A Comprehensive Approach to Characterizing 

the Hazards of Explosive Countermeasures with 

Respect to Dismounted Troops

by Patricia S. Frounfelker, Stephen P. Swann, and Gregory K. Dietrich 

ARL-TR-5608 July 2011 

Approved for public release; distribution is unlimited.
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Army Research Laboratory 

Aberdeen Proving Ground, MD 21005-5068 

ARL-TR-5608 July 2011 

A Comprehensive Approach to Characterizing 

the Hazards of Explosive Countermeasures with 

Respect to Dismounted Troops

Patricia S. Frounfelker, Stephen P. Swann, and Gregory K. Dietrich 

Survivability/Lethality Analysis Directorate, ARL 

Approved for public release; distribution is unlimited.
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A Comprehensive Approach to Characterizing the Hazards of Explosive 

Countermeasures with Respect to Dismounted Troops 

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Patricia S. Frounfelker, Stephen P. Swann, and Gregory K. Dietrich 

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U.S. Army Research Laboratory 

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13. SUPPLEMENTARY NOTES 

14. ABSTRACT 

Active countermeasures (ACM) are designed to increase protection to military vehicles and their occupants. Current military 

operations in urban environments have increased the need to understand the danger from these countermeasure technologies to 

dismounted troops and other personnel. The area of hazard around the interaction of the ACM and threat needs to be 

understood and quantified using a combination of testing and modeling and simulation (M&S). The U.S. Army Research 

Laboratory (ARL) has developed testing procedures and M&S tools utilizing the Operational Requirement-based Casualty 

Assessment (ORCA) and MUVES-S2 models to evaluate these vulnerabilities. Hazard characterization can be used to 

influence countermeasure development as well as the Tactics, Techniques, and Procedures (TTPs) for countermeasure-equipped 

platforms and personnel operating in their proximity. This paper will present the testing and data collection methodology, the

modeling and simulation (M&S) tools, and the visualization tools developed by ARL to assess the associated risks and hazard 

areas. 

15. SUBJECT TERMS

Collateral hazards, ORCA, injury, MUVES, APS, reactive armor 

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Patricia S. Frounfelker 

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iii 

Contents 

List of Figures iv

List of Tables iv

1. Introduction 1

2. Injury Mechanisms 2

3. Terminology and Metrics 2

4. Objectives for Testing and Analysis 4

5. Test Configuration 5

6. Characterization of Blast Overpressure Effects 6

7. Characterization of Fragment Effects 6

8. Predictive Analysis of Characterized Events 8

9. Final Summary Graphics and Metrics 10

10. Conclusion 13

11. References 14

List of Symbols, Abbreviations, and Acronyms 15

Distribution 16 
iv 

List of Figures 

Figure 1. Full-scale arena. ............................................................................................................................ 5

Figure 2. Modified arena. ............................................................................................................................. 6

Figure 3. Example plots of personnel grid run analysis from 0 degrees. ..................................................... 7

Figure 4. Example plots of fragment dispersion and areas of most concern. .............................................. 8

Figure 5. Example visualization characterizing each trajectory. ................................................................. 9

Figure 6. Example visualization characterizing the interaction between threat and ACM. ....................... 10

Figure 7. Threshold map for intercept point. ............................................................................................. 11

Figure 8. Threshold map for vehicle platform. .......................................................................................... 12

List of Tables 

Table 1. AIS scale. ....................................................................................................................................... 3
1 

1. Introduction 

Active countermeasures (ACM), such as explosive reactive armor (ERA), have been installed on 

military vehicles for more than 20 years and continue to be developed for current and future 

platforms. New and innovative types of ACM, such as active protection systems (APS), are 

currently being developed and planned for military vehicles as well. The major distinction 

between the two types of countermeasures is method of threat detection. ERA only 

acknowledges and reacts to a threat at the time of impact. APS systems on the other hand 

require sensor input to both track and intercept incoming threats. These ACM have the potential 

to produce collateral hazards to dismounted troops and other personnel. Current military 

operations in urban environments have increased the need to understand the danger from these 

countermeasure technologies. The area of risk around the interaction of the countermeasure and 

threat needs to be understood and quantified using a combination of testing and modeling and 

simulation (M&S). Experimental testing allows for the characterization of a known threat 

interacting with a vehicular countermeasure system and provides the necessary data for 

executing the M&S portion of this type of analysis. Additionally, testing identifies unique defeat 

mechanisms of each ACM that require a better understanding. M&S provides a greater 

understanding of these elements by characterizing them both stochastically and discretely, which 

provides predictive insight that augments the information gained through testing. 

The U.S. Army Research Laboratory (ARL) has developed additional testing procedures and 

M&S tools utilizing the Operational Requirement-based Casualty Assessment (ORCA) and 

MUVES-S2 models to evaluate these risks in the form of identifying potential hazard zones and 

areas of risk. Hazard characterization and M&S can be used to influence countermeasure 

development as well as the Tactics, Techniques, and Procedures (TTPs) for personnel operating 

in proximity to countermeasure equipped platforms. Collateral hazard characterization also 

enables troop commanders to make informed decisions about when and where to deploy the 

countermeasures and the risks involved. This report will present the ACM testing and data 

collection methodology, the M&S tools, the visualization tools, and the associated metrics 

developed by ARL to assess the associated risks and hazard areas. 
2 

2. Injury Mechanisms 

Several different types of personnel injury mechanisms may occur due to a functioning explosive 

countermeasure and its interaction with a threat, regardless of whether the countermeasure is 

reactive armor or APS. These injuries may be due to the result of blast overpressure, penetrating 

fragments, or thermal energy. Injuries due to blast overpressure may include eardrum damage 

and lung damage. Injuries due to penetrating fragments may range from superficial skin 

penetration to deep tissue wounds depending on the size, density, velocity, and shape of the 

penetrating fragments. Thermal energy injuries like skin burn may occur as well. Additionally, 

there is a potential for blunt trauma injuries depending on the velocity, mass, and orientation of 

the debris. 

While the potential exists for any of these injury mechanisms to occur when a threat engages an 

explosive countermeasure, the primary threats from current ACM are penetrating fragments and 

blast overpressure to dismounted Soldiers and non-combatants. Even though thermal burns and 

blunt trauma are hazardous, in order for those injury mechanisms to take effect, personnel would 

be subject to high levels of blast overpressure, as well as a large number of highly-energetic 

penetrating fragments, which are greater concern. Therefore, the testing and the scope of ARL’s 

analyses focus on penetrating fragment and blast overpressure injuries. 

3. Terminology and Metrics 

Survivability-type analyses, including collateral hazard assessments, have two considerations 

regarding personnel effects. Survivability is best characterized in terms of injury severity since 

this metric has a direct correlation to a Soldier’s threat to life. We define injury using the 

Abbreviated Injury Scale (AIS) 2005 (1). AIS scores each single injury and assigns a severity 

score. AIS is an anatomically-based, consensus-derived, international severity scoring system 

that classifies each injury by body region, tissue types, and its relative severity on a 6-point 

ordinal scale. The Maximum Abbreviated Injury Score (MAIS) is used as an anatomical 

measure of injury and acts as a means to provide a single value metric in the event of multiple 

AIS scores for a given injury and/or event. The AIS scale, referenced in table 1, ranges between 

0 and 6. 

When conducting collateral hazard assessments, our threshold of unacceptable injury for crew 

and dismounted troops is an AIS 3, or serious injury. A serious injury is one that requires 

immediate medical attention, without which there is potential for loss of life. Using the same 

scale, our threshold of unacceptable injury for civilians is an AIS 1 or AIS 2, or a minor or 
3 

moderate injury respectively. Minor and moderate injuries range from superficial to those that 

are fully reversible given medical attention and pose little threat to loss of life. Personnel who 

exceed these thresholds of unacceptable injury would be considered a medical casualty. 

Table 1. AIS scale. 

MAIS Injury Level Head Injury Example Type of Injury 

0 None No injury None 

1 Minor Minor laceration of scalp Superficial 

2 Moderate Major laceration of scalp Reversible injuries, medical 

attention required 

3 Serious Fracture of skull Reversible injuries, hospitalization 

required 

4 Severe Depressed skull fracture, 

penetration > 2 cm 

Non-reversible injuries, not fully 

recoverable without medical care 

5 Critical Depressed skull fracture, 

laceration of spinal artery 

Non-reversible injuries, not fully 

recoverable with medical care 

6 Maximal Massive brain stem crush Virtually unsurvivable 

Incapacitation is a secondary concern and is related solely to operationally-relevant personnel. 

Incapacitation is the inability to perform, at a level required for combat effectiveness, the 

physical or mental tasks required in a particular role at a specific time after wounding. 

Incapacitated personnel impaired to a level below minimum capabilities are considered an 

operational casualty. While incapacitation is dependent on injury, the level of incapacitation is 

not directly dependent on the injury severity. For example, an abdominal wound perforating the 

colon is a significant wound with an AIS of up to 4. However at 30 seconds post-wounding 

time, the individual may remain fairly capable, depending on the task defined, since the infection 

associated with the perforated intestines does not set in for hours or days. Conversely, an open 

fracture of the forearm is an AIS 2, yet if an individual’s task is to fire an M4 carbine, at 30 

seconds post wounding, they would be considered incapacitated due to their inability to fire the 

weapon. 
4 

4. Objectives for Testing and Analysis 

As previously discussed, several current armored military vehicles contain some form of ERA. 

Further, program managers (PMs) for these same vehicles, as well as many vehicles under 

development are considering APS, in place of, or in addition to ERA. In light of current military 

dogma focusing on combined arms operations, and the need to conduct military operations in 

urban terrain (MOUT), Soldiers and non-combatants operate in proximity to these vehicles. The 

Army must quantify the hazards associated with penetrating fragments and blast overpressure 

resulting from the interaction of a threat and an explosive countermeasure. 

Experimentally, collateral hazards are assessed at a given distance from the detonation point in 

terms of probability of injury given an event (P(Injury|Event)), shot, or hit due to blast 

overpressure and penetrating fragments for the explosive countermeasure alone, the threat alone, 

and the combined effects of the countermeasure and threat. Once P(Injury|Event) is 

characterized experimentally for a known distance from the detonation point, ARL determines 

the hazard as a function of increasing distance out to a point where the potential for injury 

becomes negligible. A hazardous area can then be calculated based on the operational 

characteristics, methods of employment, and mount locations on a vehicle. This area acts as a 

basis for the comparison of ACM solutions and ultimately developing TTPs by the user, should 

the system be fielded. 

However, there are some additional considerations when discussing this characterization effort. 

The system level tests are typically expensive in terms of time, materials, and assets, especially 

when involving production or full up system level prototypes. The data collection and analysis 

process for these tests are time consuming and labor intensive. Due to these expenses and risks, 

there are often a limited number of tests representing limited test conditions and do not provide 

statistically strong test matrices. Finally, it is important to note that collateral hazard analysis 

does not represent a safety assessment, but is intended as a data point in the comparison of ACM 

alternatives and as information for the user. 
5 

5. Test Configuration 

There are several challenges that must be addressed when testing ACM for collateral hazards. 

The first is designing cost-effective tests that provide the necessary data to fully characterize the 

primary injury mechanisms. The second is creating test plans where the data collection material 

and instrumentation will not interfere with the ACM system itself. Fragment collection media 

and blast overpressure sensors must not interfere with camera angles, flight paths of threats or 

countermeasures, nor disrupt APS sensors, such as optical sensors or radar. 

The two main types of test configurations leveraged by ARL are full-scale arena events and 

modified arena events (figures 1 and 2). The full-scale arena events are performed according to 

the Joint Munitions Effectiveness Manual (JMEM) to characterize the behavior of the ACM and 

each threat of interest individually (2). Within this test configuration, one half of the recovery 

arena is comprised of celotex bundles, which capture all fragments to identify mass, shape factor 

and other characteristics of each fragment. The other half of the recovery arena is comprised of 

velocity screens and high-speed cameras, which capture fragment velocity. The data from these 

events are packaged into a z-data file format which creates statistically relevant mass/velocity 

bins based on spatial distributions. The second test configuration was developed by ARL to 

characterize the combined interaction of the ACM and the threat munition. The modified arena 

event uses marine-grade plywood to capture fragment characteristics and discrete fragment 

trajectory from either a dynamic or static test event. BOP gauges are incorporated into each test 

configuration as well, positioned both outside and inside the protected platform (when 

applicable). 

Figure 1. Full-scale arena. 

Top view

ACM

Not to Scale
6 

Figure 2. Modified arena. 

6. Characterization of Blast Overpressure Effects 

The BOP instrumentation (free-field pressure gauge or blast test device) collects the pressuretime history needed to model the personnel effects of the BOP, specifically eardrum rupture and 

percent lung damage. This information is processed through two submodels within ARL’s 

ORCA Model (3). The first embedded model is INJURY 8.2 (4), which is used to predict lung 

tissue damage. The U.S. Department of Energy (DoE) (5) auditory injury criterion is used as the 

second embedded model to predict ear drum rupture. The pressure-time history traces collected 

are used as the input insult against personnel without hearing protection facing the direction of 

the blast. Injuries as a result of lung tissue damage range from a MAIS 2 to a MAIS 5. Injuries 

from ear drum rupture are considered a MAIS 1. Each distance where BOP data were recorded 

is evaluated to determine where BOP damage effects become negligible. Extrapolation may be 

needed to determine this maximum distance if the distances used in testing were not great 

enough. 

7. Characterization of Fragment Effects 

Penetrating fragment data collected from the modified arena witness panels include length, 

width, depth, and location of damage on the panels. This information is entered into a preprocessing tool used to calculate a limit striking velocity and a mass (if not recovered from the 

witness panel) for each impacting fragment based on the damage to the marine-grade plywood. 
7 

The mass and velocity of each fragment along with other known or measured fragment 

properties (such as material density and shape factor) are then used as the insult against 

personnel with and without personnel protective equipment (PPE) within the ORCA model. The 

ORCA model is used to calculate the personnel vulnerability metrics, specifically injury severity 

characterizations for each fragment. To assess the average likelihood of injury over the body, 

two assumptions are used within this portion of the characterization. The first assumption is that 

each fragment collected could have hit anywhere on the personnel simulated. The second 

assumption is that the probability of hit equals one (the probability of hit is assessed in a 

different portion). In order to accommodate both assumptions, each fragment is processed in a 

grid analysis within ORCA. A grid analysis uses the same fragment threat conditions with a 

uniform grid of shot lines against the entire ORCAman at various aspect angles and/or elevations 

around his body. Grid runs reveal the vulnerability of a Soldier to the witnessed insults at 

various impacts to the body and provide a probability of MAIS > X injury given a hit (figure 3) 

depending on which level of unacceptable injury is being illustrated. 

Figure 3. Example plots of personnel grid run 

analysis from 0 degrees. 

The assessment of fragment data collected will reveal the level of injury and/or incapacitation for 

each tested distance away from the intercept point. Also a general pattern can be analyzed for all 

impacting fragments from each discrete modified arena event. Each hit location, color coded by 

the probability of injury, can be illustrated as in figure 4. This will provide characteristics of the 

fragment spray, such as spray bounds, symmetry, focused groups, and shifting. Further 

characterization can then be completed to highlight areas of most concern about the intercept 

point. 

Fragment B

P(MAIS≥3│hit) = 0.5

Fragment A

P(MAIS≥3│hit) = 0.2

Fragment C

P(MAIS≥3│hit) = 0.8
8 

Figure 4. Example plots of fragment dispersion and areas of most concern. 

8. Predictive Analysis of Characterized Events 

ORCA, in conjunction with another ARL model MUVES-S2 (6), are used to fully characterize 

and predict the potential for damaging effects of fragmentation from each event. MUVES-S2 

creates the virtual environment, which is used to lay out target arrays, provide fragment 

characteristics and fly-out trajectories for threats, controls metric reporting, and acts as a 

“physics engine” for all events during a simulation run that do not involve personnel. The 

personnel injury portion of a simulation uses ORCA, which characterizes, evaluates, and 

quantifies insults to personnel. These two models, working in conjunction, provide metrics for 

injuries to personnel using the aforementioned AIS scale, with emphasis on the associated MAIS 

score for both stochastic and discrete modeling scenarios. 

The initial conditions that MUVES-S2 uses for each fragment (mass, velocity, shape factor, 

density, and trajectory) are created using data gathered during testing. When modeling a discrete 

event, individual fragment characteristics are modeled using information garnered from impact 

locations on plywood panels during modified arena tests. These fragments are considered to be 

traveling in straight line paths, with velocity degradation taking place due to air drag. A 

stochastic event on the other hand, is modeled using statistically derived data gathered during a 

full-scale arena test. This test assumes a symmetrical fly-out based upon a judicious choice of 

test setup and creates mass velocity pairings and bins them by the angle of fly-out. This data is P(MAIS≥3│hit) P(MAIS≥3│hit)

Fragment Dispersion

Areas of Most Concern

Height of 

Fragment Impact

Height of 

Fragment Impact 0 20 40 60 80 100 120 140 160 180

Number of Serious or 

Greater Injuries

Degrees

Degrees

Degrees

Notional results
9 

then used to generate a z-data file, which in turn creates the initial conditions for MUVES-S2 to 

perform modeling runs with variable fragment fly-outs. 

The second portion of this type of modeling effort is creating the associated target geometry for 

both discrete and stochastic simulations. Discrete simulations use an array format with 

ORCAmen placed in concentric half-rings at specified distances from the initiation of fragment 

fly-out. Each set of initial fly-out conditions is run once, as there is no variability. This provides 

a snapshot of a single event. A stochastic run, on the other hand, aligns the ORCAmen in grid 

fashion around the point of initiation, with each ORCAman being placed one at a time on the 

grid, in order to prevent any shielding of the ORCAmen at further ranges due to those 

ORCAmen who are closer to the detonation point. These runs are performed 25 times for each 

individual ORCAman with their position on the grid changing by 25, 50, and 100 cm depending 

on the size of the grid being run (30 m, 50 m, and 100 m respectively). The ORCAmen for both 

discrete and stochastic modeling can be positioned in varying elevations to simulate terrain 

differences, in varying postures, with varying levels of PPE, and at variable ranges. 

The analysis associated with characterizing collateral hazards due to explosive countermeasures 

is directly linked to the type of inputs that are used in the modeling as mentioned previously. 

That said, for a discrete modeling run the following metrics/products are generated (for each 

specified range) and color coded by the MAIS value generated: 

• Count of impacts to the ORCAmen that result in a MAIS = X 

• Visualization of fragment fly-outs that impacted ORCAmen (figure 5). 

Figure 5. Example visualization characterizing each trajectory. 

1x

2x

3x

4x

5x

Notional results
10 

This portion of the analysis, in addition to the visualization of the discrete event and a count of 

injuries resulting in a given MAIS score, provides a means to quantify what the potential hazard 

is to dismounted troops for any single event. 

The second portion of this characterization relies on the stochastic method of modeling and 

provides the following products that will directly feed into the final summary graphics and 

metrics: 

• Survivability map of the probability of receiving a MAIS > X given an event due to 

penetrating fragments for the threat only 

• Survivability map of the probability of receiving a MAIS > X given an event due to 

penetrating fragments for the countermeasure only 

• Survivability map of the probability of receiving a MAIS > X given an event due to 

penetrating fragments for the combination of the threat and the countermeasure (figure 6) 

Figure 6. Example visualization characterizing the interaction between threat and ACM. 

9. Final Summary Graphics and Metrics 

Once P(Injury|Event) is characterized experimentally for a known distance from the detonation 

point, ARL determines the hazard as a function of increasing distance out to a point where the 

potential for injury becomes negligible, as previously discussed. A hazardous area, given an 

ideal intercept, can then be calculated based on the operational characteristics, methods of 

employment, and mount locations on a vehicle. 

Notional results

0%

0‐5%

5‐10%

10‐15%

15‐20%

20‐25%

25‐30%

30‐35%

35‐40%

40‐45%

45‐50%

50‐55%

55‐60%

60‐65%

65‐70%

70‐75%

75‐80%

80‐85%

85‐90%

90‐95%

95‐99%

99‐100%

P(MAIS > X)

Low Resolution – Large Grid High Resolution – Small Grid
11 

All outputs created are designed to provide answers to questions that are posed by two unique 

communities: requirements writers and evaluators, and TTP writers and operators. The 

survivability maps and metrics associated solely with the threat, ACM, and the combination of 

the two give PMs, requirements writers, and evaluators the necessary information to baseline the 

performance of various systems, as well as make side-by-side comparisons of their potential for 

injury. The visualizations of fragment fly-outs also provide this community with a snapshot of a 

single event and the ability to quantify, discretely, the maximum range at which penetrating 

fragments have the potential for various levels of injury. This can be used as both a benchmark 

and/or a means of comparison. The survivability maps and metrics this analysis provides with 

respect to the vehicle platform are of particular value to TTP writers and operators as it 

highlights a distinct area of hazard. This hazard area can be used to minimize the risk to 

dismounted troops when positioned in the vicinity of vehicle platforms equipped with explosive 

countermeasures. 

ARL recommends both communities use the following summary graphics and metrics for both 

BOP and penetrating fragments (figures 7 and 8) where the green and yellow area represents a 

greater than or equal to 50% chance of receiving a minor injury (MAIS 1) and a serious injury 

(MAIS 3), respectively. 

Figure 7. Threshold map for intercept point. 

MAIS Hazard Area 

(m2)

Max Distance

(m)

X A α

Y B β

BOP

CM-Threat 

Intercept 

Point

α β

Notional results
12 

Figure 8. Threshold map for vehicle platform. 

Specifically, these summary graphics and metrics contain: 

• Focus for requirement writers and evaluators (figure 7) 

• Threshold map identifying the area that penetrating fragments have a P(MAIS>X) > 

50% given an event with respect to the intercept point 

• Maximum range from the intercept point that a Soldier would receive a P(MAIS>X) 

> 50% given an event 

• Total area, with respect to the intercept point, producing a P(MAIS>X) > 50% given 

an event 

• Focus for TTP writers and operators (figure 8) 

• Threshold map identifying the worst-case area that penetrating fragments have a 

P(MAIS>X) > 50% given an event with respect to the vehicle platform 

• Worst-case range from the vehicle that a Soldier would receive a P(MAIS>X) > 50% 

given an event 

• Worst-case area, with respect to the vehicle, producing a P(MAIS>X) > 50% given an 

event 

Max CM-Threat 

Intercept Range (γ)

Notional results
13 

10. Conclusion 

ACM present hazards to dismounted troops and non-combatants in the vicinity of ACMequipped platforms. In addition to standard singular fragment and combined area of effect 

characterization garnered through testing, ARL has developed methodology using ORCA and 

MUVES-S2 to better characterize and predict the risk/effects of collateral hazards to the 

dismounted Soldier. Collateral hazards results may be used to compare hazardous areas between 

ACM solutions to assist with acquisition decisions; develop TTPs for combined arms operations 

requiring dismounted Soldiers to work near ACM-equipped platforms; and assist commanders 

deploying ACM-equipped platforms in MOUT operations near civilian populations. 
14 

11. References 

1. Association for the Advancement of Automotive Medicine (AAAM). Abbreviated Injury 

Scale 2005–Update 2008 Manual. AAAM 2008.

2. Test and Data Reduction Procedures for Munitions; FM 101-51-3-CD (EM0260); 

Headquarters, Department of the Army: Washington, DC, 2009. 

3. Gillich, P; Roach L. Methodology for Assessing Tri-Service Personnel Casualties; Aircraft 

Survivability; Joint Aircraft Survivability Program Office: Arlington, VA, Spring 2009; 

23−25. 

4. Stuhmiller, J.; Ho, K.; Vorst, M.; Dodd, K.; Fitzpatrick, T.; Mayorga, M. A Model of Blast 

Overpressure Injury to the Lung. J. Biomechanics 1996, 29, (2), 227−234. 

5. A Manual for the Prediction of Blast and Fragment Loading on Structures; DOE/TIC-11268; 

U.S. Department of Energy, Albuquerque Operations Office: Amarillo, Texas, 1981 

6. Eberius, N.; Benjamin, K; Frounfelker, P. Survivability Measures for Evaluation of 

Personnel in Military Systems Published by the National Defense Industrial Association. 

Proceedings of the 24th International Symposium on Ballistics, New Orleans, Louisiana, 

September 2008; Vol. 2.
15 

List of Symbols, Abbreviations, and Acronyms 

ACM Active countermeasures 

APS active protection systems 

ARL U.S. Army Research Laboratory 

DOE Department of Energy 

ERA explosive reactive armor 

JMEM Joint Munitions Effectiveness Manual 

M&S modeling and simulation 

MAIS Maximum Abbreviated Injury Score 

MOUT military operations in urban terrain 

ORCA Operational Requirement-based Casualty Assessment 

PM program manager 

PPE personnel protective equipment 

TTPs Tactics, Techniques, and Procedures 
NO. OF 

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 only) DTIC OCA 

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ABERDEEN PROVING GROUND

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18 

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