The effects of washing a collagen sample prior to TEM examination

Transmission electron microscopy (TEM) is an important analysis technique to visualize (bio)macromolecules and their assemblies, including collagen fibers. Many protocols for TEM sample preparation of collagen involve one or more washing steps to remove excess salts from the dispersion that could hamper analysis when dried on a TEM grid. Such protocols are not standardized and washing times as well as washing solvents vary from procedure to procedure, with each research group typically having their own protocol. Here, we investigate the influence of washing with water, ethanol, but also methanol and 2-propanol, for both mineralized and unmineralized collagen samples via a protocol based on centrifugation. Washing with water maintains the hydrated collagen structure and the characteristic banding pattern can be clearly observed. Conversely, washing with ethanol results in dehydration of the fibrils, often leading to aggregation of the fibers and a less obvious banding pattern, already within 1 min of ethanol exposure. As we show, this process is fully reversible. Similar observations were made for methanol and propanol. Based on these results, a standardized washing protocol for collagenous samples is proposed.     

LinkingPark: An automatic semantic table interpretation system

In this paper, we present LinkingPark, an automatic semantic annotation system for tabular data to knowledge graph matching. LinkingPark is designed as a modular framework which can handle Cell-Entity Annotation (CEA), Column-Type Annotation (CTA), and Columns-Property Annotation (CPA) altogether. It is built upon our previous SemTab 2020 system, which won the 2nd prize among 28 different teams after four rounds of evaluations. Moreover, the system is unsupervised, stand-alone, and flexible for multilingual support. Its backend offers an efficient RESTful API for programmatic access, as well as an Excel Add-in for ease of use. Users can interact with LinkingPark in near real-time, further demonstrating its efficiency.     

Machine fault diagnosis with small sample based on variational information constrained generative adversarial network

In actual engineering scenarios, limited fault data leads to insufficient model training and over-fitting, which negatively affects the diagnostic performance of intelligent diagnostic models. To solve the problem, this paper proposes a variational information constrained generative adversarial network (VICGAN) for effective machine fault diagnosis. Firstly, by incorporating the encoder into the discriminator to map the deep features, an improved generative adversarial network with stronger data synthesis capability is established. Secondly, to promote the stable training of the model and guarantee better convergence, a variational information constraint technique is utilized, which constrains the input signals and deep features of the discriminator using the information bottleneck method. In addition, a representation matching module is added to impose restrictions on the generator, avoiding the mode collapse problem and boosting the sample diversity. Two rolling bearing datasets are utilized to verify the effectiveness and stability of the presented network, which demonstrates that the presented network has an admirable ability in processing fault diagnosis with few samples, and performs better than state-of-the-art approaches.     

中子平衡节块离散纵标法及CMFD加速技术研究

本文基于横向积分离散纵标方程,解析得到横向积分通量中出射通量与入射通量的关系,并根据类似于扩散方程节块展开法的输运节块中子平衡方程形式,得到了一种高效的节块离散纵标法数值迭代策略。数值结果表明,本文提出的方法可行且数值结果正确。此外,粗网有限差分(CMFD)加速技术在节块离散纵标法中也取得了非常好的应用效果。     

Seismic behavior of geosynthetic-reinforced retaining walls backfilled with cohesive soil

This study investigates the seismic performance of geosynthetic-reinforced modular block retaining walls backfilled with cohesive, fine grained clay-sand soil mixture. Shaking table tests were performed for three ½ scaled (wall height 190 cm) and ¼ scaled model walls to investigate the effects of backfill type, the influence of reinforcement length and reinforcement stiffness effects. The El Centro and Kobe earthquake records of varying amplitudes were used as base acceleration. Displacement of the front wall, accelerations at different locations, strains on the reinforcements, and the visual observations of the facing and the backfill surface were used to evaluate the seismic performance of model walls. The model walls were subjected to rigorous shaking and the walls did not exhibit any stability problems or signs of impending failure. The maximum deformations observed on the models with cohesive backfill was less than half of the deformation of the sand model. The load transfers between the geogrid and cohesive soil was comparable to that of sand and hence the needed reinforcement length was similar as well. As a result; the model walls with cohesive backfills performed within acceptable limits under seismic loading conditions when compared with granular backfilled counterparts.     

对空监视雷达探测威力检飞的几个问题

以新的观点论证了对空监视雷达探测威力检验飞行中所需的采样点数和飞行架次，讨论了预警机雷达威力检飞的特殊问题，包括地区选择、航线设计和目标机标定等。     

径向基函数神经网络的再学习算法及其应用

为了应用径向基函数神经网络逐步地识别待研究系统,文章针对径向基函数神经网络的再学习算法开展了深入的研究.应用严格的数学推理方法,将径向基函数神经网络的再学习问题转化为矩阵求逆的附加运算.详细给出了径向基函数神经网络再学习算法中增加新训练样本和增加新基函数的数学公式,同时对如何获取新的训练样本进行了研究.     

基于粗糙集信息观的决策表属性约简方法

粗糙集理论是近年来发展起来的一种有效的处理不精确、不确定、含糊信息的数学理论方法，它被广泛应用于相容和不相容决策表的属性约简和核属性计算。利用反例指出目前基于粗糙集信息观[2、6]的决策表属性约简和核属性计算方法的局限性。对决策表的性质作了深入的研究，研究发现文献[2、6]方法的不足原因是：它们没有考虑U／ind（C）中等价类的相客性。给出了基于U／ind（C）中等价类相客性的属性约简定义和核属性定义，并给出了一种新的基于粗糙集信息观的决策表属性约简和核属性计算方法。讨论了该方法同文献[2、6]方法的区别。最后用相同实例验证了该方法的有效性。     

串行及并行A/D转换器在高速数据采集中的采样差别性分析

串行和并行接口模式是A/D转换器诸多分类中的一种,但却是应用中器件选择的一个重要指标.在同样的转换分辨率及转换速度的前提下,不同的接口方式不但影响了电路结构,更重要的是将在高速数据采集的过程中对采样周期产生较大影响.本文通过12位串行ADC ADS7822和并行ADC ADS774与AT89C51的接口电路,给出二者采样时间的差异性.