Finite-time Anti-synchronization of Memristive Stochastic BAM Neural Networks with Probabilistic Time-varying Delays

This paper investigates the drive-response finite-time anti-synchronization for memristive bidirectional associative memory neural networks (MBAMNNs). Firstly, a class of MBAMNNs with mixed probabilistic time-varying delays and stochastic perturbations is first formulated and analyzed in this paper. Secondly, an nonlinear control law is constructed and utilized to guarantee drive-response finite-time anti-synchronization of the neural networks. Thirdly, by employing some inequality technique and constructing an appropriate Lyapunov function, some anti-synchronization criteria are derived. Finally, a number simulation is provided to demonstrate the effectiveness of the proposed mechanism

Automated synthesis of local time requirement for service composition

National Research Foundation (NRF) Singapore ANR-NRF French-Singaporean research program ProMi

Verification of functional and non-functional requirements of web service composition

Abstract. Web services have emerged as an important technology nowadays. There are two kinds of requirements that are crucial to web service composition, which are functional and non-functional requirements. Functional requirements focus on functionality of the composed service, e.g., given a booking service, an example of functional requirements is that a flight ticket with price higher than $2000 will never be purchased. Non-functional requirements are concerned with the quality of service (QoS), e.g., an example of the booking service’s non-functional requirements is that the service will respond to the user within 5 sec-onds. Non-functional requirements are important to web service composition, and are often an important clause in service-level agreements (SLAs). Even though the functional requirements are satisfied, a slow or unreliable service may still not be adopted. In our paper, we propose an automated approach to verify combined functional and non-functional requirements directly based on the semantics of web service composition. Our approach has been implemented and evaluated on the real-world case studies, which demonstrate the effectiveness of our method.

A Biodegradable Polyethylenimine-Based Vector Modified by Trifunctional Peptide R18 for Enhancing Gene Transfection Efficiency In Vivo

Lack of capacity to cross the nucleus membrane seems to be one of the main reasons for the lower transfection efficiency of gene vectors observed in vivo study than in vitro. To solve this problem, a new non-viral gene vector was designed. First, a degradable polyethylenimine (PEI) derivate was synthesized by crosslinking low-molecular-weight (LMW) PEI with N-octyl-N-quaternary chitosan (OTMCS), and then adopting a designed trifunctional peptide (RGDC- TAT-NLS) with good tumor targeting, cell uptake and nucleus transport capabilities to modify OTMCS-PEI. The new gene vector was termed as OTMCS- PEI-R18 and characterized in terms of its chemical structure and biophysical parameters. Gene transfection efficiency and nucleus transport mechanism of this vector were also evaluated. The polymer showed controlled degradation and remarkable buffer capabilities with the particle size around 100–300 nm and the zeta potential ranged from 5 mV to 40 mV. Agraose gel electrophoresis showed that OTMCS-PEI-R18 could effectively condensed plasmid DNA at a ratio of 1.0. Besides, the polymer was stable in the presence of sodium heparin and could resist digestion by DNase I at a concentration of 63U DNase I/DNA. OTMCS-PEI-R18 also showed much lower cytotoxicity and better transfection rates compared to polymers OTMCS-PEI-R13, OTMCS-PEI and PEI 25 KDa in vitro and in vivo. Furthermore, OTMCS-PEI-R18/DNA complexes could accumulate in the nucleus well soon and not rely on mitosis absolutely due to the newly incorporated ligand peptide NLS with the specific nuclear delivery pathway indicating that the gene delivery system OTMCS-PEI-R18 could reinforce gene transfection efficiency in vivo

Automated runtime recovery for QoS-based service composition

No Full Tex

Optimizing selection of competing features via feedback-directed evolutionary algorithms

Software that support various groups of customers usually require complicated configurations to attain different functionalities. To model the configuration options, feature model is proposed to capture the commonalities and competing variabilities of the product variants in software family or Software Product Line (SPL). A key challenge for deriving a new product is to find a set of features that do not have inconsistencies or conflicts, yet optimize multiple objectives (e.g., minimizing cost and maximizing number of features), which are often competing with each other. Existing works have attempted to make use of evolutionary algorithms (EAs) to address this problem. In this work, we incorporated a novel feedback-directed mechanism into existing EAs. Our empirical results have shown that our method has improved noticeably over all unguided version of EAs on the optimal feature selection. In particular, for case studies in SPLOT and LVAT repositories, the feedback-directed Indicator-Based EA (IBEA) has increased the number of correct solutions found by 72.33% and 75%, compared to unguided IBEA. In addition, by leveraging a pre-computed solution, we have found 34 sound solutions for Linux X86, which contains 6888 features, in less than 40 seconds.No Full Tex