Search

The airport apron is a high-risk area for aircraft collisions due to its heavy operational load and high aircraft density. Currently, existing quantitative models for apron collision risk provide limited consideration and classification of risk areas. In response, this paper proposes a Petri net-based method for assessing aircraft collision risk. The method predicts the probability of aircraft reaching different areas at different times based on operational data, enabling the calculation of collision risks within the Petri net framework. This approach highlights areas with potential collision risks and provides a classification evaluation. Subsequently, aircraft path re-planning is carried out to reduce collision risks. The model simplifies the complex operations of the apron system, making the calculation process clearer. The results show that, during the mid-phase of aircraft taxiing, there is a significant deviation between the actual and ideal positions of aircraft. Areas with high taxiway occupancy are more prone to collision risks. On peak days, due to relatively high flight volumes, the frequency of collision risks is 14% higher than on regular days, with an average risk increase of 23.3%, and the risks are more concentrated. Therefore, reducing collision risks through path planning becomes more challenging. It is recommended to focus attention on areas with high taxiway occupancy during peak periods and carefully plan routes to ensure apron safety. [ABSTRACT FROM AUTHOR]

Interactive scientific visualization applications have been more and more used to observe, analyze and understand the complex phenomena studied in several scientific disciplines such as (nano-sciences, fluid simulations, etc.). To facilitate the coupling of the different computation, visualization and exploration codes, which are often heterogeneous and difficult to cooperate, scientists must be able to use and reuse the different parts of the applications already built in order to interchange and enrich certain parts without being experts. In this context, a middleware called FlowVR has been developed by the scientific community leading to make applications divided into set of components exchanging messages. Moreover, this middleware enables to encapsulate existing codes in components, interconnect them via data links, and deploy them on distributed computing resources. Nevertheless, FlowVR does not offer any capability to check the functional correctness of new developed component-based applications. This paper presents a framework called Model and Component-based framework for Scientific Applications (MCSA) based on a interplay of model-driven and component-based software engineering. On the one hand, we provide a domain specific language dedicated to physics/chemistry scientists in order to built their MCSA applications. This language also allows to capitalize efforts by proposing existing components that have been already developed. On the other hand, we propose a model transformation to check the correctness of instantiated models, for this, we use a new subclass of Petri net called strict Colored FIFO Nets (sCFN). Our approach has been backed by a case study relies on a molecular dynamics application. [ABSTRACT FROM AUTHOR]

This paper introduces software patterns (registration, acquire-release, and cache awareness) and data structures (Petri net, finite state machine, and protocol flag array) to support the coordinated execution of software activities (also called "components" or "agents"). Moreover, it presents and tests an implementation for Petri nets that supports real-time execution in shared memory for deployment inside one individual robot and separates event firing and handling, enabling distributed deployment between multiple robots. Experimental validation of the introduced patterns and data structures is performed within the context of activities for task execution, control and perception, and decision making for an application on coordinated navigation. [ABSTRACT FROM AUTHOR]

In order to improve quality assurance in wafer manufacturing, there are strict process requirements. Besides the well-known residency time constraints (RTCs), a dual-arm cluster tool also requires each robot arm to execute a specific set of tasks. We call such a tool an arm task-constrained dual-arm cluster tool (ATC-DACT). To do this, one of the arms is identified as the dirty one and the other as the clean one. The dirty one can deal with raw wafers, while the clean one can deal with processed wafers. This requirement raises a new problem for scheduling a cluster tool. This paper discusses the scheduling problem of ATC-DACTs with RTCs. Due to the arm task constraints, the proven, effective swap strategy is no longer applicable to ATC-DACTs, making the scheduling problem difficult. To address this problem, we explicitly describe the robot waiting as an event and build a Petri net (PN) model. Then, we propose a hybrid task sequence (HTS) as an operation strategy by combining the swap and backward strategies. Based on the HTS, the necessary and sufficient conditions for schedulability are established; also, a linear programming model is developed. We then develop an algorithm using these results to optimally schedule the system. Industrial case studies demonstrate the application of this method. [ABSTRACT FROM AUTHOR]

This paper addresses the one-dimensional cutting stock problem, focusing on minimizing total stock usage. Most procedures that deal with this problem reside on linear programming methods, heuristics, metaheuristics, and hybridizations. These methods face drawbacks like handling only low-complexity instances or requiring extensive parameter tuning. To address these limitations we develop a Petri-net model to construct cutting patterns. Using the filtered beam search algorithm, the reachability tree of the Petri net is constructed level by level from its root node to find the best solution, pruning the nodes that worsen the solution as the search progresses through the tree. Our algorithm is compared with the Least Lost Algorithm and the Generate and Solve algorithm over five datasets of instances. These algorithms share some characteristics with ours and have proven to be effective and efficient. Experimental results demonstrate that our algorithm effectively finds optimal and near-optimalsolutions for both low and high-complexity instances. These findings confirm that Petri nets are suitable for modeling and solving the one-dimensional cutting stock problem. [ABSTRACT FROM AUTHOR]

To address issues such as inadequate fault tolerance, long computation time, and limited universality in fault location for active distribution networks, this paper proposes a fault location method that combines Petri nets with an improved particle swarm optimization (PSO) algorithm. This method enhances the efficiency of fault location in distribution networks with distributed power sources, demonstrating good applicability and convergence, especially in complex network scenarios. The results from two test functions and simulation analyses of two types of node distribution networks show that (1) in the single-point fault simulation, the improved algorithm successfully located the fault in the ninth iteration, outperforming the 14th iteration result of the standard PSO algorithm. (2) In networks with randomly interconnected distributed power sources, the algorithm accurately located both single and multiple faults. (3) Experimental verification further supports the simulation results, proving the effectiveness of this method in practical applications. [ABSTRACT FROM AUTHOR]

In business process management, business process change analysis is the key link to ensure the flexibility and adaptability of the system. The existing methods mostly focus on the change analysis of a single business process from the perspective of control flow, ignoring the influence of data changes on collaborative processes with information interaction. In order to compensate for this deficiency, this paper proposes a rule-driven consistency analysis method for data changes in collaborative processes. Firstly, it analyzes the influence of data changes on other elements (such as activities, data, roles, and guards) in collaborative processes, and gives the definition of data influence. Secondly, the optimal alignment technology is used to explore how data changes interfere with the expected behavior of deviation activities, and decision rules are integrated into the Petri net model to accurately evaluate and screen out the effective expected behavior that conforms to business logic and established rules. Finally, the initial optimal alignment is repaired according to the screened effective expected behavior, and the consistency of business processes is recalculated. The experimental results show that the introduced rule constraint mechanism can effectively avoid the misjudgment of abnormal behavior. Compared with the traditional method, the average accuracy, recall rate, and F1-score of effective expected behavior are improved by 4%, 4.7%, and 4.3%, respectively. In addition, the repaired optimal alignment significantly enhances the system's ability to respond quickly and self-adjust to data changes, providing a strong support for the intelligent and automated transformation of business process management. [ABSTRACT FROM AUTHOR]

We study approximate integration of a function f over [0,1]^s based on taking the median of 2r-1 integral estimates derived from independently randomized (t,m,s)-nets in base 2. The nets are randomized by Matousek's random linear scramble with a random digital shift. If f is analytic over [0,1]^s, then the probability that any one randomized net's estimate has an error larger than 2^{-cm^2/s} times a quantity depending on f is O(1/\sqrt {m}) for any c<3\log (2)/\pi ^2\approx 0.21. As a result, the median of the distribution of these scrambled nets has an error that is O(n^{-c\log (n)/s}) for n=2^m function evaluations. The sample median of 2r-1 independent draws attains this rate too, so long as r/m^2 is bounded away from zero as m\to \infty. We include results for finite precision estimates and some nonasymptotic comparisons to taking the mean of 2r-1 independent draws. [ABSTRACT FROM AUTHOR]

To construct a user-friendly nomogram with MRI and clinicopathological parameters for the prediction of pathological complete response (pCR) after neoadjuvant therapy (NAT) in patients with breast cancer (BC). We retrospectively enrolled consecutive female patients pathologically confirmed with breast cancer who received NAT followed by surgery between January 2018 and December 2022 as the development cohort. Additionally, we prospectively collected eligible candidates between January 2023 and December 2023 as an external validation group at our institution. Pretreatment MRI features and clinicopathological variables were collected, and the pre- and post-treatment background parenchymal enhancement (BPE) and the changes in BPE on two MRIs were compared between patients who achieved pCR and those who did not. Multivariable logistic regression analysis was used to identify independent variables associated with pCR in the development cohort. These independent variables were combined into a predictive nomogram for which performance was assessed using the area under the receiver operating characteristic curve (AUC), calibration plot, decision curve analysis, and external validation. In the development cohort, there were a total of 276 female patients with a mean age of 48.3 ± 8.7 years, while in the validation cohort, there were 87 female patients with a mean age of 49.0 ± 9.5 years. Independent prognostic factors of pCR included small tumor size, HER2(+), high Ki-67 index,high signal enhancement ratio (SER), low minimum value of apparent diffusion coefficient (ADC min), and significantly decreased BPE after NAT(change of BPE). The nomogram, which incorporates the above parameters, demonstrated excellent predictive performance in both the development and external validation cohorts, with AUC values of 0.900 and 0.850, respectively. Additionally, the nomogram showed excellent calibration capacities, as indicated by Hosmer-Lemeshow test p values of 0.508 and 0.423 in the two cohorts. Furthermore, the nomogram provided greater net benefits compared to the default simple schemes in both cohorts. A nomogram constructed using tumor size, HER2 status, Ki-67 index, SER, ADC min , and changes in pre- and post-NAT BPE demonstrated strong predictive performance, calibration ability, and greater net benefits for predicting pCR in patients with BC after NAT. This suggests that the user-friendly nomogram could be a valuable imaging biomarker for identifying suitable candidates for NAT. [ABSTRACT FROM AUTHOR]

Program dependence net (PDNet) is a kind of Petri nets which can represent concurrent systems and software to apply the automata-theoretic approach for software model checking on Linear Temporal Logic (LTL). This paper presents a formal modelling method to construct a PDNet which is consistent with the behavior of multi-threaded C programs (PThread programs) from a source code. For concurrent programs with a function call and POSIX threads, we propose the formal operational semantics by the labeled transition system (LTS). We formalize the statements by the basic PDNet structure based on LTS operational semantics. Then, we propose the formal modelling method to build a basic flow to simulate the execution of PThread programs. Finally, we give a case study to illustrate the formal modelling method for verifying PThread programs on LTL properties. [ABSTRACT FROM AUTHOR]

To solve some scheduling problems of batch processes based on timed Petri net models, timed extended reachability graphs (TERGs) and approximated TERGs can be used. Such graphs abstract temporal specifications and represent parts of timed languages. By exploring the feasible trajectories in a TERG, optimal schedules can be obtained with respect to the makespans of batch processes that are modeled by timed Petri nets. Nevertheless, the rapid growth of the number of states in a TERG makes the problem intractable for large systems. In this paper, we improve the existing clustering TERG approach, and we make it suitable for large sized batch processes. We also enlarge a systematic approach to model batch processes with timed Petri nets. Finally, a comprehensive example of scheduling problem is studied for an archetypal chemical production plant in order to illustrate the efficiency of the proposed approach. • The scheduling issue of batch chemical processes is formalized with timed Petri nets. • A graph named ClusTERG approximates the state space through hierarchical clustering. • The approximation error of this ClusTERG is far less than other approximations. • Such an approach is used to obtain near-optimal schedules of a batch process. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of NO2 emissions in Mashhad City utilizing statistical evaluations and the Cisco Network Model. The present study begins by evaluating NO2 emissions through statistical analysis, followed by the application of histograms and radar statistical appraisals. Subsequently, a model execution logic is developed using the Cisco Network Model to further understand the distribution and sources of NO2 emissions in the city. Additionally, the research incorporates managerial insights by employing Petri Net modeling, which enables a deeper understanding of the dynamic interactions within the air quality management system. This approach aids in identifying critical control points and optimizing response strategies, thus enhancing the overall effectiveness of urban air pollution mitigation efforts. The findings of this study provide valuable insights into the levels of NO2 pollution in Mashhad City and offer a structured approach to modeling NO2 emissions for effective air quality management strategies which can be extended to the other megacities as well. [ABSTRACT FROM AUTHOR]

In this paper we purpose a Stochastic Petri Net based approach for analysis of communication system with fuzzy sets. There are two stages to the method that is being given. In terms of transition firing rates, the first stage is identical to that of regular stochastic petri nets with steady-state probability and continuous time Markov chain (CTMC) generated. Fuzzy steady state probabilities are computed in the second step after the transition firing rates are represented by triangular fuzzy numbers. A numerical example of communication system considered to analyse the pertinence of proposed method. The significance of this method is to study the dimensions of uncertainty and stochastic variability in system modelling. [ABSTRACT FROM AUTHOR]

In this study, we analyzed the evolution of online public opinion on emergencies using a new Stochastic Petri Net modeling approach. First, an intuitive description of the emergency online public opinion development process was conceptualized from the life cycle evolution law perspective. Then, based on Petri net theory, a Stochastic Petri Net isomorphic Markov chain model was constructed to simulate the evolution of online public opinion on emergencies. Finally, four real-life cases were selected to validate and analyze the model, demonstrating that the evolutionary leaps, complexity, critical nodes, evolutionary rate, and execution time differ across different online public opinions on emergencies. The study results indicate that this modeling approach has certain advantages in examining the evolution based on multi-factor coupling and quantifying the evolution law in online public opinion on emergencies. [ABSTRACT FROM AUTHOR]

This paper is devoted to Petri net (PN)-based models of automated manufacturing systems (AMSs) with resources in order to prevent deadlocks in them. Their sustainability can be seen as the result of their deadlock freeness, leading to correct and fluent production, because AMSs with deadlocks work neither correctly nor fluently, need reconstruction and cause downtime in production. The paradigm of such PN models, S3PRs (systems of simple sequential processes with resources), is well known from the deadlock prevention point of view. Here, an extended S3PR (ES3PR) will be explored, with respect to its modelling and deadlock prevention. While in the case of S3PRs, ordinary Petri nets (OPNs) are used for these aims, here, for ES3PRs, generalized Petri nets (GPNs) are used. The reason for such a procedure is the possible presence of multiplex-directed arcs in the structure of PN models of AMSs. The significant alternation is that while, in the former case, the elementary siphons and dependent ones are sufficient for supervisor synthesis, here, in the later case, the GPNs and their siphons have to satisfy the max cs property. [ABSTRACT FROM AUTHOR]

Petri nets are a graphical notation for describing a class of discrete event dynamic systems whose behaviours are characterised by concurrency, synchronisation, mutual exclusion and conflict. They have been used over the years for the modelling of various distributed systems applications. With the advent of pervasive systems and the Internet of Things, the Calculus of Context-aware Ambients (CCA) has emerged as a suitable formal notation for analysing the behaviours of these systems. In this paper, we are interested in comparing the expressive power of Petri nets to that of CCA. That is, can the class of systems represented by Petri nets be modelled in CCA? To answer this question, an algorithm is proposed that maps any Petri net onto a CCA process. We prove that a Petri net and its corresponding CCA process are behavioural equivalent. It follows that CCA is at least as expressive as Petri nets, i.e., any system that can be specified in Petri nets can also be specified in CCA. Moreover, tools developed for CCA can also be used to analyse the behaviours of Petri nets. [ABSTRACT FROM AUTHOR]

This survey paper explores Petri Net tools relevant to concurrent, non-deterministic, stochastic, and parallel systems. It begins with a brief introduction to Petri Nets, discussing their formalism, properties, and variants. The paper then examines several Petri Net tools, including the Oris Tool, Tapaal Tool, and Color Petri Net (CPN) Tool, detailing their modeling capabilities and presenting the results obtained from the problems addressed. [ABSTRACT FROM AUTHOR]

Decentralized supply chain operation in India adds to inefficiency in management and control difficulties for perishable product supply chain (PPSC). One of the important categories of factors that underly the problem in supply chain is the establishment of processing units that results in end – product distribution. This paper presents an optimal establishment of processing unit around production area for apple supply chain as a case study of India. This study proposed amalgamation of mixed integer linear programming (MILP) and colored petri nets (CPN) to model the PPSC. The mathematical model was set up in regard with capacity of processing unit and transportation cost with continuation in partner selection using MILP approach. The simulation model directs the maximization of system performance by running in CPN environment. The values of the results are subjected to transportation cost and production area with estimate of 25% loss during post harvesting such as transportation and environmental variation. The results corresponding to the case study provided an optimal establishment of processing units. Further, the specific partner associated with the facility and transportation cost. Decision-makers can identify the set of partners from the set of facilities dependent upon the transportation cost for further supply of product using a threshold values. Thereby, the model can be used as a decision-making system for processing companies to result in efficient PPSC management. The developed model was tested and validated on case study to demonstrate our approach's applicability. [ABSTRACT FROM AUTHOR]

Critical systems, in which failure and malfunction may result in severe human, environmental, and financial damages, are essential components in various sectors and particularly in energy domains. Although undesirable, integration error problems in the supervision of critical systems do occur, incurring significant expenses due to an operator's subjective analysis and hardware topology failures. In this work, a malleable model design approach is proposed to formulate and solve the integration error problem in critical systems' supervision in terms of reliability. A real hybrid power plant (HPP) case is considered for a case study with simulated data. A method framework with an informal approach (C4 diagram) and formal approach (hierarchical colored Petri nets) in a radial spectrum is applied to the HPP supervision design. In using formal methods, a formulation and solution to this problem through structured, scalable, and compact mathematical representations are possible. This malleable model is intended to guarantee the functional correctness and also reliability of the plant supervision system based on system software architecture. The outcomes suggest that the malleable model is appropriate for the energy domain and can be used for other types of critical systems, bringing all the benefits of this methodology to the context in which it will be applied. [ABSTRACT FROM AUTHOR]

The article discusses the concept of Self-Learning Meta (SLeM) and its applications in Auto Machine Learning (AutoML). SLeM is a framework that aims to automate various components of the machine learning process, such as data selection, model adjustment, loss setting, and algorithm designing. The framework utilizes meta-data or meta-knowledge to evaluate and predict the optimal learning methodology for different tasks. The text presents several SLeM algorithms, including data auto-selection, model auto-adjustment, loss auto-setting, and algorithm auto-designing. These algorithms have been implemented and released on an open-source platform. The text also highlights the challenges and future research directions in developing more automated and comprehensive SLeM algorithms. The work was supported by funding from the National Key Research and Development Program of China and the National Natural Science Foundation of China. [Extracted from the article]

In process mining, conformance checking determines the quality of discrete‐event process models through several measures computed with respect to a set of event sequences λ from which the model N is obtained. Precision is a measure that evaluates the surplus language of the model LN with respect to λ. This article outlines four precision evaluation approaches for Petri net (PN) workflow models obtained through process discovery techniques. First, the basics of the quality measures of process models stablished in conformance checking are summarised. Then, the methods for model precision evaluation in literature are outlined and analysed. Finally, the advantages and limitations of the methods are pointed out and compared. [ABSTRACT FROM AUTHOR]

Computer system resilience refers to the ability of a computer system to continue functioning even in the face of unexpected events or disruptions. These disruptions can be caused by a variety of factors, such as hardware failures, software glitches, cyber attacks, or even natural disasters. Modern computational environments need applications that can recover quickly from major disruptions while also being environmentally sustainable. Balancing system resilience with energy efficiency is challenging, as efforts to improve one can harm the other. This paper presents a method to enhance disaster survivability in microservice architectures, particularly those using Kubernetes in cloud-based environments, focusing on optimizing electrical energy use. Aiming to save energy, our work adopt the consolidation strategy that means grouping multiple microservices on a single host. Our aproach uses a widely adopted analytical model, the Generalized Stochastic Petri Net (GSPN). GSPN are a powerful modeling technique that is widely used in various fields, including engineering, computer science, and operations research. One of the primary advantages of GSPN is its ability to model complex systems with a high degree of accuracy. Additionally, GSPN allows for the modeling of both logical and stochastic behavior, making it ideal for systems that involve a combination of both. Our GSPN models compute a number of metrics such as: recovery time, system availability, reliability, Mean Time to Failure, and the configuration of cloud-based microservices. We compared our approach against others focusing on survivability or efficiency. Our approach aligns with Recovery Time Objectives during sudden disasters and offers the fastest recovery, requiring 9% less warning time to fully recover in cases of disaster with alert when compared to strategies with similar electrical consumption. It also saves about 27% energy compared to low consolidation strategies and 5% against high consolidation under static conditions. [ABSTRACT FROM AUTHOR]

In this article we show that the conformal nets corresponding to WZW models are rational, resolving a long-standing open problem. Specifically, we show that the Jones-Wassermann subfactors associated with these models have finite index. This result was first conjectured in the early 90s but had previously only been proven in special cases, beginning with Wassermann's landmark results in type A. The proof relies on a new framework for the systematic comparison of tensor products (a.k.a. 'fusion') of conformal net representations with the corresponding tensor product of vertex operator algebra modules. This framework is based on the geometric technique of 'bounded localized vertex operators,' which realizes algebras of observables via insertion operators localized in partially thin Riemann surfaces. We obtain a general method for showing that Jones-Wassermann subfactors have finite index, and apply it to additional families of important examples beyond WZW models. We also consider applications to a class of positivity phenomena for VOAs, and use this to outline a program for identifying unitary tensor product theories of VOAs and conformal nets even for badly-behaved models. [ABSTRACT FROM AUTHOR]

Effective quality management plays a pivotal role in ensuring the smooth operation of smart city systems, which have significant implications for safety, accessibility, affordability, and maintainability. Dependability of autonomous systems is of utmost importance, as achieving satisfactory levels of availability and reliability poses considerable challenges. Smart cities are characterized by interconnected sub-architectures, encompassing vehicle monitoring, sidewalk monitoring, and building monitoring, all of which need to function efficiently. Analytical models such as Petri nets, Markov chains, and fault trees are well-suited for evaluating complex scenarios in the context of smart cities. This paper presents analytical models that utilize fault tree and Markov chain techniques to assess the availability and reliability of smart city monitoring systems. The model is divided into shared and non-shared components, with non-shared components being specific to certain contextual applications, while shared components, such as data processing and electrical power, are essential for all smart city monitoring and management systems. The study underscores the ease with which the fault tree model can enhance availability by modifying failure requirements and resources. Case studies provide concrete examples of how availability improved from 95.3 to 99.8% by varying a configuration known as "KooN" in multiple components. This paper takes a comprehensive approach to evaluating the dependability of smart city architectures and contributes advancements, such as hierarchical modeling, sequential sensitivity analysis, and the "KooN" analytic method. These contributions expand the existing knowledge and methodologies in smart city dependability analysis. Moreover, this work aims to serve as a practical tool to assist smart city managers in optimizing their proposals. All modeling aspects and parameters are detailed thoroughly to enable effective implementation of the proposed approach by anyone using it. [ABSTRACT FROM AUTHOR]

During the last two decades, Petri Nets (PNs) are used extensively in model-based diagnosis. However, the net model becomes fairly complex when dealing with large systems; since each instance of a system variable is modelled by a corresponding place. This is due to the fact that there is only one type of tokens. Consequently, Coloured Petri Nets (CPNs) appear as more suited in this task. This paper presents a causal model-based diagnosis framework based on CPNs. It introduces a particular net model called Coloured Behavioural Petri Net (CBPN) to represent behavioural causal models. In such a net model, functional dependencies are described by means of matrices attached to transitions. The reason for such an attachment is to facilitate the net model analysis on which the diagnosis process will be accomplished. We show how the diagnostic problem can be formalized in terms of reachability in a CBPN. We introduce the CW-analysis as a backward analysis on reachability graph, then we show how it can be exploited to solve a given CBPN diagnostic problem. Finally, a transformation procedure to fold a behavioural Petri net model to an equivalent CBPN one is described. [ABSTRACT FROM AUTHOR]

This article presents a novel approach to achieve precise trajectory tracking control for a line-following quadcopter by employing a multilayer type-2 fuzzy Petri nets controller (MT2PNC). The MT2PNC dynamically adapts its parameters based on tracking errors, allowing for real-time adjustments to the quadcopter's tilt angles and flight direction. The effectiveness of the controller is thoroughly evaluated through both simulations and experimental studies. In the experimental study, a camera is integrated into the quadcopter to capture line images, which are then processed using sophisticated image processing algorithms to extract essential line information. This extracted data is subsequently fed into the MT2PNC, enabling the quadcopter to precisely follow the reference line. The simulation and experimental results conclusively demonstrate the superior control efficacy of the MT2PNC, showcasing its remarkable ability to accurately track the quadcopter's trajectory. The proposed control method exhibits great promise for line-following and trajectory-tracking applications, and its practical implementation holds substantial potential. [ABSTRACT FROM AUTHOR]

Graph convolutional networks (GCNs) have achieved remarkable performance in the 2D-to-3D human pose estimation (HPE) task. The adjacency matrix in GCNs is crucial for feature aggregation in 3D HPE. However, existing GCN-based methods excessively rely on the fixed adjacency matrix to aggregate joint features from one-hop neighbor at a single scale, which limits the feature representation of skeleton data. To better improve the performance of 3D HPE, we have designed a multi-scale parallel adaptive graph network (MPA-GNet) for 3D HPE. The proposed network consists of three parallel multi-scale subgraph networks (PMS-Net) to efficiently capture human joint features at different scales. Specially, a multi-scale feature fusion module is devised to process multi-scale graph structural features and exchange information to generate rich hierarchical representations for skeleton data. To flexible construct graph topology in different scales, a special designed adaptive attention adjacency graph convolution network and a cluster graph pooling module are designed to construct the MPA-GNet in a parallel manner and capture the local subgraphs information in each PMS-Net. Finally, we conduct experiments on two 3D human pose challenging benchmark datasets Human3.6M and HumanEva-I for evaluating the effectiveness of the proposed model. The experimental results demonstrate that our model achieves competitive performance compared with some state-of-the-art 3D HPE methods. [ABSTRACT FROM AUTHOR]

This paper discusses the problem of marking constraints in a class of partially controllable timed Petri nets. Specifically, these are Timed Event Graphs (TEGs) which include disturbed input transitions and which are subject to token capacity constraints in some paths. Using the Min-Plus algebra formalism, formal approaches are proposed to design causal control laws to guarantee compliance with marking constraints in TEGs. Sufficient conditions are defined to show the existence and synthesis of these controllers. The developed methodologies are illustrated in a case study of a disassembly system with a limited stock of components. [ABSTRACT FROM AUTHOR]

As healthcare applications of Artificial Intelligence are growing more and more, the necessity of building universal and transparent models in healthcare is of a paramount importance. Defining approaches able to combine the flexibility of data-driven and the explainability of model-based methods is still an open and promising challenge. This paper proposes an approach to combine mathematical modelling, based on Ordinary Differential Equations, Machine Learning and Fluid Stochastic Petri Nets. By adopting the proposed method and tools, a user could understand the effects of drugs and other treatments on a patient in an interpretable manner. The paper also presents a preliminary application of the approach on the human heartbeat. [ABSTRACT FROM AUTHOR]

The study attempts to assess the decision-making abilities of the knowledge-based system model based on classic fuzzy Petri nets and their subtypes depending on the mode of their operation. In addition, the relationship between the quantity and quality of the proposed decisions was investigated using additional knowledge of the structural and dynamic properties of the net used in the model. The change in the dynamics of the net model was influenced by the appropriate selection of the admissible triples of operators assigned to net transitions in accordance with the knowledge about the partial order between the triples. The theoretical considerations were supported by the analysis and evaluation of the results of the experiments carried out on the net model of the exemplary knowledge-based system for train traffic control. According to the authors, this work fits into the list of challenges formulated in the review article on the state of research on the theory and applications of fuzzy Petri nets, prepared by authors Zhou and Zain and published in Artificial Intelligence Review, 45, 405–446, 2016. [ABSTRACT FROM AUTHOR]

Timely fault diagnosis and localization of transmission lines is crucial for ensuring the reliable operation of increasingly complex power systems. This study proposes an optimized R-Net algorithm based on a feature pyramid network (FPN) and densely connected convolutional network (D-Net) for transmission line fault diagnosis and localization. The R-Net network is enhanced by reshaping the anchor points using an improved K-means algorithm and incorporating an FPN for multi-scale feature extraction. The backbone network is further optimized using D-Net to strengthen inter-layer connections and improve feature reuse. Experimental results demonstrate that the optimized R-Net achieves an overall average accuracy of 0.64, outperforming the original network by 1.30%. The accuracy improvement is particularly significant for ground wire defects (2.40%). The D-Net-based R-Net, despite having fewer parameters, maintains high accuracy (0.6502). Compared to other object detection algorithms, such as YOLO-v3 and Faster R-CNN, the optimized R-Net exhibits superior performance in terms of mean average precision (15.58% and 2.45% higher, respectively) and parameter efficiency (17M vs. 38M and 81M). Considering both performance and speed, the optimized R-Net achieves a processing rate of 10.5 frames per second. This study provides an efficient and accurate tool for transmission line fault diagnosis and localization, with significant practical implications for power system operation and maintenance. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Liquid Crystal & Displays is the property of Chinese Journal of Liquid Crystal & Displays and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We present a Python package called Modular Petri Net Assembly Toolkit (MPAT) that empowers users to easily create large-scale, modular Petri Nets for various spatial configurations, including extensive spatial grids or those derived from shapefiles, augmented with heterogeneous information layers. Petri Nets are powerful discrete event system modeling tools in computational biology and engineering. However, their utility for automated construction of large-scale spatial models has been limited by gaps in existing modeling software packages. MPAT addresses this gap by supporting the development of modular Petri Net models with flexible spatial geometries.

We present a formalism for Petri nets based on polynomial-style finite-set configurations and etale maps. The formalism supports both a geometric semantics in the style of Goltz and Reisig (processes are etale maps from graphs) and an algebraic semantics in the style of Meseguer and Montanari, in terms of free coloured props, and allows the following unification: for P a Petri net, the Segal space of P-processes is shown to be the free coloured prop-in-groupoids on P. There is also an unfolding semantics à la Winskel, which bypasses the classical symmetry problems: with the new formalism, every Petri net admits a universal unfolding, which in turn has associated an event structure and a Scott domain. Since everything is encoded with explicit sets, Petri nets and their processes have elements. In particular, individual-token semantics is native. (Collectivetoken semantics emerges from rather drastic quotient constructions à la Best–Devillers, involving taking π0 of the groupoids of states [ABSTRACT FROM AUTHOR]

Petri nets are a graphical notation for describing a class of discrete event dynamic systems whose behaviours are characterised by concurrency, synchronisation, mutual exclusion and conflict. They have been used over the years for the modelling of various distributed systems applications. With the advent of pervasive systems and the Internet of Things, the Calculus of Context-aware Ambients (CCA) has emerged as a suitable formal notation for analysing the behaviours of these systems. In this paper, we are interested in comparing the expressive power of Petri nets to that of CCA. That is, can the class of systems represented by Petri nets be modelled in CCA? To answer this question, an algorithm is proposed that maps any Petri net onto a CCA process. We prove that a Petri net and its corresponding CCA process are behavioural equivalent. It follows that CCA is at least as expressive as Petri nets, i.e., any system that can be specified in Petri nets can also be specified in CCA. Moreover, tools developed for CCA can also be used to analyse the behaviours of Petri nets.

Abstract This work investigates quantitative supervisory control of discrete event systems modeled with Petri nets under the fixed‐initial‐credit energy objective. A weight function referred to as an energy function is defined on a Petri net to characterize the energy level of a transition. The proposed fixed‐initial‐credit energy problem aims to design a supervisor such that the energy level of a transition sequence in a supervised system is higher than 0 under a given initial energy level. The problem is eventually transformed into a two‐player game between a system and a supervisor; supervisor synthesis is reduced to finding a winning strategy in the two‐player game. Instead of enumerating the complete state space of the underlying Petri net, two information structures are utilized, namely the conventional basis reachability graph and the newly proposed essential marking graph, to construct two‐player games based on each of them. It is shown that a winning strategy for a supervisor decoded from the game based on the basis reachability graph of the Petri net is a solution to the problem but is in general restrictive. Further, it is shown that the set of strategies for a supervisor in the game based on the essential marking graph is consistent with that from the game based on the reachability graph of a Petri net. The two developed approaches do not require an exhaustive exploration of the state space of a plant, thus achieving higher efficiency.

The present paper develops a new framework to predict the mean flow through an array of cylinders in which the flow around the array (array‐scale) and the flow around individual cylinders (element‐scale) are modeled separately using actuator disc theory and empirical drag models respectively, and then coupled through the net drag force. Applying this framework only requires knowledge of the array geometry and incident flow. The framework is validated using high‐fidelity direct numerical simulations for arrays of between 7 and 109 cylinders having different arrangements (staggered, concentric, random) and bounding shapes (circular, square) in both two‐ and three‐dimensional flows. In general, the framework outperforms existing models which require calibration and are only valid for part of the practical parameter space. The demonstrated scale separation suggests different combinations of element‐scale and array‐scale models/theories may be used for other arrangements of bluff bodies. Plain Language Summary: The temporally and spatially averaged velocity, or mean flow, within an array of cylinders is important for many practical applications including, for example, estimating the rates of sediment transport and nutrient uptake within a patch of aquatic vegetation, the hydrodynamic forces on offshore structures, and the efficiency of power generation with a turbine farm. However, a general predictive tool for mean flow is currently lacking. To develop this predictive capacity, this study uses the idea of scale separation to integrate models from different research fields, including the actuator disc theory originally developed for design of turbines and propellers, and element‐scale drag models initially proposed for aquatic vegetated flows, to develop a unifying, predictive, analytical framework to estimate the mean flow. The framework is validated using high‐fidelity direct numerical simulations of arrays of cylinders with different arrangements (staggered, concentric, random) and array shapes (square, circular). With only knowledge of the array geometry and incident flow, the framework predicts the mean flow exceptionally well across a wide range of practical flow conditions. This leads to a step‐change in our ability to predict the fraction of incoming flow passing through a porous array, hydrodynamic load on offshore structures, and sediment flux within aquatic vegetation systems when combined with bed load transport models. The framework could be an important guide for coastal and riverine restoration projects using aquatic vegetation and the design of offshore structures and turbine farms. Key Points: A predictive framework based on an idea of scale separation is proposed for the mean velocity of the flow passing through an array of cylindersThe framework is versatile, being applicable for a wide range of practically meaningful cylinder arrangements, array shapes and array flow blockagesHigh‐fidelity, three‐dimensional direct numerical simulations are employed to investigate flow through the array of cylinders and validate the new predictive framework [ABSTRACT FROM AUTHOR]

Air-rail intermodal transportation (ARIT) plays a crucial role in China's intermodal transportation system. This study aims to model and optimize issues such as inefficiency and complexity in China's ARIT freight transportation using Business Process Reengineering and Stochastic Petri Nets theories. The Petri Net (PN) model for incoming freight transport in ARIT is based on actual operations, employing a new method involving Stochastic Petri Nets and isomorphic Markov Chains theory for performance analysis. Performance analysis results help intuitively identify areas needing optimization. Based on optimization principles, elements such as railway container packaging are improved, resulting in an optimized PN model for ARIT. Finally, data analysis shows that the optimized ARIT model reduces total delay by 7.7% compared to the original. This demonstrates that the new method, combining Markov Chain performance analysis and optimization principles, is feasible and effective for ARIT optimization. [ABSTRACT FROM AUTHOR]

Accurate aircraft turnaround time prediction is an important way to coordinate the operation time of airport ground service and improve the efficiency of airport operation. In this paper, by analyzing the aircraft turnaround operation process, a description model based on Time Transition Petri Net is proposed. The model describes the flight turnaround operation process and the logical relationship of the operation. According to the model, a dynamic prediction method of turnaround time based on Bayesian theorem is designed. According to the actual landing time of the flight, the aircraft turnaround time is predicted. The specific method is to obtain the prior probability distribution and joint distribution law of each operation link according to the flight history data, and use Shapiro-Wilke to test the prior probability distribution of each link. Based on the analysis and comparison between the actual turnaround data of a large airport in China and the forecast data proposed in this paper, the root-mean-square error 3.75 minutes and the mean absolute error 3.40 minutes can be calculated. This paper contributes to the improvement of flight punctuality rate and airport clearance level. [ABSTRACT FROM AUTHOR]

Conducting model selection on data gives rise to selection uncertainty which, when ignored, invalidates subsequent classical inference which assumes that the model is given before the analysis and is in all its aspects correctly specified. In selective inference, the randomness induced by selection is dealt with by conditioning confidence intervals and p-values on the subspace of the data which leads to the same model selection as the observed data. The main challenge is the characterization of this selection event. We develop an algorithm for conducting approximate post-selection inference for parameters after model selection events which may not be characterizable as polyhedrons. We apply this on the adaptive lasso, the adaptive elastic net and the group lasso. We conduct experiments on simulated and real data, illustrating that the algorithm can both successfully control the false-positive rate and is computationally efficient. [ABSTRACT FROM AUTHOR]

As a typical information system, a cloud file system enables the storage, retrieval, and management of data on remote servers or server clusters. The reliable design of such systems is critical to ensure the security of data and availability of services. However, designing correct-by-construction systems is challenging due to the complexity of and concurrency inherent in cloud file systems. Further, existing works on cloud file system analysis often focus on specific systems or lack formal modeling and verification, leading to potential design flaws and security vulnerabilities. To address these issues, we propose MSCFS-RP, which is a formal analysis model based on colored Petri nets. Leveraging the strengths of colored Petri nets in representing diverse information types with colored tokens and defining explicit rules for concurrent interactions, our model captures the writing and reading processes of clients, meta servers, and clusters. With strong formalism and support for verification using CPN Tools, we rigorously evaluate key properties such as replication consistency under various scenarios. The results demonstrate that MSCFS-RP satisfies these properties, validating its effectiveness and trustworthiness in managing information within cloud storage systems. [ABSTRACT FROM AUTHOR]

The equipment of airport security screening checkpoints undergoes frequent modifications due to technological or organizational changes. New solutions complement or replace existing ones to improve the effectiveness of the equipment or formal requirements. An example of this process is the replacement of the walk-through metal detection gate with a newer solution: the body scanner. The present study aimed to analyze the capacity of an airport security checkpoint under different operational conditions, depending on the equipment used. For this purpose, a previously created model (implemented as a colored, timed, stochastic Petri net) was used. Simulation studies were performed in four real-world operational scenarios, and their results were compared to those of a nominal scenario. The results show that, in terms of capacity, it may be advantageous to redirect a more significant stream of passengers to a station equipped with a specific device, depending on the specific operational situation. The results demonstrate the necessity of analyzing the applied strategy of operation with each change in the operational environment. In particular, for an airport with characteristics similar to Katowice Airport, using older technology is beneficial under nominal conditions and after increasing the staffing so that both genders work simultaneously. In other cases, the more intensive use of Body Scanner-equipped security screening checkpoints is advantageous. [ABSTRACT FROM AUTHOR]

Biomedical signal acquisition devices (e.g., Electrocardiography—ECG) are relevant for diagnosing and monitoring persons who have developed a variety of diseases, such as cardiovascular diseases. These devices comply with regulatory requirements before being marketed to prevent misleading measures, and they should also pass through corrective and preventive maintenance to keep them working correctly. We designed and implemented a simulation-based system to support these needs and assist manufacturers and healthcare facilities. This article focuses on demonstrating the effectiveness of our system for validating ECG devices. Our system comprises software in a computing device and a biomedical signals transducer. The system relies on coloured Petri nets modeling language, a frequency-based filtering method, and publicly available databases. We validated the system using the PhysioNet database and comparison tests to verify the expected signal and outputs based on MATLAB filters and the commercial ECG device ENGC901448 from Instramed. The system was proven reliable, low-cost, and portable. Our proposal is relevant to providing evidence for certification and assisting healthcare facilities in conducting testing and corrective and preventive maintenance. [ABSTRACT FROM AUTHOR]

This research introduces the NeuRaiSya (Neural Railway System Application), an innovative railway signaling system integrating deep learning for passenger analysis. The objectives of this research are to simulate the NeuRaiSya and evaluate its effectiveness using the GreatSPN tool (graphical editor for Petri nets). GreatSPN facilitates evaluations of system behavior, ensuring safety and efficiency. Five models were designed and simulated using the Petri nets model, including the Dynamics of Train Departure model, Train Operations with Passenger Counting model, Timestamp Data Collection model, Train Speed and Location model, and Train Related-Issues model. Through simulations and modeling using Petri nets, the study demonstrates the feasibility of the proposed NeuRaiSya system. The results highlight its potential in enhancing railway operations, ensuring passenger safety, and maintaining service quality amidst the evolving railway landscape in the Philippines. [ABSTRACT FROM AUTHOR]

This paper studies the discrete differential geometry of the checkerboard pattern inscribed in a quadrilateral net by connecting edge midpoints. It turns out to be a versatile tool which allows us to consistently define principal nets, Koenigs nets and eventually isothermic nets as a combination of both. Principal nets are based on the notions of orthogonality and conjugacy and can be identified with sphere congruences that are entities of Möbius geometry. Discrete Koenigs nets are defined via the existence of the so-called conic of Koenigs. We find several interesting properties of Koenigs nets, including their being dualizable and having equal Laplace invariants. Isothermic nets can be defined as Koenigs nets that are also principal nets. We prove that the class of isothermic nets is invariant under both dualization and Möbius transformations. Among other things, this allows a natural construction of discrete minimal surfaces and their Goursat transformations. [ABSTRACT FROM AUTHOR]

Computational biological models have proven to be an invaluable tool for understanding and predicting the behaviour of many biological systems. While it may not be too challenging for experienced researchers to construct such models from scratch, it is not a straightforward task for early stage researchers. Design patterns are well-known techniques widely applied in software engineering as they provide a set of typical solutions to common problems in software design. In this paper, we collect and discuss common patterns that are usually used during the construction and execution of computational biological models. We adopt Petri nets as a modelling language to provide a visual illustration of each pattern; however, the ideas presented in this paper can also be implemented using other modelling formalisms. We provide two case studies for illustration purposes and show how these models can be built up from the presented smaller modules. We hope that the ideas discussed in this paper will help many researchers in building their own future models. [ABSTRACT FROM AUTHOR]