**4.6 Nipah virus and other henipaviruses**

Very little information on the virucidal efficacy of microbicides for Nipah virus or other henipaviruses was identified during this literature search. Claims as to utility of certain microbicides for these paramyxoviruses include the following: "Paramyxoviruses are susceptible to common soaps and disinfectants; lipid solvents (alcohol and ether) and sodium hypochlorite solutions were used effectively in outbreaks for cleaning and disinfection" [79]. This sort of information, without supporting primary literature, is only marginally useful. Important information, including microbicide concentration, contact time, matrix and methodology used to determine virucidal efficacy, are missing from this brief statement. It is clear from **Table 7** that considerable knowledge gaps exist for virucidal efficacy of microbicides for these Priority List paramyxoviruses.


*2.8. In Vitro Scratch Test and Time-Lapse Video Microscopy (TLVM)*

Briefly, HaCaT cells were seeded in 12-wells until complete cellular confluence was reached. Successively the confluent monolayer was scratched with a sterile tip (Ø = 0.1 mm) and the diluted UF\_Ret20 samples were added to the medium.

The in vitro cell migration was analyzed by a video microscopy time-lapse station (TLVM) (OKOLAB, Pozzuoli, Italy), assembled with an inverted microscope (AxioVision200, Zeiss, Germany), a CCD-gray-camera (ORCA ER, Hamamatsu Photonics, Hamamatsu City, Japan) that records the images, a motorized stage incubator that logs the position and maintains the in vitro condition of the cell culture (37 ◦C, 5% CO2 in humidified air) and the custom-tailored software OKO-Vision 4.3 software that follows the overall process and allows image analysis. The TVLM tracks the wound repair process in real-time for 48–72 h, due to the selection and recording of representative images of the experiments. The quantitative analysis of wound closure rates is calculated as [(Area t0 − Area t)/Area t0] × 100 directly by the software or alternatively by manual mode tracking for each image of the wound area over time. For each well a minimum of 5 fields of view was used for deriving the overall averaged curves of wound closures (%) as a function of the time, thus ensuring the statistical significance of the experiment. the position and maintains the in vitro condition of the cell culture (37 °C, 5% CO2 in humidified air) and the custom-tailored software OKO-Vision 4.3 software that follows the overall process and allows image analysis. The TVLM tracks the wound repair process in real-time for 48–72 h, due to the selection and recording of representative images of the experiments. The quantitative analysis of wound closure rates is calculated as [(Area t0 − Area t)/Area t0] × 100 directly by the software or alternatively by manual mode tracking for each image of the wound area over time. For each well a minimum of 5 fields of view was used for deriving the overall averaged curves of wound closures (%) as a function of the time, thus ensuring the statistical significance of the experiment. *2.9. Statistical Analysis* All data were analyzed by means of two-tailed non homoscedastic Student's *t*-test,

wound healing assay, the concentration of FBS in the medium was reduced to 1% *v*/*v* to

Briefly, HaCaT cells were seeded in 12-wells until complete cellular confluence was reached. Successively the confluent monolayer was scratched with a sterile tip (Ø = 0.1

The in vitro cell migration was analyzed by a video microscopy time-lapse station (TLVM) (OKOLAB, Pozzuoli, Italy), assembled with an inverted microscope (AxioVision200, Zeiss, Germany), a CCD-gray-camera (ORCA ER, Hamamatsu Photonics, Hamamatsu City, Japan) that records the images, a motorized stage incubator that logs

slow down the migration phenomena that could then be better evaluated.

*Fermentation* **2021**, *7*, x FOR PEER REVIEW 5 of 11

*2.8. In Vitro Scratch Test and Time-lapse Video Microscopy (TLVM)*

mm) and the diluted UF\_Ret20 samples were added to the medium.

#### *2.9. Statistical Analysis* and *p* < 0.05 was considered as statistically significant.

All data were analyzed by means of two-tailed non homoscedastic Student's *t*-test, and *p* < 0.05 was considered as statistically significant. **3. Results and Discussion**

#### **3. Results and Discussion** In line with current circular economy approaches, a waste material from one of the

In line with current circular economy approaches, a waste material from one of the production sectors of excellence of the Campania region, namely the dairy industry, was processed and recovered to obtain on the one side purified water for cleaning use within the manufacturing facility, and on the other a spray-dried concentrate that could be tested as substrate for the growth of *L. fermentum* and as bioactive compound useful in promoting tissue regeneration. An overview of the process approach is presented in Figure 1. production sectors of excellence of the Campania region, namely the dairy industry, was processed and recovered to obtain on the one side purified water for cleaning use within the manufacturing facility, and on the other a spray-dried concentrate that could be tested as substrate for the growth of *L. fermentum* and as bioactive compound useful in promoting tissue regeneration. An overview of the process approach is presented in Figure 1.

**Figure 1. Figure 1.** Overall downstream process of discarded whey and obtainment of Overall downstream process of discarded whey and obtainment of added value fractions. added value fractions.


**1. Introduction**

Traumatic spinal injuries (TSI) can comprise column fractures, spinal cord injury or both. They are among the most severe injuries with potential long-term, physical, psychological, and social consequences. The leading causes of TSIs are widely reported as falls and motor vehicle crashes [1–5]; some of these occur in occupational settings, placing a significant financial burden on both employer and employee, the compensation system, and the health care system. Safe Work Australia calculated the cost of work-related falls to be at \$6,640,000, in 2012–2013 [6,7]. The economic cost of work-related

injury and illness, more broadly, has been estimated at ranging from 1.8% to 6.5%, of a country's gross domestic product [8]. Safe Work Australia's current "Work Health and Safety Strategy" [9] has set one of its three national targets as a 30% reduction in serious workplace injury compensation claims, by 2022.

The global epidemiology of TSI has recently been estimated in a systematic review [10]. The modelled overall global incidence was 10.5 cases per 100,000 persons; meaning around 768,000 new cases across the world per year. These authors reviewed 102 studies, reporting the TSI from work-related falls to be more common in low-to-middle-income countries, than in high-income countries. Road traffic crashes and falls were the most common mechanisms of injury in all income strata, however, low-to-middle-income countries had a higher proportion of cord injuries, only, as part of the injury profile in TSI. While comprehensive, this review was unable to report, more specifically, the industry context of the work-related TSIs. Numerous studies describing TSI epidemiology have been single hospital or trauma centre-based only and have not apportioned attributable risk to work-related incidents [2,3,11]. The authors recognised the paucity of the literature, globally, in examining TSI in population-based studies [10], particularly relative to the preponderance of studies considering solely cord injuries. If we are to consider only injuries to the spinal cord, and which are attributable to work globally, this was estimated at between 10–25%, in 2001 [12]. More recently, the Australian Institute for Health and Welfare (AIHW) reported the incidence and prevalence of traumatic spinal cord injuries across Australia [13]; "working for an income" was the second most common specified activity type at the time of injury, over the five-year period; accounting for 13% of all spinal injuries. A notable limitation of the AIHW data is that it is compiled using only data provided by consenting patients from the participating spinal cord injury units (SCIUs), in Australia, therefore likely to be an underestimate, based on an analysis of state-wide record-linked data, in New South Wales (NSW) [14]. Further, a vital missing component of these reports is the examination of the activity, at the time of injury.

The primary aim of this study was to describe the population-based epidemiology and occupational context of hospitalised traumatic spinal column and cord injuries, that occurred while 'working for an income' in NSW, the most populous state in Australia. The study does not include work-related 'back' injuries, for example, only musculoskeletal injuries without any spinal column or cord injuries. Traumatic spinal cord and column injuries are severe, and highly unlikely to avoid a period of hospitalisation; hence, the capacity of this study to accurately describe the health system burden of these injuries. A secondary aim was to describe the completeness of activity-coded data.

#### **2. Methods**

The study is set in the most populous Australian state—NSW, with approximately 7.5 million inhabitants, spread over 800,000 km2 in suburban, rural, and very remote areas [15]. The study time period covered from 1 June 2013–30 June 2016.

Study inclusion criteria were: patients aged ≥16 years, who had a recorded TSI as the reason for their index admission and an indication that this injury was related to work. Work-relatedness was defined by the International Classification of Diseases and Related Health Problems, 10th Version, Australian Modification (ICD10-AM) [16] code U73.0, or at least some of the costs covered by funding from workers compensation, in either the index admission or any subsequent episode of care within the acute care continuous period of stay.

Study exclusion criteria were: patients aged <16 years, and those for whom the first admission with a record of spinal injury was for rehabilitation.

Spinal injuries included all traumatic spinal cord injuries or spinal column injuries, defined on the basis of specific ICD-10-AM [16] codes (Appendix A). Probabilistic data linkage was undertaken by the Centre for Health Record Linkage (CHeReL), linking all patients where a TSI code was either a principal or additional diagnosis, for any separation within the Admitted Patient Data Collection (APDC). The APDC contains records for all patients admitted to all public hospitals across the state of NSW. Corresponding records were linked with the Emergency Department (ED) Data, NSW Mortality data, NSW Ambulance data collections, and the NSW District Network Return (DNR) activity-based funding data. The NSW APDC contains all inpatient records including patient demographics, comprehensive admission, transfer and discharge data, ICD-10-AM [16] diagnosis codes, Australian Refined-Diagnostic Related Group (AR-DRG) codes, procedure codes, separation mode (discharges, transfers and deaths), and financial information from all NSW public hospitals, private hospitals, and day procedure centres. The NSW DNR records the individual patient-level health service activity, enabling the estimation of the costs incurred by the health service providers, in a bottom-up approach. The first hospital episode for the patient, satisfying inclusion criteria conditions, as well as all other contiguous episodes of care, including nested/non-nested transfers, was recognised as the 'index event'. The Socio-Economic Indexes for Areas (SEIFA); developed by the Australian Bureau of Statistics, to rank areas in the country according to the relative socio-economic disadvantage or advantage, was derived from the patients' residential postcodes to describe the population relative to their education and occupation, within this index.

Activity and the place of injury were derived from the first record, or from subsequent records of the first continuous period of stay, if relevant information was missing in the first record. The sub-codes of the root U73.0 were used to identify the attributed industry (U73.00–U73.09). Type of TSI and body region affected was derived from any record within the first continuous period of stay. Length of stay was calculated for the first continuous period of stay. Costs were derived from the NSW DNR, which included the ED and the admitted costs.

Descriptive statistics were used to report the prevalence of various factors. Values were reported as the mean and the standard deviation (SD), in normally distributed continuous variables, or proportions; median and interquartile range (IQR) for non-normally distributed continuous variables. All statistical analyses were performed using Stata version 15.0 (Stata Corporation, College Station, TX, USA). Standardised reporting of the demographic and other variables, as recommended by De Vivo et al. [17], was followed where possible. The incidence of injury was based on the NSW Labour Force, aged 15 years and over [18],—note that the numerator data are based on persons 16 years and over—with 95% confidence intervals calculated, assuming a Poisson distribution [19].

This study was approved by the Cancer Institute NSW, Population and Health Services Research Ethics Committee: AU RED Reference: HREC/16/CIPHS/19, Cancer Institute NSW reference number: 2016/07/647.

#### **3. Results**

From this record-linked administrative dataset, 824 patients aged 16 years or over were identified as having sustained a TSI in NSW, while working for an income over the period June 2013–June 2016. Injury victims were predominantly male (86.2%), with a gender ratio of 6.22. Almost half of these injuries occurred as a result of a fall (49.2%), and more than half of these (55.1%) were falls from building structures, scaffolding or ladders (Table 1). Of the 254 (31%) transport-related spinal injuries, 50% occurred in an off-road setting. Fewer workplace injuries occurred in summer (20%), compared with other seasons, but the variation between seasons was not substantial (note that the proportion of 'winter injuries' includes one more month—June 2013—than the other seasons). Workplace-related spinal injuries represented almost 5% of all acute TSI, identified in this dataset.

Table 2 shows that the most common injury was a fracture and/or dislocation to the lumbosacral region (58%). Comorbid head, chest or abdominal injury was also sustained in 21% of incidents. Table 2 also quantifies the measure of resource required caring for these injuries. The mean length of the acute-care stay was more than two weeks (16.1 days). The total number of acute-care bed days, used by the 824 injured persons, was 13,302 at a total cost of \$ 19,500,000 (95%CI \$16 M–\$23 M). The mean (SD) per patient cost for acute admission was \$23,681.00 (\$62,304.00); median (IQR) per patient cost was \$7436.50 (\$3381.50–\$20,393.00).

**Table 1.** Characteristics of the patient population and context of injury occurrence (*n* = 824).

\* derived from patient's residential postcode # lowest education and occupation status ## highest education and occupation status.

For the 47 patients who had inpatient rehabilitation (130 hospital separations), the mean (SD) length of stay in rehabilitation was 40 days (44).

**Table 2.** Injury detail and consequent hospitalisation.

Table 3 further investigates the activity within each broad mechanism of injury category, permitting a greater understanding of the circumstances of injury, within certain activities. The most common type of fall injury was related to falling from a building or structure (24.4%), while the most common transport injury was sustained as an occupant of a heavy vehicle (23.6%). Over half the workplace TSIs caused by a mechanical force resulted from being struck by a projected or falling object (54.1%).

The external cause activity was coded as 'other or unspecified' in 44.5% of cases, and 'missing' in another 11.5% of cases (Table 1). Of the remaining cases where codes were specified to an industry, 78.8% of all falls occurred to persons in the construction industry, as did 48.3% of all mechanical force injuries. Transport-related injuries occurred predominantly in the transport and storage industry (51%) or agriculture/forestry industry (41.5%), and rarely in the construction industry (4%).

The annual rate of workplace-related spinal injuries was 7.2 (95% CI 6.8–7.7) per 100,000 persons, with the rate per 100,000 persons for males (11.6, 95% CI 10.7–12.5) being six times that of females (2.2, 95% CI 1.8–2.6).

#### **4. Discussion**

This study identified 824 persons during a three-year period who sustained TSIs while working for income. Almost half of these injuries resulted from a fall (49.2%); often from building structures, scaffolding or ladders (55.1%). Given that only 18% of TSI occurred in the 16–29 years age group, developmental vulnerability, and inexperience do not seem to be major contributing factors. The SEIFA index classifying education and occupational status shows a slightly higher proportion in the lowest three quintiles (67%), than in the higher two. The only comparator to this study analysed NSW hospitalisation and workers compensation data almost 20 years ago and considered all injury types, and not TSI alone [20].

Almost half of all work-related spinal injuries in our study were due to falls. Fall risk in Australian workplaces has been the focus of national attention over recent years. A Working at Heights Association survey [21], in 2014, found concerning failure rates of unsafe equipment installation, where 94% of fixed ladders were identified as potentially fatal. Despite numerous publications of codes of practice promoting workplace health and safety (WHS), and strong WHS laws, our results suggest that increased local surveillance of safety systems and stricter enforcement of relevant legislation may be required to reduce risks and, therefore, fall-related injuries.

Heavy transport vehicle crashes were the leading cause of transport-related spinal injuries in this study. A heavy vehicle driving crash risk is known to be reduced by the consumption of caffeinated substances [22], however, increased with night shift driving, insufficient breaks, and lack of vehicle safety devices [23]. Industry safety for heavy vehicle drivers has a long chain of responsibility that involves general practitioners in driver licensing, logistics managers, employers of various sizes, loading managers, goods consigners, and many others. Where multiple parties may be responsible at different stages in risk profiles, it is clear that all parties must work seamlessly together to reduce overall risk.

The reliability of the external cause codes in ICD10-AM [16] has been previously proven to be questionable, with high levels of missing activity codes, and 'non-specific' coding to describe events surrounding injury cause [24,25]. Analysis of a nationally representative sample of injury admissions across Australia, in 2002, revealed an underestimation of around 32%, in hospital coding, of the true burden of occupational injury [26]. The sub-codes of the root U73.0, used to identify the attributed industry (U73.00–U73.09), within which the activity was being undertaken at the time of injury; was missing or coded as 'other' or unspecified, in more than half of all cases (56%). Lack of coding specificity clearly hampers the provision of information to injury prevention activities and policy and likely leads to underestimation of actual events. The importance of improving the quality of clinical documentation, and particularly the level of specificity surrounding the injury events has been previously highlighted by Soo et al. [25]. These authors suggested that this perhaps indicated a relatively lower perceived importance of activity information, compared to injury mechanism codes, which have been more completely coded [25].

A particular strength of this study is its comprehensive profile of work-related TSIs, drawn from a population-based record linkage; not previously offered in Australia. Despite the undoubted under-reporting due to medical coding issues, these data aptly inform the current policy targets from the Australian Work Health and Safety Strategy 2012–2022 [9], to achieve a "reduction in the incidence rate of claims resulting in one or more weeks off work of at least 30%" for workplace injuries. Baseline data for this policy included serious non-fatal workers' compensation claims, reportedly around 11.5 claims per 1000 employees, over the 2009–2010 to 2011–2012 period. The objective of 30% reduction is acknowledged as 'ambitious' but thought to be attainable, if 'concerted and sustainable effort is made to target the most common causes of injury in those industry sectors that experience both high numbers and high rates of injury' [27]. This study did not consider TSIs related to other workplace injuries, however, this injured population certainly would be included within a '30% reduction' target. A replicated follow-up record linkage, in 2022, would ideally quantify the measure of any reduction achieved.

Limitations of this study included the unavailability of various important variables about the injured worker, such as ethnicity, level of education and experience, the employment situation (e.g., whether permanent/part-time/casual), and specific occupation. Indigenous status was not identified within the APDC collection; the population of NSW has 2.9% of indigenous people. These variables may influence the risk profile within particular industries or worker groups. The source data coding issues have been discussed earlier. Finally, the APDC does not include hospitalisation data for patients who were admitted to private hospitals across NSW. The degree of under-representation that this presents is uncertain, however, such severe injuries are much more likely to be treated within the public hospital system.

### **5. Conclusions**

Work-related traumatic spinal injuries create a significant burden of cost and disability for the Australian workforce but are preventable, and also fall under a current focus of the Safe Work Australia policy to reduce serious injury compensation claims, by 30%, by 2022. This study demonstrates that the construction industry is still experiencing a high burden of work-related spinal trauma, particularly related to falling from heights, with a need for more effective policies, risk management strategies, and countermeasures for prevention. Transport injury prevention efforts equally need to address heavy vehicle occupant risk. Further research is required to better understand the factors contributing to traumatic spinal injuries in the workplace, including worker risk profiles, job design, work environment, culture, and leadership. Finally, the design and tailoring of industry-specific injury prevention strategies would be greatly assisted by improving the quality of medical record documentation, including, specifically, a reduction in the use of non-specific external cause codes. A more accurate identification of the activities and sectors which give rise to workplace spinal trauma, has policy relevance, as injury circumstances inform public health initiatives to target injury prevention.

**Author Contributions:** Conceptualisation, L.N.S. and T.D.; Methodology, L.N.S., H.M. and B.V.; Software, H.M. and L.N.S.; Formal Analysis, L.N.S, H.M. and B.V.; Data Curation, L.N.S., H.M. and B.V., Writing-Original Draft Preparation, L.N.S.; Writing-Review & Editing, J.W.M., R.I., H.M. and T.D.; Supervision, J.W.M.; Project Administration, L.N.S.; Funding Acquisition, L.N.S.

**Funding:** This research was funded by icare™; the article processing charge was funded by icare™.

**Acknowledgments:** The authors would like to thank the Meteor coding assistant team at the Australian Institute of Health and Welfare, for their strategic advice in the clarification of search and usage of activity codes.

**Conflicts of Interest:** The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.


**The Use of GNSS GPS Technology for Offshore Oil and Gas Platform Subsidence Monitoring Gas Platform Subsidence Monitoring**

**The Use of GNSS GPS Technology for Offshore Oil and**

DOI: 10.5772/intechopen.73565

Heri Andreas, Hasanuddin Z. Abidin, Irwan Gumilar, Dina A. Sarsito and Dhota Pradipta Dina A. Sarsito and Dhota Pradipta Additional information is available at the end of the chapter

Heri Andreas, Hasanuddin Z. Abidin, Irwan Gumilar,

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.73565

#### **Abstract**

Due to oil and gas exploitation, offshore oil and gas platform may experience subsidence. Continuing subsidence may deform the platform infrastructures, adding the risk for any failure on the platform objects. The failure means disaster. Therefore the subsidence information is mandatory for risk assessment and safety requirement. Repeatedly or continuous monitoring of accurate positions on the platform by using global navigation satellite system global positioning system (GNSS GPS) technology may reveal the changing of even small positions which are representing subsidence on the platform. This chapter will be deeply discussed on the use of GNSS GPS technology for offshore oil and gas platform subsidence monitoring, especially in Indonesia, the archipelago country where long baseline between reference station in the land and monitoring station at the sea slightly exists. The capability and especially the high performance of this technology on deriving subsidence information along with data sample of long baseline will be highlighted.

**Keywords:** GNSS GPS, accuracy, oil and gas platform, subsidence, monitoring


**5. Conclusions**

The MexAB-OprM efflux pump spans the complete cell wall of *P. aeruginosa*, crossing the peptidoglycan in the periplasmic compartment. Here we analyzed the possible specific interaction of the MexA and OprM proteins with the PG purified from *E. coli* or *P. aeruginosa* revealing, when proteins are purified in DDM, no interaction for OprM and an interaction of MexA only with the PG of *E. coli* that is three times thicker in vivo than the one of *P. aeruginosa*. Nevertheless, previous association of the two proteins before analysis reveals a stabilization of the complex–PG interaction in both cases. This is further reinforced with the PG of *P. aeruginosa* after the exchange of DDM detergent for C12E8 for the two proteins. Even if no covalent binding is expected, this synergistic interaction could correspond to a required step of the assembly mechanism, suggesting the PG to be an important actor of the process. It could help to stabilize MexA to keep it in the proper quaternary structure in order to increase the speed of the pump formation with OprM present in the opposite membrane.

**Supplementary Materials:** Supplementary Materials can be found at https://www.mdpi.com/1422 -0067/22/10/5328/s1. Figure S1: Pull-down experiments performed with the PG extracted from *P. aeruginosa* and with the MexA and OprM proteins mixed at two different ratios, 2:1 and 3:1., Figure S2: Pull-down experiments performed with the PG extracted from *P. aeruginosa* and with the MexA and OprM modified proteins, OprMΔCter corresponding to a truncation of 13 residues at its C-terminus.

**Author Contributions:** Conceptualization, G.P. and I.B.; Funding acquisition, I.B.; Investigation, M.M., M.L., M.S., D.M.-L. and G.P.; Methodology, D.M.-L., G.P. and I.B.; Project administration, D.M.-L., G.P. and I.B.; Supervision, G.P. and I.B.; Validation, D.M.-L., G.P. and I.B.; Writing—original draft, I.B.; Writing—review and editing, D.M.-L. and G.P. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by Agence Nationale de la Recherche, grant numbers ANR-17-CE11-0028-02 and ANR-12-BSV8-0010. MM was supported by ANR-12-BSV8-0010. M.L. was supported by a ministerial doctoral funding.

**Acknowledgments:** We thank Maria Antónia Sousa Correia da Silva for her participation in the pull-down experiments during her mobility training in IB laboratory.

**Conflicts of Interest:** The authors declare no conflict of interest.


**7. Conclusions**

In this paper, we proposed a hybrid method based on finite difference and Haar wavelets approximations. The scheme is applied for the numerical solution of (1 + 1)- and (1 + 2)-dimensional time fraction partial differential equations. The accuracy and applicability of the scheme is validated through some test problems. The tabulated data and graphical solution show that the scheme works very well for time fractional problems.

**Author Contributions:** Conceptualization, A.G. and S.H.; Methodology, A.G.; Software, A.G.; Validation, S.H., M.H. and M.A.J.; Formal Analysis, A.G.; Investigation, M.A.J.; Resources, P.K.; Writing–Original Draft Preparation, A.G.; Writing–Review and Editing, M.H.; Visualization, P.K.; Supervision, S.H. and P.K.; Project Administration, P.K.; Funding Acquisition, P.K.

**Funding:** The project was supported by the Center of Excellence in Theoretical and Computational Science (TaCS-CoE), Faculty of Science, King Mongkut's University of Technology Thonburi (KMUTT).

**Acknowledgments:** The authors are thankful to anonymous reviewers for their fruitful suggestion which improved the quality of the manuscript. Also we are thankful for the financial support of the Center of Excellence in Theoretical and Computational Science(TaCS-CoE), Faculty of Science, King Mongkut's University of Technology Thonburi (KMUTT).

**Conflicts of Interest:** The authors declare no conflict of interest.


**9.3. Embedding dimension**

The Embedding Theorem serves to remake from the observed or measured time series, the evolution of the states in the phase space, where the exponents of Lyapunov and the fractal dimension can be calculated (for example). It uses the method of delayed coordinates (reconstruction with delays). If we have the data series x1 , x2 , x3 , x4 … xn, we can form the set of points (x1 , x2 …, xp), (x2 , x3 …, xp + 1 ) …, and (xi , xi + 1 …, xp+i). These points determine a trajectory in the space RP . The dynamics of the empirical system represented by the "minimum" dynamics (in a dimensional sense) of this set of points:

If the system is random, the fractal dimension grows, as the dimension of the embedding space increases, that is, p.

If the system is periodic, the fractal dimension grows to a value k and then remains constant and whole (it is not fractal).

If the system is chaotic, the fractal dimension stabilizes for a certain embedding dimension p. Also, at least one exponent of Lyapunov will be positive.

#### **9.4. Matrix and correlation dimension**

The decomposition of time series into main analysis components gives rise to the correlation matrix. This matrix is two-dimensional (M × M) constructed by placing the values of the correlation function for τ = 0 along the main diagonal. Then, the values for τ = 1 put to the right and left of the diagonal, following with τ = 2 and so on until completing the matrix, which can be as large as 16 × 16 [37, 60].

In conclusion, the calculation of the entropy of Kolmogorov:

random and it is impossible to make any prediction.

prediction can make, nor elaboration of scenarios.

this case, a learning process.

**9.6. Determination of information loss**

mation, < ΔI > [51]:

〈Δ*I*〉

The expressions for <Δ*I* >*i*

information is greater [51, 64].

sustainability of a learning process.

The coefficients of Lyapunov λx

**1.** Check if the entropy of Kolmogorov is between zero and infinity (0 < SK < ∞), which allows verifying the presence of chaotic behavior. If the Kolmogorov entropy is equal to 0, no information loss, and the system is regular and predictable. If SK = ∞, the system is entirely

Initial Condition and Behavior Patterns in Learning Dynamics: Study of Complexity and…

**2.** Determine the amount of information needed to predict the future behavior of a system, in

**3.** Calculate the speed with which the system loses (or downgrade information over time).

**4.** To establish the maximum horizon of temporal predictability of the system, from which no

There is a relationship between the entropy of Kolmogorov and the characteristic parameter of chaos, the exponent of Lyapunov, λ, which shows that it is proportional to the loss of infor-

Persuasion (X (t)), Positivity/Negativity (Y (t)), and Internal Information/External Information

If λ < 0, the movement is not chaotic, information does not lose, because the prediction is

If λ > 0, the movement is chaotic, the prediction is less accurate and, therefore, the loss of

In chaos theory, the calculation of the Lyapunov coefficients is fundamental because it allows studying the effect of the initial condition, the irreversibility of the processes, the entropy, the time of predictability, the complexity, and, based on these parameters, to characterize the

**10. Experimental results: Application of the chaos data analyzer**

*i* = −*λ* \_\_\_\_*i*

log2, *i* = *X*,*Y*, *Z* (7)

http://dx.doi.org/10.5772/intechopen.74140

59

are associated with the time series of the Inquiry/

are in bits/time and show the relationship between the entropy of

*i* log2 = −*λi* ⇒ 〈Δ*I*〉

, λy, and λz

(Z (t)) coefficients obtained according to learning dynamics.

**software (CDA) to the experimental time series**

accurate. (It is the main idea of the current educational paradigm).

Kolmogorov and the exponent of Lyapunov, λ.

The number of significant eigenvalues of the correlation matrix, of the order of the correlation dimension, is a measure of the complexity of the system [37]. A procedure of these characteristics was applied the time series of X (t), Y (t), and Z (t).

#### **9.5. The entropy of Kolmogorov and its relation to the loss of information**

Following Farmer [61, 62], one of the essential differences between chaotic and predictable behavior is that chaotic trajectories continuously generate new information, while predictable trajectories do not. Metric entropy makes this notion more rigorous. In addition to providing a good definition of "chaos," metric entropy provides a quantitative way to describe "how chaotic" a dynamic system is.

The entropy of Kolmogorov [48, 49] is the average information loss [51, 63], when "l" (cell side in units of information) and τ (time) become infinitesimal:

$$S\_{\mathbf{x}} = -\lim\_{\text{res}\to 0} \lim\_{l\to 0} \lim\_{l\to 0} \lim\_{l\to \infty} \frac{l}{l^{\text{tr}}} \sum\_{a\dots a} P\_{a\dots a} \log P\_{a\dots a} \quad \text{(Kolmogorov entropy)}\tag{6}$$

Expressed in information bits/sec or bits/orbits for a continuous system and bits/iteration for a discrete system.

The entropy difference of Kolmogorov (ΔSK) between one cell and another (SK n+1 - SK n) represents the additional information needed to know, in which cell (i n+1) system will be found in the future. Therefore, the difference (SK n+1 - SK n) measures the loss of system information over time.

In conclusion, the calculation of the entropy of Kolmogorov:


**Author details**

Mahadev C. Khetagoudar1 , Mahadev B. Chetti2 and Dinesh C. Bilehal1 \*

1 P.G. Department of Chemistry, Karnataka University, Dharwad, Karnataka, India

2 Indian Council of Agricultural Research, New Delhi, India

\*Address all correspondence to<EMAIL_ADDRESS>
© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


**4.**

**Table**  *Effective doses and their compliance with radiation risk levels in patient groups with a single CT scan of the chest on COVID-19.*

#### *Radiation Doses and Risk Assessment during Computed Tomography of the Chest in COVID-19 … DOI: http://dx.doi.org/10.5772/intechopen.100177*

#### **Figure 1.**

*Average doses, mSv (A), and radiation risk values by age groups of patients (B) with a single, double and triple computed tomography of the chest. On the ordinate axis: (A) – average effective dose and confidence intervals (p* ≤ *0.05), mSv; (B) – values of radiation risk per 100,000 people; on the abscissa: age groups. Legend: number of CT scans of the patient, single, double, triple. The dashed lines show the boundaries between the levels of radiation risk (\*).*

old), 3.43 0.08 mSv in group 3 (adults 19–64 years old) and 3.28 0.19 mSv in group 4 (older persons – 65 years and older).

These doses are comparable with the ED values shown in the report [16] on the evaluation of DRLs for adult CT in European countries and in studies of other authors [17–19]. The DRLs for CT of adult chest organs in European countries were: 4.7–6.31 mSv in Netherlands [11, 14], 5.1–5.95 mSv in Germany [16, 18], 6.8 mSv in Austria [16], 5.95–10.4 mSv in Great Britain [16, 19], 7.31 mSv in Finland [16], 8.5–10.5 in Denmark [16], as well as 7.65 mSv in Australia [20].

In our earlier study [21], with standard protocols on different CT scanners, the values of the ED were in the ranges of 2.4–6.04 mSv and 8.4–15.33 mSv, for a singlephase and multiphase with contrast CTs, respectively. The use of low-dose protocols (tube voltage from 80 to 100 kV with automatic modulation of current) made it possible to reduce the ED to 1.6 mSv, when applying the iterative reconstruction algorithm MBIR for single-phase CTs and to 4.41 mSv when applying the iterative reconstruction algorithm ASIR for multiphase CTs [22].

Based on the risk indicator value for exposure of 1 mSv with age and sex (**Table 3**) were calculations radiation risk values and radiation risk levels after chest CT radiation per 100,000 exposed persons (**Table 4**). The maximum radiation risk values for a single CT were observed (**Figure 1B**) in groups of children (24.1\*105 ) and adolescents (23.3\*105 ). As can be seen in **Figure 2B**, the radiation risk values for a single CT were 31.2\*105 in women children (12–14 years old) and 29.3\*105 in women adolescents (15–19 years old), which exceeds the risk values for men in these groups by 2.3 and 1.9 times, respectively. For the group of adult patients the

*Radiation Doses and Risk Assessment during Computed Tomography of the Chest in COVID-19… DOI: http://dx.doi.org/10.5772/intechopen.100177*

#### **Figure 2.**

*Average doses for a single CT scan (A), radiation risk values for a single (B), twice repeated (C) and triple (D) CT, distribution by levels of radiation risk by age groups and depending on the sex of patients. On the ordinate axis: (A) – average effective dose and confidence intervals (p* ≤ *0.05), mSv; (B–D) – values of radiation risk per 100,000 people; on the abscissa axis: age groups. Legend: – men, – women. The dashed lines show the boundaries between the levels of radiation risk (\*).*

average risk was 14.4\*105 , (11.2\*105 ) in men and 17.4\*105 in women, which is 1.6 times higher than in men. Nevertheless, all these risk values are in the range of 10\*105 –100\*105 , which corresponds to the level LOW. For the group of older age patients, the radiation risk was 2.6\*105 , which corresponds to the leval rang of 1\*105 –10\*105 , VERY LOW.

We have compared the calculations with estimates of radiation risks in other studies.

For example, when planning the limits of exposure of astronauts [23], the risk of oncological diseases and genetic effects are rather low: 0.2\*106 for leukemia, 0.2\*106 for other types of malignant neoplasms, and 0.05\*106 for genetic effects, per year per dose of additional irradiation of 1 mSv. Spontaneous incidence are

50\*106 for leukemia, 1000–2000\*106 for other types of malignant neoplasms and 8000\*106 for genetic effects per year.

In publication 103 of the ICRP [11], new views of the ICRP on the principles and approaches to ensuring radiation safety, are formulated in comparison with the previous document - Publication 60 of the ICRP [15]. Epidemiological data obtained since the publication of Publication 60 of the ICRP served as a reason for revising the values of the nominal risk factors per unit dose for radiogenic cancers and hereditary effects (**Table 5**).

As we can see, the new risk values in Publication 103 are slightly lower as those specified in Publication 60. But, at the same time, for children compared with adults, they were increased in terms of Malignant neoplasms from 1.5 to 1.68, for hereditary defects from 2.25 to 3.0, and in the total number of negative effects from 1.61 to 2.0. Our results are comparable to these guidelines.

I.A. Tsalafoutas, G.V. Koukourakis [24] emphasize that stochastic negative effects can be caused even by small doses of radiation, and give the following example of calculating the risk associated with radiation during CT. The assumption of a 5% probability of risk per 1 Sv (1,000 mSv) for the occurrence of cancer or hereditary effects means that the examination, which leads to patient exposure in ED = 10 mSv (typical for CT of the abdomen and pelvis), implies 0.05% chance of such risks. That is, for every 10,000 patients, who underwent CT with a dose of 10 mSv, five people can be expected, to develop cancer or hereditary effects as the result of radiation.

There was calculation individual of effective dose and risk of malignancy based on Monte Carlo simulations after whole body CT [25]. The Excess Relative Risk (ERRMC), as a measurement of the exceeding risk of an exposed person compared to a non-exposed person, calculated using the solid cancer mortality in the United States as baseline (female: 17,500/100,000; male: 22,100/100,000).

There was calculation individual of Effective Dose and estimation of organspecific additional Lifetime Attributable Risk (LAR) of cancer mortality after Whole Body Computed Tomography based on Monte Carlo simulations and report VII about Biologic Effects of Ionizing Radiation (BEIR VII). Considering the effective doses of 1.48 0.15 mSv for the lungs, the LAR for mortality from lung cancer [n / 100,000] was 13.25 4.24.

In our study, it was shown that with a single chest CT scan in patients with suspected COVID-19, additional (to a spontaneous level) cases of oncological pathology per 100,000 people may occur: 24.1 cases in children, 23.3 cases in adolescents, 14.4 cases in adults, 2.6 cases in older persons.

The average effective dose will increase in proportion to the increase in the number of CTs performed on the patient from 2.6–3.4 mSv with a single CT scan to the calculated values of 7.8–10.3 mSv with three times CTs. This will lead to a threefold increase in radiation risks to levels per 100,000 people may occur

#### **Table 5.**

*Comparison of the risk of negative effects of exposure from a dose of 1 mSv, number of cases per 105 people (ICRP Publication 103, 2007 [11]; ICRP Publication 60, 1991 [15]).*

*Radiation Doses and Risk Assessment during Computed Tomography of the Chest in COVID-19… DOI: http://dx.doi.org/10.5772/intechopen.100177*

(**Figure 1B**): 72.3 cases in children, 69.8 cases in adolescents, 43.2 cases in adults, 7.9 cases in older persons. Due to the increased post-radiation risks in children; сcurrently, both the European and the American Society of Pediatric Radiology do not recommend the use of CT to diagnose COVID-19 pneumonia in children. CT is indicated only for severe, where concurrent pathology need to be excluded.

In men, the average radiation doses in the four age groups were slightly higher than in women (**Figure 2A**). However, with an increase in the number of CT scans from one (**Figure 2B**) to two (**Figure 2C**) and up to three (**Figure 2D**) in females, the increase in the calculated radiation risk compared to men is more significant, especially in women children (in 2.3 times) and among women adolescent (in 1.9 times). The radiation risk in men and women in all subgroups by age up to 65 years remains at the LOW level (10\*105 –100\*105 ), and in the older subgroup at the WERY LOW level (1\*105 –10\*105 ). However, with a three-fold CT scan in groups of children and adolescents, the radiation risk in women approaches the border of the MODERATE level (100\*105 –300\*105 ), and in the old group to the border of the LOW level (10\*105 –100\*105 ).

By evaluating the lung irradiation with the doses used in the ongoing clinical trials to treat COVID -19 patients, our data shows that a radiation dose 0.5 Gy provides an acceptable Risk Identification Checklist (RIC) estimate (LAR 1%), irrespective of sex and age at exposure [26]. However, a promising direction is the use of modern CT scanners, which allow the use of low-dose algorithms for CT diagnostics [27], while significantly reducing the radiation exposure to patients.

#### **4. Conclusions**

Because the study established effective radiation doses for chest CTs of patients with the diagnosis of COVID-19, the radiation risks for a single, double and triple chest CTs in different age and sex of patients were calculated. It has been found, that the radiation risk due to a single, double and triple chest CTs for patients under 65 years old is LOW, and for 65 years old and older patents is VERY LOW. Taking into account the radiation risk during CT is necessary to reduce the long-term consequences of radiation exposure on the population.

#### **Financing**

The study was performed without external funding.

#### **Conflict of interest**

The authors declare no conflict of interest.

#### **Conformity with the principles of ethics**

The study was approved by the local ethics committee.

#### **Abbreviations**

#### *Computed-Tomography (CT) Scan*

## **Author details**

Elena Ivanovna Matkevich and Ivan Vasilievich Ivanov\* Department of Occupational Health, I.M. Sechenov First Moscow State Medical University of the Ministry of Health of the Russian Federation (Sechenov University), Moscow, Russian Federation

\*Address all correspondence to<EMAIL_ADDRESS>
© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Radiation Doses and Risk Assessment during Computed Tomography of the Chest in COVID-19… DOI: http://dx.doi.org/10.5772/intechopen.100177*

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#### **Chapter 4**


**Supplementary Materials:** Supplementary materials can be found at http://www.mdpi.com/1422-0067/21/14/ 4961/s1.

**Author Contributions:** Conceptualization; B.C., G.E.J.-G., R.M.-G. and R.C.R.P.; Methodology; G.E.J.-G., R.M.-G., L.A.S.-P., W.L.G.-M., I.G.-A., R.A.P.-R. and R.S.-S.; Formal analysis; B.C., G.E.J.-G., R.M.-G., L.A.S.-P., W.L.G.-M., I.G.-A. and A.B.; Resources; B.C., J.J.M., R.C.R.P., R.A.P.-R. and A.B.; Writing; B.C., G.E.J.-G., R.M.-G. and A.B.; Funding acquisition and Project Administration; B.C. and J.J.M. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by Bilateral Agreement CNR (Italy)/CINVESTAV (Mexico) (Reference CNR2018/4) and SEP-CINVESTAV (Reference 242) grants (BC) and the National Institutes of Health (NHI), grants R01 AR055299 and AR071439 (R.C.R.P.).

**Acknowledgments:** We are grateful to Jesús Pablo Gómez Islas for technical assistance and to Steve J Winder for critical reading of the manuscript.

**Conflicts of Interest:** The authors declare no conflict of interest.