Turing patterns induced by self-diffusion in a predator–prey model with schooling behavior in predator and prey

Nonlinear Dynamics - This article considers a reaction–diffusion predator–prey model with schooling behavior both in predator and prey species and subject to the homogeneous Neumann...     

Novel Tridentate Bisoxime Chemosensor for Selective Recognition of Cu2+ and Zn2+ with Different Mechanisms

Journal of Applied Spectroscopy - A novel tridentate bisoxime chemosensor HL has been developed as a two-in-one platform for colorimetric recognition of Cu2+ (from blue to green), and fluorescent...     

Novel biogeography-based optimization algorithm with hybrid migration and global-best Gaussian mutation

The Biogeography-Based Optimization algorithm and its variants have been used widely for optimization problems. To get better performance, a novel Biogeography-Based Optimization algorithm with Hybrid migration and global-best Gaussian mutation is proposed in this paper. Firstly, a linearly dynamic random heuristic crossover strategy and an exponentially dynamic random differential mutation one are presented to form a hybrid migration operator, and the former is used to get stronger local search ability and the latter strengthen the global search ability. Secondly, a new global-best Gaussian mutation operator is put forward to balance exploration and exploitation better. Finally, a random opposition learning strategy is merged to avoid getting stuck in local optima. The experiments on the classical benchmark functions and the complexity functions from CEC-2013 and CEC-2017 test sets, and the Wilcoxon, Bonferroni-Holm and Friedman statistical tests are used to evaluate our algorithm. The results show that our algorithm obtains better performance and faster running speed compared with quite a few state-of-the-art competitive algorithms. In addition, experimental results on Minimum Spanning Tree and K-means clustering optimization show that our algorithm can cope with these two problems better than the comparison algorithms.     

Point and interval forecasting for carbon price based on an improved analysis-forecast system

Effective analysis and forecasting of carbon prices, which is an essential endeavor for the carbon trading market, is still considered a difficult task because of the nonlinearity and nonstationarity inherent in carbon prices. Previous studies have failed at the analysis and interval prediction of carbon prices and are limited to point forecasts. Therefore, an improved carbon price analysis and forecasting system that consists of an analysis module and a forecasting module is established in this study; more importantly, the forecasting module includes point forecasting and interval forecasting as well. Aimed at investigating the characteristics of the carbon price series, a chaotic analysis based on the maximum Lyapunov exponent is performed, the determination of appropriate distribution functions based on our newly proposed hybrid optimization algorithm is conducted, and different distribution functions are effectively designed in the analysis module. Furthermore, in the point forecasting model, the phase space reconstruction technique is applied to reconstruct the sequences decomposed by variational mode decomposition due to the chaotic characteristics of the carbon price series, and the reconstructed sequences are considered as the optimal input–output variables of the forecasting model. Then, an adaptive neuro-fuzzy inference system model is trained by the newly proposed hybrid optimization algorithm, which is developed for the first time in the domain of carbon price point forecasting. Moreover, based on the results of point forecasting and the distribution function of the carbon price series determined by the analysis module, the interval forecasting results can be obtained and implemented to provide more reliable information for decision making. Empirical results based on the carbon price data of the European Union Emissions Trading System and Shenzhen of China demonstrate that the proposed system achieves better results than other benchmark models in point forecasting as well as interval forecasting.     

Optimal Sensor Placement Method Considering the Importance of Structural Performance Degradation for the Allowable Loadings for Damage Identification

Considering the importance of damage for the structural performance and for decreasing the identification error, this paper proposes an optimal sensor placement method based on a weighted standard deviation norm (WSDN) index. The standard deviation of the identified damage parameters is solved using the series expansion theory and probabilistic method to quantify the effect of a measurement error on damage identification. The damage estimation weight (DEW) index, which can reflect the importance of each element in the structural capabilities, is established based on a performance-damage curve. A significant DEW for a specified element indicates that the element is important for the structure and that its identification error should be small. The WSDN index is obtained from the Hadamard product of the standard deviations (SDs) and DEWs. Thus, the identification error of the entire structure is measured using the weighting coefficient. The optimal sensor placement (OSP) procedure is performed by minimizing the WSDN index. The proposed method can clearly decrease the uncertainties of the identification results for the important elements. Other OSP criteria, including the condition number, information entropy, and standard deviation norm, which aim to decrease the identification error, are discussed in this paper for comparison with the proposed method. Two numerical examples and an experiment, which pertain to the deformation performance, buckling features, and dynamic characteristics, are discussed to verify the advantages of the proposed method.     

碱金属盐催化1,2,4-三唑与α,β-不饱和酮及二酰亚胺的氮杂Michael反应

发展了一种高效的碱金属盐催化1,2,4-三唑与α,β-不饱和酮及α,β-不饱和二酰亚胺的氮杂Michael加成反应的新方法,以中等到优异的产率得到目标产物.该方法原料易得,底物普适性好,反应条件温和,易实现克级规模的制备.产物容易转化为相应的γ-氨基醇.     

Facile galvanic replacement deposition of nickel on copper substrate in deep eutectic solvent and its activation ability for electroless Ni–P plating

Journal of Solid State Electrochemistry - Electroless nickel-phosphorus plating is an important surface treatment method for copper due to its good corrosion resistance and nonmagnetic properties....     

简支及悬臂箱梁在角隅轴向荷载作用下的翼板纵向应力

基于能量变分原理，拟定轴向荷载作用下箱梁的纵向位移函数，得到关于翼板剪切变形引起的位移差函数的基本微分方程，继而推导出箱梁翼板纵向应力表达式，并首次得出角隅轴向荷载作用下翼板出现应力不均匀分布的荷载及边界条件。通过对一模型箱梁进行计算，并与通用有限元软件ANSYS壳单元计算结果进行比较，验证了该方法和所推导公式的正确性。研究结果表明，当作用于简支箱梁截面角隅处的轴向荷载（合力无偏心）为集中或分布荷载时，翼板不产生纵向应力不均匀现象；当作用于悬臂箱梁截面角隅处的轴向荷载（合力无偏心）为集中荷载时，翼板不产生纵向应力不均匀现象，而当荷载轴向分布时，翼板将产生纵向应力不均匀现象。实际工程中，横力弯曲使悬臂箱梁产生剪力滞效应，这种效应会与轴向分布荷载产生的效应叠加，设计时对此应予以充分考虑。