Relations between physiologic parameters and pulse transit time during loaded breathing

Pulse transit time (PTT) is a non-invasive measure, defined as time taken for the pulse pressure waves to travel from the R-wave of electrocardiogram to a selected peripheral site. Baseline PTT value is known to be influenced by physiologic variables like heart rate (HR), blood pressure (BP) and arterial compliance (AC). However, few quantitative data are available describing the factors which can influence PTT measurements in a child during breathing. The aim of this study was to investigate the effects of changes in breathing efforts on PTT baseline and fluctuations. Two different inspiratory resistive loading (IRL) devices were used to simulate loaded breathing in order to induce these effects. It is known that HR can influence the normative PTT value however the effect of HR variability (HRV) is not well-studied. Two groups of 3 healthy children ( 0.05) HR changes during all test activities. Results showed that HRV is not the sole contributor to PTT variations and suggest that changes in other physiologic parameters are also equally important. Hence, monitoring PTT measurement can be indicative of these associated changes during tidal or increased breathing efforts in healthy children.

Wear-type rail corrugation prediction: Field study

Rail corrugation consists of undesirable periodic fluctuations in wear on railway track and costs the railway industry substantially for it's removal by regrinding. Much research has been performed on this problem, particularly over the past two decades, however, a reliable cure remains elusive for wear-type corrugations. Recently the growth behaviour of wear-type rail corrugation-has been investigated using theoretical and experimental models as part of the RailCRC Project (#18). A critical part of this work is the tuning and validation of these models via an extensive field testing program. Rail corrugations have been monitored for 2 years on sites throughout Australia. Measured rail surface profiles are used to determine corrugation growth rates on each site. Growth rates and other characteristics are compared with theoretical predictions from a computer model for validation. The results from several pertinent sites are presented and discussed.

Logistics by rail:is this the key to more sustainable traffic policies in Northern Germany?

Despite the fact that Germany has a well- expanded traffic infrastructure, the country con-fronts a strong growth in freight volumes and it is very likely that in the forthcoming yearsit will not be able to reasonably cope with the increasing demand. Against this back-ground, the aim of this paper is to provide an in-depth analysis of the transport sector in North Germany in an effort to identify, if any, the possibilities of collapse as a result of the continuous increase in the demand of freight traffic. The research based on the DelphiTechnique, collects, analyses and summarizes the opinions of a group of experts in theaforementioned issues. Results indicate that railways could represent the solution to theforecasted growing freight volumes in the next years, not only in Germany, but also in thewhole European transport sector. In spite of continuous efforts undertaken by the politicsand the economy, the existing logistics and freight traffic concepts are not sufficient. Fi-nancing is too scarce; traffic concepts take issues like sustainability, environment protec-tion and working conditions into little consideration.

Implementation of Genetic Algorithms for Transit Points Arrangement

The problem of transit points arrangement is presented in the paper. This issue is connected with accuracy of tariff distance calculation and it is the urgent problem at present. Was showed that standard method of tariff distance discovering is not optimal. The Genetic Algorithms are used in optimization problem resolution. The UML application class diagram and class content are showed. In the end the example of transit points arrangement is represented.

The challenges of representing human behaviour in simulation models:some case examples from supply chain, evacuation modelling and rail disruption

As more of the economy moves from traditional manufacturing to the service sector, the nature of work is becoming less tangible and thus, the representation of human behaviour in models is becoming more important. Representing human behaviour and decision making in models is challenging, both in terms of capturing the essence of the processes, and also the way that those behaviours and decisions are or can be represented in the models themselves. In order to advance understanding in this area, a useful first step is to evaluate and start to classify the various types of behaviour and decision making that are required to be modelled. This talk will attempt to set out and provide an initial classification of the different types of behaviour and decision making that a modeller might want to represent in a model. Then, it will be useful to start to assess the main methods of simulation in terms of their capability in representing these various aspects. The three main simulation methods, System Dynamics, Agent Based Modelling and Discrete Event Simulation all achieve this to varying degrees. There is some evidence that all three methods can, within limits, represent the key aspects of the system being modelled. The three simulation approaches are then assessed for their suitability in modelling these various aspects. Illustration of behavioural modelling will be provided from cases in supply chain management, evacuation modelling and rail disruption.

Action research: minimising the impact of disruptive incidents for rail passengers through improved sharing of information

This paper reports on an action research project based in the UK rail industry; it used a novel type of Soft Systems Methodology (known as PrOH Modelling) to facilitate change in a major Train Operating Company (TOC). The project looked at a number of different disruptive incidents to compare and contrast practice via the Mitigate, Prevent, React and Recover (MPRR) Framework. One incident is detailed in depth. The paper also looks at the general process of conducting action research. This work will be of interest for researchers in the rail sector and for those conducting operations action research projects.

Optimization of adaptive traffic signal control with transit signal priority at isolated intersections using parallel genetic algorithms

Optimization of adaptive traffic signal timing is one of the most complex problems in traffic control systems. This dissertation presents a new method that applies the parallel genetic algorithm (PGA) to optimize adaptive traffic signal control in the presence of transit signal priority (TSP). The method can optimize the phase plan, cycle length, and green splits at isolated intersections with consideration for the performance of both the transit and the general vehicles. Unlike the simple genetic algorithm (GA), PGA can provide better and faster solutions needed for real-time optimization of adaptive traffic signal control. ^ An important component in the proposed method involves the development of a microscopic delay estimation model that was designed specifically to optimize adaptive traffic signal with TSP. Macroscopic delay models such as the Highway Capacity Manual (HCM) delay model are unable to accurately consider the effect of phase combination and phase sequence in delay calculations. In addition, because the number of phases and the phase sequence of adaptive traffic signal may vary from cycle to cycle, the phase splits cannot be optimized when the phase sequence is also a decision variable. A "flex-phase" concept was introduced in the proposed microscopic delay estimation model to overcome these limitations. ^ The performance of PGA was first evaluated against the simple GA. The results show that PGA achieved both faster convergence and lower delay for both under- or over-saturated traffic conditions. A VISSIM simulation testbed was then developed to evaluate the performance of the proposed PGA-based adaptive traffic signal control with TSP. The simulation results show that the PGA-based optimizer for adaptive TSP outperformed the fully actuated NEMA control in all test cases. The results also show that the PGA-based optimizer was able to produce TSP timing plans that benefit the transit vehicles while minimizing the impact of TSP on the general vehicles. The VISSIM testbed developed in this research provides a powerful tool to design and evaluate different TSP strategies under both actuated and adaptive signal control. ^