自适应融合层级特征的混合退化图像复原算法

多种退化类型混合的图像比单一类型的退化图像降质更严重,很难建立精确模型对其复原,研究端到端的神经网络算法是复原的关键.现有的基于操作选择注意力网络的算法(operation-wiseattentionnetwork,OWAN)虽然有一定的性能提升,但是其网络过于复杂,运行较慢,复原图像缺乏高频细节,整体效果也有提升的空间.针对这些问题,提出一种基于层级特征融合的自适应复原算法.该算法直接融合不同感受野分支的特征,增强复原图像的结构;用注意力机制对不同层级的特征进行动态融合,增加模型的自适应性,降低了模型冗余;另外,结合L1损失和感知损失,增强了复原图像的视觉感知效果.在DIV2K,BSD500等数据集上的实验结果表明,该算法无论是在峰值信噪比和结构相似性上的定量分析,还是在主观视觉质量方面,均优于OWAN算法,充分证明了该算法的有效性.     

负载羧基化球状介孔纳米颗粒TFN膜的研究

在薄层复合膜(thin-film composite membrane, TFC膜)中引入无机纳米颗粒，形成薄层纳米复合膜(thin-film nanocomposite membrane, TFN膜)，近几年作为反渗透膜开始应用于水处理研究。但是无机纳米颗粒在TFC膜中的性能的不稳定性和膜的机械强度等变成了突出问题。合成制备了粒径约为110 nm修饰羧基的介孔氧化硅球状纳米颗粒(MSN—COOH)，并将其成功地化学键合在TFC膜的表面功能层交联网络中。与TFC膜相比，键合有MSN—COOH的TFN膜，水通量提高了56.2%，保持高脱盐率；由于单分散介孔纳米颗粒表面亲水官能团的引入，使膜表面的亲水性有很大程度提高，单分散介孔纳米颗粒在基体中的有序排列，使膜表面粗糙度降低，提高了膜的抗污染能力。与普通TFN膜相比较，具有更好的稳定性和柔韧性，可以在长时间高压过滤操作下保持稳定。     

Supporting primary students' learning of fraction conceptual knowledge through digital games

With the advent of mobile technologies, well-designed fraction apps can be used to help children gain fraction knowledge, a challenging topic for both teachers and students. The present pilot study adopted a quasi-experimental design to investigate whether children can learn fraction concepts equally well if half of the lesson time (20 min) is replaced with game-based learning. Keeping the total lesson time (40 min) identical, the control group (N = 33) received traditional instruction, and the experimental group (N = 32) was presented with a blended learning approach spending half of the class time (20 min) playing tablet-based fraction games, where each of the learners had their own tablet. The results suggested that in the posttest, the experimental group achieved similar learning gains to the control group and appear to have achieved better performance in the transfer test than the control group. This paper also discusses the efficiency of game-based learning, the mechanism of how fraction games might enhance learning, and the potential of integrating game-based learning in educational settings.     

不同硬度PTMG型CPUE与硫化橡胶的性能对比研究

通过改变硬段含量、扩链系数等制备了几种不同硬度的四氢呋喃均聚醚(PTMG)浇注型聚氨酯弹性体(CPUE),讨论了PTMG型CPUE与硫化橡胶(VR)的物理机械性能、拉伸应力应变行为以及动态力学性能的区别。结果表明,随CPUE硬度的提高,材料的刚性增强,力学性能是低硬度VR的数倍。通过对CPUE硬度的调节,可以使之兼具聚氨酯和橡胶的优良性能。在玻璃态温度下CPUE的储能模量随硬度的减小而降低,但都高于VR,而且CPUE的硬度越大,二者相差越大,温度越低,相差也越大;在室温以上温度范围内,CPUE具有比VR更小的力学损耗。     

基于力测量法研究滑石的晶面各向异性

层状硅酸盐矿物滑石在解离时会形成具有明显性质差异的2种表面，滑石的晶面各向异性会导致矿物不同表面表现出不同的润湿性以及不同的电荷密度。通过原子力显微镜力（AFM）曲线测量以及经典DLVO理论计算，对滑石的非极性面和极性面进行深入研究，首先利用AFM力曲线测量和计算，得到不同pH值时氮化硅的电位值，利用AFM直接力测量法对符合粗糙度要求的滑石非极性面和极性面样品在10 mmol/L的KCl溶液中进行测量，得到不同pH值环境下的力曲线，通过DLVO理论拟合计算得到滑石非极性面和极性面的表面电位值。滑石的非极性面电位值在研究的pH范围内一直为负，且恒定，极性面电位值随pH升高而降低，零电点出现在pH=7.6左右。将计算得到的滑石表面电位与氮化硅之间的作用力拟合曲线与实际测得的作用力进行对比发现，计算值与测量值基本一致，拟合度较高。     

Heat propagation in thermally conductive polymers of PA6 and hexagonal boron nitride

Thermally conductive polymers offer new possibilities for the heat dissipation in electric and electronic components, for example, by a three‐dimensional shaping of the heat sinks. To face safety regulations, improved fire performance of those components is required. In contrast to unfilled polymers, those materials exhibit an entirely different thermal behavior. To investigate the flammability, a phosphorus flame retardant was incorporated into thermally conductive composites of polyamide 6 and hexagonal boron nitride. The flame retardant decreased the thermal conductivity only slightly. However, the burning behavior changed significantly, due to a different heat propagation, which was investigated using a thermographic camera. An optimum content of hexagonal boron nitride for a sufficient thermal conductivity and fire performance was found between 20 and 30 vol%. The improvement of the fire performance was due to a faster heat release out of the pyrolysis zone and an earlier decomposition of the flame retardant. For higher contents of hexagonal boron nitride, the heat was spread faster within the part, promoting an earlier ignition and increasing the decomposition rate of the flame retardant.