esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Iron, ROS and Iron Chelators That Can Cause DNA Damage in Cancer
### Iron Chelators and ROS
#### 5.1.3 DP44 mT and Analogues  
One of the most widely researched iron chelators that produces toxic levels of ROS is the tridentate iron chelator di-2-pyridyl ketone-4,\(A\),-dimethyl-3-thiosemicarbazone (Dp44 mT). Dp44 mT binds to iron in the 2:1 ratio and is more selective for Fe(III). It is a member of the thiosemicarbazones chelator family in which triapine and pyridoxal isonicotinoyl hydrazone (PIH) had relative success; Dp44 mT is an analogy of these two thiosemicarbazones [184; 127; 132]. Dp44 mt contains the donor atoms of imine, nitrogen, sulphur and pyridyl [33]. It was initially shown to induce apoptosis in lung carcinoma in vivo (up to 47% against the control). They suggested that its cytotoxicity could result from its redox activity [185]. Additionally, they showed that the incubation with Dp44 mT for two hours increased ROS. Later, its antiproliferative effect was seen by inducing DNA double-strand breaks culminating in cell cycle arrest [184]. It also exhibited selective inhibition of top2A, demonstrated in breast cancer cells and overcoming the altered autophagy in cancer cells [184; 186]. It has since been used in a leukemic cell line, melanoma mouse xenograft, and osteosarcoma, where it increased levels of ROS [187; 188; 189]. Dp44 mt also significantly increased autophagy via activation of the AMPK pathway whilst inhibiting metastasis of the Wnt/\(\beta\)-catenin signalling pathway in several cancer cell lines [191]. Dp44 mt has been shown to increase the tumour suppressor phosphate and tensin homolog (PTEN) and NRDG1 [190]. This was shown to reduce p-AKT levels and has been repeated in pancreatic cancer [192; 190]. Dp44 mt has also been shown to inhibit mTORC1 in a ROS independent fashion, reducing p-AKT in A549 and A427 lung cancer cell lines [193]. The ability of Dp44 mT to induce ROS has been shown to be crucial to its cytotoxic profile [194]. Dp44 mT has high antiproliferative and cytotoxicity; however, it can induce cardiotoxicity if used at non-optimal doses. This cardiotoxicity required the design of second-generationiron chelators, the main compound being di-2-pyridylketone 4-cyclohexyl-4-methyl-3-thiosemicarbazone (DpC) [195].  
DpC, unlike Dp44 mT, can be given intravenously and orally. It has demonstrated many of the same effects that Dp44 mT has, such as overcoming chemotherapy drug resistance and increasing the expression of NDRG-1 without the oxidation of oxyhemoglobin and cardiac toxicity [196; 197]. DpC, like Dp44 mt, is tridentate and has soft donors such as N and S in the coordination sphere. This allows complexes to participate in redox reactions and has exhibited an increase in ROS; this aids its antiproliferative effects. It was recently shown to inhibit neuroblastoma growth in vitro and in vivo [198]. DpC has also recently been shown to inhibit TGF-\(\beta\) and Wnt/\(\beta\)-catenin signalling in pancreatic cancer, thus inhibiting the cross talk between pancreatic cancer cells and pancreatic stellate cells [199].
# Degree-Constrained Steiner Problem in Graphs with Capacity Constraints
## 7 Evaluation of the Solutions
### Runs and Results  
#### 7.2.1 Effect of the Degree Bounds  
In the first case, random graphs were generated with 70 nodes and 30 randomly selected nodes for the group to cover. The following parameters were set: \(C_{max}=5\), \(D_{min}=2\), and \(D_{max}\) varied from 3 to 12. Note that with these values, the node set \(V_{1}\) was always empty. For each value of \(D_{max}\), 10 random graphs were generated, and the values in Table 2 are the average values of 10 runs.  
In the table, c(St), c(DCMStT), and c(DCMStH) are the average costs of the minimum Steiner trees, the degree-constrained Steiner trees, and the hierarchies, respectively; arc(St), arc(DCMStT), and arc(DCMStH) are the number of arcs in the three solutions.
## 5 Discussion
For instance, municipalities could use the predictions to prioritize corrective or preventive measures. Furthermore, since the model uses data typically available in modern cities (historical noise levels and station locations), it is highly replicable in other cities with different acoustic dynamics by simply adapting the graph's spatial configuration. Finally, while the proposed model shows solid predictive performance, its effectiveness depends significantly on the quality and representativeness of the data used. Exceptional acoustic situations or extraordinary events may affect its accuracy. Therefore, future research should incorporate additional sources of contextual information, such as one-off urban events (holidays, public works, and mass events) and weather variables influencing noise propagation. Additionally, exploring adaptive approaches capable of dynamically updating the model could help improve its predictive performance in continuously changing real-world scenarios.
## 2 Materials and Methods
### Antioxidant Activity  
Antioxidant activity was evaluated using a DPPH (2,2-diphenyl-1-picrylhydrazyl) assay. DPPH was dissolved in 2-butanone:methanol (1:1, \(v/v\)) at a concentration of 0.04 mg of DPPH/mL. A solution of 10 mg of gallic acid in 10 mL of solvent was used as a positive control. Solvent was used as a blank control. Oil samples were prepared by dissolving 5 mg of oil in 1 mL of solvent. A total of 40 \(\mu\)L of each oil sample and control was mixed with 1 mL of the DPPH solution. After 30 min incubation in the dark, radical scavenging activity was determined spectrophotometrically at 517 nm. Three measurements were made for each sample and the average absorbance was calculated. Antioxidant activity was calculated from the average absorbances as follows:  
\[\text{DPPH radical scavenging assay (\%)}=\text{(A}_{\text{blank}}-\text{A}_{\text{test}} )/\text{(A}_{\text{blank}}-\text{Am}_{\text{control}})\times 100\]
## 2 Research Questions and Methodology
### Methodology
#### 2.2.2 Peak Load Stability and Dynamic Clustering Parameters  
The system maintains sub-second response times during peak loads through (1) request coalescing--grouping identical requests within 100 ms windows, reducing redundant inference by 67%, (2) adaptive batching--dynamically adjusting batch_size based on queue depth (8 for <10 requests, 32 for 10-50, 64 for >50), and (3) a circuit breaker pattern with fallbacks: primary RL inference (<200 ms), hourly cached recommendations (<50 ms), and pre-computed university lists (<20 ms).  
DBSCAN epsilon adapts using eps_adaptive = 0.0015 \(\times\) sqrt (local_density/global_density) \(\times\) seasonal_modifier, where base epsilon is 0.0015 radians (\(\sim\)150 m), the density factor is clamped to [0.5, 2.0], and the seasonal modifier is 1.2 during peak months (September-October), achieving 94% cluster stability across seasons.  
This approach enables the system to adapt to short-term preference changes with a response time of fewer than 10 interactions, significantly outperforming incremental Q-learning in A/B tests involving 5000 users [9]. The sliding window strategy ensures that recent interactions receive a three times higher weight than older ones, allowing rapid adaptation to evolving user preferences [36].  
Therefore, the recommendation engine functions as follows: when a user initiates a search or requests recommendations, the system identifies the state (preferences context) and queries the database for listings that meet the basic criteria (budget, number of rooms, keywords, perhaps a broad radius around the campus). Then, from that set, the RL model (with its current learned policy) selects or ranks listings to show first.
# A High Gain Modified Quadratic Boost DC-DC Converter with Voltage Stress Half of Output Voltage
## 5 Comparison Analysis  
A summary of recently proposed high gain, mostly quadratic boost-based topology, is discussed in this section. Table 1 represents the summary of the recently reported high gain boost topology, tabulating from where it specifies a few important characteristic parts of each proposed topology (i.e., components count normalized switch's voltage stress, etc.). From the specification table, it can be observed that the performance of the high gain boost converter depends on so many factors of the converter specification. It can also be noted that though the desirable performance of the high gain boost is suitable based on low voltage stress and a high voltage gain capability, sometimes it may require many passive components. So, a trade-off must be given in order to select for perfect application.  
In the comparison table, topology 9 proposed by [PERSON] et al. [37] has been reported recently, and the voltage gain of the proposed topology is denoted in Table 1. In this article, our proposed topology has already overcome the voltage gain, and the normalized voltage stress was reduced compared to their study. Figure 11 presents the ideal voltage gain vs. duty ratio of the reported literature survey from Table 1. The figure justifies the voltage gain profile of the studied circuit configuration. In the later section, the voltage gain is verified by the simulated results as well as by the experimental results.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Topology** & **Switches** & **Diode** & **Inductor** & **Capacitor** & \begin{tabular}{c} **Ideal Voltage** \\ **Gain** \\ \end{tabular} & \
\begin{tabular}{c} **Voltage** \\ **Stress (\(V_{g}\)/\(V_{in}\))** \\ \end{tabular} &
\begin{tabular}{c} **Common** \\ **Ground** \\ \end{tabular} & **Ref** \\ \hline CQBC & 1 & 3 & 2 & 2 & \(\frac{1}{(1-d)^{2}}\) & \(\frac{1}{(1-d)^{2}}\) & Yes & [29] \\ \hline Topology 1 & 1 & 5 & 2 & 4 & \(\frac{(3+d)}{(1-d)}\) & \(\frac{4}{(1-d)}\)
## 2 Materials and Methods
### BI-RADS Categories
In this study, feature extractors were created for BI-RADS categories to calculate feature points from images according to the purpose of each category. To generate an automatic lesion diagnosis model, feature points for each BI-RADS category were calculated using designed feature extractors from a large volume of breast ultrasound images in PACS. Then, pattern recognition algorithms and data mining techniques were employed to create a breast cancer auxiliary diagnosis model. For the pattern recognition algorithm, we utilized a Support Vector Machine (SVM) classifier due to its ability to handle high-dimensional feature spaces and its effectiveness in binary classification--critical for accurately distinguishing between benign and malignant breast lesions. The data mining process began with the extraction of key features from a large set of breast ultrasound images stored in the PACS system. For each BI-RADS category, we developed specific feature extractors designed to capture relevant image characteristics that are indicative of different types of lesions. These extracted features were then standardized and processed to reduce noise and improve their utility for classification tasks. The refined feature set served as input for the SVM classifier, which was trained to detect patterns that correspond to either benign or malignant lesions based on the features of each BI-RADS category. By integrating these methodologies, this developed an auxiliary diagnosis model for breast cancer which locates the lesion and automatically determines its benignity or malignancy when new breast ultrasound images are input into the CAD system. Figure 5: Examples of echo patterns in ultrasound images. Figure 6: Examples of posterior features in ultrasound images.
# Characterization of Sidr (_Ziziphus_ spp.) Honey from Different Geographical Origins
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Antibacterial** & \multicolumn{2}{c}{**Gram-Positive**} & \multicolumn{2}{c}{**Gram-Negative**} \\
**Activity** & \multicolumn{2}{c}{_Staphylococcus Streptococcus_} & \multicolumn{2}{c}{_Streptococcus_} & \multicolumn{2}{c}{_Klebsiella_} & _Escherichia coli_ & _Pseudomonas aeruginosa_ \\
**Origin** & _aureus_ & _mutans_ & _pneumoniae_ & _coli_ & _aarginosa_ \\ \hline Emirates & 21.33 \(\pm\) 0.88 & 18.33 \(\pm\) 0.88 & 22.00 \(\pm\) 0.68 & 15.33 \(\pm\) 0.33 & 16.00 \(\pm\) 1.15 \\ China & 16.00 \(\pm\) 0.58 & 16.33 \(\pm\) 0.88 & 11.00 \(\pm\) 0.56 & 8.00 \(\pm\) 0.58 & 9.67 \(\pm\) 0.33 \\ Iraq & 19.00 \(\pm\) 0.58 & 22.00 \(\pm\) 0.58 & 11.00 \(\pm\) 0.48 & 17.00 \(\pm\) 0.48 & 15.00 \(\pm\) 0.10 \\ Pakistan & 18.00 \(\pm\) 0.58 & 16.00 \(\pm\) 0.48 & 11.00 \(\pm\) 0.44 & 14.00 \(\pm\) 0.66 & 15.00 \(\pm\) 0.51 \\ Bashawer & 8.00 \(\pm\) 0.58 & 12.00 \(\pm\) 0.32 & 11.00 \(\pm\) 0.55 & 17.00 \(\pm\) 0.38 & 14.00 \(\pm\) 0.58 \\ Panjab & 21.00 \(\pm\) 0.11 & 17.00 \(\pm\) 0.58 & 10.33 \(\pm\) 0.47 & 8.00 \(\pm\) 0.64 & 9.67 \(\pm\) 0.43 \\ Saudi Arabia & 23.00 \(\pm\) 0.58 \({}^{*}\) & 23.00 \(\pm\) 0.22 \({}^{*}\) & 25.00 \(\pm\) 0.61 \({}^{*}\) & 27.33 \(\pm\) 0.45 \({}^{*}\) & 40.67 \(\pm\) 0.67 \({}^{*}\) \\ Kashmir & 11.00 \(\pm\) 0.58 & 17.00 \(\pm\) 0.66 & 15.00 \(\pm\) 0.30 & 19.33 \(\pm\) 1.85 & 14.00 \(\pm\) 0.68 \\ Libya & 15.33 \(\pm\) 0.33 & 18.33 \(\pm\) 0.33 & 21.00 \(\pm\) 0.31 & 20.33 \(\pm\) 0.88 & 10.00 \(\pm\) 0.58 \\ Egypt & 25.00 \(\pm\) 0.58 \({}^{*}\) & 29.33 \(\pm\) 0.64 \({}^{*}\) & 24.00 \(\pm\) 0.34 \({}^{*}\) & 29.16 \(\pm\) 0.60 \({}^{*}\) & 29.00 \(\pm\) 0.58 \({}^{*}\) \\ India & 11.00 \(\pm\) 0.58 & 10.33 \(\pm\) 0.88 & 17.00 \(\pm\) 0.58 & 15.00 \(\pm\) 0.10 & 11.33 \(\pm\) 0.88 \\ Yemen & 8.33 \(\pm\) 0.33 & 9.67 \(\pm\) 0.33 & 8.00 \(\pm\) 0.44 & 11.00 \(\pm\) 0.58 & 9.00 \(\pm\) 0.58 \\ \hline \hline \end{tabular} \({}^{*}\)Significant.  
\end{table} 
Table 6: The inhibition zone of Sidr honey against various pathogenic microorganisms by well diffusion method. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**MIC of Bacterial Strains (g/mL)**} \\ \cline{2-5}
**MIC** & \multicolumn{2}{c}{**Gram-Positive**} & \multicolumn{2}{c}{**Gram-Negative**} \\ \cline{2-5}
**Honey** & _Staphylococcus Streptococcus_ & _Klebsiella_ & _Escherichia coli_ & _Pseudomonas aeruginosa_ \\ \hline Emirates & 0.1 & 0.2 & 0.2 & 0.2 & 0.2 \\ \hline China & 0.1 & 0.2 & 0.2 & 0.2 & 0.2 \\ Iraq & 0.4 \({}^{*}\) & 0.4 \({}^{*}\) & 0.4 \({}^{*}\) & 0.1 & 0.2 \\ \hline Pakistan & 0.2 & 0.2 & 0.2 & 0.1 & 0.1 \\ \hline Bashawer & 0.2 & 0.2 & 0.2 & 0.1 & 0.1 \\ \hline \hline \end{tabular} \({}^{*}\)Significant.  
\end{table} 
Table 7: Minimal inhibitory concentration (MIC) of Sidr honey against antibiotic resistant bacterial strains.
## 3 Results
### Skin Elasticity  
The results of skin elasticity are shown in Figure 4. At the start (0 weeks), the R2 skin elasticity in the control and test groups were 0.6541 \(\pm\) 0.0844 and 0.6835 \(\pm\) 0.0599 and those of R7 were 0.3207 \(\pm\) 0.0512 and 0.3015 \(\pm\) 0.0498, respectively. The R2 skin elasticity in the test group significantly increased at 2 weeks (0.6898 \(\pm\) 0.0598, +5.46%, \(p\) < 0.01) and 4 weeks (0.7353 \(\pm\) 0.0644, +12.41%, \(p\) < 0.001), and that of R7 in the test group also significantly increased at 2 weeks (0.3291 \(\pm\) 0.0389, +9.16%, \(p\) < 0.01) and 4 weeks (0.3528 \(\pm\) 0.0431, +17.01%, \(p\) < 0.001). The control group's R2 skin elasticity was only significantly enhanced at 4 weeks (0.7084 \(\pm\) 0.0553, 3.63%, \(p\) < 0.01), and the rate of increase in R2 in the test group at 4 weeks was better (12.41%) than that in the control group (3.63%). Furthermore, the skin elasticity in the test group, compared to the control group, was significantly different at both 2 weeks (_p_ < 0.01) and 4 weeks (_p_ < 0.001) but only for R2.  
Figure 4: (**a**) R2 and (**b**) R7 skin elasticity before (0 weeks) and after (2 and 4 weeks) using the collagen peptide buccal delivery film of the control group (gray) and test group (white). The boxplot shows the average (\(\bullet\)), median (—), and max–min whiskers (**I**). The changes were significantly different between the control and test groups, before and after intake, with \(p\) < 0.05 (*), \(p\) < 0.01 (**), and \(p\) < 0.001 (***).
# Rogue Node Detection Based on a Fog Network Utilizing Parked Vehicles
## 6 Conclusions  
Aiming at rogue node detection in the IoV, this paper aims to use stable parked vehicles on both sides of the road to dynamically form a fog network, in which fog nodes works in a collaborative way to detect rogue nodes efficiently. We let the number of fog nodes correspond to the moving vehicle density on the road. In addition, to obtain the density of moving vehicles on the road, the Greenshield model is used to model the current traffic conditions. A U-test-based rogue node detection algorithm is then described. Simulation experiments show that our scheme achieves high rogue node detection performance. With the increase of the proportion of rogue nodes, the proposed scheme can also maintain a high TPR. Compared with other schemes, the proposed scheme does not rely on any infrastructure and does not need to increase additional hardware equipment investment. In the future, the proposed prediction model in this paper can be further optimized. In addition, the response mechanism for rogue nodes will be discussed in detail.  
Conceptualization, J.H.; software, [PERSON] and J.W.; validation, [PERSON] and J.W.; writing--review and editing, [PERSON] and J.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by The Science & Technology Development Fund of Tianjin Education Commission for Higher Education (No.2019 ZD23).  
Not applicable.  
Figure 16: Variation of the running time vs. the number of moving vehicles.
# Non-Uniform Clustering Algorithm for UWSNs Based on Energy Equalization
## 5 Simulation Results
### Algorithm Coverage Analysis  
The comparison of network coverage with the number of rounds of network operation for DBR, LEACH, UCUBG and the present algorithm is shown in Figure 6a. From it, it can be seen that the network coverage of DBR, LEACH, UCUBG and the present algorithm all decrease with the increasing number of rounds of network operation. Only the LEACH and DBR algorithms have some coverage in the end, which is because these two algorithms run to the end and surviving nodes can still be found. Nevertheless, this algorithm is better than other algorithms in terms of network coverage at the middle stage of network operation. This is mainly due to the slower death rate of nodes in this algorithm and the higher number of surviving nodes in the network. Thus, network coverage is better.  
Figure 5: Variation in the number of surviving nodes.  
The comparison of network coverage with the number of dead nodes for DBR, LEACH, UCUBG and the present algorithm is shown in Figure 6b. It can be seen from it that the present algorithm has higher network coverage than the other three when the same number of nodes die. This is because the coverage redundancy index is introduced in the selection of cluster heads in this algorithm. Thus, nodes with a high coverage redundancy have a higher likelihood of becoming cluster heads. The network coverage of other algorithms has a greater impact than that of this algorithm in the case of the same number of dead nodes in the network. Therefore, the network coverage of this algorithm is superior to that of the other three for the same number of dead nodes. The UCUBG algorithm has the second-best coverage because it also considers the number of neighboring nodes to choose cluster heads. Nodes with a high number of neighboring nodes have higher coverage redundancy to some extent. Compared with the DBR planar routing protocol, the LEACH algorithm has slightly better coverage. The reason for it is that LEACH has a rotation of cluster head nodes in each round, while the DBR algorithm flooding routing falls into the local optimum.  
In addition, network coverage is almost constant when nodes just start to die. This is because the density of network nodes is high, and the coverage redundancy of each node is relatively high in the beginning. When nearly half of the nodes die, the advantage of considering coverage redundancy is more obvious. Meanwhile, the network is sparser, and the impact of coverage redundancy is smaller when fewer nodes remain.
# Dealing with Deadlocks in Industrial Multi Agent Systems
## 5 More Complicated Examples  
### Example 3  
Consider a PN model in the form presented in Figure 5.  
This PN model has RT with 77 nodes (including \(\mathbf{M}_{0}\)). There are three deadlocks in the PN, namely  
\[\mathbf{M}_{33} =(3,\,1,\,1,\,0,\,0,\,0,\,0,\,3,\,2,\,0,\,0,\,0,\,0,\,0,\,0,\,0)^{T} \tag{34}\] \[\mathbf{M}_{36} =(3,\,1,\,0,\,1,\,0,\,0,\,0,\,3,\,2,\,0,\,0,\,0,\,0,\,0,\,0,\,0)^{T}\] (35) \[\mathbf{M}_{44} =(4,\,1,\,0,\,0,\,0,\,0,\,0,\,2,\,2,\,0,\,1,\,0,\,0,\,0,\,0,\,1)^{T} \tag{36}\]  
Because of the large RT it is difficult or practically impossible (by commonly available means) to analyze RT in order to find the matrix \(\mathbf{L}\) and the vector \(\mathbf{b}\) in (5). It remains to use the approach based on siphon and traps. Using the Matlab-based tool in [30] we obtain the following minimal siphons and traps  
\[\begin{array}{l}\par\par\par\par\par\par\par\par\par\par\par\par\par\par\par \par\par\par\par\par\par\par\par\par\par\  
\[\mathbf{S}=\left(\begin{array}{cccccccccccccccc}0&0&1&1&1&1&0&0&0&0&0&1&1&1&0&0 \\ 0&0&0&0&1&1&1&0&0&1&1&0&0&1&1&0\\ 0&0&0&0&1&1&1&0&0&1&0&1&1&1&1&0\end{array}\right) \tag{37}\]  
\[\mathbf{N}_{c}=\mathbf{S}.\mathbf{N}=\left(\begin{array}{cccccccccccccccc}-1& 1&0&0&1&0&0&0&0&1&0&-1&0\\ 0&-1&0&1&-1&0&1&0&0&0&1&0&-1\\ -1&0&0&1&0&0&1&0&0&1&0&0&-1\end{array}\right) \tag{38}\]  
where the incidence matrix of the PN model is the following  
\[\mathbf{N}=\left(\begin{array}{cccccccccccc}-1&0&0&0&0&0&0&1&0&0&0&0\\ 1&-1&0&0&-1&0&0&0&0&0&0&0&0\\ 0&1&-1&0&0&0&0&0&0&0&0&0\\ 0&0&0&0&1&-1&0&0&0&0&0&0&0\\ 0&0&1&-1&0&0&0&0&0&0&0&0\\ 0&0&0&0&0&1&-1&0&0&0&0&0\\ 0&0&0&0&0&0&0&0&1&0&0&0&-1\\ 0&0&0&0&0&0&0&0&0&0&-1&1&0\\ 0&0&0&0&0&0&0&0&0&-1&1&0\\ 0&0&0&0&0&0&0&0&-1&1&0&0\\ -1&1&0&1&0&0&0&1&-1&0&0&0\\ 0&-1&0&1&-1&0&1&0&0&1&0&-1\\ 0&0&-1&0&0&-1&0&1&0&0&1&0&-1\\ 0&-1&1&0&-1&1&0&0&0&0&0&0\end{array}\right) \tag{39}\]  
The uncontrolled PN model together with its supervisor is displayed in Figure 6. This form of the mutual interaction between the supervisor and the PN model is used in order to avoid intricate interconnections between the uncontrolled model and the supervisor in the same figure. Marking of monitors \(V_{S_{1}}-V_{S_{3}}\) were established by the relation \(M_{0}(S_{i})-M_{1}(V_{S_{i}})>0\) for \(i=1,2,3\). Because \(M_{0}(S_{1})=2\) it is sufficient to put \(M_{1}(V_{S_{1}})=1\). Similarly, because \(M_{0}(S_{2})=3\) it is sufficient to put \(M_{1}(V_{S_{2}})=2\) and because \(M_{0}(S_{3})=4\) it is sufficient to put \(M_{1}(V_{S_{3}})=2\). In such a way controlled PN model (30) has RT with 56 nodes (it creates the state space of reachable markings) and no deadlocks. In case of putting \(M_{1}(V_{S_{3}})=3\) a new deadlock would occur in controlled system, namely \(\mathbf{M}_{14}=(4,\,1,\,0,\,0,\,0,\,0,\,3,\,2,\,0,\,0,\,0,\,1,\,0,\,1,\,0,\,0, 0)^{T}\).