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Enterprises are both an emerging force in national reform and a major force in its future economic development. The professors in China and on abroad have do much research on enterprise development and put forward lots of valuable theories. P systems have been proposed for about 14 years, and its applications refer to numbers of areas. However, to investigate the survival and development of firms in the aspect of P systems is rarely. In this study, it makes analysis on the survival and development of small and medium-sized enterprises in an aspect of bionics, trying to find out the origin which makes the enterprise exuberant and everlasting. Have the enterprises compare to biological cells and construct a membrane framework in order to interpret an enterprise’s survival. At the same time it also highlights the irreplaceable role of enterprise DNA in the inheritance of the enterprise entity.

Anomaly detection in time series has attracted much attention recently and is quite a challenging task. In this paper, a novel deep-learning approach (AL-CNN) that classifies the time series as normal or abnormal with less domain knowledge is proposed. The proposed algorithm combines Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) to effectively model the spatial and temporal information contained in time-series data, the techniques of Squeeze-and-Excitation are applied to implement the feature recalibration. However, the difficulty of selecting multiple parameters and the long training time of a single model make AL-CNN less effective. To alleviate these challenges, a hybrid dynamic membrane system (HM-AL-CNN) is designed which is a new distributed and parallel computing model. We have performed a detailed evaluation of this proposed approach on three well-known benchmarks including the Yahoo S5 datasets. Experiments show that the proposed method possessed a robust and superior performance than the state-of-the-art methods and improved the average on three used indicators significantly.

The Eclat algorithm is a typical frequent pattern mining algorithm using vertical data. This study proposes an improved Eclat algorithm called ETPAM, based on the tissue-like P system with active membranes. The active membranes are used to run evolution rules, i.e., object rewriting rules, in parallel. Moreover, ETPAM utilizes subsume indices and an early pruning strategy to reduce the number of frequent pattern candidates and subsumes. The time complexity of ETPAM is decreased from O(t2) to O(t) as compared with the original Eclat algorithm through the parallelism of the P system. The experimental results using two databases indicate that ETPAM performs very well in mining frequent patterns, and the experimental results using four databases prove that ETPAM is computationally very efficient as compared with three other existing frequent pattern mining algorithms.

Membrane computing is a fast developing natural computing field with wide applications. Cell-like P system is the one of membrane computing and it is a class of distributed and parallel computing model which is inspired from cell. Then, Shortest Path Faster Algorithm is another name of queue optimization algorithm for the Bellman-Ford algorithm. It is usually used to find single-source shortest paths with negative edges and negative weights. In this paper, we use rewrite rules and dissolution rules of cell-like P system, combined with queue thresholds to improve the SPFA algorithm. We use the average distance between the point where the queue is removed and the point to be queued as the threshold. The node whose distance is less than the threshold is inserted at the beginning of the queue, otherwise, the node is inserted at the end of the queue. A directional weighted digraph is applied to prove the feasibility of the algorithm in this paper. This method takes full advantage of the parallelism of the P system and finds the shortest path with fewer steps and shorter time.

Constrained optimization problems (COPs) which can be seen everywhere in life has been widely used in science and engineering. Given the complexity of this problem, it is difficult to solve them with traditional optimization techniques. In this paper, we have applied a new algorithm which is called Multi-population genetic algorithm (MPGA) based on an improved dynamic cell-like P system. This algorithm combines global search and local search, and have a significant effect on overcoming immature convergence problem. The sensitivity of calculated results to genetic control parameters is greatly reduced. We have also used Taguchi methods to select the initial population to reduce the number of iterations. The performance is tested on several well-known engineering design problems. This research can be applied in most practical problems and is of great help to optimize the operation.

In this paper, an application of P systems based on the MapReduce structure is proposed, which provides new ideas and methods for the parallel problem solving. P system is abstracted membrane computing model that simulates the function of the living cells, and all objects in it can evolve in a maximal parallelism and distributed manner. It could be used to solve some problems for less time of computation. MapReduce, one of the key technologies of cloud computing, is used to handle huge data in parallel by both map and reduce process. It can be connected with membrane computing for their commonalities. The most value problem is frequently encountered in daily life, faced with massive data in the big data environment, how can we effectively solve this problem is particularly important. So, a class of P system based on the MapReduce structure for the most value problem solving is constructed in this paper and it proves to be feasible through a simple online store example. This is a new attempt in applications of membrane system.

In this work, a new criterion concerning the global exponential stability of impulsive neural networks with time-varying delays is presented by employing the impulsive delayed differential inequality method. The criterion is independent of the time-varying delays and does not require the differentiability of delay functions. An example and its simulation showing the effectiveness of the present criterion is given finally. Copyright © 2012 John Wiley & Sons, Ltd.

FP-Growth algorithm is an algorithm of mining frequent patterns based on the data structure of FP-Tree(Frequent Pattern-Tree), which runs slowly and takes up a lot of ram during the mining process of large transaction database and even sometimes cannot construct the corresponding FP-tree. On the basis of the original algorithm, a new improved approach is put forward, which uses less ram and can meet the requirement of large transaction database. Firstly, calculate the support of all items in the transaction database, exclude the item which cannot satisfy the minimum support and get frequent items, then construct their sub-databases in turn according to ascending order of the support of these frequent items; finally, merge frequent item sets of each sub-database in order to gain frequent patterns of the entire transaction database. In this paper, some instances and experiments are carried out. Experimental results demonstrate that the improved FP-Growth algorithm is more efficient than traditional FP-Growth algorithm and suitable for the mining of large-scale transaction databases.

With equity-based crowdfunding, an entrepreneur raises financing from a large number of individuals, instead of soliciting a small group of investors. This paper compares two forms of financing: the equity-based crowdfunding and the traditional loan form. We conclude that the equity-based crowdfunding can raises larger number of money for entrepreneurs than the traditional loan form owing to the entrepreneurs' moral hazard. And the equity-based crowdfunding is suitable for a project with high risk and high return.

A membrane system for decision-making problems is proposed in this paper to provide new ideas for parallel solutions in today’s big data environment. Because a membrane system has great parallelism, it could reduce the calculation time complexity and is suitable for solving some complex problems such as decision-making problems. First, the possibility of such a problem’s parallelism is analyzed, and then the membrane system is established with all its rules to solve the problem. Finally, an example is given to prove its feasibility. This represents another attempt in the application of a membrane system.

Current smartphone operating systems are capable of running applications written by third party developers. And this capability has raised concerns about the threat that smartphones pose to the public privacy and security. Recently, it is widespread for the malwares to collect sensitive information owned by third-party applications as well as to escalate its privilege to the system level. Hence the smartphone OS has been developed with a greater emphasis on security and privacy protection. In this paper, under the condition that the rapid development usage of Android platform, we purpose to introduce the permission mechanism, which requires developers to declare what sensitive resources their applications would use, has users agree with this request when they install the application and constrains the application to the requested resources during runtime. In the end we give some tips for users to protect sensitive information and user privacy.

In this paper, we propose a new technique for clustering, which is the combination of membrane computing and Bin-packing technique. A new kind of P system (graph P system) is constructed which is different with the traditional ones. New rules and membrane structure are described. Cluster analysis is transformed by Bin-packing technique. All processes of the algorithm are implemented on this P system, including rules for mutation, swap and the calculation of energy change. Finally we apply the technique in the heart disease data set and obtain some results.

Enterprise life cycle theory is the most direct business growth study of enterprise development. This theory divides SMEs’ (small and medium-size enterprises) life into different stages, and do research respectively in each stage on the growth of them. It holds that companies are like biological organisms, they also have the growth from birth to death, from gloom to doom. It makes an analog of SMEs’ stages development combined with the core concept of membrane computing and the knowledge of biology. It is on a living point of view to analyze the growth of an enterprise, but also a continuation of the theoretical research on bionic-evolution theory. The discussion on how to extend the life of the enterprise and how to make it grow as longevity enterprise is of profound practical effect to the cultivation of domestic life-oriented enterprises.

Particle Swarm Optimization algorithm is a kind of excellent optimization algorithm, and has been widely used in many fields. In order to overcome the premature convergence and improve the accuracy of the PSO, we combine some related theories of membrane computing with PSO. The new algorithm can effectively balance the global search and partial optimization. Simulation results based on three bench functions show that the new algorithm can effectively solve the problem of premature, and effectively improve the convergence precision. At the same time, the algorithm in solving TSP problem also shows good optimization ability.

Logistics company alliance building problem studies that how to build alliance with a method of great rate and low cost. It presents an improved algorithm about graph clustering base on PSO to solve the problem. Optimize the clustering result through PSO, and expand the solution space through disturbance strategies in order to obtain the optimal solution. Experimental result shows that the algorithm can solve the problem in a high rate and low cost.

Graph clustering is an important area of cluster analysis with wide applications in social networks, data transformation and bioinformatics. The purpose of this paper is to propose a new method in graph clustering, the discrete Morse technique. We propose a new optimization outline using Morse theory, describe graph clustering into simplicial complex, and present an algorithm to construct the discrete vector fields. An example show the work procedure of the proposed method.