氧化铝/氮化硅双层膜晶硅钝化的开尔文探针力显微镜研究

对晶体硅(c-Si)太阳能电池而言, 氧化铝(AlOx)是一种广泛使用的钝化材料, 因为它具有优异的沉积保形性和良好的钝化质量. 为了确保AlOx发挥其良好的钝化效果, 在沉积后退火并氢化处理是必不可少的. 通过在AlOx薄膜上沉积氢化氮化硅(SiNx:H)来实现氢化, 利用开尔文探针力显微镜研究了在不同热处理和氢化作用下, AlOx/SiNx:H双层薄膜功函数的变化, 并基于沉积薄膜所含氢与固定电荷展开了讨论. 发现钝化质量和功函数之间有相关性, 影响因素包括薄膜厚度、氢化与热处理顺序.     

模糊选择函数半序合理的充分条件

在G?del t-模下,研究了模糊选择函数的半序合理性.首先给出了模糊选择函数的合理性条件FA1.然后研究了该条件与模糊选择函数半序合理性之间的关系,得到了半序合理的一个充分条件.     

全局优化问题的一个新的无参数填充函数

求全局最优化问题的填充函数算法被提出以来，参数的选取和调整一直是制约算法有效性的因素。如何在实际的计算过程中选取合适的参数，直接影响和决定了运算速度和效率。因此，构造不含参数的填充函数就显得极为重要。提出一个新的无参数的填充函数，对其理论性质进行了分析，并给出相应的填充函数算法，数值计算验证了算法的有效性。     

RNAGCN: RNA tertiary structure assessment with a graph convolutional network

RNAs play crucial and versatile roles in cellular biochemical reactions. Since experimental approaches of determining their three-dimensional (3D) structures are costly and less efficient, it is greatly advantageous to develop computational methods to predict RNA 3D structures. For these methods, designing a model or scoring function for structure quality assessment is an essential step but this step poses challenges. In this study, we designed and trained a deep learning model to tackle this problem. The model was based on a graph convolutional network (GCN) and named RNAGCN. The model provided a natural way of representing RNA structures, avoided complex algorithms to preserve atomic rotational equivalence, and was capable of extracting features automatically out of structural patterns. Testing results on two datasets convincingly demonstrated that RNAGCN performs similarly to or better than four leading scoring functions. Our approach provides an alternative way of RNA tertiary structure assessment and may facilitate RNA structure predictions. RNAGCN can be downloaded from https://gitee.com/dcw-RNAGCN/rnagcn.