Search Results (432)

Selective algorithmic auditing requires deciding which process evidence should receive attention when exhaustive review is infeasible. This Review introduces a four-layer framework that connects process representation, learning, inspection allocation, and governance within a single budgeted sequential decision problem over event streams. Unlike prior reviews centered on predictive process monitoring, explainability, cost analysis, or bibliometric structure, the proposed framework examines how these functions interact when human review, computation, latency, and documentation capacity are constrained. A structured and targeted survey of 89 unique publication families is used to illustrate and critically examine event-log, Petri-net, graph, object-centric, neural, uncertainty-aware, sequential, bandit, reinforcement learning, and audit architecture approaches. The reviewed evidence indicates that substantial bodies of work address the individual layers, but cross-layer evaluation remains fragmented and uses heterogeneous datasets, objectives, and validation protocols. The synthesis identifies five priorities: audit-ready benchmarks, explicit inspection budget protocols, calibrated uncertainty, transfer across organizational contexts, and reproducible governance interfaces. The main contribution is a computational framework and a corpus-bounded research agenda that connects representation, learning, inspection allocation, and governance for selective algorithmic auditing. Full article

The industrial chain forms a network of interconnected enterprises spanning from raw material procurement to final product distribution, enabling cost reduction, operational efficiency improvement, and enhanced market competitiveness. However, business resource conversion among enterprises often leads to information asymmetry, which impedes effective interconnection and collaboration. To address this challenge, WFC-Net (Workflow-Control Petri Net with Resource Fusion), a model tailored for multi-collaboration scenarios among interconnected enterprises in the industrial chain, is proposed. The proposed approach integrates WF-Net workflow characteristics with C-Net dependency modeling and Petri net transitions to enable precise workflow control and effective business resource coordination. The resource attributes are incorporated as first-class net elements, and a receding-horizon optimization policy is employed to dynamically adapt to varying operational conditions. Comparative simulation experiments demonstrate that WFC-Net achieves superior performance across all metrics, with an 89.1% authorization success rate and 36.0% event completion rate, outperforming Petri Net, C-Net, and WF-Net baselines. The full-rank reachability matrix provides a necessary condition for linearized controllability, offering a theoretical foundation for stable authorization behavior under the receding-horizon policy. These results demonstrate that WFC-Net consistently achieves or approximates target business-side metrics while ensuring controllability and soundness. Full article

Microservice-based systems evolve continuously through API changes, service splits and merges, dependency churn, and deployment-topology drift driven by DevOps practices. This rapid evolution erodes the assumptions encoded in formal models, test suites, and architectural descriptions, leaving practitioners with limited guidance on how to keep verification artifacts aligned with evolving implementations. Prior surveys cover microservices design, deployment, performance, and isolated verification techniques, but, to our knowledge, none consolidate the intersection of evolution and formal/rigorous verification, nor map how repository-derived signals and CI/CD pipelines support continuous verification of microservice-based systems. This paper addresses that gap through a systematic literature review (SLR). Following the Kitchenham et al. guidelines and PRISMA-style reporting, we defined a review protocol with four research questions covering (i) formal modeling and verification approaches, (ii) the impact of architectural and API evolution on verifiability, (iii) repository-derived signals for updating formal artifacts, and (iv) continuous and incremental verification in CI/CD pipelines. Structured searches in IEEE Xplore, ACM Digital Library, SpringerLink, and Scopus returned 1187 records, which were screened in seven stages and reduced to 18 included studies (13 primary studies that supply evidence and five background studies retained for transparency and framing) through inclusion/exclusion criteria and a weighted 15-item quality-assessment instrument. We synthesized the included studies thematically and compared them along five dimensions: verification technique, evolution dimension, automation level, empirical evidence, and tool/artifact availability. The synthesis shows that actor-based model checking, TLA⁺ control-plane verification, static code analysis, continuous certification, and runtime/self-adaptive testing form five complementary clusters, but empirical evaluation is dominated by small academic case studies, only a minority of approaches automate model updates from repository signals, and few are integrated end-to-end into CI/CD. We discuss methodological limitations of the body of work, including a lack of industrial-scale validation and shared datasets, contrast intrinsic (build-time, model-driven) and extrinsic (recovery-based) verification strategies, and outline a research agenda toward repository-aware, pipeline-integrated verification for evolving microservices. The present synthesis distils a set of immediate, evidence-based actions for practitioners together with a focused agenda of open research challenges. Full article

Sustainable development has become a strategic priority for the European Union in response to climate change, resource constraints and environmental degradation. Despite common policy objectives, Member States exhibit substantial differences in sustainability performance and transition capacities. This study investigates sustainable development pathways in the European Union using an integrated framework combining cluster analysis, transition analysis, panel econometrics and a conceptual Petri Net model. The empirical analysis is based on a balanced panel dataset covering the 27 EU Member States during the period 2015–2024. K-means clustering was employed to identify sustainable development regimes, transition matrices were used to evaluate regime persistence, and panel data models were estimated to examine the determinants of environmental performance measured through greenhouse gas emissions per capita. The results confirm the existence of four distinct sustainable development regimes characterized by different combinations of economic performance, environmental pressure and resource efficiency. Transition analysis confirms heterogeneous transition dynamics characterized by a generally high degree of regime persistence, while only a limited number of countries experienced regime changes during the study period. The econometric findings confirm that renewable energy deployment and resource productivity are the principal determinants associated with lower greenhouse gas emissions. Based on these results, a Petri Net framework was developed to represent alternative sustainability pathways and transition mechanisms. The integrated analytical framework confirms that combining sustainability regime identification, transition dynamics, panel econometric modelling and conceptual Petri Net representation provides a more comprehensive understanding of sustainable development pathways in the European Union. The findings provide relevant insights for policymakers seeking to support the transition towards more sustainable development trajectories in the European Union. Full article

The implementation of safe and reliable Autonomous Driving Stacks in complex urban environments remains a formidable engineering challenge. While classical modular pipelines provide necessary component-level interpretability, they are inherently rigid, often struggling to adapt to novel environments and failing to provide robust scene interpretation in highly interactive scenarios. In this paper, we present a modular End-to-End ROS-based autonomous driving architecture that upgrades a classical modular baseline by injecting learning-based models into its individual processing layers, integrating GaussianCaR and CLIP for dense semantic BEV perception, expanding the Hierarchical Petri Net state space for safe multi-agent reasoning, refining the planning layer with continuous curve optimization, and replacing the previous reactive controller with an Adaptive Nonlinear Model Predictive Control strategy for superior trajectory tracking. Validated in the CARLA simulator across challenging traffic scenarios and adverse environmental conditions, the proposed architecture raises the Driving Score from 53.81% to 66.46% over the previous baseline, driven by a substantial increase in the Infraction Penalty from 0.59 to 0.79, reflecting a fundamental shift towards safer and more conservative driving behavior at the cost of a moderate reduction in route completion. Against pure End-to-End approaches, our architecture achieves the highest Driving Score at 73.9% and the strongest Infraction Penalty at 0.913, demonstrating that modular interpretability and competitive End-to-End performance are not mutually exclusive. Code will be made publicly available online. Full article

Sustainable management of sugar agro-industrial value chains requires a multidimensional approach that integrates economic, environmental, and social criteria. Current literature addresses risk management, circularity, and artificial intelligence in isolation, without an integrated framework that generates synergistic value. The objective of this research is to validate an integrated framework for the sustainable management of sugar agro-industrial value chains. A mixed-methods, qualitative-quantitative, descriptive-retrospective study was conducted on the Cuban sugar agro-industry during 2023–2025. The procedure was structured into five phases and 10 stages; Petri net simulation was used to validate its logical consistency. Material, economic-financial, and knowledge flows were mapped; 16 stakeholder groups and their influence–dependence relationships were analyzed; 41 risks were identified, of which six were classified as critical. Simulation-based scenario modeling, which integrates risk, circularity, and AI interventions, projects an average potential reduction of 33.4% in total chain lead time, pending empirical validation. Petri nets confirmed the absence of connectivity errors, free-choice violations, and flow noise, formally validating the logical consistency of the procedure. The research supports the hypothesis that an integrated framework combining risk management, circularity, and AI, validated using Petri nets for logical consistency, projects improvements in the efficiency and sustainability of the value chain. Full article

Petri nets represent a highly versatile mathematical formalism for modeling discrete event and hybrid systems. For the development of modern complex production processes for Industry 4.0, integrating these formal models with industrial communication standards is an appropriate and effective option. The main aim of the proposed article is to design a new web-based software tool for the modeling, simulation, and control of mechatronic systems with OPC Unified Architecture support. To accomplish this task, an original software solution called PetriLink is proposed. This platform leverages an intuitive graphical interface and significantly expands the formalism by combining hybrid Petri nets with Colored Petri Nets (CPN) data extensions and a reactive OPC UA subscription model. These new features greatly expand the area of systems that can be modeled and controlled, bridging the gap between theoretical academic tools and practical industrial automation. Furthermore, the structural flexibility of the implemented Petri net models enables the explicit representation of symmetric cyber-physical architectures, as well as the design of asymmetric, event-driven control strategies (e.g., using inhibitor and reset arcs) for enhanced system robustness. The platform was evaluated on a reference net of 5000 places and 2500 transitions, where an incremental dirty-flag evaluation mechanism keeps the per-step engine cost below 1 ms for sparse industrial markings and at about 350 µs for a moderate workload of one hundred concurrent tokens, yielding a speed-up of up to roughly three orders of magnitude over naive full re-evaluation and confirming consistent soft real-time behavior on commodity hardware. Offering a graphical environment for the design of discrete event and hybrid system control algorithms, it can be used for education, research and practice in cyber-physical systems (Industry 4.0). Full article

Controller Area Network (CAN) remains a key in-vehicle communication protocol for distributed automotive control systems, where predictable communication timing is essential for coordinated operation of electronic control units (ECUs). This paper presents a cross-validated framework for timing analysis of automotive CAN networks by combining Deterministic and Stochastic Petri net (DSPN) modeling with worst-case response-time (WCRT) analysis. A DSPN model is developed to represent CAN message generation, priority-based arbitration, bus access, and non-preemptive frame transmission. The model is implemented in TimeNet to evaluate bus utilization, queue occupancy, and access-delay behavior under representative automotive traffic. In parallel, analytical WCRT equations are used to derive conservative latency bounds for each message class. The proposed framework links stochastic performance observations from DSPN simulation with deterministic schedulability guarantees from WCRT analysis, enabling consistency checks between average-case and worst-case timing results. A case study based on a 500 kbit/s automotive CAN configuration with six priority classes is presented. The results show that the network operates at approximately 35.9% bus utilization and that all message classes satisfy their timing requirements with a substantial margin, with the maximum worst-case response time remaining below 2 ms. The study further discusses the modeling assumptions, abstraction limits, and sensitivity of timing behavior to frame length and traffic configuration. The proposed framework provides a practical methodology for timing-oriented design and early-stage validation of automotive CAN communication systems. Full article

As Intelligent Transportation Systems (ITS) transition towards automated ecosystems, the deployment of advanced wireless charging technologies becomes a critical infrastructure requirement. Central to the management of these networks is the Open Charge Point Protocol (OCPP), which ensures interoperability across diverse hardware vendors. However, the reliance on digital communication for power transfer introduces significant cybersecurity vulnerabilities. This paper presents a methodology for evaluating the impact of cyber-threats on urban transport services, with a specific focus on the communication layers that support these Advanced Wireless Power Transfer (WPT) environments. Utilising Stochastic Petri net (SPN) ontology, we model the operational states of an Electric Vehicle (EV) service—including the activation and the arrival phases—to quantify how protocol-level vulnerabilities affect service reliability. We introduce an Extended Vulnerability List (EVL) and analyse two distinct scenarios: a public transport service and a weather forecasting integration. Our results demonstrate that as wireless charging moves towards standardization, the security of the OCPP-based backbone is a fundamental necessity for preventing service disruption. The proposed assessment framework provides a roadmap for securing the next generation of dynamic wireless charging infrastructures against evolving cyber-physical threats. Full article

This paper presents a Digital Twin (DT)-based framework for the control, monitoring, and intelligent optimization of an Assembly/Disassembly/Repair Mechatronic Production Line (A/D/R MPL), developed as a laboratory platform aligned with Industry/Education 4.0/5.0 paradigms. The A/D/R MPL is assisted by two complementary cyber–physical robotic systems: an Assembly/Disassembly/Replacement Cyber–Physical Robotic System (A/D/R CPRS), and a Mobile Cyber–Physical Robotic System (MCPRS), enabling both fixed and mobile intelligent operations. The CPRS is equipped with an industrial robotic manipulator (IRM) responsible for A/D/R tasks, while the A/D Mechatronic Line (A/D ML) consists of seven interconnected workstations (WS1–WS7) dedicated to storage, transport, quality control, and final product handling. MCPRS includes a wheeled mobile robot (WMR), carrying a robotic manipulator (RM) and Mobile Visual Servoing System (MVSS). Each workstation is connected to a local slave programmable logic controller (PLC), which communicates via PROFIBUS with a master PLC located at the CPRS level. Additional communication infrastructures include LAN PROFINET and LAN Ethernet for local integration, and WAN Ethernet connectivity enabled through open platform Communication-Unified Architecture (OPC-UA), ensuring interoperability, scalability, and remote accessibility. Also, MODBUS TCP as serial industrial communication is used between the master PLC and the MCPRS. Virtual environment supports task planning through Augmented Reality (AR) and real-time monitoring through Virtual Reality (VR). The system behaviour is modelled with synchronized hybrid Petri Nets (SHPNs) which describe the discrete and hybrid dynamics of A/D/R processes. Artificial intelligence (AI) techniques are integrated into the DT framework for optimal task scheduling and adaptive decision-making. As a laboratory-scale implementation, the proposed system provides a comprehensive platform for experimentation, validation, and education. It supports Education 4.0/5.0 objectives by facilitating hands-on learning, human–machine interaction, and the integration of emerging technologies such as AI, Digital Twins, AR/VR, and cyber–physical systems. At the same time, it embodies Industry 4.0/5.0 principles, including interoperability, decentralization, sustainability, robustness, and human-centric design. Full article

Cross-organizational collaboration in digital manufacturing systems is often hindered by fragmented data handoff, inconsistent versions, and delayed downstream collaboration across lifecycle stages and heterogeneous systems. To address this issue, a digital thread (DT)-based business process collaboration paradigm is proposed, and its corresponding modeling and execution method is developed in this paper. Firstly, a digital thread-based business collaboration architecture is constructed, in which the unified digital model (UDM) and the authoritative source of truth (ASoT) are integrated to support consistent referencing and authoritative state control of shared data objects. Subsequently, a business process collaboration model and a data-state-driven business collaboration mechanism are established by integrating data state visibility with dual resource constraints. Finally, the proposed method is formalized and evaluated through timed colored Petri net (TCPN) simulation using production business data from a civil aircraft manufacturing enterprise. The results indicate that, under identical resource constraints, the nominal makespan is reduced by approximately 11.3% as collaboration triggering is shifted from document-level handoff to data-state-driven collaboration, thereby improving process parallelism without increasing physical resource inputs. Full article

Accurate and rapid fault diagnosis is critical for active distribution networks characterized by growing structural complexity and diverse load profiles. This paper proposes a two-stage fault diagnosis framework that synergistically combines colored Petri nets (CPN) and fuzzy colored Petri nets (FCPN). In the first stage, a CPN fault zone search model employing a breadth-first search (BFS) strategy is developed to identify suspected faulty components by processing circuit breaker operation information and grid topology. In the second stage, an FCPN diagnosis model is constructed by extending hierarchical fuzzy Petri nets through color assignment to confidence tokens. A key feature of this model is a dedicated initial confidence assessment module that dynamically evaluates the reliability of protection and circuit breaker actions by synthesizing device self-check alarms and operational timing information, thereby overcoming the limitation of empirical, static confidence assignment in existing methods. The resulting initial confidence values are then propagated through a hierarchical confidence inference module to determine the fault likelihood of each suspected component. Comparative simulations across four fault scenarios demonstrate that the proposed method achieves higher diagnostic accuracy and stronger fault tolerance than state-of-the-art approaches, correctly identifying all faulty components even under degraded alarm conditions. Full article

Qualitative analysis of biochemical reaction systems reveals fundamental system-level properties that are independent of precise kinetic parameters, often context-dependent, or experimentally inaccessible. By focusing on structural and topological features—such as conservation relations, feedback loops, and pathway interconnections—qualitative analysis identifies invariant behaviors, robustness mechanisms, and potential failure modes inherent to the signaling network. In this study, we use Petri nets as a formal modeling framework to conduct qualitative analysis of the integrated MAPK and PI3K/Akt signaling network. By exploiting structural properties including place invariants, transition invariants, and siphons, the analysis establishes a direct correspondence between the Petri net structure and biologically meaningful conservation laws, signaling modules, and characteristic dynamic behaviors. The results demonstrate that the proposed model is structurally consistent, biologically plausible, and modular. Minimal semi-positive place invariants confirm mass conservation, indicating that proteins and enzymes circulate within closed molecular pools. Minimal semi-positive transition invariants identify canonical kinase–phosphatase cycles underlying sustained and reversible signaling. Hierarchical decomposition reveals a modular organization reducible to reusable enzymatic motifs, reflecting biological reuse across cascades and supporting scalability. Additionally, the identification of sixteen siphons that are also traps highlights persistent subsystems that ensure continuous regulator availability, confirming the robustness and dynamic sustainability of the integrated network. Full article

Simple recursive rules often conceal surprisingly complex structures, and the Tribonacci sequence is an interesting example of how elementary arithmetic can lead to complex and visually expressive patterns. In this research paper, we present triangular arrays generated by a Petri net encoding of the Tribonacci recurrence, extending our previous Fibonacci construction. Token propagation realizes the delayed dependencies of the recurrence and gives rise to a directed global triangular geometry. Parity and modular colourings highlight persistent self-similar motifs reminiscent of classical fractal patterns. We also outline higher-order k-bonacci extensions and compare the resulting visual structures. Within this framework, global fractal-like visibility tends to degrade as the order increases, while extreme orders can enter a different limiting regime. Full article

The increasing operational variability in distribution networks (e.g., abrupt load changes and distributed generation integration) increases the demands on voltage regulation devices and, in particular, on transformers with on-load tap changers (OLTCs). This paper develops and validates a discrete supervisory control scheme based on Petri nets, implemented in Stateflow and coupled to an electromagnetic model of the OLTC transformer in Simulink/Simscape. The Petri net formalizes the conditional and sequential logic of OLTC operation, enabling state- and time-dependent decisions (e.g., delays between maneuvers) to improve voltage regulation and reduce unnecessary tap operations. The evaluation is performed by simulation under transient scenarios that include sudden load variations anda phase-to-ground fault in the IEEE 13-node standard network, specifically at node 634. In the base case, the controller maintains the voltage within the tolerance band $\pm 1.875\%$ during 96% of the simulated time, with an 88% reduction in RMS error (from 1.92% to 0.23%) and 100% operational efficiency (16 effective maneuvers, with a single hunting event). Subsequently, the scheme is validated on the standard IEEE 13-node network, with four disturbances applied over 600 s (two load increments, photovoltaic injection, and a temporary line disconnection). In this case, regulation remains within a precision zone of $\pm 0.3\%$ for 96.8% of the time, with an average RMS error of 0.23% and 100% efficiency, with no hunting events. The results confirm that a Petri net-based supervisory logic can simultaneously improve the OLTC’s voltage quality and switching efficiency, providing a reproducible alternative for distribution network automation. Full article