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This paper discusses the problem of control laws for Discrete Event Dynamic Systems (DEDSs) represented by Timed Event Graphs (TEGs) under Generalized Marking Constraints (GMCs). The behaviour of TEGs is described using linear equations while GMCs are expressed by weighted inequalities in Min-Plus algebra. We formulate the problem in terms of control linear Min-Plus models. Hence, an algebraic method to calculate control laws ensuring the respect of GMCs is suggested. And once sufficient conditions are satisfied, we propose casual feedbacks to guarantee these marking specifications. Besides, the proposed control strategy is applied to a dual-armed cluster tool, a well-known industrial application. [ABSTRACT FROM AUTHOR]

The correspondence pruning task relies on both local and global contexts, which are considered to be essential in inferring the probability of inliers. Many previous approaches seek to devise various structures to make effective use of them, but they either use only a plain structure or base it on their own hypothetical relationships, which leads to some limitations remaining to be improved. Derived from this, we propose our LG-Net including a simple yet effective LGA block and a well-designed GPA block to extract local and global information respectively. Specifically, the LGA block combines local topology into the neighborhood and enhances the representative ability by enabling the interaction of the adjacency neighbors with a simple Softmax operation. Meanwhile, the GPA block prefers correspondences with higher inlier-probability to restrict the interference of outliers. With the guide of relatively reliable prior, it will facilitate the robustness of gathering rich global contextual information. As a consequence, our LG-Net takes both local and global context into account to help successfully recover correspondences. Extensive experiments have demonstrated the better performance of our method comparing with existing state-of-the-art methods on camera pose estimation and homography estimation. • Proposed LGA block captures local topology for completing neighborhood interactions. • GPA module leverages inlier information and enhances global context aggregation. • LG-Net surpasses current works on pose and homography estimation. [ABSTRACT FROM AUTHOR]

Conditional Preference Networks (CP-nets) have emerged as a prominent and intuitive model for representing conditional preferences within the AI community. However, the literature lacks a concrete investigation on their applicability within Multiobjective Evolutionary Algorithms (MOEAs). Many optimization problems that are tackled by MOEA have a room for conditional preferences on the decision space and thus reasoning about CP-nets in MOEA is of great importance. In this work, we motivate the use of CP-nets within the MOEA framework and provide a simple yet powerful approach that can be augmented during the search to return solutions that are not only optimized for the objectives but also preferred by the decision maker's CPnet. Furthermore, we consider the case where the decision maker has no clear idea about her preferences at the beginning of the search but willing to engage during the search to answer simple comparison questions. Our experimental results show that it is possible to return solutions that are both optimal and preferred even when the search starts with empty preference information. The obtained solution space achieved at least 23% improvement in the average penalty score. [ABSTRACT FROM AUTHOR]

An optimisation methodology is presented using the primary coolant circulation system of a nuclear reactor as its case study, the purpose of which is to find combinations of selected design and maintenance parameters to maximise the reactor safety and minimise monetary expenditure. The parameter space was sampled by a Monte Carlo method and Petri net modelling was used to predict the performance of each of these options. The optimal solutions were then extracted from the data via computation of the Pareto front, with further analysis conducted on parameter sets of interest. [ABSTRACT FROM AUTHOR]

Natural hazard-triggered technological accidents (Natechs) are a type of multihazard coupled event with complex disaster evolution trends and serious consequences, resulting in high difficulty in collaborative responses. Currently, research on Natechs primarily revolves around evolutionary mechanisms and risk assessment. However, this complex form of disaster still requires relevant studies on response processes and organizational collaboration. The stochastic Petri net simulation method aligns well with this need, offering a suitable approach for addressing these gaps. Stochastic Petri nets (SPN) were introduced to clarify the Natech response process and analyze the synergistic relationship between organizations during the Natech response. First, the natech response process was framed by the logic of "scenario-task-organization"; the SPN's components were designed considering the characteristics of Natech events, and a proposal was made on how to construct the SPN model corresponding to a collaborative Natech response. Second, the performance of the analysis model was evaluated using the isomorphic Markov chain, and a method for examining organizational collaborative relationships was proposed. Finally, the model is validated by an example, and the characteristics of organizational task execution in Natech responses are summarized according to the busy probability of places and variation in the utilization of transitions. Analysis of task coordination relationships is proposed based on the SPN's structure, and the core and auxiliary workgroups are determined according to the actual task execution requirements to achieve better identification of responsible parties. Further, ways to improve Natech response collaboration are recommended: The role of the natural disaster response department is highlighted, for example, as it provides crucial information support during rescue operations. Relying solely on government departments may prove ineffective in responding to Natechs; thus, it may be necessary to involve social forces in the response effort. Flexible adjustments to deployment should be made to ensure the full utilization of limited disaster relief resources. [ABSTRACT FROM AUTHOR]

An efficient computing system for massively parallel systems is established. The stochastic Petri net abstracts and models the high-performance computer work scheduling system. The IB switch is modeled. Stochastic Petri nets are used for performance analysis. Finally, the proposed method is combined with InfiniBand interconnection architecture to evaluate the system's delay. The experimental results prove the feasibility of this algorithm. [ABSTRACT FROM AUTHOR]

The public health sector, considered to be vital, especially in this pandemic crisis of COVID‐19, requires automation and control of drug distribution in pharmacies commonly called: "Dispensing." In this paper, we address the medication assignment problem for automated dispensing cabinets (ADCs). First, we use a network of conflicting timed event graphs (CTEGs), a class of timed Petri nets with shared resources, to model pharmaceutical cabinets. Second, we develop a new method for controlling CTEGs under mutual exclusion constraints (MECs) to solve the problem of drug assignment, using a control approach based on Min‐Plus algebra. Finally, a case study of assigning drugs is given to illustrate the proposed methodology and show the efficiency of the developed control laws. [ABSTRACT FROM AUTHOR]

This study proposes an analytical control method for the meeting of capacity constraints in discrete event systems with disturbances. More precisely, it consists of computing control laws for partially controllable and observable timed event graphs that are subject to marking constraints. To resolve the issue, linear Min‐Plus models are used to describe the behavior of these graphs, and the constraints are expressed by inequalities in Min‐Plus algebra. Sufficient conditions for the existence of causal control laws to guarantee marking specifications are established. Finally, to illustrate the efficiency of the proposed approaches in this paper, an application for a disassembly process with some disturbance inputs and limited component capacities is carried out. [ABSTRACT FROM AUTHOR]

Process Mining offers a powerful framework for uncovering, analyzing, and optimizing real-world business processes. Petri nets provide a versatile means of modeling process behavior. However, traditional methods often struggle to effectively compare complex Petri nets, hindering their potential for process enhancement. To address this challenge, we introduce PetriNet2Vec, an unsupervised methodology inspired by Doc2Vec. This approach converts Petri nets into embedding vectors, facilitating the comparison, clustering, and classification of process models. We validated our approach using the PDC Dataset, comprising 96 diverse Petri net models. The results demonstrate that PetriNet2Vec effectively captures the structural properties of process models, enabling accurate process classification and efficient process retrieval. Specifically, our findings highlight the utility of the learned embeddings in two key downstream tasks: process classification and process retrieval. In process classification, the embeddings allowed for accurate categorization of process models based on their structural properties. In process retrieval, the embeddings enabled efficient retrieval of similar process models using cosine distance. These results demonstrate the potential of PetriNet2Vec to significantly enhance process mining capabilities.

Flexible manufacturing systems (FMS) should be able to respond to changing manufacturing requirements and environments. From the layout point of view, FMS need to be rearranged to fit the new requirements. However, rearranging the layout is often undesirable due to its unpredicted high costs and production disruption. This paper proposes a practical approach to mitigate the effects and repercussions of changing environments and avoid rearranging the layout. A robust layout approach is presented, where changes in product demand and mix are absorbed by altering product routes and not rearranging the layout. In this approach, the problem is decomposed into two sub-problems: sub-problem 1 (SP1) where a robust layout is constructed, and sub-problem 2 (SP2) to obtain the best routes of products. To solve SP1, design of experiments is used to find a critical period, which is the period most affected under demand changes. Then, the layout for the critical period is determined using a hybridized genetic-tabu search algorithm. Then SP2 is solved by a branch and cut algorithm to obtain the optimal routes of the products in each period. The performance of the proposed methodology is illustrated using a case study and is benchmarked against rival ones from the literature. [ABSTRACT FROM AUTHOR]

In molecular biological studies, considerable attention is paid to macrocyclic nanoscale compounds known as calix[4]arenes. An imperative concern in biochemical membranology and molecular biotechnology is the exploration of effectors capable of modifying the intensity of redox reactions within the inner mitochondrial membrane and influencing the activity of its Ca 2+ transport systems. The simulation model development is relevant to formalize and generalize the experimental data and assess the conformity of experimental results with theoretical predictions. Experiments were carried out on a suspension of isolated rat myometrial mitochondria. The synthesized thiacalix[4]arene C-1193, containing four sulfur atoms, was employed. Demonstrations of time-dependent and concentration-dependent (0.01–10 μM) inhibition of Ca 2+ accumulation and reactive oxygen species (ROS) formation by mitochondria in the presence of C-1193 were observed. While C-1193 inhibited the oxidation of NADH and FADH2, it did not induce mitochondrial swelling. The thiacalix[4]arene also inhibited the synthesis of nitric oxide, with a Ki of 5.5±1.7 nM, positioning it as a high-affinity blocker of endogenous NO generation in mitochondria. These results are the basis for the possible application of the synthesized thiacalix[4]arene as a tool in researching biochemical processes in mitochondria. A simulation model employing functional hybrid Petri nets was developed, reproducing the functional activity of mitochondria, including simultaneous NADH oxidation, ROS formation, NO synthesis, and Ca 2+ accumulation. The derived equations formalize and describe the time dependencies of the listed processes in the medium under the influence of thiacalix[4]arene C-1193.

International transport corridors (ITCs) are intricate logistical networks essential for global trade flows. The effective modeling of these corridors provides invaluable insights into optimizing the transport system. However, existing approaches have significant limitations in dynamically representing the complexities and uncertainties inherent in ITC operations and at the early stages of ITC development when data are limited. This gap is addressed through the application of Evaluation Petri Nets (E-Nets), which facilitate the detailed, flexible, and responsive macro-modeling of international transport corridors. This paper proposes a novel methodology for developing E-Net-based macro-models of corridors by incorporating key parameters like transportation time, costs, and logistics performance. The model is scalable, enabling analysis from an international perspective down to specific country segments. E-Nets overcome limitations of conventional transport models by capturing the interactive, stochastic nature of ITCs. The proposed modeling approach and scalability provide strategic insights into optimizing corridor efficiency. This research delivers a streamlined yet comprehensive methodology for ITC modeling using E-Nets. The presented framework has substantial potential for enhancing logistics system analysis and planning.

This paper introduces a novel algorithm for the efficient verification of a Petri net-based concurrent control system. The proposed method is based on the computation of transition invariant coverage to detect possible errors in the modelled system. Transition invariants play a crucial role in ensuring the correctness and reliability of such systems; however, existing methods often struggle with high computational demand, especially in the case of large and complex systems. The proposed approach addresses this challenge by performing a fast polynomial-time analysis to identify uncovered transitions, thereby streamlining the verification process. The effectiveness and efficiency of the proposed technique is verified experimentally with a set of 386 test modules (benchmarks) and compared against two well-known established methods: the classical method proposed by Martínez–Silva (as a reference algorithm) and PIPE (Platform Independent Petri Net Editor) tool. The results of the experiments confirm high performance of the presented algorithm, which was able to compute results for all the tested cases. In contrast, both the reference algorithm as well as the PIPE tool failed to deliver results for all examined models within one hour. The proposed algorithm is especially useful in early design stages, offering system designers timely insights into potential issues.

Ventricular assist devices (VADs) are designed to provide sufficient blood flow to patients with severe heart failure. Once implanted, the patient becomes dependent on the VAD, making it essential to prevent situations that could harm the patient while receiving circulatory support. VADs are classified as critical systems (CS), and adverse events (AEs) can lead to serious consequences, including hospitalization or even death. At present, patient care is provided through in-person consultations, with incidents reported via medical device reports (MDRs) to the Food and Drug Administration (FDA). However, there is no real-time monitoring of AEs or oversight of these events. In response to this gap, a system for supervising critical adverse processes in patients with implanted VADs (SCVAD) is proposed, based on horizontally and vertically integrated architecture. This system aims to address the complexity of AEs by considering multiple domains of operation: the device, the patient, and the medical team, as well as the interactions between these entities. In this context, the formalism of Petri nets (PN) is used to develop models that represent adverse processes based on the actions recommended by the medical team. These models allow for the mapping of events with the potential to cause harm to the patient. Therefore, the medical team will be able to monitor adverse processes, as the models in interpreted PN can be isomorphically transcribed into computable algorithms that can be processed on compatible devices, enabling the tracking of complications caused by adverse processes.

Intrusion Detection Systems (IDSs) are a crucial component of modern corporate firewalls. The ability of IDS to identify malicious traffic is a powerful tool to prevent potential attacks and keep a corporate network secure. In this context, Machine Learning (ML)-based methods have proven to be very effective for attack identification. However, traditional approaches are not always applicable in a real-time environment as they do not integrate concrete traffic management after a malicious packet pattern has been identified. In this paper, a novel combined approach to both identify and discard potential malicious traffic in a real-time fashion is proposed. In detail, a Long Short-Term Memory (LSTM) supervised artificial neural network model is provided in which consecutive packet groups are considered as they flow through the corporate network. Moreover, the whole IDS architecture is modeled by a Petri Net (PN) that either blocks or allows packet flow throughout the network based on the LSTM model output. The novel hybrid approach combining LSTM with Petri Nets achieves a 99.71% detection accuracy—a notable improvement over traditional LSTM-only methods, which averaged around 97%. The LSTM–Petri Net approach is an innovative solution combining machine learning with formal network modeling for enhanced threat detection, offering improved accuracy and real-time adaptability to meet the rapid security needs of virtual environments and CPS. Moreover, the approach emphasizes the innovative role of the Intrusion Detection System (IDS) and Intrusion Prevention System (IPS) as a form of “virtual sensing technology” applied to advanced network security. An extensive case study with promising results is provided by training the model with the popular IDS 2018 dataset.