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The development of sepsis can lead to many organ dysfunction and even death. Myocardial injury is one of the serious complications of sepsis leading to death. New evidence suggests that microRNAs (miRNAs) play a critical role in infection myocardial injury. However, the mechanism which miR-208a-5p regulates sepsis-induced myocardial injury remains unclear. To mimic sepsis-induced myocardial injury in vitro, rat primary cardiomyocytes were treated with LPS. Cell viability and apoptosis were tested by CCK-8 and flow cytometry, respectively. The secretion of inflammatory factors was analyzed by ELISA. mRNA and protein levels were detected by RT-qPCR and Western blotting. The interaction among SP1, XIAP and miR-208a-5p was detected using dual luciferase report assay. Ultrasonic analysis and HE staining was performed to observe the effect of miR-208a-5p in sepsis-induced rats. Our findings indicated that miR-208a-5p expression in primary rat cardiomyocytes was increased by LPS. MiR-208a-5p inhibitor reversed LPS-induced cardiomyocytes injury through inhibiting the apoptosis. Furthermore, the inflammatory injury in cardiomyocytes was induced by LPS, which was rescued by miR-208a-5p inhibitor. In addition, downregulation of miR-208a-5p improved LPS-induced sepsis myocardial injury in vivo. Mechanistically, XIAP might be a target gene of miR-208a-5p. SP1 promoted transcription of miR-208a by binding to the miR-208a promoter region. Moreover, silencing of XIAP reversed the regulatory of miR-208a-5p inhibitor on cardiomyocytes injury. To sum up, those findings revealed silencing of miR-208a-5p could alleviate sepsis-induced myocardial injury, which would grant a new process for the treatment of sepsis., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

A novel multifunctional fluorescent probe LL2 was prepared via a one-step condensation reaction between 3-formyl-N-butylcarbazole and 2-Hydroxy-1-naphthylhydrazone. LL2 can work as a colorimetric probe for Cu 2+ , and can also selectively recognize Cu 2+ via ratiometric fluorescence signal. After the addition of Cu 2+ , the probe LL2 responded rapidly within 5 s and reached stability within 30 s. In natural light, when Cu 2+ were added to the solution, the color of probe LL2 changed from yellowish to colorless, while there was a discernible fluorescence changed from green to blue under a 365 nm UV lamp. The ratiometric fluorescence intensity (F 449 /F 510 ) showed a good linear relationship (R 2  = 0.9902) with Cu 2+ concentration in the concentration range of 0-5 μmol/L, and the minimum detection limit was 1.96 μM. Cell imaging experiments showed that LL2 could capture fluorescence signals in the green and blue channels of HepG2 cells through fluorescence confocal microscope, and successfully recognize exogenous Cu 2+ in HepG2 cells. In addition, fluorescence co-localization experiments showed that LL2 could target both lipid droplets and lysosomes. Meanwhile, LL2 could be applied to filter paper strip assay and detection of Cu 2+ in actual water samples. These results indicated that probe LL2 has a good capability for monitoring Cu 2+ in environment and living cells., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

With the rapid development of bioinformatics and the availability of genetic sequencing technologies, genomic data has been used to facilitate personalized medicine. Cloud computing, features as low cost, rich storage and rapid processing can precisely respond to the challenges brought by the emergence of massive genomic data. Considering the security of cloud platform and the privacy of genomic data, we first introduce P2GT which utilizes key-policy attribute-based encryption to realize genomic data access control with unbounded attributes, and employs equality test algorithm to achieve personalized medicine test by matching digitized single nucleotide polymorphisms (SNPs) directly on the users' ciphertext without encrypting multiple times. We then propose an enhanced scheme P2GT+, which adopts identity-based encryption with equality test supporting flexible joint authorization to realize privacy-preserving paternity test, genetic compatibility test and disease susceptibility test over the encrypted SNPs with P2GT. We prove the security of proposed schemes and conduct extensive experiments with the 1,000 Genomes dataset. The results show that P2GT and P2GT+ are practical and scalable enough to meet the privacy-preserving and authorized genetic testing requirements in cloud computing.

In Location-Based Social Networks (LBSNs), registered users submit their reviews for visited point-of-interests (POIs) to the system providers (SPs). The SPs anonymously publish submitted reviews to build reputations for POIs. Unfortunately, the user profile and trajectory contained in reviews can be easily obtained by adversaries who SPs has compromised with. Even worse, existing techniques, such as cryptography and generalization, etc., are infeasible due to the necessity of public publication of reviews and the facticity of reviews. Inspired by pseudonym techniques, we propose an approach to exchanging reviews before users submit reviews to SPs. In our approach, we introduce two attacks, namely review-based location correlation attack (RLCA) and semantic-based long-term statistical attack (SLSA). RLCA can be exploited to link the real user by reconstructing the trajectory, and SLSA can be launched to establish a connection between locations and users through the difference of semantic frequency. To resist RLCA, we design a method named User Selection to Resist RLCA (USR-RLCA) to exchange reviews. We propose a metric to measure the correlation between a user and a trajectory. Based on the metric, USR-RLCA can select reviews resisting RLCA to exchange by suppressing the number of locations on each reconstructed trajectory below the correlation. However, USR-RLCA fails to resist SLSA because of ignoring the essential semantics. Hence, we design an enhanced USR-RLCA named User Selection to Resist SLSA (USR-SLSA). We first propose a metric to measure the indistinguishability of locations concerning the difference of semantic frequency in a long term. Then, USR-SLSA can select reviews resisting SLSA to exchange by allowing two reviews whose indistinguishability is below the probability difference after the exchange to be exchanged. Evaluation results verify the effectiveness of our approach in terms of privacy and utility., Competing Interests: No.

In this paper, we derive a new fixed-time stability theorem based on definite integral, variable substitution and some inequality techniques. The fixed-time stability criterion and the upper bound estimate formula for the settling time are different from those in the existing fixed-time stability theorems. Based on the new fixed-time stability theorem, the fixed-time synchronization of neural networks is investigated by designing feedback controller, and sufficient conditions are derived to guarantee the fixed-time synchronization of neural networks. To show the usability and superiority of the obtained theoretical results, we propose a secure communication scheme based on the fixed-time synchronization of neural networks. Numerical simulations illustrate that the new upper bound estimate formula for the settling time is much tighter than those in the existing fixed-time stability theorems. Moreover, the plaintext signals can be recovered according to the new fixed-time stability theorem, while the plaintext signals cannot be recovered according to the existing fixed-time stability theorems., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

For wireless communication networks, cognitive radio (CR) can be used to obtain the available spectrum, and wideband compressed sensing plays a vital role in cognitive radio networks (CRNs). Using compressed sensing (CS), sampling and compression of the spectrum signal can be simultaneously achieved, and the original signal can be accurately recovered from the sampling data under sub-Nyquist rate. Using a set of wideband random filters to measure the channel energy, only the recovery of the channel energy is necessary, rather than that of all the original channel signals. Based on the semi-tensor product, this paper proposes a new model to achieve the energy compression and reconstruction of spectral signals, called semi-tensor product compressed spectrum sensing (STP-CSS), which is a generalization of traditional spectrum sensing. The experimental results show that STP-CSS can flexibly generate a low-dimensional sensing matrix for energy compression and parallel reconstruction of the signal. Compared with the existing methods, STP-CSS is proved to effectively reduce the calculation complexity of sensor nodes. Hence, the proposed model markedly improves the spectrum sensing speed of network nodes and saves storage space and energy consumption.

With the emergence and widespread application of cloud computing, the use of cloud platforms to solve the problem of secure multi-party computation has emerged as a new research direction. The traditional computation of a solid geometry is performed through mutual interactions between two parties, which is not suitable in an untrusted cloud computing environment. In this paper, we first design a basic protocol for a secure Euclidean distance calculation that is suitable for cloud platforms and can serve as a building block for other protocols on cloud platforms. Using the solution of the Euclidean distance problem as such a building block, we provide a new method that converts the problems of calculating solid triangular areas and solid tetrahedral volumes into the calculation of distances and determinants in three-dimensional space. Then, we discuss solid point-line distance calculations, which extent the idea of the spatial geometry security problem. We present protocols for the above problems and prove that the proposed protocols can resist conspiracy among users and the untrusted cloud platform so that they can effectively ensure the privacy of the users. We also analyze the performances of these solutions. The analysis results show that our scheme is more versatile., Competing Interests: The authors have declared that no competing interests exist.

Intrusion detection systems play an important role in preventing security threats and protecting networks from attacks. However, with the emergence of unknown attacks and imbalanced samples, traditional machine learning methods suffer from lower detection rates and higher false positive rates. We propose a novel intrusion detection model that combines an improved conditional variational AutoEncoder (ICVAE) with a deep neural network (DNN), namely ICVAE-DNN. ICVAE is used to learn and explore potential sparse representations between network data features and classes. The trained ICVAE decoder generates new attack samples according to the specified intrusion categories to balance the training data and increase the diversity of training samples, thereby improving the detection rate of the imbalanced attacks. The trained ICVAE encoder is not only used to automatically reduce data dimension, but also to initialize the weight of DNN hidden layers, so that DNN can easily achieve global optimization through back propagation and fine tuning. The NSL-KDD and UNSW-NB15 datasets are used to evaluate the performance of the ICVAE-DNN. The ICVAE-DNN is superior to the three well-known oversampling methods in data augmentation. Moreover, the ICVAE-DNN outperforms six well-known models in detection performance, and is more effective in detecting minority attacks and unknown attacks. In addition, the ICVAE-DNN also shows better overall accuracy, detection rate and false positive rate than the nine state-of-the-art intrusion detection methods.

This paper is concerned with the fixed-time synchronization control of inertial memristor-based neural networks with discrete delay. We design four different kinds of feedback controllers, under which the considered inertial memristor-based neural networks can realize fixed-time synchronization perfectly. Moreover, the obtained fixed-time synchronization criteria can be verified by algebraic operations. For any initial synchronization error, the settling time of fixed-time synchronization is bounded by a fixed constant, which can be calculated beforehand based on system parameters and controller parameters. Numerical simulations are given to illustrate the effectiveness of our theoretical results., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Object detection in a camera sensing system has been addressed by researchers in the field of image processing. Highly-developed techniques provide researchers with great opportunities to recognize objects by applying different algorithms. This paper proposes an object recognition model, named Statistic Experience-based Adaptive One-shot Detector (EAO), based on convolutional neural network. The proposed model makes use of spectral clustering to make detection dataset, generates prior boxes for object bounding and assigns prior boxes based on multi-resolution. The model is constructed and trained for improving the detection precision and the processing speed. Experiments are conducted on classical images datasets while the results demonstrate the superiority of EAO in terms of effectiveness and efficiency. Working performance of the EAO is verified by comparing it to several state-of-the-art approaches, which makes it a promising method for the development of the camera sensing technique.

With the growing number of vehicles and popularity of various services in vehicular cloud computing (VCC), message exchanging among vehicles under traffic conditions and in emergency situations is one of the most pressing demands, and has attracted significant attention. However, it is an important challenge to authenticate the legitimate sources of broadcast messages and achieve fine-grained message access control. In this work, we propose SmartVeh, a secure and efficient message access control and authentication scheme in VCC. A hierarchical, attribute-based encryption technique is utilized to achieve fine-grained and flexible message sharing, which ensures that vehicles whose persistent or dynamic attributes satisfy the access policies can access the broadcast message with equipped on-board units (OBUs). Message authentication is enforced by integrating an attribute-based signature, which achieves message authentication and maintains the anonymity of the vehicles. In order to reduce the computations of the OBUs in the vehicles, we outsource the heavy computations of encryption, decryption and signing to a cloud server and road-side units. The theoretical analysis and simulation results reveal that our secure and efficient scheme is suitable for VCC., Competing Interests: The authors declare no conflict of interest.

This paper mainly studies the globally fixed-time synchronization of a class of coupled neutral-type neural networks with mixed time-varying delays via discontinuous feedback controllers. Compared with the traditional neutral-type neural network model, the model in this paper is more general. A class of general discontinuous feedback controllers are designed. With the help of the definition of fixed-time synchronization, the upper right-hand derivative and a defined simple Lyapunov function, some easily verifiable and extensible synchronization criteria are derived to guarantee the fixed-time synchronization between the drive and response systems. Finally, two numerical simulations are given to verify the correctness of the results.

This paper is devoted to studying the fixed-time synchronization of memristor-based BAM neural networks (MBAMNNs) with discrete delay. Fixed-time synchronization means that synchronization can be achieved in a fixed time for any initial values of the considered systems. In the light of the double-layer structure of MBAMNNs, we design two similar feedback controllers. Based on Lyapunov stability theories, several criteria are established to guarantee that the drive and response MBAMNNs can realize synchronization in a fixed time. In particular, by changing the parameters of controllers, this fixed time can be adjusted to some desired value in advance, irrespective of the initial values of MBAMNNs. Numerical simulations are included to validate the derived results., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

It is generally known that the states of network nodes are stable and have strong correlations in a linear network system. We find that without the control input, the method of compressed sensing can not succeed in reconstructing complex networks in which the states of nodes are generated through the linear network system. However, noise can drive the dynamics between nodes to break the stability of the system state. Therefore, a new method integrating QR decomposition and compressed sensing is proposed to solve the reconstruction problem of complex networks under the assistance of the input noise. The state matrix of the system is decomposed by QR decomposition. We construct the measurement matrix with the aid of Gaussian noise so that the sparse input matrix can be reconstructed by compressed sensing. We also discover that noise can build a bridge between the dynamics and the topological structure. Experiments are presented to show that the proposed method is more accurate and more efficient to reconstruct four model networks and six real networks by the comparisons between the proposed method and only compressed sensing. In addition, the proposed method can reconstruct not only the sparse complex networks, but also the dense complex networks.

Finite time synchronization, which means synchronization can be achieved in a settling time, is desirable in some practical applications. However, most of the published results on finite time synchronization don't include delays or only include discrete delays. In view of the fact that distributed delays inevitably exist in neural networks, this paper aims to investigate the finite time synchronization of memristor-based Cohen-Grossberg neural networks (MCGNNs) with both discrete delay and distributed delay (mixed delays). By means of a simple feedback controller and novel finite time synchronization analysis methods, several new criteria are derived to ensure the finite time synchronization of MCGNNs with mixed delays. The obtained criteria are very concise and easy to verify. Numerical simulations are presented to demonstrate the effectiveness of our theoretical results.

Applications of wireless body area networks (WBANs) are extended from remote health care to military, sports, disaster relief, etc. With the network scale expanding, nodes increasing, and links complicated, a WBAN evolves to a body-to-body network. Along with the development, energy saving and data security problems are highlighted. In this paper, chaotic compressive sensing (CCS) is proposed to solve these two crucial problems, simultaneously. Compared with the traditional compressive sensing, CCS can save vast storage space by only storing the matrix generation parameters. Additionally, the sensitivity of chaos can improve the security of data transmission. Aimed at image transmission, modified CCS is proposed, which uses two encryption mechanisms, confusion and mask, and performs a much better encryption quality. Simulation is conducted to verify the feasibility and effectiveness of the proposed methods. The results show that the energy efficiency and security are strongly improved, while the storage space is saved. And the secret key is extremely sensitive, [Formula: see text] perturbation of the secret key could lead to a total different decoding, the relative error is larger than 100%. Particularly for image encryption, the performance of the modified method is excellent. The adjacent pixel correlation is smaller than 0.04 in different directions including horizontal, vertical, and diagonal; the entropy of the cipher image with a 256-level gray value is larger than 7.98.

In a general (k, n) scalable secret image sharing (SSIS) scheme, the secret image is shared by n participants and any k or more than k participants have the ability to reconstruct it. The scalability means that the amount of information in the reconstructed image scales in proportion to the number of the participants. In most existing SSIS schemes, the size of each image shadow is relatively large and the dealer does not has a flexible control strategy to adjust it to meet the demand of differen applications. Besides, almost all existing SSIS schemes are not applicable under noise circumstances. To address these deficiencies, in this paper we present a novel SSIS scheme based on a brand-new technique, called compressed sensing, which has been widely used in many fields such as image processing, wireless communication and medical imaging. Our scheme has the property of flexibility, which means that the dealer can achieve a compromise between the size of each shadow and the quality of the reconstructed image. In addition, our scheme has many other advantages, including smooth scalability, noise-resilient capability, and high security. The experimental results and the comparison with similar works demonstrate the feasibility and superiority of our scheme., Competing Interests: The authors have declared that no competing interests exist.

WSNs (Wireless sensor networks) are nowadays viewed as a vital portion of the IoTs (Internet of Things). Security is a significant issue in WSNs, especially in resource-constrained environments. AKA (Authentication and key agreement) enhances the security of WSNs against adversaries attempting to get sensitive sensor data. Various AKA schemes have been developed for verifying the legitimate users of a WSN. Firstly, we scrutinize Amin-Biswas's currently scheme and demonstrate the major security loopholes in their works. Next, we propose a lightweight AKA scheme, using symmetric key cryptography based on smart card, which is resilient against all well known security attacks. Furthermore, we prove the scheme accomplishes mutual handshake and session key agreement property securely between the participates involved under BAN (Burrows, Abadi and Needham) logic. Moreover, formal security analysis and simulations are also conducted using AVISPA(Automated Validation of Internet Security Protocols and Applications) to show that our scheme is secure against active and passive attacks. Additionally, performance analysis shows that our proposed scheme is secure and efficient to apply for resource-constrained WSNs.

User anonymity is one of the key security features of an authenticated key agreement especially for communicating messages via an insecure network. Owing to the better properties and higher performance of chaotic theory, the chaotic maps have been introduced into the security schemes, and hence numerous key agreement schemes have been put forward under chaotic-maps. Recently, Xie et al. released an enhanced scheme under Farash et al.'s scheme and claimed their improvements could withstand the security loopholes pointed out in the scheme of Farash et al., i.e., resistance to the off-line password guessing and user impersonation attacks. Nevertheless, through our careful analysis, the improvements were released by Xie et al. still could not solve the problems troubled in Farash et al‥ Besides, Xie et al.'s improvements failed to achieve the user anonymity and the session key security. With the purpose of eliminating the security risks of the scheme of Xie et al., we design an anonymous password-based three-party authenticated key agreement under chaotic maps. Both the formal analysis and the formal security verification using AVISPA are presented. Also, BAN logic is used to show the correctness of the enhancements. Furthermore, we also demonstrate that the design thwarts most of the common attacks. We also make a comparison between the recent chaotic-maps based schemes and our enhancements in terms of performance.

The percolation for interdependent networks with identical dependency map follows a second-order phase transition which is exactly the same with percolation on a single network, while percolation for random dependency follows a first-order phase transition. In real networks, the dependency relations between networks are neither identical nor completely random. Thus in this paper, we study the influence of randomness for dependency maps on the robustness of interdependent lattice networks. We introduce approximate entropy(ApEn) as the measure of randomness of the dependency maps. We find that there is critical ApEnc below which the percolation is continuous, but for larger ApEn, it is a first-order transition. With the increment of ApEn, the pc increases until ApEn reaching ApEnc (') and then remains almost constant. The time scale of the system shows rich properties as ApEn increases. Our results uncover that randomness is one of the important factors that lead to cascading failures of spatially interdependent networks.

The Telecare Medicine Information Systems (TMISs) provide an efficient communicating platform supporting the patients access health-care delivery services via internet or mobile networks. Authentication becomes an essential need when a remote patient logins into the telecare server. Recently, many extended chaotic maps based authentication schemes using smart cards for TMISs have been proposed. Li et al. proposed a secure smart cards based authentication scheme for TMISs using extended chaotic maps based on Lee's and Jiang et al.'s scheme. In this study, we show that Li et al.'s scheme has still some weaknesses such as violation the session key security, vulnerability to user impersonation attack and lack of local verification. To conquer these flaws, we propose a chaotic maps and smart cards based password authentication scheme by applying biometrics technique and hash function operations. Through the informal and formal security analyses, we demonstrate that our scheme is resilient possible known attacks including the attacks found in Li et al.'s scheme. As compared with the previous authentication schemes, the proposed scheme is more secure and efficient and hence more practical for telemedical environments.

Biometrics authenticated schemes using smart cards have attracted much attention in multi-server environments. Several schemes of this type where proposed in the past. However, many of them were found to have some design flaws. This paper concentrates on the security weaknesses of the three-factor authentication scheme by Mishra et al. After careful analysis, we find their scheme does not really resist replay attack while failing to provide an efficient password change phase. We further propose an improvement of Mishra et al.'s scheme with the purpose of preventing the security threats of their scheme. We demonstrate the proposed scheme is given to strong authentication against several attacks including attacks shown in the original scheme. In addition, we compare the performance and functionality with other multi-server authenticated key schemes.

The telecare medical information systems (TMISs) enable patients to conveniently enjoy telecare services at home. The protection of patient's privacy is a key issue due to the openness of communication environment. Authentication as a typical approach is adopted to guarantee confidential and authorized interaction between the patient and remote server. In order to achieve the goals, numerous remote authentication schemes based on cryptography have been presented. Recently, Arshad et al. (J Med Syst 38(12): 2014) presented a secure and efficient three-factor authenticated key exchange scheme to remedy the weaknesses of Tan et al.'s scheme (J Med Syst 38(3): 2014). In this paper, we found that once a successful off-line password attack that results in an adversary could impersonate any user of the system in Arshad et al.'s scheme. In order to thwart these security attacks, an enhanced biometric and smart card based remote authentication scheme for TMISs is proposed. In addition, the BAN logic is applied to demonstrate the completeness of the enhanced scheme. Security and performance analyses show that our enhanced scheme satisfies more security properties and less computational cost compared with previously proposed schemes.

Paclitaxel (PTX) is the front-line chemotherapeutic agent against human non-small cell lung cancer (NSCLC). However, its therapeutic efficacy is restricted by the increasing frequency of chemotherapeutic resistance in NSCLC. Accumulating evidence has shown the potential role of microRNAs (miRNAs) in the chemotherapeutic sensitivity of cancer cells. Previously it was reported that microRNA-7 (miR-7) acts as an important tumor suppressor in NSCLC. Therefore, the present study was conducted to determine the regulatory role of miR-7 in PTX chemotherapy for NSCLC. Four NSCLC cell lines were used to analyze the correlation of the PTX-sensitivity and endogenoaus miR-7 expression. miR-7 expression was up- and downregulated using miR-7 mimics and inhibitors respectively, and the role of miR-7 in sensitizing NSCLC cells to PTX was assessed by cell viability and apoptosis assays. The molecular mechanism of PTX sensitivity was determined by quantitative polymerase chain reaction and western blotting. It was found that the sensitivity of NSCLC cells to PTX was dependent on endogenous miR-7. Upregulation of miR-7 enhanced the PTX-sensitivity of NSCLC cells by suppressing cell proliferation and promoting cell apoptosis, while the inhibition of miR-7 abrogated the antiproliferative proapoptotic effects of PTX. Pretreatment of miR-7 mimics enhanced the PTX-mediated downregulation of epidermal growth factor receptor (EGFR) in NSCLC cells. These results have identified miR-7 as a potential EGFR-targeting sensitizer in PTX therapy. These data may facilitate the development of novel chemotherapeutic approaches for NSCLC.

The vulnerability to real-life networks against small initial attacks has been one of outstanding challenges in the study of interrelated networks. We study cascading failures in two interrelated networks S and B composed from dependency chains and connectivity links respectively. This work proposes a realistic model for cascading failures based on the redistribution of traffic flow. We study the Barabási-Albert networks (BA) and Erdős-Rényi graphs (ER) with such structure, and found that the efficiency sharply decreases with increasing percentages of the dependency nodes for removing a node randomly. Furthermore, we study the robustness of interrelated traffic networks, especially the subway and bus network in Beijing. By analyzing different attacking strategies, we uncover that the efficiency of the city traffic system has a non-equilibrium phase transition at low capacity of the networks. This explains why the pressure of the traffic overload is relaxed by singly increasing the number of small buses during rush hours. We also found that the increment of some buses may release traffic jam caused by removing a node of the bus network randomly if the damage is limited. However, the efficiencies to transfer people flow will sharper increase when the capacity of the subway network α(S) > α0.

The study of the foraging behavior of group animals (especially ants) is of practical ecological importance, but it also contributes to the development of widely applicable optimization problem-solving techniques. Biologists have discovered that single ants exhibit low-dimensional deterministic-chaotic activities. However, the influences of the nest, ants' physical abilities, and ants' knowledge (or experience) on foraging behavior have received relatively little attention in studies of the collective behavior of ants. This paper provides new insights into basic mechanisms of effective foraging for social insects or group animals that have a home. We propose that the whole foraging process of ants is controlled by three successive strategies: hunting, homing, and path building. A mathematical model is developed to study this complex scheme. We show that the transition from chaotic to periodic regimes observed in our model results from an optimization scheme for group animals with a home. According to our investigation, the behavior of such insects is not represented by random but rather deterministic walks (as generated by deterministic dynamical systems, e.g., by maps) in a random environment: the animals use their intelligence and experience to guide them. The more knowledge an ant has, the higher its foraging efficiency is. When young insects join the collective to forage with old and middle-aged ants, it benefits the whole colony in the long run. The resulting strategy can even be optimal.

In this paper, the synchronization control of memristor-based recurrent neural networks with impulsive perturbations or boundary perturbations is studied. We find that the memristive connection weights have a certain relationship with the stability of the system. Some criteria are obtained to guarantee that memristive neural networks have strong noise tolerance capability. Two kinds of controllers are designed so that the memristive neural networks with perturbations can converge to the equilibrium points, which evoke human's memory patterns. The analysis in this paper employs the differential inclusions theory and the Lyapunov functional method. Numerical examples are given to show the effectiveness of our results., (Crown Copyright © 2014. Published by Elsevier Ltd. All rights reserved.)

In order to meet the demands of operation monitoring of large scale, autoscaling, and heterogeneous virtual resources in the existing cloud computing, a new method of live virtual machine (VM) migration detection algorithm based on the cellular neural networks (CNNs), is presented. Through analyzing the detection process, the parameter relationship of CNN is mapped as an optimization problem, in which improved particle swarm optimization algorithm based on bubble sort is used to solve the problem. Experimental results demonstrate that the proposed method can display the VM migration processing intuitively. Compared with the best fit heuristic algorithm, this approach reduces the processing time, and emerging evidence has indicated that this new approach is affordable to parallelism and analog very large scale integration (VLSI) implementation allowing the VM migration detection to be performed better.

We present an algebraic approach to reveal attractor transitions in Boolean networks under single control based on the recently developed matrix semitensor product theory. In this setting, the reachability of attractors is estimated by the state transition matrices. We then propose procedures that compute the shortest control sequence and the result of each step of input (control) exactly. The general derivation is exemplified by numerical simulations for two kinds of gene regulation networks, the protein-nucleic acid interactions network and the cAMP receptor of Dictyostelium discoideum network.

We study the problem of synchronization-based parameters identification of dynamical systems from time series. Through theoretical analysis and numerical examples, we show that some recent research reports on this issue are not perfect or even incorrect. Long-time full rank and finite-time full rank conditions of Gram matrix are pointed out, which are sufficient for parameters identification of dynamical systems. The influence of additive noise on the proposed parameter identifier is also investigated. The mean filter is used to suppress the estimation fluctuation caused by the noise.

Time-delay chaotic systems have some very interesting properties, and their parameter estimation has received increasing interest in the recent years. It is well known that parameter estimation of a chaotic system is a nonlinear, multivariable, and multimodal optimization problem for which global optimization techniques are required in order to avoid local minima. In this work, a seeker-optimization-algorithm (SOA)-based method is proposed to address this issue. In the SOA, search direction is based on the empirical gradients by evaluating the response to the position changes, and step length is based on uncertainty reasoning by using a simple fuzzy rule. The performance of the algorithm is evaluated on two typical test systems. Moreover, two state-of-the-art algorithms (i.e., particle swarm optimization and differential evolution) are also considered for comparison. The simulation results show that the proposed algorithm is better than or at least as good as the other two algorithms and can effectively solve the parameter estimation problem of time-delay chaotic systems.

Through the construction of suitable objective function, the parameter estimation of the dynamical system could be converted to the problem of parameter optimization. Based on the chaotic ant swarm optimization approach, we investigate the problem of parameter optimization for the dynamical systems in the presence of noise. We systematically analyze the basic relationships among the complexity of objective function, the length of time series, and the performance of the searching algorithm. Furthermore, we consider the effect of measurable additive noise on the objective function. Numerical simulations are also provided to show the effectiveness and feasibility of the proposed methods.

Together with attractive couplings, repulsive couplings play crucial roles in determining important evolutions in natural systems, such as in learning and oscillatory processes of neural networks. The complex interactions between them have great influence on the systems. A detailed understanding of the dynamical properties under this type of couplings is of practical significance. In this paper, we propose a model to investigate the dynamics of attractive and repulsive couplings, which give rise to rich phenomena, especially for amplitude death (AD). The relationship among various dynamics and possible transitions to AD are illustrated. When the system is in the maximally stable AD, we observe the transient behavior of in-phase (high frequency) and out-of-phase (low frequency) motions. The mechanism behind the phenomenon is given.

In this paper, a novel unknown parameter identifier of nonlinear dynamical systems is designed through the integrator theory, and the corresponding sufficient conditions for the existence of unknown parameter identifiers are presented. In order to illustrate the effectiveness of the proposed method, simulation results are given. The effects of system noise and measurement noise for the proposed method are discussed in detail. The comparative analysis between the proposed method based on integrator theory and the approach based on adaptive synchronization is also given.

In this paper, adaptive generalized projective synchronization and parameter identification in different chaotic systems are carefully revisited. We use a concrete counterexample to demonstrate that the result in a previous work [R. Li, W. Xu, and S. Li, Phys. Lett. A 367, 199 (2007)] is imperfect, where a scheme of generalized projective synchronization is proposed for parameter identification with some drawbacks on ignoring the conditions which ensure the parameter convergence. We further discuss the two conditions of parameter convergence, which are linear independence and persistent excitation. A special relationship between them is addressed to estimate unknown model parameters effectively.

This paper explores piecewise-linear systems to construct dynamic logic architecture. We present three schemes to obtain various basic logic gates, adders, and memory by using piecewise-linear systems. These schemes can switch easily among different operational roles by changing parameters. The proposed schemes are computationally efficient and easy to use. It is convenient for us to study and analyze them with the theory of linear systems., ((c) 2008 American Institute of Physics.)

This paper comments on a recent paper by Yu and Cao [Chaos, 16, 023119 (2006)]. We find that the theorem in this paper is incorrect by numerical simulations. The consequence of the incorrectness is analyzed as well.

This paper investigates an important problem concerning the complexity of three-layer binary neural networks (BNNs) with one hidden layer. The neuron in the studied BNNs employs a hard limiter activation function with only integer weights and an integer threshold. The studies are focused on implementations of arbitrary Boolean functions which map from [0, 1]n into [0, 1]. A deterministic algorithm called set covering algorithm (SCA) is proposed for the construction of a three-layer BNN to implement an arbitrary Boolean function. The SCA is based on a unit sphere covering (USC) of the Hamming space (HS) which is chosen in advance. It is proved that for the implementation of an arbitrary Boolean function of n-variables (n > or = 3) by using SCA, [3L/2] hidden neurons are necessary and sufficient, where L is the number of unit spheres contained in the chosen USC of the n-dimensional HS. It is shown that by using SCA, the number of hidden neurons required is much less than that by using a two-parallel hyperplane method. In order to indicate the potential ability of three-layer BNNs, a lower bound on the required number of hidden neurons which is derived by using the method of estimating the Vapnik-Chervonenkis (VC) dimension is also given.