出口船舶建造贷款贷后风险管理

根据银行对出口在建船舶贷款形成的不良资产情况,作者认为进行随机的贷后检查是防范与控制风险的重要途径.在建船舶贷款的贷后风险管理主要包括贷款资金的封闭式管理、船厂的风险管理、造船项目的监管、船东付款管理、外部风险管理和危机管理六个方面.     

试论中国经济转型与社会稳定

我国处在社会经济转型与持续调整增长阶段，社会非稳定因素与日俱增，社会经济转型给整个社会带来进步与繁荣，也给社会带来冲突与动荡，但是，中国的社会经济顺利转型又必须在长期稳定的社会环境中才能进行，因此，保持政治稳定，社会安定是实现社会经济转型的前提。     

“国有民营”一符合地铁本质特征的运营模式

指出地铁运营模式的选择应充分考虑到地铁的本质特性,分析地铁具有线路部分的自然垄断性与运营部分的非自然垄断性、公共性与企业性并存、极强的外部效应等特点,得出“国有民营”是一种符合地铁长质特征的运营模式的结论,并推出新加坡地铁的例证。     

1885-1945年缅甸闽籍华侨的社会结构

早期缅甸闽侨以年轻男子居多,由于华侨社会内部性别比失衡,他们大多与当地人通婚.1885年后缅甸全境沦为英国殖民地,仰曼铁路向北延伸,缅甸闽侨的地域结构特征逐渐由"二元"转为"一体".同期,缅甸闽侨大多经商为生,并创立了各类社团组织.1930年后英属缅甸社会动荡不安,但远离当地政治的闽侨所受影响较小.第一代闽侨总体文化水平不高,但大多以自身中华文化特征为荣.     

The effects of federal transit subsidy policy on investment decisions: The case of San Francisco's Geary Corridor

In the United States, federal funding for public transit often accounts for a large proportion of a local agency's budget, especially for capital investments. For this reason, local governments can be expected to plan a portfolio of projects that maximize federal contributions. This study examines the financial effects of federal transit subsidy policy on local transit investment decisions. Data from a System Planning Study for the Geary Corridor in San Francisco are used as an illustration. It is found that federal transit subsidy policy provides financial incentives for local decision-makers to select capital-intensive investment options that may not be efficient or effective. While federal financial incentives are not the only factor influencing local investment decisions, some reform of the current subsidy policy may be necessary to reduce the incentive for ineffective use of public resources.     

Predicting the adoption of connected autonomous vehicles: A new approach based on the theory of diffusion of innovations

On the grounds that individuals heavily rely on the information that they receive from their peers when evaluating adoption of a radical innovation, this paper proposes a new approach to forecast long-term adoption of connected autonomous vehicles (CAVs). The concept of resistance is employed to explain why individuals typically tend to defer the adoption of an innovation. We assume that there exists a social network among individuals through which they communicate based on certain frequencies. In addition, individuals can be subject to media advertisement based on certain frequencies. An individual’s perceptions are dynamic and change over time as the individual is exposed to advertisement and communicates with satisfied and dissatisfied adopters. We also explicitly allow willingness-to-pay (WTP) to change as a result of peer-to-peer communication. An individual decides to adopt when (i) there is a need for a new vehicles; (ii) his/her WTP is greater than CAV price; and (iii) his/her overall impression about CAVs reaches a cutoff value. Applicability of the proposed approach is shown using a survey of employees of the University of Memphis. Our results show that the automobile fleet will be near homogenous in about 2050 only if CAV prices decrease at an annual rate of 15% or 20%. We find that a 6-month pre-introduction marketing campaign may have no significant impact on adoption trend. Marketing is shown to ignite CAV diffusion but its effect is capped. CAV market share will be close to 100% only if all adopters are satisfied with their purchases; therefore, the probability that an individual becomes a satisfied adopter plays an important role in the trend of adoption. The effect of the latter probability is more pronounced as time goes by and is also more prominent when CAV price reduces at greater rates. Some caveats may be inserted when considering the study results as the findings are subject to sample bias and data limitations.     

Potentials of using social media to infer the longitudinal travel behavior: A sequential model-based clustering method

This study explores the possibility of employing social media data to infer the longitudinal travel behavior. The geo-tagged social media data show some unique features including location-aggregated features, distance-separated features, and Gaussian distributed features. Compared to conventional household travel survey, social media data is less expensive, easier to obtain and the most importantly can monitor the individual’s longitudinal travel behavior features over a much longer observation period. This paper proposes a sequential model-based clustering method to group the high-resolution Twitter locations and extract the Twitter displacements. Further, this study details the unique features of displacements extracted from Twitter including the demographics of Twitter user, as well as the advantages and limitations. The results are even compared with those from traditional household travel survey, showing promises in using displacement distribution, length, duration and start time to infer individual’s travel behavior. On this basis, one can also see the potential of employing social media to infer longitudinal travel behavior, as well as a large quantity of short-distance Twitter displacements. The results will supplement the traditional travel survey and support travel behavior modeling in a metropolitan area.     

Forecasting current and next trip purpose with social media data and Google Places

Trip purpose is crucial to travel behavior modeling and travel demand estimation for transportation planning and investment decisions. However, the spatial-temporal complexity of human activities makes the prediction of trip purpose a challenging problem. This research, an extension of work by Ermagun et al. (2017) and Meng et al. (2017), addresses the problem of predicting both current and next trip purposes with both Google Places and social media data. First, this paper implements a new approach to match points of interest (POIs) from the Google Places API with historical Twitter data. Therefore, the popularity of each POI can be obtained. Additionally, a Bayesian neural network (BNN) is employed to model the trip dependence on each individual’s daily trip chain and infer the trip purpose. Compared with traditional models, it is found that Google Places and Twitter information can greatly improve the overall accuracy of prediction for certain activities, including “EatOut”, “Personal”, “Recreation” and “Shopping”, but not for “Education” and “Transportation”. In addition, trip duration is found to be an important factor in inferring activity/trip purposes. Further, to address the computational challenge in the BNN, an elastic net is implemented for feature selection before the classification task. Our research can lead to three types of possible applications: activity-based travel demand modeling, survey labeling assistance, and online recommendations.     

Travel analytics: Understanding how destination choice and business clusters are connected based on social media data

Understanding how destination choice and business clusters are connected is of great importance for designing sustainable cities, fostering flourishing business clusters, and building livable communities. As sharing locations and activities on social media platforms becomes increasingly popular, such data can reveal destination choice and activity space which can shed light on human-environment relationships. To this end, this research models the relationship between characteristics of business clusters and check-in activities from Los Angeles County, California. Business clusters are analyzed via two lenses: the supply side (employment data by industry) and the demand side (on-line check-in data). Spatial and statistical analyses are performed to understand how land use and transportation network features affect the popularity of the identified clusters and their relationships. Our results suggest that a cluster with more employment opportunities and more types of employment is associated with more check-ins. A business cluster that has access to parks or recreational services is also more popular. A business cluster with a longer road network and better connectivity of roads is associated with more check-ins. The visualization of the common visitors between clusters reveals that there are a few clusters with outstanding strong ties, while most have modest ties with each other. Our findings have implications on the influence of urban design on the popularity of business clusters.     

社会转型期大学生思想道德教育对策思考

随着网络化、经济全球化的进程,中国社会正处在深刻变革和快速转型期,人们的价值观念、思维方式和行为方式发生剧烈变化,传统的道德规范受到了强烈冲击。正处在人生观、价值观、道德观的形成时期的大学生,面临极大的道德冲突与思想困惑。文章在分析大学生的思想道德现状及其成因的基础上,就社会转型期如何加强大学生思想道德建设进行探讨。