高效液相色谱-紫外光谱分析氢醌-双(β-羟乙基)醚

建立了高效液相色谱-紫外光谱法测定聚氨酯扩链剂氢醌-双(β-羟乙基)醚(HQEE)工业品中HQEE主含量和对苯二酚残留的方法。在氰基柱上,以甲醇-水-1%乙酸溶液(体积比10∶50∶40)为流动相,流速1.0 mL/min时,HQEE、对苯二酚及其他相关杂质得到良好分离,紫外光谱285 nm检测。方法简便、快捷,结果准确可靠,在工业生产中可有效地指导HQEE合成过程的质量控制。     

基于单邻点多波段预测的高光谱图像无损压缩算法

提出了一种基于聚类-单邻点、多波段预测-熵编码的高光谱数据无损压缩方法。根据谱向特征,进行高光谱图像矢量聚类。对各个分类,采用单个空间位置邻点、多个波段作为预测数据,训练预测系数,进行三维预测。残差采用Golomb-Rice编码。实验证实了算法的有效性。     

基于耐震时程法的进水塔结构抗震性能分析

进水塔结构作为水电站的进水建筑物,其抗震性能对水电站的安全运行有重要意义。以沙牌水电站的高耸独立进水塔结构为研究对象,应用3条耐震时程曲线对其进行抗震性能分析与评估,并将计算结果与天然地震动下的增量动力分析结果进行比较,对比分析进水塔塔顶位移、基底剪力及地震损伤历程。实例研究表明：耐震时程法可以较好地分析结构的抗震响应,与增量动力分析结果的均值相匹配。耐震时程法可以利用较少的时程分析得到不同动力强度作用下的结构响应,且计算结果精度好、效率高,为进水塔结构的抗震性能分析提供一种新的手段。     

基于IDA的套箍加固拱上立柱抗震性能评估

以德阳金花大桥为工程实例,结合拱上立柱的实际震害,利用地震作用下的非线性计算模型,建立基于IDA的钢筋混凝土套箍加固拱上立柱抗震性能评估分析方法。通过分析表明,剪跨比对拱上立柱的抗震性能影响显著,高剪跨比立柱抗震性能相对较差,在地震作用下拱上立柱为易损构件;套箍加固拱上立柱的抗震性能提高明显,易损性显著降低,延性亦有一定程度改善。分析结果与震害调查具有一致性,可为拱上立柱抗震评估提供参考。     

考虑楼板作用的SRC柱-钢梁混合框架动力时程分析

为了解楼板空间作用对型钢混凝土(SRC)柱-钢梁混合框架抗震性能的影响,利用有限元软件ABAQUS分别建立带有楼板和不带楼板的两跨三层SRC柱-钢梁框架,选取2组天然波和1组人工波对其进行弹塑性分析,对比2种框架结构的型钢应力分布、混凝土板损伤、层间相对位移角以及框架基底剪力,分析楼板在结构抗震中的影响规律。结果表明:增加楼板可以有效增加框架抗侧刚度,最大可使层间位移角降低38.7%;同时可以减小核心区梁端塑性区域的面积,减缓型钢上翼缘应力发展速度;而且楼板的存在可使最大基底剪力提升60.7%,有利于减小结构损伤和提高抗震性能。     

人工主余震作用下桥墩结构损伤预测评估分析

主余震作用下,断层距（R_JB）和余震次数的不同对结构造成的损伤也大不相同,进行主余震序列下桥梁损伤评估时需要考虑断层距、余震次数等影响因素。基于OpenSees平台,以一座连续梁桥为例,根据人工主余震构造时不同的影响因素,选用余震衰减和PGA调幅的人工主余震构造法构造主余震序列进行结构损伤分析,并与实际主余震事件的损伤结果作对比,研究人工构造主余震序列预测结构损伤的可行性。结果表明：基于PGA调幅构造主余震的方法,当考虑余震衰减时,能够较好模拟出结构在实际主余震地震作用下造成的最终损伤,能够较合理的预测结构在实际主余震序列中的损伤指标;对区域主余震作用下的桥梁进行损伤评估时,需要考虑断层距和余震次数影响因素,桥梁距断层距R_JB越近时结构的损伤状态越严重,余震发生次数越多则结构的损伤增量就越大,并且结构在人工主余震作用下R_JB越大的地方,结构损伤增量误差程度越小,预测的损伤状态就越接近实际的损伤状态。     

连云港地震台BBVS-120、CTS-1E型地震计系统性能对比

通过对连云港地震台BBVS-120、CTS-1E型地震计台基噪声功率谱分析,正弦标定和脉冲标定计算分析,近震和远震震级对比分析,研究在实际工作中2种地震计性能差异,对比观测数据质量,检验稳定性,以期为地震监测及相关研究工作提供依据。     

Predicting Terrestrial Flagstone Reservoirs in the Sha-I Member of the Qibei Depression in the Dagang Oilfield

There are few 3-D seismic profiles and drillings in the middle part of the Qibei depression in the Dagang oilfield, and more than 70% of the 2-D seismic profiles were completed before the 1980s. Meanwhile, changes in the terrestrial formations in this region have been large and complex. These factors have made it difficult to predict reservoirs in this area. The purpose of this paper is to establish a methodology for predicting potential gas and oil reservoirs. Our research combines sequence stratigraphy, well-logs, and seismic analysis to elucidate the prediction of flagstone reservoirs in the S1 (Sha-I) Member in the middle of the Qibei depression. Previous research indicates that these rocks were deposited in an environment that had a semiarid, northern subtropical, and warm, humid climate. The objective strata currently consist mainly of lake fades, deeper lake facies, and shore-shallow lake facies. The study reveals that the lower section of the S1 Member is an important objective region for explora     

Automated Prediction of CMEs Using Machine Learning of CME – Flare Associations

Machine-learning algorithms are applied to explore the relation between significant flares and their associated CMEs. The NGDC flares catalogue and the SOHO/LASCO CME catalogue are processed to associate X and M-class flares with CMEs based on timing information. Automated systems are created to process and associate years of flare and CME data, which are later arranged in numerical-training vectors and fed to machine-learning algorithms to extract the embedded knowledge and provide learning rules that can be used for the automated prediction of CMEs. Properties representing the intensity, flare duration, and duration of decline and duration of growth are extracted from all the associated (A) and not-associated (NA) flares and converted to a numerical format that is suitable for machine-learning use. The machine-learning algorithms Cascade Correlation Neural Networks (CCNN) and Support Vector Machines (SVM) are used and compared in our work. The machine-learning systems predict, from the input of a flare’s properties, if the flare is likely to initiate a CME. Intensive experiments using Jack-knife techniques are carried out and the relationships between flare properties and CMEs are investigated using the results. The predictive performance of SVM and CCNN is analysed and recommendations for enhancing the performance are provided.     

西北地区农业旱灾与预测研究

西北地区干旱灾害在中国乃至世界上具有代表性,常常对农业生产、社会经济和人民生活带来巨大威胁。利用西北五省区(陕西、甘肃、宁夏、青海、新疆)1951～2000年的50年农业旱灾面积统计资料,分析了农业旱灾的时空强度变化。表明西北地区农业旱灾有增加趋势,以20世纪90年代增加最明显。利用西北地区均匀分布的40个站点50年的降水和气温资料,采用变换的Z指数法,探讨了干旱强度和地表径流量的时间变化与西北地区农业旱害的关系。表明降水量的减少及河川径流枯期与农业旱灾面积负相关关系密切。应用波谱分析与逐步自回归方法,分别建立了西北地区干旱指数的拟合回归预测模型,农业旱灾面积、成灾面积和绝收面积预测模型,并对西北地区干旱灾害的趋势进行了预测。表明西北地区干旱灾害在21世纪前十年有所减缓。经检验,模型预测效果良好,根据预测结果可以采取有针对性的减灾措施,减少西北地区农业灾害损失。