This paper presents the newly introduced class of (simple) kernel P systems ((s)kP systems) and investigates through a 3-colouring problem case study the expressive power and efficiency of kernel P systems. It describes two skP systems that model the problem and analyses them in terms of efficiency and complexity. The skP models prove to be more succinct (in terms of number of rules, objects, number of cells and execution steps) than the corresponding tissue P system, available in the literature, that solves the same problem, at the expense of a greater length of the rules.

P systems have been proven to be useful as modeling tools in many fields, such as Systems Biology and Ecological Modeling. For such applications, the acceleration of P system simulation is often desired, given the computational needs derived from these kinds of models. One promising solution is to implement the inherent parallelism of P systems on platforms with parallel architectures.In this respect, GPU computing proved to be an alternative to more classic approaches in Parallel Computing. It provides a low cost, and a manycore platform with a high level of parallelism. The GPU has been already employed to speedup the simulation of P systems.In this paper, we look over the available parallel P systems simulators on the GPU, with special emphasis on those included in the PMCGPU project, and analyze some useful guidelines for future implementations and developments.

This paper focuses on power system fault diagnosis based on Weighted Corrective Fuzzy Reasoning Spiking Neural P Systems with real numbers (rWCFRSNPSs) to propose a graphic fault diagnosis method, called FD-WCFRSNPS. In the FD-WCFRSNPS, an rWCFRSNPS is proposed to model the logical relationships between faults and potential warning messages triggered by the corresponding protective devices. In addition, a matrixbased reasoning algorithm for the rWCFRSNPS is devised to reason about the fault alarm messages using parallel representations. Besides, a layered modeling method based on rWCFRSNPSs is developed to adapt to topological changes in power systems and a Temporal Order Information Processing Method based on Cause-Effect Networks is designed to correct fault alarm messages before the fault reasoning. Finally, in a case study considering a local subsystem of a 220kV power system, the diagnosis results of five test cases prove that the proposed FD-WCFRSNPS is viable and effective.

In recent years, the increasing importance of the computational systems biology is leading to an impressive growth of the knowledge of several real-life phenomena. In this frame work, membrane computing is an emergent branch within natural computing that has been succesfully used to model biological phenomena. The study of these phenomena usually requires the execution of virtual experiments using mechanisms of simulation, implying the development of ad-hoc tools to simulate. However, the advance of the research is demanding general solutions to avoid the necessity of custom software developments for each matter of study, when there are some common problems to resolve. MeCoSim (Membrane Computing Simulator) is a first step in this direction providing the users a customizable application to generate custom simulators based on membrane computing by simply writing a configuration file.

To reveal fault propagation paths is one of the most critical studies for the analysis of power system security; however, it is rather difficult. This paper proposes a new framework for the fault propagation path modeling method of power systems based on membrane computing. We first model the fault propagation paths by proposing the event spiking neural P systems (Ev-SNP systems) with neurotransmitter concentration, which can intuitively reveal the fault propagation path due to the ability of its graphics models and parallel knowledge reasoning. The neurotransmitter concentration is used to represent the probability and gravity degree of fault propagation among synapses. Then, to reduce the dimension of the Ev-SNP system and make them suitable for large-scale power systems, we propose a model reduction method for the Ev-SNP system and devise its simplified model by constructing single-input and single-output neurons, called reduction-SNP system (RSNP system). Moreover, we apply the RSNP system to the IEEE 14-and 118-bus systems to study their fault propagation paths. The proposed approach first extends the SNP systems to a large-scaled application in critical infrastructures from a single element to a system-wise investigation as well as from the post-ante fault diagnosis to a new ex-ante fault propagation path prediction, and the simulation results show a new success and promising approach to the engineering domain. INDEX TERMS Spiking neural P system, membrane computing, fault propagation path, fault propagation relationship, power system.

Cell-like P systems with symport/antiport rules are computing models inspired by the conservation law, in the sense that they compute by changing the places of objects with respect to the membranes, and not by changing the objects themselves. In this work, a variant of these kinds of membrane systems, called cell-like P systems with evolutional symport/antiport rules, where objects can evolve in the execution of such rules, is introduced. Besides, inspired by the autopoiesis process (ability of a system to maintain itself), membrane creation rules are considered as an efficient mechanism to provide an exponential workspace in terms of membranes. The presumed efficiency of these computing models (ability to solve computationally hard problems in polynomial time and uniform way) is explored. Specifically, an efficient solution to the SAT problem is provided by means of a family of recognizer cell-like P systems with evolutional symport/antiport rules and membrane creation which make use of communication rules involving a restricted number of objects.

Summary. Population Dynamics P systems refer to a formal framework for ecological modelling. The semantics of the model associates probabilities to rules, but at the same time, the model is based on P systems, so the rules are applied in a maximally parallel way. Since the success of the first model using this framework [5], initially called multienvironment probabilistic P systems, several simulation algorithms have been defined in order to better reproduce the behaviour of the ecosystems with the models.BBB and DNDP are previous attempts, which define blocks of rules having the same left-hand side, but do not define a deterministic behaviour when different rules are competing for the same resources. That is, different blocks of rules present in their lefthand side common objects, being applicable at the same time. In this paper, we introduce a new simulation algorithm, called DCBA, which performs a proportional distribution of resources.

The fault prediction and abductive fault diagnosis of three-phase induction motors are of great importance for improving their working safety, reliability, and economy; however, it is difficult to succeed in solving these issues. This paper proposes a fault analysis method of motors based on modified fuzzy reasoning spiking neural P systems with real numbers (rMFRSNPSs) for fault prediction and abductive fault diagnosis. To achieve this goal, fault fuzzy production rules of three-phase induction motors are first proposed. Then, the rMFRSNPS is presented to model the rules, which provides an intuitive way for modelling the motors. Moreover, to realize the parallel data computing and information reasoning in the fault prediction and diagnosis process, three reasoning algorithms for the rMFRSNPS are proposed: the pulse value reasoning algorithm, the forward fault prediction reasoning algorithm, and the backward abductive fault diagnosis reasoning algorithm. Finally, some case studies are given, in order to verify the feasibility and effectiveness of the proposed method.