Income, Time Effects and Direct Preferences in a Multimodal Choice Context: Application of Mixed RP/SP Models with Non-Linear Utilities

Transport problems typically involve at least two types of constraints, on income and on time. Therefore, the indirect utility function depends either on the income available after having subtracted the cost of the discrete alternative and on the free time left after having worked and travelled by each competing option. In the typical linear-in-the-attributes and in-the-parameters specification, that represents the first grade approximation of the indirect utility function, the effect of income and time constraints cancel out and only the cost and time of the alternatives matter in the comparison between them. From a microeconomic point of view this is equivalent to assume that income and time effects could be disregarded; which is not always the case. To account for these effects the utility function should include second order attributes; however, in non-linear utility functions it may not be easy to distinguish among several effects that could be relevant: direct preferences for good and leisure, and simple interactions between attributes other than income and time effects. This paper analyses these effects from a theoretical point of view focusing on the possible confounding problem in detecting income and time effects. We use a dataset collected for a modal choice context and containing both revealed and stated preference data, and estimate several NL models examining the effect of the different second-order terms on detecting income and time effects. We compared specifications including square cost and time attributes, interactions between time and cost, cost divided by the income available to be spent on free time, and time multiplied by free time. Our results confirm the strong effect of direct preferences for goods and leisure time on choice, and the potential confounding effect between quadratic attributes and other non-linear omitted terms. Finally, we also found that care should be taken in highlighting income and time effects using mixed data sources, since confounding effects can occur when non-linearities are accounted for in both data sets.     

二元复合驱阶段见效特征

针对孤东油田油藏条件，通过室内试验研究，确定了以胜利石油磺酸盐为活性剂主剂和聚合物的二元复合驱油体系，通过两者之间的协同效应，增加水驱波及体积和洗油效率。该体系在孤东油田七区西馆上54—61层进行试验，取得了较好的阶段性降水增油效果。本文分析了复合驱见效特征和影响见效因素，为下步跟踪分析调整具有指导意义。     

拟声波构建技术在砂泥岩薄互层储层预测中的应用

拟声波构建技术依据对储层敏感的非声波地球物理测井信息构建具有声波量纲的新曲线，进而形成拟声波曲线。由于该声波曲线既能反映地层速度和波阻抗的变化，又能反映地层岩性的细微差别，可以较好地解决薄互层储层定量预测中的岩性识别、薄层分辨等难题。针对歧口凹陷南大港地区地层实测声波时差曲线对砂、泥岩的岩性反映不明显，无法进行储层反演预测的问题，采用了拟声波构建技术。将对储层岩性敏感性好的自然电位曲线转换成拟声波曲线，以此为约束条件实现了储层预测的目的，取得了较好的应用效果。     

C++程序设计教学中的技能培养

本文阐述了知识与技能的关系,以及C++程序设计教学中知识和技能的双重性要求。以选择语句为例,演示了"先知识后技能"的教学过程。实践证明,这样的教学安排行之有效。     

模型驱动下的Web应用系统自动生成

为了提高Web应用系统开发效率,提出了模型驱动下的Web应用系统自动生成方法。这种生成方法是以Me—taEdit＋作为元建模工具,首先创建Web应用系统元模型、定制DSL,进而建立Web应用系统领域模型,然后通过MetaEdit＋提供的生成器定义语言MERL,软件开发人员可以很方便地设计出Web应用系统所需的JSP生成器、Servlet生成器、Jayabeans生成器和数据库生成器,从Web应用系统图形模型直接生成整个Web应用系统。最后通过一个WebShopping实例详细介绍了模型驱动下的Web应用系统生成方法及生成过程。经测试,所生成的Web应用系统可以在Windows操作系统中的Web应用服务器上正确运行。     

基于Visual C＋＋的GPRS服务器制作

针对GPRS无线通信服务器界面制作问题．提出一种基于Visual C＋＋制作GPRS服务器的方案。在该方案中,解决GPRS无线通信的驱动．分析GPRS无线通信中的ASCII码显示处理问题,实现GPRS服务器的网络连接和数据接收。实验结果表明,该方案正确、可靠。     

深入浅出讲解“Windows程序设计—Visual C++”课程

针对Windows程序设计——Visual C++课程涉及知识面广、实践性强、复杂难学的特点,分析了该课程难点之所在及传统教学过程中存在的问题,并从教学内容、教学方法和模式等方面采取了一些改革措施。实践证明,改革措施能够激发学生的编程兴趣,提高学生自主学习的能力,使学生较快进入状态,提高教学质量。     

矿物质催化合成SP及SP-C

探索出一种以矿物质（橡胶填充剂）为催化剂合成ＳＰ及ＳＰ－Ｃ的新工艺。选择了最佳的工艺条件。此法制成的ＳＰ及ＳＰ－Ｃ产品性能优良、质量稳定。     

基于Hg~(2+)诱导DNA双链形成的汞离子检测试纸条研制

Hg2+能特异性地与2个胸腺嘧啶碱基(T)共价结合,介导T-T错配形成稳定的T-Hg2+-T结构。基于DNA修饰的纳米金探针研制了一种可检测水溶液中Hg2+浓度的层析试纸条。试纸条包含1条控制线和2条测试线,检测结果在5 min内可见,裸眼可见检测灵敏度为100 nmol/L。金标条阅读仪分析结果在10 nmol/L~10μmol/L范围内具有良好的线性关系,且对常见二价重金属离子Cu2+,Ni2+,Mg2+,Zn2+,Pb2+具有很好的选择性。该试纸条灵敏特异、快捷简单、操作方便、成本低廉,在环境监测和食品安全的现场快速检测中具有很好的应用前景。     

适用于智能传感器系统的SVM集成研究

以支持向量机(SVM)为代表的人工智能技术在智能传感器系统中得到了广泛的应用,但传统的SVM有"灾难性遗忘"现象,即会遗忘以前学过的知识,并且不能增量学习新的数据,这已无法满足智能传感器系统实时性的要求。而Learn++算法能够增量地学习新来的数据,即使新来数据属于新的类,也不会遗忘已经学习到的旧知识。为了解决上述问题,提出了一种基于壳向量算法的Learn++集成方法。实验结果表明:该算法不但具有增量学习的能力,而且在保证分类精度的同时,提高了训练速度,减小了存储规模,可以满足当下智能传感器系统在线学习的需求。