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challenges
Article
Expert System for Bomb Factory Detection by
Networks of Advance Sensors
Carlotta Ferrari 1, Alessandro Ulrici 2and Francesco Saverio Romolo 1, 3, *
1Institut de Police Scientifique (IPS), Université de Lausanne, Dorigny, 1004 Lausanne, Switzerland;
carlotta.ferrari23@libero.it
2Department of Life Sciences, University of Modena and Reggio Emilia, Via Amendola, 2,
42122 Reggio Emilia, Italy; alessandro.ulrici@unimore.it
3Legal Medicine Section, Department Saimlal, Sapienza Università di Roma, Viale Regina Elena, 336,
00161 Rome, Italy
*Correspondence: francescosaverio.romolo@uniroma1.it; Tel.: +39-06-4991-2581
Academic Editor: Palmiro Poltronieri
Received: 31 October 2016; Accepted: 26 December 2016; Published: 3 January 2017
Abstract:
(1) Background: Police forces and security administrations are nowadays considering
Improvised explosives (IEs) as a major threat. The chemical substances used to prepare IEs are
called precursors, and their presence could allow police forces to locate a bomb factory where the
on-going manufacturing of IEs is carried out. (2) Methods: An expert system was developed and
tested in handling signals from a network of sensors, allowing an early warning. The expert system
allows the detection of one precursor based on the signal provided by a single sensor, the detection
of one precursor based on the signal provided by more than one sensor, and the production of
a global alarm level based on data fusion from all the sensors of the network. (3) Results: The expert
system was tested in the Italian Air Force base of Pratica di Mare (Italy) and in the Swedish Defence
Research Agency (FOI) in Grindsjön (Sweden). (4) Conclusion: The performance of the expert system
was successfully evaluated under relevant environmental conditions. The approach used in the
development of the expert system allows maximum flexibility in terms of integration of the response
provided by any sensor, allowing to easily include in the network all possible new sensors.
Keywords: improvised explosive; precursor; network; chemometrics; security; forensic science
1. Introduction
The behaviour of terrorists using explosives changed in last decade of bombings: improvised
explosives (IEs) produced with chemical substances available on the market substituted commercial
and military explosives [
1
–
4
]. The chemical substances used in home-made preparations of IEs are
called precursors. The European Parliament and the Council adopted the Regulation (EU) No. 98/2013
on the marketing and use of explosives precursors on 15 January 2014 [
5
]. According to this regulation,
seven precursors shall not be available to the general public anymore in concentrations greater than
their limit values listed in Table 1and other precursors, listed in Table 2, will be monitored to report
suspicious transactions when purchased by the public.
The traces of precursors used in IEs production (particulates, vapours and/or waterborne)
present in the environment surrounding the vicinity of a “bomb factory” could allow police forces
to locate sites, where the on-going manufacturing of IEs is suspected. This approach for protecting
citizens from bombings is expected to be more effective than simply patrolling a possible target,
because the production time of IEs is much longer than the time needed to transport an improvised
explosive device (IED) close to the target from the manufacturing site [
6
]. Between 2009 and 2011,
the LOTUS project developed sensors to detect precursors of both drugs of abuse and IEs in the
Challenges 2017,8, 1; doi:10.3390/challe8010001 www.mdpi.com/journal/challenges
Challenges 2017,8, 1 2 of 18
environment. Another project, called “Bomb factory detection by Networks of Advanced Sensors”
(BONAS) [
7
], recently studied a network of wireless sensors to detect precursors of IEs outside
explosives manufacturing sites. Results from the BONAS project about the analysis of the precursor
hydrogen peroxide have been recently published [
8
]. The different sensors of the BONAS project
were specifically designed to be deployed in sensitive locations and easily camouflaged. This network
allows an early threat alarm thanks to an expert system, which is a system allowing the detection of
one precursor based on the signal provided by a single sensor, the detection of one precursor based
on the signal provided by more than one sensor, and the production of a global alarm level based on
data fusion from all the sensors of the network. The aim of the present work is to show how the expert
system was successfully developed and tested to play its key role in protecting the security of citizens.
Table 1.
Precursors listed in the Annex I of the European Parliament and the Council adopted the
Regulation (EU) No. 98/2013 on the marketing and use of explosives precursors on 15 January 2014 [
5
],
which are available to the public only in limited concentration and in Annex II the eight substances
that are required to be reported as suspicious transactions when purchased by the public.
ANNEX I Regulated Substance Limit Value (w/w)
1 Hydrogen peroxide 12%
2 Nitromethane 30%
3 Nitric acid 3%
4 Potassium chlorate 40%
5 Potassium perchlorate 40%
6 Sodium chlorate 40%
7 Sodium perchlorate 40%
Table 2.
Precursors listed in the Annex II of the European Parliament and the Council adopted the
Regulation (EU) No. 98/2013 on the marketing and use of explosives precursors on 15 January 2014 [
5
],
which are required to be reported as suspicious transactions when purchased by the public.
ANNEX II Regulated Substance
1 Hexamine
2 Sulphuric acid
3 Acetone
4 Potassium nitrate
5 Sodium nitrate
6 Calcium nitrate
7 Calcium ammonium nitrate
8Ammonium nitrate (in concentration of 16 % by weight of nitrogen in relation to
ammonium nitrate or higher)
2. Expert System for Advanced Sensors Network Data Analysis
Sensors Network Expert System Overview
The BONAS expert system was aimed to perform pattern recognition of the data collected by
each network sensor, estimating the predicted probability of detection for each target. The system
was then designed to integrate the responses from each sensor to provide the end-user with a global
alarm level, summarizing information from the entire network into a unique evaluation of the possible
criminal threat of an IE production site. Sensors able to detect explosives were considered for inclusion
in the network too.
The expert system follows a three step workflow while providing a real time response for any
new measurement:
1.
The first step is estimating the predicted probability of the presence of each target for each sensor
included in the BONAS network. The classification models adopted are computed by means of
Challenges 2017,8, 1 3 of 18
a pattern recognition technique on a dataset of experimental data acquired from laboratory and
field conditions. These classification models are then applied to any new data received to obtain
the corresponding predicted probability value.
2.
The expert system integrates the output given from multiple sensors measuring the same target
to provide the user with a unique alarm value for each compound. In this context, sensor location
is also considered and only output from sensors located within a user-defined distance range are
integrated, giving a positive detection within a reasonable time delay.
3.
At the last step, the output from the previous steps is further integrated to provide the user
with a defined global alarm. This alarm can be triggered by the detection of a single explosive
compound, such as trinitrotoluene (TNT) or RDX, because these substances are generally forbidden
to the general public. When precursors are detected, the alarm value is triggered when specific
couples of targets defined by the user are detected from sensors located within a user-defined
distance range and giving a positive detection within a reasonable time delay.
A compact representation of the information provided by the expert system is shown in the
dedicated user interface developed in collaboration with TEKEVER [
7
]. A schematic representation of
the three step approach developed for the BONAS expert system is shown in Figure 1.
Challenges 2017, 8, 1 3 of 19
1. The first step is estimating the predicted probability of the presence of each target for each sensor
included in the BONAS network. The classification models adopted are computed by means of
a pattern recognition technique on a dataset of experimental data acquired from laboratory and
field conditions. These classification models are then applied to any new data received to obtain
the corresponding predicted probability value.
2. The expert system integrates the output given from multiple sensors measuring the same target
to provide the user with a unique alarm value for each compound. In this context, sensor location
is also considered and only output from sensors located within a user-defined distance range
are integrated, giving a positive detection within a reasonable time delay.
3. At the last step, the output from the previous steps is further integrated to provide the user with
a defined global alarm. This alarm can be triggered by the detection of a single explosive
compound, such as trinitrotoluene (TNT) or RDX, because these substances are generally
forbidden to the general public. When precursors are detected, the alarm value is triggered when
specific couples of targets defined by the user are detected from sensors located within a user-
defined distance range and giving a positive detection within a reasonable time delay.
A compact representation of the information provided by the expert system is shown in the
dedicated user interface developed in collaboration with TEKEVER [7]. A schematic representation
of the three step approach developed for the BONAS expert system is shown in Figure 1.
Figure 1. Schematic representation of the three-step approach developed for the BONAS expert
system. The alarms due to individual targets are provided as output of Step 2 while the global alarm
level is obtained as output of Step 3.
Figure 1.
Schematic representation of the three-step approach developed for the BONAS expert system.
The alarms due to individual targets are provided as output of Step 2 while the global alarm level is
obtained as output of Step 3.
Challenges 2017,8, 1 4 of 18
3. Materials and Methods
3.1. Advanced Sensors Network and IE Precursors
The BONAS project developed a network of advanced sensors able to detect traces of precursors
used in IEs’ production present in the environment surrounding the vicinity of a “bomb factory”.
Sensors based on a wide range of analytical methods were selected in order to enable the detection of
precursors in different forms, i.e., particulates, vapours and/or waterborne.
The sensors developed and tested throughout the project include a quartz-enhanced photo-acoustic
spectroscopy (QEPAS) sensor, an electrochemical (EC) sensor, a light detection and ranging
(LIDAR)/differential absorption LIDAR detection system (DIAL) sensor, and a surface-enhanced
Raman spectroscopy (SERS) sensor.
The substances considered in the project were selected among the IE precursors taken into account
by the Regulation (EU) No. 98/2013 on the marketing and use of explosive precursors. Overall,
classification models for the classification of five precursors with different forms were computed.
These precursors are listed in Table 3as codes, because their names are Confidential EU.
Table 3. Precursors considered as targets of the different sensors included in the network.
Sensor Precursor Code Precursor Form
QEPAS B02 Vapor
B10 Vapor
EC
B01 Waterborne
B08 Waterborne
B15 Waterborne
LIDAR B02 Vapor
B10 Vapor
SERS B15 Particulate
Three key requirements were identified in the development of the present expert system:
(i) minimization of false positive detections at sensor level (described in Section 3.2); (ii) flexibility of
the system in terms of type and number of sensors included at any time in the network (described in
Section 3.3); (iii) flexibility of the system in terms of choice of the parameters that determine the global
alarm activation (described in Section 3.4), in order to allow the user to easily customize the system
according to the condition-specific needs.
All data analysis were performed using the PLS Toolbox ver. 7.5 and ver. 7.8.2 (Eigenvector
Research Inc., Manson, WA, USA) and all routines were written in Matlab
©
platform 7.11 R2010b
(The Mathworks Inc., Natick, MA, USA).
3.2. STEP 1: Supervised Pattern Recognition at Sensor Level
At the first step, the expert system is requested to estimate the predicted probability of presence
of each target for each sensor included in the sensor network. To this aim, different classification
rules were chosen, based on the nature of the sensor data. In particular, for QEPAS and EC, Partial
Least Squares-Discriminant Analysis (PLS-DA) [
9
] models were developed using experimental data
acquired from laboratory and field conditions.
The PLS-DA classification models were validated by means of an external test set, and their
performance was evaluated on the basis of the following parameters:
•
sensitivity (SENS): the percentage of objects of each modelled class correctly accepted by the
class model;
•
specificity (SPEC): the percentage of objects of the other classes correctly rejected by the class model;
•efficiency (EFF): the geometric mean of sensitivity and specificity.
Challenges 2017,8, 1 5 of 18
Once the optimal model for each target substance has been selected, for any new measurement the
probability of the presence of the different target substances can be computed and stored. An alarm is
then triggered if this value exceeds the corresponding probability threshold. In order to better exploit
the collected analytical information, two different thresholds are considered:
•
a lower threshold (maxeff_thresh): probability threshold which provides the best compromise
between false positive and false negative results;
•
a higher threshold (maxspec_thresh): probability threshold which allows to minimize false
positive results.
Following the double threshold approach, two different alarm levels are considered: a lower
alarm level is provided when the predicted probability exceeds the value of maxeff_thresh but is lower
than maxspec_thresh, because it means that the predicted probability value is below the threshold that
minimizes the false positive results, while a higher level alarm is given when the predicted probability
exceeds the value of maxspec_thresh as well.
In the case of the SERS sensor, a simpler classification rule was used, based on the correlation
coefficient between the signal measured on the sample and the corresponding signal measured on the
pure target. Concerning the LIDAR sensor, it provided a concentration value of the precursor B10 [
7
].
In addition to the display of the detected target names and of the obtained predicted probability
values, a simple colour-based code has been implemented to summarize this information in the expert
system human interface and facilitate the interpretation of results. In particular, a green bar is used to
indicate that no detection has occurred while a blue or yellow bar is displayed when a lower or higher
alarm has been triggered, respectively. A numeric value of 1, 2 and 3 has been assigned to green, blue
and yellow colour codes, respectively (Table 4).
Table 4. Interpretation of the colour code used for the output of the expert system after the Step 1.
Colour Code Interpretation Associated Value
Grey The target substance is not monitored by any of the
sensors included at that moment in the network. 0
Green The target substance can be detected by the network
but is not detect at that moment. 1
Blue The target substance is detected by the sensor at the
lower threshold. 2
Yellow The target substance is detected by the sensor at the
higher threshold. 3
A schematic overview of the approach used in the first step of the expert system is provided in
Figure 2.
Challenges 2017, 8, 1 5 of 19
Once the optimal model for each target substance has been selected, for any new measurement
the probability of the presence of the different target substances can be computed and stored. An
alarm is then triggered if this value exceeds the corresponding probability threshold. In order to
better exploit the collected analytical information, two different thresholds are considered:
a lower threshold (maxeff_thresh): probability threshold which provides the best compromise
between false positive and false negative results;
a higher threshold (maxspec_thresh): probability threshold which allows to minimize false
positive results.
Following the double threshold approach, two different alarm levels are considered: a lower
alarm level is provided when the predicted probability exceeds the value of maxeff_thresh but is
lower than maxspec_thresh, because it means that the predicted probability value is below the
threshold that minimizes the false positive results, while a higher level alarm is given when the
predicted probability exceeds the value of maxspec_thresh as well.
In the case of the SERS sensor, a simpler classification rule was used, based on the correlation
coefficient between the signal measured on the sample and the corresponding signal measured on
the pure target. Concerning the LIDAR sensor, it provided a concentration value of the precursor B10
[7].
In addition to the display of the detected target names and of the obtained predicted probability
values, a simple colour-based code has been implemented to summarize this information in the
expert system human interface and facilitate the interpretation of results. In particular, a green bar is
used to indicate that no detection has occurred while a blue or yellow bar is displayed when a lower
or higher alarm has been triggered, respectively. A numeric value of 1, 2 and 3 has been assigned to
green, blue and yellow colour codes, respectively (Table 4).
Table 4. Interpretation of the colour code used for the output of the expert system after the Step 1.
Colour Code
Interpretation
Associated Value
Grey
The target substance is not monitored by any
of the sensors included at that moment in the
network.
0
Green
The target substance can be detected by the
network but is not detect at that moment.
1
Blue
The target substance is detected by the sensor
at the lower threshold.
2
Yellow
The target substance is detected by the sensor
at the higher threshold.
3
A schematic overview of the approach used in the first step of the expert system is provided in
Figure 2.
Figure 2. Scheme of the expert system step for pattern recognition at sensor level.
The colour code obtained by each sensor for each target substance at this step is stored in a
matrix, which is passed to the function of the second step of the expert system. In this way, this
information can then be integrated with that of the other sensors of the network and a unique alarm
level for each target substance can be therefore provided to the end-user.
Figure 2. Scheme of the expert system step for pattern recognition at sensor level.
The colour code obtained by each sensor for each target substance at this step is stored in a matrix,
which is passed to the function of the second step of the expert system. In this way, this information
can then be integrated with that of the other sensors of the network and a unique alarm level for each
target substance can be therefore provided to the end-user.
Challenges 2017,8, 1 6 of 18
3.2.1. QEPAS Sensor
For the BONAS project, the CREO team developed a QEPAS sensor allowing IR analysis of
vapour [
10
]. At the first step, a database of experimental signals was created in order to calculate
the PLS-DA classification model at the basis of the developed expert system. In order to obtain
a representative spectra database, signals were acquired both in laboratory and on field conditions.
In particular, a laboratory dataset was created in laboratory by acquiring spectra of four target
substances, i.e., B01, B02, B03 and B10 and four interferents, i.e., Int02, Int05, Int10 and Int11 at two
different concentration levels have been acquired. In order to evaluate the reproducibility of the
system, at least two samples (replicates) of each substance at each concentration were considered.
The laboratory spectra database was initially investigated by means of Principal Component Analysis
(PCA) considering several data pre-treatments (data not shown for conciseness reasons). The results of
this explorative analysis suggested focusing on two main target molecules, i.e., B02 e B10.
Considering that the QEPAS sensor is able to analyze vapors, the database was then updated
with data acquired during on-field tests carried out according to realistic IE preparation scenarios.
During this test campaign, two target substances included in the priority list for BONAS and one
interferent were tested, i.e., target B02, target B10 and Int10. The composition of the updated datasets
used for classification model computation of both target B02 and target B10 are reported in Table 5.
Table 5.
Composition of the QEPAS dataset for target B02 and target B10 classification model computation.
Target Class # Spectra Training Set # Spectra Test Set Total Number of Spectra
B02 Class TARGET B02 139 90 229
Class NO TARGET B02
340 363 703
B10 Class TARGET B10 94 59 153
Class NO TARGET B10
385 395 780
Subsequently, these datasets have been used to compute the classification models for the two
targets and the selected classification model was then used to analyze the data acquired during on
field tests carried out in realistic scenarios.
3.2.2. EC Sensor
For the BONAS project, the UCBL team [
7
] developed an electrochemical sensor dedicated to
the achievement of simultaneous monitoring of different explosive precursors. The BONAS EC
sensor uses electrodes to oxidize or reduce molecules soluble in water using portable electrodes,
easily hidden in the sewage system, operating stand-alone by battery, with wireless communication
capability. The electrochemical chips used for the acquisition of the database signals were of one
counter electrode, one pseudo-reference electrode and eight working electrodes. These electrodes were
divided into two series of four electrodes each; in each series, three electrodes were modified with
different electrodeposited metals (gold, palladium and platinum), resulting in four voltammograms
per analysis. In addition to the analysis of the individual signals of the different electrode surfaces,
another approach based on the creation of a unique signature for each sample was applied so as to
take full advantage of the information provided by the different electrodes on the same sample. To this
aim, the signals acquired with the four electrode surfaces were merged together as a sequence and
each block of data then was scaled to unit variance, in order to assign equal importance to information
provided by the different electrodes. A schematic representation of this approach is shown in Figure 3.
Challenges 2017,8, 1 7 of 18
Challenges 2017, 8, 1 7 of 19
Figure 3. Electrochemical data analysis approach.
In addition to the three main target substances analysed by this sensor, i.e., B01, B08 and B15,
data were acquired also on additional target substances, i.e., B04, B05 and B11, and on the interferent
Int09, selected as components of common cleaning products. The measurements were realized in: (i)
NaCl 0.1 M electrolyte solution, (ii) tap drinking water, (iii) tap non-drinkable water iv) soap water
(GEH dish washing soap 0.1% v/v in tap drinking water) and (iv) artificial sewage water prepared,
using tap drinking water. The composition of the dataset used for the calculation of the PLS-DA
classification models of target B01, target B08 and target B15 is reported in Table 6.
Table 6. Composition of the training set and test set used to compute and validate the classification
models of the EC sensor.
Target
Class
# Spectra Training
Set/Electrode
# Spectra Test
Set/Electrode
Total Number of
Spectra/Electrode
B01
Class TARGET B01
25
11
36
Class NO TARGET
B01
187
122
309
B08
Class TARGET B08
33
22
55
Class NO TARGET
B08
179
111
290
B15
Class TARGET B15
33
21
54
Figure 3. Electrochemical data analysis approach.
In addition to the three main target substances analysed by this sensor, i.e., B01, B08 and B15, data
were acquired also on additional target substances, i.e., B04, B05 and B11, and on the interferent Int09,
selected as components of common cleaning products. The measurements were realized in: (i) NaCl
0.1 M electrolyte solution; (ii) tap drinking water; (iii) tap non-drinkable water (iv) soap water (GEH
dish washing soap 0.1% v/v in tap drinking water) and (v) artificial sewage water prepared, using tap
drinking water. The composition of the dataset used for the calculation of the PLS-DA classification
models of target B01, target B08 and target B15 is reported in Table 6.
Table 6.
Composition of the training set and test set used to compute and validate the classification
models of the EC sensor.
Target Class # Spectra Training
Set/Electrode
# Spectra Test
Set/Electrode
Total Number of
Spectra/Electrode
B01 Class TARGET B01 25 11 36
Class NO TARGET B01
187 122 309
B08 Class TARGET B08 33 22 55
Class NO TARGET B08
179 111 290
B15 Class TARGET B15 33 21 54
Class NO TARGET B15
179 112 291
Challenges 2017,8, 1 8 of 18
Similarly to the QEPAS sensor, also in this case these datasets have been used for the computation
of classification models to be applied on the data acquired during on field tests carried out in
realistic scenarios.
3.2.3. SERS Sensor
For the BONAS project, the Serstech team [
7
] developed a SERS based sensor, used for detecting
particles and/or vapours in the air surrounding a potential IED factory. Briefly, the sensor system
consists of a miniaturized Raman spectrometer in combination with a sampling system for collecting
particles and/or vapours from the surrounding air. The sampling system collects air through
an inlet fan and directs this to a cooled SERS-surface were particles are trapped onto the surface.
The SERS-substrate is then dried by heating and placed in position for measurement by the Raman
spectrometer. The resulting spectral information is transferred to the command centre for data analysis.
In order to test the possibility to successfully integrate this sensor in the expert system, a very
preliminary data analysis approach was applied during the on field test campaign. The approach was
based on the calculation of the linear correlation coefficient (R) between any new acquired spectrum
and a reference spectrum, after having applied a pre-processing step by means of linear detrend in
order to remove baseline shifts. Furthermore, on the basis of a meeting with the specialists in charge
of the sensor, only the data in spectral range between 812 and 880 nm have been considered for data
analysis. For alarm triggering, it was set a criteria of correlation coefficient above a threshold of 0.8
with a significance level of 0.05.
3.2.4. Lidar/DIAL Remote Sensor
For the BONAS project, the ENEA [
7
] and CSM teams [
7
] developed a lidar/DIAL remote sensor.
As far it is concerned, no pattern recognition analysis has been required, since the sensor output is not
provided as raw data or signals but directly as target B10 concentration values. In this case, therefore,
the expert system just compares the concentration value in output with a concentration threshold
defined experimentally that can be easily modified by the user.
This information is then integrated with the response of the other sensors during the second step
of the expert system in order to trigger an alarm.
In order to take into account the limited selectivity of the sensor response, at this step the
concentration values obtained for target B10 were also converted to equivalent concentration values
of the other target substance the sensor can detect, i.e., target B02 . This is achieved by applying
a correction factor defined according of the optical absorption characteristic of target B02. Therefore,
whenever lidar/DIAL remote sensor has a positive detection, alarms are triggered for all the target
substances it can detect (i.e., B10 and B02). However, this aspect is considered at the second step of
the expert system and, when other sensors confirm the detection of only one of the lidar/DIAL target
substances, the alarms triggered for the other one is set back to “no detection”.
3.3. STEP 2: High-Level Data Fusion for Single Target Substance Detection
At the second step, the expert system is requested to integrate the output given from multiple
sensors measuring the same target to provide the user with a unique alarm value for each compound.
Over the years, a considerable number of data fusion methods have been developed, which can
be roughly subdivided into three different levels as follows [11]:
•
low-level fusion, where the raw signals provided by the different sensors are combined before
performing any data pre-processing;
•
mid-level fusion, which is based on the combination of features extracted from the data of
each sensor;
•
high-level fusion, where the response of the different sensors (e.g., detection decision) are
combined to obtain a unique response.
Challenges 2017,8, 1 9 of 18
Considering the need of having a flexible expert system able to include any new sensor as well as
to consider the possibility of temporary inactivity (e.g., battery drained waiting for replacement) of
some sensors, the high level data fusion approach has been selected to be used in the development of
the expert system.
In particular, high level data fusion was used at this stage to enable the expert system to integrate
the information about the alarms triggered for the same target substance by all the sensors which are
included at that moment in the network and which are able to detect it.
To this aim, a dedicated routine was developed, which runs over all the target substances the
sensors network is monitoring at that moment and it checks if any alarm has been triggered for each of
them. If this is not the case, nothing happens and the green colour bar is displayed in the user interface.
Similarly, when only one sensor is detecting that target substance, the same alarm level and the same
color code given by that sensor is kept.
Whenever more than one sensor detects the same target substance, the information about their
relative distance is first considered. For this reason, the distances between the positions of all those
sensors are computed and compared with the defined threshold of maximum distance. For the
stand-off sensors, the coordinates of the actual sampling volume are computed and considered in the
calculation of their relative distance to the other sensors.
At this point, if all the sensors are closer to each other than the distance threshold, the final alarm
level is calculated as a weighted sum of the alarm levels of the individual sensors. The alarm of each
sensor is thus weighted according to the efficiency value of the model used at Step 1 for its definition
so as to take into account also the ability of that model to correctly discriminate samples belonging or
not to the target class. If this value is greater than 4 (orange colour code) then the alarm is set to the
maximum level, i.e., 5, corresponding to a red colour code.
The colour codes used to represent the Step 2 output in the human interface are summarized in
Table 7.
Table 7. Interpretation of the colour code used to represent the Step 2 output.
Colour Code Interpretation Associated Value
Grey The target substance is not monitored by any of the sensors
included at that moment in the network. 0
Green The target substance can be detected by the network but is not
detect at that moment. 1
Blue The target substance is detected by the network at the lowest
threshold by one sensor only. 2
Yellow The target substance is detected by one sensor only at the
highest threshold or by two sensors at the lower threshold. 3
Orange The target substance is detected by at least two sensors at the
.higher threshold 4
Red The target substance is detected by at least two sensors at the
higher threshold. 5
It has to be underlined that the approach used in the development of the expert system allows
maximum flexibility in terms of integration of the response provided by any sensor, even if not
considered in the previous step of the expert system. This characteristic is particularly important since
it allows to easily include in the network any possible new sensor by just communicating to the expert
system the information about the code of the detected target substance, the concentration/probability
measured, the concentration/probability threshold and its location. The flexibility of the expert system
in easily integrating the response of sensors not considered at Step 1 has been successfully tested in the
integration of the lidar/DIAL remote sensor response.
3.4. STEP 3: Data Fusion for Multiple Target Substances Detection
At the last step, the expert system further integrates the outputs of Step 1 and Step 2 to provide
the user with a unique evaluation of the possible criminal threat of an IE production site, defined
Challenges 2017,8, 1 10 of 18
taking into account the information obtained from the entire network of sensors. The global alarm level
provided as output of this third step, in fact, considers sensor location, the total number of detected
targets as well as the detection of specific targets known to be used together to prepare a specific IE in
a reasonably short time window.
In this context, particular attention has been paid to the development of a system characterized by
maximum flexibility in terms of choice of the parameters that determine the alarm activation, in order
to allow the user to easily customize the system according to the condition-specific needs. This means
that the end user can easily modify the conditions that define an alarm triggering event as well as the
global alarm threshold used to indicate the presence of a possible threat.
In the set up adopted, the global alarm ranges from 1 to 100 and the alarm threshold was set to 50.
In case explosive compounds are being monitored by the sensors network, the detection of just one
of them is considered as an alarm triggering event and the global alarm score is increased above the
threshold according to its probability of detection. When only precursors are monitored, this threshold
can be exceeded only in case specific couples of target substances are detected, while otherwise the
alarm level is lower or equal to 50. The reason of this approach is to avoid an excessive number of
false positives, because precursors can be used in legal activities. The first part of the score (from 1
to 50) is in fact just a weighed sum of the information provided by Step 2, where the weight to be
given to each detected target substance is calculated according to the total number of target substances
monitored at that time. The second part of the score (from 51 to 100) is instead defined according to the
number of specific couples of target substances, which are being detected, and to their probability level
of detection. The names of all the couples of target substances are displayed when detected together.
We stress once again the point that the system has maximum flexibility and that therefore the
final user, according to the specific needs, can easily implement different choices about the parameters,
which define the alarm activation. The user can thus easily modify the list of individual targets
whose detection determine the alarm initiation in order to include, for example, not only explosive
compounds but also precursors of particular interest. Similarly, also the list of couples of targets
substances can be updated without requiring any modification of the routine code.
As mentioned above and similarly to what described in Step 2 (see Section 3), also in this context
the information about the relative distance between sensors is taken into account before combining the
information about couples of target substances, i.e., the alarm is raised above the threshold only if the
sensors which have detected the couple of target substances are located within the distance threshold
defined by the user.
4. Discussion
The discussion of the results is organized in a first section (4.1), where we report about applying
the PLS-DA classification models developed for the QEPAS and EC sensors to the relevant test set
signals in laboratory conditions (Step 1), and a second section (4.2) where we discuss the results of
the final validation in real scenario both at single sensor level (Step 1) and by the whole network of
sensors (Step 2 and Step 3).
4.1. Validation of PLS-DA Classification Models in Laboratory Conditions
With regards to QEPAS sensor, efficiency values in prediction equal to 98.13% and 100.00% were
obtained for target B10 and target B02, respectively (see Table 8).
Table 8.
PLS-DA classification results obtained in prediction on QEPAS spectra acquired for models
validation in laboratory conditions.
B10 B02
SENS SPEC EFF SENS SPEC EFF
100.00 96.30 98.13 100.00 100.00 100.00
Challenges 2017,8, 1 11 of 18
The PLS-DA classification model for the EC sensor has been calculated for the three main targets,
i.e., B01, B08 and B15, and the results obtained for the prediction of the test set samples are reported in
Table 9.
Table 9.
PLS-DA classification results obtained in prediction on the EC signals acquired for models
validation in laboratory conditions.
B01 B08 B15
SENS SPEC EFF SENS SPEC EFF SENS SPEC EFF
90.90 100.00 95.34 100.00 99.10 99.55 100.00 100.00 100.00
4.2. Expert System Validation in a Real Case Scenario
A final validation of the whole expert system structure was carried out during two demos of the
project, one at the Italian Air Force base of Pratica di Mare (Italy) and one at the Swedish Defence
Research Agency (FOI) in Grindsjön (Sweden).
During this demo, real case scenario tests were in fact performed in order to verify the detection
abilities of the individual sensors as well as the expert system ability to successfully integrate their
response. No details related to test protocols followed during the demo are described as they are
Confidential EU.
4.2.1. Validation of Pattern Recognition Models at Sensor Level
Considering that these tests were carried out on field and in real case scenario, target
concentrations were not constant during the whole test due to several factors such as weather
conditions. It can be however noticed that positive detections were obtained during all the tests
while no false positive detections occurred (Tables 10 and 11).
As for the QEPAS sensor, three tests were performed for each of the two target substances, i.e.,
target B10 and target B02. During these trials, the QEPAS sensor was placed inside a dumpster used
for camouflage and used to analyse vapours of IE precursors generated during an IE production (or
the simulation of an IE production). The results are reported in terms of alarm colour codes triggered
after Step 1 (please refer to Table 4for colour code interpretation).
Table 10. Results of the three tests performed at FOI for target B10.
# Test # Measurement Cycle Target B10 Target B02
Test 1
1 3 1
2 1 1
3 1 1
4 2 1
5 1 1
Test 2
1 3 1
2 3 1
3 3 1
4 3 1
Test 3
1 3 1
2 3 1
3 3 1
4 3 1
5 1 1
As for the QEPAS sensor, also the capability of the electrochemical sensor to detect IE precursors
directly in sewage water drains from simulated bomb factories has been performed during the final
demonstration in Sweden. Three different scenarios have been tested in this context, each of them
involving the use of one the three main targets at one step of an IE preparation. It has to be underlined
Challenges 2017,8, 1 12 of 18
that all the experiments performed during this test campaign were carried out using local tap water
and a sink system found in loco (Figure 4).
Table 11. Results of the three tests performed at FOI for target B02.
# Test # Measurement Target B10 Target B02
Test 4
1 1 3
2 1 3
3 1 3
4 1 3
5 1 3
Test 5
1 1 1
2 1 3
3 1 3
4 1 3
5 1 3
Test 6
1 1 3
2 1 1
3 1 3
4 1 3
5 1 3
6 1 1
Challenges 2017, 8, 1 12 of 19
Table 11. Results of the three tests performed at FOI for target B02.
# Test
# Measurement
Target B10
Target B02
Test 4
1
1
3
2
1
3
3
1
3
4
1
3
5
1
3
Test 5
1
1
1
2
1
3
3
1
3
4
1
3
5
1
3
Test 6
1
1
3
2
1
1
3
1
3
4
1
3
5
1
3
6
1
1
As for the QEPAS sensor, also the capability of the electrochemical sensor to detect IE precursors
directly in sewage water drains from simulated bomb factories has been performed during the final
demonstration in Sweden. Three different scenarios have been tested in this context, each of them
involving the use of one the three main targets at one step of an IE preparation. It has to be underlined
that all the experiments performed during this test campaign were carried out using local tap water
and a sink system found in loco (Figure 4).
Figure 4. EC sensor experimental set-up used during the final demonstration carried out at FOI
(Grindsjön).
The results obtained by applying the final classification models (see Section 4.1) to the
measurements acquired during the experiments performed in the final demonstration are shown in
Table 12.
Table 12. Results obtained for the EC sensor during the tests performed during the final
demonstration at FOI.
# Test
Target
Alarm Colour Code
B01
B08
B15
Figure 4.
EC sensor experimental set-up used during the final demonstration carried out at FOI (Grindsjön).
The results obtained by applying the final classification models (see Section 4.1) to the
measurements acquired during the experiments performed in the final demonstration are shown
in Table 12.
It can be noticed that true positive alarms were obtained for the target substances during all the
tests. However, in some of the tests carried out on target B01, false positive alarms were obtained also
for target B08. In order to investigate the reason for this anomalous behaviour not observed during
the laboratory test, EC signals acquired during the tests on target B01 were compared to the average
signals previously acquired in tap water on target B01 and target B08. It was observed that, despite the
signals acquired during the final demonstration showed most of the typical features of the average B01
signals, the intensity registered especially using the Pd and Pt electrodes was lower than usual and
Challenges 2017,8, 1 13 of 18
more similar to the intensity registered in the presence of target B08. A possible explanation for this
behaviour could be the degradation of the solution used during the tests due to the high instability of
target B01. Furthermore, it has to be underlined that a more limited number of signals acquired in
a lower number of experimental conditions was available for this target substance. For these reasons,
a further training of the expert system with a larger number of samples, acquired, e.g., at lower
concentrations, could allow for significant improvement of the expert system performance.
Table 12.
Results obtained for the EC sensor during the tests performed during the final demonstration
at FOI.
# Test Target Alarm Colour Code
B01 B08 B15
Test 1 B15 1 1 3
Test 2 B01 3 1 1
Test 3 B08 1 3 1
Test 4 B01 3 3 1
Test 5 B15 1 1 3
Test 6 B01 3 3 1
Test 7 B15 1 1 3
Test 8 Blank 1 1 1
Test 9 Blank 1 1 1
Test 10 B15 1 1 3
Test 11 B08 1 3 1
Test 12 Blank 1 1 1
Test 13 B15 1 1 3
Test 14 B01 3 3 1
Test 15 Blank 1 1 1
Test 16 B08 1 3 1
Test 17 Blank 1 1 1
Test 18 B08 1 3 1
Test 19 B08 1 3 1
Test 20 Blank 1 1 1
As mentioned in Section 3.2, in the case of the SERS sensor, no pattern recognition was applied
and the possibility to successfully integrate the sensor in the network was evaluated by triggering the
alarm on the basis of the correlation coefficient value calculated between the signal measured on the
sample and the corresponding signal measured on the pure target. During the final demo, four tests
were performed by applying a real case scenario involving the use of Target B15. The spectra obtained
during these tests are reported in Figure 5, where it can be observed that positive detections by the
Raman spectrometer were obtained in all experiments.
Despite that positive detections were observed in all experiments, the initiated alarms obtained
from the analysis of the pretreated data (Table 13) show that false negative detections were also
observed. In particular, the false alarm obtained for the Test 4 spectrum acquired was likely due to
a slight misalignment of the main absorption peak. The application of different data pretreatment
methods as well as of different data analysis approaches is expected to greatly help in solving this issue.
In conclusion, the tests performed during the demo at FOI confirmed the capability of the SERS
sensor in sampling particles from the atmosphere and successfully acquiring spectra on the collected
analytical data. The promising results obtained suggest that the acquisition of a more representative
database of experimental data could allow developing a more reliable and robust data analysis step as
well as to extend the list of target substances for this sensor.
Challenges 2017,8, 1 14 of 18
Challenges 2017, 8, 1 14 of 19
Figure 5. Raman spectra pretreated by means of linear detrend.
Despite that positive detections were observed in all experiments, the initiated alarms obtained
from the analysis of the pretreated data (Table 13) show that false negative detections were also
observed. In particular, the false alarm obtained for the Test 4 spectrum acquired was likely due to a
slight misalignment of the main absorption peak. The application of different data pretreatment
methods as well as of different data analysis approaches is expected to greatly help in solving this
issue.
Table 13. Results of the analysis of the spectra pretreated by linear detrend.
# Test
Target
Alarm Color Code
Test 1
B15
2
Test 2
B15
1
Test 5
B15
1
Test 6
B15
2
Blank
B15
1
In conclusion, the tests performed during the demo at FOI confirmed the capability of the SERS
sensor in sampling particles from the atmosphere and successfully acquiring spectra on the collected
analytical data. The promising results obtained suggest that the acquisition of a more representative
database of experimental data could allow developing a more reliable and robust data analysis step
as well as to extend the list of target substances for this sensor.
4.2.2. Validation of Expert System Data Fusion at Sensors Network Level
In addition to validating the classification models developed for the individual sensors, an
extensive validation of the capability of the expert system to effectively integrate the information
provided by the different sensors has also been carried out during the two test campaigns at the
Italian Air Force base of Pratica di Mare and in Sweden at FOI.
During both test campaigns, the command and control (C2) centre has shown its ability to
communicate with all the sensors included in the network and to process, by means of the expert
system, the data received. The live alarm monitor end‐user application developed by Tekever
allowed a real-time monitoring of the information provided by the expert system. In fact, the user
interface reports:
on the left, the alarm obtained for each target substance considering the information of all the
sensors able to monitor it (Step 2 output);
Figure 5. Raman spectra pretreated by means of linear detrend.
Table 13. Results of the analysis of the spectra pretreated by linear detrend.
# Test Target Alarm Color Code
Test 1 B15 2
Test 2 B15 1
Test 5 B15 1
Test 6 B15 2
Blank B15 1
4.2.2. Validation of Expert System Data Fusion at Sensors Network Level
In addition to validating the classification models developed for the individual sensors,
an extensive validation of the capability of the expert system to effectively integrate the information
provided by the different sensors has also been carried out during the two test campaigns at the Italian
Air Force base of Pratica di Mare and in Sweden at FOI.
During both test campaigns, the command and control (C2) centre has shown its ability to
communicate with all the sensors included in the network and to process, by means of the expert
system, the data received. The live alarm monitor end-user application developed by Tekever
allowed a real-time monitoring of the information provided by the expert system. In fact, the user
interface reports:
•
on the left, the alarm obtained for each target substance considering the information of all the
sensors able to monitor it (Step 2 output);
•
in the middle, the global alarm which summarizes the information from the entire network into
a unique evaluation of the possible criminal threat of an IE production site (Step 3 output);
•on the right, a map showing the location of the different sensors is also reported. To indicate the
sensors’ positions, markers coloured according to the colour code of Step 2 are used in order to
show a possible positive detection of the sensor;
•
a circle with a radius equal to the value maximum distance threshold for data integration is also
reported for each sensor.
In this way, the interface allows to monitor the data based on which sensors are integrated.
As an example, the user interface obtained during an experiment performed at FOI is shown in
Figure 6. From the interface, it can be noticed that only one sensor is showing positive detections and
that two targets are being detected. The location of the sensor corresponded to the lidar/DIAL remote
sensor, while the two detected targets were target B02 and target B10. The double detection provided
Challenges 2017,8, 1 15 of 18
by the sensor is due to its limited selectivity. Since two target substances are being detected at the
lower alarm level, a low global alarm level is provided as output of Step 3.
Challenges 2017, 8, 1 15 of 19
in the middle, the global alarm which summarizes the information from the entire network
into a unique evaluation of the possible criminal threat of an IE production site (Step 3
output);
on the right, a map showing the location of the different sensors is also reported. To indicate
the sensors’ positions, markers coloured according to the colour code of Step 2 are used in
order to show a possible positive detection of the sensor;
a circle with a radius equal to the value maximum distance threshold for data integration is
also reported for each sensor.
In this way, the interface allows to monitor the data based on which sensors are integrated.
As an example, the user interface obtained during an experiment performed at FOI is shown in
Figure 6. From the interface, it can be noticed that only one sensor is showing positive detections and
that two targets are being detected. The location of the sensor corresponded to the lidar/DIAL remote
sensor, while the two detected targets were target B02 and target B10. The double detection provided
by the sensor is due to its limited selectivity. Since two target substances are being detected at the
lower alarm level, a low global alarm level is provided as output of Step 3.
Figure 6. User interface during an experiment performed at FOI, which shows the results for the two
possible targets giving positive detection by Lidar.
Another example of a user interface obtained during the test campaign at FOI is shown in Figure
7. In this case, in addition to the detection described above, also another sensor, i.e., the
electrochemical sensor, showed a positive detection for target B08 with a probability above the
threshold which minimizes false positives (colour code yellow, probability of 100%). However, since
no couples of targets known to be used together in the preparation of IEs have been detected, the
global alarm still shows a low value.
Figure 6.
User interface during an experiment performed at FOI, which shows the results for the two
possible targets giving positive detection by Lidar.
Another example of a user interface obtained during the test campaign at FOI is shown in Figure 7.
In this case, in addition to the detection described above, also another sensor, i.e., the electrochemical
sensor, showed a positive detection for target B08 with a probability above the threshold which
minimizes false positives (colour code yellow, probability of 100%). However, since no couples of
targets known to be used together in the preparation of IEs have been detected, the global alarm still
shows a low value.
Challenges 2017, 8, 1 16 of 19
Figure 7. Example of user interface obtained during the test campaign at FOI, which shows the
positive detections for three targets obtained by two sensors. No couples of targets are known to be
used together in the preparation of one IE.
A third example of a user interface obtained during the test campaign at FOI is shown in Figure
8. It can be noticed that, as in the previous case, three target substances are being detected by two
sensors. However, since target B02 and target B15 are one of the couples of targets known to be used
together in the preparation of one IE, a much higher global alarm level is obtained in this case as
output of Step 3.
Figure 8. Example of a user interface obtained during the test campaign at FOI, which shows the
positive detections for three targets obtained by two sensors. Since a couple of target detected is
known to be used together for the preparation of one IE, the global alarm has increased above the
threshold of 50.
Figure 7.
Example of user interface obtained during the test campaign at FOI, which shows the positive
detections for three targets obtained by two sensors. No couples of targets are known to be used
together in the preparation of one IE.
Challenges 2017,8, 1 16 of 18
A third example of a user interface obtained during the test campaign at FOI is shown in Figure 8.
It can be noticed that, as in the previous case, three target substances are being detected by two sensors.
However, since target B02 and target B15 are one of the couples of targets known to be used together in
the preparation of one IE, a much higher global alarm level is obtained in this case as output of Step 3.
Challenges 2017, 8, 1 16 of 19
Figure 7. Example of user interface obtained during the test campaign at FOI, which shows the
positive detections for three targets obtained by two sensors. No couples of targets are known to be
used together in the preparation of one IE.
A third example of a user interface obtained during the test campaign at FOI is shown in Figure
8. It can be noticed that, as in the previous case, three target substances are being detected by two
sensors. However, since target B02 and target B15 are one of the couples of targets known to be used
together in the preparation of one IE, a much higher global alarm level is obtained in this case as
output of Step 3.
Figure 8. Example of a user interface obtained during the test campaign at FOI, which shows the
positive detections for three targets obtained by two sensors. Since a couple of target detected is
known to be used together for the preparation of one IE, the global alarm has increased above the
threshold of 50.
Figure 8.
Example of a user interface obtained during the test campaign at FOI, which shows the
positive detections for three targets obtained by two sensors. Since a couple of target detected is known
to be used together for the preparation of one IE, the global alarm has increased above the threshold
of 50.
Finally, we want to emphasize that during the final demonstration at the Swedish Defence
Research Agency, also the sensors developed in another project (EMPHASIS) [
12
] were able to
communicate with the BONAS Central Command Unit (CCU) in order to have their data processed by
means of the BONAS expert system. In particular, the positive detections of the EMPHASIS sensors
were successfully integrated in the second step of the BONAS expert system and did participate to
determine both the individual target substance alarms and the global alarm of the sensor network.
This test campaign, therefore, confirmed that the approach used in the development of the BONAS
expert system allows maximum flexibility in terms of integration of the response provided by any
sensor, even if not considered at the beginning of the expert system development. It is important to
note that, despite the use of IR and Raman sensors, any positive detection produced by the network is
not yet a forensic identification. To obtain a forensic identification of a chemical substance (e.g., the IE
TATP) a suitable number of laboratory analyses must be carried out [13,14].
The capability to integrate the response any possible new sensor is the basis to establish a large
network supporting safer smart cities, where many existing sensors can be networked in a grid
allowing new elements of new types to collect information to be provided to the security authority [
15
].
5. Conclusions
This paper describes the innovative expert system approach developed in the BONAS research
project to exploit the information provided by the whole sensor network and to provide the end-user
with a unique global evaluation of the possible criminal threat of an IE production site.
Three key requirements have been identified in the development of the present expert system:
(i) minimization of false positive detections at the sensor level (developed within Step 1); (ii) flexibility
Challenges 2017,8, 1 17 of 18
of the system in terms of type and number of sensors included at any time in the network (developed
within Step 2); (iii) flexibility of the system in terms of choice of the parameters that determine the
global alarm activation (developed within Step 3), in order to allow the user to easily customize the
system according to the condition-specific needs.
In order to meet these requirements, an expert system which follows a three-step workflow has
been created.
During the first step, the expert system estimates the predicted probability of presence of each
target for each sensor included in the BONAS network. To this aim, classification models were
computed by means of a PLS-DA algorithm with a dataset of experimental data acquired by means of
QEPAS and EC sensors both in laboratory and in field conditions. The computed classification models
were then applied to any new data received to obtain the corresponding predicted probability value.
Two different probability thresholds were then considered for alarm activation, one which allows to
maximize classification efficiency and one which allows to maximize classification specificity (i.e., to
minimize false positive results).
During the second step, the expert system integrates the output given from multiple sensors
measuring the same target to provide the user with a unique alarm value for each compound. In this
context, the application of a high-level data fusion approach allows to meet the requirement of having
a flexible expert system able to include at this stage any new sensor (e.g., LIDAR sensor) as well as to
consider the possibility of temporary inactivity of some sensors.
During the last step, the expert system further integrates the outputs from the previous steps to
provide the user with a unique global evaluation of the possible criminal threat of an IE production
site. The global alarm level provided as output of this third step is, in fact, defined considering the
sensors’ locations, the short time, if any, between the positive detections of the total number of detected
targets as well as the detection of specific targets known to be used together to prepare a specific IE.
At this stage, the end user can easily customize the system according to the condition-specific needs by
modifying the parameters that determine the alarm activation (e.g., the list of individual targets or the
list of couples of targets substances whose detection determine the alarm).
Finally, a dedicated user interface has been developed in collaboration with TEKEVER (reference)
to provide the user with a compact representation of the information given by the expert system.
The performance of the developed sensor network was evaluated under relevant environmental
conditions, i.e., in the presence of interferents and pollutants in both air and water, in the Italian
Air Force base of Pratica di Mare and in Swedish Defence Research Agency facility in Grindsjön.
An extensive validation of the capability of the expert system was carried out to effectively integrate
the information provided by the different sensors showing that the end-users were effectively provided
with a global alarm level, summarizing information from the entire network into a unique evaluation
of the possible criminal threat of an IE production site. The expert system during the validation
showed its capability to handle information not only from the BONAS sensors, but also from the
sensors developed in another project, EMPHASIS. This flexibility in terms of integration of the response
provided by any sensor allows easily including in the network any possible new sensor and establishing
a large network supporting safer smart cities.
Acknowledgments:
The project BONAS was funded under the 7th Framework Programme of the European
Commission, grant agreement No. 261685. We acknowledge Christophe A. Marquette, Directeur Adjoint of
the Institut de Chimie et Biochimie Moléculaires et Supramoléculaires Equipe Génie Enzymatique, Membranes
Biomimétiques et Assemblages Supramoléculaires (GEMBAS) of the University Lyon 1 (France) for the tests carried
out with the electrochemical sensor, Pedro Santo Antonio from TEKEVER, Obidos (Portugal), for developing the
software connecting the sensors of the BONAS network.
Author Contributions:
Francesco Saverio Romolo proposed the general approach for the expert system to handle
the network of sensors (Step 1, Step 2 and Step 3), and contributed to the writing of this article. Carlotta Ferrari
developed and validated chemometric models, and contributed to the writing of this article. Alessandro Ulrici
contributed to the validation of the results of the expert system, and supervised the writing of this article.
Challenges 2017,8, 1 18 of 18
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.
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©
2017 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC-BY) license (http://creativecommons.org/licenses/by/4.0/).
