如何获取免费科技文献全文文章

文中介绍了利用Googlescholar等学术搜索引擎获取免费科技文献全文文章的几种途径与方法。     

平阳高速公路穿越东村小窑煤矿采空区设计

近年来山西省的高速公路穿越煤矿采空区和铝土矿采空区的工程很多,采空区的处治设计直接影响到高速公路的运营安全和使用寿命。结合工程实践,针对平阳高速公路煤矿采空区的特点,阐述穿越东村小窑煤矿采空区的三种处治设计方案,并对方案进行对比,可供同行参考。     

我国铁路运输系统节能问题的研究分析

从技术、管理和应用3个层面总结归纳了国内铁路运输系统节能问题的研究现状以及应用的研究方法,分析探讨了铁路运输系统节能研究面临的理论问题和现实问题,最后从应用角度提出了基于数据挖掘的能耗模型研究思路和基于组织管理的节能研究的主要内容.     

热力隧道在非降水施工中的力学特性监测

城市热力供水工程逐渐由近地层明挖向深地层暗挖转移,通过对北京市两个典型地质热力隧道的现场原位的监测,分析浅埋暗挖热力隧道在非降水施工过程中对于砂卵石地质和粉质黏土地质条件下的初支结构内力、围岩性状、水文特征的变化及对地层的影响.试验监测结果表明,隧道围岩的土压力分布不同,水压力分布相同,初衬钢筋内力分布不同,砂卵石地质条件下的隧道初衬受力情况较好,隧道开挖对地表沉降影响较小,研究结果可在开挖城市供热、供水及供气等小断面隧道时参考.     

重型货运车辆出行时空差异性和影响因素分析

重型货运车辆能促进城市经济生产和物流效率,但也因显著的排放、噪声和拥堵等外部属性对城市空间品质有明显影响,对其活动时空特征和影响因素分析是当前城市空间品质提升和交通管理精细化的迫切需求。基于深圳注册的12 t以上重型货车GPS轨迹数据,考虑不同类型货运车辆作业差异,提出利用统计抽样中最小样本量的观察结果确定不同类型重型货车轨迹数据出行切割阈值水平的方法;针对5类重型货车,分析出车强度和出行时段的差异性,与小客车进行对比分析发现,重型货车出行活动与小客车出行在时间上存在错峰特征,且夜间出行比例较高;重型货车的活动空间具有组内差异性,集装箱卡车具有显著的跨城市活动需求,更多承担中长距离运输功能,而土方车和重型罐式车更多承担本地化运输服务功能;重型货车的活动空间具有集聚特征,出行活动的节点强度具有无标度幂律分布特征;利用广义加性模型分析两类典型货车活动空间的影响因素,集装箱卡车活动与港口及交通仓储园区等物流设施呈现非线性关系,而普通大货车与工业园区等产业用地具有紧密关系。本文可以为重型货车交通精细化管理和交通需求模型建设提供新的思路和方法。     

西江长洲枢纽库尾段采砂工程对航道通航水流条件的影响

西江航道桂平航运枢纽—长洲水利枢纽之间存在约34 km的水位未衔接段,而大藤峡水利枢纽建设选取的江口料场位于长洲枢纽库区回水末端和上游附近滩段。为研究库尾段采砂工程对上游通航水流条件的影响,须同步考虑上游来水和下游库区回水的变化,建立桂平三江口上游黔江河段16 km和桂平枢纽—长洲枢纽约156 km的长河段二维水流数学模型,分析采砂工程对航道通航水流条件的影响。结果表明,采砂工程实施后上游桂平航运枢纽引航道水位降落0.09 m,布岭沙弯道凸岸侧边滩挖除引起主流一定左偏。整体上,除设计流量外,库尾段采砂工程对航道水流条件影响较小。     

基于Web GIS的公路施工管理系统研究

基于Web从组织机构和功能两个方面分析公路施工管理系统的实际需求,分析公路施工管理系统中主要模块之间的关系,并据此设计基于Web的公路施工管理系统的总体结构。该系统利用GIS、图形显示等技术,为施工管理搭建了一个地图可视化平台,在电子图库、三维分析、工程量计算等方面进行了一定的探索。     

Web数据库中最小失败子查询问题研究

随着网络的普及,越来越多的网络用户通过Web数据库提供的查询接口进行数据查询。但是用户往往会遇到发出查询条件而没有返回结果的现象即空查询结果问题。造成这种情况的原因可能是一方面用户查询意图模糊,不能够准确地描述查询要求。另一方面,用户明确查询需求但Web数据库却找不到任何符合查询条件的结果。针对这种情况可以分析发现找到造成查询失败的子查询条件是解决问题的关键。本文的主要研究分两个步骤:首先,将用户发出的查询条件分解为单个的原子查询条件;然后找到造成查询失败的最小失败子查询(Minimal Failing Sbuquery,MFS)集合。     

Classification of Livebus arrivals user behavior

With the increasing use of Intelligent Transport Systems, large amounts of data are created. Innovative information services are introduced and new forms of data are available, which could be used to understand the behavior of travelers and the dynamics of people flows. This work analyzes the requests for real-time arrivals of bus routes at stops in London made by travelers using Transport for London's LiveBus Arrivals system. The available dataset consists of about one million requests for real-time arrivals for each of the 28 days under observation. These data are analyzed for different purposes. LiveBus Arrivals users are classified based on a set of features and using K-Means, Expectation Maximization, Logistic regression, One-level decision tree, Decision Tree, Random Forest, and Support Vector Machine (SVM) by Sequential Minimal Optimization (SMO). The results of the study indicate that the LiveBus Arrivals requests can be classified into six main behaviors. It was found that the classification-based approaches produce better results than the clustering-based ones. The most accurate results were obtained with the SVM-SMO methodology (Precision of 97%). Furthermore, the behavior within the six classes of users is analyzed to better understand how users take advantage of the LiveBus Arrivals service. It was found that the 37% of users can be classified as interchange users. This classification could form the basis of a more personalized LiveBus Arrivals application in future, which could support management and planning by revealing how public transport and related services are actually used or update information on commuters.     

A mathematical programming technique for matching time-stamped records in logistics and transportation systems

Time-stamped data for transportation and logistics are essential for estimating times on transportation legs and times between successive stages in logistic processes. Often these data are subject to recording errors and omissions. Matches must then be inferred from the time stamps alone because identifying keys are unavailable, suppressed to preserve confidentiality, or ambiguous because of missing observations. We present an integer programming (IP) model developed for matching successive events in such situations and illustrate its application in three problem settings involving (a) airline operations at an airport, (b) taxi service between an airport and a train station, and (c) taxi services from an airport. With data from the third setting (where a matching key was available), we illustrate the robustness of estimates for median and mean times between events under different random rates for “failure to record”, different screening criteria for outliers, and different target times used in the IP objective. The IP model proves to be a tractable and informative tool for data matching and data cleaning, with a wide range of potential applications.