Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\n
We wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
IntechOpen is proud to announce that 191 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\n
Throughout the years, the list has named a total of 261 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\n
We wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
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1. Introduction
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Accidents at industrial facilities may result in serious consequences to workers, public, property, and the environment. Risk management approaches are aimed at insuring that processes and systems are designed and operated to meet “acceptable or tolerable risk levels” as required by regulatory bodies. Risk assessment usually encompasses the following steps: hazard identification, risk analysis, and risk evaluation. When the risk evaluation is carried out in a quantitative way, the risk assessment is considered a probabilistic risk assessment (PRA).
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Fault tree analysis (FTA) and event tree analysis (ETA) are the most used techniques in PRAs. However, uncertainties in PRAs may lead to inaccurate risk level estimations and consequently to wrong decisions [1]. Lack of knowledge about systems under study during the PRAs is one of the main causes of uncertainties, which leads to simplification of assumptions, as well as imprecision and inaccuracies in the parameters used as inputs to PRA (e.g., component reliabilities, failure probabilities, and human error rates).
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A framework to use the method of moments for determining the likelihoods of different outcomes from event trees in an uncertain data environment using fault trees is described in this work. Illustrative examples using this approach for propagating uncertainty in basic events of fault trees, following log-normal distributions, are also presented. The probability distributions of top events are compared with analyses available in the literature using different approaches, such as Monte Carlo simulation and Wilks and Fenton-Wilkinson methods.
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2. Basics of risk assessment
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There are many concepts of risk used in different scientific, technological, or organization areas. In a general sense, risk can be defined as the potential of loss (e.g., material, human, or environment) resulting from exposure to a hazard (e.g., fire, explosion, or earthquake). Sometimes, risk is measured through the assessment of the probability of occurrence of an undesired event and the magnitude of consequences [2]. In this way, risk assessment encompasses the answers to the following questions [3]:
What can go wrong that may lead to an outcome of hazard exposure (scenario Si\n)?
How likely is this to happen, and if so, what is its frequency (Fi\n)?
If it happens, what are the likely consequences (Ci\n)?
\n\n
Therefore, risk, Ri\n, for a scenario Si\n, can be quantitatively expressed as function of these three variables, as given by Eq. (1):
According to Christensen et al. [4], hazard is an inherent property of a risk source potentially causing consequences or effects. This hazard concept does not include the probability of adverse outcome, which is the core difference from risk term. In this chapter, hazard is then considered as the properties of agents or situations capable of having adverse effects on facilities, human health, or environment, such as dangerous substance, sources of energy, or natural phenomena.
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2.1 Probabilistic risk assessment (PRA)
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PRA provides an efficient way for quantifying the risks, even in an environment of uncertainties regarding possible scenarios, data, or modeling. Risk assessment is part of risk management carried out before deciding about risk treatment and prioritizing actions to reduce risks (risk-based decision-making). Figure 1 shows a framework for PRA under uncertainty environment [5, 6].
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Figure 1.
Framework for probabilistic risk assessment under uncertainty (based on Refs. [5, 6]).
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PRA starts with the hazard identification and scenario development, proceeds through quantification of frequencies and consequences, and ends with risk analysis and evaluation [5].
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The first step of a PRA process consists of finding, recognizing, and recording risk sources (hazard identification). The accident scenario development (sequence or chain of undesired events) consists of identifying the initiating events (IEs) and the sequences of events following these IEs. The latter are the critical events that initiate an accident, such as pipe rupture, overpressures, or explosion. The sequences of events are the combinations of success or failure of the barriers or controls requested by IEs (defense-in-depth layers), for example, emergency shutdown systems, human actions, or physical protection. Each sequence can lead to a desired or undesired outcome (end state) such as uncontrollable release of toxic gases, radiation exposure, or facility shutdown [6].
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Fault trees (FTs) and event trees (ETs) are often used in PRAs for quantifying the likelihood of event sequences. FTs quantify frequencies or probabilities of top events (such as IEs or failure of defense-in-depth layers) through causal relationship of basic events (e.g., system components, human actions, or subsystems). ETs identify and evaluate each sequence frequency using data generated by FTs [5].
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The consequence assessment of each accident scenario to people, property, or environment depends on many factors, such as magnitude of the event, number of people exposed to harm, atmospheric conditions, mitigating measures, etc. The consequence modeling involves the use of analytical or empirical physical or phenomenological models, such as plume dispersion, blast impact (TNT equivalent), or Monte Carlo simulation [7, 8].
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Risk analysis is the combination and integration of the probabilities (or frequencies) and the consequences for identified hazards, taking into account the effectiveness of any existing controls and barriers. It provides an input to risk evaluation and decisions about risk treatment and risk management strategies [6].
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There are many uncertainties associated with the analysis of risk related to both probability and consequence assessments. An assessment of uncertainties is necessary to perform risk evaluation and to take decisions. The major categories of uncertainties are associated with data, methods, and models used to identify and analyze risks. Uncertainty assessment involves the determination of the variation or imprecision in the results, based on uncertainties of basic parameters and assumptions used in the analyses. Uncertainty propagation of failure probability distributions in FTs and ETs, as well as variability analysis of physical processes (named stochastic uncertainty) and the uncertainties in knowledge of these processes (named epistemic uncertainty), have to be properly accounted for in PRA results [9].
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Risk evaluation involves comparing estimated levels of risk with risk criteria defined, once the context of analysis has been established. Uncertainty assessment is important to adjust the categorization of the risk ranking, supporting the decision-makers in meeting risk criteria of standards and guidelines, as well as in visualizing and communicating risks [10].
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2.2 Techniques for PRA
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The main techniques used for probabilistic risk assessment are fault tree analysis (FTA) and event tree analysis (ETA) [11].
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FTA is a graphical relationship among events leading to a “top event” at the apex of the tree. Beginning with the top event, the intermediate events are hierarchically placed at different levels until the required level of detail is reached (the basic events at the bottom of the tree). The interactions between the top event and other events can be generally represented by “OR” or “AND” gates, as shown in Figure 2(a) and (b), respectively.
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Figure 2.
Intermediate events connected by “OR” (a) and “AND” (b) gates in a fault tree.
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Minimal cut sets (MCSs) of a fault tree are the combinations of basic events which are the shortest pathways that lead to the top event. MCSs are used for qualitative and quantitative assessments of fault trees and can be identified with support of Boolean algebra, specialized algorithms, or computer codes [12]. The probability of the top event can be assessed if the probability values or probability density functions (pdfs) of the basic events are available, using the identified MCSs. For instance, using the set theory concepts [13], the probability equations of the two FTs in Figure 2(a) and (b) can be expressed by Eqs. (2) and (3), respectively:
where P(A) and P(B) are the independent probabilities of the basic events and P(A|B) and P(B|A) are the conditional (dependent) probabilities.
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ETA is also a graphical logic model that identifies and quantifies possible outcomes (accident scenarios) following an undesired initiating event [14]. It provides systematic analysis of the time sequence of intermediate events (e.g., success or failure of defense-in-depth layers, as protective system or operator interventions), until an end state is reached. Consequences can be direct (e.g., fires, explosions) or indirect (e.g., domino effects on adjacent plants or environmental consequences).
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\nFigure 3 shows an example of an event tree construction, starting with the initiating event of frequency of occurrence, λ, where P1\n and P2\n are the probabilities of subsequent events (event 1 and event 2) leading to the possible scenarios S1\n, S2\n, S3\n, and S4\n, with frequencies F1\n, F2\n, F3\n, and F4\n, respectively, each one with different consequences. If the success and the failure of each event are mutually exclusive (binary trees) and the probabilities of event occurrence are independent of each other, the frequency of each scenario is calculated as shown in Figure 3.
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Figure 3.
Sequence of events in an event tree leading to different accident scenarios.
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2.3 Uncertainty sources in PRA
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Many types of data must be collected and treated for use in PRAs in order to quantify the accident scenarios and accident contributors. Data include, among others, component reliability and failure rates, repair times, initiating event probabilities, human error probabilities, and common cause failure (CCF) probabilities. These data are usually represented by uncertainty bounds or probability density functions, measuring the degree of knowledge or confidence in the available data.
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Uncertainties can be highly significant in risk-based decisions and are important for establishing research priorities after a PRA process. For well-understood basic events for which a substantial experience base exists, the uncertainties may be small. When data from experience are limited, the probability of basic events may be highly uncertain, and even knowing that a given probability is small, most of the time one does not know how small it is.
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The development of scenarios in a PRA introduces uncertainties about both consequences and probabilities. Random changing of physical processes is an example of stochastic uncertainties, while the uncertainties due to lack of knowledge about these processes are the epistemic uncertainties. Component failure rates and reliability data are typically uncertain, sometimes because unavailability of information and sometimes because doubts about the applicability of available data.
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PRA of complex engineering systems such as those in nuclear power plants (NPPs) and chemical plants usually exhibits uncertainties arising from inadequate assumptions, incompleteness of modeling, CCF and human reliability issues, and lack of plant-specific data. For this type of facility, the major of sources of uncertainties are [15]:
Uncertainties in input parameters—parameters of the models (e.g., FTs and ETs) for estimating event probabilities and assessing magnitude consequences are not exactly known because of the lack of data, variability of plants, processes or components, and inadequate assumptions.
Modeling uncertainty—inadequacy of conceptual, mathematical, numerical, and computational models.
Uncertainty about completeness—systematic expert reviewing can minimize the difficulties in assessing or quantifying this type of uncertainty.
\n\n
The main focus of this work is the treatment of uncertainties regarding numerical values of the parameters used in fault and event trees in the scope of PRA and their propagation in these models. If a probability density function (pdf) is provided for the basic events (e.g., normal, log-normal, or triangular), a pdf or confidence bounds can be obtained for an FT top event or an ET scenario sequence.
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3. Methods of uncertainty propagation used in PRA
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There are several available methods for propagating uncertainties such as analytical methods (method of moments and Fenton-Wilkinson (FW) method), Monte Carlo simulation, Wilks method (order statistic), and fuzzy set theory. They are different from each other, in terms of characterizing the input parameter uncertainty and how they propagate from parameter level to output level [16].
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The analytical methods consist in obtaining the distribution of the output of a model (e.g., fault or event trees) starting from probability distribution of input parameters. An exact analytical distribution of the output however can be derived only for specific models such as normal or log-normal distributions [17].
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The Fenton-Wilkinson (FW) method is a kind of analytical technique of approximating a distribution using log-normal distribution with the same moments. It is a moment-matching method for obtaining an exact analytical distribution for the output (closed form). This kind of closed form is helpful, when more detailed uncertainty analyses are required, for instance, in parametric studies involving uncertainty importance assessments, which require re-estimating the overall uncertainty distribution many times [18].
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The method of moments is another kind of analytical method where the calculations of the mean, variance, and higher order moments are based on approximate models (generally using Taylor series). As the method is only an approximation, when the variance in the input data are large, higher order terms in the Taylor expansion have to be included. This introduces much more complexity in the analytical model, especially for complex original models, as in the case of PRAs [19].
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The Monte Carlo simulation estimates the output parameter (e.g., probability of the top event of an FT) by simulating the real process and its random behavior in a computer model. It estimates the output occurrence by counting the number of times an event occurs in simulated time, starting to sample the pdf from the input data [20].
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The fuzzy set theory is used when empirical information for input data are limited and probability theory is insufficient for representing all type of uncertainties. In this case, the so-called possibility distributions are subjectively assigned to input data, and fuzzy arithmetic is carried out. For uncertainty analysis in FTAs, instead of assuming the input parameter as a random variable, it is considered as a fuzzy number, and the uncertainty is propagated to the top event [21].
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The Wilks method is an efficient sampling approach, based on order statistics, which can be used to find upper bounds to specified percentiles of the output distribution. Order statistics are statistics based on the order of magnitudes and do not need assumptions about the shape of input or output distributions. According to the authors’ knowledge, this method has been of little use in the field of reliability modeling and PRA, although it is used in other aspects of NPP safety, such as uncertainty in input parameters associated with the loss-of-coolant accident (LOCA) phenomena [22].
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The mentioned methods for uncertainty propagation have many differences and similarities, advantages and disadvantages, as well as benefits and limitations. Table 1 summarizes a comparison of these methods.
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Method
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Propagation technique
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Benefits
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Limitations
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Method of moments
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Analytical (probability theory and statistics)
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Conceptually simple and does not require the specification of pdf of input data
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Difficult to apply for complex systems and large fault trees
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Monte Carlo simulation
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Simulation
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Estimates are closes to exact solutions, especially for simple and small systems
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Computationally intensive for large and complex systems. Requires pdf of input data and does not reveal contributors to the uncertainty
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Fuzzy set theory
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Fuzzy arithmetic
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It does not require detailed information of pdf. Suited when empirical information is very scarce
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It is inherently conservative because the inputs are treated in a fully correlated way
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Fenton-Wilkinson (FW) method
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Analytical (closed-form approximation)
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Improves understanding of contributions to uncertainties and has low computational costs
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Closed form for top events is not easily obtained. Applicable only to log-normal distribution. Estimates are most accurate in the central range
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Wilks method
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Order statistics
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Conservative and computationally inexpensive
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Low accuracy in low tails of the distributions
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Table 1.
Comparison of methods for uncertainty propagation.
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A brief discussion about the comparison of the mentioned methods is given as follows.
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The method of moments is an efficient technique that does not require the specification of the probabilistic distributions of the basic event probabilities. It is difficult to be applied to complex fault trees with many replicated events [23]. This can be solved with the use of computer codes that automatically get the minimal cut sets (MCSs) of the fault trees. It is a simple method, easily explainable and suited for screening studies, due to inherent conservatism and simplicity [24].
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The Monte Carlo simulation is computationally intensive for large and complex systems and requires pdf of input data. It has the disadvantage of not readily revealing the dominant contributors to the uncertainties. With current computer technology and availability of user-friendly software for Monte Carlo simulation, computational cost is no longer a limitation.
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The fuzzy set theory does not need detailed empirical information like the shape of distribution, dependencies, and correlations. Fuzzy numbers are a good representation of uncertainty when empirical information is very scarce. It is inherently conservative because the inputs are treated as fully correlated [25].
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The Fenton-Wilkinson (FW) method improves the understanding of the contributions to the uncertainty distribution and reduces the computational costs involved, for instance, in conventional Monte Carlo simulation for uncertainty estimation. It is applicable only when the uncertainties in the basic events of the model are log-normally distributed. FW estimates are most accurate in the central range, and the tails of the distributions are poorly represented. The Wilks method requires relatively few samples and is computationally inexpensive. It is useful for providing an upper bound (conservative) for the percentiles of the uncertainty distribution. However, its calculated values are less accurate than the FW estimates over practically the entire range of the distribution. For both Wilks and FW methods, the greatest errors are found in the low tails of the distributions, but in almost all reliability applications the high tails are of more interest than the low tails [26].
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4. Method of moments for uncertainty propagation in FTA and ETA
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The method of moments uses first and second moments of the input parameters (mean and variance) to estimate the mean and variance of the output function using propagation of variance or coefficient of variation. As a measure of uncertainty, the coefficient of variation is defined as a ratio of the standard deviation to the mean, which indicates the relative dispersion of uncertain data around the mean. The uncertainty measure is a readily interpretable and dimensionless measure of error, differently for standard deviation, which is not dimensionless [27].
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In PRA, the method of moments can be used to propagate the uncertainties of the inputs (i.e., event probabilities) and propagate the uncertainty for the outputs. The probability density functions (pdfs) for the inputs can be estimated from reliability data of gathered components or from historical records of undesired events. Hypothesizing that the events (or basic events) are independent, probabilistic approaches for propagating uncertainties in FTs and ETs are given as follows in Sections 4.1 and 4.2, respectively [28].
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4.1 Method of moments applied to FTA
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The uncertainty propagation in a fault tree begins with the propagation of uncertainties of basic events through “OR” and “AND” gates, until it reaches the top event. The fault tree should be represented by MCSs in order to avoid direct dependence between intermediate events, facilitating probabilistic calculations.
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For an “OR” gate of a fault tree, the probability of the output event, Por\n, is given by Eq. (4):
where Pi\n denotes the probability of ith (i = 1, 2, 3, …, n) independent events (or basic events) and n is the number of input events.
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The uncertainty propagation through the “OR” gate is given by Eq. (5) that calculates the coefficient of variation of output, \n\n\nC\nor\n′\n\n\n, as function of the coefficients of variation of inputs, \n\n\nC\ni\n′\n\n\n, according to Eqs. (6) and (7) [29]:
where si\n denotes the standard deviations of ith (i = 1, 2, 3, …, n) input, n is the number of input events, and sor\n is the standard deviation of the output of “OR” gate.
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For an “AND” gate of a fault tree, the probability of output event, Pand\n, is given by Eq. (8):
where Pi\n denotes the probability of ith (i = 1, 2, 3, …, n) independent events (or basic events) and n is the number of input events.
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The uncertainty propagation through the “AND” gate is given by Eq. (9). It calculates the coefficient of variation of output, \n\n\nC\nand\n\n\n, as function of the coefficients of variation of inputs, \n\n\nC\ni\n\n\n, according to Eqs. (10) and (11) [29]:
where si\n denotes the standard deviations of ith (i = 1, 2, 3, …, n) input, n is the number of input events, and sand\n is the standard deviation of output of the “AND” gate.
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4.2 Method of moments applied to ETA
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Uncertainty propagation in an event tree is similar (or analogous) to uncertainty propagation of an “AND” gate of a fault tree. The frequency of occurrence of each accident scenario, \n\n\nF\nseq\n\n\n, is given by Eq. (12),
where λ is the frequency of occurrence of the initiating event and Pi\n denotes the probabilities of ith (i = 1, 2, 3, …, n) subsequent independent events leading to the accident scenario and n is the number of input events. These values can be obtained from fault trees constructed for each ith event or system failure of the event tree.
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The uncertainty propagation through the accident sequence is given by Eq. (13) that provides the coefficient of variation of accident sequence, \n\n\nC\nseq\n\n\n, as function of the coefficients of variation of subsequent events, \n\n\nC\ni\n\n\n, according to Eqs. (14) and (15), respectively:
where si\n denotes the standard deviations of ith (i = 1, 2, 3, …, n) subsequent event of the sequence, n is the number of input events, and sseq\n is the standard deviation of the accident sequence.
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4.3 Propagation of log-normal distributions
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Many uncertainty distributions associated with the basic events of fault trees (reliability or failure probability data) often can be approximated in reliability and safety studies by log-normal functions. If a random variable ln(x) has a normal distribution, the variable x has then a log-normal distribution. The log-normal probability density function (pdf), f(x) is then given by Eq. (16) [30]:
where \n\n\nχ\n95\n\n\n,\n\n\n\nχ\n50\n\n\n, and \n\n\nχ\n5\n\n\n are the 95th, 50th (median), and 5th percentiles, respectively.
\n
\nEF is often used as an alternative to the standard deviation of “underlying” normal distribution, σ, for characterizing the spread of a log-normal distribution, and these two quantities are related by Eq. (18):
\n
\n\nEF\n=\nexp\n\n\n1.645\n\nσ\n\n\n.\n\nE18
\n
The mean, P, and standard deviation, s, of the log-normal variable, x, can be given by the following Eqs. (19) and (20), respectively:
\nEqs. (4)–(20) are used for uncertainty propagation of log-normal pdf in fault and event trees, as illustrated in the following examples.
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5. Illustrative examples
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In order to validate the proposed approach for implementing the method of moments, two cases were tested.
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5.1 Case study 1
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The first case, taken from Chang et al. [8], introduces a fault tree (Figure 4) describing a generic top event “system failure,” T, with seven basic events (X(1) to X(7)), characterized by the log-normal distributions. This simple example was chosen in order to compare the results of the method of moments with the uncertainty propagation analyses using Monte Carlo simulation.
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Figure 4.
Fault tree analysis for a generic top event “system failure” (adapted from Chang et al. [8]).
\n
The log-normal distributions assigned to the basic events (represented by median and mean values of probabilities, error factors, and standard deviations) are shown in Table 2. An analysis of the fault tree shows that its minimal cut sets (MCSs) are X(1), X(6), X(7), X(2)X(4), X(2)X(5), X(3)X(4), and X(3)X(5), which are used to estimate the top event probability and propagate the uncertainties. The application of the method of moments is carried out in a bottom-up approach. Starting from basic events of the fault tree, the coefficients of variation of the intermediate events are estimated using Eqs. (4)–(7) for “OR” gates and Eqs. (8)–(11) for “AND” gates. This procedure is repeated interactively until the top event is reached, and its standard deviation is obtained. Considering that, in the same way as the basic events, the top event has also a log-normal distributions, Eqs. (16)–(20) are used to estimate the 5th percentile, median, and 95th percentile for the top event, as shown in Table 3. These estimates are slightly lower than the values obtained by Chang et al. [8] with the Monte Carlo simulation (percent difference less than 4%). This good agreement can also be verified through the probability density function (obtained with Eq. (16)), as shown in Figure 5.
\n
\n
\n
\n
\n
\n
\n\n
\n
Basic events
\n
Median of log-normal pdf (\n\n\n\nχ\n50\n\n\n\n)\n
\n
\nEF of log-normal pdf
\n
Mean of log-normal pdf (P)\n
\n
Standard deviation of log-normal pdf (s)
\n
\n\n\n
\n
X(1)
\n
1.00 × 10−3\n
\n
3
\n
1.25 × 10−3\n
\n
9.37 × 10−4\n
\n
\n
\n
X(2)
\n
3.00 × 10−2\n
\n
3
\n
3.75 × 10−2\n
\n
2.81 × 10−2\n
\n
\n
\n
X(3)
\n
1.00 × 10−2\n
\n
3
\n
1.25 × 10−2\n
\n
9.37 × 10−3\n
\n
\n
\n
X(4)
\n
3.00 × 10−3\n
\n
3
\n
3.75 × 10−3\n
\n
2.81 × 10−3\n
\n
\n
\n
X(5)
\n
1.00 × 10−2\n
\n
3
\n
1.25 × 10−2\n
\n
9.37 × 10−3\n
\n
\n
\n
X(6)
\n
3.00 × 10−3\n
\n
3
\n
3.75 × 10−3\n
\n
2.81 × 10−3\n
\n
\n
\n
X(7)
\n
1.00 × 10−3\n
\n
3
\n
1.25 × 10−3\n
\n
9.37 × 10−4\n
\n
\n\n
Table 2.
Basic event distribution for a generic top event “system failure” (\n\n\nχ\n50\n\n\n and EF values were taken from Ref. [8]).
Comparison of pdf obtained by method of moments and by the Monte Carlo simulation for the top event of Figure 4.
\n
\n
\n
5.2 Case study 2
\n
The second case study illustrates the application of the method of moments for assessing the uncertainty of a fault tree taken from a probabilistic safety analysis of a nuclear power plant (NPP). The fault tree shown in Figure 6 was constructed using MCSs and basic event distributions provided by El-Shanawany et al. [26]. It represents a fault tree analysis for the top event “nuclear power plant core melt,” taking into account loss of off-site and on-site power systems and failure of core residual heat removal. The basic events A, B, C, D, E, F, G, H, I, J, K, L, and M are related to off-site power system failure, operator errors, emergency diesel generators (EDGs) failures, pump failures, and common cause failures (CCFs). A detailed description of each one of these basic events is given in the caption of Figure 6. An accurate logical analysis of this drawn fault tree can demonstrate that its MCSs are ABC, ABD, ABE, ABF, ABH, ABI, ABJ, AFG, and AKLMH, which describes the illustrative example analyzed in the literature.
\n
Figure 6.
Fault tree analysis for a nuclear power plant core melt.
\n
The log-normal distributions assigned to the basic events (represented by mean values of probabilities, error factors, and standard deviations) are shown in Table 4. Such distributions are also used in Ref. [26], to compare the results of this current work, using the method of moments, with the analyses of uncertainty propagation using Wilks method, Monte Carlo simulation, and Fenton-Wilkinson (FW) method.
\n
\n
\n
\n
\n
\n\n
\n
Basic events
\n
Mean of log-normal pdf (P)\n
\n
Error factor of log-normal pdf (EF)
\n
Standard deviation of log-normal pdf (s)
\n
\n\n\n
\n
A
\n
6.00 × 10−2\n
\n
5
\n
7.60 × 10−2\n
\n
\n
\n
B
\n
6.60 × 10−6\n
\n
5
\n
8.36 × 10−6\n
\n
\n
\n
C
\n
1.00 × 10−2\n
\n
5
\n
1.27 × 10−2\n
\n
\n
\n
D
\n
2.13 × 10−3\n
\n
5
\n
2.70 × 10−3\n
\n
\n
\n
E
\n
8.33 × 10−4\n
\n
5
\n
1.06 × 10−3\n
\n
\n
\n
F
\n
5.20 × 10−5\n
\n
5
\n
6.59 × 10−5\n
\n
\n
\n
G
\n
6.10 × 10−5\n
\n
5
\n
7.73 × 10−5\n
\n
\n
\n
H
\n
4.20 × 10−5\n
\n
5
\n
5.32 × 10−5\n
\n
\n
\n
I
\n
1.58 × 10−3\n
\n
5
\n
2.00 × 10−3\n
\n
\n
\n
J
\n
1.00 × 10−4\n
\n
5
\n
1.27 × 10−4\n
\n
\n
\n
K
\n
9.00 × 10−2\n
\n
5
\n
1.14 × 10−1\n
\n
\n
\n
L
\n
1.00 × 10−1\n
\n
5
\n
1.27 × 10−1\n
\n
\n
\n
M
\n
1.20 × 10−4\n
\n
5
\n
1.52 × 10−4\n
\n
\n\n
Table 4.
Basic event distribution for illustrative example (P and EF values were taken from Ref. [26]).
\n
The application of the method of moments is carried out in a similar way as in the first case study. Considering that the top event is also log-normally distributed, its 5th percentile, median, and 95th percentile are estimated. As can be seen in Table 5, the median values of the method of moments show a good agreement with Wilks method and are 25.8% and 20.4% greater than the results of Monte Carlo simulation and FW method, respectively. This is also illustrated in Figure 7, where the cumulative distribution function obtained by method of moments is compared with the data in the mentioned literature [26]. As can be seen, the results of the method of moments agree reasonably with the Wilks method, being slightly lower, moving toward the analyses of uncertainty propagation using Monte Carlo simulation, which is considered for many purposes to be close to the exact solution for simple models.
Comparison of cumulative distribution function for core melt frequency obtained by the method of moments with data from literature [26].
\n
Overall, uncertainty propagation using the method of moments in fault trees, as shown in the two case studies, or in event trees, is quite simple in small systems and does not require the specification of probability density functions of basic events but only their means and standard deviations. For more complex systems and large fault and event trees, computer implementation of the described bottom-up approach can be performed, for instance, using specialized computer software for obtaining the minimal cut sets and quantitatively assessing the top event probabilities [31], as well as matrix computations for obtaining the standard deviations along the trees, as proposed by Simões Filho [32].
\n
\n
\n
\n
6. Final remarks
\n
This work addresses the uncertainty propagation in fault and event trees in the scope of probabilistic risk assessment (PRA) of industrial facilities. Given the uncertainties of the primary input data (component reliability, system failure probabilities, or human error rates), the method of moments is proposed for the evaluation of the confidence bounds of top event probabilities of fault trees or event sequence frequencies of event trees. These types of analyses are helpful in performing a systematic PRA uncertainty treatment of risks and system reliabilities associated with complex industrial facilities, mainly in risk-based decision-making.
\n
Two illustrative examples using the method of moments for carrying out the uncertainty propagation in fault trees are presented, and their results are compared with available analyses in literature using different uncertainty assessment approaches. The method of moments proved to be conceptually simple to be used. It confirmed findings postulated in literature, when dealing with simple and small systems. More complex systems will require the support of specialized reliability and risk assessment software, in order to implement the proposed approach.
\n
\n
Acknowledgments
\n
The authors would like to thank the following institutions, which sponsored this work: Development Center of Nuclear Technology/Brazilian Nuclear Energy Commission (CDTN/CNEN) and Brazilian Innovation Agency (FINEP).
\n
Conflict of interest
The authors are the only responsible for the printed material included in this paper.
\n',keywords:"accident, fault tree, event tree, nuclear, probabilistic risk assessment, reliability, uncertainty",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/65179.pdf",chapterXML:"https://mts.intechopen.com/source/xml/65179.xml",downloadPdfUrl:"/chapter/pdf-download/65179",previewPdfUrl:"/chapter/pdf-preview/65179",totalDownloads:977,totalViews:0,totalCrossrefCites:1,dateSubmitted:"September 20th 2018",dateReviewed:"December 13th 2018",datePrePublished:"January 16th 2019",datePublished:"July 1st 2020",dateFinished:"January 12th 2019",readingETA:"0",abstract:"Probabilistic risk assessment (PRA), sometimes called probabilistic safety analysis, quantifies the risk of undesired events in industrial facilities. However, one of the weaknesses that undermines the credibility and usefulness of this technique is the uncertainty in PRA results. Fault tree analysis (FTA) and event tree analysis (ETA) are the most important PRA techniques for evaluating system reliabilities and likelihoods of accident scenarios. Uncertainties, as incompleteness and imprecision, are present in probabilities of undesired events and failure rate data. Furthermore, both FTA and ETA traditionally assume that events are independent, assumptions that are often unrealistic and introduce uncertainties in data and modeling when using FTA and ETA. This work explores uncertainty handling approaches for analyzing the fault trees and event trees (method of moments) as a way to overcome the challenges of PRA. Applications of the developed frameworks and approaches are explored in illustrative examples, where the probability distributions of the top event of fault trees are obtained through the propagation of uncertainties of the failure probabilities of basic events. The application of the method of moments to propagate uncertainty of log-normal distributions showed good agreement with results available in the literature using different methods.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/65179",risUrl:"/chapter/ris/65179",signatures:"Vanderley de Vasconcelos, Wellington Antonio Soares, Antônio Carlos Lopes da Costa and Amanda Laureano Raso",book:{id:"7687",type:"book",title:"Reliability and Maintenance",subtitle:"An Overview of Cases",fullTitle:"Reliability and Maintenance - An Overview of Cases",slug:"reliability-and-maintenance-an-overview-of-cases",publishedDate:"July 1st 2020",bookSignature:"Leo Kounis",coverURL:"https://cdn.intechopen.com/books/images_new/7687.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",isbn:"978-1-78923-952-2",printIsbn:"978-1-78923-951-5",pdfIsbn:"978-1-83880-736-8",isAvailableForWebshopOrdering:!0,editors:[{id:"111582",title:"Dr.",name:"Leo",middleName:"Dimitrios",surname:"Kounis",slug:"leo-kounis",fullName:"Leo Kounis"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"159882",title:"Dr.",name:"Wellington A.",middleName:null,surname:"Soares",fullName:"Wellington A. Soares",slug:"wellington-a.-soares",email:"soaresw@cdtn.br",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null},{id:"164740",title:"D.Sc.",name:"Vanderley",middleName:null,surname:"Vasconcelos",fullName:"Vanderley Vasconcelos",slug:"vanderley-vasconcelos",email:"vasconv@cdtn.br",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null},{id:"286702",title:"Dr.",name:"Antônio C. L.",middleName:null,surname:"Costa",fullName:"Antônio C. L. Costa",slug:"antonio-c.-l.-costa",email:"aclc@cdtn.br",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null},{id:"286703",title:"BSc.",name:"Amanda L.",middleName:null,surname:"Raso",fullName:"Amanda L. Raso",slug:"amanda-l.-raso",email:"amandaraso@hotmail.com",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Basics of risk assessment",level:"1"},{id:"sec_2_2",title:"2.1 Probabilistic risk assessment (PRA)",level:"2"},{id:"sec_3_2",title:"2.2 Techniques for PRA",level:"2"},{id:"sec_4_2",title:"2.3 Uncertainty sources in PRA",level:"2"},{id:"sec_6",title:"3. Methods of uncertainty propagation used in PRA",level:"1"},{id:"sec_7",title:"4. Method of moments for uncertainty propagation in FTA and ETA",level:"1"},{id:"sec_7_2",title:"4.1 Method of moments applied to FTA",level:"2"},{id:"sec_8_2",title:"4.2 Method of moments applied to ETA",level:"2"},{id:"sec_9_2",title:"4.3 Propagation of log-normal distributions",level:"2"},{id:"sec_11",title:"5. Illustrative examples",level:"1"},{id:"sec_11_2",title:"5.1 Case study 1",level:"2"},{id:"sec_12_2",title:"5.2 Case study 2",level:"2"},{id:"sec_14",title:"6. Final remarks",level:"1"},{id:"sec_15",title:"Acknowledgments",level:"1"},{id:"sec_18",title:"Conflict of interest",level:"1"}],chapterReferences:[{id:"B1",body:'\nReinert JM, Apostolakis GE. Including model uncertainty in risk-informed decision making. Annals of Nuclear Energy. 2006;33:354-369\n'},{id:"B2",body:'\nU.S. Nuclear Regulatory Commission (USNRC). WASH-1400: Reactor Safety Study (NUREG-75/014). Washington, DC: USNRC; 1975\n'},{id:"B3",body:'\nInnal F, Mourad C, Bourareche M, Antar AS. Treatment of uncertainty in probabilistic risk assessment using Monte Carlo analysis. In: Proceedings of the 3rd International Conference on Systems and Control; 29–31 October 2013. Algiers, Algeria\n'},{id:"B4",body:'\nChristensen FM, Andersen O, Duijm NJ, Harremoës P. Risk terminology: A platform for common understanding and better communication. Journal of Hazardous Materials. 2003;A103:181-203\n'},{id:"B5",body:'\nStamatelatos M. Probabilistic Risk Assessment Procedures Guide for NASA Managers and Practitioners: Version 1.1. Washington, DC: Office of Safety and Mission Assurance, NASA Headquarters; 2002\n'},{id:"B6",body:'\nInternational Standard Organization (ISO). Risk Management: Risk Assessment Techniques (ISO IEC/FDIS 31010). Geneva, Switzerland: ISO/IEC; 2009\n'},{id:"B7",body:'\nU.S. Nuclear Regulatory Commission (USNRC). Fire Dynamics Tools (FDTs): Quantitative Fire Hazard Analysis Methods for the U.S. Nuclear Regulatory Commission Fire Protection Inspection Program (NUREG 1805). Washington, DC: USNRC; 2004\n'},{id:"B8",body:'\nChang SH, Park JY, Kim MK. The Monte-Carlo method without sorting for uncertainty propagation analysis in PRA. Reliability Engineering. 1985;10:233-243\n'},{id:"B9",body:'\nEl-Shanawany AB. Quantification of uncertainty in probabilistic safety analysis [Thesis]. London: Imperial College London, Department of Mechanical Engineering; 2017\n'},{id:"B10",body:'\nGoerlandt F, Reniers G. On the assessment of uncertainty in risk diagrams. Safety Science. 2016;84:67-77\n'},{id:"B11",body:'\nVasconcelos V, Soares WA, Marques RO. Integrated engineering approach to safety, reliability, risk management and human factors. In: Felice F, Petrillo A, editors. Human Factors and Reliability Engineering for Safety and Security in Critical Infrastructures: Decision Making, Theory, and Practice. Cham, Switzerland: Springer International Publishing AG; 2018. pp. 77-107\n'},{id:"B12",body:'\nReliasoft. System Analysis Reference: Reliability, Availability and Optimization. Tucson AZ: ReliaSoft Corporation; 2015\n'},{id:"B13",body:'\nVesely WE, Goldberg FF, Roberts NH, Haasl DF. Fault Tree Handbook (NUREG-0492). Washington, DC: USNRC, Office of Nuclear Regulatory Research; 1981\n'},{id:"B14",body:'\nU.S. Nuclear Regulatory Commission (USNRC), PRA Procedures Guide. A Guide to the Performance of Probabilistic Risk Assessments for Nuclear Power Plants (NUREG/CR-2300). Washington, DC: USNRC; 1983\n'},{id:"B15",body:'\nDurga Rao K, Kushwaha HS, Verma AK, Srividya A. Epistemic uncertainty propagation in reliability assessment of complex systems. International Journal of Performability Engineering. 2007;3(4):71-84\n'},{id:"B16",body:'\nSuresh PV, Babar AK, Venkat Raj V. Uncertainty in fault tree analysis: A fuzzy approach. Fuzzy Sets and Systems. 1996;83:135-141\n'},{id:"B17",body:'\nUlmeanu AP. Analytical method to determine uncertainty propagation in fault trees by means of binary decision diagrams. IEEE Transactions on Reliability. 2012;61(1):84-94\n'},{id:"B18",body:'\nDezfuli H, Modarres M. Uncertainty analysis of reactor safety systems with statistically correlated failure data. Reliability Engineering. 1985;11:47-64\n'},{id:"B19",body:'\nCheng D. Uncertainty analysis of large risk assessment models with applications to the railway safety and standards board safety risk model [Thesis]. Glasgow: University of Strathclyde, Department of Management Science; 2009\n'},{id:"B20",body:'\nRaychaudhuri S. Introduction to Monte Carlo simulation. In: Proceedings of the 2008 Winter Simulation Conference; 7–10 December 2008. Miami, USA\n'},{id:"B21",body:'\nFerdous R, Khan F, Sadiq R, Amyotte P, Veitch B. Fault and event tree analyses for process systems risk analysis: Uncertainty handling formulations. Risk Analysis. 2011;31(1):86-107\n'},{id:"B22",body:'\nLee SW, Chung BD, Bang YS, Bae SW. Analysis of uncertainty quantification method by comparing Monte Carlo method and Wilks’ formula. Nuclear Engineering and Technology. 2014;46(4):481-488\n'},{id:"B23",body:'\nAhmed S, Metcalf DR, Pegram JW. Uncertainty propagation in probabilistic risk assessment: A comparative study. Nuclear Engineering and Design. 1981;68:1-31\n'},{id:"B24",body:'\nRushdi AM, Kafrawy KF. Uncertainty propagation in fault tree analyses using an exact method of moments. Microelectronics and Reliability. 1988;28(6):945-965\n'},{id:"B25",body:'\nFerdous R. Quantitative risk analysis in an uncertain and dynamic environment [Thesis]. Newfoundland, Canada: Faculty of Engineering & Applied Science, Memorial University of Newfoundland; 2011\n'},{id:"B26",body:'\nEl-Shanawany AB, Ardron KH, Walker SP. Lognormal approximations of fault tree uncertainty distributions. Risk Analysis. 2018;38(8):1576-1584\n'},{id:"B27",body:'\nApostolakis G, Lee YT. Methods for the estimation of confidence bounds for the top-event unavailability of fault trees. Nuclear Engineering and Design. 1977;41:411-419\n'},{id:"B28",body:'\nBier VM. Uncertainty analysis as applied to probabilistic risk assessment. In: Covello VT, Lave LB, Moghissi AA, Uppuluri VRR, editors. Uncertainty in Risk Assessment, Risk Management, and Decision Making. New York: Plenum Press; 1987. pp. 469-478\n'},{id:"B29",body:'\nAhn K. On the use of coefficient of variation for uncertainty analysis in fault tree analysis. Reliability Engineering and System Safety. 1995;47:229-230\n'},{id:"B30",body:'\nReliasoft. Life Data Analysis Reference. Tucson, AZ: ReliaSoft Corporation; 2015\n'},{id:"B31",body:'\nMisra KB. Handbook of Performability Engineering. Cham, Switzerland: Springer International Publishing AG; 2008\n'},{id:"B32",body:'\nSimões Filho S. Análise de árvore de falhas considerando incertezas na definição dos eventos básicos [thesis]. Rio de Janeiro, Brazil: Faculdade de Engenharia Civil, Universidade Federal do Rio de Janeiro; 2006\n'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Vanderley de Vasconcelos",address:"vasconv@cdtn.br",affiliation:'
Development Center of Nuclear Technology/Brazilian Nuclear Energy Commission, CDTN/CNEN, Belo Horizonte, Brazil
'},{corresp:null,contributorFullName:"Wellington Antonio Soares",address:null,affiliation:'
Development Center of Nuclear Technology/Brazilian Nuclear Energy Commission, CDTN/CNEN, Belo Horizonte, Brazil
'},{corresp:null,contributorFullName:"Antônio Carlos Lopes da Costa",address:null,affiliation:'
Development Center of Nuclear Technology/Brazilian Nuclear Energy Commission, CDTN/CNEN, Belo Horizonte, Brazil
Development Center of Nuclear Technology/Brazilian Nuclear Energy Commission, CDTN/CNEN, Belo Horizonte, Brazil
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We have created an algorithm that takes into account the significance of the nature of the natural and cultural-historical sites in the landscape and showing and presenting 14 categories of ecological forms of tourism.",signatures:"Zdena Krnacova, Peter Barancok and Katarina Pavlickova",authors:[{id:"213649",title:"Ph.D.",name:"Zdena",surname:"Krnáčová",fullName:"Zdena Krnáčová",slug:"zdena-krnacova",email:"zdena.krnacova@savba.sk"},{id:"229580",title:"Dr.",name:"Peter",surname:"Barančok",fullName:"Peter Barančok",slug:"peter-barancok",email:"peter.barancok@savba.sk"}],book:{id:"6102",title:"Mobilities, Tourism and Travel Behavior",slug:"mobilities-tourism-and-travel-behavior-contexts-and-boundaries",productType:{id:"1",title:"Edited Volume"}}}],collaborators:[{id:"206419",title:"M.A.",name:"Lars",surname:"Rettig",slug:"lars-rettig",fullName:"Lars Rettig",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/206419/images/5903_n.jpg",biography:null,institutionString:null,institution:{name:"West Coast University of Applied Sciences",institutionURL:null,country:{name:"Germany"}}},{id:"206480",title:"Prof.",name:"Mohamed",surname:"Battour",slug:"mohamed-battour",fullName:"Mohamed Battour",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Malaya",institutionURL:null,country:{name:"Malaysia"}}},{id:"207073",title:"Dr.",name:"Mario",surname:"De La Puente Pacheco",slug:"mario-de-la-puente-pacheco",fullName:"Mario De La Puente Pacheco",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/207073/images/5300_n.jpg",biography:"Professor of the Department of Political Science at the Universidad del Norte (Colombia). PhD in International Economics, Universidad Rey Juan Carlos (Spain). Member of the research group International Agenda recognized by the Department of Science, Technology and Information (Colciencias) (Colombia). Research areas: Medical Tourism, Health Tourism, Tourism and Post-conflict. Contact: mdelapuente@uninorte.edu.co.",institutionString:null,institution:{name:"Universidad del Norte",institutionURL:null,country:{name:"Colombia"}}},{id:"207340",title:"Dr.",name:"Gian Luigi",surname:"Corinto",slug:"gian-luigi-corinto",fullName:"Gian Luigi Corinto",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"207365",title:"Prof.",name:"Fabio",surname:"Curzi",slug:"fabio-curzi",fullName:"Fabio Curzi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"207589",title:"Dr.",name:"Enrique",surname:"Marinao",slug:"enrique-marinao",fullName:"Enrique Marinao",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"207813",title:"Prof.",name:"Chin-Cheng",surname:"Ni",slug:"chin-cheng-ni",fullName:"Chin-Cheng Ni",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"National Tsing Hua University",institutionURL:null,country:{name:"Taiwan"}}},{id:"207814",title:"Dr.",name:"Chien-Yu",surname:"Tsao",slug:"chien-yu-tsao",fullName:"Chien-Yu Tsao",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"207820",title:"MSc.",name:"Ying-Hsiang",surname:"Wang",slug:"ying-hsiang-wang",fullName:"Ying-Hsiang Wang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"215226",title:"Dr.",name:"Morten",surname:"Friedrichsen",slug:"morten-friedrichsen",fullName:"Morten Friedrichsen",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null}]},generic:{page:{slug:"our-story",title:"Our story",intro:"
The company was founded in Vienna in 2004 by Alex Lazinica and Vedran Kordic, two PhD students researching robotics. While completing our PhDs, we found it difficult to access the research we needed. So, we decided to create a new Open Access publisher. A better one, where researchers like us could find the information they needed easily. The result is IntechOpen, an Open Access publisher that puts the academic needs of the researchers before the business interests of publishers.
",metaTitle:"Our story",metaDescription:"The company was founded in Vienna in 2004 by Alex Lazinica and Vedran Kordic, two PhD students researching robotics. While completing our PhDs, we found it difficult to access the research we needed. So, we decided to create a new Open Access publisher. A better one, where researchers like us could find the information they needed easily. The result is IntechOpen, an Open Access publisher that puts the academic needs of the researchers before the business interests of publishers.",metaKeywords:null,canonicalURL:"/page/our-story",contentRaw:'[{"type":"htmlEditorComponent","content":"
We started by publishing journals and books from the fields of science we were most familiar with - AI, robotics, manufacturing and operations research. Through our growing network of institutions and authors, we soon expanded into related fields like environmental engineering, nanotechnology, computer science, renewable energy and electrical engineering, Today, we are the world’s largest Open Access publisher of scientific research, with over 4,200 books and 54,000 scientific works including peer-reviewed content from more than 116,000 scientists spanning 161 countries. Our authors range from globally-renowned Nobel Prize winners to up-and-coming researchers at the cutting edge of scientific discovery.
\\n\\n
In the same year that IntechOpen was founded, we launched what was at the time the first ever Open Access, peer-reviewed journal in its field: the International Journal of Advanced Robotic Systems (IJARS).
\\n\\n
The IntechOpen timeline
\\n\\n
2004
\\n\\n
\\n\\t
Intech Open is founded in Vienna, Austria, by Alex Lazinica and Vedran Kordic, two PhD students, and their first Open Access journals and books are published.
\\n\\t
Alex and Vedran launch the first Open Access, peer-reviewed robotics journal and IntechOpen’s flagship publication, the International Journal of Advanced Robotic Systems (IJARS).
\\n
\\n\\n
2005
\\n\\n
\\n\\t
IntechOpen publishes its first Open Access book: Cutting Edge Robotics.
\\n
\\n\\n
2006
\\n\\n
\\n\\t
IntechOpen publishes a special issue of IJARS, featuring contributions from NASA scientists regarding the Mars Exploration Rover missions.
\\n
\\n\\n
2008
\\n\\n
\\n\\t
Downloads milestone: 200,000 downloads reached
\\n
\\n\\n
2009
\\n\\n
\\n\\t
Publishing milestone: the first 100 Open Access STM books are published
\\n
\\n\\n
2010
\\n\\n
\\n\\t
Downloads milestone: one million downloads reached
\\n\\t
IntechOpen expands its book publishing into a new field: medicine.
\\n
\\n\\n
2011
\\n\\n
\\n\\t
Publishing milestone: More than five million downloads reached
\\n\\t
IntechOpen publishes 1996 Nobel Prize in Chemistry winner Harold W. Kroto’s “Strategies to Successfully Cross-Link Carbon Nanotubes”. Find it here.
\\n\\t
IntechOpen and TBI collaborate on a project to explore the changing needs of researchers and the evolving ways that they discover, publish and exchange information. The result is the survey “Author Attitudes Towards Open Access Publishing: A Market Research Program”.
\\n\\t
IntechOpen hosts SHOW - Share Open Access Worldwide; a series of lectures, debates, round-tables and events to bring people together in discussion of open source principles, intellectual property, content licensing innovations, remixed and shared culture and free knowledge.
\\n
\\n\\n
2012
\\n\\n
\\n\\t
Publishing milestone: 10 million downloads reached
\\n\\t
IntechOpen holds Interact2012, a free series of workshops held by figureheads of the scientific community including Professor Hiroshi Ishiguro, director of the Intelligent Robotics Laboratory, who took the audience through some of the most impressive human-robot interactions observed in his lab.
\\n
\\n\\n
2013
\\n\\n
\\n\\t
IntechOpen joins the Committee on Publication Ethics (COPE) as part of a commitment to guaranteeing the highest standards of publishing.
\\n
\\n\\n
2014
\\n\\n
\\n\\t
IntechOpen turns 10, with more than 30 million downloads to date.
\\n\\t
IntechOpen appoints its first Regional Representatives - members of the team situated around the world dedicated to increasing the visibility of our authors’ published work within their local scientific communities.
\\n
\\n\\n
2015
\\n\\n
\\n\\t
Downloads milestone: More than 70 million downloads reached, more than doubling since the previous year.
\\n\\t
Publishing milestone: IntechOpen publishes its 2,500th book and 40,000th Open Access chapter, reaching 20,000 citations in Thomson Reuters ISI Web of Science.
\\n\\t
40 IntechOpen authors are included in the top one per cent of the world’s most-cited researchers.
\\n\\t
Thomson Reuters’ ISI Web of Science Book Citation Index begins indexing IntechOpen’s books in its database.
\\n
\\n\\n
2016
\\n\\n
\\n\\t
IntechOpen is identified as a world leader in Simba Information’s Open Access Book Publishing 2016-2020 report and forecast. IntechOpen came in as the world’s largest Open Access book publisher by title count.
\\n
\\n\\n
2017
\\n\\n
\\n\\t
Downloads milestone: IntechOpen reaches more than 100 million downloads
\\n\\t
Publishing milestone: IntechOpen publishes its 3,000th Open Access book, making it the largest Open Access book collection in the world
We started by publishing journals and books from the fields of science we were most familiar with - AI, robotics, manufacturing and operations research. Through our growing network of institutions and authors, we soon expanded into related fields like environmental engineering, nanotechnology, computer science, renewable energy and electrical engineering, Today, we are the world’s largest Open Access publisher of scientific research, with over 4,200 books and 54,000 scientific works including peer-reviewed content from more than 116,000 scientists spanning 161 countries. Our authors range from globally-renowned Nobel Prize winners to up-and-coming researchers at the cutting edge of scientific discovery.
\n\n
In the same year that IntechOpen was founded, we launched what was at the time the first ever Open Access, peer-reviewed journal in its field: the International Journal of Advanced Robotic Systems (IJARS).
\n\n
The IntechOpen timeline
\n\n
2004
\n\n
\n\t
Intech Open is founded in Vienna, Austria, by Alex Lazinica and Vedran Kordic, two PhD students, and their first Open Access journals and books are published.
\n\t
Alex and Vedran launch the first Open Access, peer-reviewed robotics journal and IntechOpen’s flagship publication, the International Journal of Advanced Robotic Systems (IJARS).
\n
\n\n
2005
\n\n
\n\t
IntechOpen publishes its first Open Access book: Cutting Edge Robotics.
\n
\n\n
2006
\n\n
\n\t
IntechOpen publishes a special issue of IJARS, featuring contributions from NASA scientists regarding the Mars Exploration Rover missions.
\n
\n\n
2008
\n\n
\n\t
Downloads milestone: 200,000 downloads reached
\n
\n\n
2009
\n\n
\n\t
Publishing milestone: the first 100 Open Access STM books are published
\n
\n\n
2010
\n\n
\n\t
Downloads milestone: one million downloads reached
\n\t
IntechOpen expands its book publishing into a new field: medicine.
\n
\n\n
2011
\n\n
\n\t
Publishing milestone: More than five million downloads reached
\n\t
IntechOpen publishes 1996 Nobel Prize in Chemistry winner Harold W. Kroto’s “Strategies to Successfully Cross-Link Carbon Nanotubes”. Find it here.
\n\t
IntechOpen and TBI collaborate on a project to explore the changing needs of researchers and the evolving ways that they discover, publish and exchange information. The result is the survey “Author Attitudes Towards Open Access Publishing: A Market Research Program”.
\n\t
IntechOpen hosts SHOW - Share Open Access Worldwide; a series of lectures, debates, round-tables and events to bring people together in discussion of open source principles, intellectual property, content licensing innovations, remixed and shared culture and free knowledge.
\n
\n\n
2012
\n\n
\n\t
Publishing milestone: 10 million downloads reached
\n\t
IntechOpen holds Interact2012, a free series of workshops held by figureheads of the scientific community including Professor Hiroshi Ishiguro, director of the Intelligent Robotics Laboratory, who took the audience through some of the most impressive human-robot interactions observed in his lab.
\n
\n\n
2013
\n\n
\n\t
IntechOpen joins the Committee on Publication Ethics (COPE) as part of a commitment to guaranteeing the highest standards of publishing.
\n
\n\n
2014
\n\n
\n\t
IntechOpen turns 10, with more than 30 million downloads to date.
\n\t
IntechOpen appoints its first Regional Representatives - members of the team situated around the world dedicated to increasing the visibility of our authors’ published work within their local scientific communities.
\n
\n\n
2015
\n\n
\n\t
Downloads milestone: More than 70 million downloads reached, more than doubling since the previous year.
\n\t
Publishing milestone: IntechOpen publishes its 2,500th book and 40,000th Open Access chapter, reaching 20,000 citations in Thomson Reuters ISI Web of Science.
\n\t
40 IntechOpen authors are included in the top one per cent of the world’s most-cited researchers.
\n\t
Thomson Reuters’ ISI Web of Science Book Citation Index begins indexing IntechOpen’s books in its database.
\n
\n\n
2016
\n\n
\n\t
IntechOpen is identified as a world leader in Simba Information’s Open Access Book Publishing 2016-2020 report and forecast. IntechOpen came in as the world’s largest Open Access book publisher by title count.
\n
\n\n
2017
\n\n
\n\t
Downloads milestone: IntechOpen reaches more than 100 million downloads
\n\t
Publishing milestone: IntechOpen publishes its 3,000th Open Access book, making it the largest Open Access book collection in the world
\n
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That is exactly what he does, diving into Machine Learning algorithms and technologies to help TECNALIA to decide whether something is great in theory or will actually impact on the product or processes of its projects. So, he is expert at framing experiments, developing hypotheses, and proving whether they’re true or not, in order to investigate fundamental problems with a longer time horizon. He is also able to design and develop PoCs and system prototypes in simulation. He has participated in several national and internacional R&D projects.\n\nAs another relevant part of his everyday research work, he usually publishes his findings in reputed scientific refereed journals and international conferences, occasionally acting as reviewer and Programme Commitee member. Concretely, since 2018 he has published 9 JCR (8 Q1) journal papers, 9 conference papers (e.g. ECML PKDD 2021), and he has co-edited a book. He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:"Polytechnic University of Timişoara",institution:{name:"Polytechnic University of Timişoara",country:{name:"Romania"}}},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:null},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. Science CV available at: https://www.cienciavitae.pt//pt/8E1C-A8B3-78C5 and ORCID: https://orcid.org/0000-0002-0298-3974',institutionString:"University of Lisbon",institution:{name:"Universidade Lusófona",country:{name:"Portugal"}}},{id:"241400",title:"Prof.",name:"Mohammed",middleName:null,surname:"Bsiss",slug:"mohammed-bsiss",fullName:"Mohammed Bsiss",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241400/images/8062_n.jpg",biography:null,institutionString:null,institution:null},{id:"276128",title:"Dr.",name:"Hira",middleName:null,surname:"Fatima",slug:"hira-fatima",fullName:"Hira Fatima",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/276128/images/14420_n.jpg",biography:"Dr. Hira Fatima\nAssistant Professor\nDepartment of Mathematics\nInstitute of Applied Science\nMangalayatan University, Aligarh\nMobile: no : 8532041179\nhirafatima2014@gmal.com\n\nDr. Hira Fatima has received his Ph.D. degree in pure Mathematics from Aligarh Muslim University, Aligarh India. Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. She is a member of Indian Mathematical Society.",institutionString:null,institution:null},{id:"414880",title:"Dr.",name:"Maryam",middleName:null,surname:"Vatankhah",slug:"maryam-vatankhah",fullName:"Maryam Vatankhah",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Borough of Manhattan Community College",country:{name:"United States of America"}}},{id:"414879",title:"Prof.",name:"Mohammad-Reza",middleName:null,surname:"Akbarzadeh-Totonchi",slug:"mohammad-reza-akbarzadeh-totonchi",fullName:"Mohammad-Reza Akbarzadeh-Totonchi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Ferdowsi University of Mashhad",country:{name:"Iran"}}},{id:"414878",title:"Prof.",name:"Reza",middleName:null,surname:"Fazel-Rezai",slug:"reza-fazel-rezai",fullName:"Reza Fazel-Rezai",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"American Public University System",country:{name:"United States of America"}}},{id:"302698",title:"Dr.",name:"Yao",middleName:null,surname:"Shan",slug:"yao-shan",fullName:"Yao Shan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Dalian University of Technology",country:{name:"China"}}},{id:"125911",title:"Prof.",name:"Jia-Ching",middleName:null,surname:"Wang",slug:"jia-ching-wang",fullName:"Jia-Ching Wang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"National Central University",country:{name:"Taiwan"}}},{id:"357085",title:"Mr.",name:"P. Mohan",middleName:null,surname:"Anand",slug:"p.-mohan-anand",fullName:"P. Mohan Anand",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356696",title:"Ph.D. Student",name:"P.V.",middleName:null,surname:"Sai Charan",slug:"p.v.-sai-charan",fullName:"P.V. Sai Charan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"357086",title:"Prof.",name:"Sandeep K.",middleName:null,surname:"Shukla",slug:"sandeep-k.-shukla",fullName:"Sandeep K. Shukla",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356823",title:"MSc.",name:"Seonghee",middleName:null,surname:"Min",slug:"seonghee-min",fullName:"Seonghee Min",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Daegu University",country:{name:"Korea, South"}}},{id:"353307",title:"Prof.",name:"Yoosoo",middleName:null,surname:"Oh",slug:"yoosoo-oh",fullName:"Yoosoo Oh",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:"Yoosoo Oh received his Bachelor's degree in the Department of Electronics and Engineering from Kyungpook National University in 2002. He obtained his Master’s degree in the Department of Information and Communications from Gwangju Institute of Science and Technology (GIST) in 2003. In 2010, he received his Ph.D. degree in the School of Information and Mechatronics from GIST. In the meantime, he was an executed team leader at Culture Technology Institute, GIST, 2010-2012. In 2011, he worked at Lancaster University, the UK as a visiting scholar. In September 2012, he joined Daegu University, where he is currently an associate professor in the School of ICT Conver, Daegu University. Also, he served as the Board of Directors of KSIIS since 2019, and HCI Korea since 2016. From 2017~2019, he worked as a center director of the Mixed Reality Convergence Research Center at Daegu University. From 2015-2017, He worked as a director in the Enterprise Supporting Office of LINC Project Group, Daegu University. His research interests include Activity Fusion & Reasoning, Machine Learning, Context-aware Middleware, Human-Computer Interaction, etc.",institutionString:null,institution:{name:"Daegu Gyeongbuk Institute of Science and Technology",country:{name:"Korea, South"}}},{id:"262719",title:"Dr.",name:"Esma",middleName:null,surname:"Ergüner Özkoç",slug:"esma-erguner-ozkoc",fullName:"Esma Ergüner Özkoç",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Başkent University",country:{name:"Turkey"}}},{id:"346530",title:"Dr.",name:"Ibrahim",middleName:null,surname:"Kaya",slug:"ibrahim-kaya",fullName:"Ibrahim Kaya",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"419199",title:"Dr.",name:"Qun",middleName:null,surname:"Yang",slug:"qun-yang",fullName:"Qun Yang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Auckland",country:{name:"New Zealand"}}},{id:"351158",title:"Prof.",name:"David W.",middleName:null,surname:"Anderson",slug:"david-w.-anderson",fullName:"David W. Anderson",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Calgary",country:{name:"Canada"}}}]}},subseries:{item:{id:"95",type:"subseries",title:"Urban Planning and Environmental Management",keywords:"Circular economy, Contingency planning and response to disasters, Ecosystem services, Integrated urban water management, Nature-based solutions, Sustainable urban development, Urban green spaces",scope:"
\r\n\tIf we aim to prosper as a society and as a species, there is no alternative to sustainability-oriented development and growth. Sustainable development is no longer a choice but a necessity for us all. Ecosystems and preserving ecosystem services and inclusive urban development present promising solutions to environmental problems. Contextually, the emphasis on studying these fields will enable us to identify and define the critical factors for territorial success in the upcoming decades to be considered by the main-actors, decision and policy makers, technicians, and public in general.
\r\n
\r\n\tHolistic urban planning and environmental management are therefore crucial spheres that will define sustainable trajectories for our urbanizing planet. This urban and environmental planning topic aims to attract contributions that address sustainable urban development challenges and solutions, including integrated urban water management, planning for the urban circular economy, monitoring of risks, contingency planning and response to disasters, among several other challenges and solutions.
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Since 2015 he heads the research department Sanitation, Water and Solid Waste for Development (Sandec) at the Swiss Federal Institute of Aquatic Research and Technology (Eawag).",institutionString:"Swiss Federal Institute of Aquatic Science and Technology, Switzerland",institution:null},editorTwo:{id:"290571",title:"Dr.",name:"Rui Alexandre",middleName:null,surname:"Castanho",slug:"rui-alexandre-castanho",fullName:"Rui Alexandre Castanho",profilePictureURL:"https://mts.intechopen.com/storage/users/290571/images/system/290571.jpg",biography:"Rui Alexandre Castanho has a master\\'s degree in Planning, Audit, and Control in Urban Green Spaces and an international Ph.D. in Sustainable Planning in Borderlands. Currently, he is a professor at WSB University, Poland, and a visiting professor at the University of Johannesburg, South Africa. Dr. Castanho is a post-doc researcher on the GREAT Project, University of Azores, Ponta Delgada, Portugal. He collaborates with the Environmental Resources Analysis Research Group (ARAM), University of Extremadura (UEx), Spain; VALORIZA - Research Center for the Enhancement of Endogenous Resources, Polytechnic Institute of Portalegre (IPP), Portugal; Centre for Tourism Research, Development and Innovation (CITUR), Madeira, Portugal; and AQUAGEO Research Group, University of Campinas (UNICAMP), Brazil.",institutionString:"University of Johannesburg, South Africa and WSB University, Poland",institution:{name:"University of Johannesburg",institutionURL:null,country:{name:"South Africa"}}},editorThree:null,series:{id:"24",title:"Sustainable Development",doi:"10.5772/intechopen.100361",issn:null},editorialBoard:[{id:"181486",title:"Dr.",name:"Claudia",middleName:null,surname:"Trillo",slug:"claudia-trillo",fullName:"Claudia 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