亚磷酸酯功能化聚硅氧烷扩链聚乳酸的阻燃性能研究

将聚氨基环氧基硅氧烷（PSQ）以及亚磷酸酯功能化的聚氨基环氧基硅氧烷（PPSQ）与聚乳酸（PLA）通过熔融共混制备了扩链PLA。研究了亚磷酸酯基团的引入对PSQ在PLA中的扩链和阻燃性能的影响，探究PPSQ在PLA中的阻燃机理。结果表明，相比于PSQ，PPSQ可以明显提高PLA的分子量和复合黏度，具有更好的扩链作用；PPSQ的引入可以明显改善PLA的阻燃性能，使得PLA?PPSQ的极限氧指数提高至25.4 %，比纯PLA提升了31.6 %，且使得PLA的热释放速率峰值和总热释放量分别下降了18.1 %和16.6 %。分析可知，由于亚磷酸酯基团的引入，促使PLA?PPSQ中的硅元素在燃烧过程中向炭层表面迁移形成更多富含碳化硅和氧化硅的致密炭层，起到隔热隔氧的作用，进而发挥优异的阻燃性能。     

煤矿井下小断层延伸长度定量化预测研究

以山西A矿开采的3号煤层采掘活动已揭露的小断层资料为基础,选取断层落差、倾角、延伸长度3个参数,通过多线性回归方法构建NW、NE 2个方向的断层延伸长度预本构模型,并进行现场应用。结果表明,构建的小断层预测模型预测的小断层延伸长度的准确率在90%以上,可以为后续揭露小断层延伸长度的确定提供可靠的参数指导。     

能源互联网内多类设备协同运行的能量管理系统软件设计

针对能源互联网中的海量分布式设备和电动汽车的需求，利用C#和Java编程语言设计并开发面向能源互联网的能量管理系统。该系统采用典型B/S 架构，以Asp.Net动态网页开发技术和数据库技术为核心，由云端后台算法、云端服务器的MySQL数据库、前端的网页三大部分构成。详细介绍了软件平台的系统框架，分为上下两层。其中，上层为日前-实时协同的多时间尺度能量管理系统模块，下层包含分布式设备协调控制子系统和电动汽车协调控制子系统。最后，通过仿真算例分析，能量管理系统能对海量的分布式设备和电动汽车进行优化调度，验证了该软件的可移植性、有效性和实用性。     

316L不锈钢non-Masing特性分析和疲劳寿命预测

对316L不锈钢在不同应变范围下分别进行了293 K和873 K试验温度下的低周疲劳试验，讨论了材料循环特性的幅值相关性和温度相关性，比较了不同条件下non-Masing特性，并利用能量方法进行了低周疲劳寿命预测。实验结果表明, 在不同条件下，循环初期会出现不同程度的循环硬化现象，随后会出现循环软化、饱和直至材料失效；与873 K 试验条件相比，材料在293 K温度下的non-Masing特性更为显著；在大应变范围下，两温度下材料的non-Masing特性更加明显。采用能量方法进行疲劳寿命预测时，预测结果均位于两倍分散带内，且基于non-Masing特性得到了比基于Masing特性更为精确的预测结果。在873 K温度和大应变范围下，显著的动态应变时效效应导致考虑non-Masing与Masing特性的预测结果相差不大。     

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。     

三维增材鞋面印花机对位平台的冗余驱动控制策略

针对自动鞋面印花机在进行对版时定位精度低,影响鞋面印刷质量的问题,提出了基于冗余驱动的印花机对位平台。在原有的三轴并联机构对位平台的基础上通过增加1个Y轴,有效地提高了对位平台Y向的刚度和承载能力,从而提高了印花机对位平台的定位精度。由于冗余驱动机构运动过程中存在机构运动耦合,利用几何法进行解耦,提出了基于电子凸轮的控制策略。同时提出了对机构换向间隙补偿的控制策略,进一步提高对位平台的定位精度,保证了鞋面的印刷质量。经过实验验证,改进后的印花机对位平台Y向定位精度提高了85.7%,Z向旋转定位精度提高了72.9%,X向和Y向换向间隙分别提高了50% 和75%,Z向旋转换向间隙提高了42.86%。     

A Step toward Molecular Evolution of RNA: Ribose Binds to Prebiotic Fatty Acid Membranes,and Nucleosides Bind Better than Individual Bases Do

A major challenge in understanding how biological cells arose on the early Earth is explaining how RNA and membranes originally colocalized. We propose that the building blocks of RNA (nucleobases and ribose) bound to self-assembled prebiotic membranes. We have previously demonstrated that the bases bind to membranes composed of a prebiotic fatty acid, but evidence for the binding of sugars has remained a technical challenge. Here, we used pulsed-field gradient NMR spectroscopy to demonstrate that ribose and other sugars bind to membranes of decanoic acid. Moreover, the binding of some bases is strongly enhanced when they are linked to ribose to form a nucleoside or – with the addition of phosphate – a nucleotide. This enhanced binding could have played a role in the molecular evolution leading to the production of RNA.     

高速列车转向架构架损伤、等效应力及寿命分布特性研究

为分析高速列车转向架构架损伤、等效应力及寿命分布特性，对构架疲劳关键测点进行动应力线路实测并对测点实测时域数据波形进行解析；基于实测应力时间历程及雨流计数法编制二维应力谱，利用Goodman等寿命方程将二维应力谱等效转换为一维应力谱；阐述线性累积损伤及非线性累积损伤模型的建立方法并对实测数据的线性累积损伤及非线性累积损伤进行了计算及对比分析；分别基于线性累积损伤理论及非线性累积损伤理论推导出各理论下的等效应力，基于实测数据对两种等效应力进行了计算及对比分析；通过结合非线性累积损伤及线性累积损伤理论计算的等效应力及不同可靠度下的材料S-N曲线计算并对比分析构架结构的疲劳寿命。研究结果表明，与非线性疲劳分析理论相比，线性疲劳分析理论对高速列车转向架构架的疲劳特性评估偏于保守。     

A novel PEM fuel cell remaining useful life prediction method based on singular spectrum analysis and deep Gaussian processes

Accurate remaining useful life (RUL) prediction of proton exchange membrane fuel cells (PEMFCs) can assess the reliability of fuel cells to determine the occurrence of failures and mitigate their operational risk. However, is it quite challenging to design a high-precision prediction method because the implicit degradation details of PEMFCs are difficult to learn well from the measurement data with high-frequency noise. Recognizing this, a novel RUL prediction method based on singular spectrum analysis (SSA) and deep Gaussian process (DGP) is proposed in this paper. The SSA-based method is firstly employed to preprocess the measurement data, which can strengthen the effective information of PEMFC degradation data at the same time remove the noise and spikes that interfere with degradation prediction. As a deep structural model, DGP has strong feature learning ability which can represent the nonlinear details of degradation data and give more accurate prediction results. At the same time, it serves as a probabilistic model that can provide the confidence interval to enhance reliability of RUL prediction. The effectiveness of the proposed method is evaluated by experimental data of the PEMFCs under steady-state conditions, and the results show that the SSA-DGP method has higher accuracy and reliability than conventional methods.     

Performance degradation prediction of proton exchange membrane fuel cell using a hybrid prognostic approach

The proton exchange membrane fuel cell has been widely used for industrial systems; however, its performance gradually degrades during use. Therefore, the study on the performance degradation prediction of fuel cells is helpful to extend its lifespan. In this paper, a novel hybrid approach using a combination of model-based adaptive Kalman filter and data-driven NARX neural network is proposed to predict the degradation of fuel cells. The overall degradation trend (i.e., irreversible degradation process) is captured by an empirical aging model and adaptive Kalman filter. Meanwhile, the detail degradation information (i.e., reversible degradation process) is depicted by the NARX neural network. Moreover, the correlation analysis of the reversible voltage time series is carried out to obtain the number of delays of the NARX neural network based on the autocorrelation function and the partial autocorrelation function. Then, the total degradation prediction is the sum of the overall degradation prediction and the detail degradation prediction. Finally, the prognostic capability of the proposed method is verified by two aging datasets, and the results show the effectiveness and superiority of the proposed method which can provide accurate degradation forecasting and remaining useful life.