基于出行阻抗的城市地铁网络客流分布计算

为提高城市地铁网络客流分布预测的准确性,结合乘车阻抗、进站候车阻抗和换乘阻抗等出行阻抗建立综合出行阻抗函数,基于阻抗函数对有效路径集进行搜索与筛选,建立基于综合出行阻抗的多路径客流分布计算模型,并设立换乘惩罚系数对不同路径的客流分配进行合理修正.以深圳地铁新开通的11号线为契机,结合地铁1号线、2号线、3号线、5号线分析网络客流分布的变化.结果 表明:新开通的11号线使各线路中的换乘客流有不同程度的增大,增加了乘客出行的可选路径,且11号线车站附近形成新的人口及岗位聚集圈,进一步增加了11号线自身的客流,使得线网客运总量增加,与实际运营中的客流特征相符,验证了所建模型的有效性和适用性.     

多目标模糊优化问题的神经网络解法

基于函数联接神经网络，提出了一种解决工程结构多目标模糊优化问题的新算法。该算法以设计人员对目标函数值的满意程度作学习样本，采用神经网络取代传统的隶属度函数，从而较好地解决了隶属函数的描述问题。在解决多目标模糊优化问题中，该算法较传统算法具有更大的灵活性。     

平面广告误用心理暗示原因之研究

摘要当前广告在生产生活中发挥着越来越大的作用,作为广告创作者如何做好广告是必须面对的实际问题。本文针对当下一些平面广告出现的运用心理暗示产生的错误效果,着重研讨了产生走入误区的原因,也提出了避免陷入错误运用心理暗示误区的思维和方法,对企业策划广告时有较好的现实意义。     

钢筋混凝土抗滑桩在治理大型滑坡中的应用

以治理黄土滑坡工程为例,从抗滑桩的施工方案选定、施工方案的实施出发,重点介绍了抗滑桩施工工艺及质量控制措施,以保证施工质量及安全。     

列车用柴油发电机组隔振系统隔振性能分析

针对某型出口动车采用的具有双层隔振系统的柴油发电机组和车体间的隔振问题进行了研究,首先建立了机组双层隔振系统的振动传递模型和隔振性能评估表达式,然后按照柴油发电机组在列车上的安装方式,将其安装在固定于地面的基础台架上,对3种不同的隔振刚度条件进行了隔振性能试验,给出了不同刚度工况下隔振性能随机组转速的变化规律,其结果可为机组隔振系统性能研究和设计提供参考.     

乙酰半胱氨酸对特发性肺间质纤维化肺成纤维细胞增殖及胶原合成的影响及机制

目的探讨乙酰半胱氨酸(NAC)对特发性肺间质纤维化(IPF)肺成纤维细胞增殖及胶原合成的影响及其机制。方法分离培养IPF患者及正常对照肺成纤维细胞,MTT法检测NAC(5、10、20、40mmol/L)对细胞增殖的影响;再分为NAC(20mmol/L)、重组人转化生长因子-β1(TGF-β1组)单用及联用组和正常对照组,RT-PCR检测I型前胶原mRNA的表达变化,Western blotting检测α-肌动蛋白(α-SMA)及信号转导与转录激活因子-3(STAT3)的表达。结果 5、10、20、40mmol/L NAC对正常及IPF患者肺成纤维细胞增殖均具有抑制作用,且具有明显的剂量依赖性。与正常对照组相比,NAC组能够明显抑制正常及IPF肺成纤维细胞Ⅰ型前胶原mRNA的表达(分别为0.28±0.04vs.0.52±0.07,P=0.001;0.56±0.08 vs.0.74±0.03,P=0.017);NAC联用TGF-β则可以减弱其抑制作用(0.75±0.03 vs.0.28±0.04,P=0.012;0.71±0.03 vs.0.56±0.08,P=0.000)。正常肺成纤维细胞表达一定水平的α-SMA,而IPF肺成纤维细胞则表达水平明显增高(0.97±0.06 vs.0.63±0.04,P=0.001),NAC能够明显抑制IPF肺成纤维细胞α-SMA及STAT3表达(分别为0.48±0.02 vs.0.97±0.06,P=0.000及0.43±0.05 vs.0.76±0.02,P=0.000),NAC联用TGF-β则可以减弱其抑制作用(0.93±0.04 vs.0.48±0.02及0.89±0.03 vs.0.43±0.05,P均=0.000)。结论 NAC能够抑制正常及IPF肺成纤维细胞增殖及胶原合成,这可能与抑制α-SMA及STAT3的表达有关,但这种作用可被TGF-β所抑制。     

公交客流预测的神经网络模型

通过分析神经网络的作用机理和公交年客流量的影响冈素,以城市人口、居民收入、生产总值等9个因素作为输入神经元,输出神经元为每年的公交客流量,建立了公交客流预测的径向基神经网络模型(RBF)和BP神经网络模型,以合肥市公交量的调查数据为例,对网络进行学习与训练仿真实验,结果表明所建模型具有较高的预测精度,效果较好.     

Fabrication and Interfacial Structure of Ni-YSZ-LSM Porous Scaffold for Solid Oxide Fuel Cells

A novel process route using tape casting and stacking for fabricating porous scaffold of solid oxide fuel cells (SOFC) was demonstrated. The linear shrinkages of anode (Ni-YSZ, YSZ stands for 3% Y_2O_3(mole fraction) stabilized ZrO_2) and cathode (LSM-YSZ, LSM stands for La_(0.8)Sr_(0.2)Mn0_3) were optimized to be uniform with that of electrolyte during sintering, by controlling the content of pore former. The micromorphology and interface microstructure of the cross-section of the porous scaffold were observed by optical microscope and scanning electron microscope, respectively. The element distribution and phase composition were analyzed by energy dispersive spectrometer and X-ray diffraction, respectively. The results showed that the porous scaffold with regular pore shape and high specific surface area was obtained after sintering at 1 350℃. The fabricated porous scaffold had defect free interfacial structures due to the uniform shrinkage of anode, cathode and electrolyte layers. In addition, it was shown that diffusions of Zr, Ni and La caused a progressive boundary between YSZ, Ni-YSZ and LSM-YSZ layers. The interface between anode and electrolyte (Ni-YSZ/YSZ) was mainly composed of Ni, YSZ and a small amount of NiO, and the interface between cathode and electrolyte (LSM-YSZ/YSZ) was mainly composed of YSZ, LSM and a small amount of La_2Zr_2O_7.     

Automatic semantic analysis of software requirements through machine learning and ontology approach

Nowadays, software requirements are still mainly analyzed manually, which has many drawbacks (such as a large amount of labor consumption, inefficiency, and even inaccuracy of the results). The problems are even worse in domain analysis scenarios because a large number of requirements from many users need to be analyzed. In this sense, automatic analysis of software requirements can bring benefits to software companies. For this purpose, we proposed an approach to automatically analyze software requirement specifications (SRSs) and extract the semantic information. In this approach, a machine learning and ontology based semantic role labeling (SRL) method was used. First of all, some common verbs were calculated from SRS documents in the E-commerce domain, and then semantic frames were designed for those verbs. Based on the frames, sentences from SRSs were selected and labeled manually, and the labeled sentences were used as training examples in the machine learning stage. Besides the training examples labeled with semantic roles, external ontology knowledge was used to relieve the data sparsity problem and obtain reliable results. Based on the SemCor and WordNet corpus, the senses of nouns and verbs were identified in a sequential manner through the K-nearest neighbor approach. Then the senses of the verbs were used to identify the frame types. After that, we trained the SRL labeling classifier with the maximum entropy method, in which we added some new features based on word sense, such as the hypernyms and hyponyms of the word senses in the ontology. Experimental results show that this new approach for automatic functional requirements analysis is effective.     

基于复配硬质沥青的大掺量高性能热再生技术

借鉴法国耐久性高模量沥青混合料的设计理念,充分利用沥青路面回收材料中沥青老化的特性和级配细化的特点,采用硬质颗粒复配低标号沥青,并通过丰度系数指标实现大掺量高性能热再生沥青混合料。室内性能评价与验证表明,热再生沥青混合料实现了大掺量,且性能满足现行施工技术规范要求,同时满足法国耐久性高模量沥青混合料的性能指标,为热再生沥青混合料的设计和应用提供了新的思路,具有较好的经济社会效益。