Advances in Production Engineering & Management

ISSN 1854-6250

Volume 17 | Number 4 | December 2022 | pp 494–504

Journal home: apem-journal.org

https://doi.org/10.14743/apem2022.4.451

Original scientific paper

Impact of Cobot parameters on the worker productivity:
Optimization challenge
Javernik, A.a, Buchmeister, B.a, Ojstersek, R.a,*
aUniversity

of Maribor, Faculty of Mechanical Engineering, Maribor, Slovenia

ABSTRACT

ARTICLE INFO

In the era of Industry 4.0 and the introduction of new technologies, collaborative workplaces represent the potential to increase the efficiency of manufacturing systems. The presented research focuses on studying the impact of
changing the speed and acceleration of a Cobot to the number of finished products at a collaborative workstation, the average assembly time, and the utilization of the Cobot and worker. In a laboratory experiment, it was demonstrated
that changing the parameters of the Cobot significantly affects the optimization
parameters of the collaborative workstation productivity. The results indicate
an increase in production capacity with an increase in the speed and acceleration of the Cobot, while at the same time highlighting the importance of uniform
utilization and occupancy of the Cobot and worker. The findings are particularly interesting from the influence of the Cobot’s audio and video effects on
worker, when reducing the average assembly time while increasing the Cobot's
capabilities. The results and findings presented open up important new areas
of research in the field of social, time and financial justification of collaborative
workplaces.

Keywords:
Collaborative robot;
Cobot;
Collaborative operation;
Robot parameters;
Worker productivity;
Working scenarios;
Manufacturing efficiency;
Optimization
*Corresponding author:
robert.ojstersek@um.si
(Ojstersek, R.)

Article history:
Received 10 September 2022
Revised 19 December 2022
Accepted 22 December 2022
Content from this work may be used under the terms of
the Creative Commons Attribution 4.0 International Licence (CC BY 4.0). Any further distribution of this work
must maintain attribution to the author(s) and the title of
the work, journal citation and DOI.

1. Introduction
In the era of Industry 4.0 [1], where the role of making the right decisions in optimizing existing
or newly proposed manufacturing system is key to achieving global competitiveness of the company. The optimization of manufacturing systems and its efficiency refers to both machines (technology) and workers (their knowledge and social paradigms) [2, 3]. The correct and equal distribution of occupancy between workers and machines plays a key role in sustainable production
systems in terms of social, environmental and financial aspects. Implementing an effective model
to acquire new knowledge about technologies that can increase efficiency is crucial [4]. According
to the literature [5], classical single-objective approaches do not provide satisfactory solutions,
especially when dealing with problems with high market dynamics and continuous optimization
trends. Given the global shortage of workers (especially in developed countries), collaborative robots (Cobots) represent a new opportunity for companies willing to invest into new financially
feasible technologies. In this case the research question appears: how efficient we can place them
into the existing or newly proposed manufacturing system [6]. Proper design of collaborative
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Impact of Cobot parameters on the worker productivity: Optimization challenge

workplaces (cooperation between worker and Cobot) is the main challenge for researchers and
engineers who want to increase manufacturing efficiency [7]. In doing so, we encounter the issues
of collaboration safety, time and economic justification, and their impact on the entire manufacturing system under consideration [8]. The researchers point out the importance of using simulation modelling methods [9] that allow us to effectively evaluate the time efficiency of collaborative
workplaces from the individual optimization parameters of the system [10] in a real-world industrial environment [11]. Different applications of collaborative workplaces [12] require the use of
different optimization approaches [13], where we need to ensure safe and efficient parameters of
the collaborative machines (Cobot) due to the specificity of the considered cases [14]. In this case,
data-driven predictive models [15] prove to be the most reliable methods, where we use numerical and graphical simulation results to investigate the appropriateness of introducing and correctly determining collaborative machines [16]. Collaborative workplaces and their design in
manufacturing systems present new challenges also from the ergonomic design point of view [17,
18], where the classical methods of ergonomic studies do not satisfy the criteria of a collected big
data at high production dynamics. The impact of collaborative workplaces on the efficiency of the
manufacturing systems [19] and the link with adaptive models [20] to monitor the importance of
effective implementations are based on the performance of preliminary studies in which researchers compare simulation and real-world data of manufacturing systems [21]. The major limitation
of the existing research is in the area of describing how the parameters of the Cobot affect the
efficiency of the co-worker, not only from the worker's point of view, but also with respect to the
efficiency of the Cobot and the manufacturing system as a combined unit.
In this research, we address the research question of whether changing the operating parameters of a Cobot affects the efficiency of the manufacturing system and the occupancy of the
worker itself. Based on the results, limitations, and issues of previous research works [19, 21], in
this paper, we aim to present an experimental method to study the change of speed and acceleration of a Cobot on the effectiveness of a collaborative workplace. We focus on the multi-objective
evaluation of the collaborative workplace optimization parameters with the detailed study of the
assembly times, production quantities, and occupancy of the collaborative workplace from the
Cobot and worker perspective.

2. Problem description

Cobot’s properly set parameters can have a big impact on the efficiency and capacities of the manufacturing system. To set up a proper parameter of robots/machines can be a real challenge, especially when it comes to Cobot’s. It is not necessary that highest working parameters brings the
highest number of finished orders, shortest operating times and highest machine utilization. The
optimum working parameters depends on the structure of collaborative workplace, application
type and defined sequence of tasks [22].
Most integrators/production engineers define Cobot’s working parameters their preference
and existing knowledge or based on suggestions from Cobot’s producers. In many cases, parameters are not set up properly or are even set up to the maximum limit of the machine. With higher
defined parameters, integrators want to achieve more finished products in less time. In most
cases, such a decision leads to positive results as the machine’s operating time is reduced, but is it
really only about the machine operating time, or there are also other variables we have overlooked
inside the collaborative workplace?
At observing of collaborative workplaces and their operations, we noticed that worker movements change or adapt according to parameters of the collaborative device [19, 22]. Such a finding,
immediately raised a question of whether the speed and acceleration of the Cobot could have an
impact on workers performance indicators (average worker assembly time, number of finished
products and worker occupancy)? To determine the correctness or incorrectness of our predictions in the best possible way, we had proposed the design of the collaborative workplace and
determine the most suitable type of collaborative application, presented in section three.
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2.1 Collaborative workplaces
To find out if there is a relation between the parameters of the collaborative robot and the assembly time of the workers, we had to design the structure of the experiment, a collaborative workplace layout and a collaborative operation. In the initial research phase, we had to think about
what type of application and type of collaboration between the Cobot and the worker to design.
In Fig. 1 four types of collaboration between the Cobot and the worker are presented. The first
type is a caged cell. At caged cell type, a collaboration level is zero, the robots are in cages and
there is no possible interaction between the worker and the robot. At this type mainly industrial
robots are installed. At second type, so called coexistence, the Cobot and the worker work in the
same space without fence between them, but they do not share the same workspace. Next type of
collaboration is sequential or synchronized. The worker and the Cobot share their workspace but
not at the same time, movements needs to be sequential. The last type of collaboration according
to literature [23] is cooperation. At this type of work, the level of collaboration is the highest, the
worker and the Cobot share the same workspace at the same time. In cooperation type the use of
Cobot instead of industrial robot is necessary, because of integrated safety features that allow to
work in direct contact. Even that in Cobot safety features are installed, the integrator still needs
to perform risk assessment and design the operation as save as possible [24].
To mention, definition of collaboration types differs between the individuals, because the community is still split about their opinions and definitions. But there is not a lot of differences between the sources, mainly in names and small details.

2.2 Manufacturing efficiency

Fig. 1 Types of Cobot and worker collaboration

Initially, the definition of manufacturing productivity in terms of production is the ratio of output
to input in production and is a measure of efficiency. If something is produced, we want to know
how long it takes to produce it. While productivity focuses more on increasing the quantity produced, efficiency refers to the quality and effectiveness of the manufacturing system. In our research work we are focused on the collaborative workplace’s efficiency in correlation to its performance parameters. We are focusing on evaluating the ability to do or produce products without
wasting material, time, cost, or energy. The efficiency is often expressed as a percentage, with 100
% being the ideal goal so that the product is produced at the lowest average total cost per item at
the highest possible workplace occupancy. In presented research work we are measuring efficiency with the parameter of the number of hours of productive quality assembly work divided
by the number of minutes available in the experiment run. The optimization methods and workers
training can lead to improved manufacturing efficiency. For this purpose, the key performance
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Impact of Cobot parameters on the worker productivity: Optimization challenge

indicators (KPI) were used to help evaluate Cobot and worker efficiency when the parameters of
Cobot speeds and accelerations are changing. In general: if you want to increase manufacturing
efficiency, you essentially want to produce more output in the same amount of time. Finding a
constant balance between productivity and manufacturing efficiency is critical to keeping your
manufacturing running optimally. In Fig. 2 we can see optimization perspectives with which company can achieve high manufacturing system KPI’s.

Fig. 2 Manufacturing efficiency, optimization perspectives

3. Experiment description

The presented experiment was prepared according to the collaborative workplace standards
listed in the literature [7], the research focuses on the reproducibility of the experiment in a laboratory environment, initial simulation model in Siemens software is shown in Fig. 3, where the
selected building blocks are standard elements, as presented in subsection 3.1. The experimental
environment is presented in detail in the subsections of the collaborative workspace description
and the experimental design, where the experiment design is prepared in correlation with different experts from the field of social, medicine and technical sciences.

Fig. 3 Collaborative workplace simulation model for laboratory experiments

3.1 Collaborative workplace description

The experiment was conducted with the laboratory environment, so the collaborative workplace
was adapted to laboratory conditions in terms of size and application type. Collaborative workplace consisted of a worktable ③, a collaborative robot UR3e ⑦, a collaborative gripper Robotiq
2F-85 ⑥, a switch with indicators light in green and red colour ④ and button ⑤, and two types
of semi-finished products ① ②. A Lego brick size of 4×2 represented the semi-finished product
1 ① while a brick size of 2×2 represented the semi-finished product 2 ②. The finish product
consisted of one semi-finished product ① and two semi-finished products ② assembled together, as shown in Fig. 4.
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Fig. 4 Layout of the collaborative workplace (left) and the finished product (right)

The assembly operation consisted of assembling three semi-finished products into one finished
product. The sequence of assembling was predefined to get the most objective results. At the beginning of the collaborative operation, the indicator light glowed red. The red light signalled that
the Cobot is working and the worker is not approved to enter the robot area, but the worker can
prepare himself. At the beginning, the collaborative robot picked up the semi-finished product and
brought it to the collaboration/assembly area. When the robot reached the assembly area, it
stopped, the indicator light changed from red to green and the worker was allowed to assemble.
After the worker attached two semi-finished products ② to the semi-finished product ①, he or
she pressed the button to send the signal about the completed work. The indicator light changed
back from green to red and the Cobot moved the finished product into the box. The procedure was
repeated until the total time of 30 minutes, for each experiment, was reached.
3.2 Experiment design

To test our hypothesis optimal, we decided to design an assembly operation, a collaborative assembly operation, where the Cobot and the worker work sequential. At sequential type, they share
the same workspace but at different time intervals. The sequential type of collaboration in our
case was ideal because we still provided the contact between them and we could obtain objective
results about a relation between the parameters of the robot and the operation time of the worker.
In the next phase of experiment planning, we had to determine the structure of the experiment,
length of the experiment and Cobot parameters (speed and acceleration). We decided to limit our
experiment to 30 minutes and divide it into four phases as seen in Table 1.
The experiment was divided into four phases. In the first phase, the Cobot and the worker
worked separately. The worker manually assembled the finished products for 5 minutes. The goal
of the first phase for the worker was to get used to such a type of the work and to the proximity of
the Cobot. After 5 minutes, phase 2 has begun. Phase 2 lasted 10 minutes and consisted of collaborative work. The worker assembled the finished products with the help of the Cobot. The speed
and acceleration of the Cobot were set to 60 %. After phase 2, phase 3 had started, it was set up
the same way as phase 1, mainly to relax the worker. The worker and Cobot worked separately
for 5 minutes. The goal of phase 3 was to release the pressure from the worker from the previous
phase in which the worker collaborated with the Cobot. In the last phase of the experiment, phase
4, the worker worked with the Cobot again. Phase 4 lasted the same amount of time as phase 2,
but the parameters of the Cobot were set higher, at 100 %. Cobot parameters (Table 2) were define based on attempted test in laboratory, simulation model presented in Fig. 3 and the specifics
of the application type. After the experiment was completed, we obtained the results, describing
collaborative workplace efficiency from phase 2 and phase 4.
Phase No.
Phase 1
Phase 2
Phase 3
Phase 4

498

Time length (min)
5
10
5
10

Table 1 Structure of the experiment
Description
Manual assembly operation
Collaborative assembly operation (CR speed and acceleration 60 %)
Manual assembly operation
Collaborative assembly operation (CR speed and acceleration 100 %)

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Speed (%)
60
100

4. Results

Table 2 Cobot speeds and accelerations

Linear movements
(mm/s) Acceleration (%)
600
60
1000
100

(mm/s2 )
1500
2500

add

Speed (%)
60
100

Joint movements
(°/s)
Acceleration (%)
206
60
344
100

(°/s2 )
310
516

The experiment involved nine participants, both men’s and women’s, aged from 20 to 50 years.
Until the end of experiment participants did not know the real goal of the experiment. With unknown, research goal (caning speeds and accelerations of the Cobot), we provided that they could
not had an impact on their assembly task. The results in Table 3 show the average assembly time
and number of finished products of each participant in two different scenarios corelated to phase
2 and phase 4. In both scenarios, the worker collaborated with the Cobot, but in scenario 1 (S1)
the Cobot parameters were set to 60 % meanwhile in scenario 2 (S2) the parameters were set to
100 % of the specified speed and acceleration. Throughout the experiment we focus on next optimization parameters: worker average assembly times, number of finished products and Cobot and
worker occupancy. Each worker average assembly times were saved to the time variable of the
Cobot data collecting unit. According to the number of finished products and stored assembly
times in the time variable, the average assembly time for each scenario was calculated.
4.1 Results of average assembly times and number of finished products

From the results seen in Table 3 and Fig. 5, it is clear that with increased Cobot parameters assembly times did shorten. Although the number of participants in our experiment was low, we did
not perceive a single longer assembly time while the parameters of the Cobot were increased in
comparison with the S1, where Cobot parameters were lower. The average assembly time in S1
was 3.4 s with a standard deviation of 0.81. The number of participants in which the average assembly time was below average was 5 out of 9 participants. In scenario S2, where the speed of the
robot was increased to 100 %, the average assembly time decreased to 2.6 s, in this case the standard deviation is lower at 0.66. In S2, 6 out of 9 participants had a shorter average assembly time
than the average total time.
Increasing the parameters of the Cobot from 60 % to 100 % contributed to the 23.4 % decrease
in average assembly time of the worker. The minimum decrease in average assembly time that
occurred in our experiment was 8.70 %, while the maximum decrease was 36.90 %.
The presented results of the experiment confirm our prediction. Higher define parameters of
the Cobot had an impact on the worker and resulted in a higher working speed. Due to the higher
working speed, the average assembly time is shorter, and we are able to deliver a higher number
of finished products. Using the number of finished products parameters, we see that the total number of products produced in S1 is 497 pieces for nine participants in total. On average, each participant assembled 55.2 pieces in 10 minutes, with a standard deviation of 3.9. A significantly
higher number of finished products is seen in S2, where participants assembled a total of 761
pieces in 10 minutes, while each worker assembled an average of 85.5 pieces with a standard
deviation of 7.6, as shown in Fig. 6.
Table 3 Average assembly times and Number of finished products per participant in S1 and S2
Participants
Average assembly
Number of finished
Average assembly
Number of finished
time (s) – S1
products (pcs) – S1
time (s) – S2
products (pcs) – S2
1
2.82
58
2.34
87
2
3.00
57
2.42
86
3
2.82
58
1.97
92
4
2.65
59
2.27
88
5
2.48
60
1.70
96
6
3.59
54
2.27
88
7
4.98
48
3.34
76
8
4.24
51
3.45
75
9
4.02
52
3.67
73

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Fig. 5 Average assembly times, results comparison (S1 vs S2)

Fig. 6 Number of finished products, results comparison (S1 vs S2)

4.2 Results of Cobot and worker occupancy

Table 4 shows the occupancy of the collaborative workspace (Cobot and worker time occupancy),
where the values are evaluated according to the total assembly time, which is in total 10 minutes
for the individual scenario. The results show the three optimization parameters, where the Cobot
occupancy indicates the total operating time of the Cobot. The worker occupancy is divided into
the assembly and setup time parameters. Notice: the worker's assembly setup time is performed
when the robot performs its operations.
Participants

500

1
2
3
4
5
6
7
8
9

Table 4 Cobot and worker occupancy in S1 and S2
Cobot occupancy
Worker occupancy
Cobot occupancy
(min) – S1
(min) – S1
(min) – S2
Assembly
Setup
time
time
7.27/10
2.73/10
1.87/10
6.60/10
7.15/10
2.85/10
1.95/10
6.53/10
7.27/10
2.73/10
1.87/10
6.98/10
7.40/10
2.60/10
1.78/10
6.68/10
7.52/10
2.48/10
1.70/10
7.28/10
6.77/10
3.23/10
2.21/10
6.68/10
6.02/10
3.98/10
2.72/10
5.77/10
6.39/10
3.61/10
2.47/10
5.69/10
6.52/10
3.48/10
2.38/10
5.54/10

Worker occupancy
(min) – S2
Assembly
Setup
time
time
4.40/10
3.19/10
3.47/10
2.52/10
3.02/10
2.19/10
3.32/10
2.41/10
2.72/10
1.97/10
3.32/10
2.41/10
4.43/10
3.22/10
4.31/10
3.13/10
4.46/10
3.24/10

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Impact of Cobot parameters on the worker productivity: Optimization challenge

Fig. 7 Workplace occupancy in S1

Fig. 7 and the results in Table 4 show that in S1, the participating robot is occupied on average
6.92 minutes of the available 10 min total time. The standard deviation of average Cobot occupancy is 4.9. A worker at a collaborative workstation is engaged in assembling operation for an
average of 3.08 minutes while spending another 2.11 minutes preparing the parts to be assembled, giving a total time occupancy of 5.18 in the available time of 10 min, where the standard
deviation is 4.9.
Results of the S2 in Fig. 8 shows, that the Cobot works an average of 6.42 min of the available
10 min. With the parameters S2, the worker is occupied for 3.71 min for the assembly operation
and 2.69 min for the setup time, which totals 6.40 min of the available 10 min workplace operation
time. For parameters S2, the standard deviation of the Cobot occupancy is 5.7 and for the worker
it is 6.3.

5. Discussion

Fig. 8 Workplace occupancy in S2

Based on the results shown, we conclude that in total 264 more pieces are produced in S2 at the
collaborative workplace than with the parameters representing scenario S1. The average number
of finished products at the collaborative workplace in scenario S2 increases by 54.9 %, which significantly increases the number of manufactured products. At the same time, the standard
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deviation results, which are 3.7 higher for S2, indicate a higher probability of non-constant operation of the collaborative workplace assembly operations when repeated operations are performed in several shifts with several different workers. The results presented indicate that worker
workload increases at higher speeds and accelerations of the Cobot. Based on the evaluated optimization parameter of the average assembly time, we find that it is 23.5 % shorter in scenario S2,
which is an interesting observation from the justifying the collaborative workplace point of view.
The worker changed the work speed (assembling operation) when the Cobot mode of operation
changed (the transition of the Cobot parameters from S1 to S2). Obviously, the visual and audio
effects presences (perception of the Cobot) had a positive effect on the shorter average assembly
time, even though the worker had exactly the same time to assemble the product in both evaluated
scenarios (S1 and S2).
It is also interesting to analyse the occupancy of the participants (Cobot and worker) in the
collaborative workplace. The results show that the occupancy of the Cobot in the S2 scenario decreased by 7.2 % compared to the time occupancy in the S1 scenario. The results prove that the
Cobot is able to perform multiple activities simultaneously and possibly participate in more complex activities or collaborate in a workplace with two workers when the operating parameters are
increased. In this case, it is useful to study the utilization of the Cobot in detail, because only with
a detailed analysis we can ensure the justification for the introduction of such a machine in existing or newly proposed production system. In contrast to the decrease in the time occupancy of the
Cobot in S2, the occupancy of the worker in S2 increases, by 23.6 %, the occupancy is higher both
during assembly and setup time. With respect to the time occupancy parameter, we find that the
importance of consistent collaborative workplace occupancy is critical to the economic and social
justification of a collaborative workplace. When introducing new collaborative workstations, the
use of simulation techniques is highly justified, assuming that they represent the minimum cost
of the initial investment and reduce the risk that the new investment is not justified.
As mentioned in the introduction, usually companies start to increase the parameters (speeds
and accelerations) of the Cobot to reduce the cycle time of the machine operations to provide
enough collaborative workplace capacity. But as our results show, the integrators probably also
affect the worker's working speed unconsciously. It is difficult to fully determine the reason for
the increased worker operating speed, but a few options emerged during the experiment that may
be important contributors. Different speeds and accelerations of the Cobot provide different volumes of noise. The loudness of electro motors in joints could be a factor for faster movements and
greater willingness of the worker. The other aspect that should be considered is visualization. In
collaborative operation, the worker is constantly in "contact" with the Cobot, as with assembling
the products together as at observing the robot's movements. With defined and constant parameters of the Cobot, workers get to use of work sequence and begin to memorize specific positions
of the Cobot at specific time frames. If the remembered positions start to differ from previously
remembered start to deviate from previous memorized positions, this could trigger some kind of
alarm in the worker, whose spontaneous reaction could be an increased working speed.
According to the existing literature [19, 21], the presented work highlights the importance of
a detailed study of the feasibility of introducing collaborative workplaces, where the setting parameters of the collaborative machine can significantly affect the efficiency of such a workplace.
Proper integration of a Cobot affects both economic, time and social justification. The main contribution of the presented work is evident in the results that highlight the study of the importance
of the visual and acoustic impact on the worker and consequently on its use. Considering the number of participants considered, the trend of results shown allows further work and extension of
the experiment, which must be transferred to a real-world environment where the working hours
of the collaborative workplace would be longer. With longer working hours and the evaluation of
the collaborative workplace efficiency, the speed and acceleration of the Cobot would have a significant impact on the worker's workload, concentration, and therefore on the quality of the work
performed.
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6. Conclusions
In the research paper, the importance of Cobot parameters on worker productivity is presented.
The results of the study provide useful and interesting answers to our research question, but new
doubts have also been raised. The results show that the parameters of the Cobot have an impact
on the assembly times of the workers. Higher speed and acceleration of the Cobot contribute to a
higher working speed of the worker, which leads to shorter assembly times in our study. Despite
the positive results, the limit of the Cobot parameters must be considered at different collaborative work operations. The speed and acceleration limit should be set in such a way that it does not
affect the correct performance of the Cobot and, most importantly, it does not negatively affect the
worker (performance, physical health, mental health, etc.).
Future research will focus on the analysis of more participants, different ages, educations and
genders. We will focus our research on studying the mental and physical health of workers working with Cobot. Favourable research results will lead to the transfer of the laboratory experiment
to a real-world production environment, where the speed and acceleration of the Cobot will have
a longer-term effect on the efficiency of the worker and the entire collaborative workplace. When
evaluated in a real-world environment, the challenges will enable a more detailed and practical
application value of the presented research work, which is crucial in the era of Industry 4.0 and
the arrival of new technologies. Cost, time and social justified of the collaborative workplaces certainly represent one of the more attractive research areas from the manufacturing efficiency point
of view.

Acknowledgement

The authors gratefully acknowledge the support of the Slovenian Research Agency (ARRS), Research Core Funding No.
P2-0190. The authors acknowledge the use of research equipment system for development and testing cognitive production approaches in industry 4.0: Collaborative robots with equipment and Sensors, hardware and software for ergonomic analysis of a collaborative workplace, procured within the project "Upgrading national research infrastructures - RIUM", which was co-financed by the Republic of Slovenia, the Ministry of Education, Science and Sport and the
European Union from the European Regional Development Fund.

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