International benchmarking of longitudinal train dynamics simulators: results

This paper presents the results of the International Benchmarking of Longitudinal Train Dynamics Simulators which involved participation of nine simulators (TABLDSS, UM, CRE-LTS, TDEAS, PoliTo, TsDyn, CARS, BODYSIM and VOCO) from six countries. Longitudinal train dynamics results and computing time of four simulation cases are presented and compared. The results show that all simulators had basic agreement in simulations of locomotive forces, resistance forces and track gradients. The major differences among different simulators lie in the draft gear models. TABLDSS, UM, CRE-LTS, TDEAS, TsDyn and CARS had general agreement in terms of the in-train forces; minor differences exist as reflections of draft gear model variations. In-train force oscillations were observed in VOCO due to the introduction of wheel–rail contact. In-train force instabilities were sometimes observed in PoliTo and BODYSIM due to the velocity controlled transitional characteristics which could have generated unreasonable transitional stiffness. Regarding computing time per train operational second, the following list is in order of increasing computing speed: VOCO, TsDyn, PoliTO, CARS, BODYSIM, UM, TDEAS, CRE-LTS and TABLDSS (fastest); all simulators except VOCO, TsDyn and PoliTo achieved faster speeds than real-time simulations. Similarly, regarding computing time per integration step, the computing speeds in order are: CRE-LTS, VOCO, CARS, TsDyn, UM, TABLDSS and TDEAS (fastest).     

Longitudinal train dynamics: an overview

This paper discusses the evolution of longitudinal train dynamics (LTD) simulations, which covers numerical solvers, vehicle connection systems, air brake systems, wagon dumper systems and locomotives, resistance forces and gravitational components, vehicle in-train instabilities, and computing schemes. A number of potential research topics are suggested, such as modelling of friction, polymer, and transition characteristics for vehicle connection simulations, studies of wagon dumping operations, proper modelling of vehicle in-train instabilities, and computing schemes for LTD simulations. Evidence shows that LTD simulations have evolved with computing capabilities. Currently, advanced component models that directly describe the working principles of the operation of air brake systems, vehicle connection systems, and traction systems are available. Parallel computing is a good solution to combine and simulate all these advanced models. Parallel computing can also be used to conduct three-dimensional long train dynamics simulations.     

PI_3激酶参与AT_1受体介导Ang II调节SHR脑神经元电活动的信号转导

目的　探索AT1受体介导AngII调节SHR和WKY鼠脑神经元电活动的信号转导机制。 方法　原代培养SHR和WKY新生鼠脑干和下丘脑神经元 ,用全细胞膜片钳以电流箝方式记录神经元的放电活动 ,观察AngII及各种信号分子抑制剂对脑神经元放电频率的影响。结果　AngⅡ (10 0nmol·L-1)可使WKY新生鼠脑神经元的放电频率从 (0 .4 4± 0 .0 8)Hz增加到 (1.39± 0 .16 )Hz。这种效应在SHR鼠更加显著。由AngⅡ引起的这两种鼠脑神经元放电频率的增加均可被AT1受体拮抗剂Losartan完全消除。使用PKC抑制剂calphostinC和钙调蛋白激酶II抑制剂KN 93能完全阻断AngⅡ对WKY鼠脑神经元的作用 ,但对SHR鼠脑神经元的放电频率仅阻抑约 5 0 %。磷脂酰肌醇 3(PI3 )激酶抑制剂LY2 94 0 0 2 (10 μmol·L-1)对SHR鼠脑神经元的放电频率可产生部分阻断作用 ,但对WKY鼠脑神经元无明显影响。结论　AngII增加SHR和WKY鼠脑神经元放电频率的作用均由AT1受体介导。PI3 激酶在AngⅡ调节SHR鼠脑神经元电活动的信号转导机制中发挥重要作用     

EFFECT OF ANGIOTENSIN Ⅱ RECEPTOR SUBTYPE ⅠON THE FIRING RATE IN CATECHOLAMINERGIC TUMOR CELLS

AngiotensinⅡ (AngⅡ ) playsanimportantroleintheregulationofbloodpressure[1 ] .Theac tionofAngⅡismediatedmainlybystimulationoftheangiotensintype 1(AT1 )receptorandinvolvesintheactivityofcatecholaminergicsystem .Im munostainingstudieshaveindicatedthattheAT1 re ceptorexistsonthecatecholaminergicneurons[2 ] .AdministrationofAngⅡintorat’sbrainincreasedtheconcentrationsofdopamine ,epinephrine ,andnorepinephrineinthebrain[3 ] .Althoughanatomicalandfunctionalevidencessupporttheinteractionbe twe…     

Crowdsourced data for bicycling research and practice

ABSTRACT

Cities are promoting bicycling for transportation as an antidote to increased traffic congestion, obesity and related health issues, and air pollution. However, both research and practice have been stalled by lack of data on bicycling volumes, safety, infrastructure, and public attitudes. New technologies such as GPS-enabled smartphones, crowdsourcing tools, and social media are changing the potential sources for bicycling data. However, many of the developments are coming from data science and it can be difficult evaluate the strengths and limitations of crowdsourced data. In this narrative review we provide an overview and critique of crowdsourced data that are being used to fill gaps and advance bicycling behaviour and safety knowledge. We assess crowdsourced data used to map ridership (fitness, bike share, and GPS/accelerometer data), assess safety (web-map tools), map infrastructure (OpenStreetMap), and track attitudes (social media). For each category of data, we discuss the challenges and opportunities they offer for researchers and practitioners. Fitness app data can be used to model spatial variation in bicycling ridership volumes, and GPS/accelerometer data offer new potential to characterise route choice and origin-destination of bicycling trips; however, working with these data requires a high level of training in data science. New sources of safety and near miss data can be used to address underreporting and increase predictive capacity but require grassroots promotion and are often best used when combined with official reports. Crowdsourced bicycling infrastructure data can be timely and facilitate comparisons across multiple cities; however, such data must be assessed for consistency in route type labels. Using social media, it is possible to track reactions to bicycle policy and infrastructure changes, yet linking attitudes expressed on social media platforms with broader populations is a challenge. New data present opportunities for improving our understanding of bicycling and supporting decision making towards transportation options that are healthy and safe for all. However, there are challenges, such as who has data access and how data crowdsourced tools are funded, protection of individual privacy, representativeness of data and impact of biased data on equity in decision making, and stakeholder capacity to use data given the requirement for advanced data science skills. If cities are to benefit from these new data, methodological developments and tools and training for end-users will need to track with the momentum of crowdsourced data.     

The impact of urban form on automobile travel: disentangling causation from correlation

A longstanding question within the field of transportation demand management is the strength of the relationship between urban form and mobility behavior. Although several studies have identified a strong correlation between these variables, there is as yet scant evidence to support policy interventions that target land use as a means of influencing travel. To the contrary, some of the more recent research has cast skepticism on the proposition that the relationship is causative, recognizing the possibility that households endogenously self-select themselves into communities that support their preferences for particular transportation modes. Focusing on individual automobile travel, the present study seeks to contribute to this line of inquiry by estimating econometric models on a panel of travel-diary data collected in Germany between 1996 and 2003. Specifically, we employ the two-part model (2PM)—a procedure involving probit and OLS estimators—to assess the determinants of the discrete decision to use the car and the continuous decision of distance traveled. Beyond modeling variables that capture the urban form features that are commonly suggested to influence mobility behavior, including mixed use and public transit, this study employs instrumental variables to control for potential endogeneity emerging from the simultaneity of residential and mode choices. Unlike much of the work to date, our results suggest that urban form has a causative impact on car use, a finding that is robust to alternative econometric specifications.

Containerization in an LDC environment

This paper examines one sector of the economy of an LDC, namely, port investment and planning, and considers the ways in which some of the arguments concerning choice of technology in LDCs apply to this sector.     

Management and Protection of the Maritime Cultural Resource in Ireland

Government in partnership with a number of Irish universities has begun a program of mapping underwater archaeological sites around the island of Ireland. This was encouraged by a strong legislative base and by a growing awareness of the value of the extensive submerged cultural resource around the coast. The results of these programs have been incorporated into the Geographic Information System and now form the basis of integrated management and research plans.