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Although quantum-behaved particle swarm optimization (QPSO) algorithm has the advantages of few parameters and simple implementation, it suffers from these problems of low precision while calculating high-dimensional complex problems and being caught in local optimum during the later stage of iteration. To tackle these shortages effectively, a modified QPSO (QDQPSO) algorithm is presented. In order to improve the search efficiency and convergence speed of the algorithm, the idea of quasi opposite-based learning is used in the initialization stage. For enhancing the overall performance of the algorithm, the double evolutionary mechanism is applied to update the individual location during the iterative process. Furthermore, perturbation at global optimum position and bound constraint handling are considered to help the algorithm to escape from local optimum and maintain the diversity of population. According to the results obtained by QDQPSO and other nine optimization algorithms on 28 benchmark functions under different dimensions, it is found that QDQPSO performs better on the accuracy and stability of the optimal solution. Subsequently, Wilcoxon rank-sum test and Friedman test demonstrate significant advantages of the improved algorithm. Finally, QDQPSO algorithm displays superior performance in solving five practical optimization problems compared to several optimization methods. [ABSTRACT FROM AUTHOR]

It is important to design insecticides having both low drug resistance and less undesirable toxicity for desert locust control. Specific GPCRs of Schistocerca gregaria , especially β-adrenergic-like octopamine receptor (SgOctβR), can be considered as its potential effective insecticide targets. However, either the unavailability of SgOctβR’s structure or the inadequate capability of its sequence lead the development of insecticide for Schistocerca gregaria meets its plateau. To relax this difficulty, this paper develops a promising progressive structure simulation from SgOctβR’s sequence, to its predicted structure of SgOctβR in vacuum, to its conformation as well as its complex with endogenous ligand octopamine in a solvent-membrane system. The combined approach of multiple sequence alignment, static structural characterization, and dynamic process of conformational change during binding octopamine reveal three important aspects. The first one is the characterization of SgOctβR’s active pocket, including the attending secondary structure elements, its hydrophobic residues and nonpolar surface. The second one is the interaction with octopamine, especially the involved hydrogen bonds and an aromatic stacking of pi–pi interactions. The third one is the potential binding sites, including six highly conserved residues and one highly variable residue for locust insecticide design. This work is definitely helpful for the further structure-based drug design for efficient and eco-friendly insecticides, as well as site-directed mutagenesis biochemical research of SgOctβR. [ABSTRACT FROM AUTHOR]

In order to overcome the premature convergence problem of quantum-behaved particle swarm optimization algorithm (QPSO) in the face of high-dimensional complexity, an improved QPSO based on good point set and double attractor point mechanism (GDQPSO) is presented. In the initialization phase of GDQPSO, the idea of good point set is used to allow particles to explore more uniformly and comprehensively at the beginning of the iteration. To better balance the exploration ability of individuals and the global search capability of the algorithm, the local attraction point is redefined by combining dynamic and fixed methods. Then the global perturbation mechanism is considered to maintain the diversity of the population. And the comparison results with nine intelligent algorithms on 18 benchmark functions indicate that GDQPSO has obvious advantages in convergence accuracy and stability. Finally, the simulation experiments demonstrate very competitive performance of GDQPSO while solving the portfolio model with transaction fee and financing cost when compared with other evolutionary algorithms. • The idea of good point set is introduced to initialize the swarm. • Two different methods is used to redefine the local attraction point. • The global disturbance mechanism is applied to maintain population diversity • A portfolio model with transaction fee and financing cost is established. [ABSTRACT FROM AUTHOR]

Abstract: The influence of ultrasound to the activity of tyrosinase was investigated. According to the analyses of ultraviolet–visible spectrometry and Fourier transform infrared spectroscopy, both monophenolase and diphenolase activities of tyrosinase treated by ultrasound were higher than that of control, with the decrease of lag time and the increase of activity. No oxytyrosinase was observed and β-sheet conformation was predominant in the tyrosinase under ultrasound treatment. Moreover, with the observation of atomic force microscopy, the large molecular groups of tyrosinase were broken into small ones under the treatment of ultrasound. The present result suggested the activity of tyrosinase could be activated under the tested ultrasound treatment, mainly due to the increased likelihood of the combination of substrate and enzyme, or the possible change and exposure of the active site in tyrosinase. [Copyright &y& Elsevier]

Silicon nitride based materials have emerged as the promising candidates for high-power electronics and next-generation gate dielectrics. Herein, the crucial characteristics of electron mobility and lattice thermal conductivity of β -Si 3 N 4 are investigated from first-principles. The predicted electron mobility and averaged lattice thermal conductivity is 228.4 cm2/Vs and 325.06 W/m·K at 300 K, which demonstrates a good agreement with literature data. The electron mobility exhibits strong temperature-dependence at a low carrier concentration where the polar-optical phonon scattering dominates. For the heavy doping case, the ionized impurity scattering becomes dominant. A well-trained momentum tensor potential (MTP) with an accuracy comparable to density functional theory shows advantages in predicting thermal transport properties over a large-scale system containing thousands of atoms. The relaxation lifetimes for heat-carrying acoustic phonons are over tens of picoseconds which can explain the high thermal conductivity of β -Si 3 N 4 , but the nanoscale grain size crucially limits the thermal transport properties. • POP scattering primarily limits electron mobility at low doping concentrations. • Electron mobility is temperature-independent at heavily doping concentrations. • The MTP potential is trained with an accuracy comparable to DFT. • Long lifetimes for heat-carrying acoustic phonons are predicted. • The nanoscale grain size crucially limits thermal conductivity of β -Si 3 N 4. [ABSTRACT FROM AUTHOR]