indicates that the VTEC predictions from the NN model developed in this chapter were closer to the GPS VTEC observations in most of the times than the VTEC predictions of the IRI-Plas and NeQuick. Table 2 summarizes the RMSEs (computed using Eq. (6)) for each of the days and models illustrated in Figure 6. The RMSEs for each of the models were computed with reference to the GPS observations. Table 2 shows that the prediction errors for the NN model were predominantly lower than for the other two models, except for the December solstice day when the NeQuick prediction error was lower.
Diurnal RMSEs of the 3 models for the days illustrated in Figure 6.
Asides the demonstrated capability of neural networks to very accurately learn and predict variations in the ionosphere, the better performance of the NN model could also be linked to the fact that more volume of regional GPS data (GPS data from the Nigerian region) were used in the NN model than the volume used in either of the NeQuick or IRI-Plas models.
A regional VTEC model over Nigeria was developed using the method of computer neural networks and GPS-VTEC data from 14 stations spanning the period from years 2011 to 2016. A total of seven input layer neurons (namely, Year, Day of Year, Hour of Day, Geomagnetic Longitude, Geomagnetic Latitude, SSN, and DST indices) were used to learn the studied output (GPS-TEC). By simulating 20 different networks that differed in their number of hidden layer neurons, the network with 6 hidden layer neuron was determined to be the best in terms of minimizing the prediction errors (using the RMSE as criterion for measuring the prediction error).
The neural network model was demonstrated to be proficient in predicting the VTEC variation patterns in terms of diurnal variations, seasonal variations, long-term solar cycle variations, and spatial variations across Nigeria.
When compared with two popular global ionospheric models (the NeQuick and the IRI-Plas), predictions from the neural network model was observed to be more accurate in terms of closeness to the GPS-VTEC values. Typical RMSEs for the neural network model predictions were between 1.3 and 10.8 TECU, the mean RMSE was 5.6 TECU. For the IRI-Plas model, the RMSEs were between 8.5 and 12.4 TECU, and the mean was 11.2 TECU. For the NeQuick, the RMSEs were between 2.8 and 11.0 TECU, and the mean was 6.7 TECU. The work done in this chapter further validates neural networks as excellent candidates for modeling of ionospheric parameters.
The author acknowledges the Office of the Surveyor General of the Federation (OSGF) of Nigeria for making the NIGNET GPS data available. Thanks to Dr. Gopi Seemala for providing software that was used in processing of the GPS RINEX data and for his immeasurable support during the period of carrying out the work in this chapter. The author also appreciates developers of the NeQuick and IRI-Plas models for making their models available. I thank the WDC and the WDC-SILSO for, respectively, making DST index and SSN data available. Thanks to developers of the Apex Coordinate Conversion Utility Software that was also used in this chapter. Most importantly, I thank the Centre for Atmospheric Research, the Indian Institute of Geomagnetism, and the CV Raman Fellowship for providing support and opportunity to carry out the work in this chapter. I heartily appreciate the Mathworks® for providing me with sponsored license for the MATLAB software that was used for this chapter.
Face recognition is a central issue in computer vision and pattern recognition. The variations in lighting conditions, pose and viewpoint changes, facial expressions, makeup, aging, and occlusion are challenges that significantly affect recognition accuracy. Generally, the challenges in face recognition can be classified into four main categories as follows:
In face recognition, image representation (IR) techniques play an important role in improving the accuracy performance. Commonly, an IR system is to transform the input signal into a new representation which reduces its dimensionality and explicates its latent structures. Over the past decades, the subspace methods, such as principal component analysis (PCA) [26], linear discriminant analysis (LAD) [27, 28], and nonnegative matrix factorization (NMF) [29, 30] have been successfully used in feature extraction. In particularly, PCA is known as a powerful technique for dimensionality reduction and multivariate analysis. PCA seeks a linear combination of variables such that the maximum variance is extracted from the variables by projecting data onto an orthogonal base which is represented in the directions of largest variance. In image representation, eigenfaces (PCA) result in dense representations for facial images, which mainly applied the global structure of the whole facial image. Likewise, LAD finds a linear transformation that maximizes discrimination between classes.
NMF is known as an unsupervised data-driven approach in which all elements of the decomposed matrix and the obtained matrix factors are forced to be nonnegative. Furthermore, NMF is able to represent an object as various parts, for instance, a human face can be decomposed into eyes, lips, and other elements. In order to make NMF algorithms more efficient, one has proposed some constraints into the cost function such as sparsity [31, 32], orthogonally [33], discrimination [34], graph regularization [35, 36], and pixel dispersion penalty [37]. Additionally, proposing an appropriate distance metric for an NMF model plays an important role in enhancing the efficacy of the estimated linear subspace of the given data. NMF techniques commonly apply the squared Frobenius norm (Fr) or the generalized Kullback–Leibler (KL) divergence for the independent and identically distributed noise data. But in many cases, they produce an arbitrarily biased subspace when data is corrupted by outliers [38]. To overcome this drawback,
Recently, matrix factorization techniques have been extended to complex matrix factorizations (CMFs) where the input data are complex matrices. These models have been obtaining promising results in facial expression recognition and data representation tasks [43, 44, 45]. The main idea of complex methods for face and facial expression recognition is that the original signal is projected on to the complex field by a mapping such that the distances of two data points in the original space and projection space are equivalent. Particularly, by transforming the real values of pixel intensive to complex domain, it is shown that the squared Frobenius norm of corresponding complex vectors and the cosine dissimilarity of real-valued vectors are equivalent. As a result, the real optimization problem with cosine divergence is replaced by optimizing a complex function with the Frobenius norm. Most of the mentioned CMF models were applied to facial expression and object recognition.
In this chapter, we present two complex matrix factorization-based models for face recognition. In the following sections, we denote
The image analysis methods on the complex domain, which are called structured complex matrix factorization (StCMF) and constrained complex matrix factorization (CoCMF), are proposed.
In complex domain, the updating rule for StCMF and CoCMF is derived based on gradient descent method.
A thorough experimental study on face recognition is conducted, the results show that the proposed StCMF and CoCMF yield better performance compared to extensions of the real NMFs.
Assume that we are given an initial data matrix
where
Most NMF techniques estimate the linear subspace of the given data by the Frobenius norm (F) or the generalized Kullback–Leibler (KL) divergence which have the following forms:
The problem (1) is non-convex; thus, it may result in several local minimal solutions. To find an optimization solution, the iterative methods are commonly used. Generally, there are three classes of algorithms for solving this problem including multiplicative update, gradient descent, and alternating nonnegative least squares algorithms. The most popular approach to solve (1) is the multiplicative update rules proposed by Lee and Seung [30]. For example, the iteratively updating rules of a Frobenius NMF cost function are given by
Given the representations of two images,
One of interesting properties of the cosine distance measurement is suppression outlier which is proved in [46]. The comparison between Frobenius norm and cosine divergence is showed in Figure 1. Liwiki et al. [46] show that the Frobenius distance between the original and the same subject is smaller; in contrary, a large distance between the original image and the image of a different person or occlusion image results from the cosine-based measure.
Sample images for making comparison between dissimilarity measures.
Let us consider two mappings:
and
The nonlinear function
It is proven that the cosine dissimilarity distance of a pair of data in the input real space equals to the Frobenius distance of the corresponding data in complex domain [47]. This observation is the first motivation of StCMF and CoCMF by mapping the samples into the complex space with a nonlinear mapping function
Any function of a complex variable
A necessary condition for
In case of real-valued function of complex variables, we also have one special property which is useful for optimization theory described later.
Let the input data matrix
The idea of structured complex matrix factorization (StCMF) is to build a learned base which is embedded within original space. The basis matrix in StCMF is constructed by the linear combination of the complex training examples. Given the complex data matrix
where
Considering a dataset of
Note that
In [51], Zhou et al. illustrated that the small-cone constraint on the bases
Since 0 < det(
It can be seen that (12) and (16) are non-convex minimization problems with respect to both variables
Fixing
Algorithm 1: |
---|
Input: Output: 1. Initialize any feasible 2. Iterations, for k = 1, 2, … |
Then,
Taking advanced of Wirtinger calculus, the gradient is evaluated in the forms
We summarize the projected gradient method for optimizing (21) and (22) in
To investigate the recognition performance of the proposed StCMF and CoCMF methods, we have conducted extensive experiments on the ORL dataset [55] and the Georgia Tech face dataset [56] in two scenarios for face recognitions including holistic face and key point occluded face.
First, we give brief description about the data collections and experiment setting. Second, the performance comparisons and corresponding results are shown.
The ORL dataset contains 400 grayscale images corresponding to 40 people’s face. The images were captured at different times, under different lighting conditions, with different facial expression (open or close eyes, smiling or non-smiling) and facial details (glasses or no glasses). All the face images are manually aligned and cropped. For the computational efficiency, each cropped image is resized to 28 × 23 for face recognition without occlusion and 32 × 32 pixels for face recognition with occlusion. Figure 2 shows some instances of such random face on ORL dataset.
Sample facial images from ORL dataset [
The Georgia Tech face dataset (GT) contains images of 50 people taken during 1999 and stored in JPEG format. For each individual, there are 15 color images captured at resolution of 640 × 480 pixels. Most of the images were taken in two different sessions to take into account the variations in illumination conditions, facial expression, and appearance. In our experiments, original images are normalized, cropped and scaled into 31 × 23 pixels, and finally converted into gray level images. Examples of GT dataset are shown in Figure 3.
Sample facial images from GT dataset [
We use the nearest neighbor (NN) classifier for all face recognition with/without occlusion experiments. The platform was a 3.0 GHz Pentium V with 1024 MB RAM running Windows. Code was written in MATLAB.
For this case, in order to evaluate the performance of the proposed StCMF and CoCMF, we make the comparisons with seven representative algorithms, namely, NMF [29], P-NMF [57], P-NMF (Fr) [58], P-NMF (KL) [58], OPNMF (Fr) [59], OPNMF (KL) [59], NNDSVD-NMF [60], and GPNMF [60]. Different training numbers ranging from five to nine images were randomly chosen from each individual to construct the training set, and the rest images constitute the test set which was used to estimate the accuracy of face recognition [61]. The learning basic images in all selected algorithms are
No. Trains | StCMF | CoCMF | GPNMF | NMF | PNMF | P-NMF (Fr) | P-NMF (KL) | OPNMF (Fr) | OPNMF (KL) | NNDSVD-NMF |
---|---|---|---|---|---|---|---|---|---|---|
5 | 86.5 | 84.5 | 82.4 | 83.7 | 85.0 | 80.0 | 79.0 | 43.0 | ||
6 | 87.5 | 84.4 | 85.81 | 85 | 84.4 | 83.0 | 82.0 | 39.3 | ||
7 | 87.5 | 83.3 | 87.33 | 85.6 | 85.9 | 84.4 | 80.0 | 36.8 | ||
8 | 88.75 | 88.75 | 88.5 | 88.8 | 88.0 | 84.3 | 83.0 | 40.8 | ||
9 | 92.5 | 85 | 90.75 | 87.25 | 87.5 | 84.0 | 83.0 | 42.3 | ||
Avg. | 88.55 | 85.19 | 86.96 | 86.07 | 86.16 | 83.14 | 81.4 | 40.44 |
Face recognition accuracy on the ORL dataset with different train numbers.
Table 1 shows the detailed recognition accuracies of compared algorithms. As can be seen, our algorithms significantly outperform the other algorithms in all the cases. Almost algorithms achieve the best accuracy when the number of training face images per class is eight exceptionally our proposed methods and GPNMF. Besides, there is the same trend between the number of training images and accuracy rate; that is, the lower training numbers lead to a decreasing rate of recognition. StCMF achieves the best performance (97.50%) when the number of training samples is chosen largest. However, CoCMF achieves higher improvement in general.
It is observed that the above-selected algorithms employ a different kind of measurements such as Frobenius (Fr) and Kullback–Leibler (KL) and add more graph to regularize as well as adjust basic NMF to projective NMF. In a reprocessing image, centered aligning image technique is applied for other methods to enhance effective recognition rate that cannot be focused on our StCMF and CoCMF models. However, the best recognition rate of all obtained by our proposed CoCMF method which has extra regularizes term.
One of the difficulties in NMF is the estimation of the number of components or
Table 2 shows the recognition rates versus feature dimension by the competing methods on GT dataset. GT dataset exists with many challenging samples that are harder to recognize. Thus, the performance of all methods is lower than those of ORL dataset. In this dataset, the implement was done similarly as those in the previous section in choosing algorithms to compare as well as dividing randomly into two different sets, each containing a different number of testing and training images. In our experiments, we set
No. Trains | StCMF | CoCMF | GPNMF | NMF | PNMF | P-NMF (Fr) | P-NMF (KL) | OPNMF (Fr) | OPNMF (KL) | NNDSVD-NMF |
---|---|---|---|---|---|---|---|---|---|---|
5 | 59.14 | 54.70 | 46.84 | 58.90 | 57.97 | 57.89 | 48.08 | 23.80 | ||
7 | 60.96 | 59.38 | 52.50 | 60.20 | 60.88 | 60.44 | 48.68 | 23.83 | ||
9 | 62.5 | 62.40 | 54.93 | 64.03 | 63.35 | 62.48 | 48.84 | 24.30 | ||
11 | 65.37 | 65.20 | 57.25 | 63.75 | 63.38 | 63.17 | 49.36 | 27.35 | ||
13 | 69.00 | 67.40 | 61.60 | 65.60 | 64.05 | 63.50 | 49.50 | 30.20 | ||
Avg. | 63.39 | 61.82 | 54.63 | 62.50 | 61.93 | 61.50 | 48.90 | 25.90 |
Face recognition accuracy on the GT dataset with different train numbers.
For a more convincing experimental assessment of the power of our proposed models in occlusion processing, we test the performance on occluded images of ORL database. In cropped 112 × 92 dimension test image gallery, occlusion was simulated by using a sheltering patch with different size ranges in set {10 × 10, 15 × 15, 20 × 20, 25 × 25, 30 × 30} and placed at random locations before resized in 28 × 21. Figure 4 shows examples of occluded ORL images.
Occluded face samples from ORL dataset with patch sizes of 15 × 15, 20 × 20, 25 × 25, 30 × 30, and 35 × 35.
In this experiment, we take randomly the training images with the ratio 4:6 for training/testing and test several times on each sort of percent of randomly occluded test image. Table 3 shows the detailed recognition accuracy on all selected algorithms and our proposed methods. It can be seen that the recognition rate of all methods is increased when the size of occlusion batch is decreased. Obviously, StCMF and CoCMF outperform other tested approaches even if occlusion. This reveals that StCMF and CoCMF are more robust outlier than the other.
Occluded Size | StCMF | CoCMF | GPNMF | NMF | PNMF | P-NMF (Fr) | P-NMF (KL) | OPNMF (Fr) | OPNMF (KL) | NNDSVD-NMF |
---|---|---|---|---|---|---|---|---|---|---|
15×15 | 75.16 | 74.32 | 72.55 | 69.16 | 71.25 | 74.18 | 45.16 | 54.46 | ||
20×20 | 64.52 | 65.45 | 62.15 | 67.52 | 71.23 | 65.00 | 41.52 | 25.62 | ||
25×25 | 65.54 | 55.18 | 52.38 | 65.54 | 62.19 | 55.00 | 35.54 | 19.83 | ||
30×30 | 54.53 | 45.62 | 43.87 | 48.53 | 55.21 | 45.89 | 28.53 | 13.22 | ||
35×35 | 43.25 | 33.63 | 31.06 | 43.25 | 38.79 | 33.39 | 23.25 | 16.13 | ||
Avg. | 60.60 | 54.84 | 52.40 | 58.80 | 59.73 | 54.69 | 34.80 | 25.85 |
Face recognition accuracy on the occluded ORL image with different occlusion sizes.
In this paper, we have proposed a new approach to complex matrix factorization to face recognition. Preliminary experimental results show that StCMF and CoCMF achieve promising results for face recognition by utilizing the robustness of cosine-based dissimilarity and extend the main spirits of NMF from real number field to complex field which adds flexible constraints for the real-valued function of complex variables. We have also noted how strong is the proficiency of StCMF as well as CoCMF on face recognition task. Our proposed methods are simple frameworks which do not need more complicated regularizes like NMFs in the real domain. We believe that this capability of proposed methods will be stable in other application tasks. In future work, three aspects of the proposed system will be centered on. First, we add more regularized rules into objective function to a range of further application such as speech and sound processing. Second, we employ other classifiers such as complex neural network or complex SVM to treat well the complex-valued feature. Last, kernel methods will be exploited in both feature extraction and classification of StCMF and CoCMF constructed paradigm to develop the performance of nonlinear contexts.
This research is partially supported by the Ministry of Science and Technology under Grant Number 108-2634-F-008-004 through Pervasive Artificial Intelligence Research (PAIR) Labs, Taiwan.
"I work with IntechOpen for a number of reasons: their professionalism, their mission in support of Open Access publishing, and the quality of their peer-reviewed publications, but also because they believe in equality. Throughout the world, we are seeing progress in attracting, retaining, and promoting women in STEMM. IntechOpen are certainly supporting this work globally by empowering all scientists and ensuring that women are encouraged and enabled to publish and take leading roles within the scientific community." Dr. Catrin Rutland, University of Nottingham, UK
",metaTitle:"Advantages of Publishing with IntechOpen",metaDescription:"We have more than a decade of experience in Open Access publishing. \n\n ",metaKeywords:null,canonicalURL:null,contentRaw:'[{"type":"htmlEditorComponent","content":"We have more than a decade of experience in Open Access publishing. The advantages of publishing with IntechOpen include:
\\n\\nOur platform – IntechOpen is the world’s leading publisher of OA books, built by scientists, for scientists.
\\n\\nOur reputation – Everything we publish goes through a two-stage peer review process. We’re proud to count Nobel laureates among our esteemed authors. We meet European Commission standards for funding, and the research we’ve published has been funded by the Bill and Melinda Gates Foundation and the Wellcome Trust, among others. IntechOpen is a member of all relevant trade associations (including the STM Association and the Association of Learned and Professional Society Publishers) and has a selection of books indexed in Web of Science's Book Citation Index.
\\n\\nOur expertise – We’ve published more than 4,500 books by more than 118,000 authors and editors.
\\n\\nOur reach – Our books have more than 130 million downloads and more than 146,150 Web of Science citations. We increase citations via indexing in all the major databases, including the Book Citation Index at Web of Science and Google Scholar.
\\n\\nOur services – The support we offer our authors and editors is second to none. Each book in our program receives the following:
\\n\\nOur end-to-end publishing service frees our authors and editors to focus on what matters: research. We empower them to shape their fields and connect with the global scientific community.
\\n\\n"In developing countries until now, advancement in science has been very limited, because insufficient economic resources are dedicated to science and education. These limitations are more marked when the scientists are women. In order to develop science in the poorest countries and decrease the gender gap that exists in scientific fields, Open Access networks like IntechOpen are essential. Free access to scientific research could contribute to ameliorating difficult life conditions and breaking down barriers." Marquidia Pacheco, National Institute for Nuclear Research (ININ), Mexico
\\n\\nInterested? Contact Ana Pantar (book.idea@intechopen.com) for more information.
\\n"}]'},components:[{type:"htmlEditorComponent",content:'We have more than a decade of experience in Open Access publishing. The advantages of publishing with IntechOpen include:
\n\nOur platform – IntechOpen is the world’s leading publisher of OA books, built by scientists, for scientists.
\n\nOur reputation – Everything we publish goes through a two-stage peer review process. We’re proud to count Nobel laureates among our esteemed authors. We meet European Commission standards for funding, and the research we’ve published has been funded by the Bill and Melinda Gates Foundation and the Wellcome Trust, among others. IntechOpen is a member of all relevant trade associations (including the STM Association and the Association of Learned and Professional Society Publishers) and has a selection of books indexed in Web of Science's Book Citation Index.
\n\nOur expertise – We’ve published more than 4,500 books by more than 118,000 authors and editors.
\n\nOur reach – Our books have more than 130 million downloads and more than 146,150 Web of Science citations. We increase citations via indexing in all the major databases, including the Book Citation Index at Web of Science and Google Scholar.
\n\nOur services – The support we offer our authors and editors is second to none. Each book in our program receives the following:
\n\nOur end-to-end publishing service frees our authors and editors to focus on what matters: research. We empower them to shape their fields and connect with the global scientific community.
\n\n"In developing countries until now, advancement in science has been very limited, because insufficient economic resources are dedicated to science and education. These limitations are more marked when the scientists are women. In order to develop science in the poorest countries and decrease the gender gap that exists in scientific fields, Open Access networks like IntechOpen are essential. Free access to scientific research could contribute to ameliorating difficult life conditions and breaking down barriers." Marquidia Pacheco, National Institute for Nuclear Research (ININ), Mexico
\n\nInterested? Contact Ana Pantar (book.idea@intechopen.com) for more information.
\n'}]},successStories:{items:[]},authorsAndEditors:{filterParams:{sort:"featured,name"},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://mts.intechopen.com/storage/users/55578/images/4574_n.png",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:5822},{group:"region",caption:"Middle and South America",value:2,count:5289},{group:"region",caption:"Africa",value:3,count:1761},{group:"region",caption:"Asia",value:4,count:10546},{group:"region",caption:"Australia and Oceania",value:5,count:909},{group:"region",caption:"Europe",value:6,count:15938}],offset:12,limit:12,total:119319},chapterEmbeded:{data:{}},editorApplication:{success:null,errors:{}},ofsBooks:{filterParams:{sort:"dateEndThirdStepPublish",topicId:"8"},books:[{type:"book",id:"10696",title:"Calorimetry - New Advances",subtitle:null,isOpenForSubmission:!0,hash:"bb239599406f0b731bbfd62c1c8dbf3f",slug:null,bookSignature:"",coverURL:"https://cdn.intechopen.com/books/images_new/10696.jpg",editedByType:null,editors:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"10504",title:"Crystallization",subtitle:null,isOpenForSubmission:!0,hash:"3478d05926950f475f4ad2825d340963",slug:null,bookSignature:"Dr. Youssef Ben Smida and Dr. Riadh Marzouki",coverURL:"https://cdn.intechopen.com/books/images_new/10504.jpg",editedByType:null,editors:[{id:"311698",title:"Dr.",name:"Youssef",surname:"Ben Smida",slug:"youssef-ben-smida",fullName:"Youssef Ben Smida"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"10697",title:"Raman Spectroscopy",subtitle:null,isOpenForSubmission:!0,hash:"6e2bfc19cc9f0b441890f24485b0de80",slug:null,bookSignature:"Dr. Marianna V. Kharlamova",coverURL:"https://cdn.intechopen.com/books/images_new/10697.jpg",editedByType:null,editors:[{id:"285875",title:"Dr.",name:"Marianna V.",surname:"Kharlamova",slug:"marianna-v.-kharlamova",fullName:"Marianna V. Kharlamova"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"10700",title:"Titanium Dioxide",subtitle:null,isOpenForSubmission:!0,hash:"d9448d83caa34d90fd58464268c869a0",slug:null,bookSignature:"Dr. Hafiz Muhammad Ali",coverURL:"https://cdn.intechopen.com/books/images_new/10700.jpg",editedByType:null,editors:[{id:"187624",title:"Dr.",name:"Hafiz Muhammad",surname:"Ali",slug:"hafiz-muhammad-ali",fullName:"Hafiz Muhammad Ali"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"10699",title:"Foams",subtitle:null,isOpenForSubmission:!0,hash:"9495e848f41431e0ffb3be12b4d80544",slug:null,bookSignature:"Dr. Marco Caniato",coverURL:"https://cdn.intechopen.com/books/images_new/10699.jpg",editedByType:null,editors:[{id:"312499",title:"Dr.",name:"Marco",surname:"Caniato",slug:"marco-caniato",fullName:"Marco Caniato"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"11000",title:"Advances in Mass Transfer",subtitle:null,isOpenForSubmission:!0,hash:"f9cdf245988fe529bcab93c3b1286ba4",slug:null,bookSignature:"Prof. Badie I. Morsi and Dr. Omar M. Basha",coverURL:"https://cdn.intechopen.com/books/images_new/11000.jpg",editedByType:null,editors:[{id:"174420",title:"Prof.",name:"Badie",surname:"Morsi",slug:"badie-morsi",fullName:"Badie Morsi"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"10997",title:"Arsenic",subtitle:null,isOpenForSubmission:!0,hash:"a40cc5d83f2f1233db31ef10c547b35c",slug:null,bookSignature:"Dr. Margarita Stoytcheva and Dr. Roumen Zlatev",coverURL:"https://cdn.intechopen.com/books/images_new/10997.jpg",editedByType:null,editors:[{id:"170080",title:"Dr.",name:"Margarita",surname:"Stoytcheva",slug:"margarita-stoytcheva",fullName:"Margarita Stoytcheva"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"10698",title:"Elemental Mass Spectrometry - Basic Principles and Analytical Applications",subtitle:null,isOpenForSubmission:!0,hash:"2b139aeb749b1a8d8453a1326b48ff20",slug:null,bookSignature:"Dr. Edgar Pinto and Prof. Agostinho Almeida",coverURL:"https://cdn.intechopen.com/books/images_new/10698.jpg",editedByType:null,editors:[{id:"345122",title:"Dr.",name:"Edgar",surname:"Pinto",slug:"edgar-pinto",fullName:"Edgar Pinto"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"10861",title:"Furans and Furan Derivatives - Recent Advances and Applications",subtitle:null,isOpenForSubmission:!0,hash:"fdfc39cecd82f91b0effac994f75c877",slug:null,bookSignature:"Dr. Anish Khan, Prof. Mohammed Muzibur Rahman, Dr. M Ramesh, Dr. Salman Ahmad Khan and Dr. Abdullah M. Asiri",coverURL:"https://cdn.intechopen.com/books/images_new/10861.jpg",editedByType:null,editors:[{id:"293058",title:"Dr.",name:"Anish",surname:"Khan",slug:"anish-khan",fullName:"Anish Khan"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"10701",title:"Alkenes - Recent Advances, New Perspectives and Applications",subtitle:null,isOpenForSubmission:!0,hash:"f6dd394ef1ca2d6472220de6a79a0d9a",slug:null,bookSignature:"Dr. Reza Davarnejad",coverURL:"https://cdn.intechopen.com/books/images_new/10701.jpg",editedByType:null,editors:[{id:"88069",title:"Dr.",name:"Reza",surname:"Davarnejad",slug:"reza-davarnejad",fullName:"Reza Davarnejad"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"11072",title:"Modern Sample Preparation Techniques",subtitle:null,isOpenForSubmission:!0,hash:"38fecf7570774c29c22a0cbca58ba570",slug:null,bookSignature:"Prof. Massoud Kaykhaii",coverURL:"https://cdn.intechopen.com/books/images_new/11072.jpg",editedByType:null,editors:[{id:"349151",title:"Prof.",name:"Massoud",surname:"Kaykhaii",slug:"massoud-kaykhaii",fullName:"Massoud Kaykhaii"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}],filtersByTopic:[{group:"topic",caption:"Agricultural and Biological Sciences",value:5,count:28},{group:"topic",caption:"Biochemistry, Genetics and Molecular Biology",value:6,count:8},{group:"topic",caption:"Business, Management and Economics",value:7,count:4},{group:"topic",caption:"Chemistry",value:8,count:10},{group:"topic",caption:"Computer and Information Science",value:9,count:10},{group:"topic",caption:"Earth and Planetary Sciences",value:10,count:10},{group:"topic",caption:"Engineering",value:11,count:26},{group:"topic",caption:"Environmental Sciences",value:12,count:3},{group:"topic",caption:"Immunology and Microbiology",value:13,count:4},{group:"topic",caption:"Materials Science",value:14,count:7},{group:"topic",caption:"Mathematics",value:15,count:3},{group:"topic",caption:"Medicine",value:16,count:51},{group:"topic",caption:"Neuroscience",value:18,count:3},{group:"topic",caption:"Pharmacology, Toxicology and Pharmaceutical Science",value:19,count:3},{group:"topic",caption:"Physics",value:20,count:4},{group:"topic",caption:"Psychology",value:21,count:4},{group:"topic",caption:"Robotics",value:22,count:2},{group:"topic",caption:"Social Sciences",value:23,count:3},{group:"topic",caption:"Technology",value:24,count:1},{group:"topic",caption:"Veterinary Medicine and Science",value:25,count:2}],offset:12,limit:12,total:11},popularBooks:{featuredBooks:[{type:"book",id:"9154",title:"Spinal Deformities in Adolescents, Adults and Older Adults",subtitle:null,isOpenForSubmission:!1,hash:"313f1dffa803b60a14ff1e6966e93d91",slug:"spinal-deformities-in-adolescents-adults-and-older-adults",bookSignature:"Josette Bettany-Saltikov and Gokulakannan Kandasamy",coverURL:"https://cdn.intechopen.com/books/images_new/9154.jpg",editors:[{id:"94802",title:"Dr.",name:"Josette",middleName:null,surname:"Bettany-Saltikov",slug:"josette-bettany-saltikov",fullName:"Josette Bettany-Saltikov"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"7030",title:"Satellite Systems",subtitle:"Design, Modeling, Simulation and Analysis",isOpenForSubmission:!1,hash:"b9db6d2645ef248ceb1b33ea75f38e88",slug:"satellite-systems-design-modeling-simulation-and-analysis",bookSignature:"Tien Nguyen",coverURL:"https://cdn.intechopen.com/books/images_new/7030.jpg",editors:[{id:"210657",title:"Dr.",name:"Tien M.",middleName:"Manh",surname:"Nguyen",slug:"tien-m.-nguyen",fullName:"Tien M. Nguyen"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"10201",title:"Post-Transition Metals",subtitle:null,isOpenForSubmission:!1,hash:"cc7f53ff5269916e3ce29f65a51a87ae",slug:"post-transition-metals",bookSignature:"Mohammed Muzibur Rahman, Abdullah Mohammed Asiri, Anish Khan, Inamuddin and Thamer Tabbakh",coverURL:"https://cdn.intechopen.com/books/images_new/10201.jpg",editors:[{id:"24438",title:"Prof.",name:"Mohammed Muzibur",middleName:null,surname:"Rahman",slug:"mohammed-muzibur-rahman",fullName:"Mohammed Muzibur Rahman"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"10413",title:"A Collection of Papers on Chaos Theory and Its Applications",subtitle:null,isOpenForSubmission:!1,hash:"900b71b164948830fec3d6254b7881f7",slug:"a-collection-of-papers-on-chaos-theory-and-its-applications",bookSignature:"Paul Bracken and Dimo I. Uzunov",coverURL:"https://cdn.intechopen.com/books/images_new/10413.jpg",editors:[{id:"92883",title:"Prof.",name:"Paul",middleName:null,surname:"Bracken",slug:"paul-bracken",fullName:"Paul Bracken"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"8472",title:"Bioactive Compounds in Nutraceutical and Functional Food for Good Human Health",subtitle:null,isOpenForSubmission:!1,hash:"8855452919b8495810ef8e88641feb20",slug:"bioactive-compounds-in-nutraceutical-and-functional-food-for-good-human-health",bookSignature:"Kavita Sharma, Kanchan Mishra, Kula Kamal Senapati and Corina Danciu",coverURL:"https://cdn.intechopen.com/books/images_new/8472.jpg",editors:[{id:"197731",title:"Dr.",name:"Kavita",middleName:null,surname:"Sharma",slug:"kavita-sharma",fullName:"Kavita Sharma"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"9515",title:"Update in Geriatrics",subtitle:null,isOpenForSubmission:!1,hash:"913e16c0ae977474b283bbd4269564c8",slug:"update-in-geriatrics",bookSignature:"Somchai Amornyotin",coverURL:"https://cdn.intechopen.com/books/images_new/9515.jpg",editors:[{id:"185484",title:"Prof.",name:"Somchai",middleName:null,surname:"Amornyotin",slug:"somchai-amornyotin",fullName:"Somchai Amornyotin"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"8148",title:"Investment Strategies in Emerging New Trends in Finance",subtitle:null,isOpenForSubmission:!1,hash:"3b714d96a68d2acdfbd7b50aba6504ca",slug:"investment-strategies-in-emerging-new-trends-in-finance",bookSignature:"Reza Gharoie Ahangar and Asma Salman",coverURL:"https://cdn.intechopen.com/books/images_new/8148.jpg",editors:[{id:"91081",title:"Dr.",name:"Reza",middleName:null,surname:"Gharoie Ahangar",slug:"reza-gharoie-ahangar",fullName:"Reza Gharoie Ahangar"}],equalEditorOne:{id:"206443",title:"Prof.",name:"Asma",middleName:null,surname:"Salman",slug:"asma-salman",fullName:"Asma Salman",profilePictureURL:"https://mts.intechopen.com/storage/users/206443/images/system/206443.png",biography:"Professor Asma Salman is a blockchain developer and Professor of Finance at the American University in the Emirates, UAE. An Honorary Global Advisor at the Global Academy of Finance and Management, USA, she completed her MBA in Finance and Accounting and earned a Ph.D. in Finance from an AACSB member, AMBA accredited, School of Management at Harbin Institute of Technology, China. Her research credentials include a one-year residency at the Brunel Business School, Brunel University, UK. Prof. Salman also served as the Dubai Cohort supervisor for DBA students under the Nottingham Business School, UK, for seven years and is currently a Ph.D. supervisor at the University of Northampton, UK, where she is a visiting fellow. She also served on the Board of Etihad Airlines during 2019–2020. One of her recent articles on “Bitcoin and Blockchain” gained wide visibility and she is an active speaker on Fintech, blockchain, and crypto events around the GCC. She holds various professional certifications including Chartered Fintech Professional (USA), Certified Financial Manager (USA), Women in Leadership and Management in Higher Education, (UK), and Taxation GCC VAT Compliance, (UK). She recently won an award for “Blockchain Trainer of the Year” from Berkeley Middle East. Other recognitions include the Women Leadership Impact Award by H.E First Lady of Armenia, Research Excellence Award, and the Global Inspirational Women Leadership Award by H.H Sheikh Juma Bin Maktoum Juma Al Maktoum.",institutionString:"American University in the Emirates",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"2",institution:{name:"American University in the Emirates",institutionURL:null,country:{name:"United Arab Emirates"}}},equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"9959",title:"Biomedical Signal and Image Processing",subtitle:null,isOpenForSubmission:!1,hash:"22b87a09bd6df065d78c175235d367c8",slug:"biomedical-signal-and-image-processing",bookSignature:"Yongxia Zhou",coverURL:"https://cdn.intechopen.com/books/images_new/9959.jpg",editors:[{id:"259308",title:"Dr.",name:"Yongxia",middleName:null,surname:"Zhou",slug:"yongxia-zhou",fullName:"Yongxia Zhou"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"9685",title:"Agroecosystems",subtitle:"Very Complex Environmental Systems",isOpenForSubmission:!1,hash:"c44f7b43a9f9610c243dc32300d37df6",slug:"agroecosystems-very-complex-environmental-systems",bookSignature:"Marcelo L. Larramendy and Sonia Soloneski",coverURL:"https://cdn.intechopen.com/books/images_new/9685.jpg",editors:[{id:"14764",title:"Dr.",name:"Marcelo L.",middleName:null,surname:"Larramendy",slug:"marcelo-l.-larramendy",fullName:"Marcelo L. Larramendy"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"9385",title:"Renewable Energy",subtitle:"Technologies and Applications",isOpenForSubmission:!1,hash:"a6b446d19166f17f313008e6c056f3d8",slug:"renewable-energy-technologies-and-applications",bookSignature:"Tolga Taner, Archana Tiwari and Taha Selim Ustun",coverURL:"https://cdn.intechopen.com/books/images_new/9385.jpg",editors:[{id:"197240",title:"Associate Prof.",name:"Tolga",middleName:null,surname:"Taner",slug:"tolga-taner",fullName:"Tolga Taner"}],equalEditorOne:{id:"186791",title:"Dr.",name:"Archana",middleName:null,surname:"Tiwari",slug:"archana-tiwari",fullName:"Archana Tiwari",profilePictureURL:"https://mts.intechopen.com/storage/users/186791/images/system/186791.jpg",biography:"Dr. Archana Tiwari is Associate Professor at Amity University, India. Her research interests include renewable sources of energy from microalgae and further utilizing the residual biomass for the generation of value-added products, bioremediation through microalgae and microbial consortium, antioxidative enzymes and stress, and nutraceuticals from microalgae. She has been working on algal biotechnology for the last two decades. She has published her research in many international journals and has authored many books and chapters with renowned publishing houses. She has also delivered talks as an invited speaker at many national and international conferences. Dr. Tiwari is the recipient of several awards including Researcher of the Year and Distinguished Scientist.",institutionString:"Amity University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"3",totalChapterViews:"0",totalEditedBooks:"1",institution:{name:"Amity University",institutionURL:null,country:{name:"India"}}},equalEditorTwo:{id:"197609",title:"Prof.",name:"Taha Selim",middleName:null,surname:"Ustun",slug:"taha-selim-ustun",fullName:"Taha Selim Ustun",profilePictureURL:"https://mts.intechopen.com/storage/users/197609/images/system/197609.jpeg",biography:"Dr. Taha Selim Ustun received a Ph.D. in Electrical Engineering from Victoria University, Melbourne, Australia. He is a researcher with the Fukushima Renewable Energy Institute, AIST (FREA), where he leads the Smart Grid Cybersecurity Laboratory. Prior to that, he was a faculty member with the School of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA. His current research interests include power systems protection, communication in power networks, distributed generation, microgrids, electric vehicle integration, and cybersecurity in smart grids. He serves on the editorial boards of IEEE Access, IEEE Transactions on Industrial Informatics, Energies, Electronics, Electricity, World Electric Vehicle and Information journals. Dr. Ustun is a member of the IEEE 2004 and 2800, IEC Renewable Energy Management WG 8, and IEC TC 57 WG17. He has been invited to run specialist courses in Africa, India, and China. He has delivered talks for the Qatar Foundation, the World Energy Council, the Waterloo Global Science Initiative, and the European Union Energy Initiative (EUEI). His research has attracted funding from prestigious programs in Japan, Australia, the European Union, and North America.",institutionString:"Fukushima Renewable Energy Institute, AIST (FREA)",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"National Institute of Advanced Industrial Science and Technology",institutionURL:null,country:{name:"Japan"}}},equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"2160",title:"MATLAB",subtitle:"A Fundamental Tool for Scientific Computing and Engineering Applications - Volume 1",isOpenForSubmission:!1,hash:"dd9c658341fbd264ed4f8d9e6aa8ca29",slug:"matlab-a-fundamental-tool-for-scientific-computing-and-engineering-applications-volume-1",bookSignature:"Vasilios N. Katsikis",coverURL:"https://cdn.intechopen.com/books/images_new/2160.jpg",editors:[{id:"12289",title:"Prof.",name:"Vasilios",middleName:"N.",surname:"Katsikis",slug:"vasilios-katsikis",fullName:"Vasilios Katsikis"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"9161",title:"Frailty in the Elderly",subtitle:"Understanding and Managing Complexity",isOpenForSubmission:!1,hash:"a4f0f2fade8fb8ba35c405f5ad31a823",slug:"frailty-in-the-elderly-understanding-and-managing-complexity",bookSignature:"Sara Palermo",coverURL:"https://cdn.intechopen.com/books/images_new/9161.jpg",editors:[{id:"233998",title:"Ph.D.",name:"Sara",middleName:null,surname:"Palermo",slug:"sara-palermo",fullName:"Sara Palermo"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}}],offset:12,limit:12,total:5330},hotBookTopics:{hotBooks:[],offset:0,limit:12,total:null},publish:{},publishingProposal:{success:null,errors:{}},books:{featuredBooks:[{type:"book",id:"9154",title:"Spinal Deformities in Adolescents, Adults and Older Adults",subtitle:null,isOpenForSubmission:!1,hash:"313f1dffa803b60a14ff1e6966e93d91",slug:"spinal-deformities-in-adolescents-adults-and-older-adults",bookSignature:"Josette Bettany-Saltikov and Gokulakannan Kandasamy",coverURL:"https://cdn.intechopen.com/books/images_new/9154.jpg",editors:[{id:"94802",title:"Dr.",name:"Josette",middleName:null,surname:"Bettany-Saltikov",slug:"josette-bettany-saltikov",fullName:"Josette Bettany-Saltikov"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"7030",title:"Satellite Systems",subtitle:"Design, Modeling, Simulation and Analysis",isOpenForSubmission:!1,hash:"b9db6d2645ef248ceb1b33ea75f38e88",slug:"satellite-systems-design-modeling-simulation-and-analysis",bookSignature:"Tien Nguyen",coverURL:"https://cdn.intechopen.com/books/images_new/7030.jpg",editors:[{id:"210657",title:"Dr.",name:"Tien M.",middleName:"Manh",surname:"Nguyen",slug:"tien-m.-nguyen",fullName:"Tien M. Nguyen"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"8472",title:"Bioactive Compounds in Nutraceutical and Functional Food for Good Human Health",subtitle:null,isOpenForSubmission:!1,hash:"8855452919b8495810ef8e88641feb20",slug:"bioactive-compounds-in-nutraceutical-and-functional-food-for-good-human-health",bookSignature:"Kavita Sharma, Kanchan Mishra, Kula Kamal Senapati and Corina Danciu",coverURL:"https://cdn.intechopen.com/books/images_new/8472.jpg",editors:[{id:"197731",title:"Dr.",name:"Kavita",middleName:null,surname:"Sharma",slug:"kavita-sharma",fullName:"Kavita Sharma"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"10201",title:"Post-Transition Metals",subtitle:null,isOpenForSubmission:!1,hash:"cc7f53ff5269916e3ce29f65a51a87ae",slug:"post-transition-metals",bookSignature:"Mohammed Muzibur Rahman, Abdullah Mohammed Asiri, Anish Khan, Inamuddin and Thamer Tabbakh",coverURL:"https://cdn.intechopen.com/books/images_new/10201.jpg",editors:[{id:"24438",title:"Prof.",name:"Mohammed Muzibur",middleName:null,surname:"Rahman",slug:"mohammed-muzibur-rahman",fullName:"Mohammed Muzibur Rahman"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"10413",title:"A Collection of Papers on Chaos Theory and Its Applications",subtitle:null,isOpenForSubmission:!1,hash:"900b71b164948830fec3d6254b7881f7",slug:"a-collection-of-papers-on-chaos-theory-and-its-applications",bookSignature:"Paul Bracken and Dimo I. Uzunov",coverURL:"https://cdn.intechopen.com/books/images_new/10413.jpg",editors:[{id:"92883",title:"Prof.",name:"Paul",middleName:null,surname:"Bracken",slug:"paul-bracken",fullName:"Paul Bracken"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"9515",title:"Update in Geriatrics",subtitle:null,isOpenForSubmission:!1,hash:"913e16c0ae977474b283bbd4269564c8",slug:"update-in-geriatrics",bookSignature:"Somchai Amornyotin",coverURL:"https://cdn.intechopen.com/books/images_new/9515.jpg",editors:[{id:"185484",title:"Prof.",name:"Somchai",middleName:null,surname:"Amornyotin",slug:"somchai-amornyotin",fullName:"Somchai Amornyotin"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"8148",title:"Investment Strategies in Emerging New Trends in Finance",subtitle:null,isOpenForSubmission:!1,hash:"3b714d96a68d2acdfbd7b50aba6504ca",slug:"investment-strategies-in-emerging-new-trends-in-finance",bookSignature:"Reza Gharoie Ahangar and Asma Salman",coverURL:"https://cdn.intechopen.com/books/images_new/8148.jpg",editors:[{id:"91081",title:"Dr.",name:"Reza",middleName:null,surname:"Gharoie Ahangar",slug:"reza-gharoie-ahangar",fullName:"Reza Gharoie Ahangar"}],equalEditorOne:{id:"206443",title:"Prof.",name:"Asma",middleName:null,surname:"Salman",slug:"asma-salman",fullName:"Asma Salman",profilePictureURL:"https://mts.intechopen.com/storage/users/206443/images/system/206443.png",biography:"Professor Asma Salman is a blockchain developer and Professor of Finance at the American University in the Emirates, UAE. An Honorary Global Advisor at the Global Academy of Finance and Management, USA, she completed her MBA in Finance and Accounting and earned a Ph.D. in Finance from an AACSB member, AMBA accredited, School of Management at Harbin Institute of Technology, China. Her research credentials include a one-year residency at the Brunel Business School, Brunel University, UK. Prof. Salman also served as the Dubai Cohort supervisor for DBA students under the Nottingham Business School, UK, for seven years and is currently a Ph.D. supervisor at the University of Northampton, UK, where she is a visiting fellow. She also served on the Board of Etihad Airlines during 2019–2020. One of her recent articles on “Bitcoin and Blockchain” gained wide visibility and she is an active speaker on Fintech, blockchain, and crypto events around the GCC. She holds various professional certifications including Chartered Fintech Professional (USA), Certified Financial Manager (USA), Women in Leadership and Management in Higher Education, (UK), and Taxation GCC VAT Compliance, (UK). She recently won an award for “Blockchain Trainer of the Year” from Berkeley Middle East. Other recognitions include the Women Leadership Impact Award by H.E First Lady of Armenia, Research Excellence Award, and the Global Inspirational Women Leadership Award by H.H Sheikh Juma Bin Maktoum Juma Al Maktoum.",institutionString:"American University in the Emirates",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"2",institution:{name:"American University in the Emirates",institutionURL:null,country:{name:"United Arab Emirates"}}},equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"2160",title:"MATLAB",subtitle:"A Fundamental Tool for Scientific Computing and Engineering Applications - Volume 1",isOpenForSubmission:!1,hash:"dd9c658341fbd264ed4f8d9e6aa8ca29",slug:"matlab-a-fundamental-tool-for-scientific-computing-and-engineering-applications-volume-1",bookSignature:"Vasilios N. Katsikis",coverURL:"https://cdn.intechopen.com/books/images_new/2160.jpg",editors:[{id:"12289",title:"Prof.",name:"Vasilios",middleName:"N.",surname:"Katsikis",slug:"vasilios-katsikis",fullName:"Vasilios Katsikis"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"9385",title:"Renewable Energy",subtitle:"Technologies and Applications",isOpenForSubmission:!1,hash:"a6b446d19166f17f313008e6c056f3d8",slug:"renewable-energy-technologies-and-applications",bookSignature:"Tolga Taner, Archana Tiwari and Taha Selim Ustun",coverURL:"https://cdn.intechopen.com/books/images_new/9385.jpg",editors:[{id:"197240",title:"Associate Prof.",name:"Tolga",middleName:null,surname:"Taner",slug:"tolga-taner",fullName:"Tolga Taner"}],equalEditorOne:{id:"186791",title:"Dr.",name:"Archana",middleName:null,surname:"Tiwari",slug:"archana-tiwari",fullName:"Archana Tiwari",profilePictureURL:"https://mts.intechopen.com/storage/users/186791/images/system/186791.jpg",biography:"Dr. Archana Tiwari is Associate Professor at Amity University, India. Her research interests include renewable sources of energy from microalgae and further utilizing the residual biomass for the generation of value-added products, bioremediation through microalgae and microbial consortium, antioxidative enzymes and stress, and nutraceuticals from microalgae. She has been working on algal biotechnology for the last two decades. She has published her research in many international journals and has authored many books and chapters with renowned publishing houses. She has also delivered talks as an invited speaker at many national and international conferences. Dr. Tiwari is the recipient of several awards including Researcher of the Year and Distinguished Scientist.",institutionString:"Amity University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"3",totalChapterViews:"0",totalEditedBooks:"1",institution:{name:"Amity University",institutionURL:null,country:{name:"India"}}},equalEditorTwo:{id:"197609",title:"Prof.",name:"Taha Selim",middleName:null,surname:"Ustun",slug:"taha-selim-ustun",fullName:"Taha Selim Ustun",profilePictureURL:"https://mts.intechopen.com/storage/users/197609/images/system/197609.jpeg",biography:"Dr. Taha Selim Ustun received a Ph.D. in Electrical Engineering from Victoria University, Melbourne, Australia. He is a researcher with the Fukushima Renewable Energy Institute, AIST (FREA), where he leads the Smart Grid Cybersecurity Laboratory. Prior to that, he was a faculty member with the School of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA. His current research interests include power systems protection, communication in power networks, distributed generation, microgrids, electric vehicle integration, and cybersecurity in smart grids. He serves on the editorial boards of IEEE Access, IEEE Transactions on Industrial Informatics, Energies, Electronics, Electricity, World Electric Vehicle and Information journals. Dr. Ustun is a member of the IEEE 2004 and 2800, IEC Renewable Energy Management WG 8, and IEC TC 57 WG17. He has been invited to run specialist courses in Africa, India, and China. He has delivered talks for the Qatar Foundation, the World Energy Council, the Waterloo Global Science Initiative, and the European Union Energy Initiative (EUEI). His research has attracted funding from prestigious programs in Japan, Australia, the European Union, and North America.",institutionString:"Fukushima Renewable Energy Institute, AIST (FREA)",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"National Institute of Advanced Industrial Science and Technology",institutionURL:null,country:{name:"Japan"}}},equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"3568",title:"Recent Advances in Plant in vitro Culture",subtitle:null,isOpenForSubmission:!1,hash:"830bbb601742c85a3fb0eeafe1454c43",slug:"recent-advances-in-plant-in-vitro-culture",bookSignature:"Annarita Leva and Laura M. R. Rinaldi",coverURL:"https://cdn.intechopen.com/books/images_new/3568.jpg",editors:[{id:"142145",title:"Dr.",name:"Annarita",middleName:null,surname:"Leva",slug:"annarita-leva",fullName:"Annarita Leva"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}}],latestBooks:[{type:"book",id:"9515",title:"Update in Geriatrics",subtitle:null,isOpenForSubmission:!1,hash:"913e16c0ae977474b283bbd4269564c8",slug:"update-in-geriatrics",bookSignature:"Somchai Amornyotin",coverURL:"https://cdn.intechopen.com/books/images_new/9515.jpg",editedByType:"Edited by",editors:[{id:"185484",title:"Prof.",name:"Somchai",middleName:null,surname:"Amornyotin",slug:"somchai-amornyotin",fullName:"Somchai Amornyotin"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"9021",title:"Novel Perspectives of Stem Cell Manufacturing and Therapies",subtitle:null,isOpenForSubmission:!1,hash:"522c6db871783d2a11c17b83f1fd4e18",slug:"novel-perspectives-of-stem-cell-manufacturing-and-therapies",bookSignature:"Diana Kitala and Ana Colette Maurício",coverURL:"https://cdn.intechopen.com/books/images_new/9021.jpg",editedByType:"Edited by",editors:[{id:"203598",title:"Ph.D.",name:"Diana",middleName:null,surname:"Kitala",slug:"diana-kitala",fullName:"Diana Kitala"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"7030",title:"Satellite Systems",subtitle:"Design, Modeling, Simulation and Analysis",isOpenForSubmission:!1,hash:"b9db6d2645ef248ceb1b33ea75f38e88",slug:"satellite-systems-design-modeling-simulation-and-analysis",bookSignature:"Tien Nguyen",coverURL:"https://cdn.intechopen.com/books/images_new/7030.jpg",editedByType:"Edited by",editors:[{id:"210657",title:"Dr.",name:"Tien M.",middleName:"Manh",surname:"Nguyen",slug:"tien-m.-nguyen",fullName:"Tien M. Nguyen"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"10413",title:"A Collection of Papers on Chaos Theory and Its Applications",subtitle:null,isOpenForSubmission:!1,hash:"900b71b164948830fec3d6254b7881f7",slug:"a-collection-of-papers-on-chaos-theory-and-its-applications",bookSignature:"Paul Bracken and Dimo I. Uzunov",coverURL:"https://cdn.intechopen.com/books/images_new/10413.jpg",editedByType:"Edited by",editors:[{id:"92883",title:"Prof.",name:"Paul",middleName:null,surname:"Bracken",slug:"paul-bracken",fullName:"Paul Bracken"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"9154",title:"Spinal Deformities in Adolescents, Adults and Older Adults",subtitle:null,isOpenForSubmission:!1,hash:"313f1dffa803b60a14ff1e6966e93d91",slug:"spinal-deformities-in-adolescents-adults-and-older-adults",bookSignature:"Josette Bettany-Saltikov and Gokulakannan Kandasamy",coverURL:"https://cdn.intechopen.com/books/images_new/9154.jpg",editedByType:"Edited by",editors:[{id:"94802",title:"Dr.",name:"Josette",middleName:null,surname:"Bettany-Saltikov",slug:"josette-bettany-saltikov",fullName:"Josette Bettany-Saltikov"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"8148",title:"Investment Strategies in Emerging New Trends in Finance",subtitle:null,isOpenForSubmission:!1,hash:"3b714d96a68d2acdfbd7b50aba6504ca",slug:"investment-strategies-in-emerging-new-trends-in-finance",bookSignature:"Reza Gharoie Ahangar and Asma Salman",coverURL:"https://cdn.intechopen.com/books/images_new/8148.jpg",editedByType:"Edited by",editors:[{id:"91081",title:"Dr.",name:"Reza",middleName:null,surname:"Gharoie Ahangar",slug:"reza-gharoie-ahangar",fullName:"Reza Gharoie Ahangar"}],equalEditorOne:{id:"206443",title:"Prof.",name:"Asma",middleName:null,surname:"Salman",slug:"asma-salman",fullName:"Asma Salman",profilePictureURL:"https://mts.intechopen.com/storage/users/206443/images/system/206443.png",biography:"Professor Asma Salman is a blockchain developer and Professor of Finance at the American University in the Emirates, UAE. An Honorary Global Advisor at the Global Academy of Finance and Management, USA, she completed her MBA in Finance and Accounting and earned a Ph.D. in Finance from an AACSB member, AMBA accredited, School of Management at Harbin Institute of Technology, China. Her research credentials include a one-year residency at the Brunel Business School, Brunel University, UK. Prof. Salman also served as the Dubai Cohort supervisor for DBA students under the Nottingham Business School, UK, for seven years and is currently a Ph.D. supervisor at the University of Northampton, UK, where she is a visiting fellow. She also served on the Board of Etihad Airlines during 2019–2020. One of her recent articles on “Bitcoin and Blockchain” gained wide visibility and she is an active speaker on Fintech, blockchain, and crypto events around the GCC. She holds various professional certifications including Chartered Fintech Professional (USA), Certified Financial Manager (USA), Women in Leadership and Management in Higher Education, (UK), and Taxation GCC VAT Compliance, (UK). She recently won an award for “Blockchain Trainer of the Year” from Berkeley Middle East. Other recognitions include the Women Leadership Impact Award by H.E First Lady of Armenia, Research Excellence Award, and the Global Inspirational Women Leadership Award by H.H Sheikh Juma Bin Maktoum Juma Al Maktoum.",institutionString:"American University in the Emirates",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"2",institution:{name:"American University in the Emirates",institutionURL:null,country:{name:"United Arab Emirates"}}},equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"10201",title:"Post-Transition Metals",subtitle:null,isOpenForSubmission:!1,hash:"cc7f53ff5269916e3ce29f65a51a87ae",slug:"post-transition-metals",bookSignature:"Mohammed Muzibur Rahman, Abdullah Mohammed Asiri, Anish Khan, Inamuddin and Thamer Tabbakh",coverURL:"https://cdn.intechopen.com/books/images_new/10201.jpg",editedByType:"Edited by",editors:[{id:"24438",title:"Prof.",name:"Mohammed Muzibur",middleName:null,surname:"Rahman",slug:"mohammed-muzibur-rahman",fullName:"Mohammed Muzibur Rahman"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"9959",title:"Biomedical Signal and Image Processing",subtitle:null,isOpenForSubmission:!1,hash:"22b87a09bd6df065d78c175235d367c8",slug:"biomedical-signal-and-image-processing",bookSignature:"Yongxia Zhou",coverURL:"https://cdn.intechopen.com/books/images_new/9959.jpg",editedByType:"Edited by",editors:[{id:"259308",title:"Dr.",name:"Yongxia",middleName:null,surname:"Zhou",slug:"yongxia-zhou",fullName:"Yongxia Zhou"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"8472",title:"Bioactive Compounds in Nutraceutical and Functional Food for Good Human Health",subtitle:null,isOpenForSubmission:!1,hash:"8855452919b8495810ef8e88641feb20",slug:"bioactive-compounds-in-nutraceutical-and-functional-food-for-good-human-health",bookSignature:"Kavita Sharma, Kanchan Mishra, Kula Kamal Senapati and Corina Danciu",coverURL:"https://cdn.intechopen.com/books/images_new/8472.jpg",editedByType:"Edited by",editors:[{id:"197731",title:"Dr.",name:"Kavita",middleName:null,surname:"Sharma",slug:"kavita-sharma",fullName:"Kavita Sharma"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"8760",title:"Structure Topology and Symplectic Geometry",subtitle:null,isOpenForSubmission:!1,hash:"8974840985ec3652492c83e20233bf02",slug:"structure-topology-and-symplectic-geometry",bookSignature:"Kamal Shah and Min Lei",coverURL:"https://cdn.intechopen.com/books/images_new/8760.jpg",editedByType:"Edited by",editors:[{id:"231748",title:"Dr.",name:"Kamal",middleName:null,surname:"Shah",slug:"kamal-shah",fullName:"Kamal Shah"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},subject:{topic:{id:"384",title:"Chemical Biology",slug:"chemical-biology",parent:{title:"Bioorganic Chemistry",slug:"biochemistry-genetics-and-molecular-biology-bioorganic-chemistry"},numberOfBooks:37,numberOfAuthorsAndEditors:1291,numberOfWosCitations:2284,numberOfCrossrefCitations:964,numberOfDimensionsCitations:2559,videoUrl:null,fallbackUrl:null,description:null},booksByTopicFilter:{topicSlug:"chemical-biology",sort:"-publishedDate",limit:12,offset:0},booksByTopicCollection:[{type:"book",id:"8852",title:"Chemistry and Applications of Benzimidazole and its Derivatives",subtitle:null,isOpenForSubmission:!1,hash:"e95984a2b87df5a7ca051cb3345d5e7a",slug:"chemistry-and-applications-of-benzimidazole-and-its-derivatives",bookSignature:"Maria Marinescu",coverURL:"https://cdn.intechopen.com/books/images_new/8852.jpg",editedByType:"Edited by",editors:[{id:"250975",title:"Ph.D.",name:"Maria",middleName:null,surname:"Marinescu",slug:"maria-marinescu",fullName:"Maria Marinescu"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"5855",title:"Protein Phosphorylation",subtitle:null,isOpenForSubmission:!1,hash:"c5f88bc57e9b8606807624451a48a5a1",slug:"protein-phosphorylation",bookSignature:"Claude Prigent",coverURL:"https://cdn.intechopen.com/books/images_new/5855.jpg",editedByType:"Edited by",editors:[{id:"98783",title:"Dr.",name:"Claude",middleName:null,surname:"Prigent",slug:"claude-prigent",fullName:"Claude Prigent"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"5935",title:"Solubility of Polysaccharides",subtitle:null,isOpenForSubmission:!1,hash:"f2e1999c512e400b58f4065789d080ee",slug:"solubility-of-polysaccharides",bookSignature:"Zhenbo Xu",coverURL:"https://cdn.intechopen.com/books/images_new/5935.jpg",editedByType:"Edited by",editors:[{id:"176645",title:"Dr.",name:"Zhenbo",middleName:null,surname:"Xu",slug:"zhenbo-xu",fullName:"Zhenbo Xu"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"5843",title:"Quantitative Structure-activity Relationship",subtitle:null,isOpenForSubmission:!1,hash:"009d82593f285d019aaecb2670da39cf",slug:"quantitative-structure-activity-relationship",bookSignature:"Fatma Kandemirli",coverURL:"https://cdn.intechopen.com/books/images_new/5843.jpg",editedByType:"Edited by",editors:[{id:"104919",title:null,name:"Fatma",middleName:null,surname:"Kandemirli",slug:"fatma-kandemirli",fullName:"Fatma Kandemirli"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"5945",title:"Amino Acid",subtitle:"New Insights and Roles in Plant and Animal",isOpenForSubmission:!1,hash:"b7d91fed8804240b70bcc3e803f3b73a",slug:"amino-acid-new-insights-and-roles-in-plant-and-animal",bookSignature:"Toshiki Asao and Md. Asaduzzaman",coverURL:"https://cdn.intechopen.com/books/images_new/5945.jpg",editedByType:"Edited by",editors:[{id:"106510",title:"Dr.",name:"Toshiki",middleName:null,surname:"Asao",slug:"toshiki-asao",fullName:"Toshiki Asao"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"5769",title:"Fatty Acids",subtitle:null,isOpenForSubmission:!1,hash:"026ff00026816b4cca7116ca6e1e7fbd",slug:"fatty-acids",bookSignature:"Angel Catala",coverURL:"https://cdn.intechopen.com/books/images_new/5769.jpg",editedByType:"Edited by",editors:[{id:"196544",title:"Prof.",name:"Angel",middleName:null,surname:"Catala",slug:"angel-catala",fullName:"Angel Catala"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"5797",title:"Carotenoids",subtitle:null,isOpenForSubmission:!1,hash:"95f1843c0526c96e4aa0be620d8db749",slug:"carotenoids",bookSignature:"Dragan J. Cvetkovic and Goran S. Nikolic",coverURL:"https://cdn.intechopen.com/books/images_new/5797.jpg",editedByType:"Edited by",editors:[{id:"195521",title:"Prof.",name:"Dragan",middleName:"J.",surname:"Cvetkovic",slug:"dragan-cvetkovic",fullName:"Dragan Cvetkovic"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"5836",title:"Bisphenol A",subtitle:"Exposure and Health Risks",isOpenForSubmission:!1,hash:"446599b9e5cf929537d445edc546c449",slug:"bisphenol-a-exposure-and-health-risks",bookSignature:"Pinar Erkekoglu and Belma Kocer-Gumusel",coverURL:"https://cdn.intechopen.com/books/images_new/5836.jpg",editedByType:"Edited by",editors:[{id:"109978",title:"Prof.",name:"Pınar",middleName:null,surname:"Erkekoglu",slug:"pinar-erkekoglu",fullName:"Pınar Erkekoglu"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"5497",title:"Nitric Oxide Synthase",subtitle:"Simple Enzyme-Complex Roles",isOpenForSubmission:!1,hash:"be2bf109fabe37c7514acc5712b9995b",slug:"nitric-oxide-synthase-simple-enzyme-complex-roles",bookSignature:"Seyed Soheil Saeedi Saravi",coverURL:"https://cdn.intechopen.com/books/images_new/5497.jpg",editedByType:"Edited by",editors:[{id:"14680",title:"Dr.",name:"Seyed Soheil",middleName:null,surname:"Saeedi Saravi",slug:"seyed-soheil-saeedi-saravi",fullName:"Seyed Soheil Saeedi Saravi"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"5507",title:"Current Topics in Lactation",subtitle:null,isOpenForSubmission:!1,hash:"ac8a108f23ad313d4ea64202d68c7502",slug:"current-topics-in-lactation",bookSignature:"Isabel Gigli",coverURL:"https://cdn.intechopen.com/books/images_new/5507.jpg",editedByType:"Edited by",editors:[{id:"175679",title:"Dr.",name:"Isabel",middleName:null,surname:"Gigli",slug:"isabel-gigli",fullName:"Isabel Gigli"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"5508",title:"Carbohydrate",subtitle:null,isOpenForSubmission:!1,hash:"e594b777fe1d4981c5b1adbe5a40f19c",slug:"carbohydrate",bookSignature:"Mahmut Caliskan, I. Halil Kavakli and Gul Cevahir Oz",coverURL:"https://cdn.intechopen.com/books/images_new/5508.jpg",editedByType:"Edited by",editors:[{id:"51528",title:"Prof.",name:"Mahmut",middleName:null,surname:"Çalışkan",slug:"mahmut-caliskan",fullName:"Mahmut Çalışkan"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"5683",title:"Advances in Lipoprotein Research",subtitle:null,isOpenForSubmission:!1,hash:"b1bebf38c2a7e785165e7d020b1ec933",slug:"advances-in-lipoprotein-research",bookSignature:"Turgay Isbir",coverURL:"https://cdn.intechopen.com/books/images_new/5683.jpg",editedByType:"Edited by",editors:[{id:"55739",title:"Prof.",name:"Turgay",middleName:null,surname:"Isbir",slug:"turgay-isbir",fullName:"Turgay Isbir"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}],booksByTopicTotal:37,mostCitedChapters:[{id:"38477",doi:"10.5772/45943",title:"Lipid Peroxidation: Chemical Mechanism, Biological Implications and Analytical Determination",slug:"lipid-peroxidation-chemical-mechanism-biological-implications-and-analytical-determination",totalDownloads:12231,totalCrossrefCites:57,totalDimensionsCites:165,book:{slug:"lipid-peroxidation",title:"Lipid Peroxidation",fullTitle:"Lipid Peroxidation"},signatures:"Marisa Repetto, Jimena Semprine and Alberto Boveris",authors:[{id:"36452",title:"Dr.",name:"Marisa",middleName:"Gabriela",surname:"Repetto",slug:"marisa-repetto",fullName:"Marisa Repetto"}]},{id:"41116",doi:"10.5772/51572",title:"Algal Polysaccharides, Novel Applications and Outlook",slug:"algal-polysaccharides-novel-applications-and-outlook",totalDownloads:13496,totalCrossrefCites:50,totalDimensionsCites:128,book:{slug:"carbohydrates-comprehensive-studies-on-glycobiology-and-glycotechnology",title:"Carbohydrates",fullTitle:"Carbohydrates - Comprehensive Studies on Glycobiology and Glycotechnology"},signatures:"Stefan Kraan",authors:[{id:"142720",title:"Dr.",name:"Stefan",middleName:null,surname:"Kraan",slug:"stefan-kraan",fullName:"Stefan Kraan"}]},{id:"40938",doi:"10.5772/48294",title:"Dehydrogenase Activity in the Soil Environment",slug:"dehydrogenase-activity-in-the-soil-environment",totalDownloads:6093,totalCrossrefCites:53,totalDimensionsCites:126,book:{slug:"dehydrogenases",title:"Dehydrogenases",fullTitle:"Dehydrogenases"},signatures:"Agnieszka Wolińska and Zofia Stępniewska",authors:[{id:"141696",title:"Dr.",name:"Agnieszka",middleName:"Maria",surname:"Wolinska",slug:"agnieszka-wolinska",fullName:"Agnieszka Wolinska"}]}],mostDownloadedChaptersLast30Days:[{id:"53367",title:"Lactate, Not Pyruvate, Is the End Product of Glucose Metabolism via Glycolysis",slug:"lactate-not-pyruvate-is-the-end-product-of-glucose-metabolism-via-glycolysis",totalDownloads:2522,totalCrossrefCites:5,totalDimensionsCites:6,book:{slug:"carbohydrate",title:"Carbohydrate",fullTitle:"Carbohydrate"},signatures:"Avital Schurr",authors:[{id:"72322",title:"Dr.",name:"Avital",middleName:null,surname:"Schurr",slug:"avital-schurr",fullName:"Avital Schurr"}]},{id:"50574",title:"Bioinformatics for RNA‐Seq Data Analysis",slug:"bioinformatics-for-rna-seq-data-analysis",totalDownloads:3945,totalCrossrefCites:3,totalDimensionsCites:5,book:{slug:"bioinformatics-updated-features-and-applications",title:"Bioinformatics",fullTitle:"Bioinformatics - Updated Features and Applications"},signatures:"Shanrong Zhao, Baohong Zhang, Ying Zhang, William Gordon,\nSarah Du, Theresa Paradis, Michael Vincent and David von Schack",authors:[{id:"176364",title:"Dr.",name:"Shanrong",middleName:null,surname:"Zhao",slug:"shanrong-zhao",fullName:"Shanrong Zhao"}]},{id:"54169",title:"Importance of Fatty Acids in Physiopathology of Human Body",slug:"importance-of-fatty-acids-in-physiopathology-of-human-body",totalDownloads:3765,totalCrossrefCites:13,totalDimensionsCites:25,book:{slug:"fatty-acids",title:"Fatty Acids",fullTitle:"Fatty Acids"},signatures:"Katalin Nagy and Ioana-Daria Tiuca",authors:[{id:"178879",title:"Ph.D.",name:"Ioana",middleName:null,surname:"Gug",slug:"ioana-gug",fullName:"Ioana Gug"},{id:"204524",title:"Ms.",name:"Katalin",middleName:null,surname:"Nagy",slug:"katalin-nagy",fullName:"Katalin Nagy"}]},{id:"57644",title:"Polysaccharides: Structure and Solubility",slug:"polysaccharides-structure-and-solubility",totalDownloads:3260,totalCrossrefCites:15,totalDimensionsCites:47,book:{slug:"solubility-of-polysaccharides",title:"Solubility of Polysaccharides",fullTitle:"Solubility of Polysaccharides"},signatures:"Mark Q. Guo, Xinzhong Hu, Changlu Wang and Lianzhong Ai",authors:[{id:"202384",title:"Dr.",name:"Qingbin",middleName:null,surname:"Guo",slug:"qingbin-guo",fullName:"Qingbin Guo"},{id:"203883",title:"Dr.",name:"Changlu",middleName:null,surname:"Wang",slug:"changlu-wang",fullName:"Changlu Wang"},{id:"203884",title:"Prof.",name:"Xinzhong",middleName:null,surname:"Hu",slug:"xinzhong-hu",fullName:"Xinzhong Hu"}]},{id:"50934",title:"Bioinformatics: Basics, Development, and Future",slug:"bioinformatics-basics-development-and-future",totalDownloads:4496,totalCrossrefCites:3,totalDimensionsCites:5,book:{slug:"bioinformatics-updated-features-and-applications",title:"Bioinformatics",fullTitle:"Bioinformatics - Updated Features and Applications"},signatures:"Ibrokhim Y. Abdurakhmonov",authors:[{id:"213344",title:"Dr.",name:"Ibrokhim Y.",middleName:null,surname:"Abdurakhmonov",slug:"ibrokhim-y.-abdurakhmonov",fullName:"Ibrokhim Y. Abdurakhmonov"}]},{id:"38477",title:"Lipid Peroxidation: Chemical Mechanism, Biological Implications and Analytical Determination",slug:"lipid-peroxidation-chemical-mechanism-biological-implications-and-analytical-determination",totalDownloads:12232,totalCrossrefCites:57,totalDimensionsCites:165,book:{slug:"lipid-peroxidation",title:"Lipid Peroxidation",fullTitle:"Lipid Peroxidation"},signatures:"Marisa Repetto, Jimena Semprine and Alberto Boveris",authors:[{id:"36452",title:"Dr.",name:"Marisa",middleName:"Gabriela",surname:"Repetto",slug:"marisa-repetto",fullName:"Marisa Repetto"}]},{id:"52975",title:"Site‐Directed Mutagenesis by Polymerase Chain Reaction",slug:"site-directed-mutagenesis-by-polymerase-chain-reaction",totalDownloads:3652,totalCrossrefCites:0,totalDimensionsCites:0,book:{slug:"polymerase-chain-reaction-for-biomedical-applications",title:"Polymerase Chain Reaction for Biomedical Applications",fullTitle:"Polymerase Chain Reaction for Biomedical Applications"},signatures:"Fabiola Castorena‐Torres, Katia Peñuelas‐Urquides and Mario\nBermúdez de León",authors:[{id:"188810",title:"Dr.",name:"Mario",middleName:null,surname:"Bermúdez De León",slug:"mario-bermudez-de-leon",fullName:"Mario Bermúdez De León"},{id:"188821",title:"Dr.",name:"Fabiola",middleName:null,surname:"Castorena Torres",slug:"fabiola-castorena-torres",fullName:"Fabiola Castorena Torres"},{id:"198351",title:"Dr.",name:"Katia",middleName:null,surname:"Peñuelas Urquides",slug:"katia-penuelas-urquides",fullName:"Katia Peñuelas Urquides"}]},{id:"53489",title:"Effect of Quality Carbohydrates on the Prevention and Therapy of Noncommunicable Diseases: Obesity and Type 2 Diabetes",slug:"effect-of-quality-carbohydrates-on-the-prevention-and-therapy-of-noncommunicable-diseases-obesity-an",totalDownloads:1088,totalCrossrefCites:0,totalDimensionsCites:0,book:{slug:"carbohydrate",title:"Carbohydrate",fullTitle:"Carbohydrate"},signatures:"Claudia Vega and Marcela Alviña",authors:[{id:"191303",title:"Dr.",name:"Claudia",middleName:null,surname:"Vega",slug:"claudia-vega",fullName:"Claudia Vega"},{id:"191683",title:"MSc.",name:"Marcela",middleName:null,surname:"Alviña",slug:"marcela-alvina",fullName:"Marcela Alviña"},{id:"202949",title:"Prof.",name:"Hector",middleName:null,surname:"Araya",slug:"hector-araya",fullName:"Hector Araya"}]},{id:"57402",title:"Solubility of Chitin: Solvents, Solution Behaviors and Their Related Mechanisms",slug:"solubility-of-chitin-solvents-solution-behaviors-and-their-related-mechanisms",totalDownloads:3596,totalCrossrefCites:21,totalDimensionsCites:40,book:{slug:"solubility-of-polysaccharides",title:"Solubility of Polysaccharides",fullTitle:"Solubility of Polysaccharides"},signatures:"Jagadish C. Roy, Fabien Salaün, Stéphane Giraud, Ada Ferri",authors:[{id:"27644",title:"Prof.",name:"Fabien",middleName:null,surname:"Salaün",slug:"fabien-salaun",fullName:"Fabien Salaün"},{id:"150004",title:"Prof.",name:"Yan",middleName:null,surname:"Chen",slug:"yan-chen",fullName:"Yan Chen"},{id:"189338",title:"Prof.",name:"Ada",middleName:null,surname:"Ferri",slug:"ada-ferri",fullName:"Ada Ferri"},{id:"189339",title:"Dr.",name:"Stéphane",middleName:null,surname:"Giraud",slug:"stephane-giraud",fullName:"Stéphane Giraud"},{id:"189340",title:"M.Sc.",name:"Jagadish",middleName:"Chandra",surname:"Roy",slug:"jagadish-roy",fullName:"Jagadish Roy"},{id:"218812",title:"Prof.",name:"Guan",middleName:null,surname:"Jinping",slug:"guan-jinping",fullName:"Guan Jinping"}]},{id:"53176",title:"Guidelines for Successful Quantitative Gene Expression in Real- Time qPCR Assays",slug:"guidelines-for-successful-quantitative-gene-expression-in-real-time-qpcr-assays",totalDownloads:2680,totalCrossrefCites:3,totalDimensionsCites:4,book:{slug:"polymerase-chain-reaction-for-biomedical-applications",title:"Polymerase Chain Reaction for Biomedical Applications",fullTitle:"Polymerase Chain Reaction for Biomedical Applications"},signatures:"Antônio José Rocha, Rafael de Souza Miranda, Antônio Juscelino\nSudário Sousa and André Luis Coelho da Silva",authors:[{id:"188806",title:"Dr.",name:"Antônio",middleName:"José",surname:"Rocha",slug:"antonio-rocha",fullName:"Antônio Rocha"}]}],onlineFirstChaptersFilter:{topicSlug:"chemical-biology",limit:3,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[{type:"book",id:"10176",title:"Microgrids and Local Energy Systems",subtitle:null,isOpenForSubmission:!0,hash:"c32b4a5351a88f263074b0d0ca813a9c",slug:null,bookSignature:"Prof. Nick Jenkins",coverURL:"https://cdn.intechopen.com/books/images_new/10176.jpg",editedByType:null,editors:[{id:"55219",title:"Prof.",name:"Nick",middleName:null,surname:"Jenkins",slug:"nick-jenkins",fullName:"Nick Jenkins"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}}],offset:8,limit:8,total:1},route:{name:"profile.detail",path:"/profiles/310790/guenter-mueller-czygan",hash:"",query:{},params:{id:"310790",slug:"guenter-mueller-czygan"},fullPath:"/profiles/310790/guenter-mueller-czygan",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)}()