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This work deals with the language-based opacity verification and enforcement problems in discrete event systems modeled with labeled Petri nets. Opacity is a security property that relates to privacy protection by hiding secret information of a system from an external observer called an "intruder". A secret can be a subset of a system's language. In this case, opacity is referred to as language-based opacity. A system is said to be language-based opaque if an intruder, with a partial observation on the system's behavior, cannot deduce whether the sequences of events corresponding to the generated observations are included in the secret language or not. We propose a novel and efficient approach for language-based opacity verification and enforcement, using the concepts of basis markings and basis partition. First, a sufficient condition is formulated to check language-based opacity for labeled Petri nets by solving an integer-programming problem. A unique graph, called a modified basis reachability graph (MBRG), is then derived to verify different language-based opacity properties. The proposed method relaxes the acyclicity assumption of the unobservable transition subnet thanks to the basis partition notion. A new embedded insertion function technique is also provided to deal with opacity enforcement. This technique ensures that no new observed behavior is created. A verification algorithm is developed to check the enforceability of a system. Finally, once a system is proved to be enforceable, an algorithm is given to construct a new structure, called an insertion automaton, which synthesizes all possible insertion functions that ensure opacity.

Given the non-repeatability, complexity, and unpredictability of unconventional public health emergencies, building accurate models and making effective response decisions based only on traditional prediction-response decision-making methods are difficult. To solve this problem, under the scenario-response paradigm and theories on parallel emergency management and discrete event system (DES), the parallel simulation decision-making framework (PSDF), which includes the methods of abstract modeling, simulation operation, decision-making optimization, and parallel control, is proposed for unconventional public health emergency response processes. Furthermore, with the example of the severe acute respiratory syndrome (SARS) response process, the evolutionary scenarios that include infected patients and diagnostic processes are transformed into simulation processes. Then, the validity and operability of the DES-PSDF method proposed in this paper are verified by the results of a simulation experiment. The results demonstrated that, in the case of insufficient prior knowledge, effective parallel simulation models can be constructed and improved dynamically by multi-stage parallel controlling. Public health system bottlenecks and relevant effective response solutions can also be obtained by iterative simulation and optimizing decisions. To meet the urgent requirements of emergency response, the DES-PSDF method introduces a new response decision-making concept for unconventional public health emergencies.

This paper proposes a deadlock prevention method to design a maximally permissive liveness-enforcing pure Petri net supervisor for a discrete event system, if such a supervisor exists; otherwise, it obtains the most permissive one in the sense that no other pure liveness-enforcing supervisors via linear monitors can be more permissive than it. This paper exploits an iterative method. At each iteration, a first-met bad marking (FBM) is singled out and an integer linear programming problem (ILPP) is configured. If a feasible solution can be found for the ILPP, then a place invariant (PI) is designed to prohibit the FBM from being reached while no legal marking is forbidden. If the ILPP has no solution, we collect all these FBMs that cannot be optimally controlled. For each of such FBMs, another ILPP is designed to find the least number of legal markings whose reachability conditions contradict the current considered FBM and enumerate all the optimal solutions of this ILPP. Based on it, we develop a 0-1 linear programming problem to find the maximal number of legal markings after removing all the contradictory legal markings. Then, the new sets of legal markings and FBMs are obtained, and we return to the iteration stage to redesign a PI to control each FBM if the ILPP has a feasible solution. Repeat the above process until no FBM can be reached. Finally, a most permissive pure liveness-enforcing supervisor via linear monitors is derived. Two Petri net models are used to illustrate the proposed method., (Copyright © 2019 ISA. Published by Elsevier Ltd. All rights reserved.)

Background: Cigarette smoking is one of the most important health-related risk factors in terms of morbidity and mortality. In this study, we introduced a new method for deriving the transitional probabilities of smoking stages from a cross-sectional study and simulated a long-term smoking behavior for adolescents., Methods: In this study in 2010, a total of 4853 high school students were randomly selected and were completed a self-administered questionnaire about cigarette smoking. We used smoothed age- and sex-specific prevalence of smoking stages in a probabilistic discrete event system for estimating of transitional probabilities. A nonhomogenous discrete time Markov chain analysis was used to model the progression of the smoking in 10 years ahead in the same population. The mean age of the students was 15.69 ± 0.73 years (range: 14-19)., Results: The smoothed prevalence proportion of current smoking varies between 3.58 and 26.14%. The age-adjusted odds of initiation in boys is 8.9 (95% confidence interval [CI]: 7.9-10.0) times of the odds of initiation of smoking in girls. Our study predicted that the prevalence proportion of current smokers increased from 7.55% in 2010 to 20.31% (95% CI: 19.44-21.37) for 2019., Conclusions: The present study showed a moderately but concerning prevalence of current smoking in Iranian adolescents and introduced a novel method for estimation of transitional probabilities from a cross-sectional study. The increasing trend of cigarette use among adolescents indicated the necessity of paying more attention to this group.

This paper deals with the problem of forbidden states in discrete event systems based on Petri net models. So, a method is presented to prevent the system from entering these states by constructing a small number of generalized mutual exclusion constraints. This goal is achieved by solving three types of Integer Linear Programming problems. The problems are designed to verify the constraints that some of them are related to verifying authorized states and the others are related to avoiding forbidden states. The obtained constraints can be enforced on the system using a small number of control places. Moreover, the number of arcs related to these places is small, and the controller after connecting them is maximally permissive., (© 2013 ISA. Published by Elsevier Ltd. All rights reserved.)