Limit on right handed weak coupling parameters from inelastic neutrino interactions

Right handed weak quark currents coupled to the usual left handed weak lepton current would be seen in inclusive antineutrino scattering on nuclei as a contribution at largey with the quark (not antiquark) structure function. We do not see such a term, and can therefore put an upper limit on the relative strengths of such right handed currents: \(\varrho ^2 = \frac{{\sigma _R }}{{\sigma _L }}< 0.009\) , 90% confidence. This measurement puts limits on the mixing angle of left-right symmetric models. In distinction to similar limits derived from muon decay or β decay, our limits are also valid if the right handed neutrino is heavy.     

聚合物在碳纤维表面的电沉积

用电沉积方法将苯乙烯-马来酸酐、乙酸乙烯酯-马来酸酐、甲基丙烯酸甲酯-马来酸酐共聚物沉积于碳纤维表面。通过实验初步证实了此过程的负离子-自由基机理,以及沉积层在纤维表面的物理粘附。由于电沉积的聚合物中间层改善了碳纤维增强塑料中纤维-基体树脂间的界面粘接作用,使原来的剪切破坏基本上发生于界面的情况转变成发生于基体本身为主。并将单向碳纤维增强环氧树脂复合材料的层间剪切强度,由原来的600kg/cm~2左右提高到1000kg/cm~2以上,经沸水浸泡100小时后的强度损失也减少了。     

Ca(1)(-)(x)Na(2)(x)Al(2)B(2)O(7): a structure with tunable density of Na(+) vacancies

A series of samples with the composition Ca(1)(-)(x)Na(2)(x)Al(2)B(2)O(7) (0 < x < or = 1) was investigated and a hexagonal structure with unusually large range of homogeneity (at least from x = 0.01 to 0.95) was revealed. The hexagonal phase consists of [Al(2)B(2)O(7)](infinity)(2)(-) lamellae stacked along the c axis, as in CaAl(2)B(2)O(7) and Na(2)Al(2)B(2)O(7). Nevertheless, the configuration and stacking sequence of the [Al(2)B(2)O(7)](infinity)(2)(-) lamellae are different in these three structures. In the hexagonal structure of Ca(1)(-)(x)()Na(2)(x)()Al(2)B(2)O(7), Ca and half Na cations (Na1) statistically occupy the same crystallographic site which is located between the [Al(2)B(2)O(7)](infinity)(2)(-) lamellae, the other half Na cations (Na2) distribute in the planes bisecting the [Al(2)B(2)O(7)](infinity)(2)(-) lamellae. Depending on the composition, the site occupation factor of Na2 site can vary in the same range as x, leading to a tunable density of Na(+) vacancies in the structure. The AlO(4) tetrahedra and BO(3) triangles in the structure tilt in appropriate ways to improve the bond valence sum of Na2 cations which are not sufficiently bonded to the anions.     

一般增长曲线模型参数阵的BLU估计

考虑一般增长曲线模型：Ｙ＝Ｘ１ＢＸ２＋εＥ（Ｖｅｃ（ε））＝０Ｖ（Ｖｅｃ（ε））＝σ２ＶＩｎ（Ｖ０）本文对任一可估函数ＫＢＬ给出了它的ＢＬＵ估计（最佳线性无偏估计），并得到了方差σ２的一个无偏估计．     

中子成像技术在元素分析中的应用

中子成像作为一种快速、直观的无损检测技术,在核工业、航空航天、新能源、地质、考古、先进制造等多个领域得到广泛应用。中子成像利用中子不带电、穿透能力强、对轻元素敏感、可区分同位素和近邻元素等特性,非常适合开展含氢元素、近邻元素和同位素等材料的无损检测。本文概述了中子成像技术的基本原理及特点,并结合中国先进研究堆(CARR)中子成像装置上的应用案例, 重点介绍了国内外中子成像技术在储氢材料、燃料电池、岩石、核燃料元件、古代文物等领域的典型应用。随着中子成像技术的不断发展和广泛应用,有望为我国更多领域研究提供更强有力的技术支撑。     

锗元素测定方法综述

锗在国防工业、航空航天和通信等领域中的战略性,锗含量的测定对于保证材料质量和满足国际标准至关重要。本文综述了锗含量测定方法的多种技术,包括分光光度法、原子荧光光谱法、原子吸收光谱法、电感耦合等离子体原子发射光谱法、电感耦合等离子体质谱法以及滴定法。在每个检测方法的介绍中,详细探讨了方法的原理、前处理步骤以及应用范围,并分别总结了各个方法的优势和不足。最后,强调了锗含量测定方法的意义,特别是在满足出口监管和促进科学研究方面的作用。同时对锗元素的测定方法进行了展望,为未来的发展提供了参考方向。     

An Algorithm Framework for Drug-Induced Liver Injury Prediction Based on Genetic Algorithm and Ensemble Learning

In the process of drug discovery, drug-induced liver injury (DILI) is still an active research field and is one of the most common and important issues in toxicity evaluation research. It directly leads to the high wear attrition of the drug. At present, there are a variety of computer algorithms based on molecular representations to predict DILI. It is found that a single molecular representation method is insufficient to complete the task of toxicity prediction, and multiple molecular fingerprint fusion methods have been used as model input. In order to solve the problem of high dimensional and unbalanced DILI prediction data, this paper integrates existing datasets and designs a new algorithm framework, Rotation-Ensemble-GA (R-E-GA). The main idea is to find a feature subset with better predictive performance after rotating the fusion vector of high-dimensional molecular representation in the feature space. Then, an Adaboost-type ensemble learning method is integrated into R-E-GA to improve the prediction accuracy. The experimental results show that the performance of R-E-GA is better than other state-of-art algorithms including ensemble learning-based and graph neural network-based methods. Through five-fold cross-validation, the R-E-GA obtains an ACC of 0.77, an F1 score of 0.769, and an AUC of 0.842.