Low melting point nanocrystalline Sn–Ag solder synthesized by a refined chemical reduction method

The commercial market of Sn-Pb solder is gradually decreasing due to its toxicity, calling for Pb-free substitute materials. Sn-Ag alloy is a potential candidate in terms of good mechanical property. The major problematic issue of using Sn-Ag is their high melting temperature, consequently this study is dedicated to lowering the melt- ing temperature of Sn3.5Ag （wt%） alloy by developing nanomaterials using a chemical reduction approach. The resultant nanocrystalline Sn3.5Ag is characterized by field emission scanning electron microscope. The size dependence of the melting temperature is discussed based on differential scanning calorimetry results. We have reduced the melting temperature to 209.8 ℃ in the nanocrystalline Sn3.5Ag of （32.4± 8.0） nm, compared to ～221 ℃ of the bulk alloy. The results are consistent with the prediction made by a relevant theoretical model, and it is possible to further lower the melting temperature using the chemical reduction approach developed by this study.     

A Comparison of Box—Jenkins and objective methods for determining the order of a non-seasonal ARMA Model

This paper evaluates different procedures for selecting the order of a non-seasonal ARMA model. Specifically, it compares the forecasting accuracy of models developed by the personalized Box-Jenkins (BJ) methodology with models chosen by numerous automatic procedures. The study uses real series modelled by experts (textbook authors) in the BJ approach. Our results show that many objective selection criteria provide structures equal or superior to the time-consuming BJ method. For the sets of data used in this study, we also examine the influence of parsimony in time-series forecasting. Defining what models are too large or too small is sensitive to the forecast horizon. Automatic techniques that select the best models for forecasting are similar in size to BJ models although they often disagree on model order.     

无反射波动方程模拟堤防复杂构造的波传问题

为了更好地了解浅层复杂介质中的波场特征 ,以及更好解释野外堤防的探测资料 ,该文利用无反射波动方程和交错网格的有限差分法研究了堤防复杂构造中波传问题 ;并结合实际堤防中物性参数 ,模拟出自激自收地震剖面 ,有效地避免了层间的多次反射。讨论了无反射波动方程的特性 ,推导出多次反射满足的波动方程 ,从特征线分析的角度探讨了无反射波动方程采用交错网格的波传特性和数值计算的稳定性 ,以及利用套网格技术解决源点奇异性的有效性。     

Biodiversity and biogeography of phages in modern stromatolites and thrombolites

Viruses, and more particularly phages (viruses that infect bacteria), represent one of the most abundant living entities in aquatic and terrestrial environments. The biogeography of phages has only recently been investigated and so far reveals a cosmopolitan distribution of phage genetic material (or genotypes). Here we address this cosmopolitan distribution through the analysis of phage communities in modern microbialites, the living representatives of one of the most ancient life forms on Earth. On the basis of a comparative metagenomic analysis of viral communities associated with marine (Highborne Cay, Bahamas) and freshwater (Pozas Azules II and Rio Mesquites, Mexico) microbialites, we show that some phage genotypes are geographically restricted. The high percentage of unknown sequences recovered from the three metagenomes (>97%), the low percentage similarities with sequences from other environmental viral (n = 42) and microbial (n = 36) metagenomes, and the absence of viral genotypes shared among microbialites indicate that viruses are genetically unique in these environments. Identifiable sequences in the Highborne Cay metagenome were dominated by single-stranded DNA microphages that were not detected in any other samples examined, including sea water, fresh water, sediment, terrestrial, extreme, metazoan-associated and marine microbial mats. Finally, a marine signature was present in the phage community of the Pozas Azules II microbialites, even though this environment has not been in contact with the ocean for tens of millions of years. Taken together, these results prove that viruses in modern microbialites display biogeographical variability and suggest that they may be derived from an ancient community.