格拉斯磁环匀强磁场分析

以毕奥-萨伐尔定律为基础,建立亥姆霍兹线圈以及格拉斯磁环中心磁场分布模型,并计算了它们内部的轴向磁感应强度分布,绘出了空间分布图,分析了轴向磁感应强度与x、y坐标的关系,分析结果得到了亥姆霍兹线圈和格拉斯磁环内部匀强磁场分布区域,通过比较得到格拉斯磁环内部的匀强磁场区域比亥姆霍兹线圈内部的匀强磁场分布区域更大的结论。     

Towards a model of hydrodynamic damping for a circular cylinder with helical strakes at low KC

This paper investigates the hydrodynamic damping of a circular cylinder with helical strakes at Keulegan-Carpenter (KC) number from 0.07 to 3 in the presence of steady currents. Experiments were performed with a straked cylinder oscillating in either in-line or cross currents over Reynolds number (based on the oscillating velocity amplitude) varying from 1260 to 54,000. With in-line current being present, the measured drag coefficients of the straked cylinder are found to depend on the ratio between the oscillating velocity amplitude and the steady current velocity. This phenomenon is further confirmed by computational fluid dynamics using large-eddy simulations. The drag coefficients obtained from the numerical simulations agree well with the experimentally determined values. Similar phenomenon is observed for the cases with cross background current. Based on the experimental data, empirical formulae are proposed to evaluate drag coefficients. These results are of importance in estimating the resonant motion and the fatigue life of risers, e.g. water intake risers, in the flow regime of low KC. Finally, recommendations are provided for fatigue analysis of risers with helical strakes from the perspective of engineering practice.     

基于家庭属性差异的大学生出行方式选择行为研究

为剖析家庭属性差异对大学生出行方式选择行为的影响，基于非集计理论，构建家庭属性差异的大学生出行选择多元Logit 模型. 根据四川省2 571 份大学生出行行为调查问卷，运用SPSS 软件标定模型参数，获取影响大学生出行选择的主要家庭属性因素，并进行敏感性分析. 结果表明：家庭平均年收入、经济净流对大学生出行方式选择有显著的影响；以航空运输为参考，家庭平均年收入、经济净流对公路运输方式选择的影响大于铁路运输；“祖辈替孙辈购买机票”的折扣票务形式可提高大学生选择航空出行的概率.     

Locating emergency vehicles with an approximate queuing model and a meta-heuristic solution approach

In this paper, the location of emergency service (ES) vehicles is studied on fully connected networks. Queuing theory is utilized to obtain the performance metrics of the system. An approximate queuing model the (AQM) is proposed. For the AQM, different service rate formulations are constructed. These formulations are tested with a simulation study for different approximation levels. A mathematical model is proposed to minimize the mean response time of ES systems based on AQM. In the model, multiple vehicles are allowed at a single location. The objective function of the model has no closed form expression. A genetic algorithm is constructed to solve the model. With the help of the genetic algorithm, the effect of assigning multiple vehicles on the mean response time is reported.     

放宽条件定点集结模式下编组站车辆 集结排队系统分析

集结模式决定了货车集结过程的结束条件，定点集结是一种高效率的集结方式，有利于提高运输质量.针对放宽条件定点集结模式下编组站车辆集结过程，建立离散时间批到达批服务排队模型，利用嵌入式马尔可夫链方法求得离去时刻瞬时系统集结车辆队长分布，并求得任意时刻车辆集结队长分布，在此基础上分别分析了最小编成辆数，车组大小分布，车流到达强度，服务时间间隔分布对车辆平均集结队长，集结延误时间，效率，一昼夜发送车流量等系统指标的影响.分析结果表明，各因素对车辆集结排队系统影响明显.因此，利用本文提出的模型能为编组站的精细化管理和车流组织优化提供决策参考.     

论孔子人才观的历史定位与普适价值

孔子的人才观虽然产生于春秋时期动荡的社会背景下,却揭示了人才的最本质内涵———＂仁、智、勇＂。三者作为人才内涵的核心三要素,形成一个内在的三角稳定结构,它能够持久地推动包括成才在内的人的生命境界的发展,因而具有超越时代和社会阶层的普适价值。     

Experimental study and modeling of car-following behavior under high speed situation

To investigate the car-following behavior under high speed driving conditions, we performed a set of 11-car-platoon experiments on Hefei airport highway. The formation and growth of oscillations have been analyzed and compared with that in low speed situations. It was found that there is considerable heterogeneity for the same driver over different runs of the experiment. This intra-driver heterogeneity was quantitatively depicted by a new index and incorporated in an enhanced two-dimensional intelligent driver model. Using both the new high-speed and the previous low-speed experimental data, the new and three existing models were calibrated. Simulation results show that the enhanced model outperforms the three existing car-following models that do not take into account this intra-driver heterogeneity in reproducing the essential features of the traffic in the experiments.     

Compliance with MARPOL Annex VI regulation 14 by ships in the Gulf of Guinea sub-region: Issues,challenges and opportunities

The negative environmental and health impacts association with high sulphur dioxide emissions from shipboard machineries have been raised by various stakeholders within the marine transportation sector. It is against this backdrop that the International Maritime Organisation under the MARPOL Annex VI regulation 14 has capped sulphur emission to 0.1% for Sulphur Emission Control Areas and 0.5% for the other shipping nations. However, ship owners in the Gulf of Guinea (GoG) sub-region are facing multitudes of challenges in meeting up with this new IMO regulation. This paper aims to identify the main barriers hampering effective compliance to this new regulation by ships operating in the GoG, rank the barriers, and then discuss the possible opportunities that may arise as a result of addressing the challenges. To identify the main barriers, experts with several years of experience in the maritime industry from Ghana and Cameroun were used while multi-criteria decision-making (MCDM) method combining analytic hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS) was employed to rank the barriers. Other methods such as fuzzy AHP (FAHP), rank-order centroid (ROC) and TOPSIS were combined to validate the result of the study. The findings indicate that lack of infrastructure, lack of comprehensive marine air pollution laws and high capital and operational costs of sulphur reduction solutions emerged as the top three ranked barriers. The findings of this study can be useful to ship owners and policy makers in dealing with the issues of marine air pollution.     

Adoption of alternative fuel vehicles: Influence from neighbors,family and coworkers

During the last years, many governments have set targets for increasing the share of biofuels in the transportation sector. Understanding consumer behavior is essential in designing policies that efficiently increase the uptake of cleaner technologies. In this paper we analyze adopters and non-adopters of alternative fuel vehicles (AFVs). We use diffusion of innovation theory and the established notion that the social system and interpersonal influence play important roles in adoption. Based on a nationwide database of car owners we analyze interpersonal influence on adoption from three social domains: neighbors, family and coworkers. The results point primarily at a neighbor effect in that AFV adoption is more likely if neighbors also have adopted. The results also point at significant effects of interpersonal influence from coworkers and family members but these effects weaken or disappear when income, education level, marriage, age, gender and green party votes are controlled for. The results extend the diffusion of innovation and AFV literature with empirical support for interpersonal influence based on objective data where response bias is not a factor. Implications for further research, environmental and transport policy, and practitioners are discussed.     

A hybrid deep learning based traffic flow prediction method and its understanding

Deep neural networks (DNNs) have recently demonstrated the capability to predict traffic flow with big data. While existing DNN models can provide better performance than shallow models, it is still an open issue of making full use of spatial-temporal characteristics of the traffic flow to improve their performance. In addition, our understanding of them on traffic data remains limited. This paper proposes a DNN based traffic flow prediction model (DNN-BTF) to improve the prediction accuracy. The DNN-BTF model makes full use of weekly/daily periodicity and spatial-temporal characteristics of traffic flow. Inspired by recent work in machine learning, an attention based model was introduced that automatically learns to determine the importance of past traffic flow. The convolutional neural network was also used to mine the spatial features and the recurrent neural network to mine the temporal features of traffic flow. We also showed through visualization how DNN-BTF model understands traffic flow data and presents a challenge to conventional thinking about neural networks in the transportation field that neural networks is purely a “black-box” model. Data from open-access database PeMS was used to validate the proposed DNN-BTF model on a long-term horizon prediction task. Experimental results demonstrated that our method outperforms the state-of-the-art approaches.