模块化钢结构建筑连接节点研究进展

模块化钢结构建筑是近年来建筑工业化发展的重要趋势,模块间连接节点对于结构整体性能有显著影响,因此其设计至关重要。为此,基于国内外模块化钢结构建筑发展历程,从构件装配层面、模块化结构层面和模块化建筑层面分别综述了国内外模块化钢结构建筑连接节点构造形式和研究现状。结果表明：国内外已经出现多种形式的模块化钢结构建筑连接节点并应用于实际工程中,但对其受力性能的研究相对匮乏,严重滞后于工程实践;同时,缺乏能够与建筑层面模块化作业相适应且受力性能优良的节点形式,无法满足模块化钢结构建筑在高层建筑及抗震设防区域的应用。因此,对节点装配构造、传力机理、抗震性能及其设计理论的研究具有一定的迫切性和现实意义。综述了近年来国内外模块化钢结构建筑连接节点方面的最新进展,结合作者在该领域的研究工作和成果,给出了该领域的研究方向和未来的发展趋势。     

纤维增强复合材料及其结构研究进展

纤维增强复合材料因其轻质高强、可设计性和高耐久性在基础设施领域逐步得到关注与应用,通过结构体系创新,可部分替代钢和混凝土制造各类结构构件,满足建筑、桥梁、海洋等工程结构的强度、刚度及使用功能需求。国内外有关学者已进行了大量的研究和实践,基于相关成果从以下四个方面系统总结了国内外关于纤维增强复合材料结构的研究进展： 1） 组成材料及制造工艺方面,介绍了增强材料纤维、树脂基体和芯材的种类和性能,并阐述了几种典型制造工艺; 2） 基本构件与结构应用方面,归纳总结了复合材料筋索、型材、管材、板材以及夹芯结构的受力特点及在土木工程中的主要应用; 3） 复合材料结构力学理论方面,详细介绍了其基本力学性能以及冲击、疲劳、蠕变、耐火等性能,并阐述了监测与设计方法; 4） 复合材料结构耐久性方面,总结了温度、湿度、紫外线照射、化学介质（酸、碱、盐等）的侵蚀以及多因素耦合作用对其耐久性的影响,并归纳了耐久性提升技术研究现状。通过对基础设施领域复合材料结构的发展现状、应用的局限性以及前景的系统综述,促进复合材料结构的研究与工程应用。     

区域建筑业发展竞争力动态评价研究——基于时序全局主成分分析

建筑业与区域经济发展具有高度关联性,并且随着地区经济发展更多地呈现出产业触及面广、多动力驱动发展以及差异性的特点。结合区域经济和建筑业发展的关联性,从影响建筑业发展的内外部因素方面入手构建区域建筑业发展竞争力动态评价指标体系,以时间为发展轴基于时序全局主成分分析和层次聚类分析对区域建筑业发展竞争力进行动态评价、分等级排序。研究结果表明,各省市建筑业发展竞争力来源方向具有差异性,并且处于动态的变化之中,与之相关的竞争力指标除建筑业本身的规模之外,也与地区经济、社会发展水平等密切相关,通过分析建筑业竞争力来源的差异性为提高建筑业发展水平提出合理化建议。     

增强型傅氏算法在监视线保护中的应用

分析了现有监视线保护存在的问题，即在干扰信号的作用下，容易产生误动和拒动.提出了一种终端加电阻的改进型全波傅氏算法的交流监视线保护原理，该方法能完全滤除直流衰减信号及直流、工频和高频杂散电流.通过装置实验证明，该方法可靠准确，计算量小，是一种可行的监视线保护.     

同位素方法测量煤炭灰分的进展

介绍了各种核方法测量煤炭灰分的基本原理及发展概况。评述了各主要方法的优缺点、适用条件及应用情况。     

修复零件机械加工时定位基准的选择

介绍了修复零件机械加工时定位基准选择的基本原则 ,提出了修复加工零件定位基准的 4种选择方法。论述了零件定位基准的选择要结合工件的实际情况 ,正确合理选择定位基准 ,使修复后的零件既满足零件的尺寸要求 ,又能满足零件的使用性能 ,从而延长机械零件的使用寿命。     

基于GC-MS指纹图谱和化学计量学方法鉴别陈皮

制备了陈皮挥发油提取物并建立其气相色谱-质谱（GC-MS）指纹图谱,对不同批次的陈皮指纹图谱进行相似度评价;对其共有成分峰面积数据进行主成分分析（PCA）和层次聚类分析（HCA）。建立了具有16个共有峰的新会陈皮挥发油指纹图谱,通过化学计量学分析方法能将新会陈皮和其他陈皮样品准确区分开来;不同采摘期及贮存时间陈皮的成分呈现规律性特征。方法可用于不同产地、采摘期及贮存时间陈皮的鉴别,并为陈皮的质量控制提供依据。     

Label‐free monitoring of inflammatory tissue conditions using a carrageenan‐induced acute inflammation rat model

Although the confirmation of inflammatory changes within tissues at the onset of various diseases is critical for the early detection of disease and selection of appropriate treatment, most therapies are based on complex and time‐consuming diagnostic procedures. Raman spectroscopy has the ability to provide non‐invasive, real‐time, chemical bonding analysis through the inelastic scattering of photons. In this study, we evaluate the feasibility of Raman spectroscopy as a new, easy, fast, and accurate diagnostic method to support diagnostic decisions. The molecular changes in carrageenan‐induced acute inflammation rat tissues were assessed by Raman spectroscopy. Volumes of 0 (control), 100, 150, and 200 µL of 1% carrageenan were administered to rat hind paws to control the degree of inflammation. The prominent peaks at [1,062, 1,131] cm^?1 and [2,847, 2,881] cm^?1 were selected as characteristic measurements corresponding to the C–C stretching vibrational modes and the symmetric and asymmetric C–H (CH₂) stretching vibrational modes, respectively. Principal component analysis of the inflammatory Raman spectra enabled graphical representation of the degree of inflammation through principal component loading profiles of inflammatory tissues on a two‐dimensional plot. Therefore, Raman spectroscopy with multivariate statistical analysis represents a promising method for detecting biomolecular responses based on different types of inflammatory tissues.     

Regional forecasts of photovoltaic power generation according to different data availability scenarios: a study of four methods

The development of methods to forecast photovoltaic (PV) power generation regionally is of utmost importance to support the spread of such power systems in current power grids. The objective of this study is to propose and to evaluate methods to forecast regional PV power 1 day ahead of time and to compare their performances. Four forecast methods were regarded, of which two are new ones proposed in this study. Together, they characterize a set of forecast methods that can be applied in different scenarios regarding availability of data and infrastructure to make the forecasts. The forecast methods were based on the use of support vector regression and weather prediction data. Evaluations were performed for 1 year of hourly forecasts using data of 273 PV systems installed in two adjacent regions in Japan, Kanto, and Chubu. The results show the importance of selecting the proper forecast method regarding the region characteristics. For Chubu, the region with a variety of weather conditions, the forecast methods based on single systems' forecasts and the one based on stratified sampling provided the best results. In this case, the best annual normalized root mean square error (RMSE) and mean absolute error (MAE) were 0.25 and 0.15 kWh/kWh_avg, respectively. For Kanto, with homogeneous weather conditions, the four methods performed similarly. In this case, the lowest annual forecast errors were 0.33 kWh/kWh_avg for the normalized RMSE and 0.202 kWh/kWh_avg for the normalized MAE. Copyright © 2014 John Wiley & Sons, Ltd.     

Lurgi MTP工艺循环烃组分分布研究

采用气相色谱仪对德国Lurgi MTP工艺的C4循环烃进行分析,并从MTP催化剂运行周期方向考察了循环烃的组分变化规律。分析结果表明,C4循环烃主要组分是烷烃,约占其总物质的量的57%,其余组分为烯烃和少量的甲醇、二甲醚;其中,各组分含量大小为:异丁烷异丁烯正丁烷1-丁烯反-2-丁烯。