Construction of sliced (nearly) orthogonal Latin hypercube designs

Sliced Latin hypercube designs are very useful for running a computer model in batches, ensembles of multiple computer models, computer experiments with qualitative and quantitative factors, cross-validation and data pooling. However, the presence of highly correlated columns makes the data analysis intractable. In this paper, a construction method for sliced (nearly) orthogonal Latin hypercube designs is developed. The resulting designs have flexible sizes and most are new. With the orthogonality or near orthogonality being guaranteed, the space-filling property of the resulting designs is also improved. Examples are provided for illustrating the proposed method.     

Localization of diffusion sources in complex networks with sparse observations

Locating sources in a large network is of paramount importance to reduce the spreading of disruptive behavior. Based on the backward diffusion-based method and integer programming, we propose an efficient approach to locate sources in complex networks with limited observers. The results on model networks and empirical networks demonstrate that, for a certain fraction of observers, the accuracy of our method for source localization will improve as the increase of network size. Besides, compared with the previous method (the maximum–minimum method), the performance of our method is much better with a small fraction of observers, especially in heterogeneous networks. Furthermore, our method is more robust against noise environments and strategies of choosing observers.     

Physical,hydrochemical and isotopic characteristics of springs in Beijing,China, compared to historical properties

A physical, hydrochemical and isotopic evaluation of springs in Beijing was conducted in 2009 to reveal apparent changes in the properties of those springs. The results showed that most of the 2nd class springs and more than 50 % of the 1st class springs recorded in the early 1980s were depleted, while the discharges of existing springs have also decreased sharply. In addition, the majority of springs were of the HCO₃–Ca–Mg type and good water quality, with the quality indices changing slightly compared to those recorded 30 years ago. The abundances of ²H, ¹⁸O, and ³H in the springs indicated that most of the springs were of meteoric origin with a relatively close connection to modern atmospheric precipitation. As a result, the springs have a relatively strong renewability within a shallow circulation.     

Crucial factors affecting the physicochemical properties of sol–gel produced Fe3O4@SiO2–NH2 core–shell nanomaterials

Multifunctional nanomaterials with task-specific physicochemical properties, especially core?Cshell nanostructures with Fe₃O₄ core and NH₂-functional shells (Fe₃O₄@SiO₂?CNH₂), have been extensively investigated as high-performance adsorbents, catalysts and catalyst supports; and in most cases the controllable sol?Cgel technique is the choice for fabrication of this kind of widely applied materials. Herein, we demonstrated that mono-dispersed and spherical Fe₃O₄@SiO₂?CNH₂ nanomaterials with magnetic response core, NH₂-functional shell structure can be facilely prepared by co-condensation of TEOS with APTMS using a versatile sol?Cgel process. It was shown that the proper usage of APTMS and appropriate pre-hydrolysis time of TEOS were crucial and key steps for formation of highly uniform and desirable amino loading Fe₃O₄@SiO₂?CNH₂ materials. The TEOS pre-hydrolysis and the critical time (around 90?min) before the addition of APTMS prove to be vital for uniform structure evolution, while the appropriate concentration of APTMS (~2.28?mmol?L^?1 in our system) leads to well-dispersed materials with relatively high loading of amino functionality. The as-prepared Fe₃O₄@SiO₂?CNH₂ magnetic nanoparticles prepared under optimum conditions possessing superparamagnetic behavior, uniform core?Cshell structure (~200?nm in diameter), relatively large BET surface area (~138?m²/g) and high incorporation of amino-functionality (~2.90?wt?%).