Maintenance data categories (based on ([14], Table 8)).
\\n\\n
\\n"}]',published:!0,mainMedia:{caption:"Milestone",originalUrl:"/media/original/124"}},components:[{type:"htmlEditorComponent",content:'
Barely three months into the new year and we are happy to announce a monumental milestone reached - 150 million downloads.
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\n\nWe are so proud to have worked with so many bright minds throughout the years who have helped us spread knowledge through the power of Open Access and we look forward to continuing to support some of the greatest thinkers of our day.
\n\nThank you for making IntechOpen your place of learning, sharing, and discovery, and here’s to 150 million more!
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Equipment reliability and maintenance (RM) data are widely collected in the oil and gas industry and are needed for predictive analysis, e.g., for oil and gas production systems, to achieve safe and cost-efficient solutions. An important benefit of such activity is optimized maintenance [1], for example, when finding the optimal inspection intervals for pipelines (see e.g., [2–4]) or when deciding upon the appropriate testing modes for safety instrumented systems (see e.g., [5]).
\nIn the current chapter, we focus on the maintenance part of the RM data collection and mainly on the measures linked to maintenance activities associated with failed items, i.e., equipment with downtime. Thus, attention in this chapter is primarily on the time interval from when a failure of a reparable item occurs to the time when it is back in an upstate.
\nUpdated information about equipment downtime is important and relevant for various types of analysis within the oil and gas industry to inform decision-making. In particular, the measure “mean time to repair,” commonly abbreviated as “MTTR” is widely used within this industry for the purpose of reliability, availability and maintainability analysis (see e.g., [6–9]). It is also widely used in, for example, design planning (e.g., [10, 11]) and also in relation to safety integrity level verification for safety instrumented systems (e.g., [5, 12, 13]).
\nHowever, there are several obstacles related to the collection of such data. One of the challenges is simply to get sufficiently high quality in the data for downtime predictions, for example, for assessment of the expected repairing time, i.e., the “MTTR.” The problem is typically twofold; the relevant population is too small (e.g., technology is changing, and old data, which has taken years to collect, may not be relevant anymore), and the taxonomy used for the data collection may not be sufficiently clear, which may give room for interpreting information differently.
\nThis is the reason why the international standard ISO 14224 [14] is such an important document. The document is partly a result of industry field feedback implemented for more than 25 years within the OREDA project (see [1, 15]), which has led to the ISO 14224 standard [16]. It represents a main guidance document for collection of RM data within the oil and gas industry, applicable in par with the dependability standard IEC 60300-3-2 [17], and outlines principles and taxonomy for how to achieve quality data. It is a way of ensuring data quality, e.g., through a consistent taxonomy being used (see e.g., [18, 19]).
\nA much-ignored issue when collecting downtime data is the difficulty to measure the time to detect the failure, i.e., the exact time of failure. This time is normally referred to as the “detection time” (TD) (see e.g., [14]). It is a key value needed, for example, when assessing the expected time to achieve the repair of the failed item, i.e., the so-called mean time to repair, widely referred to by the abbreviation “MTTR.” The main problem is that, in situations where failures are hidden and these are not evident before some demand occurs, it is most difficult for a data collector to assess or specify the exact time of the failures. Besides, it may not be possible to confirm whether in fact these values are true or not. Often, rather one attempts to ignore the issue by claiming that the precision of this value is negligible, as TD ≪ MTTR, or MTTR ≪ MTTF, where MTTF is the common abbreviation for the “mean time to failure.” In other words, the numeric value of TD is assumed to be of low importance to the predictive analysis.
\nIn addition, when using the data on repair times from a database, one normally mixes together situations where the failure is detected at once (being in continuous mode) and those situations where the failure is hidden (or dormant) for a while until a demand occurs (e.g., revealed from a functional test of the equipment). Consequently, to guide for more consistent data, the newly issued [14] has tried to limit the use of MTTR, despite the strong position and frequent use of this measure in the oil and gas industry.
\nSimilarly, the time to mobilize or carry out the repair may also be subject to uncertainty, depending on how the data collector interprets these measures. Having a consistent interpretation of the key terms used to predict downtime is crucial in order to obtain high-quality maintenance data.
\nAn objective of this chapter is to critically examine key terms and associated measures used in maintenance data collection, by studying the repair time taxonomy defined in Ref. [14], i.e., the different terms used in data collection to define downtime in relation to failed items (see Section 2).
\nThe remaining part of the chapter is structured as follows. Section 2 gives a brief description and definitions of key measures, mostly based on the terminology in [14, 20], such as the MTTR. Section 3 provides two example cases illustrating the effects of using different interpretations of the MTTR. Section 4 links data collection to decision-making and presents findings from data collection experiences identified from published literature, addressing several challenging issues that could compromise the maintenance data quality and use. Then Section 5 provides a discussion on to what extent the industry is in line with ISO 14224 taxonomy and how to handle the issues identified. In Section 6 we give some conclusions.
\nAs indicated in the previous section, ISO 14224 [14] gives guidance both on how to collect and how to analyze downtime data and is a document strongly linked to ISO/TR 12489 [21] on reliability modeling and also the two dependability documents [17, 20].
\nThe recently issued [14] specifies four different maintenance data categories; see Table 1, where details are given on what is the minimum data that must be collected. For example, any corrective maintenance action shall be paired with the associated failure event (i.e., failure record). Downtime data is also labeled as minimum, meaning that the data collector must specify the total length of the downtime interval.
\nData category | \nExamples | \nMinimum data | \n
---|---|---|
Identification | \nEquipment tag number, failure reference | \nUnique maintenance identification, equipment identification/location, failure record | \n
Maintenance data | \nMaintenance category = preventive or corrective | \nDate of maintenance, maintenance category | \n
Maintenance resources | \nType of main resource(s) and number of days used, e.g., drilling rig, diving vessel, service vessel | \n(No data specified as minimum) | \n
Maintenance times | \nTime duration for active maintenance work being done on the equipment | \nActive maintenance time, downtime | \n
Maintenance data categories (based on ([14], Table 8)).
It is common to split the downtime interval in three main parts, i.e., the active repair time, and the activities before and after (such as waiting, delay, start-up). Obtaining accurate information regarding these down time segments is challenging. One such example is already mentioned, i.e., the detection time.
\nAnother example is the “active repair time,” which could easily be confused with the “active maintenance time.“ [14] distinguishes between similar terms such as the “active maintenance time,” “active repair time” and “overall repairing time” and uses these when addressing measures, i.e., expected values, such as the “mean active repair time,” “mean overall repairing time,” “mean time to restoration” and “mean time to repair.” We will further address and discuss the meaning of these terms and measures in the following sections.
\nThe mobilization time is normally a main part of the repair preparations. It includes mobilization of all types of resources required, such as vessels, personnel and ROVs. It includes all activities carried out to get the necessary resources available to execute the active repair of the failed items.
\nIn Ref. [14], there is also a relevant note to the entry associated to the definition, which states that “time spent before starting the maintenance is dependent on access to resources, e.g., spare parts, tools, personnel, subsea intervention and support vessels.” The mobilization time is therefore sometimes difficult to distinguish from delays caused by manufacturing time and transportation.
\nIn practical terms, the mobilization of intervention vessels is often described using the term “opportunity maintenance,” meaning that the intervention vessel is on site or called for in relation to other activities. For example, the vessel could already be on site when the failure is detected, or some critical failure is somehow making the mobilization more urgent and prioritized. To deal with such situations, it is important to have clear procedures for how to collect the mobilization data. Typically, the mobilization time TM is specified as TM = 0, if other items are mainly responsible for the vessel order. However, for analysis purposes, it is important to be aware of which maintenance activities are included in the order and which are not.
\nInformation about an intervention vessel can also be found in long-term schedules, where the time when the vessel is on site largely depends on the planned route. For example, the vessel could plan for 30 days at a “Site A,” then 30 days at a “Site B” (which could then be operated by a different company), then at “Site C,” etc. Mobilization will then depend on both whether the item is critical for production and safety and whether the intervention vessel by chance is at the site or soon is coming to this site.
\nFurthermore, it is not straightforward how to cope with the issue of multiple maintenance and mobilization activities. Often, several maintenance calls are issued, and the allocation of a maintenance vessel needs to be cost-efficient. Thus, one may find that there is a need to remobilize the vessel, as it was not able to complete the maintenance actions on site before moving to the next location.
\nThe active maintenance time is, as defined in Ref. [20], “Duration of the maintenance action, excluding logistic delays.” Per the definition, other delays such as technical delays are thus included in the active maintenance time. The measure is often referred to as “the effective time to repair” (see e.g., [12]). This is regardless of whether the repair is performed in one go.
\nHowever, there is nothing stating that the maintenance activity must be completed during the downtime. Hence, parts of the maintenance, or in some situations (and especially for preventive maintenance) most of the maintenance, relate to an upstate of the equipment. As mentioned, several separate activities may be performed before the maintenance is completed. For example, one may try different options and run checks on whether the performance is satisfying, or one might have to wait for equipment parts to arrive, etc. However, the time used to run-down or start-up the equipment is considered as part of the uptime and not the downtime.
\nIn practice, the active maintenance time could include various testing of the equipment, to check its condition. Depending on the urgency of getting the item back into operation, the duration of this activity might be significant. This also relates to, e.g., wells that are alternating in production, which usually makes the maintenance activity less urgent and allows for more “experimentation.” The most cost-efficient solution is then perhaps not the most time-efficient one. For example, one could opt to use time-demanding repairing tools with lower cost and lower efficiency.
\nThe active maintenance time is, by including technical delay, interpreted in Ref. [14] as, “the calendar time during which maintenance work on the item is actually performed.” Although normally not the case, it is possible for the active maintenance time interval to be larger than the total downtime. This would be true in situations where the equipment is running in operation while the maintenance activity is ongoing.
\nThe effective time needed to achieve the repair of an item is called the “active repair time” (see [14]) and is a key part of the downtime as shown in Table 2, in Phase No. 3. It consists of, per the IEC 60050-192 [20], three possible activities, i.e., the fault localization time, the fault correction time and the function checkout time. See also ([21], Figure 5), which compares repair time taxonomies provided in [20–22] (currently also available in the International Association of Drilling Contractors (IADC) Lexicon definition of “mean active repair time” [23].
\nPhase No. | \nDescription | \nState of equipment | \n
---|---|---|
1 | \nTime of failure and then run-down | \nUptime | \n
2 | \nPreparations and delays | \nDowntime | \n
3 | \nActive repair time | \nDowntime | \n
4 | \nWaiting and delays | \nDowntime | \n
5 | \nStart-up | \nUptime | \n
Equipment state categorization.
The active repair time is different from the active maintenance time, as the preparations and delays are not included (Phase No. 2 and No. 4). But, it is not necessarily the same as the number of man-hours needed to achieve the repair. The number of man-hours also relates to the amount of personnel working with the repair and is thus not directly linked to the active repair time. Similarly, the time of resource use may only capture a part of, and give a misleading picture of, the overall active repair time. For example, an ROV is often used for subsea maintenance operations, but the use of the actual ROV time may be split on several simultaneous maintenance operations. There may be several other ROV activities carried out, while the ROV is subsea.
\nThe expectation of the active repair time is a relevant key performance indicator (KPI) for downtime, i.e., the mean active repair time (MART). It is listed in ([14], Annex E), to have purpose and value through “indication of the productivity and work content of repair activities.” It is noted that if one is also interested in the preparation and delay times, then the mean downtime (MDT), comprising Phase Nos. 2–4, is a relevant KPI or measure.
\nISO/TR 12489:2013 defines the mean overall repairing time (MRT) as the “expected time to achieve the following actions:
\nThe time spent before starting the repair; and,
The effective time to repair; and,
The time before the item is made available to be put back into operation.”
[12] gives the same understanding of the elements included but instead refers to MRT as the “mean repair time,” which although somewhat similar introduces a variation of the term that is found in practice but which is distinct from the “mean time to repair” (see Section 2.6).
\nBy including the time spent before starting the repair and the time to prepare the item for operation, the measure is synonymous with the MDT when fault detection time is equal to zero. The MDT is simply the expectation of the downtime, i.e., the mean time the equipment is not in a standby or operating mode (upstate).
\nDetection time is the period from when a fault occurs to the time when this is detected, where fault in this context refers to the equipment being unable to perform as required due to some internal state, in line with the definition in Ref. [20]. The term “fault detection time” is sometimes used for more specificity, as, e.g., in Ref. [21]. However, this term is not the same as the “fault localization time,” i.e., the time taken to complete fault localization, although the two terms appear similar. Fault localization takes place after the fault is detected and during the period of corrective maintenance action. Fault localization often includes the activity of diagnosing at what time the fault occurred (see e.g., [24]).
\nFault detection may be achieved through manual or automatic operations, depending on modes of operation and system characteristics. Faults of safety systems with possible long detection time, e.g., revealed through functional testing, often make it challenging to identify the exact detection time. Assumptions and estimates are then normally made based on the testing intervals.
\nThe expectation of the fault detection time is called the “mean fault detection time” and is abbreviated as the “MFDT” (see e.g., [21]). For immediately revealed failures, the value of MFDT is equal to zero and negligible for failures with short detection time. Otherwise, this value strongly depends on the test policy for the equipment. For hidden failures, the detection time may represent the main part of the downtime.
\nSometimes, for assessment of reliability and maintenance performance, the abbreviation “MFDT” is also used for the ”mean fractional dead time.” This term has a completely different meaning, i.e., a measure for the average unavailability expressing the expected fraction of time in a nonfunctional condition (refer to use in, e.g., ([25], p. 428, [26])). Obviously, the two terms should not be mixed.
\nRestoration time (or time to restoration; see [14]) includes also the fault detection time in addition to the elements comprised by the overall repairing time (see above). The expectation of this value, i.e., the mean time to restoration (MTTRes), thus includes the full picture of:
\nFault detection time.
Preparation and delays (administrative, logistic and technical delays).
Active repair time.
Delays after the item is repaired (mainly administrative).
The variation in the meaning of MTTR as the ‘mean time to restoration’ versus the ‘mean time to repair’ makes it unclear whether all the four elements are captured, i.e. the full picture above. It is often challenging to separate what definition is used in practice by only looking at the abbreviation “MTTR.” The change of the meaning of MTTR in 1999 (see [27]) has the engineering population still divided between those using the present time definition, ”mean time to restoration” and those keeping with the old definition, “mean time to repair” and a reluctance to change [28].
\n[14] also defines the mean time to repair (MTTR) as “expected time to achieve repair of a failed item.” The problem with this definition is the fault detection time, which is either zero when the failure is revealed immediately, or it is unknown. If it is possible to include the detection time, the MTTR is equal to the MTTRes; otherwise MRT is considered a more appropriate measure. When using the MTTR, the meaning could be all of the three measures above, as illustrated in Figure 1, depending on the length of fault detection, active repair time and preparations and delays, which is causing unnecessary confusion to data collection and analysis. [21] therefore avoids the use of the term MTTR.
\nDifferent measures that could represent the mean time to repair (MTTR).
The length of downtime and associated measures are important in computations of availability, where availability is often estimated (e.g., by Monte Carlo simulations) by the use of terms such as the MTTR and MTTF. This is the case when calculating the intrinsic (or inherent) availability for some component (AI), where one considers the corrective maintenance downtime of the system:
\nwhere MTTF is the mean time to failure. However, as already mentioned [14] notes that it is more appropriate to refer to the active maintenance time observed in the field, i.e., the MTTRes, which represents a more meaningful term compared with, e.g., the MTTR. The formula should therefore be instead.
\nThe MTTRes here is not the same as the mean downtime, MDT, although the two measures may have the same value. Replacing MDT with MTTRes, and MTTF with mean uptime (MUT), would give the operational availability instead of the AI and is perhaps a more relevant measure from a maintenance perspective. Both are used to express proportion of time that the equipment or system is in an upstate, but the MDT is generally not considered an intrinsic property. The duration of MDT could in practice include a variety of delays (e.g., detection, isolation, spare parts, standby, repair duration, reinstatement, etc.; see ([14], Annex C)). The mean up- and downtime are measures that depend strongly on the system performance, i.e., reliability, maintainability and maintenance support, and therefore dependent on the context in which they are used. The MTTRes, by focusing on the maintenance resources and disregarding external resources, thus allows for a more intrinsic analysis.
\nTo illustrate the effects of different interpretations of maintenance times, two example cases are provided that illustrate the challenges related to situations where failures are detected with short-time versus long-time intervals. In the first case, we use a data set for subsea control modules (SCMs) obtained from the OREDA database. In the second case, we have constructed a data set for downhole safety valves (DHSVs) based on reliability data collected annually by the Petroleum Safety Authority Norway (PSA) to analyze and map the risk level on the Norwegian Continental Shelf, the so-called RNNP project (see e.g., [29]). Both data sets have been randomized and anonymized for confidentially reasons and are thus solely for illustration purposes.
\nThe data set consists of 375 SCMs, with a total time in service of 1.44 × 107 hour. During this period, a total of 255 failures have occurred (counting any type of failure severity, i.e., critical, degraded or incipient). An estimator for the mean time to failure, MTTF, is thus given as 1.44 × 107/255 = 56,471 hour. We now consider the implications of varying definitions of the repair time, in this context referred to simply as MTTR. Let us assume that there is uncertainty on all of the components that together form the MTTRes, i.e., fault detection time, administrative delay prior to repair, logistic delay, technical delay, active repair time and administrative delay post repair. For simplicity, we will assume that each of these parameters is represented by a triangular distribution denoted T1:
\nT1 (1; 8; 20) hours, i.e., minimum = 1 hour, peak value = 8 hour and maximum value = 20 hour.
All of the components of the MTTRes are assumed to be independent of each other.
This distribution is not based on any real data set, but delays and repair times on an SCM could realistically be at least 20 hour.
\nRecalling from the previous chapter we have:
\nMART = active repair time (fault localization + correction + function checkout).
MRT (overall repair time) = MART + administrative delay prior to repair + logistical delay + technical delay + administrative delay post repair.
MTTRes (time to restoration) = MRT + fault detection time.
Thus, we can establish three definitions for the intrinsic (inherent, technical) system availability, A:
\nFor now, we assume that the MTTF is deterministic, and thus all uncertainty is placed on the MTTR component. Running a Monte Carlo simulation using N = 10,000 gives the distributions for A1, A2 and A3 shown in Figure 2.
\nIntrinsic availability distribution for A1, (left) using MTTR = MTTRes. Mean = 99.915% and stdev = 1.5 × 10−4. A2, (center) using MTTR = MRT. Mean = 99.932% and stdev = 1.4 × 10−4. A3, (right) using MTTR = MART. Mean = 99.983% and stdev = 7.0 × 10−5.
As Figure 2 shows, widening the definition of repair time to include delays and fault detection will lower the system availability and increase the standard deviation, as there is obviously more uncertainty. The deviations between the three definitions of repair time are not significant in this particular case, as the MTTF is relatively much larger. Consider however Figure 3, where a sensitivity analysis is run for the MTTF, showing the expected absolute and relative difference between A1 and A3 (blue line) and A2 and A3 (red line), where A3 is a main element of both A2 and A3, i.e., the differences thus indicating the contribution of detection time. As the MTTF becomes lower, the significance in the varying interpretation of repair time becomes as expected greater.
\nExpected absolute (left) and expected relative (right) difference between A1 and A3 (upper line) and A2 and A3 (lower line), as a function of varying MTTF [hours].
Figure 3 also shows the expected relative difference between the two measures, showing a decreasing value in the approximate MTTF interval = [500; 10,000]. Indicating that especially when the MTTF is higher than 10,000 hours, i.e., around 1 year, it is fully acceptable to ignore the contribution of detection time in such calculations.
\nWhile there is the obvious point that MTTR generally plays a more important part of availability the lower the MTTF is, there are also other reasons why the MTTR generally is skewed towards the right of Figure 2. When collecting subsea data, for example, for OREDA, this process is often time-demanding and costly; see e.g., [15] when done manually, which is often the case. For this reason, the priority is often to collect failures and any maintenance related directly to these. This comes at the cost of sometimes disregarding opportunity maintenance or other types of no-failure maintenance, where the equipment in question is actually in a downtime state, thus overestimating the total time in service and consequently also the MTTF. Essentially, the actual MTTF is bound to be lower than what is often used as an estimate, and thus the difference in repair time definition becomes greater. In addition to component-related maintenance, there are also at times planned shutdowns of wells or even fields, which are not always captured for the same reasons and which also emphasize the point made. Furthermore, as mentioned in Section 2.1, there is the difficulty of distinguishing mobilization time from delays caused by manufacturing time and transportation, meaning there is a possibility of potentially both longer or shorter times added to, or subtracted from, extended definitions of MTTR. According to Ref. [15], quality checks on various equipment items (not necessarily subsea items) yielded wrong interpretations or coding used during data collection in 39% of the cases. Such errors could also swing both the MTTF and the MTTR in either direction but will certainly give rise to variations in the expected system availability.
\nA main objective of this second example case is to address the important issue of hidden failures (also called dormant failures). These are failures that, according to Ref. [14], are not immediately evident to operations and maintenance personnel. This means that it may take some time before detection, as it is not possible to detect these failures unless some specific action, such as a periodic test, is performed.
\nFor the second data set, we refer to a population of 8714 DHSV tests collected from 73 facilities operating at the Norwegian Continental Shelf during the time period in 2012–2016 from the RNNP project [29], which in contrast to the SCM population is taken from a wide range of oil and gas operators. The availability requirements refer to an industry standard (see Table 3) based on the requirements set by the Norwegian oil and gas operator Statoil. These requirements are also referred to as the “failure fraction,” FF, the ratio between number of safety critical failures revealed from periodic testing,
Barrier element: DHSV | \n\n |
---|---|
Number of facilities where tests were performed in 2016 | \n73 | \n
Average number of tests for facilities where tests were performed in 2016 | \n119 | \n
Number of facilities with percentage failures in 2016 greater than the industry standard | \n25 | \n
Total (mean) percentage of failures in 2016 | \n0.023 (0.026) | \n
Total (mean) percentage failures 2012–2016 | \n0.021 (0.021) | \n
Industry standard for availability (Statoil value) | \n0.02 | \n
General calculations and comparison with industry standards [29].
This measure may further be used to estimate the MTTF (see [30, 31]), which will then also depend on the time between the tests (the periodic testing interval).
\nFor simplicity, we assume that the valves are tested at the maximum interval, i.e., twice a year (as defined in Ref. [32]), meaning that if a failure is detected from testing, the valve failure has occurred at some point in time within the interval = [0, 6] months. The data set then corresponds to an estimated 4357 DHSVs. These valves are associated with a total number of 200 failures recorded from this population.
\nFurthermore, we assume that, except for the detection time TD, each of the parameters is representable by the triangle distribution defined in Example I, T1 (1; 8; 20) hours. In addition, another triangular probability distribution is made for the time to detect the failure, denoted T2 (1; 2160; 4320) hours, i.e., a peak (mean) value of 3 months. This corresponds to a total downtime of 4.32 × 105 hours.
\nAlthough the various delay times may be correlated, we assume the parameters are independent of each other. For the time to failure, we calculate this based on the RNNP data in Table 3, showing the percentage of critical failures, i.e., exceeding the acceptance criteria of the barrier testing. The calculations based on PSA data [29] yield a total time in service of 7.49 × 107 hours. An estimator for the mean time to failure, MTTF, is thus given as 7.49 × 107/200 = 3.74 × 105 hours. We now consider the implications of varying definitions of the repair time, in this context referred to simply as MTTR. We assume that there is uncertainty on all of the components which together form the MTTRes.
\nFigure 4 shows, similarly to Figure 2 for Example I, that widening the definition of repair time to include delays and fault detection will lower the system availability and increase the standard deviation. For this example, in contrast to the previous, the detection time is significant. Also for this example, the MTTF is relatively high; however, now the differences between using MTTRes and MRT are much greater, where the deviations in the mean availability between the A1 (i.e., MTTR = MTTRes) and A2 (i.e., MTTR = MRT) is equal to 0.547% versus a deviation of 0.061% given for Example I. And although there is significant uncertainty related to the value of MTTF, the relevance of detection time is significant.
\nIntrinsic availability distribution for A1, (left) using MTTR = MTTRes. Mean = 99.416 and stdev = 2.3 × 10−3. A2, (center) using MTTR = MRT. Mean = 99.990% and stdev = 0.2 × 10−4. A3, (right) using MTTR = MART. Mean = 99.997% and stdev = 0.1 × 10−4.
Consider also the value of the MTTF in Figure 5, where a sensitivity analysis is run, showing both the expected absolute and relative difference between A1 and A3 (blue line) and A2 and A3 (red line). As the MTTF becomes lower, the significance in the varying interpretation of repair time becomes as expected greater also in this example.
\nExpected absolute (figure to the left) and expected relative (figure to the right) difference between A1 and A3 (upper line) and A2 and A3 (lower line), as a function of varying MTTF [hours].
Example II indicates that the MTTF must be significantly higher before the availability deviations converge towards zero, at least a factor of 10 higher than the SCM situation (Example I). Only when the MTTF is in the region of 500,000 hours (60 years or more) can the contribution from detection time be considered negligible in this example.
\nBesides, one may claim that one could see significant variations in this value. For example, in the 2010 edition of the PDS handbook [30], which presents recommended data for safety instrumented systems, DHSVs are assigned with overall failure rates in the range between 2.0 and 6.7 per 106 hours depending on the data source, corresponding to a MTTF value inside the interval = [58, 17] years. Knowing also that there are significant differences between companies and operating conditions, the estimated value of detection time and the measure selected (i.e., MRT or MTTRes) could significantly influence the availability calculations.
\nInformation about downtime is highly important for decision-making in the oil and gas industry, including for subsea systems. For example, such data is needed to track maintenance KPIs and achieve the so-called maintenance excellence; refer to references [33, 34].
\nIn general, having useful information about RM is important for high-quality decision-making, and it is one of the six key dimensions that are used to evaluate decision quality [35]:
\nHelpful frame (what is it that I am deciding?)
Creative alternatives (what are my choices?)
Useful information (what do I know?)
Clear values (what consequences do I care about?)
Sound reasoning (am I thinking straight about this?)
Commitment to follow through (will I really take action?)
The above six dimensions can be visualized as a chain of decision links (see also ([36], p. 55)), where the decision is not stronger than its weakest link, which simply means that poor information (i.e., the RM data in this context) deteriorates the decision quality. It is also pointed out in Ref. [35] that the information or data should be “useful” and hence should be compared with its area of use, in this case within the area of RM data applicability and how such data may create business value by contributing to good decisions.
\nDespite the link to business value, and the broad consensus that use of RM data strongly depends on its quality, collecting such data about RM performance in the industry has typically received considerably less attention compared with the use of the information.
\nTo some extent it is inevitable that data is not always suited for the decision-making, as the system requirements, design and operations change over time. Neither are the databases sufficiently flexible to adopt for the data needs all the time. The data sources are, in many situations, unmanageable at the time when they are needed. For example, when analyzing and predicting downtime for some subsea safety valve, one typically uses the data already collected and at the time available from some database or source, such as OREDA (see e.g., [1, 37]), WellMaster (see e.g., [38]) or some internal database. There may be at the time limited room for collecting new and more relevant information. Data collection requires time, personnel and software resources. It could take years to build a quality database, and strategic decisions should be taken about what equipment data and format are relevant in the future. At the time of analysis and decision-making, there may not be sufficient resources (e.g., time) to collect additional information.
\nRM data collection is often an issue of resources, such as cost. In Ref. [14], it is mentioned that “collecting RM data is costly and therefore it is necessary that this effort is balanced against the intended use and benefits.” The RM data collection activity is considered an investment, but it is an issue that strongly depends on both how and why the data are collected. Besides, it is not always clear what time perspective one is considering. Several years of data collection could be required for the data to have significant population and value in decision-making.
\nA similar point is made in Ref. [39], with reference to the Well Integrity Management System (WIMS). This is a software application for data collection, and [39] claim that it is important that both accurate and reliable information are achieved, at least from a user perspective. Nowadays data is shared with, and should be compatible with, other systems used by the oil and gas companies, such as, the data management system SAP, to synchronize data quality. The taxonomy used in the RM database should to a large extent also be reflected in the sources where the data is first recorded, such that the transfer of failure data does not have to go through several interpretation steps where essential information is missing.
\nAnother issue is cost and ownership, which is a main reason why the data collection process is often delayed. Experience shows that often the data collection is performed at a quite late stage compared to when the actual failure occurred. The main information is typically captured by, e.g., reports from the maintenance provider. Then it is later the task of some data collector to transfer the relevant information into a RM database, including identification of missing and low-quality information. For example, the time to mobilize is normally given with low accuracy in such reports. The same is the situation when studying data from typical replacements, for example, choke replacements. The number of hours to replace such an item is too often given as an estimate, for example, 24 hours, as the detailed information may not be available from the maintenance reports.
\nThe information provided by the MTTR, which is a common and much used KPI in the oil and gas industry (see e.g., [40, 41]), is also challenging. The KPI has a strong position within this industry, especially for availability calculations, and it is highly difficult to avoid the use of this term by replacing it with MRT and MTTRes, as suggested in Ref. [21]. A quick search in “Google scholar” (January 2016), for the period 2013–2016, confirms that the term “mean time to repair” (MTTR) is used significantly and way more than the term “mean time to restoration” (MTTRes). The search results confirm that on several accounts, the term used is MTTR, while the actual basis for estimating this value is equivalent to “mean time to restoration,” which is the specific term and abbreviation used in the two IEC documents [20, 22] but then with the meaning of MTTRes. The abbreviation is also used for the mean time to recover (or recovery), synonymous with the “mean time to repair.”
\nRM data is used for a vast array of different applications, both operational and engineering. Data use today is as relevant as it was 30 years ago, for example, according to Ref. [42], reliability data is used to design the operational phase in terms of evaluating the operational performance of equipment, adjusting maintenance intervals, optimizing test intervals, establishing failure probability distributions, optimizing spare parts and logistics and job priority scheduling. Of similar importance to the engineering of equipment components, reliability data provide input to analysis such as safety integrity level analyses, RAM studies, required maintainability, selection of equipment and parts based on reliability experience, choice of maintenance strategy and qualification testing.
\nThe OREDA database, which has a taxonomy based directly on ISO 14224, contains more than 39,000 failures and 73,000 maintenance records, as well as over 2000 years of operating experience from subsea fields. Its current member list includes BP, Engie E&P Norge, Eni, Gassco, Petrobras, Shell, Statoil and Total. Expert members from the OREDA project are also involved in the development of ISO 14224, to make sure that the challenges experienced in practice are captured when revising the standard. When the latest edition was issued [14], it was possible to include several definitions of key measures relevant for downtime predictions, such as, the new definition of the mean time to restoration (MTTRes).
\nConsidering the cost of subsea maintenance operations, downtime and mobilization of vessels, the range of affected decisions, operations and procedures and not least the extent to which safety systems are used in today’s industry, the importance of RM data quality cannot be emphasized enough. We will next discuss some challenges relating to the practical use of the ISO 14224 taxonomy, what impacts this may have on decisions, operations and design and some suggestions to improvements.
\nOne of the key challenges when collecting RM data is related to the fact that the RM database in many cases exists as a separate entity which may only partially communicate or not communicate at all, with other relevant systems (e.g., SAP). It is not rare to observe that data stored in an RM system such as OREDA or WellMaster is essentially data extracted from another system or database, converted into an appropriate format and then re-entered. This creates several challenges relating to both data integrity and quality:
\nCreates an unnecessary overhead; data could simply be stored in one place, possibly supplemented by automatic or manual conversion if a special format or interpretation was required, as is often the case for standards that use a specific taxonomy.
Requiring access to multiple data systems, means spending more time interviewing and more time spent on data interpretation, since it is likely that the different systems have different data formats, requiring multiple “reformatting” efforts to reach taxonomical compliance. The data are not even necessarily found within the same company, but may be located with suppliers or sub-suppliers.
Since crucial data, such as maintenance or vessel data, may not be stored directly into a RM system, then there is also the risk that original records do not contain all required fields, meaning that if the persons responsible for the operations cannot be contacted or do not recall specifics, then required data will be impaired, severely inaccurate or lost forever.
The mobilization time, as previously mentioned, includes all resources, including personnel. The source for obtaining such data varies. Sometimes, such as in the event of unexpected, significant failures, specific detailed maintenance reports may outline the mobilization time of resources used. In other cases, such data are available through data stored in vessel log reports, and the data stored into the RM database is typically an interpretation of this. These reports however seldom store records of other resources than the vessels themselves, such as personnel. Maintenance records for which no records exist in neither vessel logs nor other systems have unknown mobilization times and must therefore be estimated. This is typically done based on expected duration to bring the vessel to its destination. Such estimates will thus be optimistic figures in cases where there were delays, where there were shortages in spare parts or where there were other delays that should have been considered, but were not. Conversely, in some cases, due to lack of accurate maintenance records or other source documents, the time allocated to mobilization becomes too large if the same maintenance campaign covers several maintenance jobs but where mobilization is not split across all maintenance records. There is also a fair chance that data collection will not necessarily capture any remobilization related to maintenance jobs, if these occur at a point after the maintenance data was collected or if this remobilization is not logically mapped to the original mobilization activity.
\nIt may be challenging to use statistical data to estimate the mobilization time due to the issues discussed above. In many situations, one must rely on experts having insights into the planning of maintenance activities, to assess and recommend representative values of mobilization time. For example, the mobilization is often highly area and company sensitive, where the mobilization time could be significantly shorter within one specific geographic area, e.g., due to a higher number of cooperating vessels operating within the area.
\nThe abbreviation for “mean fault detection time” (i.e., MFDT) is, as already mentioned in Section 2.5, an abbreviation that may have two distinct meanings, both of which are relevant for assessment of reliability and maintenance performance. However, in practice, this discrepancy is not widely problematic, as the specific meaning of both terms is considered well known.
\nIn many situations, it may be reasonable to ignore the contribution of detection time when making assessments involving downtime measures. However, as we may see from the two examples given in the previous section, the significance partly depends on the equipment dealt with and obviously the type of assessment. It is especially important to distinguish between the two types of “MTTR,” i.e., “MRT” and “MTTRes,” when the failure rate is high and the time of failure is uncertain.
\nThe detection time may naturally be subject to high uncertainty when dealing with hidden failures. However, that is not always the situation. Detection time is closely linked to equipment degradation modeling, such as, the use of so-called P-F interval models (see e.g., ([25], p. 394)). A P-F interval is the time from when there is a potential for failure (TP) due to the equipment being in a condition where it is possible to reveal some fault (e.g., from periodic functional testing, condition monitoring or inspection) to the time when the failure occurs (TF); see Figure 6. If the item is subject to periodic testing, and the test interval is shorter than the P-F interval, then one may have a situation where a fault is always detected from testing before any failure occurs. By using such models, it is possible to assess the time from when the fault is revealed to the time of the fault by comparing the condition of the item and the P-F interval model.
\nP-F interval model (equipment degradation model).
Detection time should also be seen in relation to the probability of detection, which relates to both the quality of testing and inspection activities, and to the incentives of accepting versus failing tests. For functional testing of equipment exposed to hidden failures, there may be strong incentives of passing tests that are close to the acceptance criterion. For example, if failing a valve leakage test leads to a more frequent test schedule, documentation work, etc., then the test personnel might pass a test even though some initial result shows that the leakage is 2.1 bar and thus just over the acceptance criterion of 2.0 bar. One could find it convenient to extend the test or make some adjustments on site to achieve a time interval where the leakage result is found acceptable. The incentives of failing or accepting tests is likely to both influence testing schedules and the statistics concerning how many of the results in the area around the acceptance criterion are reported as “failures” and thus also the estimated MTTF value.
\nAs with MFDT, MTTR is also an abbreviation that may have several different meanings. Within the oil and gas industry, the letters used in the abbreviation could refer to several terms which all make sense within the area of analysis, and may thus cause analytic ambiguity. The “TT” term consistently refers to “time to.” However, the two other letters are found highly inconsistent in use. “M” could refer to “minimum,” “mean” or “maximum” and likewise the “R” term could also refer to different meaningful words, such as “repair,” “recovery,” “respond” or “restore/restoration.” It is therefore sometimes confusing when the abbreviation is not explicitly defined.
\nThe use of the term “mean time to restoration,” with the abbreviation MTTRes, as suggested in ISO 14224 [14] clearly reduces the chance of making wrong assumptions regarding use of MTTR.
\nThe use of MTTR very much is an issue of quality. By comprising both MTTRes and MRT, it represents a measure that fails to give consistent information. In some situations, MTTR will provide the MTTRes information, and in some situations, it will provide a combination of the two. To use the MTTRes makes it clear that the intended meaning is captured. By keeping the MTTR as a useful term, one must ensure that the “R” refers to “restore” and not “repair.” For decision-making purposes, the MTTR measure is better avoided, as also recommended in the international standard [14].
\nThe failure fraction, while being a measure that is simple to understand and use, also has some limitations with respect to time aspects. Using the FF as an availability measure, for example, makes it difficult to draw conclusions when the population varies significantly in detection time and number of demands. The value expressed from using this measure does not separate between equipment tested monthly or tested once a year, which makes it difficult to use the values for estimating the MTTF.
\nFailure fraction is typically used for equipment linked to hidden failures. If assuming that the fault does not occur during the testing, the measure provides relevant information about the number of tests that reveal hidden failures. However, such an assumption strongly depends on the testing interval, as there is a possibility that the fraction of test inducted faults may be high, and one could then argue that the FF, and consequently also the MTTF, would be far higher if the testing interval was reduced.
\nA main challenge is that the failure fraction ignores the number of demands or faults that occurred since the last test, which is important information regarding the availability of the system. Hence, it is possible that instead of one failure, there should have been recorded, e.g., two failures, where a shorter test interval could have detected the two distinct faults initiated at different points in time instead of only the one that was registered at the point of testing. Furthermore, some of the failures could be observed from a demand and maintained prior to the functional testing and thus not be included in the FF statistics.
\nTypically, the quality of data influences the quality of decision-making. The process also comprises other elements, as listed in Section 4. Part of this quality relates to consistency in the use of the downtime measures for, e.g., availability calculations, which we exemplify in the current chapter.
\nBased on the above discussion, the use of downtime measures is found ambiguous in the sense that they can be given different interpretations, such as the situation now appears to be with the term “mean time to repair,” and, in particular, the abbreviation “MTTR,” and may thus contribute to reduced data and decision-making quality. The attempt of [20] to reduce ambiguity by defining “MTTR” as “mean time to restore” and thereby include the detection time is welcome in that sense (see also [27]). However, as the “MTTR” is still being widely used with previous definitions, the “mean time to repair” and the “mean time to restore” could be difficult to separate. The ISO 14224 [14] term “mean time to restoration” with the abbreviation “MTTRes” is considered less ambiguous.
\nIn this chapter, we have looked at different terms used to describe downtime. Different terms are used in data collection to define downtime, where some may be questioned to not provide adequate quality needed for associated analyzes and decision-making where the RM data is used.
\nThe ‘mean time to repair’ is a term that is well-established and widely used for e.g. availability calculations. Although the meaning of this term has shifted over time, we find examples where MTTR refers to different meanings and thus is a challenging term to use. The solution proposed in Ref. [14], to avoid the use of this term, and instead use MTTRes and MRT, is considered an acceptable way to deal with this challenge. Data collection experience indicates that to complete a change of the MTTR meaning is difficult, as the term has such a strong position within the oil and gas industry.
\nAnother challenge discussed in this chapter relates to the mobilization of maintenance resources. Experience shows that it is difficult to both interpret and quantify mobilization times in practice. Part of this problem is that resources may be linked to several maintenance activities on site, which provides an opening for different ways of recording the actual time used to mobilize and to repair the specific item. It becomes an interpretation issue, which may influence the values used for prediction of time needed to achieve repair of a failed item. Limited guidance, except for adequate definition, is given in Ref. [14] on this issue.
\nIn general, we recommend that data collection is given higher attention compared with the situation today. Typically, investments are focused on building models and using the data rather than obtaining them and ensuring high quality. Especially, more focus should be on achieving high-quality data from maintenance operations.
\nThe current book chapter is based on the paper: “Maintenance data collection for subsea systems: A critical look at terms and information used for prediction of down time” [43], presented at the European Safety and Reliability (ESREL) conference in Portorož, Slovenia, June 18–22, 2017.
\nThe under-five mortality rate (U5MR) represents children who die before reaching the age of 5 per 1000 live births [1]. The definition of live birth is a sign of life such as breathing, heartbeat, or voluntary muscular movements of the newborn after separation from the mother, regardless of the gestational age [2]. The majority of these deaths occur by preventable or treatable diseases, especially in low- and middle-income countries (LMICs). The most common causes of death under 5 years old are acute respiratory infections, diarrhea, malaria, and birth complications [3]. Underlying problems are also critical in U5MR. Malnutrition is the leading cause of about 45% of all childhood deaths [4].
The World Bank divides the world’s economies into four groups according to their income status: low-income, lower-middle-income, upper-middle-income, and high-income countries. The classification is made annually based on the gross national income (GNI) per capita in US dollars (USD) of the preceding year. According to the current data for 2021, countries with a GNI per capita of less than 1046 USD are classified as low-income countries, and those with less than 12,695 USD are classified as middle-income countries [5].
Child mortality under the age of 5 consists of several subgroups. These are neonatal mortality (birth—the first month of life), infant mortality (birth—the first year of life), and child mortality (1–5 years). Looking at the U5MR in each subgroup, it is observed that the neonatal group has the highest risk. Among this population, the youngest has the highest mortality rate. A review showed that approximately one-third of all neonatal deaths occur within the first day after birth, and nearly three-fourths occur within the first week of life [6].
Data from the last three decades show that the fastest decline in U5MR occurred between 2000 and 2009. Globally, the annual rate of reduction (ARR) was 1.9 percent in 1990–1999, 4.0 percent in 2000–2009, and 3.4 percent in 2010–2019. While 1 out of every 11 children could not see their 5th birthday in the 1990s, this number dropped to 1 in 27 in 2019. In 34 LMICs, U5MR was reduced by more than two-thirds between 1990 and 2019 [7].
Although there was a similar decrease in neonatal mortality rates (NMR), it was slower than the decline in child mortality rates between 1 and 59 months. Between 1990 and 2019, the average ARR for child mortality in the first 1–59 months was 3.6 percent, compared to only 2.5 percent in the neonatal period. As a result of these rates, deaths in children under 5 years of age began to concentrate in the neonatal period. While the ratio of newborn deaths to all childhood deaths under 5 was 40 percent in 1990, this rate increased to 47 percent in 2019 (Figure 1) [7].
Distribution of global under-five deaths by age, 1990–2019 [
The U5MR is a good indicator of the healthcare system and general social-economic development. The reasons are as follows:
First, these data precede results, not inputs. For example, the number of doctors and nurses per child is an input, it is related to child health, but positive/negative inputs do not necessarily mean that outcomes will be positive/negative. The U5MR clearly shows the extent to which these inputs achieve their goal.
The U5MR is closely related to some negative socio-economic factors (malnutrition, low immunization rates, poor maternal health, and education). Therefore, it is a strong indicator of inequality and systemic health problems.
At the same time, as many of these deaths are preventable, the rate reflects better than any other measure the lack of access to critical and essential quality health care, including family planning, antenatal and postnatal services, and disease prevention and case management.
For these and similar reasons, many global initiatives, mainly the United Nations Global Strategy for Women’s Children’s and Adolescents’ Health (2016–2030) [8] and the Sustainable Development Goals (SDG) [9], have set child survival goals to improve the U5MR.
In this chapter, we aim to examine the causes of death under the age of 5 and the methods of prevention.
In LMICs, maternal and neonatal diseases, respiratory infections and tuberculosis, enteric and other infections, and malaria are the leading causes of under-five mortality. While in high-income countries, complex neonatal conditions such as prematurity top the list of causes of under-five mortality, in LMICs, infectious diseases still rank high among causes of death.
Since the decline in NMR has not been achieved to a similar extent as in U5MR, the rate of deaths due to maternal and neonatal diseases among under-five mortality has increased recently, especially in high-income countries. In one study, the two most common causes of death in the neonatal period in LMICs were perinatal asphyxia and severe infections such as sepsis, meningitis, and pneumonia [10]. Approximately 23% of the 2.5 million annual infant deaths in the neonatal period are related to infections [7]. Deaths are due to complications associated with prematurity account for about one-fifth of neonatal deaths in these countries. These three causes account for 90% of total neonatal deaths in LMICs [11].
Despite improvements in living conditions and increases in vaccination rates, respiratory infections are the leading cause of under-five death after neonatal diseases in LMICs. It is estimated that approximately 100 million pneumonia episodes occur annually under 5 [12]. With the increase in Pneumococcal conjugate and Haemophilus influenza type b vaccination, changes in the etiology of pneumonia have occurred recently. Among bacterial pathogens, non-type b Haemophilus influenza and Staphylococcus aureus are more common, whereas RSV stands out among viral pathogens. Although less common, CMV pneumonia can be fatal in HIV-infected and immunosuppressed patients [12].
Within this group of diseases, tuberculosis occupies a special place in LMICs. About a quarter of the world’s population is infected with Mycobacterium tuberculosis, which is not always easy to treat with its extrapulmonary involvement and resistant strains [13]. About half of all people with tuberculosis live in eight countries: Bangladesh, China, India, Indonesia, Nigeria, Pakistan, Philippines, and South Africa [14]. In 2019, there were a total of 50,000 deaths due to tuberculosis in children under 5 years of age [15].
Enteral infections are the leading cause of death in children under 5 years among all infections, causing 1.5–2 million deaths per year [16, 17]. In LMICs, there are six episodes of diarrhea per year in infants and three episodes per year in children [18]. Rotavirus, Cryptosporidium, Shigella, and enterotoxigenic Escherichia coli (ETEC) are the most common pathogens observed in the moderate and severe diarrheal disease under 5 years of age in LMICs [19]. In the past, severe dehydration and fluid loss were the main causes of death from diarrhea. Other causes, such as septic bacterial infections, are now increasingly responsible for all diarrheal deaths [20].
Infection with Plasmodium falciparum potentially is fatal and most commonly manifests as a nonspecific febrile illness often without localizing signs. About 635,000 deaths from malaria occurred in LMICs in 2019, of which more than 50% were children under 5 years of age [14]. Moreover, malaria in pregnancy leads to fetal growth retardation, low birth weight, and later malaria infection in the infant, which increases neonatal mortality [21, 22]. In sub-Saharan Africa, a region at high risk of malaria, peripheral malaria is detected in the blood of about half of pregnant women during antenatal care [23].
In addition to these main causes, some factors increase the frequency and treatability or mortality of these diseases. The Covid-19 pandemic, which has been ongoing for about two years, has placed a heavy burden on the health care system. A survey by UNICEF conducted in 77 countries shows that almost 68 percent of countries reported slight reductions in health checks and immunizations for children. In addition, 63 percent of countries reported interruptions in antenatal checkups and 59 percent in postnatal care [24]. In regions with a high burden of HIV, malaria, and tuberculosis, the Covid-19 pandemic may increase HIV-related deaths by 10%, tuberculosis-related deaths by 20%, and malaria-related deaths by up to 36% over 5 years [7].
Some other exacerbating underlying factors include food insecurity, inadequate feeding practices, household air pollution, lack of hygiene, and access to safe water or adequate sanitation.
While the U5MR was about 85 per 1000 live births worldwide in 1990, it dropped to 38 per 1000 live births by 2019, a decline of over 50%. According to 2019 data, the global NMR was 17 per 1000 live births. While this rate was 11 per 1000 for children aged 1 month to 1 year, it was 10 per 1000 for children aged 1–5 years. The gap between the highest and lowest regional U5MR narrowed from 171 deaths per 1000 live births in 1990 to 73 in 2019. In 1990, the number of countries with a mortality rate above 100 per 1000 live births was over 50, while this number has dropped to 5 in 2019. Variation in NMR and U5MR in LMICs by year is shown in Figures 2–5.
Number of under-5 deaths between 1990 and 2019. Data were obtained from the healthdata.org website.
U5MR between 1990 and 2019. Data were obtained from the
Number of neonatal deaths between 1990 and 2019. Data were obtained from the
NMR between 1990 and 2019. Data were obtained from the
Despite these developments, U5MR in low-income countries remains high. These countries are all located in sub-Saharan Africa. In sub-Saharan Africa, the region with the highest U5MR, one in 13 children does not reach the age of five. This rate is 15–20 times higher than in developed countries. Of the 37 countries in this region, 31 have a high mortality rate (U5MR above 50 per 1000 live births). Although sub-Saharan Africa and Central and South Asia account for about 50% of children under 5, 80% of all under 5 deaths in 2019. On a country basis, nearly half of under-five deaths in 2019 occurred in five countries: Nigeria, India, Pakistan, the Democratic Republic of the Congo and, Ethiopia. Nigeria and India alone account for nearly a third. The alteration of mortality rates by regions over time is shown in Figure 6.
U5MR (deaths per 1000 live births) by regions, 1990, 2000, and 2019 [
When evaluating neonatal mortality, sub-Saharan Africa has the highest rate in 2019 (27 per 1000), followed by Central and South Asia (24 per 1000). Nearly 80% of neonatal deaths in 2019 occurred in these two regions. From 1990 to 2019, sub-Saharan Africa was the only SDG region without a decline in neonatal mortality due to the relatively low decrease in NMR and increase in the birth rate. In this period, newborn deaths did not decline in 21 of 48 countries in sub-Saharan Africa, and the total number of newborn deaths remained stable at about 1 million per year.
The difference between the sexes has tended to narrow over time in under-five mortality. In general, the risk of death is higher for boys under the age of 5 than for girls. However, due to regional risks, the mortality rate of girls is much higher than expected in some countries. These countries are located in the South and West Asia. After 1990, there were similar developments in this area, and the number of countries with higher than expected girl mortality dropped from 25 to 7.
The SDG aims to finish under-five mortality from preventable causes by reducing the NMR to 12 and below per 1000 live births and the U5MR to 25 and below per 1000 live births by 2030. If the current trend in under-five mortality continues, 27 percent of the 195 countries evaluated in the latest UNICEF report will not meet the SDG target. Achieving the 2030 target requires high-level efforts and a focus on child mortality in the remaining 53 countries. All these countries are in the status of LMICs. In light of current data, it is estimated that 48 million children under the age of 5 will die between 2020 and 2030. Nearly 80% of these deaths are expected to occur in sub-Saharan Africa and Central and South Asia, where almost all of the countries they host are LMICs. Moreover, if current trends continue, it does not seem possible for nearly 90% of sub-Saharan African countries to reach the neonatal mortality target of the SDG [7].
Access to life-saving interventions is at the forefront of consistently reducing mortality in LMICs. Examining the main causes of under-five deaths in these countries, it is observed that care during and after birth, vaccinations, and preventive and therapeutic services in early childhood play a crucial role [25]. An analysis conducted in 118 LMICs found that disrupting access to basic life-saving interventions could cause millions of under-five deaths in as little as six months [26]. We can consider protective interventions under several headings:
Due to the increase in the birth rate and the dependence of mother-infant life on basic life-saving interventions, prenatal, natal and postnatal care is a priority issue in LMICs. At this point, comprehensive prenatal care, skilled and rapid care during childbirth, regular postnatal care for mother and child, and high-quality and adequate care for premature and sick newborns should be the goal. For reasons other than congenital anomalies causing one in 10 neonatal deaths [27], the following precautions should be taken:
Home visits during pregnancy can play an active role in reducing NMR. A large-scale study in India has shown that antenatal care provided by Accredited Social Health Activists significantly reduces infant mortality rates under 1-year-old [28]. All pregnant women should be visited by experienced teams at least four times during pregnancy, including one visit in the first trimester. At these visits, preeclampsia and eclampsia can be diagnosed and treatment initiated in appropriate cases. In addition, these visits provide an opportunity to start folic acid and iron supplements that improve fetal health and to test for HIV, which is at risk of transmission from mother to fetus. Home visits can also help ensure that adequate precautions are taken in areas where malaria is common. Tetanus vaccinations recommended during pregnancy can be given during these visits. Finally, these visits can encourage pregnant women to deliver in the hospital and provide information about birth complications and nutrition.
Facility-based delivery should be provided to all pregnant women and, expectant mothers should be encouraged in this regard. Basic hygiene rules such as hand washing, sterile cord-cutting, and cord care should be followed in these facilities. To achieve this, using clean delivery kits (CDK) should be expanded for both home and facility deliveries. The use of CDK containing soap, gloves, cord ties, and other sterile equipment has been shown to reduce neonatal mortality and neonatal tetanus in studies in LMIC countries [29, 30, 31]. Drugs (oxytocin, etc.) and materials (partograph, etc.) used in the active management of labor should also be available. In communities where access to facilities is not possible, the number of staff with basic emergency obstetric and newborn care (BEmONC) training should be increased. However, a fast and safe referral and transport system should be ready in case of complications where the capabilities and knowledge of these staff may be inadequate. The purpose of this transportation is to transport the patient to facilities where comprehensive emergency obstetric and newborn care (CEmONC) can be provided.
In the postnatal period, attention should be paid to hypothermia and allow the mother to feed her baby within the first hour of life. It should be ensured that the mother and newborn stay in the facility for the first 24 hours, and an early postnatal visit should be scheduled to recognize the danger signs. In cases of suspected or proven infection, neonates should be treated with antibiotics, and newborns at risk should be followed up in neonatal intensive care units with adequate personnel and equipment.
Vaccination is an essential component of primary health care and an indisputable human right. Also, it is the single most effective prevention method for reducing mortality in children under 5. The following vaccines are at the forefront of preventing early childhood deaths: measles, polio, diphtheria, tetanus and pertussis, Haemophilus influenza type B, pneumococcal, and rotavirus vaccines. Reaching large populations with effective community health programs is crucial for vaccination success. Despite significant advances in immunization, too many children worldwide—including around 20 million infants each year—do not have adequate access to vaccines [32].
Since it is more difficult to diagnose tuberculosis (TB) in childhood, prevention may be more feasible than cure. At this point, neonatal use of the BCG vaccine, which is still the only vaccine against tuberculosis, could be the solution. Neonatal administration of BCG has been associated with a lower prevalence of TB disease [33].
Access to antibiotics and antimalarial to treat diarrhea, malaria, and lower respiratory tract infections also contribute to reducing child mortality. In diarrhea, oral rehydration solution and zinc supplementation help reduce mortality.
Community health workers play a vital role in providing health care to underprivileged communities. Public health workers organize public education programs, especially in the field of preventive health services. The performance of these workers is critical to increasing vaccination and breastfeeding rates, disseminating malaria prophylaxis devices such as insecticide-treated bed nets, and improving hygiene behaviors.
Studies in rural areas of three LMICs found that proximity to health facilities was a significant risk factor for infant, child, and general under-five mortality [34, 35, 36]. Similarly, a meta-analysis found that a distance of over 5 km from the health facility can increase mortality by up to 60% in newborns and children under five years of age [37]. In these and similar situations, governments should eliminate inequality of opportunity among communities and ensure access to health services for all. For example, maternity waiting homes, initiated under the leadership of the World Health Organization (WHO), can be expanded for pregnant women with limited access to facilities.
The integrated management of childhood diseases, in collaboration with national and international organizations, will help to address regional and cross-national disparities and share experience and knowledge. Finally, it is essential to build a people-centered and high-quality health system to implement all these preventive measures. At the same time, families’ desire for medical care and behavior to seek medical help when their children are ill should be encouraged.
Encouraging breastfeeding is a practical and cost-effective method of preventing early childhood deaths. However, about two out of three infants do not have access to exclusive breastfeeding for 6 months. Breast milk provides all the energy and nutrients a baby needs in the first months of life, and continues to meet at least one-third of the baby’s nutritional needs during the second half of the first year [38]. Breastfeeding not only improves the child’s nutrition but also protects the child from the dirty water used in food preparation and cleaning kitchen utensils. Vitamin A supplementation should be encouraged as it has been proven to reduce the risk of illness and death from measles and diarrhea. Access to clean water and ensuring hygiene will be particularly effective in reducing the incidence of diarrheal diseases. Screening for acute malnutrition allows early detection of cases with a risk for mortality.
The U5MR has been significantly reduced over the last 30 years through concerted action and resource allocation. The next goal of countries achieving the SDG targets should be to ensure the protection of children by eliminating inequalities that arise for reasons such as household income, race and ethnicity, and subnational division. While there are some developments in child health globally, 75% of countries failing to meet the SDG targets are found in sub-Saharan Africa clearly showing the inequality that children face in terms of their chances of survival due to birthplace. These inequalities are exacerbated in times of crisis, such as the Covid-19 pandemic, and have an even more negative impact on vulnerable children [39]. Attention should be given to sub-Saharan Africa, where mortality rates are highest in all age groups and the population continues to grow, considering that 11 million children could be saved by 2030 if all countries achieve the SDG targets.
On the other hand, newborns, whose mortality rate is declining more slowly than other age groups and therefore continues to increase their share in the under-five mortality rate, also deserve special attention. Moreover, it is more difficult to reduce the number of deaths during this period due to the establishment of more advanced health care facilities and the need for quality health care services over a longer time, beginning with the antenatal period. In addition to these, it is necessary to provide adequate health services during childbirth and in the early postnatal period. These services, along with NMR, will also reduce maternal mortality rates, the risk of stillbirths, and disability.
When we look at under-five deaths as a whole, it is essential that evidence-based life-saving interventions are accessible and accepted in the community. In addition, the impact of contextual factors such as female empowerment, nutrition, and health system resources should not be ignored in achieving success.
Another issue that should not be ignored in child mortality is the reliability of the data. In resource-poor areas, deaths may not be reported, or the cause of death may not be identified. This information may not be made available to national authorities and international health organizations promptly. Therefore, there is a need for a high-level integrated database at local, national, regional, and global levels, especially for under-five mortality data.
In summary, ending preventable child deaths is only possible by improving access to well-equipped health care professionals during pregnancy and childbirth, life-saving interventions such as vaccinations, breastfeeding, and the provision of low-cost medicines, and access to water and sanitation, which are now lacking in low-income countries.
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Saleh"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},subject:{topic:{id:"613",title:"Statistics",slug:"numerical-analysis-and-scientific-computing-statistics",parent:{id:"95",title:"Numerical Analysis and Scientific Computing",slug:"numerical-analysis-and-scientific-computing"},numberOfBooks:2,numberOfSeries:0,numberOfAuthorsAndEditors:41,numberOfWosCitations:37,numberOfCrossrefCitations:22,numberOfDimensionsCitations:39,videoUrl:null,fallbackUrl:null,description:null},booksByTopicFilter:{topicId:"613",sort:"-publishedDate",limit:12,offset:0},booksByTopicCollection:[{type:"book",id:"10961",title:"Computational Statistics and Applications",subtitle:null,isOpenForSubmission:!1,hash:"1c6b5b7c26b825d27383de72ec2e34f1",slug:"computational-statistics-and-applications",bookSignature:"Ricardo López-Ruiz",coverURL:"https://cdn.intechopen.com/books/images_new/10961.jpg",editedByType:"Edited by",editors:[{id:"9849",title:"Prof.",name:"Ricardo",middleName:null,surname:"López-Ruiz",slug:"ricardo-lopez-ruiz",fullName:"Ricardo López-Ruiz"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"5832",title:"Uncertainty Quantification and Model Calibration",subtitle:null,isOpenForSubmission:!1,hash:"1c1cdab73d684310946ba7c3b390ca9c",slug:"uncertainty-quantification-and-model-calibration",bookSignature:"Jan Peter Hessling",coverURL:"https://cdn.intechopen.com/books/images_new/5832.jpg",editedByType:"Edited by",editors:[{id:"20815",title:"Dr.",name:"Jan Peter",middleName:null,surname:"Hessling",slug:"jan-peter-hessling",fullName:"Jan Peter Hessling"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}],booksByTopicTotal:2,seriesByTopicCollection:[],seriesByTopicTotal:0,mostCitedChapters:[{id:"54755",doi:"10.5772/68049",title:"Fitting Models to Data: Residual Analysis, a Primer",slug:"fitting-models-to-data-residual-analysis-a-primer",totalDownloads:2508,totalCrossrefCites:10,totalDimensionsCites:17,abstract:"The aim of this chapter is to show checking the underlying assumptions (the errors are independent, have a zero mean, a constant variance and follows a normal distribution) in a regression analysis, mainly fitting a straight‐line model to experimental data, via the residual plots. Residuals play an essential role in regression diagnostics; no analysis is being complete without a thorough examination of residuals. The residuals should show a trend that tends to confirm the assumptions made in performing the regression analysis, or failing them should not show a tendency that denies them. Although there are numerical statistical means of verifying observed discrepancies, statisticians often prefer a visual examination of residual graphs as a more informative and certainly more convenient methodology. When dealing with small samples, the use of the graphic techniques can be very useful. Several examples taken from scientific journals and monographs are selected dealing with linearity, calibration, heteroscedastic data, errors in the model, transforming data, time‐order analysis and non‐linear calibration curves.",book:{id:"5832",slug:"uncertainty-quantification-and-model-calibration",title:"Uncertainty Quantification and Model Calibration",fullTitle:"Uncertainty Quantification and Model Calibration"},signatures:"Julia Martin, David Daffos Ruiz de Adana and Agustin G. Asuero",authors:[{id:"190870",title:"Dr.",name:"Agustín G.",middleName:null,surname:"Asuero",slug:"agustin-g.-asuero",fullName:"Agustín G. Asuero"},{id:"190871",title:"Dr.",name:"Julia",middleName:null,surname:"Martín",slug:"julia-martin",fullName:"Julia Martín"},{id:"203694",title:"Mr.",name:"David",middleName:null,surname:"Daffos Ruiz De Adana",slug:"david-daffos-ruiz-de-adana",fullName:"David Daffos Ruiz De Adana"},{id:"203695",title:"Mr.",name:"Alberto",middleName:null,surname:"Romero Gracia",slug:"alberto-romero-gracia",fullName:"Alberto Romero Gracia"}]},{id:"54982",doi:"10.5772/intechopen.68484",title:"Polynomial Chaos Expansion for Probabilistic Uncertainty Propagation",slug:"polynomial-chaos-expansion-for-probabilistic-uncertainty-propagation",totalDownloads:2869,totalCrossrefCites:6,totalDimensionsCites:11,abstract:"Uncertainty propagation (UP) methods are of great importance to design optimization under uncertainty. As a well-known and rigorous probabilistic UP approach, the polynomial chaos expansion (PCE) technique has been widely studied and applied. However, there is a lack of comprehensive overviews and studies of the latest advances of the PCE methods, and there is still a large gap between the academic research and engineering application for PCE due to its high computational cost. In this chapter, latest advances of the PCE theory and method are elaborated, in which the newly developed data-driven PCE method that does not depend on the complete information of input probabilistic distribution as the common PCE approaches is introduced and improved. Meanwhile, the least angle regression technique and the trust region scenario are, respectively, extended to reduce the computational cost of data-driven PCE to accommodate it to practical engineering design applications. In addition, comprehensive comparisons are made to explore the relative merits of the most commonly used PCE approaches in the literature to help designers to choose more suitable PCE techniques in probabilistic design optimization.",book:{id:"5832",slug:"uncertainty-quantification-and-model-calibration",title:"Uncertainty Quantification and Model Calibration",fullTitle:"Uncertainty Quantification and Model Calibration"},signatures:"Shuxing Yang, Fenfen Xiong and Fenggang Wang",authors:[{id:"200594",title:"Dr.",name:"Fenfen",middleName:null,surname:"Xiong",slug:"fenfen-xiong",fullName:"Fenfen Xiong"},{id:"200601",title:"Prof.",name:"Shuxing",middleName:null,surname:"Yang",slug:"shuxing-yang",fullName:"Shuxing Yang"},{id:"205382",title:"Mr.",name:"Fenggang",middleName:null,surname:"Wang",slug:"fenggang-wang",fullName:"Fenggang Wang"}]},{id:"55003",doi:"10.5772/intechopen.68507",title:"Uncertainty Quantification and Reduction of Molecular Dynamics Models",slug:"uncertainty-quantification-and-reduction-of-molecular-dynamics-models",totalDownloads:1442,totalCrossrefCites:2,totalDimensionsCites:5,abstract:"Molecular dynamics (MD) is an important method underlying the modern field of Computational Materials Science. Without requiring prior knowledge as inputs, MD simulations have been used to study a variety of material problems. However, results of molecular dynamics simulations are often associated with errors as compared with experimental observations. These errors come from a variety of sources, including inaccuracy of interatomic potentials, short length and time scales, idealized problem description and statistical uncertainties of MD simulations themselves. This chapter specifically devotes to the statistical uncertainties of MD simulations. In particular, methods to quantify and reduce such statistical uncertainties are demonstrated using a variety of exemplar cases, including calculations of finite temperature static properties such as lattice constants, cohesive energies, elastic constants, dislocation energies, thermal conductivities, surface segregation and calculations of kinetic properties such as diffusion parameters. We also demonstrate that when the statistical uncertainties are reduced to near zero, MD can be used to validate and improve widely used theories.",book:{id:"5832",slug:"uncertainty-quantification-and-model-calibration",title:"Uncertainty Quantification and Model Calibration",fullTitle:"Uncertainty Quantification and Model Calibration"},signatures:"Xiaowang Zhou and Stephen M. Foiles",authors:[{id:"201277",title:"Dr.",name:"Xiaowang",middleName:null,surname:"Zhou",slug:"xiaowang-zhou",fullName:"Xiaowang Zhou"},{id:"205437",title:"Dr.",name:"Stephen M.",middleName:null,surname:"Foiles",slug:"stephen-m.-foiles",fullName:"Stephen M. Foiles"}]},{id:"55556",doi:"10.5772/intechopen.68947",title:"An Improved Wavelet‐Based Multivariable Fault Detection Scheme",slug:"an-improved-wavelet-based-multivariable-fault-detection-scheme",totalDownloads:1543,totalCrossrefCites:3,totalDimensionsCites:3,abstract:"Data observed from environmental and engineering processes are usually noisy and correlated in time, which makes the fault detection more difficult as the presence of noise degrades fault detection quality. Multiscale representation of data using wavelets is a powerful feature extraction tool that is well suited to denoising and decorrelating time series data. In this chapter, we combine the advantages of multiscale partial least squares (MSPLSs) modeling with those of the univariate EWMA (exponentially weighted moving average) monitoring chart, which results in an improved fault detection system, especially for detecting small faults in highly correlated, multivariate data. Toward this end, we applied EWMA chart to the output residuals obtained from MSPLS model. It is shown through simulated distillation column data the significant improvement in fault detection can be obtained by using the proposed methods as compared to the use of the conventional partial least square (PLS)‐based Q and EWMA methods and MSPLS‐based Q method.",book:{id:"5832",slug:"uncertainty-quantification-and-model-calibration",title:"Uncertainty Quantification and Model Calibration",fullTitle:"Uncertainty Quantification and Model Calibration"},signatures:"Fouzi Harrou, Ying Sun and Muddu Madakyaru",authors:[{id:"197090",title:"Dr.",name:"Fouzi",middleName:null,surname:"Harrou",slug:"fouzi-harrou",fullName:"Fouzi Harrou"}]},{id:"55887",doi:"10.5772/intechopen.69385",title:"Bayesian Uncertainty Quantification for Functional Response",slug:"bayesian-uncertainty-quantification-for-functional-response",totalDownloads:1605,totalCrossrefCites:1,totalDimensionsCites:1,abstract:"This chapter addresses the stochastic modeling of functional response, which is a major concern in engineering implementation. We first introduce a general framework and several conventional models for functional data, including the functional linear model, penalized regression splines, and the spatial temporal model. However, in engineering practice, a naive mathematical modeling of functional response may fail due to the lack of expressing the underlying physical mechanism. We propose a series of quasiphysical models to handle the functional response. A motivating example of metamaterial design is thoroughly discussed to demonstrate the idea of quasiphysical models. In real applications, various uncertainties have to be taken into account, such as that of the permittivity or permeability of the substrate of the metamaterial. For the propagation of uncertainty, simulation‐based methods are discussed. A Bayesian framework is presented to deal with the model calibration in the case of functional response. Experimental results illustrate the efficiency of the proposed method.",book:{id:"5832",slug:"uncertainty-quantification-and-model-calibration",title:"Uncertainty Quantification and Model Calibration",fullTitle:"Uncertainty Quantification and Model Calibration"},signatures:"Xiao Guo, Yang He, Binbin Zhu, Yang Yang, Ke Deng, Ruopeng Liu\nand Chunlin Ji",authors:[{id:"198216",title:"Dr.",name:"Chunlin",middleName:null,surname:"Ji",slug:"chunlin-ji",fullName:"Chunlin Ji"},{id:"199075",title:"Dr.",name:"Xiao",middleName:null,surname:"Guo",slug:"xiao-guo",fullName:"Xiao Guo"}]}],mostDownloadedChaptersLast30Days:[{id:"54982",title:"Polynomial Chaos Expansion for Probabilistic Uncertainty Propagation",slug:"polynomial-chaos-expansion-for-probabilistic-uncertainty-propagation",totalDownloads:2869,totalCrossrefCites:6,totalDimensionsCites:11,abstract:"Uncertainty propagation (UP) methods are of great importance to design optimization under uncertainty. As a well-known and rigorous probabilistic UP approach, the polynomial chaos expansion (PCE) technique has been widely studied and applied. However, there is a lack of comprehensive overviews and studies of the latest advances of the PCE methods, and there is still a large gap between the academic research and engineering application for PCE due to its high computational cost. In this chapter, latest advances of the PCE theory and method are elaborated, in which the newly developed data-driven PCE method that does not depend on the complete information of input probabilistic distribution as the common PCE approaches is introduced and improved. Meanwhile, the least angle regression technique and the trust region scenario are, respectively, extended to reduce the computational cost of data-driven PCE to accommodate it to practical engineering design applications. In addition, comprehensive comparisons are made to explore the relative merits of the most commonly used PCE approaches in the literature to help designers to choose more suitable PCE techniques in probabilistic design optimization.",book:{id:"5832",slug:"uncertainty-quantification-and-model-calibration",title:"Uncertainty Quantification and Model Calibration",fullTitle:"Uncertainty Quantification and Model Calibration"},signatures:"Shuxing Yang, Fenfen Xiong and Fenggang Wang",authors:[{id:"200594",title:"Dr.",name:"Fenfen",middleName:null,surname:"Xiong",slug:"fenfen-xiong",fullName:"Fenfen Xiong"},{id:"200601",title:"Prof.",name:"Shuxing",middleName:null,surname:"Yang",slug:"shuxing-yang",fullName:"Shuxing Yang"},{id:"205382",title:"Mr.",name:"Fenggang",middleName:null,surname:"Wang",slug:"fenggang-wang",fullName:"Fenggang Wang"}]},{id:"54755",title:"Fitting Models to Data: Residual Analysis, a Primer",slug:"fitting-models-to-data-residual-analysis-a-primer",totalDownloads:2503,totalCrossrefCites:10,totalDimensionsCites:16,abstract:"The aim of this chapter is to show checking the underlying assumptions (the errors are independent, have a zero mean, a constant variance and follows a normal distribution) in a regression analysis, mainly fitting a straight‐line model to experimental data, via the residual plots. Residuals play an essential role in regression diagnostics; no analysis is being complete without a thorough examination of residuals. The residuals should show a trend that tends to confirm the assumptions made in performing the regression analysis, or failing them should not show a tendency that denies them. Although there are numerical statistical means of verifying observed discrepancies, statisticians often prefer a visual examination of residual graphs as a more informative and certainly more convenient methodology. When dealing with small samples, the use of the graphic techniques can be very useful. Several examples taken from scientific journals and monographs are selected dealing with linearity, calibration, heteroscedastic data, errors in the model, transforming data, time‐order analysis and non‐linear calibration curves.",book:{id:"5832",slug:"uncertainty-quantification-and-model-calibration",title:"Uncertainty Quantification and Model Calibration",fullTitle:"Uncertainty Quantification and Model Calibration"},signatures:"Julia Martin, David Daffos Ruiz de Adana and Agustin G. Asuero",authors:[{id:"190870",title:"Dr.",name:"Agustín G.",middleName:null,surname:"Asuero",slug:"agustin-g.-asuero",fullName:"Agustín G. Asuero"},{id:"190871",title:"Dr.",name:"Julia",middleName:null,surname:"Martín",slug:"julia-martin",fullName:"Julia Martín"},{id:"203694",title:"Mr.",name:"David",middleName:null,surname:"Daffos Ruiz De Adana",slug:"david-daffos-ruiz-de-adana",fullName:"David Daffos Ruiz De Adana"},{id:"203695",title:"Mr.",name:"Alberto",middleName:null,surname:"Romero Gracia",slug:"alberto-romero-gracia",fullName:"Alberto Romero Gracia"}]},{id:"55003",title:"Uncertainty Quantification and Reduction of Molecular Dynamics Models",slug:"uncertainty-quantification-and-reduction-of-molecular-dynamics-models",totalDownloads:1439,totalCrossrefCites:2,totalDimensionsCites:5,abstract:"Molecular dynamics (MD) is an important method underlying the modern field of Computational Materials Science. Without requiring prior knowledge as inputs, MD simulations have been used to study a variety of material problems. However, results of molecular dynamics simulations are often associated with errors as compared with experimental observations. These errors come from a variety of sources, including inaccuracy of interatomic potentials, short length and time scales, idealized problem description and statistical uncertainties of MD simulations themselves. This chapter specifically devotes to the statistical uncertainties of MD simulations. In particular, methods to quantify and reduce such statistical uncertainties are demonstrated using a variety of exemplar cases, including calculations of finite temperature static properties such as lattice constants, cohesive energies, elastic constants, dislocation energies, thermal conductivities, surface segregation and calculations of kinetic properties such as diffusion parameters. We also demonstrate that when the statistical uncertainties are reduced to near zero, MD can be used to validate and improve widely used theories.",book:{id:"5832",slug:"uncertainty-quantification-and-model-calibration",title:"Uncertainty Quantification and Model Calibration",fullTitle:"Uncertainty Quantification and Model Calibration"},signatures:"Xiaowang Zhou and Stephen M. Foiles",authors:[{id:"201277",title:"Dr.",name:"Xiaowang",middleName:null,surname:"Zhou",slug:"xiaowang-zhou",fullName:"Xiaowang Zhou"},{id:"205437",title:"Dr.",name:"Stephen M.",middleName:null,surname:"Foiles",slug:"stephen-m.-foiles",fullName:"Stephen M. Foiles"}]},{id:"54841",title:"State‐of‐the‐Art Nonprobabilistic Finite Element Analyses",slug:"state-of-the-art-nonprobabilistic-finite-element-analyses",totalDownloads:1647,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"The finite element analysis of a mechanical system is conventionally performed in the context of deterministic inputs. However, uncertainties associated with material properties, geometric dimensions, subjective experiences, boundary conditions, and external loads are ubiquitous in engineering applications. The most popular techniques to handle these uncertain parameters are the probabilistic methods, in which uncertainties are modeled as random variables or stochastic processes based on a large amount of statistical information on each uncertain parameter. Nevertheless, subjective results could be obtained if insufficient information unavailable and nonprobabilistic methods can be alternatively employed, which has led to elegant procedures for the nonprobabilistic finite element analysis. In this chapter, each nonprobabilistic finite element analysis method can be decomposed as two individual parts, i.e., the core algorithm and preprocessing procedure. In this context, four types of algorithms and two typical preprocessing procedures as well as their effectiveness were described in detail, based on which novel hybrid algorithms can be conceived for the specific problems and the future work in this research field can be fostered.",book:{id:"5832",slug:"uncertainty-quantification-and-model-calibration",title:"Uncertainty Quantification and Model Calibration",fullTitle:"Uncertainty Quantification and Model Calibration"},signatures:"Wang Lei, Qiu Zhiping and Zheng Yuning",authors:[{id:"196882",title:"Prof.",name:"Zhiping",middleName:null,surname:"Qiu",slug:"zhiping-qiu",fullName:"Zhiping Qiu"},{id:"198421",title:"Dr.",name:"Lei",middleName:null,surname:"Wang",slug:"lei-wang",fullName:"Lei Wang"},{id:"204754",title:"Dr.",name:"Yuning",middleName:null,surname:"Zheng",slug:"yuning-zheng",fullName:"Yuning Zheng"}]},{id:"55556",title:"An Improved Wavelet‐Based Multivariable Fault Detection Scheme",slug:"an-improved-wavelet-based-multivariable-fault-detection-scheme",totalDownloads:1542,totalCrossrefCites:3,totalDimensionsCites:3,abstract:"Data observed from environmental and engineering processes are usually noisy and correlated in time, which makes the fault detection more difficult as the presence of noise degrades fault detection quality. Multiscale representation of data using wavelets is a powerful feature extraction tool that is well suited to denoising and decorrelating time series data. In this chapter, we combine the advantages of multiscale partial least squares (MSPLSs) modeling with those of the univariate EWMA (exponentially weighted moving average) monitoring chart, which results in an improved fault detection system, especially for detecting small faults in highly correlated, multivariate data. Toward this end, we applied EWMA chart to the output residuals obtained from MSPLS model. It is shown through simulated distillation column data the significant improvement in fault detection can be obtained by using the proposed methods as compared to the use of the conventional partial least square (PLS)‐based Q and EWMA methods and MSPLS‐based Q method.",book:{id:"5832",slug:"uncertainty-quantification-and-model-calibration",title:"Uncertainty Quantification and Model Calibration",fullTitle:"Uncertainty Quantification and Model Calibration"},signatures:"Fouzi Harrou, Ying Sun and Muddu Madakyaru",authors:[{id:"197090",title:"Dr.",name:"Fouzi",middleName:null,surname:"Harrou",slug:"fouzi-harrou",fullName:"Fouzi Harrou"}]}],onlineFirstChaptersFilter:{topicId:"613",limit:6,offset:0},onlineFirstChaptersCollection:[{id:"82166",title:"Evaluation of Principal Component Analysis Variants to Assess Their Suitability for Mobile Malware Detection",slug:"evaluation-of-principal-component-analysis-variants-to-assess-their-suitability-for-mobile-malware-d",totalDownloads:10,totalDimensionsCites:0,doi:"10.5772/intechopen.105418",abstract:"Principal component analysis (PCA) is an unsupervised machine learning algorithm that plays a vital role in reducing the dimensions of the data in building an appropriate machine learning model. It is a statistical process that transforms the data containing correlated features into a set of uncorrelated features with the help of orthogonal transformations. Unsupervised machine learning is a concept of self-learning method that involves unlabelled data to identify hidden patterns. PCA converts the data features from a high dimensional space into a low dimensional space. PCA also acts as a feature extraction method since it transforms the ‘n’ number of features into ‘m’ number of principal components (PCs; m < n). Mobile Malware is increasing tremendously in the digital era due to the growth of android mobile users and android applications. Some of the mobile malware are viruses, Trojan horses, worms, adware, spyware, ransomware, riskware, banking malware, SMS malware, keylogger, and many more. To automate the process of detecting mobile malware without human intervention, machine learning methods are applied to discover the malware more precisely. Specifically, unsupervised machine learning helps to uncover the hidden patterns to detect anomalies in the data. In discovering hidden patterns of malware, PCA is an important dimensionality reduction technique that can be applied to transform the features into PCs containing important feature values. So, by implementing PCA, the correlated features are transformed into uncorrelated features automatically to explore the anomalies in the data effectively. This book chapter explains all the variants of the PCA, including all linear and non-linear methods of PCA and their suitability in applying to mobile malware detection. A case study on mobile malware detection with variants of PCA using machine learning techniques in CICMalDroid_2020 dataset has been experimented. Based on the experimental results, for the given dataset, normal PCA is suitable to detect the malware data points and forms an optimal cluster.",book:{id:"11201",title:"Advances in Principal Component Analysis",coverURL:"https://cdn.intechopen.com/books/images_new/11201.jpg"},signatures:"Padmavathi Ganapathi, Shanmugapriya Dhathathri and Roshni Arumugam"},{id:"81645",title:"Determining an Adequate Number of Principal Components",slug:"determining-an-adequate-number-of-principal-components",totalDownloads:11,totalDimensionsCites:0,doi:"10.5772/intechopen.104534",abstract:"The problem of choosing the number of PCs to retain is analyzed in the context of model selection, using so-called model selection criteria (MSCs). For a prespecified set of models, indexed by k=1,2,…,K, these model selection criteria (MSCs) take the form MSCk=nLLk+anmk, where, for model k,LLk is the maximum log likelihood, mk is the number of independent parameters, and the constant an is an=lnn for BIC and an=2 for AIC. The maximum log likelihood LLk is achieved by using the maximum likelihood estimates (MLEs) of the parameters. In Gaussian models, LLk involves the logarithm of the mean squared error (MSE). The main contribution of this chapter is to show how to best use BIC to choose the number of PCs, and to compare these results to ad hoc procedures that have been used. Findings include the following. These are stated as they apply to the eigenvalues of the correlation matrix, which are between 0 and p and have an average of 1. For considering an additional PCk + 1, with AIC, inclusion of the additional PCk + 1 is justified if the corresponding eigenvalue λk+1 is greater than exp−2/n. For BIC, the inclusion of an additional PCk + 1 is justified if λk+1>n1/n, which tends to 1 for large n. Therefore, this is in approximate agreement with the average eigenvalue rule for correlation matrices, stating that one should retain dimensions with eigenvalues larger than 1.",book:{id:"11201",title:"Advances in Principal Component Analysis",coverURL:"https://cdn.intechopen.com/books/images_new/11201.jpg"},signatures:"Stanley L. Sclove"},{id:"81542",title:"On the Use of Modified Winsorization with Graphical Diagnostic for Obtaining a Statistically Optimal Classification Accuracy in Predictive Discriminant Analysis",slug:"on-the-use-of-modified-winsorization-with-graphical-diagnostic-for-obtaining-a-statistically-optimal",totalDownloads:14,totalDimensionsCites:0,doi:"10.5772/intechopen.104539",abstract:"In predictive discriminant analysis (PDA), the classification accuracy is only statistically optimal if each group sample is normally distributed with different group means, and each predictor variance is similar between the groups. This can be achieved by accounting for homogeneity of variances between the groups using the modified winsorization with graphical diagnostic (MW-GD) method. The MW-GD method involves the identification and removal of legitimate contaminants in a training sample with the aim of obtaining a true optimal training sample that can be used to build a predictive discriminant function (PDF) that will yield a statistically optimal classification accuracy. However, the use of this method is yet to receive significant attention in PDA. An alternative statistical interpretation of the graphical diagnostic information associated with the method when confronted with the challenge of differentiating between a variable shape in the groups of the 2-D area plot remains a problem to be resolved. Therefore, this paper provides a more comprehensive analysis of the idea and concept of the MW-GD method, as well as proposed an alternative statistical interpretation of the informative graphical diagnostic associated with the method when confronted with the challenge of differentiating between a variable shape in the groups of the 2-D area plot.",book:{id:"11201",title:"Advances in Principal Component Analysis",coverURL:"https://cdn.intechopen.com/books/images_new/11201.jpg"},signatures:"Augustine Iduseri"},{id:"81460",title:"Spatial Principal Component Analysis of Head-Related Transfer Functions and Its Domain Dependency",slug:"spatial-principal-component-analysis-of-head-related-transfer-functions-and-its-domain-dependency",totalDownloads:15,totalDimensionsCites:0,doi:"10.5772/intechopen.104449",abstract:"In this chapter, the Principal Component Analysis (PCA) was adopted to spatial variation of Head-Related Transfer Function (HRTF) or its corresponding inverse Fourier Transform, called Head-Related Impulse Response (HRIR), in order to compactly represent their spatial variation. This is called the Spatial PCA (SPCA). The SPCA was carried out for a database of HRTFs in all directions by selecting the domain as one of the HRIRs, the complex HRTFs, the frequency amplitudes of HRTFs, log-amplitudes of HRTFs, and complex logarithm of HRTFs. The minimum phase approximation was incorporated for the frequency amplitudes and log-amplitudes of HRTFs. Comparison of the accuracies in both time and frequency domains taking into account their influence on subjective evaluation showed that the log-amplitudes and complex logarithm of HRTFs are suitable for the SPCA of HRTFs.",book:{id:"11201",title:"Advances in Principal Component Analysis",coverURL:"https://cdn.intechopen.com/books/images_new/11201.jpg"},signatures:"Shouichi Takane"},{id:"81407",title:"Space-Time-Parameter PCA for Data-Driven Modeling with Application to Bioengineering",slug:"space-time-parameter-pca-for-data-driven-modeling-with-application-to-bioengineering",totalDownloads:28,totalDimensionsCites:0,doi:"10.5772/intechopen.103756",abstract:"Principal component analysis is a recognized powerful and practical method in statistics and data science. It can also be used in modeling as a dimensionality reduction tool to achieve low-order models of complex multiphysics or engineering systems. Model-order reduction (MOR) methodologies today are an important topic for engineering design and analysis. Design space exploration or accelerated numerical optimization for example are made easier by the use of reduced-order models. In this chapter, we will talk about the use of higher-order singular value decompositions (HOSVD) applied to spatiotemporal problems that are parameterized by a set of design variables or physical parameters. Here we consider a data-driven reduced order modeling based on a design of computer experiment: from high-dimensional computational results returned by high-fidelity solvers (e.g. finite element ones), the HOSVD allows us to determine spatial, time and parameters principal components. The dynamics of the system can then be retrieved by identifying the low-order discrete dynamical system. As application, we will consider the dynamics of deformable capsules flowing into microchannels. The study of such fluid-structure interaction problems is motivated by the use of microcapsules as innovative drug delivery carriers through blood vessels.",book:{id:"11201",title:"Advances in Principal Component Analysis",coverURL:"https://cdn.intechopen.com/books/images_new/11201.jpg"},signatures:"Florian De Vuyst, Claire Dupont and Anne-Virginie Salsac"},{id:"81414",title:"Prediction Analysis Based on Logistic Regression Modelling",slug:"prediction-analysis-based-on-logistic-regression-modelling",totalDownloads:14,totalDimensionsCites:0,doi:"10.5772/intechopen.103090",abstract:"The chapter aims to show an application of logistic regression modelling for prediction analysis in the offshore industry. The different variables shown in dynamic positioning incident reports are analysed and processed using logistic regression modelling. The results of the models are then analysed, showing which data influence the loss of positioning and human errors and how the model can be interpreted. 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He previously worked as a post-doctoral fellow at the Ben-Gurion University of Negev, Israel; University of the Free State, South Africa; and Central University of Technology Bloemfontein, South Africa. He obtained his Ph.D. in Organic Chemistry from Nagaoka University of Technology, Japan. He has published more than seventy-four journal articles and attended several national and international conferences as speaker and chair. Dr. Kendrekar has received many international awards. He has several funded projects, namely, anti-malaria drug development, MRSA, and SARS-CoV-2 activity of curcumin and its formulations. He has filed four patents in collaboration with the University of Central Lancashire and Mayo Clinic Infectious Diseases. His present research includes organic synthesis, drug discovery and development, biochemistry, nanoscience, and nanotechnology.",institutionString:"Visiting Scientist at Lipid Nanostructures Laboratory, Centre for Smart Materials, School of Natural Sciences, University of Central Lancashire",institution:null},{id:"428125",title:"Dr.",name:"Vinayak",middleName:null,surname:"Adimule",slug:"vinayak-adimule",fullName:"Vinayak Adimule",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/428125/images/system/428125.jpg",biography:"Dr. Vinayak Adimule, MSc, Ph.D., is a professor and dean of R&D, Angadi Institute of Technology and Management, India. He has 15 years of research experience as a senior research scientist and associate research scientist in R&D organizations. He has published more than fifty research articles as well as several book chapters. He has two Indian patents and two international patents to his credit. Dr. Adimule has attended, chaired, and presented papers at national and international conferences. He is a guest editor for Topics in Catalysis and other journals. He is also an editorial board member, life member, and associate member for many international societies and research institutions. His research interests include nanoelectronics, material chemistry, artificial intelligence, sensors and actuators, bio-nanomaterials, and medicinal chemistry.",institutionString:"Angadi Institute of Technology and Management",institution:null},{id:"284317",title:"Prof.",name:"Kantharaju",middleName:null,surname:"Kamanna",slug:"kantharaju-kamanna",fullName:"Kantharaju Kamanna",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/284317/images/21050_n.jpg",biography:"Prof. K. Kantharaju has received Bachelor of science (PCM), master of science (Organic Chemistry) and Doctor of Philosophy in Chemistry from Bangalore University. He worked as a Executive Research & Development @ Cadila Pharmaceuticals Ltd, Ahmedabad. He received DBT-postdoc fellow @ Molecular Biophysics Unit, Indian Institute of Science, Bangalore under the supervision of Prof. P. Balaram, later he moved to NIH-postdoc researcher at Drexel University College of Medicine, Philadelphia, USA, after his return from postdoc joined NITK-Surthakal as a Adhoc faculty at department of chemistry. Since from August 2013 working as a Associate Professor, and in 2016 promoted to Profeesor in the School of Basic Sciences: Department of Chemistry and having 20 years of teaching and research experiences.",institutionString:null,institution:{name:"Rani Channamma University, Belagavi",country:{name:"India"}}},{id:"158492",title:"Prof.",name:"Yusuf",middleName:null,surname:"Tutar",slug:"yusuf-tutar",fullName:"Yusuf Tutar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/158492/images/system/158492.jpeg",biography:"Prof. Dr. Yusuf Tutar conducts his research at the Hamidiye Faculty of Pharmacy, Department of Basic Pharmaceutical Sciences, Division of Biochemistry, University of Health Sciences, Turkey. He is also a faculty member in the Molecular Oncology Program. He obtained his MSc and Ph.D. at Oregon State University and Texas Tech University, respectively. He pursued his postdoctoral studies at Rutgers University Medical School and the National Institutes of Health (NIH/NIDDK), USA. His research focuses on biochemistry, biophysics, genetics, molecular biology, and molecular medicine with specialization in the fields of drug design, protein structure-function, protein folding, prions, microRNA, pseudogenes, molecular cancer, epigenetics, metabolites, proteomics, genomics, protein expression, and characterization by spectroscopic and calorimetric methods.",institutionString:"University of Health Sciences",institution:null},{id:"180528",title:"Dr.",name:"Hiroyuki",middleName:null,surname:"Kagechika",slug:"hiroyuki-kagechika",fullName:"Hiroyuki Kagechika",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/180528/images/system/180528.jpg",biography:"Hiroyuki Kagechika received his bachelor’s degree and Ph.D. in Pharmaceutical Sciences from the University of Tokyo, Japan, where he served as an associate professor until 2004. He is currently a professor at the Institute of Biomaterials and Bioengineering (IBB), Tokyo Medical and Dental University (TMDU). From 2010 to 2012, he was the dean of the Graduate School of Biomedical Science. Since 2012, he has served as the vice dean of the Graduate School of Medical and Dental Sciences. He has been the director of the IBB since 2020. Dr. Kagechika’s major research interests are the medicinal chemistry of retinoids, vitamins D/K, and nuclear receptors. He has developed various compounds including a drug for acute promyelocytic leukemia.",institutionString:"Tokyo Medical and Dental University",institution:{name:"Tokyo Medical and Dental University",country:{name:"Japan"}}},{id:"94311",title:"Prof.",name:"Martins",middleName:"Ochubiojo",surname:"Ochubiojo Emeje",slug:"martins-ochubiojo-emeje",fullName:"Martins Ochubiojo Emeje",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/94311/images/system/94311.jpeg",biography:"Martins Emeje obtained a BPharm with distinction from Ahmadu Bello University, Nigeria, and an MPharm and Ph.D. from the University of Nigeria (UNN), where he received the best Ph.D. award and was enlisted as UNN’s “Face of Research.” He established the first nanomedicine center in Nigeria and was the pioneer head of the intellectual property and technology transfer as well as the technology innovation and support center. Prof. Emeje’s several international fellowships include the prestigious Raman fellowship. He has published more than 150 articles and patents. He is also the head of R&D at NIPRD and holds a visiting professor position at Nnamdi Azikiwe University, Nigeria. He has a postgraduate certificate in Project Management from Walden University, Minnesota, as well as a professional teaching certificate and a World Bank certification in Public Procurement. Prof. Emeje was a national chairman of academic pharmacists in Nigeria and the 2021 winner of the May & Baker Nigeria Plc–sponsored prize for professional service in research and innovation.",institutionString:"National Institute for Pharmaceutical Research and Development",institution:{name:"National Institute for Pharmaceutical Research and Development",country:{name:"Nigeria"}}},{id:"436430",title:"Associate Prof.",name:"Mesut",middleName:null,surname:"Işık",slug:"mesut-isik",fullName:"Mesut Işık",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/436430/images/19686_n.jpg",biography:null,institutionString:null,institution:{name:"Bilecik University",country:{name:"Turkey"}}},{id:"268659",title:"Ms.",name:"Xianquan",middleName:null,surname:"Zhan",slug:"xianquan-zhan",fullName:"Xianquan Zhan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/268659/images/8143_n.jpg",biography:"Dr. Zhan received his undergraduate and graduate training in the fields of preventive medicine and epidemiology and statistics at the West China University of Medical Sciences in China during 1989 to 1999. He received his post-doctoral training in oncology and cancer proteomics for two years at the Cancer Research Institute of Human Medical University in China. In 2001, he went to the University of Tennessee Health Science Center (UTHSC) in USA, where he was a post-doctoral researcher and focused on mass spectrometry and cancer proteomics. Then, he was appointed as an Assistant Professor of Neurology, UTHSC in 2005. He moved to the Cleveland Clinic in USA as a Project Scientist/Staff in 2006 where he focused on the studies of eye disease proteomics and biomarkers. He returned to UTHSC as an Assistant Professor of Neurology in the end of 2007, engaging in proteomics and biomarker studies of lung diseases and brain tumors, and initiating the studies of predictive, preventive, and personalized medicine (PPPM) in cancer. In 2010, he was promoted to Associate Professor of Neurology, UTHSC. Currently, he is a Professor at Xiangya Hospital of Central South University in China, Fellow of Royal Society of Medicine (FRSM), the European EPMA National Representative in China, Regular Member of American Association for the Advancement of Science (AAAS), European Cooperation of Science and Technology (e-COST) grant evaluator, Associate Editors of BMC Genomics, BMC Medical Genomics, EPMA Journal, and Frontiers in Endocrinology, Executive Editor-in-Chief of Med One. He has\npublished 116 peer-reviewed research articles, 16 book chapters, 2 books, and 2 US patents. His current main research interest focuses on the studies of cancer proteomics and biomarkers, and the use of modern omics techniques and systems biology for PPPM in cancer, and on the development and use of 2DE-LC/MS for the large-scale study of human proteoforms.",institutionString:null,institution:{name:"Xiangya Hospital Central South University",country:{name:"China"}}},{id:"40482",title:null,name:"Rizwan",middleName:null,surname:"Ahmad",slug:"rizwan-ahmad",fullName:"Rizwan Ahmad",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/40482/images/system/40482.jpeg",biography:"Dr. Rizwan Ahmad is a University Professor and Coordinator, Quality and Development, College of Medicine, Imam Abdulrahman bin Faisal University, Saudi Arabia. Previously, he was Associate Professor of Human Function, Oman Medical College, Oman, and SBS University, Dehradun. Dr. Ahmad completed his education at Aligarh Muslim University, Aligarh. He has published several articles in peer-reviewed journals, chapters, and edited books. His area of specialization is free radical biochemistry and autoimmune diseases.",institutionString:"Imam Abdulrahman Bin Faisal University",institution:{name:"Imam Abdulrahman Bin Faisal University",country:{name:"Saudi Arabia"}}},{id:"41865",title:"Prof.",name:"Farid A.",middleName:null,surname:"Badria",slug:"farid-a.-badria",fullName:"Farid A. Badria",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/41865/images/system/41865.jpg",biography:"Farid A. Badria, Ph.D., is the recipient of several awards, including The World Academy of Sciences (TWAS) Prize for Public Understanding of Science; the World Intellectual Property Organization (WIPO) Gold Medal for best invention; Outstanding Arab Scholar, Kuwait; and the Khwarizmi International Award, Iran. He has 250 publications, 12 books, 20 patents, and several marketed pharmaceutical products to his credit. He continues to lead research projects on developing new therapies for liver, skin disorders, and cancer. Dr. Badria was listed among the world’s top 2% of scientists in medicinal and biomolecular chemistry in 2019 and 2020. He is a member of the Arab Development Fund, Kuwait; International Cell Research Organization–United Nations Educational, Scientific and Cultural Organization (ICRO–UNESCO), Chile; and UNESCO Biotechnology France",institutionString:"Mansoura University",institution:{name:"Mansoura University",country:{name:"Egypt"}}},{id:"329385",title:"Dr.",name:"Rajesh K.",middleName:"Kumar",surname:"Singh",slug:"rajesh-k.-singh",fullName:"Rajesh K. Singh",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/329385/images/system/329385.png",biography:"Dr. Singh received a BPharm (2003) and MPharm (2005) from Panjab University, Chandigarh, India, and a Ph.D. (2013) from Punjab Technical University (PTU), Jalandhar, India. He has more than sixteen years of teaching experience and has supervised numerous postgraduate and Ph.D. students. He has to his credit more than seventy papers in SCI- and SCOPUS-indexed journals, fifty-five conference proceedings, four books, six Best Paper Awards, and five projects from different government agencies. He is currently an editorial board member of eight international journals and a reviewer for more than fifty scientific journals. He received Top Reviewer and Excellent Peer Reviewer Awards from Publons in 2016 and 2017, respectively. He is also on the panel of The International Reviewer for reviewing research proposals for grants from the Royal Society. He also serves as a Publons Academy mentor and Bentham brand ambassador.",institutionString:"Punjab Technical University",institution:{name:"Punjab Technical University",country:{name:"India"}}},{id:"142388",title:"Dr.",name:"Thiago",middleName:"Gomes",surname:"Gomes Heck",slug:"thiago-gomes-heck",fullName:"Thiago Gomes Heck",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/142388/images/7259_n.jpg",biography:null,institutionString:null,institution:{name:"Universidade Regional do Noroeste do Estado do Rio Grande do Sul",country:{name:"Brazil"}}},{id:"336273",title:"Assistant Prof.",name:"Janja",middleName:null,surname:"Zupan",slug:"janja-zupan",fullName:"Janja Zupan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/336273/images/14853_n.jpeg",biography:"Janja Zupan graduated in 2005 at the Department of Clinical Biochemistry (superviser prof. dr. Janja Marc) in the field of genetics of osteoporosis. Since November 2009 she is working as a Teaching Assistant at the Faculty of Pharmacy, Department of Clinical Biochemistry. In 2011 she completed part of her research and PhD work at Institute of Genetics and Molecular Medicine, University of Edinburgh. She finished her PhD entitled The influence of the proinflammatory cytokines on the RANK/RANKL/OPG in bone tissue of osteoporotic and osteoarthritic patients in 2012. From 2014-2016 she worked at the Institute of Biomedical Sciences, University of Aberdeen as a postdoctoral research fellow on UK Arthritis research project where she gained knowledge in mesenchymal stem cells and regenerative medicine. She returned back to University of Ljubljana, Faculty of Pharmacy in 2016. She is currently leading project entitled Mesenchymal stem cells-the keepers of tissue endogenous regenerative capacity facing up to aging of the musculoskeletal system funded by Slovenian Research Agency.",institutionString:null,institution:{name:"University of Ljubljana",country:{name:"Slovenia"}}},{id:"357453",title:"Dr.",name:"Radheshyam",middleName:null,surname:"Maurya",slug:"radheshyam-maurya",fullName:"Radheshyam Maurya",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/357453/images/16535_n.jpg",biography:null,institutionString:null,institution:{name:"University of Hyderabad",country:{name:"India"}}},{id:"418340",title:"Dr.",name:"Jyotirmoi",middleName:null,surname:"Aich",slug:"jyotirmoi-aich",fullName:"Jyotirmoi Aich",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000038Ugi5QAC/Profile_Picture_2022-04-15T07:48:28.png",biography:"Biotechnologist with 15 years of research including 6 years of teaching experience. Demonstrated record of scientific achievements through consistent publication record (H index = 13, with 874 citations) in high impact journals such as Nature Communications, Oncotarget, Annals of Oncology, PNAS, and AJRCCM, etc. Strong research professional with a post-doctorate from ACTREC where I gained experimental oncology experience in clinical settings and a doctorate from IGIB where I gained expertise in asthma pathophysiology. A well-trained biotechnologist with diverse experience on the bench across different research themes ranging from asthma to cancer and other infectious diseases. An individual with a strong commitment and innovative mindset. Have the ability to work on diverse projects such as regenerative and molecular medicine with an overall mindset of improving healthcare.",institutionString:"DY Patil Deemed to Be University",institution:null},{id:"349288",title:"Prof.",name:"Soumya",middleName:null,surname:"Basu",slug:"soumya-basu",fullName:"Soumya Basu",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000035QxIDQA0/Profile_Picture_2022-04-15T07:47:01.jpg",biography:"Soumya Basu, Ph.D., is currently working as an Associate Professor at Dr. D. Y. Patil Biotechnology and Bioinformatics Institute, Dr. D. Y. Patil Vidyapeeth, Pune, Maharashtra, India. With 16+ years of trans-disciplinary research experience in Drug Design, development, and pre-clinical validation; 20+ research article publications in journals of repute, 9+ years of teaching experience, trained with cross-disciplinary education, Dr. Basu is a life-long learner and always thrives for new challenges.\r\nHer research area is the design and synthesis of small molecule partial agonists of PPAR-γ in lung cancer. She is also using artificial intelligence and deep learning methods to understand the exosomal miRNA’s role in cancer metastasis. Dr. Basu is the recipient of many awards including the Early Career Research Award from the Department of Science and Technology, Govt. of India. She is a reviewer of many journals like Molecular Biology Reports, Frontiers in Oncology, RSC Advances, PLOS ONE, Journal of Biomolecular Structure & Dynamics, Journal of Molecular Graphics and Modelling, etc. She has edited and authored/co-authored 21 journal papers, 3 book chapters, and 15 abstracts. She is a Board of Studies member at her university. She is a life member of 'The Cytometry Society”-in India and 'All India Cell Biology Society”- in India.",institutionString:"Dr. D.Y. Patil Vidyapeeth, Pune",institution:{name:"Dr. D.Y. 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He pursued post-doctoral research at College of Pharmacy, Health Science Center, Texas A & M University and was involved in another postdoctoral research at Department of Translational Neurosciences and Neurotherapeutics, John Wayne Cancer Institute, Santa Monica, California. In 2015, he worked in Harvard-MIT Health Sciences & Technology as a visiting scientist. He has substantial experience in nanotechnology-based formulation development and successfully served various Indian organizations to develop pharmaceuticals and nutraceutical products. He is an inventor in many US patents and an author in many peer-reviewed articles, book chapters and books published in various media of international repute. Dr. Mukherjee is currently serving as Principal Scientist, R&D at Esperer Onco Nutrition (EON) Pvt. 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