Determinants of vaccine hesitancy.
\r\n\tThe WHO classification in 2007; was based on the histogenesis and cell origin of the tumor. In the latest classification made in 2016; to better characterize the tumor and obtain better data on its prognosis; The combination of molecular and genetic biomarkers and histopathological features of the tumor was used. Despite all current treatment approaches, the median survival time is around 12 months in most GBM patients. Compared with the situation of some types of successfully treated cancers; the survival time of GBM patients is not at an acceptable level today. In the treatment of CNS tumors; surgery, chemotherapy, and radiation treatments (x-rays, gamma rays, electron and proton beams) are used. The therapeutic potential of chemotherapy; New strategies are needed to increase drug concentration at the diseased site, as this largely depends on the ability of the chemotherapeutic agent to achieve effective concentrations at tumor localization. Based on our better understanding of the genetic and molecular characteristics of CNS tumors; Targeted therapies, including vaccines, and treatment protocols such as immunotherapy are promising developments.
\r\n\r\n\tThis book supposes to be written by many authors who have an internationally honored place in their field to share their ideas about the treatment of CNS tumors. Surgery, Radiotherapy, Chemotherapy and Antiangiogenic Therapy Protocols, Immunotherapy, Molecular Therapy, Specific target-agents therapy with Nanoparticles and Gene Therapy for CNS tumors among the book chapters.
\r\n\tIn these sections; there are many practical pieces of information that can help the students who graduated from the Medicine Faculty and specialist doctors who are interested in Neurosurgery.
Machine learning has recently found many applications in the geosciences and remote sensing. These applications range from bias correction to retrieval algorithms, from code acceleration to detection of disease in crops. As a broad subfield of artificial intelligence, machine learning is concerned with algorithms and techniques that allow computers to “learn”. The major focus of machine learning is to extract information from data automatically by computational and statistical methods.
\n\t\t\tOver the last decade there has been considerable progress in developing a machine learning methodology for a variety of Earth Science applications involving trace gases, retrievals, aerosol products, land surface products, vegetation indices, and most recently, ocean products (\n\t\t\t\t\tYi and Prybutok, 1996, \n\t\t\t\t\tAtkinson and Tatnall, 1997, \n\t\t\t\t\tCarpenter et al., 1997, \n\t\t\t\t\tComrie, 1997, \n\t\t\t\t\tChevallier et al., 1998, \n\t\t\t\t\tHyyppa et al., 1998, \n\t\t\t\t\tGardner and Dorling, 1999, \n\t\t\t\t\tLary et al., 2004, \n\t\t\t\t\tLary et al., 2007, \n\t\t\t\t\tBrown et al., 2008, \n\t\t\t\t\tLary and Aulov, 2008, \n\t\t\t\t\tCaselli et al., 2009, \n\t\t\t\t\tLary et al., 2009). Some of this work has even received special recognition as a NASA Aura Science highlight (\n\t\t\t\t\tLary et al., 2007) and commendation from the NASA MODIS instrument team (\n\t\t\t\t\tLary et al., 2009). The two types of machine learning algorithms typically used are neural networks and support vector machines. In this chapter, we will review some examples of how machine learning is useful for Geoscience and remote sensing, these examples come from the author’s own research.
\n\t\tOne of the features that make machine-learning algorithms so useful is that they are “universal approximators”. They can learn the behaviour of a system if they are given a comprehensive set of examples in a training dataset. These examples should span as much of the parameter space as possible. Effective learning of the system’s behaviour can be achieved even if it is multivariate and non-linear. An additional useful feature is that we do not need to know a priori the functional form of the system as required by traditional least-squares fitting, in other words they are non-parametric, non-linear and multivariate learning algorithms.
\n\t\t\tThe uses of machine learning to date have fallen into three basic categories which are widely applicable across all of the Geosciences and remote sensing, the first two categories use machine learning for its regression capabilities, the third category uses machine learning for its classification capabilities. We can characterize the three application themes are as follows: First, where we have a theoretical description of the system in the form of a deterministic model, but the model is computationally
Now that we have an overview of the typical applications, the sections that follow will introduce two of the most powerful machine learning approaches, neural networks and support vector machines and then present a variety of examples.
\n\t\tNeural networks are multivariate, non-parametric, ‘learning’ algorithms (\n\t\t\t\t\t\tHaykin, 1994, \n\t\t\t\t\t\tBishop, 1995, 1998, \n\t\t\t\t\t\tHaykin, 2001a,
Its output, in turn, can serve as input to other units.
Example neural network architecture showing a network with five inputs, one output, and twelve hidden nodes.
When we perform neural network training, we want to ensure we can independently assess the quality of the machine learning ‘fit’. To insure this objective assessment we usually randomly split our training dataset into three portions, typically of 80%, 10% and 10%. The largest portion containing 80% of the dataset is used for training the neural network weights. This training is iterative, and on each training iteration we evaluate the current root mean square (RMS) error of the neural network output. The RMS error is calculated by using the second 10% portion of the data that was not used in the training. We use the RMS error and the way the RMS error changes with training iteration (epoch) to determine the convergence of our training. When the training is complete, we then use the final 10% portion of data as a totally independent validation dataset. This final 10% portion of the data is randomly chosen from the training dataset and is not used in either the training or RMS evaluation. We only use the neural network if the validation scatter diagram, which plots the actual data from validation portion against the neural network estimate, yields a straight-line graph with a slope very close to one and an intercept very close to zero. This is a stringent, independent and objective validation metric. The validation is global as the data is randomly selected over all data points available. For our studies, we typically used feed-forward back-propagation neural networks with a Levenberg-Marquardt back-propagation training algorithm (\n\t\t\t\t\t\tLevenberg, 1944, \n\t\t\t\t\t\tMarquardt, 1963, \n\t\t\t\t\t\tMoré, 1977, \n\t\t\t\t\t\tMarquardt, 1979).
\n\t\t\tSupport Vector Machines (SVM) are based on the concept of decision planes that define decision boundaries and were first introduced by Vapnik (Vapnik, 1995, 1998, 2000) and has subsequently been extended by others (\n\t\t\t\t\t\tScholkopf et al., 2000, \n\t\t\t\t\t\tSmola and Scholkopf, 2004). A decision plane is one that separates between a set of objects having different class memberships. The simplest example is a linear classifier, i.e. a classifier that separates a set of objects into their respective groups with a line. However, most classification tasks are not that simple, and often more complex structures are needed in order to make an optimal separation, i.e., correctly classify new objects (test cases) on the basis of the examples that are available (training cases). Classification tasks based on drawing separating lines to distinguish between objects of different class memberships are known as hyperplane classifiers.
\n\t\t\t\tSVMs are a set of related supervised learning methods used for classification and regression. Viewing input data as two sets of vectors in an n-dimensional space, an SVM will construct a separating hyperplane in that space, one that maximizes the margin between the two data sets. To calculate the margin, two parallel hyperplanes are constructed, one on each side of the separating hyperplane, which are “pushed up against” the two data sets. Intuitively, a good separation is achieved by the hyperplane that has the largest distance to the neighboring data points of both classes, since in general the larger the margin the better the generalization error of the classifier. We typically used the SVMs provided by LIBSVM (\n\t\t\t\t\t\tFan et al., 2005, \n\t\t\t\t\t\tChen et al., 2006).
\n\t\t\tLet us now consider some applications.
\n\t\t\tCritical in determining the speed at which the stratospheric ozone hole recovers is the total amount of atmospheric chlorine. Attributing changes in stratospheric ozone to changes in chlorine requires knowledge of the stratospheric chlorine abundance over time. Such attribution is central to international ozone assessments, such as those produced by the World Meteorological Organization (\n\t\t\t\t\t\tWmo, 2006). However, we do not have continuous observations of all the key chlorine gases to provide such a continuous time series of stratospheric chlorine. To address this major limitation, we have devised a new technique that uses the long time series of available hydrochloric acid observations and neural networks to estimate the stratospheric chlorine (Cly) abundance (\n\t\t\t\t\t\tLary et al., 2007).
\n\t\t\t\tKnowledge of the distribution of inorganic chlorine Cly in the stratosphere is needed to attribute changes in stratospheric ozone to changes in halogens, and to assess the realism of chemistry-climate models (\n\t\t\t\t\t\tEyring et al., 2006,
Hydrochloric acid is the major reactive chlorine gas throughout much of the atmosphere, and throughout much of the year. However, the observations of HCl that we do have (from UARS HALOE, ATMOS, SCISAT-1 ACE and Aura MLS) have significant biases relative to each other. We found that machine learning can also address the inter-instrument bias (\n\t\t\t\t\t\tLary et al., 2007, \n\t\t\t\t\t\tLary and Aulov, 2008). We compared measurements of HCl from the different instruments listed in Table 1. The Halogen Occultation Experiment (HALOE) provides the longest record of space based HCl observations. Figure 2 compares HALOE HCl with HCl observations from (a) the Atmospheric Trace Molecule Spectroscopy Experiment (ATMOS), (b) the Atmospheric Chemistry Experiment (ACE) and (c) the Microwave Limb Sounder (MLS).
\n\t\t\t\t2. Panels (a) to (d) show scatter plots of all contemporaneous observations of HCl made by HALOE, ATMOS, ACE and MLS Aura. In panels (a) to (c) HALOE is shown on the x-axis. Panel (e) correspond to panel (c) except that it uses the neural network ‘adjusted’ HALOE HCl values. Panel (f) shows the validation scatter diagram of the neural network estimate of Cly ≈ HCl + ClONO2 + ClO +HOCl versus the actual Cly for a totally independent data sample
A consistent picture is seen in these plots: HALOE HCl measurements are lower than those from the other instruments. The slopes of the linear fits (relative scaling) are 1.05 for the HALOE-ATMOS comparison, 1.09 for the HALOE-MLS, and 1.18 for the HALOE-ACE. The offsets are apparent at the 525 K isentropic surface and above. Previous comparisons among HCl datasets reveal a similar bias for HALOE (\n\t\t\t\t\t\tRussell et al., 1996, \n\t\t\t\t\t\tMchugh et al., 2005, \n\t\t\t\t\t\tFroidevaux et al., 2006a,
The instruments and constituents used in constructing the Cly record from 1991-2006. The uncertainties given are the median values calculated for each level 2 measurement profile and its uncertainty (both in mixing ratio) for all the observations made. The uncertainties are larger than usually quoted for MLS ClO because they reflect the single profile precision, which is improved by temporal and/or spatial averaging. The HALOE uncertainties are only estimates of random error and do not include any indications of overall accuracy.
To combine the above HCl measurements to form a continuous time series of HCl (and then Cly) from 1991 to 2006 it is necessary to account for the biases between data sets. A neural network is used to learn the mapping from one set of measurements onto another as a function of equivalent latitude and potential temperature. We consider two cases. In one case ACE HCl is taken as the reference and the HALOE and Aura HCl observations are adjusted to agree with ACE HCl. In the other case HALOE HCl is taken as the reference and the Aura and ACE HCl observations are adjusted to agree with HALOE HCl. In both cases we use equivalent latitude and potential temperature to produce average profiles. The purpose of the NN mapping is simply to learn the bias as a function of location, not to imply which instrument is correct.
\n\t\t\t\tThe precision of the correction using the neural network mapping is of the order of ±0.3 ppbv, as seen in Figure 2 (e) that shows the results when HALOE HCl measurements have been mapped into ACE measurements. The mapping has removed the bias between the measurements and has straightened out the ‘wiggles’ in 2 (c), i.e., the neural network has learned the equivalent PV latitude and potential temperature dependence of the bias between HALOE and MLS. The inter-instrument offsets are not constant in space or time, and are not a simple function of Cly.
\n\t\t\t\tSo employing neural networks allows us to: Form a seamless record of HCl using observations from several space-borne instruments using neural networks. Provide an estimated of the associated inter-instrument bias. Infer Cly from HCl, and thereby provide a seamless record of Cly, the parameter needed for examining the ozone hole recovery. A similar use of machine learning has been made for Aerosol Optical Depths, the subject of the next sub-section.
\n\t\t\t\tCly average profiles between 30 and 60 N for October 2005, estimated by neural network calibrated to HALOE HCl (blue curve), estimated by neural network calibrated to ACE HCl (green), or from ACE observations of HCl, ClONO2, ClO, and HOCl (red crosses). In each case, the shaded range represents the total uncertainty; it includes the observational uncertainty, the representativeness uncertainty (the variability over the analysis grid cell), the neural network uncertainty. The vertical extent of this plot was limited to below 1000 K (≈35 km), as there is no ACE v2.2 ClO data for the upper altitudes. In addition, above ≈750 K (≈25 km), ClO constitutes a larger fraction of Cly (up to about 10%) and so the large uncertainties in ClO have greater effect.
Panels (a) to (c) show October Cly time-series for the 525 K isentropic surface (≈20 km) and the 800 K isentropic surface (≈30 km). In each case the dark shaded range represents the total uncertainty in our estimate of Cly. This total uncertainty includes the observational uncertainty, the representativeness uncertainty (the variability over the analysis grid cell), the inter-instrument bias in HCl, the uncertainty associated with the neural network inter-instrument correction, and the uncertainty associated with the neural network inference of Cly from HCl and CH4. The inner light shading depicts the uncertainty on Cly due to the inter-instrument bias in HCl alone. The upper limit of the light shaded range corresponds to the estimate of Cly based on all the HCl observations calibrated by a neural network to agree with ACE v2.2 HCl. The lower limit of the light shaded range corresponds to the estimate of Cly based on all the HCl observations calibrated to agree with HALOE v19 HCl. Overlaid are lines showing the Cly based on age of air calculations (
Scatter diagram comparisons of Aerosol Optical Depth (AOD) from AERONET (x-axis) and MODIS (y-axis) as green circles overlaid with the ideal case of perfect agreement (blue line). The measurements shown in the comparison were made within half an hour of each other, with a great circle separation of less than 0.25 and with a solar zenith angle difference of less than 0.1 . The left hand column of plots is for MODIS Aqua and the right hand column of plots is for MODIS Terra. The first row shows the comparisons between AERONET and MODIS for the entire period of overlap between the MODIS and AERONET instruments from the launch of the MODIS instrument to the present. The second row shows the same comparison overlaid with the neural network correction as red circles. We note that the neural network bias correction makes a substantial improvement in the correlation coefficient with AERONET. An improvement from 0.86 to 0.96 for MODIS Aqua and an improvement from 0.84 to 0.92 for MODIS Terra. The third row shows the comparison overlaid with the support vector regression correction as red circles. We note that the support vector regression bias correction makes an even greater improvement in the correlation coefficient than the neural network correction. An improvement from 0.86 to 0.99 for MODIS Aqua and an improvement from 0.84 to 0.99 for MODIS Terra.
As highlighted in the 2007 IPCC report on Climate Change, aerosol and cloud radiative effects remain the largest uncertainties in our understanding of climate change (
Machine learning approaches (Neural Networks and Support Vector Machines) were used by (\n\t\t\t\t\t\tLary et al., 2009) to explore the reasons for a persistent bias between aerosol optical depth (AOD) retrieved from the MODerate resolution Imaging Spectroradiometer (MODIS) and the accurate ground-based Aerosol Robotics Network (AERONET). While this bias falls within the expected uncertainty of the MODIS algorithms, there is still room for algorithm improvement. The results of the machine learning approaches suggest a link between the MODIS AOD biases and surface type. From figure 5 we can see that machine learning algorithms were able to effectively adjust the AOD bias seen between the MODIS instruments and AERONET. Support vector machines performed the best improving the correlation coefficient between the AERONET AOD and the MODIS AOD from 0.86 to 0.99 for MODIS Aqua, and from 0.84 to 0.99 for MODIS Terra.
\n\t\t\t\tKey in allowing the machine learning algorithms to ‘correct’ the MODIS bias was provision of the surface type and other ancillary variables that explain the variance between MODIS and AERONET AOD. The provision of the ancillary variables that can explain the variance in the dataset is the key ingredient for the effective use of machine learning for bias correction. A similar use of machine learning has been made for vegetation indices, the subject of the next sub-section.
\n\t\t\tConsistent, long term vegetation data records are critical for analysis of the impact of global change on terrestrial ecosystems. Continuous observations of terrestrial ecosystems through time are necessary to document changes in magnitude or variability in an ecosystem (\n\t\t\t\t\t\tTucker et al., 2001, \n\t\t\t\t\t\tEklundh and Olsson, 2003, \n\t\t\t\t\t\tSlayback et al., 2003). Satellite remote sensing has been the primary way that scientists have measured global trends in vegetation, as the measurements are both global and temporally frequent. In order to extend measurements through time, multiple sensors with different design and resolution must be used together in the same time series. This presents significant problems as sensor band placement, spectral response, processing, and atmospheric correction of the observations can vary significantly and impact the comparability of the measurements (\n\t\t\t\t\t\tBrown et al., 2006). Even without differences in atmospheric correction, vegetation index values for the same target recorded under identical conditions will not be directly comparable because input reflectance values differ from sensor to sensor due to differences in sensor design (\n\t\t\t\t\t\tTeillet et al., 1997, \n\t\t\t\t\t\tMiura et al., 2006).
\n\t\t\t\tSeveral approaches have previously been taken to integrate data from multiple sensors. (\n\t\t\t\t\t\tSteven et al., 2003), for example, simulated the spectral response from multiple instruments and with simple linear equations created conversion coefficients to transform NDVI data from one sensor to another. Their analysis is based on the observation that the vegetation index is critically dependent on the spectral response functions of the instrument used to calculate it. The conversion formulas the paper presents cannot be applied to maximum value NDVI datasets because the weighting coefficients are land cover and dataset dependent, reducing their efficacy in mixed pixel situations (\n\t\t\t\t\t\tSteven et al., 2003). (\n\t\t\t\t\t\tTrishchenko et al., 2002) created a series of quadratic functions to correct for differences in the reflectance and NDVI to NOAA-9 AVHRR-equivalents (\n\t\t\t\t\t\tTrishchenko et al., 2002). Both the (\n\t\t\t\t\t\tSteven et al., 2003) and the (\n\t\t\t\t\t\tTrishchenko et al., 2002) approaches are land cover and dataset dependent and thus cannot be used on global datasets where multiple land covers are represented by one pixel. (\n\t\t\t\t\t\tMiura et al., 2006) used hyper-spectral data to investigate the effect of different spectral response characteristics between MODIS and AVHRR instruments on both the reflectance and NDVI data, showing that the precise characteristics of the spectral response had a large effect on the resulting vegetation index. The complex patterns and dependencies on spectral band functions were both land cover dependent and strongly non-linear, thus we see that an exploration of a non-linear approach may be fruitful.
\n\t\t\t\t(\n\t\t\t\t\t\tBrown et al., 2008) experimented with powerful, non-linear neural networks to identify and remove differences in sensor design and variable atmospheric contamination from the AVHRR NDVI record in order to match the range and variance of MODIS NDVI without removing the desired signal representing the underlying vegetation dynamics. Neural networks are ‘data transformers’ (\n\t\t\t\t\t\tAtkinson and Tatnall, 1997), where the objective is to associate the elements of one set of data to the elements in another. Relationships between the two datasets can be complex and the two datasets may have different statistical distributions. In addition, neural networks incorporate a priori knowledge and realistic physical constraints into the analysis, enabling a transformation from one dataset into another through a set of weighting functions (\n\t\t\t\t\t\tAtkinson and Tatnall, 1997). This transformation incorporates additional input data that may account for differences between the two datasets.
\n\t\t\t\tThe objective of (\n\t\t\t\t\t\tBrown et al., 2008) was to demonstrate the viability of neural networks as a tool to produce a long term dataset based on AVHRR NDVI that has the data range and statistical distribution of MODIS NDVI. Previous work has shown that the relationship between AVHRR and MODIS NDVI is complex and nonlinear (\n\t\t\t\t\t\tGallo et al., 2003, \n\t\t\t\t\t\tBrown et al., 2006, \n\t\t\t\t\t\tMiura et al., 2006), thus this problem is well suited to neural networks if appropriate inputs can be found. The influence of the variation of atmospheric contamination of the AVHRR data through time was explored by using observed atmospheric water vapor from the Total Ozone Mapping Spectrometer (TOMS) instrument during the overlap period 2000-2004 and back to 1985. Examination of the resulting MODIS fitted AVHRR dataset both during the overlap period and in the historical dataset will enable an evaluation of the efficacy of the neural net approach compared to other approaches to merge multiple-sensor NDVI datasets.
\n\t\t\t\tA comparison of the NDVI from AVHR (panel a), MODIS (panel p), and then a reconstruction of MODIS using AVHRR and machine learning (panel c). We note that the machine learning can successfully account for the large differences that are found between AVHRR and MODIS.
Remote sensing datasets are the result of a complex interaction between the design of a sensor, the spectral response function, stability in orbit, the processing of the raw data, compositing schemes, and post-processing corrections for various atmospheric effects including clouds and aerosols. The interaction between these various elements is often non-linear and non-additive, where some elements increase the vegetation signal to noise ratio (compositing, for example) and others reduce it (clouds and volcanic aerosols) (\n\t\t\t\t\t\tLos, 1998). Thus, although other authors have used simulated data to explore the relationship between AVHRR and MODIS (\n\t\t\t\t\t\tTrishchenko et al., 2002, \n\t\t\t\t\t\tVan Leeuwen et al., 2006), these techniques are not directly useful in producing a sensor-independent vegetation dataset that can be used by data users in the near term.
\n\t\t\t\tPanel (a) shows a time-series from 2000 to 2003 of the zonal mean (averaged per latitude) difference between the AVHRR and MODIS NDVIs, this highlights that significant differences exist between the two data products. Panel (b) shows a time series over the same period after the machine learning has been used to “cross-calibrate” AVHRR as MODIS, showing that the machine learning has effectively learnt how to cross-calibrate the instruments.
There are substantial differences between the processed vegetation data from AVHRR and MODIS. (\n\t\t\t\t\t\tBrown et al., 2008) showed that neural networks are an effective way to have a long data record that utilizes all available data back to 1981 by providing a practical way of incorporating the AVHRR data into a continuum of observations that include both MODIS and VIIRS. The results (\n\t\t\t\t\t\tBrown et al., 2008) showed that the TOMS data record on clouds, ozone and aerosols can be used to identify and remove sensor-specific atmospheric contaminants that differentially affect the AVHRR over MODIS. Other sensor-related effects, particularly those of changing BRDF, viewing angle, illumination, and other effects that are not accounted for here, remain important sources of additional variability. Although this analysis has not produced a dataset with identical properties to MODIS, it has demonstrated that a neural net approach can remove most of the atmospheric-related aspects of the differences between the sensors, and match the mean, standard deviation and range of the two sensors. A similar technique can be used for the VIIRS sensor once the data is released.
\n\t\t\t\t\n\t\t\t\t\tFigure 6 shows a comparison of the NDVI from AVHR (panel a), MODIS (panel p), and then a reconstruction of MODIS using AVHRR and machine learning (panel c). Figure 7 (a) shows a time-series from 2000 to 2003 of the zonal mean difference between the AVHRR and NDVIs, this highlights that significant differences exist between the two data products. Panel (b) shows a time series over the same period after the machine learning has been used to “cross-calibrate” AVHRR as MODIS, illustrating that the machine learning has effectively learnt how to cross-calibrate the instruments.
\n\t\t\t\tSo far, we have seen three examples of using machine learning for bias correction (constituent biases, aerosol optical depth biases and vegetation index biases), and one example of using machine learning to infer a useful proxy from remotely sensed data (Cly from HCl). Let us look at one more example of inferring proxies from existing remotely sensed data before moving onto consider using machine learning for code acceleration.
\n\t\t\tThe spatial distributions of atmospheric trace constituents are in general dependent on both chemistry and transport. Compact correlations between long-lived species are well-observed features in the middle atmosphere. The correlations exist for all long-lived tracers - not just those that are chemically related - due to their transport by the general circulation of the atmosphere. The tight relationships between different constituents have led to many analyses using measurements of one tracer to infer the abundance of another tracer. Using these correlations is also as a diagnostic of mixing and can distinguish between air-parcels of different origins. Of special interest are the so-called ‘long-lived’ tracers: constituents such as nitrous oxide (N2O), methane (CH4), and the chlorofluorocarbons (CFCs) that have long lifetimes (many years) in the troposphere and lower stratosphere, but are destroyed rapidly in the middle and upper stratosphere.
\n\t\t\t\tThe correlations are spatially and temporally dependent. For example, there is a ‘compact-relation’ regime in the lower part of the stratosphere and an ‘altitude-dependent\' regime above this. In the compact-relation region, the abundance of one tracer is uniquely determined by the value of the other tracer, without regard to other variables such as latitude or altitude. In the altitude-dependent regime, the correlation generally shows significant variation with altitude.
\n\t\t\t\tA family of correlations usually achieves the description of such spatially and temporally dependent correlations. However, a single neural network is a natural and effective alternative. The motivation for this case study was preparation for a long-term chemical assimilation of Upper Atmosphere Research Satellite (UARS) data starting in 1991 and coming up to the present. For this period, we have continuous version 19 data from the Halogen Occultation Experiment (HALOE) but not observations of N2O as both ISAMS and CLAES failed. In addition, we would like to constrain the total amount of reactive nitrogen, chlorine, and bromine in a self-consistent way (i.e. the correlations between the long-lived tracers is preserved). Tracer correlations provide a means to do this by using HALOE CH4 observations.
\n\t\t\t\tMachine learning is ideally suited to describe the spatial and temporal dependence of tracer-tracer correlations. The neural network performs well even in regions where the correlations are less compact and normally a family of correlation curves would be required. For example, the methane CH4-N2O correlation can be well described using a neural network (\n\t\t\t\t\t\tLary et al., 2004) trained with the latitude, pressure, time of year, and CH4 volume mixing ratio (v.m.r.). \n\t\t\t\t\t\tLary et al. (2004) used a neural network to reproduce the CH4-N2O correlation with a correlation coefficient between simulated and training values of 0.9995. Such an accurate representation of tracer-tracer correlations allows more use to be made of long-term datasets to constrain chemical models. For example, the Halogen Occultation Experiment (HALOE) that continuously observed CH4 (but not N2O) from 1991 until 2005.
\n\t\t\t\tMODIS
\n\t\t\t\tPanel (a) shows the global N2O-CH4 correlation for an entire year, after evaluating the efficacy of 3,000 different functional forms for parametric fits, we overlaid the best, an order 20 Chebyshev Polynomial. However, this still does not account for the multi-variate nature of the problem exhibited by the ‘cloud’ of points rather than a compact ‘curve’ or ‘line’. However, in panel (b) we can see that a neural network is able to account for the non-linear and multi-variate aspects, the training dataset exhibited a ‘cloud’ of points, the neural network fit reproduces a ‘cloud’ of points. The most important factor in producing a ‘spread’ in the correlations is the strong altitude dependence of the N2O-CH4 correlation.
\n\t\t\t\t\tFigure 8 (a) shows the global N2O-CH4 correlation for an entire year, after evaluating the efficacy of 3,000 different functional forms for parametric fits, we overlaid the best, an order 20 Chebyshev Polynomial. However, this still does not account for the multi-variate nature of the problem exhibited by the ‘cloud’ of points rather than a compact ‘curve’ or ‘line’. However, in Figure 8 (b) we can see that a neural network is able to account for the non-linear and multi-variate aspects, the training dataset exhibited a ‘cloud’ of points, the neural network fit reproduces a ‘cloud’ of points. The most important factor in producing a ‘spread’ in the correlations is the strong altitude dependence of the N2O-CH4 correlation.
\n\t\t\tThere are many applications in the Geosciences and remote sensing which are computationally expensive. Machine learning can be very effective in accelerating components of these calculations. We can readily create training datasets for these applications using the very models we would like to accelerate.
\n\t\t\tThe first example for which we found this effective was solving ordinary differential equations. An adequate photochemical mechanism to describe the evolution of ozone in the upper troposphere and lower stratosphere (UT/LS) in a computational model involves a comprehensive treatment of reactive nitrogen, hydrogen, halogens, hydrocarbons, and interactions with aerosols. Describing this complex interaction is computationally expensive, and applications are limited by the computational burden. Simulations are often made tractable by using a coarser horizontal resolution than would be desired or by reducing the interactions accounted for in the photochemical mechanism. These compromises also limit the scientific applications. Machine learning algorithms offer a means to obtain a fast and accurate solution to the sjpg ordinary differential equations that comprise the photochemical calculations, thus making high-resolution simulations including the complete photochemical mechanism much more tractable.
\n\t\t\tFor the sake of an example, a 3D model of atmospheric chemistry and transport, the GMI-COMBO model, can use 55 vertical levels and a 4 latitude x 5 longitude grid and 125 species. With 15-minute time steps the chemical ODE solver is called 119,750,400 times in simulating just one week. If the simulation is for a year then the ODE solver needs to be called 6,227,020,800 (or 6x109) times. If the spatial and temporal resolution is doubled then the chemical ODE solver needs to be called a staggering 2.5x1010 times to simulate a year. This represents a major computational cost in simulating a constituent’s spatial and temporal evolution. The ODEs solved at adjacent grid cells and time steps are very similar. Therefore, if the simulations from one grid cell and time step could be used to speed up the simulation for adjacent grid cells and subsequent time steps, we would have a strategy to dramatically decrease the computational cost of our simulations.
\n\t\t\tStrategy for applying a neural wrapper to accelerate the ODE solver.
\n\t\t\t\tFigure 9 shows the strategy that we used for applying a neural wrapper to accelerate the ODE solver. Figure 10 shows some example results for ozone after using a neural wrapper around an atmospheric chemistry ODE solver. The x-axis shows the actual ozone abundance as a volume mixing ratio (vmr) using the regular ODE solver without neural networks. The y-axis shows the ozone vmr inferred using the neural network solution. It can be seen that we have excellent agreement between the two solutions with a correlation coefficient of 1. The neural network has learned the behaviour of the ozone ODE very well. Without the adaptive error control the acceleration could be up to 200 times, with the full adaptive error control the acceleration was less, but usually at least a factor of two. Similarly, in Figure 11 the two panels below show the results for formaldehyde (HCHO) in the GMI model. The left panel shows the solution with SMVGear for level 1 at 01:00 UT and the right panel shows the corresponding solution using the neural network. As one would hope, the two results are almost indistinguishable.
\n\t\t\tExample results for using a neural wrapper around an atmospheric chemistry ODE solver. The x-axis shows the actual ozone v.m.r. using the regular ODE solver without neural networks. The y-axis shows the ozone v.m.r. inferred using the neural network solution. It can be seen that we have excellent agreement between the two solutions with a correlation coefficient of 1. The neural network has learned the behaviour of the ozone ODE very well.
The two panels below show the results for formaldehyde (HCHO) in the GMI model. The left panel shows the solution with SMVGear for level 1 at 01:00 UT and the right panel shows the corresponding solution using the neural network. As one would hope, the two results are almost indistinguishable.
The source of chocolate, theobroma cacao (cacao), is an understory tropical tree (\n\t\t\t\t\t\tWood, 2001). Cacao is intolerant to drought (\n\t\t\t\t\t\tBelsky and Siebert, 2003), and yields and production patterns are severely affected by periodic droughts and seasonal rainfall patterns. (\n\t\t\t\t\t\tBae et al., 2008) studied the molecular response of cacao to drought and have identified several genes responsive to drought stress (\n\t\t\t\t\t\tBailey et al., 2006). They have also been studying the response of cacao to colonization by an endophytic isolates of Trichoderma including Trichoderma hamatum, DIS 219b (\n\t\t\t\t\t\tBailey et al., 2006). One of the benefits to colonization Trichoderma hamatum isolate DIS 219b is tolerance to drought as mediated through plant growth promotion, specifically enhanced root growth (\n\t\t\t\t\t\tBae et al., 2008).
\n\t\t\t\tIn characterizing the drought response of cacao considerable variation was observed in the response of individual seedlings depending upon the degree of drought stress applied (\n\t\t\t\t\t\tBae et al., 2008). In addition, although colonization by DIS 219b delayed the drought response, direct effects of DIS 219b on cacao gene expression in the absence of drought were difficult to identify (\n\t\t\t\t\t\tBae et al., 2008). The complexity of the DIS 219b/cacao plant microbe interaction overlaid on cacao’s response to drought makes the system of looking at individual genes as a marker for either drought or endophyte inefficient.
\n\t\t\t\tThere would be considerable utility in reliably predicting drought and endophyte stress from complex gene expression patterns, particularly as the endophyte lives within the plant without causing apparent phenotypic changes in the plant. Machine‐learning models offer the possibility of highly accurate, automated predictions of plant stress from a variety of causes that may otherwise go undetected or be obscured by the complexity of plant responses to multiple environmental factors, to be considered status quo for plants in nature. We examined the ability of five different machine‐learning approaches to predict drought stress and endophyte colonization in cacao: a naive Bayes classifier, decision trees (DTs), neural networks (NN), neuro-fuzzy inference (NFI), and support vector machine (SVM) classification. The results provided some support for the accuracy of machine-learning models in discerning endophyte colonization and drought stress. The best performance was by the neuro-fuzzy inference system and the support vector classifier that correctly identified 100% of the drought and endophyte stress samples. Of the two, the approaches the support vector classifier is likely to have the best generalization (wider applicability to data not previously seen in the training process).
\n\t\t\t\tWhy did the SVM model outperform the four other machine learning approaches? We noted earlier that SVMs construct separating hyperplanes that maximize the margins between the different clusters in the training data set (the vectors that constrain the width of the margin are the support vectors). A good separation is achieved by those hyperplanes providing the largest distance between neighbouring classes, and in general, the larger the margin the better the generalization of the classifier.
\n\t\t\t\tWhen the points in neighbouring classes are separated by a nonlinear dividing line, rather than fitting nonlinear curves to the data, SVMs use a kernel function to map the data into a different space where a hyperplane can once more be used to do the separation. The kernel function may transform the data into a higher dimensional space to make it possible to perform the separation. The concept of a kernel mapping function is very powerful. It allows SVM models to perform separations even with very complex boundaries. Hence, we infer that, in the present application, the SVM model algorithmic process utilizes higher dimensional space to achieve superior predictive power.
\n\t\t\t\tFor classification, the SVM algorithmic process offers an important advantage compared with neural network approaches. Specifically, neural networks can suffer from multiple local minima; in contrast, the solution to a support vector machine is global and unique. This characteristic may be partially attributed to the development process of these algorithms; SVMs were developed in the reverse order to the development of neural networks. SVMs evolved from the theory to implementation and experiments; neural networks followed a more heuristic path, from applications and extensive experimentation to theory.
\n\t\t\t\tIn handling this data using traditional methods where individual gene responses are characterized as treatment effects, it was especially difficult to sort out direct effects of endophyte on gene expression over time or at specific time points. The differences between the responses of non-stressed plants with or without the endophyte were small and, after the zero time point, were highly variable. The general conclusion from this study was that colonization of cacao seedlings by the endophyte enhanced root growth resulting in increased drought tolerance but the direct effects of endophyte on cacao gene expression at the time points studied were minimal. Yet the neuro-fuzzy inference and support vector classification methods of analysis were able identify samples receiving these treatments correctly.
\n\t\t\t\tIn this system, each gene in the plants genome is a potential sensor for the applied stress or treatment. It is not necessary that the genes response be significant in itself in determining the outcome of the plants response or that it be consistent in time or level of response. Since multiple genes are used in characterizing the response it is always the relative response in terms of the many other changes that are occurring at the same time as influenced by uncontrolled changes in the system that is important. With this study the treatments were controlled but variation in the genetic make up of each seedling (they were from segregating open pollinated seed) and minute differences in air currents within the chamber, soil composition, colonization levels, microbial populations within each pot and seedling, and even exact watering levels at each time point, all likely contributed to creating uncontrolled variation in the plants response to what is already a complex reaction to multiple factors (drought and endophyte). This type of variation makes accessing treatment responses using single gene approaches difficult and the prediction of cause due to effect in open systems almost impossible in complex systems.
\n\t\t\tWe have seen the utility of machine learning for a suite of very diverse applications. These applications often help us make better use of existing data in a variety of ways. In parallel to the success of machine learning we also have the rapid development of publically available web services. So it is timely to combine both approached by providing online services that use machine learning for intelligent data fusion as part of a workflow that allows us to cross-calibrate multiple datasets. This obviously requires care to ensure the appropriate of datasets. However, if done carefully, this could greatly facilitate the production of seamless multi-year global records for a host of Earth science applications.
\n\t\t\tWhen it comes to dealing with inter-instrument biases in a consistent manner there is currently a gap in many space agencies’ Earth science information systems. This could be addressed by providing an extensible and reusable open source infrastructure that gap that could be reused for multiple projects. A clear need for such an infrastructure would be for NASA’s future Decadal Survey missions.
\n\t\tMachine learning has recently found many applications in the geosciences and remote sensing. These applications range from bias correction to retrieval algorithms, from code acceleration to detection of disease in crops. Machine-learning algorithms can act as “universal approximators”, they can learn the behaviour of a system if they are given a comprehensive set of examples in a training dataset. Effective learning of the system’s behaviour can be achieved even if it is multivariate and non-linear. An additional useful feature is that we do not need to know a priori the functional form of the system as required by traditional least-squares fitting, in other words they are non-parametric, non-linear and multivariate learning algorithms.
\n\t\t\tThe uses of machine learning to date have fallen into three basic categories which are widely applicable across all of the Geosciences and remote sensing, the first two categories use machine learning for its regression capabilities, the third category uses machine learning for its classification capabilities. We can characterize the three application themes are as follows: First, where we have a theoretical description of the system in the form of a deterministic model, but the model is computationally
Immunisation, a key primary healthcare component and an indisputable human right, is a public health achievement of the 20th century saving millions of lives every year. Vaccines and immunisation programmes currently prevent 3.5 to 5 million deaths every year from diseases like diphtheria, tetanus, pertussis, influenza, and measles. Also, they have prevented major epidemics of life-threatening diseases since the beginning of their widespread use in the 1900s underpinning global health security. Vaccines are now available to prevent more than 20 life-threatening diseases and are a vital tool in the battle against antimicrobial resistance.
The history of public concerns about and questioning vaccines, however, is as old as vaccines themselves. Modern communication systems have only accelerated anxieties about vaccine safety and its regulation. This has resulted in pockets of people who are reluctant or refuse recommended vaccination(s), or who chose to delay some vaccines. The SAGE Working Group on Vaccine Hesitancy documented that any delay in acceptance or refusal of vaccination despite the availability of vaccination services is vaccine hesitancy. It is complex and context-specific, varying across time, place, and vaccines. Interestingly, the Working Group retained the term ‘vaccine’ rather than ‘vaccination’ hesitancy, although the latter more correctly implies the broader range of immunisation concerns [1].
It is important to monitor the reasons why a substantial number of people hesitate to receive recommended vaccinations. This allows identification of important trends over time and designing and evaluation strategies to address vaccine hesitancy and thereby increase vaccine uptake. Empirical and theoretical frameworks that assess vaccine hesitancy focus primarily on confidence in vaccines and the system that delivers them. It is essential to acknowledge that confidence covers trust in vaccines including concerns about vaccine safety, trust in healthcare workers delivering the vaccine, and in those making the decisions to approve of vaccines for a population. Vaccination behaviour can be explained by complacency (not perceiving diseases as high risk), constraints (structural and psychological barriers), the calculation (engagement in extensive information searching), and aspects pertaining to collective responsibility (willingness to protect others). These are the five main personal determinants for vaccine hesitancy [2]. To add to it would be vaccination convenience. The physical availability of vaccines, geographical accessibility, affordability and willingness-to-pay, ability to understand or comprehend that is, language and health literacy and ability of the immunisation services to appeal may affect vaccine uptake. In addition, the actual or perceived quality of the service and the degree to which vaccination services are delivered at a time and place within a cultural context that is convenient and comfortable may also affect the decision to be vaccinated and could lead to vaccine hesitancy.
Vaccine hesitancy is a continuum with those who accept all with no doubts and refuse all vaccines with no doubts as extremes (Figure 1). This may include a proportion who accept or completely refuse vaccines but are unsure. Between the extremes are those vaccine-hesitant individuals who accept some, delay or refuse some vaccines. While high levels of hesitancy lead to low vaccine demand, low levels of hesitancy do not necessarily mean high vaccine demand [3].
Vaccine hesitancy continuum.
Provided that vaccine hesitancy is complex and context-specific it may be influenced by historic, socio-cultural, psychosocial, family, environmental, health system or institutional, economic, or political factors. Apart from these contextual factors, individual or group and vaccine or vaccination-specific concerns may also determine vaccine hesitancy. Taking about individual and group influences, they may arise from personal or social or peer perceptions of the vaccine (Table 1) [3].
Contextual factors |
|
Individual and group factors |
|
Vaccine and vaccination-related factors |
|
Determinants of vaccine hesitancy.
COVID-19 vaccine hesitancy is real. In a meta-analysis that computed the overall COVID-19 vaccine acceptance rate across the US, the vaccine acceptance was as low as 12% and higher up to 91% [4]. Similarly, in a community-based sample of the American adult population, it was found that the likelihood of getting a COVID-19 immunisation was 52% very likely and 22% not likely or not, with individuals having lower education, income, or perceived threat of getting infected being more likely to report that they were not likely to not going to get COVID-19 vaccine (that is, vaccine hesitancy) [5]. A multi-country study of six Southeast Asian countries showed that the majority (84%) would accept COVID-19 vaccines. However, the variation between countries was significant with the lowest rates reported in Vietnam (27%) and the highest rates reported in Russia (72%) [6]. The disparities in inter-regional and inter-country (even within countries) COVID-19 vaccine hesitancy has been well documented. In a global cross-sectional study that included participants from seventeen countries across regions, it was found that participants from China (95.3%), Australia (96.4%), and Norway (95.3%) were most likely to get COVID-19 vaccination. However, participants from United States (29.4%), Japan (34.6%), and Iran (27.9%) were least likely to get vaccinated or in other words likely to be vaccine hesitant [7]. In a nationwide survey reported from India, only 30% of adults had no issue with the COVID-19 vaccine or vaccination [8]. This finding corroborates with the neighbouring nation Bangladesh where the reported prevalence of vaccine hesitancy was 46.2% [9]. The overall prevalence of COVID-19 vaccine hesitancy among Chinese adults was modest at 8.4% (95% CI, 8.09 to 8.72) for primary vaccination and 8.4% (95% CI, 8.07 to 8.70) for booster vaccination [10]. COVID-19 vaccine hesitancy has been particularly higher among older people (27.0%, 95% CI 15.1 to 38.9) [11].
A literature search revealed few efforts aimed at quantifying vaccine hesitancy in the population [12, 13]. Firstly, the vaccine hesitancy index (VHI) was constructed using population characteristics aligned with factors identified by an Office for National Statistics (ONS) survey analysis; the factors included in the index were population under fifty, the proportion of Black or African or Caribbean ethnic population, children under five, population with less than degree level qualification and rental housing (social or private as a proportion of the total population) [14]. This was an improved version of the earlier published COVID-19 vulnerability index (VI) that considered income domain indicators and long-term illness [15].
The data relating to the safety and efficacy of vaccines against COVID-19 are largely from high-income countries. In addition, the rapid pace of vaccine development has been highlighted in the literature as the primary reason for COVID-19 vaccine hesitancy. A COVID-19 vaccination acceptance and hesitancy survey including data from 15 survey samples covering 10 low- and middle-income countries (LMICs) in Asia, Africa, South America, Russia (an upper-middle-income country) and the United States reported that there was considerably higher willingness to take a COVID-19 vaccine in LMIC samples (mean 80.3%; median 78%; range 30.1%) compared with the United States (mean 64.6%) and Russia (mean 30.4%). The primary reason for acceptance was explained by interest in personal protection against COVID-19, whereas concerns in relation to side effects resulted in hesitancy [16]. It is, however, important to note that reported intentions may not always translate into vaccine uptake [17]. These findings corroborate a study conducted by Africa Centres for Disease Control and Prevention, in partnership with the London School of Hygiene and Tropical Medicine, in 15 African nations. More than three-fourths (79%) of respondents in Africa would be vaccinated against COVID-19 if it were deemed safe and effective. This may be explained based on lived experience in LMICs, where many vaccine-preventable infectious diseases are still a leading cause of morbidity and mortality, resulting in a higher perceived need for or value of vaccines [18]. However, in contrast, many people including medical professionals from high-income countries have not seen the devastating effects of these diseases in their respective countries. This is because they have successfully eliminated or eradicated numerous vaccine-preventable diseases. As a consequence resulting in altered risk calculations, complacency and limited collective responsibility about vaccination decision-making [18, 19]. In a survey among the United Kingdom (UK) adults that assessed their religious and political beliefs as well as their eagerness, willingness, and hesitance to take various global COVID-19 vaccines it was found that social media use does have an effect on perceived knowledge about vaccines as well as on vaccine hesitancy (especially Twitter!). People also express concerns over the trustworthiness of foreign vaccine production and testing protocols [20].
Evidence shows that 38%, 21%, 13%, and 11% variance in COVID-19 vaccine hesitancy can be explained by vaccine confidence, vaccine complacency, sociodemographic, and other psychological factors respectively [21]. Right-wing political affiliation, higher risk propensity, and less negative mental health effects of the COVID-19 pandemic were the principal sociodemographic and psychological determinants of COVID-19 vaccine hesitancy. Other sociodemographic determinants include younger age, women, race, and employment status. However, this particular study failed to examine the variance explained by vaccine convenience factors like availability, accessibility, affordability, willingness to pay, language, and health literacy [21]. Similarly, the willingness to vaccinate among Chinese adults was associated with gender (being women), higher levels of education, married residents, increased washing hands, never smoking, a higher score of health condition, increased wearing masks, higher level of convenient vaccination, increased social distance, disease risks outweigh vaccine risk, lower level of vaccine conspiracy beliefs, and a higher level of trust in doctor and developer [10].
In a study that assessed the intention to vaccinate for different effectiveness scenarios and side effects using the health belief model, it was found that the probability of rejecting a vaccine or indecision in relation to vaccine uptake were associated with the severity of COVID-19. This includes, but not limited to, adverse side effects and effectiveness of the vaccine; decreased fear of contagion, perceived benefits including immunity, and the protection of oneself and the social environment; available information, specialists’ recommendations; action signals, such as responses from ones’ family and the government; and susceptibility, including the contagion rate per 1000 population. The vaccine scenarios used in the study revealed that the individuals preferred less risky vaccines in terms of fewer side effects, rather than effectiveness [22].
In a cross-sectional study that aimed at determining the predictors of COVID-19 vaccine hesitancy among pregnant women it was found that, vaccine hesitant women are younger and further along in pregnancy. COVID-19 vaccine hesitant pregnant women also reported hesitancy for influenza and Tdap vaccines. Vaccine hesitancy was associated with lack of information to take an informed decision, personal long term side effects, short and long term side effects on the pregnancy, and harmful ingredients in the vaccine [23].
In a qualitative analysis that explored the intention to receive or not receive COVID-19 vaccine among Malaysians using an integrated framework of theory of reasoned action and health belief model, it was found that the predictors of vaccine hesitancy were age, religious beliefs, subjective norms, susceptibility, attitude, and vaccine confidence or trust [24]. In contrast to the findings of a global survey from seventeen countries which reported increasing vaccine hesitancy with increase in age [7], this study reported that the vaccine hesitancy was higher among those young, primarily driven by perceived (low) risk of COVID-19. The study also stressed the importance of social influence; an individual is more likely to get vaccinated if one or the other in his/her closest circle is either vaccinated or intend to get vaccinated [24].
A population based cross-sectional study from Germany reported predictors of COVID-19 vaccine hesitancy among adults more than or equal to 18 years of age. Regression analysis showed that the odds of willingness to get vaccinated were lower for females in comparison to males; however, participants of older age group, higher education, health literacy, and adherence to preventive measures increased the odds of willingness to get vaccinated [25].
Vaccine hesitancy or say vaccine acceptance, be in at individual level or societal level is driven by complex factors. The Royal Society of Canada Framework (an adapted version of Hasnan and Tan framework) discusses COVID-19 vaccine acceptance as shown in Figure 2. The four major domains of factors that influence vaccine acceptance are immunisation knowledge (highlighting the importance of vaccine related reliable information, that is, easily accessible, up-to-date, and accurate tailored for each target group), healthcare workers, people in place (in accordance with the goal of the World Health Organisation Immunisation Agenda 2030) and the health care system (highlighting the role of immunisation programmes, health legislations and policies) [26]. Each of these major domains influence each other and none of these stand alone; the intersections are highlighted in white boxes. The blue circle illustrate the broader context under which each of the major domain is influenced, which includes, but not limited to, education, control of infection, communication, and communities [27, 28].
Framework of factors that influence vaccine acceptance.
The implications of the COVID-19 pandemic and its vaccine hesitancy against routine immunisation is multi-modal – one, the pandemic and related movement restrictions or other mitigation measures, partial or complete suspension of vaccination clinics or fear of COVID-19, stress, anxiety, and depression may have limited parents access to avail routine immunisation vaccines for their children [29, 30]. In a data triangulated from global, country-based, and individual-reported sources during the pandemic period, it was found that there was a decline in the number of administered doses of diphtheria pertussis tetanus-containing vaccine (DTP3) (33% fewer doses in April 2020) and the first dose of measles-containing vaccine (MCV1) (9–57% fewer doses) in the early part of 2020 [31]. The primary reason reported by WHO regional offices were substantial disruption to routine vaccination sessions, and in particular, related to interrupted vaccination demand and supply, including reduced availability of the health workforce. Similarly, a systematic review reported a decline or delay in vaccination at the time of the COVID-19 pandemic, highlighting the need for a sustained catch-up program, especially in low- and middle-income countries [32].
Secondly, the impact of COVID-19 vaccine hesitancy is not limited to pandemic vaccines but may continue to extend to routinely recommended vaccines. Though certain studies found increased vaccine confidence in parents for routine childhood vaccines as compared to the COVID-19 vaccine, certain studies highlight the concern of COVID-19 vaccine hesitancy rubbing off on routine immunisation vaccine hesitancy [33, 34, 35, 36]. In a study that attempted to understand the impact of the pandemic on routine childhood vaccine hesitancy, it was found that the routine childhood vaccine hesitancy increased during the COVID-19 pandemic, mainly due to increased risk perception [37, 38].
It is the need of the hour to leverage COVID-19 vaccination awareness campaigns to include routine immunisation call-to-action messages [39]. Clear communication between public health authorities, providers, and the general public, and from providers to parents or caregivers on the value, safety, and necessity of routine immunisation will remain a critical piece to help alleviate concerns and address vaccine hesitancy. Engaging local leaders in the community may help resonate with public health messages related to the importance of routine vaccines, especially when the discussion around public health becomes tainted with political and/or non-medical aspects. In this process of communication, it is important to maintain a delicate balance between what is known and acknowledging the uncertainties that remain. Easing societal restrictions where possible, taking the necessary steps to reach standard marketing authorization, offering a fixed monetary reward as an incentive, involving physicians in the vaccination campaign, and focusing on vaccine effectiveness while communicating risks clearly and transparently are recommended as measures to reduce vaccine hesitancy [40].
Overall, the strategies include offering pre-structured, pre-tested communication from community trusted sources such as healthcare providers, local representatives, and authorities. It should be ensured that they are culturally relevant, accessible and in multiple languages. It is important to improve the accessibility of population to vaccines and vaccine related information. This should be made possible through adoption of flexible, context specific delivery models. The success of these strategies are rested with training and education of those involved and community engagement. It is necessary to involve youth ambassadors, healthcare workers, community champions and faith leaders to raise knowledge and awareness on vaccinations. Vaccination of friends, relatives and household members should be celebrated; an approach of community immunity should be fostered; aided by locally developed action plans with a continuous, open, and transparent dialogue [41].
The implications of contextual factors, individual and group factors, vaccine, and vaccination related factors on vaccine hesitancy is long recognised. However, the additive or multiplicative, multi-modal implications of COVID-19 vaccine hesitancy on routine immunisation is less recognised. It is the need of the hour to leverage COVID-19 vaccination awareness campaigns to include routine immunisation call-to-action messages with effective monitoring and evaluation aided by implementation research strategies. The areas that should be strengthened to restore and maintain vaccine confidence includes trust in health care provider–patient encounters, public health messaging, vaccine mandates, diversity, inclusion, and representation in health sectors, and industry influence on health care.
This is a brief overview of the main steps involved in publishing with IntechOpen Compacts, Monographs and Edited Books. Once you submit your proposal you will be appointed a Author Service Manager who will be your single point of contact and lead you through all the described steps below.
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\\n\\nPlease complete the publishing proposal form. The completed form should serve as an overview of your future Compacts, Monograph or Edited Book. Once submitted, your publishing proposal will be sent for evaluation, and a notice of acceptance or rejection will be sent within 10 to 30 working days from the date of submission.
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\n\nPlease complete the publishing proposal form. The completed form should serve as an overview of your future Compacts, Monograph or Edited Book. Once submitted, your publishing proposal will be sent for evaluation, and a notice of acceptance or rejection will be sent within 10 to 30 working days from the date of submission.
\n\n2. SUBMIT YOUR MANUSCRIPT
\n\nAfter approval, you will proceed in submitting your full-length manuscript. 50-130 pages for compacts, 130-500 for Monographs & Edited Books.Your full-length manuscript must follow IntechOpen's Author Guidelines and comply with our publishing rules. Once the manuscript is submitted, but before it is forwarded for peer review, it will be screened for plagiarism.
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\n\n4. ACCEPTANCE AND PRICE QUOTE
\n\nIf the manuscript is formally accepted after peer review you will receive a formal Notice of Acceptance, and a price quote.
\n\nThe Open Access Publishing Fee of your IntechOpen Compacts, Monograph or Edited Book depends on the volume of the publication and includes: project management, editorial and peer review services, technical editing, language copyediting, cover design and book layout, book promotion and ISBN assignment.
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Her research interests include archaea metabolism, enzymes purification and characterization, gene regulation, carotenoids and bioplastics production, antioxidant\ncompounds, waste water treatments, and brines bioremediation.\nRosa María’s other roles include editorial board member for several journals related\nto biochemistry, reviewer for more than 60 journals (biochemistry, molecular biology, biotechnology, chemistry and microbiology) and president of several organizing committees in international meetings related to the N-cycle or respiratory processes.",institutionString:null,institution:{name:"University of Alicante",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"15",title:"Chemical Biology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/15.jpg",isOpenForSubmission:!0,editor:{id:"441442",title:"Dr.",name:"Şükrü",middleName:null,surname:"Beydemir",slug:"sukru-beydemir",fullName:"Şükrü Beydemir",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003GsUoIQAV/Profile_Picture_1634557147521",biography:"Dr. Şükrü Beydemir obtained a BSc in Chemistry in 1995 from Yüzüncü Yıl University, MSc in Biochemistry in 1998, and PhD in Biochemistry in 2002 from Atatürk University, Turkey. 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Waisundara",profilePictureURL:"https://mts.intechopen.com/storage/users/194281/images/system/194281.jpg",biography:"Dr. Viduranga Waisundara obtained her Ph.D. in Food Science\nand Technology from the Department of Chemistry, National\nUniversity of Singapore, in 2010. She was a lecturer at Temasek Polytechnic, Singapore from July 2009 to March 2013.\nShe relocated to her motherland of Sri Lanka and spearheaded the Functional Food Product Development Project at the\nNational Institute of Fundamental Studies from April 2013 to\nOctober 2016. She was a senior lecturer on a temporary basis at the Department of\nFood Technology, Faculty of Technology, Rajarata University of Sri Lanka. She is\ncurrently Deputy Principal of the Australian College of Business and Technology –\nKandy Campus, Sri Lanka. She is also the Global Harmonization Initiative (GHI)",institutionString:"Australian College of Business & Technology",institution:{name:"Kobe College",institutionURL:null,country:{name:"Japan"}}}]},{type:"book",id:"6820",title:"Keratin",subtitle:null,coverURL:"https://cdn.intechopen.com/books/images_new/6820.jpg",slug:"keratin",publishedDate:"December 19th 2018",editedByType:"Edited by",bookSignature:"Miroslav Blumenberg",hash:"6def75cd4b6b5324a02b6dc0359896d0",volumeInSeries:2,fullTitle:"Keratin",editors:[{id:"31610",title:"Dr.",name:"Miroslav",middleName:null,surname:"Blumenberg",slug:"miroslav-blumenberg",fullName:"Miroslav Blumenberg",profilePictureURL:"https://mts.intechopen.com/storage/users/31610/images/system/31610.jpg",biography:"Miroslav Blumenberg, Ph.D., was born in Subotica and received his BSc in Belgrade, Yugoslavia. He completed his Ph.D. at MIT in Organic Chemistry; he followed up his Ph.D. with two postdoctoral study periods at Stanford University. Since 1983, he has been a faculty member of the RO Perelman Department of Dermatology, NYU School of Medicine, where he is codirector of a training grant in cutaneous biology. Dr. Blumenberg’s research is focused on the epidermis, expression of keratin genes, transcription profiling, keratinocyte differentiation, inflammatory diseases and cancers, and most recently the effects of the microbiome on the skin. He has published more than 100 peer-reviewed research articles and graduated numerous Ph.D. and postdoctoral students.",institutionString:null,institution:{name:"New York University Langone Medical Center",institutionURL:null,country:{name:"United States of America"}}}]},{type:"book",id:"7978",title:"Vitamin A",subtitle:null,coverURL:"https://cdn.intechopen.com/books/images_new/7978.jpg",slug:"vitamin-a",publishedDate:"May 15th 2019",editedByType:"Edited by",bookSignature:"Leila Queiroz Zepka, Veridiana Vera de Rosso and Eduardo Jacob-Lopes",hash:"dad04a658ab9e3d851d23705980a688b",volumeInSeries:3,fullTitle:"Vitamin A",editors:[{id:"261969",title:"Dr.",name:"Leila",middleName:null,surname:"Queiroz Zepka",slug:"leila-queiroz-zepka",fullName:"Leila Queiroz Zepka",profilePictureURL:"https://mts.intechopen.com/storage/users/261969/images/system/261969.png",biography:"Prof. Dr. Leila Queiroz Zepka is currently an associate professor in the Department of Food Technology and Science, Federal University of Santa Maria, Brazil. She has more than fifteen years of teaching and research experience. She has published more than 550 scientific publications/communications, including 15 books, 50 book chapters, 100 original research papers, 380 research communications in national and international conferences, and 12 patents. She is a member of the editorial board of five journals and acts as a reviewer for several national and international journals. 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Working with large volumes of data has given him a good command of big data processing tools and NoSQL databases. He has also been a visiting scholar at the Knowledge Engineering and Discovery Research Institute, Auckland University of Technology.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"314575",title:"Dr.",name:"Jesus",middleName:null,surname:"L. Lobo",slug:"jesus-l.-lobo",fullName:"Jesus L. Lobo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314575/images/system/314575.png",biography:"Dr. Jesús López is currently based in Bilbao (Spain) working at TECNALIA as Artificial Intelligence Research Scientist. In most cases, a project idea or a new research line needs to be investigated to see if it is good enough to take into production or to focus on it. That is exactly what he does, diving into Machine Learning algorithms and technologies to help TECNALIA to decide whether something is great in theory or will actually impact on the product or processes of its projects. So, he is expert at framing experiments, developing hypotheses, and proving whether they’re true or not, in order to investigate fundamental problems with a longer time horizon. He is also able to design and develop PoCs and system prototypes in simulation. He has participated in several national and internacional R&D projects.\n\nAs another relevant part of his everyday research work, he usually publishes his findings in reputed scientific refereed journals and international conferences, occasionally acting as reviewer and Programme Commitee member. Concretely, since 2018 he has published 9 JCR (8 Q1) journal papers, 9 conference papers (e.g. ECML PKDD 2021), and he has co-edited a book. He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"310576",title:"Prof.",name:"Erick Giovani",middleName:null,surname:"Sperandio Nascimento",slug:"erick-giovani-sperandio-nascimento",fullName:"Erick Giovani Sperandio Nascimento",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y00002pDKxDQAW/ProfilePicture%202022-06-20%2019%3A57%3A24.788",biography:"Prof. Erick Sperandio is the Lead Researcher and professor of Artificial Intelligence (AI) at SENAI CIMATEC, Bahia, Brazil, also working with Computational Modeling (CM) and HPC. He holds a PhD in Environmental Engineering in the area of Atmospheric Computational Modeling, a Master in Informatics in the field of Computational Intelligence and Graduated in Computer Science from UFES. He currently coordinates, leads and participates in R&D projects in the areas of AI, computational modeling and supercomputing applied to different areas such as Oil and Gas, Health, Advanced Manufacturing, Renewable Energies and Atmospheric Sciences, advising undergraduate, master's and doctoral students. He is the Lead Researcher at SENAI CIMATEC's Reference Center on Artificial Intelligence. In addition, he is a Certified Instructor and University Ambassador of the NVIDIA Deep Learning Institute (DLI) in the areas of Deep Learning, Computer Vision, Natural Language Processing and Recommender Systems, and Principal Investigator of the NVIDIA/CIMATEC AI Joint Lab, the first in Latin America within the NVIDIA AI Technology Center (NVAITC) worldwide program. He also works as a researcher at the Supercomputing Center for Industrial Innovation (CS2i) and at the SENAI Institute of Innovation for Automation (ISI Automação), both from SENAI CIMATEC. He is a member and vice-coordinator of the Basic Board of Scientific-Technological Advice and Evaluation, in the area of Innovation, of the Foundation for Research Support of the State of Bahia (FAPESB). He serves as Technology Transfer Coordinator and one of the Principal Investigators at the National Applied Research Center in Artificial Intelligence (CPA-IA) of SENAI CIMATEC, focusing on Industry, being one of the six CPA-IA in Brazil approved by MCTI / FAPESP / CGI.br. He also participates as one of the representatives of Brazil in the BRICS Innovation Collaboration Working Group on HPC, ICT and AI. He is the coordinator of the Work Group of the Axis 5 - Workforce and Training - of the Brazilian Strategy for Artificial Intelligence (EBIA), and member of the MCTI/EMBRAPII AI Innovation Network Training Committee. He is the coordinator, by SENAI CIMATEC, of the Artificial Intelligence Reference Network of the State of Bahia (REDE BAH.IA). He leads the working group of experts representing Brazil in the Global Partnership on Artificial Intelligence (GPAI), on the theme \"AI and the Pandemic Response\".",institutionString:"Manufacturing and Technology Integrated Campus – SENAI CIMATEC",institution:null},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:'"Politechnica" University Timişoara',institution:null},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. Science CV available at: https://www.cienciavitae.pt//pt/8E1C-A8B3-78C5 and ORCID: https://orcid.org/0000-0002-0298-3974',institutionString:"University of Lisbon",institution:{name:"Universidade Lusófona",country:{name:"Portugal"}}},{id:"241400",title:"Prof.",name:"Mohammed",middleName:null,surname:"Bsiss",slug:"mohammed-bsiss",fullName:"Mohammed Bsiss",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241400/images/8062_n.jpg",biography:null,institutionString:null,institution:null},{id:"276128",title:"Dr.",name:"Hira",middleName:null,surname:"Fatima",slug:"hira-fatima",fullName:"Hira Fatima",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/276128/images/14420_n.jpg",biography:"Dr. Hira Fatima\nAssistant Professor\nDepartment of Mathematics\nInstitute of Applied Science\nMangalayatan University, Aligarh\nMobile: no : 8532041179\nhirafatima2014@gmal.com\n\nDr. Hira Fatima has received his Ph.D. degree in pure Mathematics from Aligarh Muslim University, Aligarh India. Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. She is a member of Indian Mathematical Society.",institutionString:null,institution:null},{id:"414880",title:"Dr.",name:"Maryam",middleName:null,surname:"Vatankhah",slug:"maryam-vatankhah",fullName:"Maryam Vatankhah",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Borough of Manhattan Community College",country:{name:"United States of America"}}},{id:"414879",title:"Prof.",name:"Mohammad-Reza",middleName:null,surname:"Akbarzadeh-Totonchi",slug:"mohammad-reza-akbarzadeh-totonchi",fullName:"Mohammad-Reza Akbarzadeh-Totonchi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Ferdowsi University of Mashhad",country:{name:"Iran"}}},{id:"414878",title:"Prof.",name:"Reza",middleName:null,surname:"Fazel-Rezai",slug:"reza-fazel-rezai",fullName:"Reza Fazel-Rezai",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"American Public University System",country:{name:"United States of America"}}},{id:"426586",title:"Dr.",name:"Oladunni A.",middleName:null,surname:"Daramola",slug:"oladunni-a.-daramola",fullName:"Oladunni A. Daramola",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Federal University of Technology",country:{name:"Nigeria"}}},{id:"357014",title:"Prof.",name:"Leon",middleName:null,surname:"Bobrowski",slug:"leon-bobrowski",fullName:"Leon Bobrowski",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Bialystok University of Technology",country:{name:"Poland"}}},{id:"302698",title:"Dr.",name:"Yao",middleName:null,surname:"Shan",slug:"yao-shan",fullName:"Yao Shan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Dalian University of Technology",country:{name:"China"}}},{id:"354126",title:"Dr.",name:"Setiawan",middleName:null,surname:"Hadi",slug:"setiawan-hadi",fullName:"Setiawan Hadi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Padjadjaran University",country:{name:"Indonesia"}}},{id:"125911",title:"Prof.",name:"Jia-Ching",middleName:null,surname:"Wang",slug:"jia-ching-wang",fullName:"Jia-Ching Wang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"National Central University",country:{name:"Taiwan"}}},{id:"332603",title:"Prof.",name:"Kumar S.",middleName:null,surname:"Ray",slug:"kumar-s.-ray",fullName:"Kumar S. Ray",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Statistical Institute",country:{name:"India"}}},{id:"415409",title:"Prof.",name:"Maghsoud",middleName:null,surname:"Amiri",slug:"maghsoud-amiri",fullName:"Maghsoud Amiri",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Allameh Tabataba'i University",country:{name:"Iran"}}},{id:"357085",title:"Mr.",name:"P. Mohan",middleName:null,surname:"Anand",slug:"p.-mohan-anand",fullName:"P. Mohan Anand",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356696",title:"Ph.D. Student",name:"P.V.",middleName:null,surname:"Sai Charan",slug:"p.v.-sai-charan",fullName:"P.V. Sai Charan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"357086",title:"Prof.",name:"Sandeep K.",middleName:null,surname:"Shukla",slug:"sandeep-k.-shukla",fullName:"Sandeep K. Shukla",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}}]}},subseries:{item:{id:"17",type:"subseries",title:"Metabolism",keywords:"Biomolecules Metabolism, Energy Metabolism, Metabolic Pathways, Key Metabolic Enzymes, Metabolic Adaptation",scope:"Metabolism is frequently defined in biochemistry textbooks as the overall process that allows living systems to acquire and use the free energy they need for their vital functions or the chemical processes that occur within a living organism to maintain life. Behind these definitions are hidden all the aspects of normal and pathological functioning of all processes that the topic ‘Metabolism’ will cover within the Biochemistry Series. Thus all studies on metabolism will be considered for publication.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/17.jpg",hasOnlineFirst:!0,hasPublishedBooks:!0,annualVolume:11413,editor:{id:"138626",title:"Dr.",name:"Yannis",middleName:null,surname:"Karamanos",slug:"yannis-karamanos",fullName:"Yannis Karamanos",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002g6Jv2QAE/Profile_Picture_1629356660984",biography:"Yannis Karamanos, born in Greece in 1953, completed his pre-graduate studies at the Université Pierre et Marie Curie, Paris, then his Masters and Doctoral degree at the Université de Lille (1983). He was associate professor at the University of Limoges (1987) before becoming full professor of biochemistry at the Université d’Artois (1996). He worked on the structure-function relationships of glycoconjugates and his main project was the investigations on the biological roles of the de-N-glycosylation enzymes (Endo-N-acetyl-β-D-glucosaminidase and peptide-N4-(N-acetyl-β-glucosaminyl) asparagine amidase). From 2002 he contributes to the understanding of the Blood-brain barrier functioning using proteomics approaches. He has published more than 70 papers. 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