The mixed Langmuir monolayers and Langmuir–Blodgett (LB) films of homo‐polystyrene (h‐PS) and the diblock copolymer polystyrene‐block‐poly(2‐vinylpyridine) (PS‐b‐P2VP) have been characterized by the Langmuir monolayer technique and tapping mode atomic force microscopy (AFM), respectively. When the content of h‐PS is below 80 wt.‐%, the mixed LB films of h‐PS/PS‐b‐P2VP mainly exhibit isolated circular nanoaggregates. With a further increase of the h‐PS content (80–95%), however, highly uniform and stable necklace‐network structures are observed in the mixed LB films.
针对基于主成分分析(PCA)的三维(3D)人脸形状重构不精确的缺点,对位移向量、缩放因子、旋转矩阵、重构系数等参数进行了精确的定义和计算,并对重构模型进行了有效的调整。根据CVL Face Database建立了113个3D人脸形状模型,经迭代运算后重构出了由特征点组成的稀疏3D人脸形状模型。利用径向基函数(RBF)得到了密集3D人脸形状模型。试验结果表明,算法可以得到精确的重构模型,对深度旋转的人脸图像和特征点定位误差也有很好的鲁棒性。 相似文献
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. 相似文献
Although both ultraviolet (UV) radiation and ultrasound (US) treatment have their capabilities in microbial inactivation, applying any one method alone may require a high dose for complete inactivation, which may affect the sensory and nutritional properties of pineapple juice. Hence, this study was intended to analyse and optimise the effect of combined US and UV treatments on microbial inactivation without affecting the selected quality parameters of pineapple juice. US treatment (33 kHz) was done at three different time intervals, viz. 10 min, 20 min and 30 min., after which, juice samples were subjected to UV treatment for 10 min at three UV dosage levels, viz. 1 J/cm2, 1.3 J/cm2, and 1.6 J/cm2. The samples were evaluated for total colour difference, pH, total soluble solids (TSS), titrable acidity (TA), and ascorbic acid content; total bacterial count and total yeast count; and the standardization of process parameters was done using Response Surface Methodology and Artificial Neural Network. The results showed that the individual, as well as combined treatments, did not significantly impact the physicochemical properties while retaining the quality characteristics. It was observed that combined treatment resulted in 5 log cycle reduction in bacterial and yeast populations while the individual treatment failed. From the optimization studies, it was found that combined US and UV treatments with 22.95 min and1.577 J/cm2 ensured a microbiologically safe product while retaining organoleptic quality close to that of fresh juice. 相似文献
