A Hybrid Particle Swarm Optimization Algorithm for the Redundancy Allocation Problem

The Redundancy Allocation Problem generally involves the selection of components with multiple choices and redundancy levels that produce maximum system reliability given various system level constraints as cost and weight. In this paper we investigate the series–parallel redundant reliability problems, when a mixing of components was considered. In this type of problem both the number of redundancy components and the corresponding component reliability in each subsystem are to be decided simultaneously so as to maximise the reliability of system. A hybrid algorithm is based on particle swarm optimization and local search algorithm. In addition, we propose an adaptive penalty function which encourages our algorithm to explore within the feasible region and near feasible region, and discourage search beyond that threshold. The effectiveness of our proposed hybrid PSO algorithm is proved on numerous variations of three different problems and compared to Tabu Search and Multiple Weighted Objectives solutions.     

Hybrid metaheuristics for the profitable arc tour problem

The profitable arc tour problem is a variant in the vehicle routing problems. It is included in the family of the vehicle routing with profit problems in which a set of vehicle tours are constructed. The objective is to find a set of cycles in the vehicle tours that maximize the collection of profits minus travel costs, subject to constraints limiting the length of cycles that profit is available on arcs. To solve this variant we adopted two metaheuristics based on adaptive memory. We show that our algorithms provide good results in terms of solution quality and running times.     

Short-Term Prediction of the Sea State Dynamics

Forecasting of the sea level plays a key role to control on- and offshore facilities. First, we start with a determinstic time series method based on the state space embedding to determine the vector field of the nonlinear dynamical system and deduce the solution of its corresponding high-order differential equation. Second, We assume that the sea state is a stochastic process governed by a deterministic part and by noise so that this dynamical system can be modelled by the Langevin equation. We extract the nonlinear dynamical system considering fluctuations directly from a measured time series by estimating the drift vector and the diffusion matrix of the Fokker-Planck equation. In order to determine the prediction accuracy, the numerical solutions of the deterministic model and the Langevin equation are compared to the data values at future time. (© 2011 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

PROMETHEE-MD-2T method for project selection

Project selection is a real problem of multicriteria group decision making (MCGDM) where each decision maker expresses his/her preferences depending on the nature of the alternatives and on his/her own knowledge over them. Thus, information, as much quantitative as qualitative, coexists. The traditional methods of MCGDM developed for project selection usually discriminates in favour of quantitative information at the expense of qualitative information, and this is due to the capability to integrate this first type of information inside their procedure. In this article, two new multicriteria 2-tuple group decision methods called “Preference Ranking Organisation Method for Enrichment Evaluation Multi Decision maker 2-Tuple-I and II” (PROMETHEE-MD-2T-I and II) are presented. They are able to integrate inside their procedure both quantitative and qualitative information in an uncertain context. This has been performed by integrating a 2-tuple linguistic representation model dealing with non-homogeneous and imprecise information data made up by valued intervals, numerical and linguistic values into the aggregation operators of Promethee methods. Although they have been developed for project selection problems, these proposed methods can be applied to all kinds of decision-making problems with heterogeneous and multigranular information.     

The interference wave and the bright and dark soliton for two integro-differential equation by using BNNM

Interference wave is an important research target in the field of navigation, electromagnetic and earth science. In this work, the nonlinear property of neural network is used to study the interference wave and the bright and dark soliton solutions. The generalized broken soliton-like equation is derived through the generalized bilinear method. Three neural network models are presented to fit explicit solutions of generalized broken soliton-like equations and Boiti–Leon–Manna–Pempinelli-like equation with 100% accuracy. Interference wave solutions of the generalized broken soliton-like equation and the bright and dark soliton solutions of the Boiti–Leon–Manna–Pempinelli-like equation are obtained with the help of the bilinear neural network method. Interference waves and the bright and dark soliton solutions are shown via three-dimensional plots and density plots.

     

Tunable gold nanoparticles shape and size in reductive and structuring media containing protic ionic liquids

Herein, a facile method was developed for preparing high concentration of monodispersed gold nanoparticles (NPs) at room temperature from gold(III) chloride by using different media based on N,N-dimethylformamide or water solutions containing a protic ionic liquid (PIL), namely, the octylammonium formate or the bis(2-ethyl-hexyl)ammonium formate, based on which both PILs were used as redox-active structuring media. The formation of gold NPs in these systems was then characterized using UV–visible spectroscopy, transmission electron microscopy, and dynamic light scattering. From these investigations, it appears that the structure and aggregation pathway of PILs in selected solvents affect strongly the formation, growth, the shape, and the size of gold NPs. In fact, by using this approach, the shape-/ size-controlled gold NPs (branched and spherical) can be generated under mild condition. This approach suggests also a wealth of potential for these designer nanomaterials within the biomedical, materials, and catalysis communities by using designer and safer media based on PILs.     

Chains in Critical Fluids and Nanopores

Summary: The conformational behavior of a polymer in a critical binary solvent confined in a porous medium is studied. The size of the polymer in bulk, which is mainly governed by the correlation length of the solvent density fluctuations, depends on the proximity to the critical point of the binary mixture. We find that in contrast to the bulk behavior, the conformational properties of the polymer in a porous medium depends strongly on the pore size. The latter controls the correlation length of the solvent density fluctuations and thus determines the polymer size.     

Synthesis of 6-amino-5-cyano-1,4-disubstituted-2(1H)-pyrimidinones via copper-(I)-catalyzed alkyne-azide 'click chemistry' and their reactivity

In this paper we present the room temperature synthesis of a novel serie of 1,4-disubstituted-1,2,3-triazoles 4a-l by employing the (3+2) cycloaddition reaction of pyrimidinones containing alkyne functions with different model azides in the presence of copper sulphate and sodium ascorbate. To obtain the final triazoles, we also synthesized the major precursors 6-amino-5-cyano-1,4-disubstituted-2(1H)-pyrimidinones 3a-r from ethyl 2,2-dicyanovinylcarbamate derivatives 2a-c and various primary aromatic amines containing an alkyne group. The triazoles were prepared in good to very good yields.     

Microwave irradiation： Novel and facile methods for the synthesis of new pyrimidinones

We describe here a rapid process for the preparation of new 9-chloromethyl-12-aryl-10,12-dihydrobenzo[5,6]chromeno[2,3-d]pyrimidin-11-ones 5a-d and 10-chloromethyl-7-aryl-7,9-dihydrobenzo[7,8]chromeno[2,3-d]pyrimidin-8-ones 6a-d by two different methods utilizing microwave irradiation. This methodology provides better yields(72%–80%) and high purity of the title compounds.