多元合金化大断面球墨铸铁组织和性能的分析

为了提升大断面球墨铸铁综合力学性能，通过复合添加微量合金元素铜、锑、锡、钼对大断面球墨铸铁进行微合金化处理，借助金相显微镜(OM)、扫描电子显微镜(SEM)及力学性能测试等手段，研究了Cu Sb Sn Mo复合微合金化大断面球墨铸铁微观组织和力学性能。结果表明，试验球墨铸铁具有良好的综合力学性能。大断面球墨铸铁中添加铜、锑、锡、钼后优化了材料的组织结构，基体组织为珠光体＋少量牛眼状铁素体；试样石墨组织细小、圆整，分布均匀。同时，合金元素的复合加入使得其抗拉强度达到800 MPa以上，硬度约为280HB，伸长率达到5%以上。拉伸断口分析表明，微合金化大断面球墨铸铁断裂模式以解理断裂为主，伴有少量的塑性变形。     

埋地管道泄漏内封堵装置设计与研究

针对流体输送埋地管道泄漏问题,设计了一种利用管道机器人携带封堵气囊进行快速应急封堵修复的埋地管道泄漏内封堵装置。采用矩阵变换方法建立了牵引系统驱动轮过弯方程,利用MATLAB软件对过弯方程进行了验证,同时利用ADAMS软件仿真分析过弯路径与驱动轮转角对牵引系统行走速度的影响。研究结果表明:由两个串联封堵器组成的应急封堵系统可满足复杂工况下的管道泄漏封堵要求;牵引系统驱动轮在弯管内部行走时,单轮速度呈周期性变化,但三个驱动轮整体周期运动特性一致;驱动轮转角在25°~40°时,牵引系统行走速度与驱动轮转角成正比,且转角为30°时驱动效果最好。该内封堵装置的结构设计可为管道泄漏应急封堵领域装备的研发提供重要参考。     

Synthesis of nanoporous Baghdadite by a modified sol-gel method and its structural and controlled release properties

In this research, a bimodal nanoporous Baghdadite (NB) (Ca3ZrSi2O9) was prepared by a modified sol-gel method using P123 as a surfactant. The effects of P123's contents on the structural and textural properties as well as the drug delivery behavior of NB were assessed in vitro. The usage of P123 offered a new route for the synthesis of NB. The synthesized NB samples with different amounts of P123 were studied through X-ray diffraction (XRD), Fourier transform infrared spectra (FTIR), N2 adsorption-desorption, field emission scanning electron microscopy (FESEM) equipped with energy-dispersive X-ray analysis spectroscopy (EDAX) and transmission electron microscopy (TEM). The results showed that a single-phase Baghdadite was obtained by this new method at the calcination temperature of 800?°C. It was found that an increase in P123's content up to 0.025?mol changed the morphology of NB samples from mountain-like to needle-like. The potential application of NB samples as drug delivery agents was assessed by estimating their release properties up to 240?h. This research revealed that the synthesized Baghdadite could be used as a potential nanoporous carrier with controlled release capability in bone tissue regeneration.     

An engineering method for complex structural optimization involving both size and topology design variables

This work presents an engineering method for optimizing structures made of bars, beams, plates, or a combination of those components. Corresponding problems involve both continuous (size) and discrete (topology) variables. Using a branched multipoint approximate function, which involves such mixed variables, a series of sequential approximate problems are constructed to make the primal problem explicit. To solve the approximate problems, genetic algorithm (GA) is utilized to optimize discrete variables, and when calculating individual fitness values in GA, a second-level approximate problem only involving retained continuous variables is built to optimize continuous variables. The solution to the second-level approximate problem can be easily obtained with dual methods. Structural analyses are only needed before improving the branched approximate functions in the iteration cycles. The method aims at optimal design of discrete structures consisting of bars, beams, plates, or other components. Numerical examples are given to illustrate its effectiveness, including frame topology optimization, layout optimization of stiffeners modeled with beams or shells, concurrent layout optimization of beam and shell components, and an application in a microsatellite structure. Optimization results show that the number of structural analyses is dramatically decreased when compared with pure GA while even comparable to pure sizing optimization.     

Design structure matrix (DSM) method application to issue of modeling and analyzing the fault tree of a wind energy asset

The perpetual energy production of a wind farm could be accomplished (under proper weather conditions) if no failures occurred. But even the best possible design, manufacturing, and maintenance of a system cannot eliminate the failure possibility. In order to understand and minimize the system failures, the most crucial components of the wind turbines, which are prone to failures, should be identified. Moreover, it is essential to determine and classify the criticality of the system failures according to the impact of these failure events on wind turbine safety. The present study is processing the failure data from a wind farm and uses the Fault Tree Analysis as a baseline for applying the Design Structure Matrix technique to reveal the failure and risk interactions between wind turbine subsystems. Based on the analysis performed and by introducing new importance measures, the “readiness to fail” of a subsystem in conjunction with the “failure riskiness” can determine the “failure criticality.” The value of the failure criticality can define the frame within which interventions could be done. The arising interventions could be applied either to the whole system or could be focused in specified pairs of wind turbine subsystems. In conclusion, the method analyzed in the present research can be effectively applied by the wind turbine manufacturers and the wind farm operators as an operation framework, which can lead to a limited (as possible) design‐out maintenance cost, failures' minimization, and safety maximization for the whole wind turbine system.     

Selective Analysis of Sulfur-Containing Species in a Heavy Crude Oil by Deuterium Labeling Reactions and Ultrahigh Resolution Mass Spectrometry

A heavy crude oil has been treated with deuterated alkylating reagents (CD₃I and C₂D₅I) and directly analyzed without any prior fractionation and chromatographic separation by high-field Orbitrap Fourier Transform Mass Spectrometry (FTMS) and Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FT-ICR MS) using electrospray ionization (ESI). The reaction of a polycyclic aromatic sulfur heterocycles (PASHs) dibenzothiophene (DBT), in the presence of silver tetrafluoroborate (AgBF₄) with ethyl iodide (C₂H₅I) in anhydrous dichloroethane (DCE) was optimized as a sample reaction to study heavy crude oil mixtures, and the reaction yield was monitored and determined by proton nuclear magnetic resonance spectroscopy (¹H-NMR). The obtained conditions were then applied to a mixture of standard aromatic CH-, N-, O- and S-containing compounds and then a heavy crude oil, and only sulfur-containing compounds were selectively alkylated. The deuterium labeled alkylating reagents, iodomethane-d₃ (CD₃I) and iodoethane-d₅ (C₂D₅I), were employed to the alkylation of heavy crude oil to selectively differentiate the tagged sulfur species from the original crude oil.     

Stimuli-Responsive Nanocomposite Hydrogels for Biomedical Applications

The complex tissue-specific physiology that is orchestrated from the nano- to the macroscale, in conjugation with the dynamic biophysical/biochemical stimuli underlying biological processes, has inspired the design of sophisticated hydrogels and nanoparticle systems exhibiting stimuli-responsive features. Recently, hydrogels and nanoparticles have been combined in advanced nanocomposite hybrid platforms expanding their range of biomedical applications. The ease and flexibility of attaining modular nanocomposite hydrogel constructs by selecting different classes of nanomaterials/hydrogels, or tuning nanoparticle-hydrogel physicochemical interactions widely expands the range of attainable properties to levels beyond those of traditional platforms. This review showcases the intrinsic ability of hybrid constructs to react to external or internal/physiological stimuli in the scope of developing sophisticated and intelligent systems with application-oriented features. Moreover, nanoparticle-hydrogel platforms are overviewed in the context of encoding stimuli-responsive cascades that recapitulate signaling interplays present in native biosystems. Collectively, recent breakthroughs in the design of stimuli-responsive nanocomposite hydrogels improve their potential for operating as advanced systems in different biomedical applications that benefit from tailored single or multi-responsiveness.     

基于特征融合的中文简历解析方法研究

针对基于规则和统计的传统中文简历解析方法效率低、成本高、泛化能力差的缺点，提出一种基于特征融合的中文简历解析方法，即级联Word2Vec生成的词向量和用BLSTM（Bidirectional Long Short-Term Memory）建模字序列生成的词向量，然后再结合BLSTM和CRF（Conditional Random Fields）对中文简历进行解析（BLSTM-CRF）。为了提高中文简历解析的效率，级联包含字序列信息的词向量和用Word2Vec生成的词向量，融合成一个新的词向量表示；再由BLSTM强大的学习能力融合词的上下文信息，输出所有可能标签序列的分值给CRF层；再由CRF引入标签之间约束关系求解最优序列。利用梯度下降算法训练神经网络，使用预先训练的词向量和Dropout优化神经网络，最终完成对中文简历的解析工作。实验结果表明，所提的特征融合方法优于传统的简历解析方法。     

关于信息化测绘时代工程测量发展的思考

如今我国信息化技术全面发展，尤其对于工程测量工作领域来讲，不管是在工程建设和管理方面都产生不小的支撑引导效用。由此，笔者具体结合如今工程建设信息化测绘核心任何以及设备布置细节，进行结构整体安全管理周期和周边地理空间信息技术发展能效整理解析，试图将工程测量最新发展机遇和技术挑战问题处理完全。希望能够借此为日后一定时期范围内相关工程测量规划主体提供合理指导性建议内容，最终为我国各类工程事业可持续发展前景绽放奠定深刻适应基础。