灰色关联在城市轨道交通线网规划方案评价中的应用

用AHP-Grey模型对城市轨道交通线网规划方案进行评价选优。建立线网规划方案评价的分层指标体系,采用灰色关联分析方法按灰色关联度大小对方案进行排序。通过在实际项目方案评价中的应用,证明灰色关联分析法的实用性和合理性。AHP-Grey法既充分体现了多指标对总目标的重要性程度,又充分利用并挖掘了决策矩阵中的有效信息,是一种简便而适用的方法。     

p57kip2和cyclin D1在人脑胶质瘤中的表达和临床意义

目的探讨p57kip2蛋白、cyclin D1蛋白在人脑胶质瘤中的表达情况及其临床意义。方法采用免疫组织化学SP法检测p57kip2、cyclin D1在55例人脑胶质瘤和10例正常脑组织中的表达。结果在55例胶质瘤中,p57kip2蛋白阳性率为38.2%(21/55),显著低于正常脑组织(80%,P<0.05)。p57kip2蛋白表达在低度恶性(Ⅰ-Ⅱ级)组显著高于高度恶性(Ⅲ-Ⅳ级)组(P<0.05),生存时间≥2年组显著高于<2年组(P<0.05)。脑胶质瘤中Cyclin D1蛋白阳性率为54.5%(30/55),显著高于正常脑组织(10%,P<0.05)。cyclin D1蛋白表达在高度恶性(Ⅲ-Ⅳ级)组显著高于低度恶性(Ⅰ-Ⅱ级)组(P<0.05),生存时间<2年组显著高于≥2年组(P<0.05)。p57kip2蛋白的表达与cyclinD1蛋白的表达存在负相关(rs=-0.277,P<0.05)。结论p57kip2蛋白和cyclin D1蛋白在脑胶质瘤中存在异常表达,p57kip2蛋白和cyclin D1蛋白的表达程度与脑胶质瘤的分化程度、预后相关。     

两亲磁性聚合物微球的制备

以FeSO4和FeCl3为主要原料,用十二烷基磺酸钠作为表面活性剂,制备了稳定的水分散性Fe3O4磁流体.再以聚乙二醇、甲苯-2,4-二异氰酸酯和丙烯酸-β-羟乙酯合成聚氨酯(PU)大分子单体.在Fe3O4磁流体存在下,以乙醇/水为分散介质,过硫酸钾作为引发剂,通过苯乙烯与PU大分子单体共聚制备出了两亲磁性聚合物微球.所制得的磁性聚合物微球粒径小,平均粒径约14μm;在磁场中的沉降速度为自然沉降速度的8倍,试验结果说明磁性聚合物微球的磁性很强,磁性聚合物微球具有简便、快速的磁分离特性.     

蛇床子素对大鼠离体子宫平滑肌收缩性能的影响

蛇床子素和维拉帕米对缩宫素、ＫＣｌ、高Ｋ＋除极化后Ｃａ２＋诱导大鼠离休子宫平滑肌收缩均产生抑制作用，对ＣａＣｌ２累积量效曲线均呈非竞争性拮抗效应，ｐＤ＇２（拮抗多数）分别为３．９±０．１５和７．４５±０．１；１００μｍｏｌ／Ｌ蛇床子素和０．０８μｍｏｌ／Ｌ维拉帕米显著抑制缩宫素诱导依赖细胞内Ｃａ２＋收缩反应，对依赖细胞外Ｃａ２＋的收缩均无明显影响。提示蛇床子素对子宫平滑肌的抑制作用类似维拉帕米，与拮抗Ｃａ２＋有关，且主要通过抑制平滑肌细胞膜上电位依赖性Ｃａ２＋通道和影响胞内Ｃａ２＋释放所致。     

加宽路基压实方法的选择

为保证强夯工程的效果，提高高填方路基不均匀沉降机理的理论水平，开展“强夯法对加宽高填方路基承栽力和不均匀沉降控制”这一课题研究很有必要，其结果将对强夯法地基处理技术起到补充和完善的作用，并且将会进一步拓宽强夯法的应用范围。     

宽带氧传感器用于发动机失火检测的研究

介绍了一种使用宽带氧传感器进行发动机失火检测的方法,在发动机各种工况下对该方法的有效性进行了试验研究.试验结果表明,使用氧传感器信号的差分信号可以更清楚地从正常燃烧中辨别出失火,差分信号波动的幅值可作为失火检测的监测参数.由于氧传感器的反应速度随发动机转速的增加而增加,因而该方法可以检测高速(5 500 r/min)工况下发生的失火.     

Empirical speed models for cycling in the Oslo road network

Transportation - Knowing the speed at which a cyclist travels is important in route and mode choice modelling. Empirical evidence suggests that it varies significantly in accordance...     

充电桩产业发展的模式探索——以城市交通企业发展充电桩产业为例

在政府对电动汽车产业的大力推动下,我国电动汽车产业已步入快速发展期,作为电动汽车基础配套设施建设的充电桩也呈井喷式增长,未来充电桩设施建设将会成为交通企业产业链的重要环节,本文就充电桩产业发展现状和城市交通企业进军充电产业模式进行简要分析和研究。     

Neural network-based fuel consumption estimation for container ships in Korea

ABSTRACT

Due to the outstanding strength of advanced machine-learning techniques, they have become increasingly common in predictive studies in recent years, particularly in predicting ship energy performance. In constructing predictive models, prior studies have mostly employed vessels’ technical parameters to establish machine-learning algorithms. To bridge this research gap and enable wider applications, this paper presents the design of a multilayer perceptron artificial neural network (MLP ANN) as a machine-learning technique to estimate ship fuel consumption. We utilized the real operational data from 100–143 container ships to estimate fuel consumption for five different container ships grouped by size. We compared the performance of two ANN models and two multiple-regression models. Four input parameters (sailing time, speed, cargo weight, and capacity) were included in the first ANN and the first regression model, while the other two models only consider two inputs from physical function. The mean absolute percentage error of the ANN models with four inputs was the smallest and less than those in extended statistical models, demonstrating the MLP’s superiority over the statistical model. The MLP ANN model can thus be applied to confirm the effectiveness of the slow-steaming method for achieving energy efficiency.     

Application of a real-coded genetic algorithm for the fitting of a ship hull surface through a single non-uniform B-spline surface

In digital ship-design processes, surface modeling needs to be as accurate as possible for effectiveness in ship production as well as numerical analysis of the performance. Traditionally, the form of a ship hull is constructed from a set of cross-sectional data. This approach entails difficulties in the cross-sectional spacing and accuracy of the characteristic curves, such as the stern and bow profiles, deck side line, bottom tangential line, and unconnected curves. Genetic algorithms (GAs) have attracted increasing attention as a multimodal optimization solution for surface reconstruction that enable construction of a single non-uniform B-spline (NUB) surface at the initial stage of ship design with constraints such as knuckles, discontinuity conditions, and bulbous bows with high curvatures, . The first, simultaneous multi-fitting GA determines the boundary curves, such as the stem and stern profiles, and finds the common knot values for both curves. Similarly, the same GA technique is applied for other boundary curves at the bottom and the deck. The second GA is employed to fit the interior data points after the boundary curves are fitted. The encoded design variables for surface construction are the locations of the vertices and the knot values. Those variables are modified for improving the surface quality until a predefined degree of precision is attained. In four instances of application, the GA technique developed in this research has been shown to provide good, single, NUB surfaces with high efficiency. In the early design stage, a single NUB surface is more convenient for performance visualization and finite-element methods. It can be readily translated into many CAD/CAM packages, which facilitate the smooth transition of data across the different design stages.