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Spiking neural P systems (SN P systems) abstract the structure and function of neurons and nervous systems. By adopting some biological observations or mathematical considerations in SN P systems, many variants have been proposed. In this work, through combining two interesting variants, SN P systems with multiple channels (SNP-MC systems) and SN P systems with autapses (SNP-AU systems), we propose SN P systems with multiple channels and autapses (SNP-MCA systems) and research their universality. SNP-MCA systems are proved to be universal in the generation of numbers. For computing functions, a universal SNP-MCA system with 25 neurons is constructed, which requires fewer neurons than the two variants.

Human computation is a technology inspired by nature. The majority of its computational sources are physiologically motivated computations. The existing calculation, on the other hand, uses some conventional techniques to complete the work. In the cloud, IaaS is one of the most basic services, offering a wide range of functions to a large number of customers. Modelling a number of species using P systems with different membrane structure types to predict the number of individuals is a major advantage of the proposed work. Even though it provides large number services (or) features to the user always it faces many numerous obstacles in Infrastructure service in cloud network, such as Authentication and Authorization, Data leakage and monitoring, End to End encryption, there is a risk of lack of security. Traditional encryption approaches also employ efficient methods to build cloud network security. It does, however, have some flaws in terms of IaaS. The work proposed membrane infrastructure based on the quantum inspiration. It will used to overcome such challenges. It is possible to minimize current constraints by employing this strategy. The paper proposes to employ basic distributed computational methods in an open and natural setting to provide membrane systems as a suitable framework for cloud environments for providing unique security among cloud users. They employ a framework for defending against intruder attacks. It is possible to address key cloud computing security difficulties by integrating Quantum Computing between the membrane environments with Cloud Computing technology. The proposed effectiveness and outcomes are monitored with the help of Internet of Things (IoT). This level introduces a revolutionary method to cloud security by incorporating quantum protocols into a membrane environment. The proposed SEDFA is the better method with all types of datasets and the Communication Cost by 5%, Encoding Time (ET) by 2 s, and Decoding Time (DT) by 0.5 s.

Membrane systems are a class of bio-inspired computing models,inspired by the structure and function of cells,tissue,organ and bio-systems.Spiking neural P systems with rules on synapses(SNPRS) are a type of membrane systems,inspired by the way that neurons communicate information.In SNPRS,each neuron is a basic unit for storing information,and each synapse is a medium for integrating and transmitting information.The whole system processes information in the distributed and parallel way.In this paper,we review the definition and related notions of SNPRS.Then,we introduce a few variants of SNPRS,and give a comparison among the variants of SNPRS.Furthermore,we provide results on the computation power of SNPRS(and their variants) working in different modes and on the application of the systems,such as solving NP-hard problems,implementing arithmetic operations,and breaking RSA.Additionally,some open problems are provided to suggest directions for further theore-tical as well as applicable research on SNPRS.

This paper deals with a synthesis method of membrane computer systems inspired from the biological cells structure. The functional concept of the computer system is based on the internal structure, chemical transformations and the way of interaction between living cells. Thus, the computer system is composed of a set of autonomous, homogeneous or heterogeneous computing cells, which communicate with each other in synchronous or asynchronous mode. The algorithmic complexity of the solved problem depends on the knowledge gained, the rules of data processing and the computer system topology. It is proposed the sequence of operations for the synthesis of membrane computer systems with implementation in reconfigurable FPGA architectures which includes: analysis of behavioural models of living organisms and their interpretation in artificial intelligence models; structure and functional description of the computing cell; cell modelling and performance evaluation using Petri nets; Van diagram of the membrane computer system topology; formal description of the membrane computer system topology by applying the JSON formatting language; validation, functional modelling and performance evaluation of the membrane computer system topology using Petri nets; Hardware Description Language (HDL) code of the cell from which the principle electrical diagram and the time diagrams are obtained; implementation of the computer system in the FPGA circuit.

Rice pests and diseases have a significant impact on the quality and yield of rice, and even have a certain impact on and cause a loss in the national agricultural industry and economy. The timely and accurate detection of pests and diseases is the basic premise of formulating effective rice pest control and prevention programs. However, the complexity and diversity of pests and diseases and the high similarity between some pests and diseases make the detection and classification task of pests and diseases extremely difficult without detection tools. The existing target detection algorithms can barely complete the task of detecting pests and diseases, but the detection effect is not ideal. In the actual situation of rice disease and insect pest detection, the detection algorithm is required to have fast speed, high accuracy, and good performance for small target detection, and so this paper improved the popular yolov5 algorithm to achieve an ideal detection performance suitable for rice disease and insect pest detection. This paper briefly introduces the current status and influence of rice pests and diseases and several target detection algorithms based on deep learning. Based on the yolov5 algorithm, the RepVGG network structure is introduced, 3*3 convolution is combined with ReLU, a training time model with multi-branch topology is adopted, and the inference time is reduced through layer merging. To improve algorithm detection speed, the SK attention mechanism is introduced to improve the receptive field of the convolution kernel to obtain more information and improve accuracy. In addition, Adaptive NMS is replaced by Adaptive NMS, the dynamic suppression strategy is adopted, and scores for learning density are set, which greatly improves the problems of missing detection and the false detection of small targets. Finally, the improved algorithm model is combined with membrane calculation to further improve the accuracy and speed of the algorithm. According to the experimental results, the accuracy of the improved algorithm is increased by about 2.7 percentage points, and the mAP is increased by 4.3 percentage points, up to 94.4%. The speed is improved by about 2.8 percentage points, and indicators such as recall rate and AP are improved.

The number system is the representation method of numbers, and number system conversion is the most basic function of a computing system. The decimal system is the most commonly used number system; however, it may not necessarily be the most suitable number system in a computing system. This paper investigates the conversion methods between different number systems and designs corresponding cells, such as P systems, to implement them. The P systems we designed include Π10_2 and Π2_10 to implement conversion between decimal and binary, Π10_m and Πm_10 to implement conversion between decimal and m-base. The operation process of each P system was explained through examples, and their feasibility and effectiveness were verified through simulation software, UPSimulator.

The existing path planning algorithm of robot in coal mine uses fixed step size and serial mode to generate path, which has problems such as low success rate, poor real-time performance and low efficiency. Combining membrane computing (MC) with Informed RRT* algorithm, this study proposes a path planning algorithm of robot in coal mine, namely MC-IRRT* algorithm. The algorithm is divided into two stages, namely fast connectivity and path optimization. In the fast connectivity stage, the multi-step cellular membrane structure is constructed, and the step size is adjusted according to the size of the space area. The large step search is used in the area with larger feasible space to accelerate the search speed. The small step search is used in the narrow space to make the search space more refined and improve the success rate of the narrow space path search. In the path optimization stage, a multi-sampling cellular membrane structure is constructed, and multiple basic membranes are calculated in parallel, and the shortest feasible path is searched in parallel in multiple elliptical areas at the same time to save time and improve the efficiency of path optimization. The simple scene experimental results show that compared with Informed RRT* algorithm, the search efficiency of MC-IRRT* algorithm in the fast connectivity stage and the path optimization phase is increased by 76% and 40% respectively. The complex scene experimental results show that the path planning of RRT* algorithm and Informed RRT* algorithm fails, and both PQ-RRT* algorithm and MC-IRRT* algorithm can find feasible paths successfully. Compared with PQ-RRT* algorithm, the rate of MC-IRRT* algorithm is increased by 12.79%, and the planned path length is shortened by 8.18%. The MC-IRRT* algorithm can not only pass through narrow feasible areas quickly, but also can choose to use smaller step at the turning point of the path so as to make the path smoother.

Membrane computing is a branch of natural computing which has been extended to solve various optimization problems. A hybrid mode membrane-computing-based algorithm (HMMCA) is proposed in this paper to solve complex unconstrained optimization problems with continuous variables. The algorithmic framework of HMMCA translates from its distributed cell-like membrane structure and communication rule. A non-deterministic evolutionary programming method and two computational rules are applied to enhance the computational performance. In a numerical simulation, 12 benchmark test functions with different variables are used to verify the algorithmic performance. The test results and comparison with three other algorithms illustrate its effectiveness and superiority. Moreover, a case study on a proton exchange membrane fuel cell (PEMFC) system parameter optimization problem is applied to validate its practicability. The results of the simulation and comparison with seven other algorithms demonstrate its practicability.

In recent years, the rapid development of the internet and the advancement of information technology have produced a large amount of large-scale data, some of which are presented in the form of large-scale graphs, such as social networks and sensor networks. Minimum vertex cover (MVC) is an important problem in large-scale graph research. This paper proposes a solver Q-MeaMetaVC based on the MVC framework PEAF and the membrane evolution algorithm framework MEAF. First, the graph is reduced and divided into two types of connected components (bipartite graph and non-bipartite graph) to reduce the scale of the problem. Second, different membrane structures are designed for different types of connected components to better represent the connected component features and facilitate solutions. Third, a membrane evolution algorithm (MEA), which includes fusion, division, cytolysis, and selection operators, is designed to solve the connected components. Then, Q-MeaMetaVC is compared with the best MVC solver in recent years on the test set, and good experimental results that are obtained verify the feasibility and effectiveness of Q-MeaMetaVC in solving the MVC of large-scale graphs.

Spiking neural P systems (SNP systems), as computational models abstracted by the biological nervous system, have been a major research topic in biological computing. In conventional SNP systems, the rules in a neuron remain unchanged during the computation. In the biological nervous system, however, the biochemical reactions in a neuron are also influenced by factors such as the substances contained in it. Based on this motivation, this paper proposes SNP systems with rules dynamic generation and removal (RDGRSNP systems). In RDGRSNP systems, the application of rules leads to changes of the substances in neurons, which leads to changes of the rules in neurons. The Turing universality of RDGRSNP systems is demonstrated as a number-generating device and a number-accepting device, respectively. Finally, a small universal RDGRSNP system for function computation using 68 neurons is given. It is demonstrated that the variant we proposed requires fewer neurons by comparing it with five variants of SNP systems.

The parallelism of rule execution in membrane computing provides support for improving computational efficiency. Membrane computing models have been applied in many fields. In arithmetic operations, designing basic arithmetic operation spiking neural P systems using fewer neurons and rule types has been an important field of membrane computing application research in recent years. We discuss the application of locally homogeneous spiking neural P systems in arithmetic operations. The purpose is to design a spiking neural P system with fewer neurons and rule types to perform arithmetic operations. We designed the addition and subtraction of a locally homogeneous spiking neural P system without weight and delay. They include two input neurons to achieve any two binary number subtraction, one input neuron to achieve any two binary number addition and subtraction, and one input neuron to achieve any n binary number addition and subtraction. This is an attempt to apply the locally homogeneous spiking neural P system in arithmetic operations. Compared with the current excellent spiking neural P system performing arithmetic operations, our designed locally homogeneous spiking neural P system is more concise. The system we designed reduces the number of neurons required for n number addition operations by k − 6 and reduces the number of rule types by 5k − 14.

As a novel biological computing device, the Spiking Neural P system (SNPS) has powerful computing potential. The application of SNPS in the field of arithmetic operation has been a hot research topic in recent years. Researchers have proposed methods and systems for implementing basic arithmetic operations using SNPS. This paper studies four basic arithmetic operations, improves the parallelization of addition and multiplication methods, and designs more effective natural number addition and multiplication SNPS, as well as SNPS for subtraction and for division of natural numbers based on multiple subtractions. The effectiveness of the proposed SNPS is verified by example. Compared with the same kind of SNPS, for the addition operation the number of neurons used in our system is reduced by 50% and the time overhead is reduced by 33%, while for the multiplication operation the number of neurons is reduced by 40%.

Considering a class R comprising recognizer membrane systems with the capability of providing polynomial-time and uniform solutions for NP-complete problems (referred to as a “presumably efficient” class), the corresponding polynomial-time complexity class PMCR encompasses both the NP and co-NP classes. Specifically, when R represents the class of recognizer presumably efficient cell-like P systems that incorporate object evolution rules, communication rules, and dissolution rules, PMCR includes both the DP and co-DP classes. Here, DP signifies the class of languages that can be expressed as the difference between any two languages in NP (it is worth noting that NP ⊆ DP and co-NP⊆co-DP). As DP-complete problems are believed to be more complex than NP-complete problems, they serve as promising candidates for studying the P vs. NP problem. This outcome has previously been established within the realm of recognizer P systems with active membranes. In this paper, we extend this result to encompass any class R of presumably efficient recognizer tissue-like membrane systems by presenting a detailed protocol for transforming solutions of NP-complete problems into solutions of DP-complete problems.

In view of problems such as unreasonable path planning, slow planning speed and poor real -time performance when mine mobile robots use traditional dynamic window algorithm to plan path in complex environment, a dynamic window algorithm of mine mobile robot based on membrane computing and particle swarm optimization was proposed. The traditional dynamic window algorithm is optimized by using randomness of particle swarm optimization and distributed parallel computing ability of membrane computing. In the dynamic window algorithm, the velocity limit space of mine mobile robot is transformed into coordinate space, and the velocity coordinate of the mine mobile robot is regarded as particle position. The speed sampling mode is changed from uniform equal sampling to random sampling, the sample particles are evenly distributed to each basic membrane. The exchange between membranes and the renewal mechanism of particles in membrane are used to evaluate the renewal of particles. The optimal speed is output continuously. The path planning of the mine mobile robot is based on the optimal output speed in continuous time interval. The simulation results show that the algorithm optimizes the speed limit region of mine mobile robot by membrane computing and particle swarm optimization algorithm, and improves the randomness of speed sampling and the rationality of planning path. Compared with the traditional dynamic window algorithm, the proposed algorithm can not only reduce the number of planning steps and the evaluation times of each step, but also shorten the planning path length by 7% -10% and the planning time by 9% -32%, and can adapt to the special environment with U -shaped obstacles.

To solve the problem of insufficient node positioning accuracy in wireless sensor networks,an algorithm based on improved particle swarm optimization by membrane computing (IPSO-MC) was proposed.Kent mapping was used to initialize the population and domain particles were introduced to improve the global optimization of the particle swarm.The weight factor and nonlinear extreme value perturbation were used to improve the local optimization ability of the particle swarm,and the Levy flight mechanism was used to optimize the individual position.Finally,the optimal solution of the particle swarm algorithm was obtained by the evolutionary rules of the membrane computing.Simulation experiments show that compared with the chicken flock algorithm,the improved particle swarm algorithm and the membrane computing,the proposed algorithm improves 3.24%,5.12% and 8.15% in the comparison of reference node ratio indicators,and the increase in the number of nodes indicators by 2.26%,7.82% and 9.81%,and the comparison of communication radius indicators increased by 2.15%,5.5% and 7.5%,respectively.This indicates that the algorithm has a good effect in node localization.

This paper proposes an extension of numerical P (NP) systems, called stochastic numerical P (StNP) systems. In StNP systems, a novel stochastic production function-repartition protocol is developed to evolve the variables. Compared with usual production function-repartition protocol in NP systems, there are two differences in stochastic production function-repartition protocol: (i) some stochastic/randomized factors are introduced in production function and/or repartition protocol; (ii) data updating way is relaxed and can be flexibly used. An StNP system has a nested structure and contains some variables and a group of programs. The programs consist of stochastic production function-repartition protocols and/or usual production function-repartition protocols. Therefore, StNP systems are a class of distributed parallel computing models with stochastic and dynamic characteristics. Data clustering problems are used as an application to demonstrate the availability and effectiveness of StNP systems. Based on StNP systems, a novel partition clustering algorithm is presented. Experimental results demonstrate advantage of StNP systems for data clustering problems.

Mobile membranes represent a model of computation inspired from the biological movement provided by endocytosis and exocytosis in the living cells. This paper presents a survey of the results treating the computational power of the mobile membranes, their efficiency in solving NP-complete problems, and connections with other formal approaches able to handle mobility.

Recent advances in evolutionary algorithms based on membrane computing have shown that the mechanism of membrane computing is an effective way to solve optimization problems. In this work, we propose a new evolutionary multi-objective algorithm that uses membrane systems to solve multi-objective optimization problems. Based on the mechanism of living cell structure and function, the algorithm introduces three factors, including membrane structure, multiset and reaction rules. The membrane structure of the proposed algorithm is inspired by the structure of the membrane system, which has multiple layers and nested structures in the skin membrane. Two special symbol-objects are designed to improve the search efficiency of the algorithm. In addition, some reaction rules are used to evolve the symbol-objects of multiset in the inner region of the membrane. In addition, the proposed method combines external archive to maintain the diversity of non-dominated solutions and enhance the search capabilities of the solutions. Our proposed method is compared to five state-of-the-art multi-objective heuristic algorithms. For comparison, six different criteria were used: the quality of the resulting approximate set, the diversity of candidate solutions, and the rate of convergence to the Pareto front. Experimental results show that the proposed method is competitive in performance in qualitative and quantitative measurements of selected test functions. Therefore, the algorithm is feasible and effective for solving multi-objective optimization problems.

Tissue-like P systems are a type of distributed parallel computing models inspired by actual biological tissue. In this paper, we consider a new variant of tissue-like P systems, which is called tissue-like P systems with evolutional symport/antiport rules. Unlike traditional models of this type, in the new P systems, objects can change during transmission. In biology, an organism that is in “homeostasis” reduces its dependence on external conditions, thereby keeping it relatively constant and maintaining a relatively stable internal environment. In our work, we remove the assumption that the quantity of objects in the environment is infinite, reducing the influence of the environment on this system, so the environment no longer provides powerful energy for cells. Moreover, the time-free mode is introduced into this type of P systems, which makes the constructed systems more robust. We investigate the computational power and computational efficiency of the constructed system. Specifically, by simulating register machines, such a P system can generate any Turing computable set of numbers. In addition, we prove that this constructed system can efficiently solve the $\mathcal {SAT} $ problem. Although we restrict P systems and consider time-free manner, the results show that this system is not only Turing universal, but also can solve NP-complete problem.

In recent years, heuristic intelligent algorithms have achieved rapid development in solving combinatorial optimization problems. Travelling salesman problem(TSP) is one of the classical NP-hard problems in combinatorial optimization, and it needs for more accurate and faster algorithms. Combining heuristic algorithm and cell-like P system, this article proposes a membrane evolutionary algorithm for solving TSP(MEATSP). MEATSP abstracts the behaviors of biological cells such as fusion, division, cytolysis and selection into operators, and obtains the optimal solution of the problem through the evolution of membrane structure and objects within the membrane. Experiments on TSPLIB data sets show that the proposed algorithm performs well in both optimal solution and average residual, and it has strong stability when solving TSP with different scales.

Consensus clustering algorithm, which integrates several clustering results obtained by common algorithms, can find a better result that is independent on parameter settings. However, this kind of algorithm is often designed based on simple, such as $K$ -means, algorithms, which is limited by the time complexity. In this work, a P system, a novel branch of bio-inspired computing with inherent parallel and distributed computation, is combined with the consensus clustering algorithm. As a result, an improved consensus clustering algorithm is constructed using the hierarchical membrane structure and parallel evolution mechanism in a cell-like P system with multi-catalysts, where the catalysts are utilized to regulate the parallelism of objects evolution. The integration strategy of the algorithm is based on a revised PAM where only the $q$ -nearest neighbors of the original medoids are considered as candidates for the new medoids. The experimental results indicate that the clustering quality of the proposed algorithm is more robust than well-known consensus clustering algorithms on data sets with noises and outliers. This work gives evidence that the effectiveness and efficiency of consensus clustering algorithms can be improved using P systems.

Cell-like P systems with channel states and symport/antiport rules (CCS P systems) are a type of nondeterministic parallel biological computing model, where there exists a channel between adjacent regions and there is a state on each channel to control the execution of symport/antiport rules. In this work, a synchronization rule is introduced into CCS P systems, a variant of CCS P systems called CCS P systems with synchronization rule (CCSs P systems) is proposed. The universality of CCSs P systems with only uniport (symport or antiport) rules is investigated. By simulating the register machine, we proved that CCSs P systems have the ability to simulate any Turing machine in the following three cases: having two membranes, two channel states and using symport rules of length at most 2; having one membrane, three channel states and using symport rules of length at most 2; and having one membrane, two channel states and using antiport rules of length at most 3.

P systems with active membranes, as a sort of basic P system, include in communication rules and out communication rules, where communication rules are controlled by polarizations. However, the communication of objects among living cells may be controlled by several factors, such as proteins, polarizations, etc. Based on this biological fact, in this article, a new class of P systems, named P systems with proteins on active membranes (known as PAM P systems) is considered, where the movement of objects is controlled by both proteins and polarizations. The computational theory of PAM P systems is discussed. More specifically, we show that PAM P systems achieve Turing universality when the systems use two membranes, one protein and one polarization. Moreover, the PAM P systems, with the help of membrane division rules, make the SAT problem solvable. These results indicate that PAM P systems are also a sort of powerful system.

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.

Since the membrane algorithm was proposed, it has been used for many optimization problems such as, traveling salesman problem, the knapsack problem, and so on. In membrane algorithms, the membranes have two functions: container and comparator. As a container, each membrane contains one evolutionary algorithm like genetic algorithm and ant colony algorithm. These algorithms are called sub-algorithms and used to evolve individuals. As a comparator, the membrane will compare the results of sub-algorithms, and select the best as the base of the next evolvement. This paper proposes a novel evolutionary algorithm called membrane evolutionary algorithm framework (MEAF). Unlike the presented membrane algorithms, the membranes in MEAF will be evolved to solve problems by using four operators that are abstracted from the life cycle of living cells. Based on MEAF, a membrane evolutionary algorithm called MEAMVC is proposed to solve the minimum vertex cover (MVC) problem. The experimental results show the advantages of MEAMVC when MEAMVC is compared with two state-of-the-art MVC algorithms proposed in recent years.

In this paper, we design a double layers self-organized spiking neural P system with anti-spikes for fingerprint recognition. The system can self-adaptively create and delete synapse between the neurons in different layers and recognize fingerprints by the spike trains emitted out of the output neurons. Data experiments are conducted on FVC2002 and FVC2004 Databases with EER (Equal Error Rate) 9.5% around, and much less parameters are involved in our SN P systems than Capsule Neural Networks. To our best knowledge, it is the first attempt of using SN P systems to do fingerprint recognition, which can also provide theoretical models for spiking neural circuits recognizing fingerprints.

Construction of biomolecular computing devices has become a hot topic. Although many biomolecular computing devices have been developed, cellular comparators, which play a critical role in facilitating decision-making, are still challenging to create. A comparator requires a computing tool that can determine values based on judging the existence of a signal. It is challenging to find such a computing tool. The clustered regularly interspaced short palindromic repeats/DNase-dead Cpf1 (CRISPR/ddCpf1) system is a novel expressing interference system with high specificity and programmability. In this paper, we have successfully designed, constructed, and tested a genetic comparator in the bacterium Escherichia coli using the CRISPR/ddCpf1 system. The comparator could indicate the maximum value for up to three signals. The comparator may have many potential applications in decision-making tasks, such as biocomputing, biotherapy, bioremediation, and artificial intelligence. In addition, our results provide important insights into the use of the CRISPR/ddCpf1 system to establish complex cellular computing devices.

Hiding secret messages (SMs) based on DNA technologies plays a critical role in DNA computing. In recent years, scientists have hidden SMs at a specific site in the plasmids of various living cells. Unfortunately, this strategy was proposed without considering the attack of next-generation sequencing (NGS). Nowadays, NGS, which is a revolutionary massively parallel sequencing technique, is developing rapidly. With the help of NGS, sequencing completely unknown genomes is becoming popular in various laboratories throughout the world. The ability to sequence completely unknown genomes is a major threat to existing hiding strategies in living cells. To prevent against the attack of NGS, this paper proposes a scheme to hide SMs in living cells (SHSM). Compared with previous studies, the main contribution of SHSM is changing the specific single hiding site into random multiple hiding sites through the application of seamless clustered regularly interspaced short palindromic repeats/CRISPR-associated9 (CRISPR/Cas9) technique. In SHSM, SMs are hidden in living cells with different puzzling messages at different sites every time to prevent against the attack of NGS. To read the SM, high-fidelity polymerase chain reaction (PCR) and Sanger sequencing are used. A hash function is used to ensure the integrity of the message. These measures mean that an attacker would not be able to determine the location of the SM using NGS. The feasibility of SHSM was validated by a wet-lab experiment, and the security was demonstrated by the system's entropy. The author hopes this study will bring attention to the threat of NGS, and advance the development of hiding SMs in living cells.

Membrane computing is a kind of distributed and parallel computing model inspired by a biological cell mechanism. The maximum parallelism of membrane computing improves the computational efficiency of its computational model. In this paper, a tissue P system named ΠKP is proposed to solve the 0-1 knapsack problem, which is one of the classic NP-hard problems. ΠKP could obtain the accurate solutions of knapsack problem and points out the number of accurate solutions, which mainly consists of three stages: first, generate all the solutions of knapsack problem by a cell division; then calculate the weights and total values in all the candidate membranes, which will be kept or dissolved according to the restriction of knapsack problem; and check out the final solutions. The instances are executed on a membrane simulator named UPSimulator, and the result of the experiments shows the whole searching procedure by the rules and proves the correctness and efficiency of the system.

Biomolecular computing devices always work in a complex biochemical environment. The weight which shows the importance degree of a specific signal may vary in different environments because it may be influenced by other signals even in the absence of direct interaction. To measure a signal accurately, several biomolecular computing devices always need to be used to detect environmental factors. This study describes a novel method to determine the varied weight of a specific signal in different environments using the differential expression of a gene to avoid using several biomolecular computing devices. We constructed and verified a signal varied weight indicator (SVWI) in the bacterium Escherichia coli using the lac operon, Ptet promoter system, and the clustered regularly interspaced short palindromic repeats/DNase-dead Cpf1 system. The SVWI could indicate the varied weight of an isopropyl β-d-thiogalactoside signal in three states: existing, non-existing, and existing with another signal of anhydrotetracycline, based on the controllable differential expression of the lacZ gene. It could also be used to indicate three different kinds of environments. The quantitative reverse transcription polymerase chain reaction, which is a traditionally used method to detect gene expression levels, was used to detect the results. The experimental results in the wet lab showed that SVWI can implement the expected functions. The SVWI has great potential in the application of biomedical, biocomputing, biosensors, and environmental monitoring. It will also inspire further research on the construction of biomolecular computing devices using controllable differential gene expression.

Ant colony algorithm is a swarm intelligence population-based algorithm, which is inspired from the foraging behavior of ant colonies. Nowadays, the ant colony algorithm has become a very popular topic, and has been widely used in many fields. Also, in the field of P systems, there are many researchers using ant colony algorithms as subalgorithms in their researches, while ant colony algorithms are not implemented in the form of evolutionary rules. In this paper, we proposed an ant colony P system ΠACPS based on the pure evolutionary rule. ΠACPS not only can provide a basic computational framework for the needy P systems researchers, but also can maximally perform the parallelism of ant colony algorithms.An example of solving TSP given in the article illustrates the effectiveness and feasibility ΠACPS.

A hybrid path planning algorithm based on membrane pseudo-bacterial potential field (MemPBPF) is proposed. Membrane-inspired algorithms can reach an evolutionary behavior based on biochemical processes to find the best parameters for generating a feasible and safe path. The proposed MemPBPF algorithm uses a combination of the structure and rules of membrane computing. In that sense, the proposed MemPBPF algorithm contains dynamic membranes that include a pseudo-bacterial genetic algorithm for evolving the required parameters in the artificial potential field method. This hybridization between membrane computing, the pseudo-bacterial genetic algorithm, and the artificial potential field method provides an outperforming path planning algorithm for autonomous mobile robots. Computer simulation results demonstrate the effectiveness of the proposed MemPBPF algorithm in terms of path length considering collision avoidance and smoothness. Comparisons with two different versions employing a different number of elementary membranes and with other artificial potential field based algorithms are presented. The proposed MemPBPF algorithm yields improved performance in terms of time execution by using a parallel implementation on a multi-core computer. Therefore, the MemPBPF algorithm achieves high performance yielding competitive results for autonomous mobile robot navigation in complex and real scenarios.

The maximum clique problem (MCP) is a classical NP-hard problem in combinatorial optimization, which has important applications in many fields. In this paper, a heuristic algorithm MEAMCP based on Membrane Evolutionary Algorithm (MEA) is proposed to solve MCP. First, we introduce the general structure of MEA, which includes four kinds of membrane operators: selection, division, fusion and cytolysis. MEA evolves the feasible solution population through the membrane operator. Second, MEAMCP is proposed and we discuss how to initialize the population and implement the four kinds of membrane operators. Finally, MEAMCP is tested on DIMACS benchmark datasets and compared with other MCP algorithms. The experiment results demonstrate that MEAMCP has better stability.

In this paper, a novel microscopic machine vision system is proposed to solve a degradation monitoring problem of low-voltage electromagnetic coil insulation in practical industrial fields, where an ensemble learning approach in a compound membrane computing framework is newly introduced. This membrane computing framework is constituted by eight layers, 29 membranes, 72 objects, and 35 rules. In this framework, multiple machine learning methods, including classical pattern recognition methods and novel deep learning methods, are tested and compared. First, the most optimal feature extraction approaches are selected. Then, the selected approaches are fused together to achieve an even better monitoring performance. Third, a large number of experiments are used to evaluate and prove the usefulness and potential of the proposed system, where a mean accuracy of 61.4% is achieved on 1035 validation images of six degradation states with single state matching, and mean accuracies of 61.0% and 77.4% are achieved on 622 test images of six degradation states with single state matching and state range matching, respectively. Finally, a mechanical device is designed to apply the system to real industrial tasks.

In this paper, membrane-based computing image segmentation, both region-based and edge-based, is proposed for medical images that involve two types of neighborhood relations between pixels. These neighborhood relations—namely, 4-adjacency and 8-adjacency of a membrane computing approach—construct a family of tissue-like P systems for segmenting actual 2D medical images in a constant number of steps; the two types of adjacency were compared using different hardware platforms. The process involves the generation of membrane-based segmentation rules for 2D medical images. The rules are written in the P-Lingua format and appended to the input image for visualization. The findings show that the neighborhood relations between pixels of 8-adjacency give better results compared with the 4-adjacency neighborhood relations, because the 8-adjacency considers the eight pixels around the center pixel, which reduces the required communication rules to obtain the final segmentation results. The experimental results proved that the proposed approach has superior results in terms of the number of computational steps and processing time. To the best of our knowledge, this is the first time an evaluation procedure is conducted to evaluate the efficiency of real image segmentations using membrane computing.

Membrane computing is a new natural computing paradigm inspired by the functioning and structure of biological cells, and has been successfully applied to many different areas, from biology to engineering. In this paper, we present kPWorkbench, a software framework developed to support membrane computing and its applications. kPWorkbench offers unique features, including modelling, simulation, agent-based high performance simulation and verification, which allow modelling and computational analysis of membrane systems. The kPWorkbench formal verification component provides the opportunity to analyse the behaviour of a model and validate that important system requirements are met and certain behaviours are observed. The platform also features a property language based on natural language statements to facilitate property specification.

P systems have been known to provide efficient polynomial (often linear) deterministic solutions to hard problems. In particular, cP systems have been shown to provide very crisp and efficient solutions to such problems, which are typically linear with small coefficients. Building on a recent result by Henderson et al., which solves SAT in square-root-sublinear time, this paper proposes an orders-of-magnitude-faster solution, running in logarithmic time, and using a small fixed-sized alphabet and ruleset (25 rules). To the best of our knowledge, this is the fastest deterministic solution across all extant P system variants. Like all other cP solutions, it is a complete solution that is not a member of a uniform family (and thus does not require any preprocessing). Consequently, according to another reduction result by Henderson et al., cP systems can also solve k-colouring and several other NP-complete problems in logarithmic time.

Enzymatic numerical P systems are inspired by the biological structure of cells and the “processing of information”regulated by enzymes on chemical objects, where natural numbers are basic entities to work with. Enzymatic numerical P systems can perform arithmetic operations but not complex numerical calculations, such as obtaining suitable values or finding the maximum or minimum element from the data sets. In this paper, a variant of enzymatic numerical P systems named an enzymatic numerical P system with an enzymatic environment is proposed, which can make numerical operations more flexible with regulations of external enzymes. We design several parallel computational frameworks for complex numerical calculations by the enzymatic numerical P system with an enzymatic environment. A parallel framework is achieved for performing support vector machine (SVM) calculations and sequential minimal optimization by the enzymatic numerical P system with an enzymatic environment. The experimental results obtained using a GPU prove that our method is 5.34 times more efficient than the traditional serial SVM calculation, without any loss in accuracy.

Membrane computing (MC), which abstracts computational models from the structure and functioning of biological cells or population of cells in tissues, has served as a rich framework for handling many problems. Various types of P systems have been proposed in the literature to perform edge-based and region-based segmentation of two-dimensional digital images. However, less attention has been paid to the segmentation of three-dimensional (3D) medical images. Hence, the main contribution of this paper is to propose a tissue-like P system for segmenting 3D medical images. To the best of our knowledge, this is the first work that practically adapts MC for 3D images. Experimental results demonstrate the efficiency of the proposed approach in segmenting 3D images, and it has the potential to be used in real-world applications.

A widely studied field in the framework of membrane computing is computational complexity theory. While some types of P systems are only capable of efficiently solving problems from the class P, adding one or more syntactic or semantic ingredients to these membrane systems can give them the ability to efficiently solve presumably intractable problems. These ingredients are called to form a frontier of efficiency, in the sense that passing from the first type of P systems to the second type leads to passing from non-efficiency to the presumed efficiency. In this work, a solution to the SAT problem, a well-known NP-complete problem, is obtained by means of a family of recognizer P systems with evolutional symport/antiport rules of length at most (2,1) and division rules where the environment plays a passive role; that is, P systems from CDEC^(2,1). This result is comparable to the one obtained in the tissue-like counterpart, and gives a glance of a parallelism and the non-evolutionary membrane systems with symport/antiport rules.

Models of computation are fundamental notions in computer science; consequently, they have been the subject of countless research papers, with numerous novel models proposed even in recent years. Amongst a multitude of different approaches, many of these methods draw inspiration from the biological processes observed in nature. P systems, or membrane systems, make an analogy between the communication in computing and the flow of information that can be perceived in living organisms. These systems serve as a basis for various concepts, ranging from the fields of computational economics and robotics to the techniques of data clustering. In this paper, such utilization of these systems—membrane system–based clustering—is taken into focus. Considering the growing number of data stored worldwide, more and more data have to be handled by clustering algorithms too. To solve this issue, bringing these methods closer to the data, their main element provides several benefits. Database systems equip their users with, for instance, well-integrated security features and more direct control over the data itself. Our goal is if the type of the database management system is given, e.g., NoSQL, but the corporation or the research team can choose which specific database management system is used, then we give a perspective, how the algorithms written like this behave in such an environment, so that, based on this, a more substantiated decision can be made, meaning which database management system should be connected to the system. For this purpose, we discover the possibilities of a clustering algorithm based on P systems when used alongside NoSQL database systems, that are designed to manage big data. Variants over two competing databases, MongoDB and Redis, are evaluated and compared to identify the advantages and limitations of using such a solution in these systems.

The Internet of Things collects a variety of sensor data, independently offers problem-solving solutions and allows them to be avoided. In order for these systems to function continuously, it is necessary to apply intelligent information routing algorithms. In order to discover new algorithms for the routing of the Internet of Things, this article reviews bioinspired algorithms, their advantages and disadvantages. We introduce membrane computing, P system and its membrane structure. Paper analyses different types of communication on the Internet of Things and classification of routing algorithms for the Internet of Things communication. The practical application of membrane computing and the possibility of applying membrane computing on the Internet of Things is also reviewed. Santrauka Daiktų internetas renka įvarius jutiklių duomenis, savarankiškai siūlo problemų sprendimus ir leidžia jų išvengti. Kad šios sistemos veiktų nenutrūkstamai, būtina taikyti intelektualiuosius informacijos valdymo ir skirstymo algoritmus. Siekiant atrasti naujus daiktų interneto informacijos skirstymo ir valdymo algoritmus, šiame straipsnyje apžvelgiami biotechnologiniai algoritmai ir jų taikymo privalumai bei trūkumai. Pristatomi membraniniai skaičiavimai, P sistema ir jos membraninė struktūra. Apžvelgiami komunikacijos tipai daiktų internete ir klasifikuojami informacijos skirstymo algoritmai, skirti daiktų interneto komunikacijai. Taip pat apžvelgiamas praktinis membraninių skaičiavimų taikymas ir galimybė taikyti membraninius skaičiavimus daiktų internete. Reikšminiai žodžiai: daiktų internetas, membraniniai skaičiavimai, P sistema, gamtiniai skaičiavimai.

ABSTRACT Membrane computing is a bio-inspired computing paradigm whose devices are the so-called membrane systems or P systems. The P system designed in this work reproduces complex biological landscapes in the computer world. It uses nested “membrane-surrounded entities” able to divide, propagate, and die; to be transferred into other membranes; to exchange informative material according to flexible rules; and to mutate and be selected by external agents. This allows the exploration of hierarchical interactive dynamics resulting from the probabilistic interaction of genes (phenotypes), clones, species, hosts, environments, and antibiotic challenges. Our model facilitates analysis of several aspects of the rules that govern the multilevel evolutionary biology of antibiotic resistance. We examined a number of selected landscapes where we predict the effects of different rates of patient flow from hospital to the community and vice versa, the cross-transmission rates between patients with bacterial propagules of different sizes, the proportion of patients treated with antibiotics, and the antibiotics and dosing found in the opening spaces in the microbiota where resistant phenotypes multiply. We also evaluated the selective strengths of some drugs and the influence of the time 0 resistance composition of the species and bacterial clones in the evolution of resistance phenotypes. In summary, we provide case studies analyzing the hierarchical dynamics of antibiotic resistance using a novel computing model with reciprocity within and between levels of biological organization, a type of approach that may be expanded in the multilevel analysis of complex microbial landscapes. IMPORTANCE The work that we present here represents the culmination of many years of investigation in looking for a suitable methodology to simulate the multihierarchical processes involved in antibiotic resistance. Everything started with our early appreciation of the different independent but embedded biological units that shape the biology, ecology, and evolution of antibiotic-resistant microorganisms. Genes, plasmids carrying these genes, cells hosting plasmids, populations of cells, microbial communities, and host's populations constitute a complex system where changes in one component might influence the other ones. How would it be possible to simulate such a complexity of antibiotic resistance as it occurs in the real world? Can the process be predicted, at least at the local level? A few years ago, and because of their structural resemblance to biological systems, we realized that membrane computing procedures could provide a suitable frame to approach these questions. Our manuscript describes the first application of this modeling methodology to the field of antibiotic resistance and offers a bunch of examples—just a limited number of them in comparison with the possible ones to illustrate its unprecedented explanatory power.

With the advancement of technologies it is becoming imperative to have a stable, secure and uninterrupted supply of power to electronic systems as well as to ensure the identification of faults occurring in these systems quickly and efficiently in case of any accident. Spiking neural P system (SNPS) is a popular parallel distributed computing model. It is inspired by the structure and functioning of spiking neurons. It belongs to the category of neural-like P systems and is well-known as a branch of the third generation neural networks. SNPS and its variants can perform the task of fault diagnosis in power systems efficiently. In this paper, we provide a comprehensive survey of these models, which can perform the task of fault diagnosis in transformers, power transmission networks, traction power supply systems, metro traction power supply systems, and electric locomotive systems. Furthermore, we discuss the use of these models in fault section estimation of power systems, fault location identification in distribution network, and fault line detection. We also discuss a software tool which can perform the task of fault diagnosis automatically. Finally, we discuss future research lines related to this topic.

In the bio-inspired area of membrane computing, a novel computing model with a generic name of P system was introduced around the year 2000. Among its several variants, string or array language generating P systems involving rewriting rules have been considered. A new picture array model of array generating P system with a restricted type of picture insertion rules and picture array objects in its regions, is introduced here. The generative power of such a system is investigated by comparing with the generative power of certain related picture array grammar models introduced and studied in two-dimensional picture language theory. It is shown that this new model of array P system can generate picture array languages which cannot be generated by many other array grammar models. The theoretical model developed is for handling the application problem of generation of patterns encoded as picture arrays over a finite set of symbols. As an application, certain floor-design patterns are generated using such an array P system.

Boolean propositional satisfiability (SAT) problem is one of the most widely studied NP-complete problems and plays an outstanding role in many domains. Membrane computing is a branch of natural computing which has been proven to solve NP problems in polynomial time with a parallel compute mode. This paper proposes a new algorithm for SAT problem which combines the traditional membrane computing algorithm of SAT problem with a classic simplification rule, the splitting rule, which can divide a clause set into two axisymmetric subsets, deal with them respectively and simultaneously, and obtain the solution of the original clause set with the symmetry of their solutions. The new algorithm is shown to be able to reduce the space complexity by distributing clauses with the splitting rule repeatedly, and also reduce both time and space complexity by executing one-literal rule and pure-literal rule as many times as possible.

Image edge detection is a fundamental problem in image processing and computer vision, particularly in the area of feature extraction. However, the time complexity increases squarely with the increase of image resolution in conventional serial computing mode. This results in being unbearably time consuming when dealing with a large amount of image data. In this paper, a novel resolution free parallel implementation algorithm for gradient based edge detection, namely EDENP, is proposed. The key point of our method is the introduction of an enzymatic numerical P system (ENPS) to design the parallel computing algorithm for image processing for the first time. The proposed algorithm is based on a cell-like P system with a nested membrane structure containing four membranes. The start and stop of the system is controlled by the variables in the skin membrane. The calculation of edge detection is performed in the inner three membranes in a parallel way. The performance and efficiency of this algorithm are evaluated on the CUDA platform. The main advantage of EDENP is that the time complexity of O ( 1 ) can be achieved regardless of image resolution theoretically.

We first give a general result about P systems with symbol-objects, which says that systems with membrane creation with only one initial membrane can simulate usual systems without using additional membranes (and this implies as a direct consequence a recent result of Mutyam and Krithivasan), then we extend the membrane creation feature to P systems with string-objects and we prove the computational universality of such systems.