A multiaxial elastic potential with error-minimizing approximation to rubberlike elasticity

This study is concerned with a new,explicit approach by means of which forms of the large strain elastic potential for multiaxial rubberlike elasticity may be obtained based on data for a single deformation mode.As a departure from usual studies,here for the first time errors may be estimated and rendered minimal for all possible deformation modes and,furthermore,failure behavior may be incorporated.Numerical examples presented are in accurate agreement with Treloar's well-known data.     

Photons can hide where they have been

Recently,the nested Mach–Zehnder interferometer[Phys.Rev.Lett.111,240402(2013)]was modified by adding Dove prisms in a paper[Quantum Stud.:Math.Found.2,255(2015)],and an interesting result is that,after the Dove prisms were inserted,a signal at the first mirror of the nested interferometer was obtained.But,according to the former original paper,the photons have never been present near that mirror.In this work,we interpret this result naturally by resorting to the three-path interference method.Moreover,we find that even though the photons have been somewhere,they can hide the trace of being there.     

12β-Acetyloxyperforatin,a New Limonoid from Harrisonia Perforata

A new A, D-seco limonoid, named 12-acetyloxyperforatin (1), along with three known ones, were isolated from the leaves of Harrisonia perforata. Their structures were elucidated on the basis of spectroscopic analysis, including extensive NMR techniques and computational modelling. These compounds showed no inhibitory activity against the 11β-HSD1 enzyme.     

Gram Matrices of Mixed-State Ensembles

International Journal of Theoretical Physics - Gram matrices arise naturally in the consideration of pair-wise overlap of a family of vectors or quantum pure states, and play an important role in...     

紫色球杆菌视紫红质光谱特性的机器学习研究

近年来，机器学习等人工智能技术被应用于蛋白质工程，其在蛋白质结构、功能预测、催化活性等研究中具有独特优势。在未知蛋白质结构的情况下，将蛋白质序列和功能特性与机器学习相结合，基于序列-活性关系（innovative sequence-activity relationship，ISAR）算法，将蛋白质氨基酸序列数字化，用快速傅里叶变换（fast four transform，FFT）进行预处理，再进行偏最小二乘回归建模，可在数据集较少情况下拟合得到最佳模型。通过机器学习对紫色球杆菌视紫红质（gloeobacter violaceus rhodopsin，GR）的突变体蛋白质氨基酸序列与光谱最大吸收波长进行建模，获得了最佳模型。用最佳索引LEVM760106建模得到的确定系数R² 为0.944，均方误差E为11.64。用小波变换进行的预处理，其R² 虽也约为0.944，但E大于11.64，不及FFT进行的预处理。方法较好地解决了蛋白质序列与功能特性之间的数学建模问题，在蛋白质工程中可为预测更优的突变体提供支持。     

Optical Trapping with Focused Surface Waves

Near-field optical trapping can be realized with focused evanescent waves that are excited at the water–glass interface due to the total internal reflection, or with focused plasmonic waves excited on the water–gold interface. Herein, the performance of these two kinds of near-field optical trapping techniques is compared using the same optical microscope configuration. Experimental results show that only a single-micron polystyrene bead can be trapped by the focused evanescent waves, whereas many beads are simultaneously attracted to the center of the excited region by focused plasmonic waves. This difference in trapping behavior is analyzed from the electric field intensity distributions of these two kinds of focused surface waves and the difference in trapping behavior is attributed to photothermal effects due to the light absorption by the gold film.