Narrow Results

This paper deals with the resolution of the Quadratic 3-dimensional Assignment Problem hereafter referred to as Q3AP. Q3AP is an extension of the well-known Quadratic Assignment Problem (QAP) and of the Axial 3-Assignment Problem (A3AP). It finds its application amongst others in Hybrid Automatic Repeat reQuest (HARQ) error-control mechanism used in wireless communication systems. This problem is computationally NP-hard. As far as we know, the largest Q3AP instance size solved to optimality is 13 whereas practical Q3AP instance size can be of 8, 16, 32 or 64. Sequential exact methods such branch-and-bound or sequential metaheuristics are therefore not suited to solve large size instances for the excessive needed computation time. In this paper, we propose parallel hybrid genetic-based metaheuristics for solving the Q3AP. The parallelism in our methods is of two hierarchical levels. The first level is an insular model where a fixed number of genetic algorithms (GA) evolve independently on separate islands and periodically exchange genetic material. The second level is a parallel transformation of individuals in each GA. Implementation has been done using ParadisEO framework, and the experiments have been performed on GRID5000, the French nation-wide computational grid. The experimental results produced by our method were confronted with those reported in the literature. The optimum or the best so far known solutions have been reached in a reasonable computation time.

A nonempty circular string C(x) of length n is said to be covered by a set U_k of strings each of fixed length k≤n iff every position in C(x) lies within an occurrence of some string u∈U_k. In this paper we consider the problem of determining the minimum cardinality of a set U_k which guarantees that every circular string C(x) of length n≥k can be covered. In particular, we show how, for any positive integer m, to choose the elements of U_k so that, for sufficiently large k, u_k≈σ^k–m, where u_k=|U_k| and σ is the size of the alphabet on which the strings are defined. The problem has application to DNA sequencing by hybridization using oligonucleotide probes.

We demonstrate enhanced ferromagnetism in copper doped two-dimensional GaN monolayer (GaN-ML). Our first principle calculation based on density functional theory predicted that nonmagnetic Cu-dopant with concentration of 6.25% to be ferromagnetic (FM) in 2D GaN layer which carries a magnetic moment of 2.0 μ_B per Cu atom and it is found to be long range magnetic coupling among the Cu-dopant. The Cu-dopant in 2D GaN-ML which can be explained in terms of p-d hybridization at Curie temperature and this dopant prefer the FM behavior in 2D GaN layer. Hence Cu doped 2D GaN layer shows strong magnetic properties so that it is a promising material in the field of spintronics.

We use the $\pi$-orbital axis vector (POAV) analysis to deal with large curvature effect of graphene in the tight-binding model. To test the validities of pseudo-magnetic fields (PMFs) derived from the tight-binding model and the model with Dirac equation coupled to a curved surface, we propose two types of spatially constant-field topographies for strongly-curved graphene nanobubbles, which correspond to these two models, respectively. It is shown from the latter model that the PMF induced by any spherical graphene nanobubble is always equivalent to the magnetic field caused by one magnetic monopole charge distributed on a complete spherical surface with the same radius. Such a PMF might be attributed to the isometry breaking of a graphene layer attached conformably to a spherical substrate with adhesion.

The electronic structure and magnetic properties of the Gd₅Si₄ compound have been investigated by the first principles full-potential linearized augmented plane wave (FP-LAPW) method based on density functional theory (DFT) using the WIEN2k code. The Coulomb corrected local-spin density approximation (LSDA + U) in the self-interaction correction (SIC) has been used for the exchange-correlation potential. Based on the calculated results, the ground state of Gd₅Si₄ is found to be ferromagnetic (FM). The optimized structural parameters and magnetic properties including the lattice constants and magnetic moments are in good agreement with experimental data. The magnetic moments of the Gd atoms in Gd₅Si₄ are smaller than that of the elemental gadolinium. The magnetic moment of Gd₅Si₄ is found to be 37.8 μ_B/f.u. DOS results show that the magnetic properties of the compound depend on the hybridization between Si-3p and Gd-5d states which have an effective role in the RKKY interaction. The existence of the very flat bands at -7 eV for spin up and at +3 eV for spin down that is mainly Gd-4f characters shows that the LSDA + U method provides the better description of our systems. The obvious overlap of electron densities between the Gd1 and Si atoms indicates a covalent-like bonding between them.

Electronic properties of spin polarized antiferromagnetic ACrO₃ (A = La, Y) are explored with Hubbard model using density functional theory (DFT). These two isostructural systems are investigated using the different Hubbard energy and analyzed the hybridization of chromium 3d orbitals and oxygen 2p orbitals and the change in energy bandgaps against the Hubbard energy. The bond length and bond angle affect significantly the orbital contributions of Cr-3d and O-2p electrons for both the system. We noticed that the Cr–O hybridization affects the orbital degeneracy and is substantiated with partial density of states. These results emphasize the contribution of Hubbard energy in correlated electron systems.

The present paper describes a hybrid self-adaptive learning global search algorithm and firefly algorithm (HSLGSAFA)-based model for task scheduling in cloud computing. The proposed hybrid model combines gravitational search algorithm (GSA), which has been successfully scheduling the task in the application, with the use of SL strategy and the FA. The basic scheme of our approach is to utilize the benefits of both SLGSA algorithm and firefly algorithm and not including their disadvantages. In HSLGSAFA, each dimension of a solution represents a task and a solution as a whole signifies all tasks’ priorities. The vital issue is how to allocate users’ tasks to exploit the income of Infrastructure as a Service (IaaS) provider while promising Quality-of-Service (QoS). The generated solution is proficient to assure user-level QoS and improve IaaS providers’ credibility and economic benefit. The HSLGSAFA method also used to design the hybridization process and suitable fitness function of the corresponding task. According to the evolved results, it has been found that our algorithm always outperforms the traditional algorithms.

The main objective of the proposed methodology is multi-objective job scheduling using hybridization of whale and BAT optimization algorithm (WBAT) which is used to change existing solution and to adopt a new good solution based on the objective function. The scheduling function in the proposed job scheduling strategy first creates a set of jobs and cloud node to generate the population by assigning jobs to cloud node randomly and evaluate the fitness function which minimizes the makespan and maximizes the quality of jobs. Second, the function uses iterations to regenerate populations based on WBAT behavior to produce the best job schedule that gives minimum makespan and good quality of jobs. The experimental results show that the performance of the proposed methods is better than the other methods of job scheduling problems.

In this paper, we extend the hybridization procedure proposed in [D. N. Arnold and F. Brezzi, Mixed and nonconforming finite element methods: Implementation, postprocessing and error estimates, ESAIM Math. Model. Numer. Anal.19 (1985) 7–32] to the Virtual Element Method for linear elasticity problems based on the Hellinger–Reissner principle. To illustrate such a technique, we focus on a specific 2D scheme, but other methods and 3D problems can be considered as well. We also show how to design a better approximation of the displacement field using a straightforward post-processing procedure. The numerical experiments confirm the theory for both two and three-dimensional problems.

In the present study we propose a new hybrid version of Differential Evolution (DE) and Particle Swarm Optimization (PSO) algorithms called Hybrid DE or HDE for solving continuous global optimization problems. In the proposed HDE algorithm, information sharing mechanism of PSO is embedded in the contracted search space obtained by the basic DE algorithm. This is done to maintain a balance between the two antagonist factors; exploration and exploitation thereby obtaining a faster convergence. The embedding of swarm directions to the basic DE algorithm is done with the help of a "switchover constant" called α which keeps a record of the contraction of search space. The proposed HDE algorithm is tested on a set of 10 unconstrained benchmark problems and four constrained real life, mechanical design problems. Empirical studies show that the proposed scheme helps in improving the convergence rate of the basic DE algorithm without compromising with the quality of solution.

Fuzzy c-Means (FCM) and Possibilistic c-Means (PCM) are the most popular algorithms of the fuzzy and possibilistic clustering approaches, respectively. A hybridization of these methods, called Possibilistic Fuzzy c-Means (PFCM), solves noise sensitivity defect of FCM and overcomes the coincident clusters problem of PCM. Although PFCM have shown good performance in cluster detection, it does not consider that different variables can produce different membership and possibility degrees and this can improve the clustering quality as it has been performed with the Multivariate Fuzzy c-Means (MFCM). Here, this work presents a generalized multivariate approach for possibilistic fuzzy c-means clustering. This approach gives a general form for the clustering criterion of the possibilistic fuzzy clustering with membership and possibility degrees different by cluster and variable and a weighted squared Euclidean distance in order to take into account the shape of clusters. Six multivariate clustering models (special cases) can be derivative from this general form and their properties are presented. Experiments with real and synthetic data sets validate the usefulness of the approach introduced in this paper using the special cases.

The semiconducting two-dimensional (2D) architectures materials have potential applications in electronics and optics. The design and search of new 2D materials have attracted extensive attention recently. In this study, first principle calculation has been done on 2D gallium nitride (GaN) monolayer with respect to its formation and binding energies. The electronic and optical properties are also investigated. It is found that the single isolated GaN sheet is forming mainly ionic GaN bonds despite a slightly weaker GaN interaction as compared with its bulk counterpart. The dielectric constant value of 2D GaN is smaller as compared to 3D GaN due to less effective electronic screening effect in the layer, which is accompanied by lesser optical adsorption range and suggested to be a promising candidate in electronic and optoelectronic devices.

In this paper, we propose a hybrid amplifier-based 8-port Cross-Polarization Modulation (XPoM) interconnection with low polarization sensitivity unlike conventional Semiconductor Optical Amplifier (SOA)-based switching. The three-stage configuration utilizes Erbium-Doped Fiber Amplifier (EDFA) as an intermediate stage to reduce the effects of inherent polarization modulation creating large Transverse Electric – Transverse Magnetic (TE–TM) relative phase shifts. The appreciable error rate of $e^{-16}$ and Extinction Ratio (ER) of 14.166 dB have been obtained with 100 GHz of port-spacing at different input powers. The low Polarization-Dependent Loss (PDL) of 1.0045 dB, small variations of azimuth and ellipticity with device angle in S–E–S configuration in contrast to that of SOA, adds to the superiority of the proposed model.

Amplified detection of nucleic acid by G-quadruplex based hybridization chain reaction.

Dow opens Photovoltaics Films Application Lab in Shanghai.

Researchers discover molecular mechanisms of left-right asymmetric control in the sea urchin.

China mulls new rule on human genetic research.

China to phase out organ donation from executed criminals.

Charles River Laboratories to expand research models business in China.

Chinese Science Academy Chief urges seizing on new technological revolution.

BGI contributes genome sequencing and bioinformatics expertise.

Taiwan government to encourage formation of smaller biotech funds.

Two computational models, Simulating WAve till SHore (SWASH) and DualSPHysics, with different computational costs and capabilities have been hybridized in this work. SWASH is a time-domain wave model based on a finite difference method for simulating nonhydrostatic, free-surface and rotational flow while DualSPHysics is a Lagrangian meshless model based on the Smoothed Particle Hydrodynamics (SPH) technique. SWASH is a reliable model to generate and propagate waves in large domains, whereas DualSPHysics is normally used in areas close to the coastline to provide a detailed description of the interaction between sea waves and coastal structures. The presented technique is a one-way coupling, with a hybridization point where the information from SWASH is passed to DualSPHysics. SWASH is used to propagate waves along the fluid domain and to calculate velocities at different depths at the position of the hybridization point. Waves in DualSPHysics are generated by means of a moving boundary (MB) whose displacement in time is reconstructed using the velocities provided by SWASH. Each particle that forms the MB is displaced with its correspondent velocity that depends on its depth. The hybridization technique is validated with experimental data and the resulting model is proved to reproduce accurately wave heights and orbital velocities. Thus, the hybrid model preserves the flexibility and capabilities of DualSPHysics with important improvements in efficiency. In addition, it simulates wave propagation even more accurately than DualSPHysics taking advantage of SWASH strengths.

This paper reported on the preparation of a novel stearic acid (SA)/wollastonite (W) composite as a form-stable phase change material (PCM) for thermal energy-storage (TES) by vacuum impregnation, and especially investigated the effect of the size grade of W on the thermal properties of the SA/W composite. Samples were characterized by scanning electron microscopy (SEM), X-ray diffraction (XRD), laser particle-size analysis, and differential scanning calorimetry (DSC). Natural W (Wr) was classified into four size grades by wet screening. The results indicate that no chemical reaction took place between SA and W, and the SA load in the SA/W composite increased with an increase in the length/diameter (L/D) ratio of the W. The SA/W composite with a W L/D ratio of 22.5 exhibited latent heats of melting and freezing of 58.64 J/g and 56.95 J/g, respectively, which was higher than those of the composite incorporating natural W. We believe that the as-prepared form-stable PCM composite could provide a potential means of TES for the concentrated solar power.

The aim of this investigation is to study the ferromagnetism and magnetic properties of LiMgP HH with double impurities, namely $C$-2$p$ and (Fe and Ni)-3$p$, connected to LiMg${}_{0.95}$Fe${}_{0.05}$P${}_{0.95}$C${}_{0.05}$ and LiMg${}_{0.95}$Ni${}_{0.05}$P${}_{0.95}$C${}_{0.05,}$respectively. To achieve this, we perform KKR-CPA combined with GGA. The ferromagnetic stability of LiMg₀P${}_{0.95}$C${}_{0.05}$is observed, where C-2$p$ is set on the spin-down of $E$${}_F$ connected to the half metallicity. In the case of LiMg${}_{0.95}$Fe${}_{0.05}$P alloy, the Fe-3$d$ states show a variation in the exchange splitting ($t_2^{+}$,$t_2^{-}$) with respect to the spin-up ${}^{(\uparrow )}$ and spin-down ${}^{(\downarrow )}$. The Fe-3$d$ states are located around the $E$${}_F$ and exhibit half-metallic characteristic. Similarly, the LiMg${}_{0.95}$Ni${}_{0.05}$P alloy also exhibits half metallic characteristic. The co-doped LiMg${}_{0.95}$Fe${}_{0.05}$P${}_{0.05}$C${}_{0.05}$ and LiMg${}_{0.95}$Ni${}_{0.05}$P${}_{0.95}$C${}_{0.05}$ alloys predict an improvement in magnetic properties due to the presence of carbon, resulting in hybridization between C-2$p$ and Fe-3$d$ in the valence band (VB) maximum and conduction band (CB) minimum on the minority states. Similarly, in the case of LiMg${}_{0.95}$Ni${}_{0.05}$P${}_{0.95}$C${}_{0.05}$, hybridization occurs between C-2$p$ and Ni-3$d$ below $E$${}_F$ in the minority states, within the range of (−0.2 to 0 Ry) in the VB.

One of the fundamental tasks of robotics is to solve the localization problem, in which a robot must determine its true pose without any knowledge on its initial location. In underwater environments, this is specially hard due to sensors restrictions. For instance, many times, the localization process must rely on information from acoustic sensors, such as transponders. We propose a method to deal with this scenario, that consists in a hybridization of probabilistic and interval approaches, aiming to overcome the weaknesses found in each approach and improve the precision of results. In this paper, we use the set inversion via interval analysis (SIVIA) technique to reduce the region of uncertainty about robot localization, and a particle filter to refine the estimates. With the information provided by SIVIA, the distribution of particles can be concentrated in regions of higher interest. We compare this approach with a previous hybrid approach using contractors instead of SIVIA. Experiments with simulated data show that our hybrid method using SIVIA provides more accurate results than the method using contractors.

Rice genome sequencing and computational annotation provide a static map for understanding this model of Gramineae species. With the development of in situ oligonucleotide synthesis technology, tiling-path microarrays have become a dynamic and efficient way for monitoring large-scale transcriptional activities and detecting novel transcribed elements missed by software. Unlike conventional cDNA or oligonucleotide arrays, tiling-path platforms employ the full extent of oligos covering given genomic regions, and thus offer excellent experimental conditions in which to assay the properties of oligos in terms of their specificity and efficiency of hybridization to their corresponding targets. Here, we report a tiling-path microarray analysis of a 1-Mb region (10 to 11 Mb) in japonica rice chromosome 10, which was tiled by a 36-mer oligo set at a resolution of 5 bp. Our analysis focused on three major factors of oligo hybridization properties, including GC content, melting temperature (Tm), and the repetitiveness of oligo sequences.

DNA microarray, also known as DNA chip or gene chip, is a powerful tool that allows the measurement of tens of thousands of genes in parallel for gene expression and many other aspects of genome research. With the availability of increasing numbers of completely sequenced organisms, genome-wide microarrays are becoming more and more popular in various biological areas. DNA microarray, like other hybridization-based techniques such as Southern and Northern blots, is based on the principle that every nucleic acid strand carries the capacity to recognize its complementary sequences through base pairing. DNA microarray has been intensively used in various areas of human disease studies. It has also been recently applied by a number of investigators to elucidate molecular programs that define osteoblast differentiation. Several cellular models have been used, including committed osteogenic precursors of murine and human origin, immortalized human cells at various stages of differentiation, and uncommitted mesodermal progenitor cells. We believe that the potential of DNA microarray in human bone studies has yet to be explored, and may dramatically expand our scope of understanding molecular programs underlying the physiological and pathological conditions of human bone. This chapter will focus primarily on detailed protocols of DNA microarrays, in particular expression arrays.