Preparation and characterization of Fe‐Tetranitro phthalocyanine/polyurethane blends

In this article, Fe‐Tetranitro phthalocyanine (Fe‐TNPc)/polyurethane (PU) blends were prepared by solution blending. The mechanical properties of the samples were studied by tensile tests. The results showed that the tensile strength and the elongation at break of the samples increased with increasing Fe‐TNPc content. The improved mechanical properties for the samples containing Fe‐TNPc was attributed to the increased microphase separation degree of PU, which was further investigated by dynamic mechanical analysis (DMA) and Fourier transform infrared analysis. The lower T_g of the soft segments and the higher T_g of the hard segments for the samples containing Fe‐TNPc indicated an increase of microphase separation degree of PU. The increased hydrogen bonded carbonyl groups in the samples with increasing Fe‐TNPc content also proved the conclusion. Quantitative evaluation of the interaction between Fe‐TNPc and PU was also investigated by analyzing the physical crosslinking density of the samples. The results indicated that the physical crosslinking density of the samples increased with increasing Fe‐TNPc content. The antibacterial properties of the samples were investigated. The results showed that the percentage bacterial inactivation toward S. aureus and E. coli of the samples were 98.9% and 90.9%, respectively, when Fe‐TNPc was added to 1%. © 2014 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2015 , 132, 41284.     

The organization of biological sequences into constrained and unconstrained parts determines fundamental properties of genotype–phenotype maps

Biological information is stored in DNA, RNA and protein sequences, which can be understood as genotypes that are translated into phenotypes. The properties of genotype–phenotype (GP) maps have been studied in great detail for RNA secondary structure. These include a highly biased distribution of genotypes per phenotype, negative correlation of genotypic robustness and evolvability, positive correlation of phenotypic robustness and evolvability, shape-space covering, and a roughly logarithmic scaling of phenotypic robustness with phenotypic frequency. More recently similar properties have been discovered in other GP maps, suggesting that they may be fundamental to biological GP maps, in general, rather than specific to the RNA secondary structure map. Here we propose that the above properties arise from the fundamental organization of biological information into ‘constrained'' and ‘unconstrained'' sequences, in the broadest possible sense. As ‘constrained'' we describe sequences that affect the phenotype more immediately, and are therefore more sensitive to mutations, such as, e.g. protein-coding DNA or the stems in RNA secondary structure. ‘Unconstrained'' sequences, on the other hand, can mutate more freely without affecting the phenotype, such as, e.g. intronic or intergenic DNA or the loops in RNA secondary structure. To test our hypothesis we consider a highly simplified GP map that has genotypes with ‘coding'' and ‘non-coding'' parts. We term this the Fibonacci GP map, as it is equivalent to the Fibonacci code in information theory. Despite its simplicity the Fibonacci GP map exhibits all the above properties of much more complex and biologically realistic GP maps. These properties are therefore likely to be fundamental to many biological GP maps.     

Sequence-Dependent Nanofiber Structures of Phenylalanine and Isoleucine Tripeptides

The molecular design of short peptides to achieve a tailor-made functional architecture has attracted attention during the past decade but remains challenging as a result of insufficient understanding of the relationship between peptide sequence and assembled supramolecular structures. We report a hybrid-resolution model to computationally explore the sequence–structure relationship of self-assembly for tripeptides containing only phenylalanine and isoleucine. We found that all these tripeptides have a tendency to assemble into nanofibers composed of laterally associated filaments. Molecular arrangements within the assemblies are diverse and vary depending on the sequences. This structural diversity originates from (1) distinct conformations of peptide building blocks that lead to different surface geometries of the filaments and (2) unique sidechain arrangements at the filament interfaces for each sequence. Many conformations are available for tripeptides in solution, but only an extended β-strand and another resembling a right-handed turn are observed in assemblies. It was found that the sequence dependence of these conformations and the packing of resulting filaments are determined by multiple competing noncovalent forces, with hydrophobic interactions involving Phe being particularly important. The sequence pattern for each type of assembly conformation and packing has been identified. These results highlight the importance of the interplay between conformation, molecular packing, and sequences for determining detailed nanostructures of peptides and provide a detailed insight to support a more precise design of peptide-based nanomaterials.     

Tuning effect of silicon substitution on magnetic and high frequency electromagnetic properties of R2Fe17 and their composites

In this work, we report the tuning effect of the Si substitution on the magnetic and high frequency electromagnetic properties of R₂Fe₁₇ compounds and their paraffin composites. It is found that the introduction of Si can remarkably improve the magnetic and electromagnetic properties of the R₂Fe₁₇ compounds, making the R₂Fe_17–xSi_x-paraffin composites excellent microwave absorption materials (MAMs). By introducing the Si element, their saturation magnetizations decrease slightly, while much higher Curie temperatures are obtained. Furthermore, better impedance match is reached due to the decrease of the high-frequency permittivity ε′ by about 40%–50%, which finally enhances the performance of the microwave absorption. The peak frequency (f_RL) of the reflection loss (RL) curve moves toward high frequency domain and the qualified bandwidth (QB, RL ≤ ?10 dB) increases remarkably. The maximum QB of 3.3 GHz (12.0–15.3 GHz) is obtained for the Sm_1.5Y_0.5Fe₁₅Si₂-paraffin composite (d = 1.0 mm) and the maximum RL of ?53.6 dB is achieved for Nd₂Fe₁₅Si₂-paraffin composite (d = 2.2 mm), both surpassing most of the reported MAMs. Additionally, a distinguished dielectric microwave absorption peak is observed, which further increases the QB in these composites.     

掘进巷道快速过断层施工技术应用

基于四候煤矿3108回风顺槽过F3断层期间,顶板出现严重破碎、下沉现象,提出了俯斜台阶法施工工艺,并对断层破碎带顶板采取MF-2型化学材料注浆加固以及"钢筋锚索网+U29梯形棚"联合支护措施。支护效果检验结果表明,联合支护有效控制了断层破碎区顶板下沉、破碎现象,保证了巷道顶板稳定性。     

Prediction of process parameters of water-assisted injection molding based on inverse radial basis function neural network

Because of the introduction of new processing parameters in water-assisted injection molding (WAIM), processes control has become more difficult. First, design of experiment (DOE) was carried out by using optimized Latin hypercubes (Opt LHS). On the basis of this, computational fluid dynamics (CFD) method was used to simulate and calculate hollowed core ratios and wall thickness differences of cooling water pipe at different positions. Then inverse radial basis function (RBF) neural network model reflecting the fitting relationship between processing parameters and molding quality was established, and accuracy of the model was detected by cross validation. Finally, expected molding quality was applied to predict processing parameters, and the obtained molding quality under the predicted processing parameters was verified by computer aided engineering (CAE) simulation and experimental methods. The results showed that mean relative precisions of processing parameters such as melt temperature, delay time, short shot size, water pressure, and mold temperature for inverse RBF model were 98.6%, 93.6%, 98.5%, 93.9%, and 97.9%, respectively, which met the accuracy requirements. Furthermore, compared with expected values of hollowed core ratios and wall thickness differences, the average errors of CAE and experiment were 2.3% and 4.9%, respectively.     

多共振分量融合CNN的行星齿轮箱故障诊断

针对行星齿轮箱中各部件所激起的振动成分混叠、早期故障特征经常被较强的各级齿轮谐波成分以及环境噪声所湮没的问题，提出一种多共振分量融合卷积神经网络（multi-resonance component fusion based convolutional neural network，简称MRCF-CNN）的行星齿轮箱故障诊断方法。首先，对振动信号进行共振稀疏分解，得到包含齿轮谐波成分的高共振分量和可能包含轴承故障冲击成分的低共振分量；其次，构建多共振分量融合卷积神经网络，将得到的高、低共振分量和原始振动信号进行自适应的特征级融合，通过有监督的方式训练模型并进行行星齿轮箱故障诊断。对行星齿轮箱实验数据的分析结果表明，该方法能够有效分类行星齿轮箱中滚动轴承和齿轮的故障，成功对行星齿轮箱故障进行诊断，同时能够进一步增强卷积神经网络对振动信号所蕴含的故障信息的辨识能力。     

智能变电站设备管控大数据分析系统研究

结合数据挖掘、大数据技术在电力系统中的应用现状，分析和设计了一种智能变电站设备管控大数据分析系统。对变电站设备运行状态的大数据特征及基本框架进行了分析，并重点阐述了变电站设备运行状态大数据分析系统的数据集成与预处理、数据存储与处理、设备状态评估模型以及数据可视化展示。实验结果表明，所设计的大数据分析系统能够大幅度提升变电站设备数据分析智能化水平，为变电站管控稳定运行提供更加有效和实用性的技术支撑。     

基于开源技术的城市地理信息平台构建方法研究

摘 要：针对城市地理信息平台构建过程中存在的数据获取困难、研发成本较高等问题， 引入开源技术，对基于开源数据、开源开发平台与开源工具的城市地理信息平台构建技术进行 研究。所提出的城市地理信息平台由数据模块与可视化模块 2 部分组成。为构建数据模块，由 开源地理信息项目(OSM)获取了建筑与区划数据，并设计了建筑合并算法以提升数据质量。建 筑数据随之基于行政区划分级存储，通过由 ASP. NET 构建的数据接口提供给具体应用。可视 化模块则基于开源地理信息系统(GIS)项目 Cesium 实现，并设计了完善的用户界面。以北京市 为例，完成了原型系统的开发与测试，证实了该技术体系的可行性，通过开源技术降低平台研 发成本，推进城市地理信息平台的研究与应用。