\r\n\tThis book chapter’s main theme will be focused on transmission dynamics, pathogenesis, mechanisms of host interaction and response, epigenetics and markers, molecular diagnosis, RNA interacting proteins, RNA binding proteins, advanced development of tools for diagnosis, possible development of concepts for vaccines and anti drugs for RNA viruses, immunological mechanisms, treatment, prevention and control. \r\n\t
",isbn:"978-1-80355-667-3",printIsbn:"978-1-80355-666-6",pdfIsbn:"978-1-80355-668-0",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,isSalesforceBook:!1,isNomenclature:!1,hash:"52f8a3a1486912beae40b34ac557fed3",bookSignature:"Ph.D. Yogendra Shah",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11369.jpg",keywords:"HIV, Dengue, Zika, West Nile Virus, Chikungunya, Rabies, SARS-CoV2, MERS-CoV, Hanta Virus, Influenza, Whole Genome Sequencing, DNA Sequencing",numberOfDownloads:181,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"October 4th 2021",dateEndSecondStepPublish:"November 1st 2021",dateEndThirdStepPublish:"December 31st 2021",dateEndFourthStepPublish:"March 21st 2022",dateEndFifthStepPublish:"May 20th 2022",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"8 months",secondStepPassed:!0,areRegistrationsClosed:!0,currentStepOfPublishingProcess:5,editedByType:null,kuFlag:!1,biosketch:"Dr. Shah obtained his Ph.D. degree in Veterinary Medicine from Hokkaido University, Japan. He was awarded the Young Science and Technology Award from the Nepal Academy of Science and Technology (NAST) in 2019. His research interests include infectious diseases, zoonotic infectious diseases, and vector-borne diseases.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"278914",title:"Ph.D.",name:"Yogendra",middleName:null,surname:"Shah",slug:"yogendra-shah",fullName:"Yogendra Shah",profilePictureURL:"https://mts.intechopen.com/storage/users/278914/images/system/278914.jpg",biography:"Dr. Yogendra Shah is a consultant microbiologist/virologist, senior research microbiologist, and lecturer at Seti Provincial Hospital, COVID-19 PCR laboratory, National Zoonoses and Food Hygiene Research Center, and Kathmandu College of Science and Technology, Nepal. He obtained a Ph.D. in Veterinary Medicine (Bacteriology) from the Graduate School of Veterinary Medicine, Hokkaido University, Japan, in 2017. His research focuses on better understanding the molecular epidemiological features/transmission dynamics of infectious diseases and zoonotic infectious diseases in Nepal by employing molecular techniques like ELISA, polymerase chain reaction (PCR), loop-mediated isothermal amplification (LAMP), and DNA sequencing. He was awarded the Young Science and Technology Award from the Nepal Academy of Science and Technology (NAST) in 2019. His research interests include infectious diseases, zoonotic infectious diseases, and vector-borne diseases. 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\n
1. Introduction to Kalman filter
\n
The simplest formulation of a Kalman filter [1] is when the state and measurement equations are both linear. However, Kalman filter has found its greatest application for non-linear systems. A typical continuous state with discrete measurements in time forming a non-linear filtering problem can be written as
where ‘x’ and ‘Z’ are, respectively, the state and measurement equations of size (n × 1) and (m × 1); u is the control input and \n\nΘ\n\n the parameter vector of size (p × 1) and ‘f’ and ‘h’ are non-linear functions. The process ‘w’ and measurement ‘v’ noises are respectively of size (n × 1) and (m × 1). These are assumed to be zero mean with covariances Q and R and their sequences are uncorrelated with each other. The states may not be in general observable but the measurements should be related to the states. In many applications for linear systems, if the unknown parameters \n\nΘ\n\n are treated as additional states, then the linear system of equations becomes non-linear. In such cases, the extended Kalman filter (EKF) formulation can be written as
where ‘\n\nX\n\n’ is the augmented state of size ((n + p) × 1). The control symbol ‘u’ is not shown for brevity. The formal solution for the above filtering problem can be summarised following Brown and Hwang [2] as
\n\nInitial state estimate\n\nX\n\n0\n0\n\n=\nX\n0\n=\nE\n\n\n\nX\n\n\nt\n0\n\n\n\n\n,\n\nE6
\n
\n\nInitial state covariance matrix\n\nP\n\n0\n0\n\n=\nP\n0\n=\nE\n\n\n\n\n\nX\n0\n–\nX\n\n\nt\n0\n\n\n\n\n\n\n\nX\n0\n–\nX\n\n\nt\n0\n\n\n\n\nT\n\n\n\n\nE7
We assume that \n\nX\n\nk\n\nk\n−\n1\n\n\n\n and \n\nP\n\nk\n\nk\n−\n1\n\n\n\n denote the estimates of the state and its covariance matrix, respectively, at time index k, based on all information available up to and including time index k−1. Then, we seek to update the state value from \n\nX\n\n\nk\n\nk\n−\n1\n\n\n\n to \n\nX\n\n\nk\nk\n\n\n using the measurement \n\nZ\n\nk\n\n\n with uncertainty denoted by \n\nR\n\nk\n\n\n based on the value of \n\nK\n\nk\n\n\n called the Kalman gain such that the updated covariance \n\nP\n\nk\nk\n\n\n having the individual terms along its major diagonal is a minimum, leading to
with \n\nP\n\n denoting uncertainty, \n\nF\n\n\nk\n−\n1\n\n\n\n is the state Jacobian matrix (∂f/∂X) evaluated at \n\nX\n=\nX\n\n\nk\n−\n1\n\n\nk\n−\n1\n\n\n.\n\n\n\nX\n\nk\n\nk\n−\n1\n\n\n\n denotes the estimate at t(k) based on the process dynamics between t(k−1) and t(k) but before using the measurement information. The measurement Jacobian \n\nH\n\nk\n\n\n = (∂h/∂X) is evaluated at \n\nX\n=\nX\n\nk\n\n.\n\n The difference between the actual measurement and the predicted model output
is called the innovation. The importance of the innovation following white Gaussian for filter performance was brought out by Kailath [3]. When the innovation is white, it means all the information has been extracted from the data and no further information is left out, thus both the models and the algorithm have done their best job.
\n
There are thus five basic filter operations, namely: (i) the state propagation, (ii) the covariance propagation, (iii) Kalman gain evaluation, (iv) the state update and (v) the covariance update. The first and fourth refer to sample values and the second, third and fifth refer to the population characteristics. At any given time point, the statistical combination of the two estimates, one from state and the other from measurement equation, are formal if only the covariances denoting their uncertainties are available. Thus the states and the covariances at all times can be estimated if the initial X0 and P0 as well as Q(k) and R(k) are specified over time, but this is not easy. These have to be specified over a time span in order to match and minimise a cost function based on the innovation, or any other in some best possible sense. A well-known criterion is the method of maximum likelihood estimation (MMLE). When Q ≡ 0, the Kalman gain matrix is zero and the technique is called as the output error method (MMLE-OEM). When Q > 0, the method is called as filter error method [4, 5]. For optimal design of the Kalman filter, the innovation follows a white Gaussian distribution which is operationally equivalent to minimising the cost function
based on summation over all the N measurements. Thus, the filter has to be tuned or in other words should solve for either the statistics X0, Θ, P0, Q and R, in Eq. (16), or for X0, Θ and K in Eq. (17). Of course, there have been many number of cost functions used in the literature, the only constraint being all should lead to reasonable answers that are acceptable. The Q ≡ 0 case leads to an optimisation of the cost function. If Q > 0, then the filter approach becomes compulsive and generally the cost function is forgotten and mostly the filter statistics are tuned manually to obtain the results.
\n
One can see straightaway that the structure of the above cost function \n\nJ\n\n becomes different due to the change of variables (different combination of the statistics can lead to the same gain!); and hence whatever the results are generated, they will be different but have to be within reasonable limits. In the RRR studies [6, 7, 8, 9], many typical cost functions have been stated to bring home the above point. One can be around the true answer but not at the answer which is not known due to the occurrence of the random unknown sequence of the noise distribution in the data. Hence, estimation theory being an inverse problem, the results are subjective and not objective as many claim. In fact, the whole of statistics is subjective from the beginning to the end and thus the results generated can be stretched to any limit but have to be meaningful, acceptable and useful for further use. As is well known, inverse problems do not have unique answers, more so with randomness being introduced. Unless the above sequence of noise distribution can be worked out correctly, there is no way to get the true answers. Thus, the statistical percolation effect affects all the unknowns in any estimation theory. This should be kept in mind to understand any result based on filter statistics or filter gains in Kalman filtering.
\n
\n
1.1 The competence and beauty of the Kalman filter
\n
The earliest Kalman filter formulation by Kalman [1] dealt with state estimation. However, it has grown at present to handle myriad other scenarios such as state and parameter estimation, data fusion and many more. The Kalman filter can ably estimate or account for time-invariant or time-varying (i) unknown, (ii) inaccurately known or (iii) even unmodellable structure of the state and measurement model equations and the parameters in them as also (iv) the deterministic or random inputs and by accounting for them suitably as process and measurement noises. It can compensate even for computational errors during the entire filter operation.
\n
Further, the state and covariance updates at a measurement depend only on the covariances of the state and the measurements and not their probability distribution (!). Hence, after assimilating the measurement information, the update is subtly reset to follow a Gaussian distribution. Thus, the use of only the estimate and covariance all over the filter tacitly implies (one can however improve the numerical values of the estimate and covariance in non-linear problems) the state and measurement variables are all distributed or approximated as quasi-Gaussian. Hence, with all such subjective features, the final result can only be an answer rather than a true or unique answer. All the above have to be checked for the consistency of the whole process of modelling, convergence of the numerical algorithm and other consistency checks among the variables occurring in the filter as discussed in [6, 10, 11, 12].
\n
\n
\n
1.2 Use of filter statistics in designing the Kalman filter
\n
Assuming that the measurements are available at N discrete time instants, the normalised innovation cost function \n\nJ\n\n fundamental to the Kalman filter as suggested by Sorenson [13] is defined as
where \n\nR\n\n is the covariance matrix of the innovation \n\n\n\nH\n\nk\n\nP\n\nk\n\nk\n−\n1\n\n\nH\n\n\nk\n\nT\n\n+\nR\n\nk\n\n\n\n.\n\n Here, \n\nR\n\n which is a function of P0, Q and R varies with time. The estimation of the system parameters \n\nX\n0\n,\nΘ\n,\nP\n0\n,\nQ\n\nand\n\nR\n\n is called filter design or filter tuning as mentioned earlier. Though there are many techniques for adaptively tuning the filter statistics [14], the recent RRR [6, 7, 8, 9] or the heuristic approach of Myers and Tapley [15] for \n\nQ\n,\nand\n\nR\n,\n\n and of Gemson [16] and Gemson and Ananthasayanam [17] for \n\n\nP\n0\n\n are perhaps the simplest ones.
\n
\n
\n
1.3 Use of filter gains and design of constant gain Kalman filter (CGKF)
\n
In the constant Kalman gain formulation (in discrete form), the update step
gets simplified to determine only the constant gain matrix K(k) by subsuming \n\nP\n0\n,\nQ\n\nand\n\nR\n\n. Hence, there are no covariance equations for propagation at all, thus enormously reducing the numerical effort and time. The constant filter gain approach is less explored than the filter statistics approach. Many attempts have been made by Wilson [18], Cook and Dawson [19], Grimble et al. [20], Kobayashi [21] and Liu et al. [22]; but these are not simple, except the modified gain extended Kalman filter (MGEF) proposed by Song and Speyer [23]. Gelb [24] and Sugiyama [25] consider the CGKF approach but their method of tuning the desired filter gain parameters is manual. The present rational procedure is based on a suitable normalised innovation cost function as in space debris [26, 27, 28, 29] and many other illustrative examples to follow.
\n
When a regular Kalman filter using the filter statistics operates on the data in general, it turns out that after the initial transients the Kalman gain matrix tends to a constant value. Such a feature has been noticed in the tracking of ballistic rockets by Sarkar [30], evolution and expansion of the space debris scenario and prediction of re-entry objects [27–29]; for parameter estimation of dynamic systems by Viswanath [31]; in rendezvous and docking studies [32, 33] and total electron content in the ionosphere [34, 35] and in integration of GPS and INS [36, 37], target tracking in wireless sensor networks by Yadav et al. [38] and many more. Due to limited space, only the first three will be discussed in this chapter. This observation provides a possible approach in which instead of tuning the usual Kalman filter statistics for \n\nX\n0\n,\nP\n0\n,\nQ\n\nand\n\nR\n,\n\n in general, a smaller number of Kalman gain elements can be worked out. This constant gain matrix K can be obtained by making the above normalised innovation cost function equal to the number of measurements by assuming the above \n\n\nR\n\n in Eq. (18) to be a constant. Then, the \n\nR\n\n can be estimated as if it is the estimation of pure noise only as is the case of MMLE-OEM [4, 5]. There could be some differences in the gain values obtained from the adaptively or manually tuned \n\nP\n0\n,\nQ\n\nand\n\nR\n\n and the constant gain approach due to the relative periods of transients and the steady-state conditions. Though the results may not be as close to the optimum, the estimates are generally acceptable. The present examples mostly utilise the genetic algorithm [39, 40] to minimise the cost function \n\nJ\n\n and obtain optimum K. However, before applying the constant Kalman gain approach, it is desirable to carry out extensive studies using any adequate adaptive filtering technique such as RRR. A comparison of the results from the adaptive technique and the constant Kalman gain approach provides confidence in the latter approach. The following sections provide example applications of designing constant gain Kalman filters.
\n
\n
\n
\n
2. Ancient Indian astronomers implicitly using the constant Kalman gain approach
\n
Ancient Indian astronomers needed to calculate the position of celestial objects like Sun, Moon and other planets for timing the Vedic rituals. But their predicted positions changed over many centuries due to unmodelled or unmodellable causes. The philosophy of ‘change, capture and correct’ is the one that is followed in the Kalman filter. The ancient Indian astronomers had understood the above philosophy. They used the above concept to update the parameters for predicting the position of celestial objects based on measurements carried out at various time intervals which can be stated as.
\n\nUpdated parameter\n=\nEarlier parameter\n+\n\nSome quantity\n\n×\n\n\nMeasured\n−\nPredicted\n\n\n\nPosition of the celestial object\n\nE22
\n
They could not have done it in any other way to update the earlier parameters called ‘cannons’. The ‘some quantity’ as we will see later on is the Kalman gain. There were no frills and fashion of distributions and the spread to infer uncertainties after combining statistically one estimate in certain units with another estimate in another unit but related to the former. The measured longitude of the celestial object is different from the state that is updated, which is the number of revolutions in a yuga just as state and measurements are in general different in many Kalman filter applications!
\n
Billard [41, 42] had stated that if the elements of Aryabhata are now wrong, they must have been accurate when he was living. Then, newer astronomical elements can be established based on the earlier astronomical elements and the new observations of the present time. Billard [41, 42] provides many cannons starting from around AD 500 by Aryabhata to AD 1600 based on later measurements carried out (over many years or even decades!) to make the predicted position of the objects consistent with new observations. One such canon around AD 898 shows a very high accuracy valid over a larger number of centuries. Sarma [43] quotes such revisions over a period of time. Nilakantha (around AD 1443) had stated that the eclipses cited in Siddhanthas as well as those currently observable can be studied and future eclipses can be predicted (extrapolation!). Also, for the eclipses occurring at other longitudes and latitudes, the predictions can be perfected (data fusion!). Based on these, the past eclipses of one’s own place can be refined equivalent to ‘smoothing’! It is strongly urged that research is undertaken on Billard’s work available in French.
\n
\n
\n
3. Typical parameter estimation studies
\n
In order to illustrate the ability of CGKF, we consider the parameter estimation of a spring, mass and damper (SMD) system with a weak non-linear spring constant and also a real flight test data of an airplane.
\n
\n
3.1 Analysis of spring, mass and damper (SMD) system
\n
The SMD system with weak non-linear spring constant in continuous time (t) is governed by the equations
where \n\n\nx\n1\n\n\n and \n\n\nx\n2\n\n\n are the displacement and velocity states. The ‘dot’ represents differentiation with respect to time (t). The unknown parameter vector Θ = [\n\n\nΘ\n1\n\n\n, \n\n\nΘ\n2\n\n\n, \n\n\nΘ\n3\n\n\n]T has the true value Θtrue= [4, 0.4, 0.6].\n\n\nΘ\n3\n\n\n being a weak parameter, it does not affect the system dynamics much and hence its estimation also has more uncertainty. The complete state vector X = [\n\n\nx\n1\n\n\n, \n\n\nx\n2\n\n\n, \n\n\nΘ\n1\n\n\n, \n\n\nΘ\n2\n\n\n, \n\n\nΘ\n3\n\n\n]T of size [(n + p) × 1] which in this case is (5 × 1). The measurement equation is given by.
\n\nZ\n\nk\n\n=\nH\n\nX\n\nk\n\n\nE25
\n
where H = \n\n\n\n\n\n\n1\n\n\n0\n\n\n0\n\n\n0\n\n\n0\n\n\n\n\n0\n\n\n1\n\n\n0\n\n\n0\n\n\n0\n\n\n\n\n\n\n is the measurement matrix of size (m × (n + p)) where m = n = 2 and p = 3.
\n
At a measurement with the additional terms to assimilate the measurement data, the above equations become
where \n\n\nK\n11\n\n,\n\nK\n12\n\n,\n\n\n\n\nK\n21\n\n\n and \n\n\nK\n22\n\n\n are the elements of the constant Kalman gain matrix K. These along with the parameter vector Θ have to be estimated by minimising the earlier mentioned innovation cost function \n\nJ\n\n. A total of N = 100 simulated measurement data are generated with initial state conditions 1 and 0, respectively, in steps of dt = 0.1 s between time 0 and 10 s.
\n
\n
\n
3.2 Remarks on the SMD parameter estimation
\n
The CGKF were based on 25 Newton-Raphson iterations and RRR results were generated based on 100 iterations for each data set (for obtaining generally four-digit accuracy though not necessary) and are compared in Table 1 below. The parameter values are to be read as for [\n\n\nΘ\n1\n\n\n, \n\n\nΘ\n2\n\n\n, \n\n\nΘ\n3\n\n\n]. For Q ≡ 0 and Q > 0 cases, the mean and standard deviation of the parameter estimates from CGKF and RRR are by and large close. In fact, the CGKF estimates are generally within about 1σ of the CRB values given by RRR. In the RRR with constant P0, Q and R, the filter is able to follow the system fairly well due to the time-varying gains providing near optimal solution. But in the CGKF approach, since the gains are constant, the filter is unable to follow the system model as well as by RRR. Thus, CGKF follows a slightly different dynamical model than RRR and hence their results are somewhat different.
\n
\n
\n
\n
\n\n
\n
SMD SYSTEM: For CGKF, the standard deviation (STDV) is based on parameter estimates and for RRR, the Cramer-Rao Bound (CRB ~ STDV) is based on filter covariance averaged over 100 simulations
Comparison of simulated SMD data results of CGKF with RRR.
\n
The gains are to be read as first column first and the second column next. For CGKF, there are only four gains associated with the two states and two measurements. But for RRR, there are ten gains associated with five states and two measurements. For the case of Q ≡ 0, all the gains should have been ideally zero but are around zero here due to the statistical percolation effect of the unforgiving noises, be it process and/or measurement. This affects not just one parameter or state but every other quantity, so the gains or any estimated quantity in the numerical algorithm can also never take their true values except perhaps with an appropriate algorithm that can capture the true values with increasing amount of data. For the Q > 0 case, the major gains marked in bold are somewhat similar.
\n
\n
\n
3.3 Analysis of real airplane flight test data
\n
This real data set discussed earlier in [6, 8, 9] is obtained along with airplane, flight test data and notations from [44]. Briefly, to explain the scenario, there is a peculiar manoeuvre when the aircraft (T 37 B) is rolling through a full rotation using the aileron, and then the elevator angle (\n\n\nδ\ne\n\n\n in deg) is imparted. The coupling between the longitudinal and lateral motion is replaced by their measured values, namely the roll angle (φm), sideslip (βm), velocity (Vm), roll rate (pm), yaw rate (rm) and the angle of attack (αm). The state equations (n = 3) for the angle of attack (α), pitch rate (q) and the pitch angle (θ), respectively, are
The unknown parameters (p = 10) are \n\n\n\n\nC\n\nL\nα\n\n\n\nC\n\nL\n\nδ\ne\n\n\n\n\nC\n\nL\n0\n\n\n\nC\n\nm\nα\n\n\n\nC\n\nm\nq\n\n\n\nC\n\nm\n\nα\ṅ\n\n\n\n\nC\n\nm\n\nδ\ne\n\n\n\n\nC\n\nm\n0\n\n\n\nθ\n0\n\n\nC\n\nN\n0\n\n\n\nT\n\n\n with the approximation \n\n\nC\n\nN\nα\n\n\n=\n\nC\n\nL\nα\n\n\n\nand\n\n\nC\n\nN\n\nδ\ne\n\n\n\n=\n\nC\n\nL\n\nδ\ne\n\n\n\n\n. The suffix \n\n\nδ\ne\n\n\n denotes control derivatives, and suffix zero refers to biases and all others are aerodynamic derivatives. The initial states are taken as the initial measurements and the initial parameter values are taken as (4, 0.15, 0.2, −0.5, −11.5, −5, −1.38, −0.06, −0.01, 0.2)T. At a measurement similar to the SMD system, there is an additional term \n\nKν\n\nk\n\n\n which is the product of the gain matrix multiplying the innovation.
\n
\n
\n
3.4 Remarks on the real flight test data results
\n
Table 2 below compares the parameter estimates and their CRBs (in parenthesis) from the RRR [6, 8, 9], Gemson [16], (derived from the filter covariance) and CGKF (based on cost function) approaches. The parameter estimates from the first two are comparable except for the parameters \n\n\nC\n\nL\n\nδ\ne\n\n\n\n\n and \n\n\n\nC\n\nm\nq\n\n\n\n which strongly affect the airplane dynamics. However, all the parameter estimates from RRR, Gemson and CGKF are quite comparable. The CGKF estimates are within about 1σ of the RRR values as in the previous SMD case. The STDV from the CGKF (corresponding to CRB) is somewhat different from the other approaches since it follows a slightly different dynamical model than RRR or Gemson as in the SMD case.
\n
\n
\n
\n
\n
\n\n
\n
\n\n\nΘ\n\n\n
\n
\n\n\nRRR\n\n\n
\n
\n\n\nGemson\n\n\n
\n
CGKF
\n
\n\n\n
\n
\n\n\n\nC\n\nN\n0\n\n\n\n\n
\n
0.2538 (0.0014)
\n
0.2503 (0.0014)
\n
0.2512 (0.0006)
\n
\n
\n
\n\n\n\nC\n\nL\n0\n\n\n\n\n
\n
0.2409 (0.0021)
\n
0.2529 (0.0018)
\n
0.2443 (0.0019)
\n
\n
\n
\n\n\n\n\nC\n\nL\nα\n\n\n\n\n
\n
4.9235 (0.0164)
\n
4.9028 (0.0168)
\n
4.8035 (0.2199)
\n
\n
\n
\n\n\n\nC\n\nL\n\nδ\ne\n\n\n\n\n\n
\n
0.1554 (0.0271)
\n
0.0879 (0.0267)
\n
0.1653 (0.0993)
\n
\n
\n
\n\n\n\nC\n\nm\n0\n\n\n\n\n
\n
−0.0425 (0.0009)
\n
−0.0507 (0.0024)
\n
−0.0459 (0.0001)
\n
\n
\n
\n\n\n\nC\n\nm\nα\n\n\n\n\n
\n
−0.5293 (0.0079)
\n
−0.6174 (0.0211)
\n
−0.4986 (0.0345)
\n
\n
\n
\n\n\n\nC\n\nm\nq\n\n\n\n\n
\n
−11.8596 (0.2402)
\n
−18.8339 (.8379)
\n
−9.3528 (2.1234)
\n
\n
\n
\n\n\n\nC\n\nm\n\nα\ṅ\n\n\n\n\n\n
\n
−6.8959 (0.4891)
\n
−7.1290 (1.544)
\n
−6.6730 (4.6084)
\n
\n
\n
\n\n\n\nC\n\nm\n\nδ\ne\n\n\n\n\n\n
\n
−0.9731 (0.0177)
\n
−1.1841 (0.471)
\n
−1.0063 (0.0019)
\n
\n
\n
\n\n\n\nθ\n0\n\n\n\n
\n
0.0003 (0.0021)
\n
−0.0037 (0.001)
\n
0.0020 (0.0003)
\n
\n\n
Table 2.
Comparison of real flight test data results (Θ, σ(Θ)).
\n
\n
\n
\n
4. Introduction to flight data analysis of a ballistic rocket (BR)
\n
During the development of any BR, it is necessary to carry out many flight trials and compare the flight performance with that based on pre-flight estimates. For a BR, an accurate estimation of drag coefficient is very important due to its direct impact on the system performance as it plays a very critical role in generating the firing tables. One also uses wind tunnel tests or computational fluid dynamic codes to obtain the aerodynamic characteristics. But there exist generally unavoidable errors due to wind tunnel wall interference and the limitation of wind tunnel Reynolds number. Hence, the assessment of the aerodynamic coefficient from the full-scale flight test of vehicles is an important area of activity and research. Such an analysis would help the BR as follows.
If it fails en route, a real-time state estimation helps to obtain the expected impact location from range safety viewpoint.
A compatibility check of measured data reduces bias and scale factor effects in the measurements. The measurement noise covariances given in the manufacturer’s catalogues being notional, such values can also be estimated.
In its external ballistics, the variation of aerodynamic drag coefficient with respect to Mach number is very important.
Comparison of pre-flight with flight test estimated drag coefficient helps to improve and modify the former.
\n
\n
4.1 State estimation of a ballistic rocket (BR)
\n
It is possible to formulate the Kalman Filter (KF) to simultaneously estimate both the state and parameter or carry out the same sequentially. In the state estimation step, the bias, scale and the random errors are estimated, called compatibility check, and thus relatively clean data are available for parameter estimation. The BRs are generally tracked by ground based radar, which provides range, azimuth and elevation measurements. Sarkar explains in [30] the extended Kalman filter [24] together with a smoother for handling the effect of both the process and measurement noise contained in the measured flight test data. Later, it is used to estimate the aerodynamic drag coefficient (which is a parameter estimation problem) using the MMLE-OEM approach. We discuss here only his trajectory estimation by using CGKF and the reader can refer to [30] for drag estimation.
\n
In order to track the trajectory of a BR, one can use either the dynamical or the kinematical equations. The former needs many inputs such as the forces and moments, propulsion and the control which may not be available and more so if the BR belongs to an adversary. Broadly, the three approaches all utilising the kinematic state equations for trajectory estimation with increasing accuracy are
The ‘generic’ ones called α, αβ or αβγ types of filters as found in Blair [45] and Bar-Shalom and Li [46].
The ‘similar’ ones like the CGKF approach which can handle similar situations.
The ‘specific’ one for a given scenario like the adaptive extended Kalman filter (AEKF) such as by Gemson [16] or the ones like the adaptive limited memory filter (ALMF) as in [15].
\n
The AEKF/ALMF deals with a specific scenario and adaptively obtains Q and R by minimising the cost function \n\nJ\n\n in Eq. (16) and the steady-state gain K follows. The second CGKF handles the same specific scenario by minimising the cost function \n\nJ\n\n in Eq. (17) and obtains the gain K directly. Due to the transformed unknown variables, the results for K will be somewhat different but close to AEKF. The gain being more robust, CGKF can handle similar situations. In order to account for model deficiencies or uncertainties in real cases, these constant gains can be increased from the ones based on simulated studies. The αβγ types of filters define a manoeuvre index called λ (based on a subjective choice of Q , and R, and the time between measurements) which leads to the various gains. Thus, λ being chosen subjectively, the model accountability is generic. Such filters also do not consider any cost function, whereas the second and third are two routes to tune the Kalman filter by minimising \n\nJ\n\n, the normalised innovation sequence (NIS) cost function. The only way to improve the performance of αβγ types of filters is to tune the λ manually as is shown later.
\n
The filter world kinematic model equations could consist of displacement, velocity, acceleration, jerk, slack and so on driven by inputs at the highest state derivative variables as chosen by the analyst. The inputs could be random white noise or even correlated noise. If the input is a random white noise, then the corresponding state variable and lower ones become Gauss-Markov (GM) processes of increasingly higher order.
\n
One cannot drive the displacement by white noise since no real-world system can be instantaneously displaced due to its finite mass and moment of inertia. It is best to introduce the input at higher levels as a white or correlated Gaussian noise as in Mehrotra and Mahapatra [47], or Singer [48]. Usually, the input being a white noise acceleration, its integration provides velocity and the further integration leads to displacement. Hence, the displacement would become a third-order GM process. If the input is at the velocity level, then the displacement becomes second-order Gauss-Markov process. The input at the jerk or the slack would increase the order of the filter equations. Hence, the analyst has to choose the input acceleration at a level to provide a reasonable balance between the model order and the anticipated dynamic rate of change of the object.
\n
\n
\n
4.2 Comparison of second and third order Gauss Markov system model in CGKF
\n
Here, we consider the nine state variables as (\n\nR\n\n, \n\n\n\n\nR\n\ṅ\n\n\n,\n\n\nR\n¨\n\n\n\n, \n\nA\n\n, \n\n\nA\ṅ\n\n,\n\n\n\nA\n\n¨\n\n,\nE\n,\n\nE\ṅ\n\n,\n\nE\n¨\n\n\n) and the three measurements as (\n\nR\n,\nA\n,\nE\n\n), both in polar coordinates, and the specific to real-time application in the so called PPR [30] frame. The estimation error of the state variables’ position and velocity based on the second and third order model shows it is more in the former than in the latter. This is due to the simple fact that in the second order model, the accelerations are not accounted for properly during the transition from boost phase to power off coast, when there is a rapid change in BR acceleration level, which is not taken into account in the second-order model unlike in the third order model [30].
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\n
\n
4.3 Filter tuning using CGKF and adaptive estimation of (P0, Q , R)
\n
We consider both the above ALMF and the simpler CGKF for real-time processing to gain confidence in the results. In the former, the choice of window length is important to reach the NIS equal to the number of measurements for filter tuning. The adaptive filter tuning of the statistics \n\nP\n0\n,\nQ\n\nand\n\nR\n\n has been carried out by varying the window length L to track the NIS Cost towards 3 as shown in Figure 1. The next Figure 2 shows the time variation of Q elements with data length of the adaptive EKF after NIS cost convergence. For a given manoeuvre in space, the choice of the coordinate system and hence the components along different axes could vary. Very rapid dynamics demand higher Q to track and slower dynamics demand lower Q. This leads to different overall constant Qs being injected in different state variables and thus the Kalman gains. In the same frame, if the origin is changed, trajectory can be hard or soft. For example, if initially the manoeuvre is very rapid in azimuth and elevation channels (with injected constant Q ), the filter cannot track the BR closely, thus giving rise to oscillatory tracking error. Other axes systems and sensitivity studies for filter statistics are available in Sarkar [30].
\n
Figure 1.
Variation of the NIS cost with length of limited memory window.
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Figure 2.
Time variation of Q elements with data length of the adaptive EKF after NIS cost convergence.
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\n
\n
4.4 Real-time tracking using CGKF
\n
The small differences in the gains from AEKF and CGKF are due to the duration of the transient and the steady state. At first, a set of R, A and E measurements are generated for one launch angle of the BR (45°). This data set is processed using AEKF to estimate P0, Q and R adaptively by equating the NIS cost function to 3, the number of measurement channels. After some initial transients, the Q and K elements become constant as can be seen in Figures 2 and 3, respectively. The steady state gains from the AEKF for 45° are used for processing the real data for a different launch angle of 75° of the same BR and the filter performs well. This is because the Q values from AEKF for 45 and 70°, being only slightly different, do not affect the gains in AEKF, so also in CGKF and thereby the filter performance. The NIS cost function for AEKF based on L = 5 is 3.05. For αβγ filter, the combination of λ equal to (0.002, 0.001, 0.001); (0.02, 0.01, 0.01); (0.05, 0.02, 0.02); (0.07, 0.05, 0.05) and (0.1, 0.1, 0.1) gave costs of 56.0, 17.0, 5.71, 3.03 and 2.85, respectively. These indicate the αβγ filter and the constant gain AEKF performance are close when λ = (0.07, 0.05, 0.05). Thus, the choice of gain elements from AEKF and CGKF is better than in the αβγ filter and the latter is simpler to implement.
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Figure 3.
Time variation of K elements with data length of the adaptive EKF after NIS cost convergence.
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5. Space debris re-entry
\n
An accurate prediction of re-entry time of large orbital space debris is useful to plan hazard assessment and mitigation strategies. The database for such an analysis of large objects is the set of two line elements (TLEs) provided by agencies like USSPACECOM. The TLE sets [49] provide information regarding orbital parameters together with rate of mean motion decay and a reference parameter B* related to the ballistic coefficient B as
B represents the sensitivity of an object to air drag and B* is an adjusted value of B using the reference value of atmospheric density ρo at a reference altitude 120 km above earth. CD is the non dimensional drag coefficient, m is the mass and Aeff is the effective area of cross-section of the object. Larger B means its orbit decays faster.
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\n
5.1 Re-entry case study of US Sat. No. 25947, Soyuz
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The Satellite No. 25947 is a rocket body that has been the test case for the third IADC Re-entry campaign. The sets of 72 orbital elements were made available for re-entry prediction during February 2, 2000, to March 3, 2000.
\n
\n
\n
5.2 Filter-world scenario: state equations
\n
The measurements are available in terms of the orbital parameters the semimajor axis ‘a’ and the eccentricity ‘e’ in both the simulated cases and the tracked TLE elements. The state equations governing the state variables (a, e, B) are [29]
where ϕ1 and ϕ2 are the functional forms of King-Hele [50] which depend on ballistic coefficient B, ‘a’ and ‘e’, and \n\n\nw\n1\n\n\n, \n\n\nw\n2\n\n\n and \n\n\nw\n3\n\n\n are, respectively, the process noises. The subscript argument inside the brackets (∙) denotes the time instant.
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\n
\n
5.3 Filter-world scenario: measurement equations
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But, in the filter implementation process, the transformed variables, namely the predicted apogee and perigee heights, are
where \n\n\nv\n1\n\n\n and \n\n\nv\n2\n\n\n are the measurement noises assumed to be white Gaussian with zero mean and covariance R assumed as constant. The predicted values of these heights in the state equations are updated by utilising the measured values \n\nha\n\nand\n\nhp\n\n, respectively, the apogee height and the perigee height.
The superscripts (+) and (−) correspond to the predicted and updated values, and suffix k denotes the time instant. Further details are available in Anilkumar [26] and Anilkumar et al. [29].
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\n
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5.4 Uncertainties in the state and measurement equations requiring Q and R
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In general, the physical parameters like mass, shape and dimensions of the re-entry objects that vary are not available accurately. Also, the atmosphere varies randomly. Further, the tumbling effect of the body and the molecule gas surface interaction leads to uncertain and varying aerodynamic drag coefficient, which makes the prediction of re-entry time a difficult problem. The re-entry objects are mainly affected by the atmospheric drag, earth’s oblateness, solar activity index F10.7 and magnetic index Ap. However, the orbital propagator utilised in this study is a very simple model of King-Hele [50] which accounts for only the atmospheric drag effect. The present propagator assumes a mean atmospheric condition as provided by the US Standard Atmosphere [51]. This model estimates only the semimajor axis and eccentricity decay with respect to one revolution, assuming a constant scale height during one revolution. This model is sufficient for the re-entry prediction as the decay of the object is mainly governed by the air drag only. The effects of other orbital perturbations and variations in the atmospheric density are accountable through the process noise and the Kalman filter is thus able to handle it through the proper gains as will be demonstrated subsequently. In all the prediction exercises, when the semimajor axis of the object reaches a height of 120 km above the earth, it is considered to have re-entered the atmosphere. This assumption is appropriate as a reference condition since there are significant variations in the atmospheric properties above 120 km with solar, magnetic activity and local time than below this height. Also, effectively, a diffusive equilibrium predominates beyond 120 km as given in Whitten et al. [52].
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5.5 Adaptive filtering approach for re-entry prediction
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It was found to be adequate to obtain P0 based on the difference between the assumed initial conditions of the states (a, e) with those from the first TLE set. For state B, the deviation of initially assumed B0 with that of the derived value of B from B∗ is used. For Q and R, the heuristic estimators of Myers and Tapley [15] have been used. A careful study of data of varying length based on adaptive filtering (both by simulation and actual data) helped to assess how the estimated B, Q and R vary with data length. Recently, the RRR [6, 7, 8, 9] has found a near optimal solution for tuning the filter statistics and thus an improvement over earlier adaptive procedures.
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5.6 The CGKF approach for re-entry prediction
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The present study utilised the genetic algorithm (GA) in the CGKF to minimise the cost function J. The fundamentals of GA, its features and other implementation aspects can be found in [39, 40]. The values of the parameters arrived at after some trials for the present GA re-entry problem are: population size = 100; bit length = 20; probability of cross over = 0.90; probability of mutation = 0.05; number of generations for convergence = 50 and tolerance for convergence: change in cost function J between generations = 0.0001.
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Starting from the 22nd TLE set, the present constant gain Kalman filter algorithm utilises a total of six gains corresponding to the three states, namely, apogee and perigee heights and the ballistic coefficient and two apogee and perigee height measurements. An important parameter in this implementation is the initial assumed value of ballistic coefficient B0. This is to be expected as the body may be tumbling, with irregular shape and with varying gas molecule and surface interaction reflected in the predicted ballistic coefficient. Further, for the drag, a very simple mean atmospheric condition is used. Figure 4 shows that as time passes, with more and more TLE data sets being available, for various initial B0 values, the predicted re-entry date comes closer. But the point is what is the best choice for the initial B0 that provides minimum variation in the predicted re-entry time right from the beginning up to the actual re-entry? This turns out to be B0 = 0.40 as shown in Figure 5. The overall problem is to find out the best possible B0 and the constant Kalman gain that predicts the re-entry time with least variation from the beginning to the end.
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Figure 4.
Variation of B with time during re-entry.
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Figure 5.
Variation of re-entry time with B0.
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A combination of adaptive filtering and the constant gain approaches provided a set of constant gains as [0.6, 0.2, 0.2, 0.6, 0.00014 and 0.0001], as nearly optimal [26, 29]. One curious fact that may be noticed is the choice of the optimum Kalman gains. The optimal gain values for the states are larger and for B it is very small, the reason being the noise-to-signal ratio is very small for the states. Hence, the filter can track the state with a large gain value close to unity or even a small non-zero value (but not zero!). However, for the ballistic coefficient B, the gains have to be smaller in order to slowly learn from the measurements; and if these gains are larger, then the estimated B will show lot of fluctuations. The actual re-entry occurred on March 4, 2000, at 5 h 50 min. The CGKF formalism based on a mean atmosphere and approximate drag effects predicted the re-entry on March 4, 2000, at 5 h 35 min. Even the MSIS-86 model [53] could have been used. This shows once again the robustness of the constant gains has the ability to handle the inaccuracies in modelling B, as well as both the unmodelled and unmodellable state and measurement noise characteristics.
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6. Evolution and expansion of the space debris scenario
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\n
6.1 Introduction
\n
The evolution of the space debris scenario consisting of characterising each and every fragment can be very unwieldy. The purpose of the present study is to demonstrate that it is not necessary to follow each and every fragment in a complex environment, which demands enormous amount of computing time. It suffices to group the fragments called equivalent fragments (EQF) in the ‘a’, ‘e’ coarse bins and propagate these with time. However, the orbital and ballistic coefficients of the EQFs need to be redefined for the above purpose of time propagation in terms of the individual fragment characteristics constituting it. After time propagation, the number of fragments and their ballistic coefficient constituting the EQFs are updated based on just the measured number of individual fragments as will be explained later. This process is continued with subsequent measurements.
\n
For studying the long-term evolution of the space debris, an initial model like in Johnson and McKnight [54], ASSEMBLE model of Anilkumar et al. [27], and Rossi et al. [55] can be assumed. At large times, the prediction could depart greatly from the real scenario due to the sensitivity of the evolution to the inaccuracies in the model parameters and the environment. There are large differences in the estimated characteristics among many debris models [26, 54]. The only way the prediction can be made to follow more closely the real situation is to update the characteristics by assimilating properly the subsequent measurements of the number density in (a, e) bins repeatedly for further evolution in time. The present procedure in addition also expands the scenario for the distribution of the ballistic coefficient of the debris as well(!) which is not generally available or measurable.
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\n
\n
6.2 The present approach and the stochastic analog tool of Rossi et al.
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The stochastic analog tool (STAT) of Rossi et al. [55] simulates the time evolution of the debris by a threefold subdivision of namely: (i) the semimajor axis ‘a’ from 6378 to 46,378 km, (ii) the eccentricity ‘e’ from 0 to 1 and (iii) the mass ‘m’ from 1 mg to 10,000 kg. The present approach considers a, log(e) and log(B) as against a, e and m of STAT. The third parameter B has been presently used because the orbital parameters are sensitive to the air drag and thus change with time. There are errors due to discretisation and approximation in specifying the arithmetic mean values for ‘a’ and geometric values ‘e’ of the EQF in the various bins. Further, there are unaccounted or even unmodellable forces during propagation. However, all such errors can be accounted for by process noise in the state equations describing the propagation of the EQF. Since the individual representative objects of each bin are propagated, the computing time is almost independent of the debris population size in both the present and STAT approaches. It is the second step that is fundamentally new and different in the present approach namely at an update apart from assimilating the measurement information it also expands the scenario to update the equivalent ballistic coefficient (EQB) for the EQFs in various bins with time.
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\n
6.2.1 Characteristics of the equivalent fragment (EQF) in terms of the fragments in a bin
\n
Presently, with 10 divisions for each of the parameters a, e and B, a total of 1000 bins are formed. Instead of handling each and every fragment, the fragments in every bin are handled as a fewer number of EQFs. Next, to follow the dynamics of these EQFs, it is necessary to assign suitable orbital and ballistic coefficient values for these EQFs in terms of the individual fragment properties. Presently, these are set as the arithmetic mean for ‘a’, geometric mean for ‘e’ and the geometric mean also for ‘B’ of the number of fragments in each bin. As the EQFs meander across the various ‘B’ bins, their ballistic coefficients are updated. This is somewhat similar to a debris with a certain value of B moving in the atmosphere though it could change its value. A priori, how well a mean defined as above can follow the dynamics and subsequently get updated in the filter is not conceptually clear. Its adequacy can only be demonstrated from subsequent results.
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\n
\n
6.2.2 Evolution of individual fragments as well as EQF in the bins
\n
Initially, about 10,000 simulated debris fragments due to an explosion are considered and the later fragments due to further breakups are accounted for as source terms. The state propagation equations for both the fragments and the EQF are identical to the earlier Eqs. (38), (39) and (40). The EQF propagated based on its assigned value of suitable ‘a’ and ‘e’ and could in general end up in just within another bin. In order to redistribute the fraction of the EQFs among the bins, a heuristic rule is used as in Rossi et al. [55] that takes the ratio between the area covered by the propagated rectangle and the area of the initial rectangle as shown in Figure 6.
\n
Figure 6.
Representation of the orbital propagation and redistribution in the eccentricity ‘e’ versus semimajor axis ‘a’ space.
\n
Subsequently, by using the measurements of the number of debris in the bins at various times, the EQB of each EQF is updated based on the weighted average of the predicted and measured number density of the fragments in the bins. This weightage is the Kalman gain as we will see later on. Without the update of the EQB of these EQF, the filter is unable to follow the true time variation of the number density of the debris fragments in the bins. Hence, the ballistic coefficient of the EQF is aptly called as the EQB.
\n
\n
\n
6.2.3 Update of the EQF characteristics
\n
The evolution of the EQF takes place in two steps, namely: (i) the propagation of the EQFs representing all the fragments in the various bins, then, redistribution of the fragments around the adjacent bins as mentioned earlier; further breakups are also accounted for by the changed number density in the various bins, and (ii) using appropriate constant Kalman gains for obtaining an updated estimate of the number density of the fragments in the various bins and the EQB of the EQFs.
\n
There is one subtle point in the estimation of the EQB of the EQF corresponding to various (a, e) bins. After update, the value of B for the EQB of EQF at times can fall outside the fixed bin interval. Presently, we have taken the propagation of the EQF always from the initial (a, e) condition based on the arithmetic and geometric mean, respectively. But, for a group of fragments, the above initial condition may not be the most appropriate. The EQF could have started its trajectory from anywhere inside or on the boundary of (a, e) bin whence the redistribution could have been different and thus the updated ballistic coefficient B as well. Such features arise due to the definition of the EQF characteristics and the coarseness of the bins, but one has to see if the final results are meaningful and acceptable.
\n
\n
\n
6.2.4 Real-world (individual fragments) and filter-world (EQF) scenarios
\n
The state and measurement equations in the real-world and filter-world scenarios are given in Table 3. In the filter state equations, the binning, formation of EQF, propagation and redistribution all lead to modelling error and need process noise to handle the situation. In a real-world scenario, there would be measurement noise due to inaccuracies in the assigned orbital characteristics of the individual fragments. In simulation studies, the propagation of each and every one of the individual debris fragments and counting their number in the various bins lead to no measurement noise.
\n
\n
\n
\n
\n\n
\n
Quantity
\n
Real World Scenario for each fragment
\n
Filter World Scenario for each EQF
\n
\n\n\n
\n
The state variables
\n
The (a, e, B) of each fragment.
\n
The (a, e, B) of each EQF.
\n
\n
\n
The initial conditions
\n
The initial (a, e) of each fragment.
\n
For the EQF from anywhere in the (a, e) bin.
\n
\n
\n
The state input
\n
Complex environment.
\n
Only the air drag effect is considered.
\n
\n
\n
The state process
\n
Random variations in the real environment.
\n
The inaccuracies in assigning (a, e, B), binning, its propagation, redistribution and the environment.
\n
\n
\n
The measured variables
\n
The number of individual fragments in the various bins.
\n
The EQFs are propagated with only air drag and later converted to the number of objects in each of the bins.
\n
\n
\n
The measurement noise
\n
Measurement errors due to tracking and data processing.
\n
No measurement noise as the EQFs are propagated and using the changed values of (a, e) are assigned to appropriate bins.
\n
\n\n
Table 3.
Real world without binning and filter world with binning (for simulation studies).
\n
The uncertainties in the initial EQB values of the EQF in the state equations (shown in Table 3) are improved by the filter by using a certain length of data. In the present study, the constant Kalman gains have been derived as explained later.
\n
\n
\n
\n
6.3 The present constant gain Kalman filter approach
\n
From simulated studies, the number of debris fragments in each three-dimensional (a, e, B) bin is known exactly. The Kalman filter by using the constant gains and the updated number of objects at various times is able to track closely the true number of fragments. Similarly, the measurements can be assimilated and the scenario expanded to get the EQB.
\n
\n
6.3.1 Filter-world state equations
\n
Thus, the states presently considered in every one of the (a, e) bins are the number of objects N and their EQB. The (a, e) bins are not changing and the EQF moves in the (a, e) plane like any other single fragment and later gets redistributed based on a certain rule. The various EQFs are specified by: (i) their number in each (a, e) bin; (ii) the equivalent semimajor axis, (iii) the equivalent eccentricity and (iv) the EQB.
\n
The state equations for the EQF in the various bins between measurements are
\n\nd\nN\n/\ndt\n=\nΣ\n\n\npropagation across the\n\n\na\ne\n\n\nbins and redistribution\n+\nsource terms\n\n\n+\nstate noise\n\nE42
The true number density NM in the various bins is obtained in simulation by propagating each and every individual debris fragment. This is used to update the predicted number density based on propagated and redistributed EQF as well as update the ballistic coefficient of the EQF in the various bins as given by
with the pre- and post-updated values denoted, respectively, by the superscripts (−) and (+). The gains KN and KB, respectively, correspond to the number density and the equivalent ballistic coefficient.
\n
Presently, the number of constant gains KN and KB to be estimated is 200 with two for each of the 100 (a, e) bins. These are obtained based on minimising the cost function.
\n\n\nΣ\n\n denotes the summation over all bins and times. The constant Kalman gains were obtained by using the genetic algorithm [39, 40]. The different parameters used in GA implementation are as follows: population size = 200; bit length = 20; probability of cross over = 0.90; probability of mutation = 0.05; Convergence: number of generations 50 or alternately change in J between generations 0.0001.
\n
The whole of Kalman filter process can be summed up in a simple way. One can have the evolution of the state (without knowing how) generated by any random process. The time variation of the state can even be assumed to be given. The measurements could be noisy or even exact. In order to track the state and also follow it smoothly by reducing the fluctuations, a simple filter can be designed with the states remaining constant between measurements. For zero Kalman gain, the filter will learn nothing from the measurements and the state will remain at the initial values. For unity Kalman gain, the state will follow the measurements. In between, there is a range of gain for which the difference between the predicted state and the measurement is minimised in a suitable sense over the range of data. For slow and fast state dynamics, gains near zero and unity, respectively, would be appropriate. Further, if the random process is known to have an inaccurate or unknown parameter, they can also be handled by additional constant gains.
\n
\n
\n
6.3.3 Evolution of debris objects generated due to explosions
\n
A single explosion at a typical altitude of 800 km and eccentricity 0.00045 resulting in about 10,000 debris of varying ballistic coefficients is simulated using the ASSEMBLE model [26, 27]. These objects were propagated accounting for only the atmospheric drag effect for a period of 600 days and thus generate the (a, e, B) data of the objects. Among many studies, updating both the number of objects and the EQB gave the best results and is described here for brevity. Further experiments like initial explosion followed by one breakup, two break ups and some launch activities were carried out. The filtering process reduces the errors in the estimates, but not below a certain value due to continuous occurrence of error due to binning, propagation and redistribution leading to a non-zero K. In all the subsequent figures, the symbol (o) denotes the true, (solid line) filter and (dashed line) with no update using K = 0.
\n
\n
\n
6.3.4 Evolution of a single breakup and additional debris
\n
Figure 7 provides the results by updating both the number density and the EQB for the typical B bin (0.6056, 1.6476). Figure 8 shows the variation of the constant Kalman gain KN across the semimajor axes bins for four ballistic bins are always between zero and unity since the state, namely the number density, is measured.
\n
Figure 7.
Breakup evolution in B bin (0.6056, 1.6476).
\n
Figure 8.
Kalman gains for number of objects (KN).
\n
Figure 9 shows the KB for various values of the semimajor axes bins. However, this takes positive and negative values. Such a thing can happen since the ballistic coefficient though a state has not been measured. Further, in other experiments that were performed, it was noted that the estimated ballistic coefficient with time in typical bins is generally within the limits of the ballistic coefficient bin values. However, at times, they move somewhat outside the limits of the bin values. The initial condition for EQF propagation from the mean values of the bins, though it could have started from anywhere inside the bins, the subsequent propagation and redistribution error could be responsible for such a behaviour. It is best to accept the approach here as the ability to mimic the dynamical behaviour.
\n
Figure 9.
Kalman gains for ballistic coefficients (KB).
\n
At the beginning 10,000 fragments were introduced and subsequently an additional 300 fragments were introduced after 120 days very much like the real-world scenario where the debris are growing but not too rapidly. Even after adding the new source terms, the constant Kalman gains obtained based on the initial cloud evolution have been used for further evolution. In general, the constant gains are robust around a range of the estimated values. Hence, even if the subsequent results are non-optimal, they are adequate to obtain acceptable estimates.
\n
\n
\n
6.3.5 Evolution of a single breakup followed by more than one breakup and some launch activities
\n
To simulate the real-world scenario, two explosions and some launches are introduced during the evolution. Once again, the constant gains obtained using the primary debris clouds suffice for all later cases as well! The results provided in Figure 10 show that the present model is able to track the number of objects even in such evolution process.
\n
Figure 10.
Estimated and observed number of objects. (Two explosions and some new launches).
\n
\n
\n
6.3.6 Application to a typical real-world scenario
\n
The catalogued TLE data of 335 debris objects in near circular orbits in the perigee and apogee bin of 700–800 km from October 1998 to September 1999 were chosen, assuming an initial ballistic coefficient for the EQFs is propagated and updated using first eight observations. A constant eccentricity for all the EQFs in all the bins is assumed since the bin size is just 10 km (unlike in the simulated scenario where it was 150) km but their semimajor axis corresponds to the mid-value of the bin. The Kalman gains from simulation studies were once again used to analyse the real-world data(!), thus demonstrating the robustness of the constant Kalman gains. The innovation here is given by the difference between the TLE data and the predicted number density. The 335 objects observed in the apogee/perigee bin from October 1998 were tracked for the next 12 months to obtain the number of objects in the semimajor axis bins. By tracking the same objects, Figure 11 provides their number for the 12 months in the 10 different semimajor axis bins.
\n
Figure 11.
Variation of the number density of the debris in the bins with time.
\n
Figure 12 shows a comparison of the number of objects from the present approach with that observed for the semimajor axis bin of (7150, 7160) km. Considering 10 equivalent objects rather than propagating and monitoring all of the 335 objects, the match is quite good.
\n
Figure 12.
Comparison of the estimated time-varying debris number density with TLE values.
\n
\n
\n
\n
\n
7. Conclusions
\n
The present CGKF approach has been demonstrated with many examples and in particular the evolution of thousands of space debris fragments. This formalism can be used even in massive atmospheric data assimilation and weather prediction problems that have tens of thousands of states and measurements.
\n
\n
Acknowledgments
\n
The author sincerely thanks all collaborators referred to in this chapter. It has been very kind of Prof. Felix Govaers to have invited me to contribute a chapter in this book and also to INTECHOPEN for publishing it.
\n
\n',keywords:"adaptive EKF, reference recursive recipe, maximum likelihood, Cramer Rao bound, constant gain Kalman filter",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/64507.pdf",chapterXML:"https://mts.intechopen.com/source/xml/64507.xml",downloadPdfUrl:"/chapter/pdf-download/64507",previewPdfUrl:"/chapter/pdf-preview/64507",totalDownloads:1782,totalViews:607,totalCrossrefCites:2,totalDimensionsCites:3,totalAltmetricsMentions:0,impactScore:1,impactScorePercentile:53,impactScoreQuartile:3,hasAltmetrics:0,dateSubmitted:"March 5th 2018",dateReviewed:"October 2nd 2018",datePrePublished:"November 23rd 2018",datePublished:"May 22nd 2019",dateFinished:"November 23rd 2018",readingETA:"0",abstract:"For designing an optimal Kalman filter, it is necessary to specify the statistics, namely the initial state, its covariance and the process and measurement noise covariances. These can be chosen by minimising some suitable cost function \n\nJ\n\n. This has been very difficult till recently when a near optimal Recurrence Reference Recipe (RRR) was proposed without any optimisation but only filtering. In many filter applications after the initial transients, the gain matrix K tends to a constant during the steady state, which points to design the filter based on constant gains alone. Such a constant gain Kalman filter (CGKF) can be designed by minimising any suitable cost function. Since there are no covariances in CGKF, only the state equations need to be propagated and updated at a measurement, thus enormously reducing the computational load. Though CGKF results may not be too close to those of RRR, they are acceptable. It accepts extremely simple models and the gains are robust in handling similar scenarios. In this chapter, we provide examples of applying the CGKF by ancient Indian astronomers, parameter estimation of spring, mass and damper system, airplane real flight test data, ballistic rocket, re-entry of space object and the evolution of space debris.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/64507",risUrl:"/chapter/ris/64507",book:{id:"7466",slug:"introduction-and-implementations-of-the-kalman-filter"},signatures:"Mudambi R. Ananthasayanam",authors:[{id:"209685",title:"Prof.",name:"Mudambi",middleName:"R",surname:"Ananthasayanam",fullName:"Mudambi Ananthasayanam",slug:"mudambi-ananthasayanam",email:"sayanam2005@yahoo.co.in",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/209685/images/system/209685.jpg",institution:{name:"Indian Institute of Science Bangalore",institutionURL:null,country:{name:"India"}}}],sections:[{id:"sec_1",title:"1. Introduction to Kalman filter",level:"1"},{id:"sec_1_2",title:"1.1 The competence and beauty of the Kalman filter",level:"2"},{id:"sec_2_2",title:"1.2 Use of filter statistics in designing the Kalman filter",level:"2"},{id:"sec_3_2",title:"1.3 Use of filter gains and design of constant gain Kalman filter (CGKF)",level:"2"},{id:"sec_5",title:"2. Ancient Indian astronomers implicitly using the constant Kalman gain approach",level:"1"},{id:"sec_6",title:"3. Typical parameter estimation studies",level:"1"},{id:"sec_6_2",title:"3.1 Analysis of spring, mass and damper (SMD) system",level:"2"},{id:"sec_7_2",title:"3.2 Remarks on the SMD parameter estimation",level:"2"},{id:"sec_8_2",title:"3.3 Analysis of real airplane flight test data",level:"2"},{id:"sec_9_2",title:"3.4 Remarks on the real flight test data results",level:"2"},{id:"sec_11",title:"4. Introduction to flight data analysis of a ballistic rocket (BR)",level:"1"},{id:"sec_11_2",title:"4.1 State estimation of a ballistic rocket (BR)",level:"2"},{id:"sec_12_2",title:"4.2 Comparison of second and third order Gauss Markov system model in CGKF",level:"2"},{id:"sec_13_2",title:"4.3 Filter tuning using CGKF and adaptive estimation of (P0, Q , R)",level:"2"},{id:"sec_14_2",title:"4.4 Real-time tracking using CGKF",level:"2"},{id:"sec_16",title:"5. Space debris re-entry",level:"1"},{id:"sec_16_2",title:"5.1 Re-entry case study of US Sat. No. 25947, Soyuz",level:"2"},{id:"sec_17_2",title:"5.2 Filter-world scenario: state equations",level:"2"},{id:"sec_18_2",title:"5.3 Filter-world scenario: measurement equations",level:"2"},{id:"sec_19_2",title:"5.4 Uncertainties in the state and measurement equations requiring Q and R",level:"2"},{id:"sec_20_2",title:"5.5 Adaptive filtering approach for re-entry prediction",level:"2"},{id:"sec_21_2",title:"5.6 The CGKF approach for re-entry prediction",level:"2"},{id:"sec_23",title:"6. Evolution and expansion of the space debris scenario",level:"1"},{id:"sec_23_2",title:"6.1 Introduction",level:"2"},{id:"sec_24_2",title:"6.2 The present approach and the stochastic analog tool of Rossi et al.",level:"2"},{id:"sec_24_3",title:"6.2.1 Characteristics of the equivalent fragment (EQF) in terms of the fragments in a bin",level:"3"},{id:"sec_25_3",title:"6.2.2 Evolution of individual fragments as well as EQF in the bins",level:"3"},{id:"sec_26_3",title:"6.2.3 Update of the EQF characteristics",level:"3"},{id:"sec_27_3",title:"Table 3.",level:"3"},{id:"sec_29_2",title:"6.3 The present constant gain Kalman filter approach",level:"2"},{id:"sec_29_3",title:"6.3.1 Filter-world state equations",level:"3"},{id:"sec_30_3",title:"6.3.2 Filter-world measurement update equations",level:"3"},{id:"sec_31_3",title:"6.3.3 Evolution of debris objects generated due to explosions",level:"3"},{id:"sec_32_3",title:"6.3.4 Evolution of a single breakup and additional debris",level:"3"},{id:"sec_33_3",title:"6.3.5 Evolution of a single breakup followed by more than one breakup and some launch activities",level:"3"},{id:"sec_34_3",title:"6.3.6 Application to a typical real-world scenario",level:"3"},{id:"sec_37",title:"7. 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Washington, DC: Government Printing Offices; 1976\n'},{id:"B52",body:'Whitten RC, Vaughan WW. Guide to Reference and Standard Atmosphere Models, AIAA-G-003-1988. 1990\n'},{id:"B53",body:'Hedin AE. MSIS-86 thermospheric model. Journal of Geophysical Research. 1987;92:4649-4662\n'},{id:"B54",body:'Johnson NL, McKnight DS. Artificial Space Debris. Orbit Book Company; 1987\n'},{id:"B55",body:'Rossi A, Cordelli A, Farinella P, Anselmo L. Collisional evolution of the earth’s orbital debris cloud. Journal of Geophysical Research. 1994;99(E11):23195-23210\n'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Mudambi R. Ananthasayanam",address:"sayanam2005@yahoo.co.in",affiliation:'
Department of Aerospace Engineering, Indian Institute of Science, Bangalore, India
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1. Introduction
Worldwide, cardiovascular diseases (CVDs) are leading morbidity and mortality burdens. It has been estimated that 17.9 million people die from CVDs each year, representing 32% of all global deaths. The World Health Organization (WHO) defines CVDs as a group of disorders that include coronary artery disease (CAD), cerebrovascular disease, peripheral arterial disease, rheumatic heart disease, congenital heart disease, deep vein thrombosis, and pulmonary embolisms [1]. The world’s biggest killer of all is ischemic heart disease, or CAD, responsible for 16% of the world’s total deaths [2]. According to a statistical report published in 2020, the global prevalence of CAD was estimated at 1655 per 100,000 people and is predicted to exceed 1845 by 2030 [3]. In the United States, CAD accounts annually for approximately 610,000 deaths and costs more than 200 billion dollars for healthcare [4].
As most CAD patients are elderly and have multiple comorbidities, they need to use medication combinations over long periods, either for treatment or prophylaxis [5, 6]. One of the major strategies used for preventing CAD is antiplatelet therapy, and the most widely used antiplatelet agent tested is aspirin [6]. However, the therapeutic window of CAD drugs is very small, and inappropriate use can lead to many consequences that affect patients’ health. For instance, aspirin plays a role in reducing the risk of cardiovascular events, but it also increases the risk of bleeding, the most common risk being gastrointestinal bleeding [7, 8]. Therefore, despite the benefit of the drug, it also causes problems that adversely affect health. Old age, polypharmacy, and comorbidities are significant risk factors for developing drug-related problems (DRPs) [9, 10].
A drug-related problem (DRP) has been defined as “an event or circumstance involving drug therapy that actually or potentially interferes with desired health outcomes” [11]. DRPs can have many negative consequences for patients and society, such as decreased quality of life for patients, increased hospitalization rates, prolonged hospital stays, increased overall healthcare costs, and even increased risk of morbidity and mortality [12, 13, 14]. For example, warfarin and oral antiplatelet agents have been reported to be implicated in nearly 50% of emergency hospital admissions of elderly Americans [15].
A further serious consequence of DRPs is the economic burden. DRPs accounted for a waste of $528.4 billion, equivalent to 16% of total US healthcare expenditures [16]. In studies of CVDs, the prevalence of patients with at least one DRP varied from nearly 30% to more than 90% [17, 18, 19]. A systematic review of DRPs concluded that the drugs most commonly involved were cardiovascular drugs [12]. In CAD patients, the drugs most implicated in DRPs were beta-blockers (BBs) (34.4%), followed by angiotensin-converting enzyme inhibitors (ACEI) (24.8%), statins (16.5%), and antithrombotics (13.1%) [20]. Different drugs are often associated with several different common DRPs. To illustrate, BBs were frequently involved in ineffective drug therapy, too low dosage, and the need for additional drug therapy, while ACEIs were commonly associated with too low dosage [20]. Studies in Ethiopia, Vietnam, and Spain have estimated that the mean numbers of DRPs for each patient with CAD were about 0.75, 0.92, and 1.51, respectively [17, 18, 21]. The prevalence of CAD patients with at least one DRP was 61.1% [21]. These statistics are relatively high and represent an alarming frequency of DRPs in patients with CAD. DRPs must therefore be noticed and recognized by healthcare professionals.
This chapter separates DRPs in CAD patients into 5 common subtypes: drug selection, dose selection, adverse drug-drug interactions (DDI), patient adherence, and cost issues. We also discuss determinants that increase the ratio of DRPs, and list interventions to limit their prevalence. Our goal is to provide health care providers with an overview of the extent of DRPs and their common types; these must be considered to ensure the safety and effectiveness of drug therapy.
2. Drug-related problems
2.1 Drug selection
Inappropriate drug selection is a common type of DRP in patients with CAD; it mainly includes ineffective drug therapy, a need for additional drug therapy, and prescription of drugs with contraindications. In an Ethiopian study, O.A. Abdela et al. found that, globally, the most common category of DRPs was inappropriate drug selection for CVDs (36.1%), and in particular for CAD (46.6%) [17]. Studies in Spain and Vietnam showed the prevalence of inappropriate drug selection of 19.4% and 3.5% for CAD patients [18, 21]. Inappropriate drug selection can have several causes. A study in Indonesia found that clinicians’ critical factor influencing statin prescribing was their lack of awareness of specific details in current guideline recommendations. Although clinicians generally know the guidelines, they remain uncertain about how to determine the level of total cholesterol in combination with other cardiovascular risk factors like diabetes and hypertension [22].
Ineffective drug therapy occurs when the drug product used is not effective for the treatment of the medical condition [23]. A need for additional drug therapy exists when the medical condition requires additional drugs to achieve synergistic or additive effects [23]. A study by A.W. Tsige et al. in Ethiopia showed that among DRPs, the prevalence of need for additional drug therapy was 30.53%, and ineffective drug therapy was 26.9% [24]. In the Netherlands, J. Tra et al. conducted a study of prescriptions for patients discharged after CADs. They found that the angiotensin-converting enzyme inhibitor, one of the most important drugs in the prescribing guideline, was often missing (21.2%) [25]. In patients who have had acute coronary syndromes, it is vital to follow prescribing guidelines for secondary prevention to avoid further serious cardiovascular events. For example, according to a study on the prescription of secondary preventative cardiovascular therapies for non-ST elevation myocardial infarction (NSTEMI), adenosine-diphosphate receptor antagonist prescribing rates had significantly increased (76%) [26]. On the other hand, a study evaluating patient adherence to prescription guidelines after acute coronary syndrome indicated that adherence to lipid-lowering therapy was the lowest. The percentage of adherence to the criterion: ‘Patient regardless of lipid level is prescribed a high-intensity statin either atorvastatin 40–80 mg or rosuvastatin 20–40 mg’, was only 16.7% in the post-ST elevation myocardial infarction group, and 33.3% in the post-non-ST elevation acute coronary syndrome group [27]. A Canadian study found that only 61% of patients with stable coronary artery disease received optimal drug therapy involving concurrent use of β-blockers, ACE inhibitor/angiotensin receptor blockers, and statins [28]. Failure to prescribe drugs that should be indicated for treatment or prevention reduces the effectiveness of treatment. For example, after myocardial infarction, patients who have conditions like heart failure, pulmonary disease, and older age are often prescribed beta-blockade therapy, which is ineffective. However, patients without these conditions benefit from such therapy [29]. Ineffective drug therapy and a need for additional drugs can lead to increased medical costs, potential drug interactions, and decreased patient adherence [30].
Medicines that cause harm to the patient or negative interaction with a combination drug are called contraindicated medicines [31]. In a multicenter study in France, research on physicians’ acceptance of pharmacists’ daily routine interventions revealed that contraindication was the most identified DRP (21.3%) [32]. However, studies on CAD patients in Vietnam and Ethiopia showed that the prevalence of contraindicated medicines leading to DRPs was only approximately 0% and 2%, respectively [17, 21]. Therefore, in the latter two countries, among CAD patients, this issue is less common than in other DRPs.
Increasing the role of clinical pharmacists and the application of prescription management software in the prescribing process to check contraindication and interaction could be effective interventions to minimize such problems. For patients to be treated with appropriate drugs, clinicians should follow treatment guidelines and update their recommendations. In addition, the patient’s response to treatment should be monitored by clinical examination and tests, and if necessary, a change of drug to suit the patient’s condition.
2.2 Dose selection
Inappropriate dose selection includes both too high and too low [23]. A study in Spain by P. Gastelurrutia et al. found that inappropriate dose selection was one of the most frequently identified DRPs, with a prevalence of 22% [33], and a study in Turkey by Urbina, Olatz et al. found inappropriate dose selection in CAD patients to have a prevalence of 41% [18]. In a Vietnamese study by T.T.A. Truong et al., this prevalence was 22.2% [21]. Inappropriate dose selection can take place for several reasons. For example, ignoring comorbidities that affect the pharmacodynamics of a drug, such as hepatic or renal failure, can lead to inappropriate dose selection. Patients with renal and hepatic dysfunction require lower doses; otherwise, failure of excretion or breakdown of the drug can cause toxicity [34]. Furthermore, differing characteristics of patients, such as weight and body mass index, can make a prescribed dose too low or high for the patient’s needs.
Sometimes high dosage prescription was considered when the duration of drug therapy was regarded as too long, possibly leading to unwanted side-effects for the patient [23]. In Spain and Vietnam, patients with CAD had a prevalence of high dose prescriptions of 8.6% and 0.1%, respectively [18, 21]. A study by Simon B. Dimmitt et al. had found that statin doses around an estimated effective dose of 50 (ED50) could reduce myocardial infarction (25%) and mortality (10%). However, the high dosage can also increase adverse events: myopathy was shown to increase 29-fold, and liver dysfunction as much as 9-fold [35]. A national study in America reported that overdoses led to nearly two-thirds of emergency hospitalizations [15]. Because the therapeutic window of CVD drugs in general, and CAD drugs in particular, is very small, an overdose is very severe and can lead to death. For example, an indirect sympathomimetic overdose can result in tachycardia, hypertension, stroke, and acute myocardial infarction [36]. Furthermore, in patients with renal dysfunction or renal failure, drugs that are eliminated by the kidney should be dosed proportionally according to creatinine clearance [37].
In contrast, a too low dosage means that the dose is not sufficient to produce the desired response [23]. In Spain and Vietnam, DRPs of patients with CAD occurring due to low dosage prescriptions were 7.9% and 22.1%, respectively [18, 21]. Taking too low a dose fails to achieve the desired therapeutic goal, increasing the possibility of cardiovascular events [23]. A systematic overview of randomized trial studies in patients with risk of cardiovascular disease found that a dose of aspirin between 75 and 150 mg daily gives adequate prophylaxis; doses lower than 75 mg daily are less effective [38]. A study was conducted in patients with acute coronary syndrome after stent implantation to compare the efficacy of different doses of rosuvastatin [39]. This study concluded that high doses of rosuvastatin could postpone ventricular remodeling, decrease the prevalence of adverse events, and significantly improve long-term prognosis.
To limit problems related to dose selection, doctors need to pay attention to each patient’s condition, comorbidities, and characteristics affecting drug pharmacokinetics and monitor and adjust drug dose depending on the tolerance of the individual patient. In addition, the clinical pharmacist can help to calculate the appropriate drug dose for each patient. Furthermore, the application software should be developed to assist in dose calculation for special populations like elderly patients or liver and/or kidney disease patients.
2.3 Adverse drug-drug interaction
Adverse drug-drug interactions (DDIs) occur when drug interaction leads to undesirable reactions that are not dose-related [23]. In patients with heart failure in Ethiopia, DDIs were the most common cause of DRPs, with a prevalence of 27.3% in 2020 and 33.4% in 2021 [24, 40]. However, a study in Taiwan found DDIs to be the second most common DRP (29.6%) [41]. In patients with CAD in Ethiopia and Vietnam, DDIs had prevalences of 21.2% and 19.3%, respectively [21, 40]. Often, patients with CAD have to take multiple medications for a long time [5], and other drugs must frequently be used to treat co-morbidities. However, the greater the number of drugs, the greater the risk of drug-drug interactions [5].
The most common DDI found in patients with heart failure was the combined use of spironolactone and digoxin, possibly resulting in increased digoxin toxicity [40]. A systematic review of secondary prevention of adverse ischemic events found that a regimen including aspirin plus clopidogrel led to a significantly higher rate of hemorrhagic events than other regimens (aspirin alone, plus ticlopidine or cilostazol, etc.) [6]. Another common drug-drug interaction between clopidogrel and proton pump inhibitors (PPIs) in patients with CAD. Clopidogrel is a P2Y12 receptor inhibitor and one of the two components of dual antiplatelet therapy [42]. PPIs are recommended for patients on dual antiplatelet therapy with a history or high risk of gastrointestinal bleeding [43]. Adverse drug interactions reduce the effectiveness of treatment. For example, some PPIs, such as omeprazole and esomeprazole, reduce the antiplatelet effect of clopidogrel by inhibiting the CYP2C19-mediated conversion of clopidogrel to the active metabolite in the liver [44]. In addition, concomitant clopidogrel-PPI therapy appears to increase the risk of major adverse cardiovascular events [45]. Meanwhile, PPIs such as lansoprazole and dexlansoprazole have been found to have less effect, and pantoprazole and rabeprazole do not affect the metabolism of clopidogrel [46, 47]. Therefore, one of the four PPIs: pantoprazole, rabeprazole, lansoprazole, or dexlansoprazole, should be chosen, and omeprazole and esomeprazole should be avoided in patients requiring a combination of clopidogrel and PPI.
To limit adverse drug-drug interactions, clinicians can use drug interaction testing tools with the assistance of a clinical pharmacist. If a severe drug-drug interaction occurs, an alternative drug should be considered. Furthermore, an online drug interaction checker (Drug.com, Medscape, etc.) should be used for checking before prescribing to patients.
2.4 Patient nonadherence
Poor patient adherence is another common DRP in coronary artery disease. Nonadherence involves the failure of a patient to take medications appropriately due to personal factors [23]. Several studies have indicated that roughly 20% and more than 50% of CAD patients are non-adherent to prescribed medications [48, 49, 50]. Many factors can affect patient adherence to treatment: lack of motivation, failure to understand instructions, forgetfulness, the complexity of the regimen, polypharmacy, multiple daily doses, adverse side effects, high cost, failure to initiate treatment before discharge, and the physician’s lack of knowledge of clinical indicators for the use of medications [51, 52]. In addition, older people have many unique difficulties that contribute to poor adherence [52], one of the main factors being forgetfulness [53]. Some studies indicate that long-term therapy involving CAD prophylaxis may decrease adherence. A Swedish study reported that the adherence rate in CAD patients after discharge rapidly decreased within 2 years. Statin, aspirin, and clopidogrel adherence rates decreased from 91.7% to 56.1%, 93.2% to 61.5%, and 81.9% to 39.4% respectively, 2 years after discharge [54].
Patient adherence greatly contributes to the success of treatment and secondary prevention strategies in CAD patients. Good adherence to evidence-based medication regimens, including β-blockers, angiotensin-converting enzyme inhibitors/angiotensin receptor blockers, antiplatelet drugs, and statins, has been shown to be associated with decreased risk of all-cause mortality (risk ratio 0.56; 95% confidence interval: 0.45–0.69), cardiovascular mortality (risk ratio 0.66; 95% confidence interval: 0.51–0.87), and cardiovascular hospitalization/myocardial infarction (risk ratio 0.61; 95% confidence interval: 0.45–0.82) [55]. In contrast, poor adherence can lead to major cardiovascular events, including death [56]. In Turkey, during one-year follow-up treatment, patients with acute coronary syndrome were found to have low adherence to statin therapy (17.8%) [57]. According to a study by C.A. Jackevicius et al. in the Canadian population, patients who did not use all of their discharge medications after acute coronary syndrome had an increased risk of death at 1 year [56]. The death rates among high-adherence and low-adherence were respectively 2310/14,345 (16%), and 261/1071 (24%) (adjusted hazard ratio, 1.25; 95% confidence interval, 1.09–1.42; p = 0.001). The study also found a similar but less pronounced dose-response-type adherence-mortality association for beta-blockers [58]. However, the harmful consequence of nonadherence depends on the type of medication. For example, the mortality rate was not associated with adherence to calcium channel blockers [58]. However, patients must adhere to the prescribed regimens to achieve treatment goals.
Drug counseling upon discharge and post-discharge follow-up may increase adherence [56]. When patients know their medical condition and the benefits of prescription medications, they are more motivated to take them exactly as recommended [59]. Moreover, appropriate prescribing upon discharge should be encouraged to improve patient adherence [52]. Prescribing fixed-dose combination pills instead of using multiple single drugs also helps to enhance adherence [60, 61]. A systematic review in low- and middle-income countries demonstrated considerable variation in nonadherence to antihypertensive medication [62]. Due to the overload of healthcare systems, especially in these low- and middle-income countries and during the COVID-19 pandemic, clinicians have too little time to educate patients [63]. A systematic review of 67 countries found that about half of the world’s population spends 5 min or less with their primary care physicians [64]. Therefore, more attention should be paid to the role of the clinical pharmacist. Clinical pharmacists can help patients understand the benefits of each medication they take, the timing and frequency of administration, and signs of side effects; they can also encourage and monitor patient adherence. A systematic review of medication adherence interventions showed significant reductions in mortality risk among heart failure patients (relative risk, 0.89; 95% CI, 0.81, 0.99). A bulk of these interventions utilized medication education (s = 50) and disease education (s = 48) [65].
2.5 Cost issue
Medical costs for CAD have increased dramatically in recent years and are expected to rise even more [66]. The result is an increased economic burden for patients themselves and countries. For example, hospital admission for acute myocardial infarction requiring percutaneous coronary intervention costs an average of $20,000 [67]. In the USA, it has been calculated that in 2016 DRPs wasted $528.4 billion, equivalent to 16% of the total US healthcare expenditure for that year [16]. Furthermore, the cost of informal healthcare for CAD alone was estimated at $1 billion and projected to increase to $1.9 billion by 2035 [68]. According to M. Guerro-Prado et al., cost issues accounted for up to 6.5% of all DRPs. Unnecessary and unnecessarily expensive treatments were the main reasons for such problems [69]. Furthermore, cost issues are also related to physicians’ prescriptions. A Chinese national study among 3362 primary healthcare sites showed that expensive medications were more likely to be prescribed than less costly alternatives, thus contributing to high medication costs [70]. Increased medication costs may likely reduce patient adherence and negatively affect their healthcare [51, 71]. Patients’ discontinuation of medication therapies affects their treatment outcomes and increases the occurrence of adverse cardiovascular events [56]. To treat these events, the costs of treatment become even greater.
WHO has listed some interventions that may reduce costs. Such interventions include providing information; government communication is vital to raise public awareness of the importance of reducing cardiovascular risk factors. Further efforts to reduce medical costs include early disease detection, optimal treatment according to recommendations, and close patient management to limit complications, hospitalization, and death. Also recommended for patients with coronary artery disease are lifestyle changes that enhance the effectiveness of treatment, thereby reducing the number of drugs needed [72]. To further avoid adding to treatment costs, clinicians should avoid prescribing unnecessary extra drugs [70]. Finally, it is necessary to encourage individuals to participate in health insurance to reduce the financial burden of illness [72].
3. Conclusions
DRPs are a global problem, causing adverse consequences in cardiology in particular and medicine in general. Drug selection, dose selection, adverse drug-drug interactions, and patient adherence are the most common categories involved in DRPs. Inability to control DRPs can diminish healthcare outcomes and increase the prevalence of adverse cardiovascular events, and DRPs can also inhibit economic growth due to medication costs. To minimize the negative impacts of DRPs we propose several key solutions: (1) appropriate prescribing according to guidelines, (2) enhancing the role of clinical pharmacists in the identification and intervention of DRPs, and (3) developing tools to check for drug interactions and contraindications. More effective definition and recognition of DRPs and application of relevant interventions can help to limit these global problems.
Department of Pharmacology and Clinical Pharmacy, Can Tho University of Medicine and Pharmacy, Vietnam
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Toxic Aspects"},signatures:"Harsimran Kaur Gill and Harsh Garg",authors:[{id:"169137",title:"Dr.",name:"Harsh",middleName:null,surname:"Garg",slug:"harsh-garg",fullName:"Harsh Garg"},{id:"169846",title:"Dr.",name:"Harsimran",middleName:null,surname:"Gill",slug:"harsimran-gill",fullName:"Harsimran Gill"}]},{id:"43317",doi:"10.5772/54833",title:"Extreme Temperature Responses, Oxidative Stress and Antioxidant Defense in Plants",slug:"extreme-temperature-responses-oxidative-stress-and-antioxidant-defense-in-plants",totalDownloads:11583,totalCrossrefCites:70,totalDimensionsCites:153,abstract:null,book:{id:"3226",slug:"abiotic-stress-plant-responses-and-applications-in-agriculture",title:"Abiotic Stress",fullTitle:"Abiotic Stress - Plant Responses and Applications in Agriculture"},signatures:"Mirza Hasanuzzaman, Kamrun Nahar and Masayuki Fujita",authors:[{id:"47687",title:"Prof.",name:"Masayuki",middleName:null,surname:"Fujita",slug:"masayuki-fujita",fullName:"Masayuki Fujita"},{id:"76477",title:"Prof.",name:"Mirza",middleName:null,surname:"Hasanuzzaman",slug:"mirza-hasanuzzaman",fullName:"Mirza Hasanuzzaman"},{id:"166818",title:"MSc.",name:"Kamrun",middleName:null,surname:"Nahar",slug:"kamrun-nahar",fullName:"Kamrun Nahar"}]},{id:"21989",doi:"10.5772/17184",title:"Bacillus-Based Biological Control of Plant Diseases",slug:"bacillus-based-biological-control-of-plant-diseases",totalDownloads:17384,totalCrossrefCites:64,totalDimensionsCites:150,abstract:null,book:{id:"432",slug:"pesticides-in-the-modern-world-pesticides-use-and-management",title:"Pesticides in the Modern World",fullTitle:"Pesticides in the Modern World - 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Almost all the parts of this plant, that are, fruit, leaves, flower bud, trunk, and pseudo-stem, can be utilized. This chapter deals with the fiber extracted from the pseudo-stem of the banana plant. It discusses the production of banana pseudo-stem fiber, which includes plantation and harvesting; extraction of banana pseudo-stem fiber; retting; and degumming of the fiber. It also deals with the characteristics of the banana pseudo-stem fiber, such as morphological, physical and mechanical, durability, degradability, thermal, chemical, and antibacterial properties. Several potential applications of this fiber are also mentioned, such as the use of this fiber to fabricate rope, place mats, paper cardboard, string thread, tea bags, high-quality textile materials, absorbent, polymer/fiber composites, etc.",book:{id:"7544",slug:"banana-nutrition-function-and-processing-kinetics",title:"Banana Nutrition",fullTitle:"Banana Nutrition - Function and Processing Kinetics"},signatures:"Asmanto Subagyo and Achmad Chafidz",authors:[{id:"257742",title:"M.Sc.",name:"Achmad",middleName:null,surname:"Chafidz",slug:"achmad-chafidz",fullName:"Achmad Chafidz"},{id:"268400",title:"Mr.",name:"Asmanto",middleName:null,surname:"Subagyo",slug:"asmanto-subagyo",fullName:"Asmanto Subagyo"}]},{id:"40180",title:"Plant Tissue Culture: Current Status and Opportunities",slug:"plant-tissue-culture-current-status-and-opportunities",totalDownloads:66452,totalCrossrefCites:43,totalDimensionsCites:89,abstract:null,book:{id:"3568",slug:"recent-advances-in-plant-in-vitro-culture",title:"Recent Advances in Plant in vitro Culture",fullTitle:"Recent Advances in Plant in vitro Culture"},signatures:"Altaf Hussain, Iqbal Ahmed Qarshi, Hummera Nazir and Ikram Ullah",authors:[{id:"147617",title:"Dr.",name:"Altaf",middleName:null,surname:"Hussain",slug:"altaf-hussain",fullName:"Altaf Hussain"}]},{id:"66996",title:"Ethiopian Common Medicinal Plants: Their Parts and Uses in Traditional Medicine - Ecology and Quality Control",slug:"ethiopian-common-medicinal-plants-their-parts-and-uses-in-traditional-medicine-ecology-and-quality-c",totalDownloads:4059,totalCrossrefCites:6,totalDimensionsCites:10,abstract:"The main purpose of this review is to document medicinal plants used for traditional treatments with their parts, use, ecology, and quality control. Accordingly, 80 medicinal plant species were reviewed; leaves and roots are the main parts of the plants used for preparation of traditional medicines. The local practitioners provided various traditional medications to their patients’ diseases such as stomachaches, asthma, dysentery, malaria, evil eyes, cancer, skin diseases, and headaches. The uses of medicinal plants for human and animal treatments are practiced from time immemorial. Stream/riverbanks, cultivated lands, disturbed sites, bushlands, forested areas and their margins, woodlands, grasslands, and home gardens are major habitats of medicinal plants. Generally, medicinal plants used for traditional medicine play a significant role in the healthcare of the majority of the people in Ethiopia. The major threats to medicinal plants are habitat destruction, urbanization, agricultural expansion, investment, road construction, and deforestation. Because of these, medicinal plants are being declined and lost with their habitats. Community- and research-based conservation mechanisms could be an appropriate approach for mitigating the problems pertinent to the loss of medicinal plants and their habitats and for documenting medicinal plants. Chromatography; electrophoretic, macroscopic, and microscopic techniques; and pharmaceutical practice are mainly used for quality control of herbal medicines.",book:{id:"8502",slug:"plant-science-structure-anatomy-and-physiology-in-plants-cultured-in-vivo-and-in-vitro",title:"Plant Science",fullTitle:"Plant Science - Structure, Anatomy and Physiology in Plants Cultured in Vivo and in Vitro"},signatures:"Admasu Moges and Yohannes Moges",authors:[{id:"249746",title:"Ph.D.",name:"Admasu",middleName:null,surname:"Moges",slug:"admasu-moges",fullName:"Admasu Moges"},{id:"297761",title:"MSc.",name:"Yohannes",middleName:null,surname:"Moges",slug:"yohannes-moges",fullName:"Yohannes Moges"}]},{id:"70658",title:"Factors Affecting Yield of Crops",slug:"factors-affecting-yield-of-crops",totalDownloads:4044,totalCrossrefCites:25,totalDimensionsCites:40,abstract:"A good understanding of dynamics involved in food production is critical for the improvement of food security. It has been demonstrated that an increase in crop yields significantly reduces poverty. Yield, the mass of harvest crop product in a specific area, is influenced by several factors. These factors are grouped in three basic categories known as technological (agricultural practices, managerial decision, etc.), biological (diseases, insects, pests, weeds) and environmental (climatic condition, soil fertility, topography, water quality, etc.). These factors account for yield differences from one region to another worldwide. The current chapter will discuss each of these three basic factors as well as providing some recommendations for overcoming them. In addition, it will provide the importance of climate-smart agriculture in the increase of crop yields while facilitating the achievement of crop production in safe environment. This goes in line with the second goal of 2030 Agenda for Sustainable Development of United Nations in transforming our world formulated as end hunger, achieve food security, improve nutrition and promote sustainable agriculture.",book:{id:"8153",slug:"agronomy-climate-change-food-security",title:"Agronomy",fullTitle:"Agronomy - Climate Change & Food Security"},signatures:"Tandzi Ngoune Liliane and Mutengwa Shelton Charles",authors:[{id:"313819",title:"Dr.",name:"Liliane",middleName:null,surname:"Tandzi",slug:"liliane-tandzi",fullName:"Liliane Tandzi"},{id:"314316",title:"Prof.",name:"Charles Shelton",middleName:null,surname:"Mutengwa",slug:"charles-shelton-mutengwa",fullName:"Charles Shelton Mutengwa"}]},{id:"59402",title:"Robotic Harvesting of Fruiting Vegetables: A Simulation Approach in V-REP, ROS and MATLAB",slug:"robotic-harvesting-of-fruiting-vegetables-a-simulation-approach-in-v-rep-ros-and-matlab",totalDownloads:2797,totalCrossrefCites:7,totalDimensionsCites:8,abstract:"In modern agriculture, there is a high demand to move from tedious manual harvesting to a continuously automated operation. This chapter reports on designing a simulation and control platform in V-REP, ROS, and MATLAB for experimenting with sensors and manipulators in robotic harvesting of sweet pepper. The objective was to provide a completely simulated environment for improvement of visual servoing task through easy testing and debugging of control algorithms with zero damage risk to the real robot and to the actual equipment. A simulated workspace, including an exact replica of different robot manipulators, sensing mechanisms, and sweet pepper plant, and fruit system was created in V-REP. Image moment method visual servoing with eye-in-hand configuration was implemented in MATLAB, and was tested on four robotic platforms including Fanuc LR Mate 200iD, NOVABOT, multiple linear actuators, and multiple SCARA arms. Data from simulation experiments were used as inputs of the control algorithm in MATLAB, whose outputs were sent back to the simulated workspace and to the actual robots. ROS was used for exchanging data between the simulated environment and the real workspace via its publish-and-subscribe architecture. Results provided a framework for experimenting with different sensing and acting scenarios, and verified the performance functionality of the simulator.",book:{id:"6265",slug:"automation-in-agriculture-securing-food-supplies-for-future-generations",title:"Automation in Agriculture",fullTitle:"Automation in Agriculture - Securing Food Supplies for Future Generations"},signatures:"Redmond R. Shamshiri, Ibrahim A. Hameed, Manoj Karkee and\nCornelia Weltzien",authors:[{id:"182449",title:"Prof.",name:"Ibrahim",middleName:"A.",surname:"Hameed",slug:"ibrahim-hameed",fullName:"Ibrahim Hameed"},{id:"203413",title:"Dr.",name:"Redmond R.",middleName:null,surname:"Shamshiri",slug:"redmond-r.-shamshiri",fullName:"Redmond R. Shamshiri"},{id:"241193",title:"Dr.",name:"Manoj",middleName:null,surname:"Karkee",slug:"manoj-karkee",fullName:"Manoj Karkee"},{id:"241194",title:"Dr.",name:"Cornelia",middleName:null,surname:"Weltzien",slug:"cornelia-weltzien",fullName:"Cornelia Weltzien"}]}],onlineFirstChaptersFilter:{topicId:"5",limit:6,offset:0},onlineFirstChaptersCollection:[{id:"82538",title:"Speed Breeding: A Propitious Technique for Accelerated Crop Improvement",slug:"speed-breeding-a-propitious-technique-for-accelerated-crop-improvement",totalDownloads:2,totalDimensionsCites:0,doi:"10.5772/intechopen.105533",abstract:"Development of climate-resilient genotypes with high agronomic value through conventional breeding consumes longer time duration. Speed breeding strategy involves rapid generation advancement that results in faster release of superior varieties. In this approach, the experimental crop is grown in a controlled environment (growth chambers) with manipulation provisions for temperature, photoperiod, light intensity, and moisture. The generation of the crop cycle can be hastened by inducing changes in the physiological process such as photosynthesis rate, flowering initiation, and duration. Speed breeding eases multiple trait improvement in a shorter span by integration of high-throughput phenotyping techniques with genotype platforms. The crop breeding cycle is also shortened by the implementation of selection methods such as single-seed descent, single plant selection, and marker-assisted selection.",book:{id:"11621",title:"Plant Breeding - New Perspectives",coverURL:"https://cdn.intechopen.com/books/images_new/11621.jpg"},signatures:"Priyanka Shanmugavel, Gowtham Ramasamy, Geethalakshmi Vellingiri, Rajavel Marimuthu and Kalaimagal Thiyagarajan"},{id:"82529",title:"Molecular and Functional Characterisation of Allergenic Non-specific Lipid Transfer Proteins of Sweet Lupin Seed Species",slug:"molecular-and-functional-characterisation-of-allergenic-non-specific-lipid-transfer-proteins-of-swee",totalDownloads:2,totalDimensionsCites:0,doi:"10.5772/intechopen.102889",abstract:"Non-specific lipid transfer proteins (nsLTPs) are small proteins abundant in plants, which function in transferring phospholipids and galactolipids across the membrane. nsLTPs also play a key role in plant resistance to biotic and abiotic stresses, growth and development, as well as in sexual reproduction, seed development, and germination. In addition, these proteins have previously been identified as food allergens. In the present study, we carried out a molecular and functional comparative characterisation of 25 sequences of nsLTPs of lupin legumes and other species. Extensive analysis was carried out; including comparison of databases, phylogeny, physical–chemical properties, functional properties of post-translational modifications, protein structure conservation, 2-D and 3D modelling, functional interaction analysis, and allergenicity including identification of IgE, T-cell, and B-cell binding epitopes. The results indicated that particular structural features of nsLTPs are essential to the functionality of these proteins, high level of structural stability and conservation. Information about different functional interactions between nsLTPs and ligands showed that nsLTPs can accommodate several of them with different structure; and that the relationship between structure and allergenicity was investigated through the identification of epitopes susceptible of being involved in cross-reactivity between species of the Fabaceae family.",book:{id:"10749",title:"Legumes Research - Volume 1",coverURL:"https://cdn.intechopen.com/books/images_new/10749.jpg"},signatures:"Maria Rodrigo-Garcia, Esther Rodriguez-de Haro, Salvador Priego-Poyato, Elena Lima-Cabello, Sonia Morales-Santana and Jose C. Jimenez-Lopez"},{id:"82530",title:"Influence of Soil Moisture Stress on Vegetative Growth and Root Yield of Some Cassava Genotypes for Better Selection Strategy in Screen House Conditions and Different Agro-Ecologies in Nigeria",slug:"influence-of-soil-moisture-stress-on-vegetative-growth-and-root-yield-of-some-cassava-genotypes-for-",totalDownloads:2,totalDimensionsCites:0,doi:"10.5772/intechopen.105526",abstract:"Cassava is a vital staple crop for many African populations particularly in Nigeria. This study was conducted to determine the effect of soil moisture on the performance of selected 12 cassava genotypes that were evaluated for yield and related traits under three percentages of field capacity (75% – control, 50%, and 25%) in the screen house and field conditions in three agro-ecologies (Ibadan-Derived Savanna, Mokwa-Southern Guinea Savanna, and Zaria-Northern Guinea Savanna) and randomized complete block design was used. Data were collected on plant height, stem girth, number of nodes and leaves, shoot weight, stomata conductant, stay-green, fresh root weight, and dry matter percentage and were analyzed using descriptive statistics and ANOVA. Genotypes differed significantly across and within locations. The higher stress level (25% field capacity – F.C.) resulted in a more significant reduction in vegetative growth than the moderate stress level of 50% F.C.; moisture levels were uniform over time for plant height and stem girth. The response to moisture levels varied widely among genotypes, indicating that they experienced a higher stress condition. Genotypes IITA-TMS-IBA980581, IITA-TMS-IBA010040, and IITA-TMS-IBA010034 were identified with good drought tolerance. Integrating physiological research with breeding efforts will help in the selection of suitable varieties for release.",book:{id:"11330",title:"Plant Response Mechanisms to Abiotic Stresses",coverURL:"https://cdn.intechopen.com/books/images_new/11330.jpg"},signatures:"Najimu Adetoro and Sikirou Mouritala"},{id:"82522",title:"Macauba (Acrocomia aculeata): Biology, Oil Processing, and Technological Potential",slug:"macauba-acrocomia-aculeata-biology-oil-processing-and-technological-potential",totalDownloads:18,totalDimensionsCites:0,doi:"10.5772/intechopen.105540",abstract:"The global production of vegetable oil has increased since the beginning of the century, reaching a peak of 209 million tons in 2020/2021 and is projected to continue to increase due to population growth and the impact of the biodiesel industry. In this context, palm oil and soybean oil have stood out. However, both palm oil and soybean oil production chains are not fully sustainable, leading to socioeconomic and environmental challenges, which have driven the search for new raw materials with sustainability potential. Macauba [Acrocomia aculeata (Jacq.) Lodd. Ex Mart.] is an oleaginous palm distributed mainly in Central and South America, and most of the Brazilian territory. It is one of the species with greater potential for economic exploitation due to its high oil productivity and use of by-products from oil extraction and processing. This chapter addresses the most up-to-date information in biology, oil production, and oil processing from fruit to oil applications.",book:{id:"11627",title:"Oilseed Crops - Biology, Production and Processing",coverURL:"https://cdn.intechopen.com/books/images_new/11627.jpg"},signatures:"Odalys García Cabrera, Larissa Magalhães Grimaldi, Renato Grimaldi and Ana Paula Badan Ribeiro"},{id:"81567",title:"Basic Animal Breeding Methods",slug:"basic-animal-breeding-methods",totalDownloads:2,totalDimensionsCites:0,doi:"10.5772/intechopen.104136",abstract:"In the era of genomic selection, basic animal breeding methods are still playing a very important role in animal selection and their improvement. Animal Breeding involves the selective breeding of domestic animals with the intention to improve desirable and heritable qualities in the next generation. An animal’s overall performance is mostly influenced by genetic potential acquired from its parents, as well as the environment, which includes nutrition, health, management, and other factors. This chapter covers a brief outline of traditional breeding methods for the selection of animals and their improvement.",book:{id:"10898",title:"Animal Husbandry",coverURL:"https://cdn.intechopen.com/books/images_new/10898.jpg"},signatures:"Mohan Singh Thakur"},{id:"82262",title:"Quality of Postharvest Degreened Citrus Fruit",slug:"quality-of-postharvest-degreened-citrus-fruit",totalDownloads:5,totalDimensionsCites:0,doi:"10.5772/intechopen.105119",abstract:"External color is a key factor that defines external citrus fruit quality. Degreening with exogenous ethylene exposure is a widely used postharvest treatment applied to promote external citrus fruit color development, mainly with those cultivars that reach internal maturity while their external peel color is still green. Ethylene plays a crucial role in the color change of citrus fruit because it induces two simultaneous, but independent, processes—chlorophyll degradation and carotenoid synthesis. However, it is important to know, in addition to the effect on skin color development, whether this treatment can negatively affect other fruit quality parameters. This chapter addresses the influence of postharvest degreening treatment on the physicochemical, nutritional, and sensory quality of citrus fruit.",book:{id:"11629",title:"Advances in Citrus Production and Research",coverURL:"https://cdn.intechopen.com/books/images_new/11629.jpg"},signatures:"Julia Morales, Lourdes Cervera, Pilar Navarro and Alejandra Salvador"}],onlineFirstChaptersTotal:338},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:0,limit:8,total:null},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:90,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:104,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:32,numberOfPublishedChapters:320,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:12,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:141,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:133,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:113,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:107,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:5,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:17,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}},{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}}]},series:{item:{id:"14",title:"Artificial Intelligence",doi:"10.5772/intechopen.79920",issn:"2633-1403",scope:"Artificial Intelligence (AI) is a rapidly developing multidisciplinary research area that aims to solve increasingly complex problems. In today's highly integrated world, AI promises to become a robust and powerful means for obtaining solutions to previously unsolvable problems. 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He is a full professor of signal processing and pattern recognition and is head of the Signals and Communications Department at ULPGC, teaching from 2001 on subjects on signal processing and learning theory. His research lines are biometrics, biomedical signals and images, data mining, classification system, signal and image processing, machine learning, and environmental intelligence. He has researched in 52 international and Spanish research projects, some of them as head researcher. He is co-author of 4 books, co-editor of 27 proceedings books, guest editor for 8 JCR-ISI international journals, and up to 24 book chapters. He has over 450 papers published in international journals and conferences (81 of them indexed on JCR – ISI - Web of Science). He has published seven patents in the Spanish Patent and Trademark Office. He has been a supervisor on 8 Ph.D. theses (11 more are under supervision), and 130 master theses. 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He has been a member of the IASTED Technical Committee on Image Processing from 2007 and a member of the IASTED Technical Committee on Artificial Intelligence and Expert Systems from 2011. \n\nHe has held the general chair position for the following: ACM-APPIS (2020, 2021), IEEE-IWOBI (2019, 2020 and 2020), A PPIS (2018, 2019), IEEE-IWOBI (2014, 2015, 2017, 2018), InnoEducaTIC (2014, 2017), IEEE-INES (2013), NoLISP (2011), JRBP (2012), and IEEE-ICCST (2005)\n\nHe is an associate editor of the Computational Intelligence and Neuroscience Journal (Hindawi – Q2 JCR-ISI). He was vice dean from 2004 to 2010 in the Higher Technical School of Telecommunication Engineers at ULPGC and the vice dean of Graduate and Postgraduate Studies from March 2013 to November 2017. 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His research interests include computer/machine vision, machine learning, pattern recognition, computational intelligence. \nDr. Papakostas served as a reviewer in numerous journals, as a program\ncommittee member in international conferences and he is a member of the IAENG, MIR Labs, EUCogIII, INSTICC and the Technical Chamber of Greece (TEE).",institutionString:null,institution:{name:"International Hellenic University",institutionURL:null,country:{name:"Greece"}}},editorTwo:null,editorThree:null},{id:"25",title:"Evolutionary Computation",coverUrl:"https://cdn.intechopen.com/series_topics/covers/25.jpg",isOpenForSubmission:!0,editor:{id:"136112",title:"Dr.",name:"Sebastian",middleName:null,surname:"Ventura Soto",slug:"sebastian-ventura-soto",fullName:"Sebastian Ventura Soto",profilePictureURL:"https://mts.intechopen.com/storage/users/136112/images/system/136112.png",biography:"Sebastian Ventura is a Spanish researcher, a full professor with the Department of Computer Science and Numerical Analysis, University of Córdoba. 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He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. 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He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:"Polytechnic University of Timişoara",institution:{name:"Polytechnic University of Timişoara",institutionURL:null,country:{name:"Romania"}}}]},{type:"book",id:"9963",title:"Advances and Applications in Deep Learning",subtitle:null,coverURL:"https://cdn.intechopen.com/books/images_new/9963.jpg",slug:"advances-and-applications-in-deep-learning",publishedDate:"December 9th 2020",editedByType:"Edited by",bookSignature:"Marco Antonio Aceves-Fernandez",hash:"0d51ba46f22e55cb89140f60d86a071e",volumeInSeries:4,fullTitle:"Advances and Applications in Deep Learning",editors:[{id:"24555",title:"Dr.",name:"Marco Antonio",middleName:null,surname:"Aceves Fernandez",slug:"marco-antonio-aceves-fernandez",fullName:"Marco Antonio Aceves Fernandez",profilePictureURL:"https://mts.intechopen.com/storage/users/24555/images/system/24555.jpg",biography:"Dr. Marco Antonio Aceves Fernandez obtained his B.Sc. 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Heshmati",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/313921/images/system/313921.jpg",biography:"Dr. Hassan Massoud Heshmati is an endocrinologist with 46 years of experience in clinical research in academia (university-affiliated hospitals, Paris, France; Mayo Foundation, Rochester, MN, USA) and pharmaceutical companies (Sanofi, Malvern, PA, USA; Essentialis, Carlsbad, CA, USA; Gelesis, Boston, MA, USA). His research activity focuses on pituitary tumors, hyperthyroidism, thyroid cancers, osteoporosis, diabetes, and obesity. He has extensive knowledge in the development of anti-obesity products. Dr. Heshmati is the author of 299 abstracts, chapters, and articles related to endocrinology and metabolism. He is currently a consultant at Endocrinology Metabolism Consulting, LLC, Anthem, AZ, USA.",institutionString:"Endocrinology Metabolism Consulting, LLC",institution:null},{id:"76477",title:"Prof.",name:"Mirza",middleName:null,surname:"Hasanuzzaman",slug:"mirza-hasanuzzaman",fullName:"Mirza Hasanuzzaman",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/76477/images/system/76477.png",biography:"Dr. Mirza Hasanuzzaman is a Professor of Agronomy at Sher-e-Bangla Agricultural University, Bangladesh. He received his Ph.D. in Plant Stress Physiology and Antioxidant Metabolism from Ehime University, Japan, with a scholarship from the Japanese Government (MEXT). Later, he completed his postdoctoral research at the Center of Molecular Biosciences, University of the Ryukyus, Japan, as a recipient of the Japan Society for the Promotion of Science (JSPS) postdoctoral fellowship. He was also the recipient of the Australian Government Endeavour Research Fellowship for postdoctoral research as an adjunct senior researcher at the University of Tasmania, Australia. Dr. Hasanuzzaman’s current work is focused on the physiological and molecular mechanisms of environmental stress tolerance. Dr. Hasanuzzaman has published more than 150 articles in peer-reviewed journals. He has edited ten books and written more than forty book chapters on important aspects of plant physiology, plant stress tolerance, and crop production. According to Scopus, Dr. Hasanuzzaman’s publications have received more than 10,500 citations with an h-index of 53. He has been named a Highly Cited Researcher by Clarivate. He is an editor and reviewer for more than fifty peer-reviewed international journals and was a recipient of the “Publons Peer Review Award” in 2017, 2018, and 2019. He has been honored by different authorities for his outstanding performance in various fields like research and education, and he has received the World Academy of Science Young Scientist Award (2014) and the University Grants Commission (UGC) Award 2018. He is a fellow of the Bangladesh Academy of Sciences (BAS) and the Royal Society of Biology.",institutionString:"Sher-e-Bangla Agricultural University",institution:{name:"Sher-e-Bangla Agricultural University",country:{name:"Bangladesh"}}},{id:"187859",title:"Prof.",name:"Kusal",middleName:"K.",surname:"Das",slug:"kusal-das",fullName:"Kusal Das",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSBDeQAO/Profile_Picture_1623411145568",biography:"Kusal K. Das is a Distinguished Chair Professor of Physiology, Shri B. M. Patil Medical College and Director, Centre for Advanced Medical Research (CAMR), BLDE (Deemed to be University), Vijayapur, Karnataka, India. Dr. Das did his M.S. and Ph.D. in Human Physiology from the University of Calcutta, Kolkata. His area of research is focused on understanding of molecular mechanisms of heavy metal activated low oxygen sensing pathways in vascular pathophysiology. He has invented a new method of estimation of serum vitamin E. His expertise in critical experimental protocols on vascular functions in experimental animals was well documented by his quality of publications. He was a Visiting Professor of Medicine at University of Leeds, United Kingdom (2014-2016) and Tulane University, New Orleans, USA (2017). For his immense contribution in medical research Ministry of Science and Technology, Government of India conferred him 'G.P. Chatterjee Memorial Research Prize-2019” and he is also the recipient of 'Dr.Raja Ramanna State Scientist Award 2015” by Government of Karnataka. He is a Fellow of the Royal Society of Biology (FRSB), London and Honorary Fellow of Karnataka Science and Technology Academy, Department of Science and Technology, Government of Karnataka.",institutionString:"BLDE (Deemed to be University), India",institution:null},{id:"243660",title:"Dr.",name:"Mallanagouda Shivanagouda",middleName:null,surname:"Biradar",slug:"mallanagouda-shivanagouda-biradar",fullName:"Mallanagouda Shivanagouda Biradar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/243660/images/system/243660.jpeg",biography:"M. S. Biradar is Vice Chancellor and Professor of Medicine of\nBLDE (Deemed to be University), Vijayapura, Karnataka, India.\nHe obtained his MD with a gold medal in General Medicine and\nhas devoted himself to medical teaching, research, and administrations. He has also immensely contributed to medical research\non vascular medicine, which is reflected by his numerous publications including books and book chapters. Professor Biradar was\nalso Visiting Professor at Tulane University School of Medicine, New Orleans, USA.",institutionString:"BLDE (Deemed to be University)",institution:{name:"BLDE University",country:{name:"India"}}},{id:"289796",title:"Dr.",name:"Swastika",middleName:null,surname:"Das",slug:"swastika-das",fullName:"Swastika Das",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/289796/images/system/289796.jpeg",biography:"Swastika N. Das is Professor of Chemistry at the V. P. Dr. P. G.\nHalakatti College of Engineering and Technology, BLDE (Deemed\nto be University), Vijayapura, Karnataka, India. She obtained an\nMSc, MPhil, and PhD in Chemistry from Sambalpur University,\nOdisha, India. Her areas of research interest are medicinal chemistry, chemical kinetics, and free radical chemistry. She is a member\nof the investigators who invented a new modified method of estimation of serum vitamin E. She has authored numerous publications including book\nchapters and is a mentor of doctoral curriculum at her university.",institutionString:"BLDEA’s V.P.Dr.P.G.Halakatti College of Engineering & Technology",institution:{name:"BLDE University",country:{name:"India"}}},{id:"248459",title:"Dr.",name:"Akikazu",middleName:null,surname:"Takada",slug:"akikazu-takada",fullName:"Akikazu Takada",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/248459/images/system/248459.png",biography:"Akikazu Takada was born in Japan, 1935. After graduation from\nKeio University School of Medicine and finishing his post-graduate studies, he worked at Roswell Park Memorial Institute NY,\nUSA. He then took a professorship at Hamamatsu University\nSchool of Medicine. In thrombosis studies, he found the SK\npotentiator that enhances plasminogen activation by streptokinase. He is very much interested in simultaneous measurements\nof fatty acids, amino acids, and tryptophan degradation products. By using fatty\nacid analyses, he indicated that plasma levels of trans-fatty acids of old men were\nfar higher in the US than Japanese men. . He also showed that eicosapentaenoic acid\n(EPA) and docosahexaenoic acid (DHA) levels are higher, and arachidonic acid\nlevels are lower in Japanese than US people. By using simultaneous LC/MS analyses\nof plasma levels of tryptophan metabolites, he recently found that plasma levels of\nserotonin, kynurenine, or 5-HIAA were higher in patients of mono- and bipolar\ndepression, which are significantly different from observations reported before. In\nview of recent reports that plasma tryptophan metabolites are mainly produced by\nmicrobiota. He is now working on the relationships between microbiota and depression or autism.",institutionString:"Hamamatsu University School of Medicine",institution:{name:"Hamamatsu University School of Medicine",country:{name:"Japan"}}},{id:"137240",title:"Prof.",name:"Mohammed",middleName:null,surname:"Khalid",slug:"mohammed-khalid",fullName:"Mohammed Khalid",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/137240/images/system/137240.png",biography:"Mohammed Khalid received his B.S. in Chemistry in July 2000, and his Ph.D. in Physical Chemistry in 2007 from the University of Khartoum, Sudan. In 2009 he joined the Dr. Ron Clarke research group at the School of Chemistry, Faculty of Science, University of Sydney, Australia as a postdoctoral fellow where he worked on the Interaction of ATP with the phosphoenzyme of the Na+, K+-ATPase, and Dual mechanisms of allosteric acceleration of the Na+, K+-ATPase by ATP. He then worked as Assistant Professor at the Department of Chemistry, University of Khartoum, and in 2014 was promoted to Associate Professor ranking. In 2011 he joined the staff of the Chemistry Department at Taif University, Saudi Arabia, where he is currently active as an Assistant Professor. His research interests include:\r\n(1) P-type ATPase Enzyme Kinetics and Mechanisms; (2) Kinetics and Mechanism of Redox Reactions; (3) Autocatalytic reactions; (4) Computational enzyme kinetics; (5) Allosteric acceleration of P-type ATPases by ATP; (6) Exploring of allosteric sites of ATPases and interaction of ATP with ATPases located in the cell membranes.",institutionString:"Taif University",institution:{name:"Taif University",country:{name:"Saudi Arabia"}}},{id:"63810",title:"Prof.",name:"Jorge",middleName:null,surname:"Morales-Montor",slug:"jorge-morales-montor",fullName:"Jorge Morales-Montor",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/63810/images/system/63810.png",biography:"Dr. Jorge Morales-Montor was recognized with the Lola and Igo Flisser PUIS Award for best graduate thesis at the national level in the field of parasitology. He received a fellowship from the Fogarty Foundation to perform postdoctoral research stay at the University of Georgia. He has 153 journal articles to his credit. He has also edited several books and published more than fifty-five book chapters. He is a member of the Mexican Academy of Sciences, Latin American Academy of Sciences, and the National Academy of Medicine. He has received more than thirty-five awards and has supervised numerous bachelor’s, master’s, and Ph.D. students. Dr. Morales-Montor is the past president of the Mexican Society of Parasitology.",institutionString:"National Autonomous University of Mexico",institution:{name:"National Autonomous University of Mexico",country:{name:"Mexico"}}},{id:"217215",title:"Dr.",name:"Palash",middleName:null,surname:"Mandal",slug:"palash-mandal",fullName:"Palash Mandal",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/217215/images/system/217215.jpeg",biography:null,institutionString:"Charusat University",institution:null},{id:"49739",title:"Dr.",name:"Leszek",middleName:null,surname:"Szablewski",slug:"leszek-szablewski",fullName:"Leszek Szablewski",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49739/images/system/49739.jpg",biography:"Leszek Szablewski is a professor of medical sciences. He received his M.S. in the Faculty of Biology from the University of Warsaw and his PhD degree from the Institute of Experimental Biology Polish Academy of Sciences. He habilitated in the Medical University of Warsaw, and he obtained his degree of Professor from the President of Poland. Professor Szablewski is the Head of Chair and Department of General Biology and Parasitology, Medical University of Warsaw. Professor Szablewski has published over 80 peer-reviewed papers in journals such as Journal of Alzheimer’s Disease, Biochim. Biophys. Acta Reviews of Cancer, Biol. Chem., J. Biomed. Sci., and Diabetes/Metabol. Res. Rev, Endocrine. He is the author of two books and four book chapters. He has edited four books, written 15 scripts for students, is the ad hoc reviewer of over 30 peer-reviewed journals, and editorial member of peer-reviewed journals. Prof. Szablewski’s research focuses on cell physiology, genetics, and pathophysiology. He works on the damage caused by lack of glucose homeostasis and changes in the expression and/or function of glucose transporters due to various diseases. He has given lectures, seminars, and exercises for students at the Medical University.",institutionString:"Medical University of Warsaw",institution:{name:"Medical University of Warsaw",country:{name:"Poland"}}},{id:"173123",title:"Dr.",name:"Maitham",middleName:null,surname:"Khajah",slug:"maitham-khajah",fullName:"Maitham Khajah",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/173123/images/system/173123.jpeg",biography:"Dr. Maitham A. Khajah received his degree in Pharmacy from Faculty of Pharmacy, Kuwait University, in 2003 and obtained his PhD degree in December 2009 from the University of Calgary, Canada (Gastrointestinal Science and Immunology). Since January 2010 he has been assistant professor in Kuwait University, Faculty of Pharmacy, Department of Pharmacology and Therapeutics. His research interest are molecular targets for the treatment of inflammatory bowel disease (IBD) and the mechanisms responsible for immune cell chemotaxis. He cosupervised many students for the MSc Molecular Biology Program, College of Graduate Studies, Kuwait University. Ever since joining Kuwait University in 2010, he got various grants as PI and Co-I. He was awarded the Best Young Researcher Award by Kuwait University, Research Sector, for the Year 2013–2014. He was a member in the organizing committee for three conferences organized by Kuwait University, Faculty of Pharmacy, as cochair and a member in the scientific committee (the 3rd, 4th, and 5th Kuwait International Pharmacy Conference).",institutionString:"Kuwait University",institution:{name:"Kuwait University",country:{name:"Kuwait"}}},{id:"195136",title:"Dr.",name:"Aya",middleName:null,surname:"Adel",slug:"aya-adel",fullName:"Aya Adel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/195136/images/system/195136.jpg",biography:"Dr. Adel works as an Assistant Lecturer in the unit of Phoniatrics, Department of Otolaryngology, Ain Shams University in Cairo, Egypt. Dr. Adel is especially interested in joint attention and its impairment in autism spectrum disorder",institutionString:"Ain Shams University",institution:{name:"Ain Shams University",country:{name:"Egypt"}}},{id:"94911",title:"Dr.",name:"Boulenouar",middleName:null,surname:"Mesraoua",slug:"boulenouar-mesraoua",fullName:"Boulenouar Mesraoua",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/94911/images/system/94911.png",biography:"Dr Boulenouar Mesraoua is the Associate Professor of Clinical Neurology at Weill Cornell Medical College-Qatar and a Consultant Neurologist at Hamad Medical Corporation at the Neuroscience Department; He graduated as a Medical Doctor from the University of Oran, Algeria; he then moved to Belgium, the City of Liege, for a Residency in Internal Medicine and Neurology at Liege University; after getting the Belgian Board of Neurology (with high marks), he went to the National Hospital for Nervous Diseases, Queen Square, London, United Kingdom for a fellowship in Clinical Neurophysiology, under Pr Willison ; Dr Mesraoua had also further training in Epilepsy and Continuous EEG Monitoring for two years (from 2001-2003) in the Neurophysiology department of Zurich University, Switzerland, under late Pr Hans Gregor Wieser ,an internationally known epileptologist expert. \n\nDr B. Mesraoua is the Director of the Neurology Fellowship Program at the Neurology Section and an active member of the newly created Comprehensive Epilepsy Program at Hamad General Hospital, Doha, Qatar; he is also Assistant Director of the Residency Program at the Qatar Medical School. \nDr B. Mesraoua's main interests are Epilepsy, Multiple Sclerosis, and Clinical Neurology; He is the Chairman and the Organizer of the well known Qatar Epilepsy Symposium, he is running yearly for the past 14 years and which is considered a landmark in the Gulf region; He has also started last year , together with other epileptologists from Qatar, the region and elsewhere, a yearly International Epilepsy School Course, which was attended by many neurologists from the Area.\n\nInternationally, Dr Mesraoua is an active and elected member of the Commission on Eastern Mediterranean Region (EMR ) , a regional branch of the International League Against Epilepsy (ILAE), where he represents the Middle East and North Africa(MENA ) and where he holds the position of chief of the Epilepsy Epidemiology Section; Dr Mesraoua is a member of the American Academy of Neurology, the Europeen Academy of Neurology and the American Epilepsy Society.\n\nDr Mesraoua's main objectives are to encourage frequent gathering of the epileptologists/neurologists from the MENA region and the rest of the world, promote Epilepsy Teaching in the MENA Region, and encourage multicenter studies involving neurologists and epileptologists in the MENA region, particularly epilepsy epidemiological studies. \n\nDr. Mesraoua is the recipient of two research Grants, as the Lead Principal Investigator (750.000 USD and 250.000 USD) from the Qatar National Research Fund (QNRF) and the Hamad Hospital Internal Research Grant (IRGC), on the following topics : “Continuous EEG Monitoring in the ICU “ and on “Alpha-lactoalbumin , proof of concept in the treatment of epilepsy” .Dr Mesraoua is a reviewer for the journal \"seizures\" (Europeen Epilepsy Journal ) as well as dove journals ; Dr Mesraoua is the author and co-author of many peer reviewed publications and four book chapters in the field of Epilepsy and Clinical Neurology",institutionString:"Weill Cornell Medical College in Qatar",institution:{name:"Weill Cornell Medical College in Qatar",country:{name:"Qatar"}}},{id:"282429",title:"Prof.",name:"Covanis",middleName:null,surname:"Athanasios",slug:"covanis-athanasios",fullName:"Covanis Athanasios",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/282429/images/system/282429.jpg",biography:null,institutionString:"Neurology-Neurophysiology Department of the Children Hospital Agia Sophia",institution:null},{id:"190980",title:"Prof.",name:"Marwa",middleName:null,surname:"Mahmoud Saleh",slug:"marwa-mahmoud-saleh",fullName:"Marwa Mahmoud Saleh",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/190980/images/system/190980.jpg",biography:"Professor Marwa Mahmoud Saleh is a doctor of medicine and currently works in the unit of Phoniatrics, Department of Otolaryngology, Ain Shams University in Cairo, Egypt. She got her doctoral degree in 1991 and her doctoral thesis was accomplished in the University of Iowa, United States. Her publications covered a multitude of topics as videokymography, cochlear implants, stuttering, and dysphagia. She has lectured Egyptian phonology for many years. Her recent research interest is joint attention in autism.",institutionString:"Ain Shams University",institution:{name:"Ain Shams University",country:{name:"Egypt"}}},{id:"259190",title:"Dr.",name:"Syed Ali Raza",middleName:null,surname:"Naqvi",slug:"syed-ali-raza-naqvi",fullName:"Syed Ali Raza Naqvi",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259190/images/system/259190.png",biography:"Dr. Naqvi is a radioanalytical chemist and is working as an associate professor of analytical chemistry in the Department of Chemistry, Government College University, Faisalabad, Pakistan. Advance separation techniques, nuclear analytical techniques and radiopharmaceutical analysis are the main courses that he is teaching to graduate and post-graduate students. In the research area, he is focusing on the development of organic- and biomolecule-based radiopharmaceuticals for diagnosis and therapy of infectious and cancerous diseases. Under the supervision of Dr. Naqvi, three students have completed their Ph.D. degrees and 41 students have completed their MS degrees. He has completed three research projects and is currently working on 2 projects entitled “Radiolabeling of fluoroquinolone derivatives for the diagnosis of deep-seated bacterial infections” and “Radiolabeled minigastrin peptides for diagnosis and therapy of NETs”. He has published about 100 research articles in international reputed journals and 7 book chapters. Pakistan Institute of Nuclear Science & Technology (PINSTECH) Islamabad, Punjab Institute of Nuclear Medicine (PINM), Faisalabad and Institute of Nuclear Medicine and Radiology (INOR) Abbottabad are the main collaborating institutes.",institutionString:"Government College University",institution:{name:"Government College University, Faisalabad",country:{name:"Pakistan"}}},{id:"58390",title:"Dr.",name:"Gyula",middleName:null,surname:"Mozsik",slug:"gyula-mozsik",fullName:"Gyula Mozsik",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/58390/images/system/58390.png",biography:"Gyula Mózsik MD, Ph.D., ScD (med), is an emeritus professor of Medicine at the First Department of Medicine, Univesity of Pécs, Hungary. He was head of this department from 1993 to 2003. His specializations are medicine, gastroenterology, clinical pharmacology, clinical nutrition, and dietetics. 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His Works are realted to the Green chemistry field, biolubricants, biodiesel, transesterification reactions for biodiesel production and the manipulation of oils for therapeutic purposes.",institutionString:null,institution:{name:"Instituto Politécnico Nacional",country:{name:"Mexico"}}},{id:"196544",title:"Prof.",name:"Angel",middleName:null,surname:"Catala",slug:"angel-catala",fullName:"Angel Catala",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/196544/images/system/196544.jpg",biography:"Angel Catalá studied chemistry at Universidad Nacional de La Plata, Argentina, where he received a Ph.D. in Chemistry (Biological Branch) in 1965. From 1964 to 1974, he worked as an Assistant in Biochemistry at the School of Medicine at the same university. From 1974 to 1976, he was a fellow of the National Institutes of Health (NIH) at the University of Connecticut, Health Center, USA. From 1985 to 2004, he served as a Full Professor of Biochemistry at the Universidad Nacional de La Plata. He is a member of the National Research Council (CONICET), Argentina, and the Argentine Society for Biochemistry and Molecular Biology (SAIB). His laboratory has been interested for many years in the lipid peroxidation of biological membranes from various tissues and different species. Dr. Catalá has directed twelve doctoral theses, published more than 100 papers in peer-reviewed journals, several chapters in books, and edited twelve books. He received awards at the 40th International Conference Biochemistry of Lipids 1999 in Dijon, France. He is the winner of the Bimbo Pan-American Nutrition, Food Science and Technology Award 2006 and 2012, South America, Human Nutrition, Professional Category. In 2006, he won the Bernardo Houssay award in pharmacology, in recognition of his meritorious works of research. 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At the National Cancer Institute (National Institute of Health, Bethesda, MD) he worked as a research associate on the molecular biology of selenium and its role in health and disease. After postdoctoral collaborations with Carlos Gutierrez-Merino (University of Extremadura, Spain) and Dario Alessi (University of Dundee, UK), he established his own laboratory in 2008. The interest of Javier's lab is the study of cell signaling with a special focus on Ca2+ signaling, and how Ca2+ transport modulates the cytoskeleton, migration, differentiation, cell death, etc. He is especially interested in the study of Ca2+ channels, and the role of STIM1 in the initiation of pathological events.",institutionString:null,institution:{name:"University of Extremadura",country:{name:"Spain"}}},{id:"198499",title:"Dr.",name:"Daniel",middleName:null,surname:"Glossman-Mitnik",slug:"daniel-glossman-mitnik",fullName:"Daniel Glossman-Mitnik",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/198499/images/system/198499.jpeg",biography:"Dr. Daniel Glossman-Mitnik is currently a Titular Researcher at the Centro de Investigación en Materiales Avanzados (CIMAV), Chihuahua, Mexico, as well as a National Researcher of Level III at the Consejo Nacional de Ciencia y Tecnología, México. 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