An investigation of container train service provision and load factors in Great Britain

The continued growth in the volume of international trade poses considerable economic and sustainability challenges, particularly as transport routes become more congested and concern grows about the role of transport movements in accelerating climate change. Rail freight plays a major role in the inland transport of containers passing through the main British container ports, and potentially could play a more significant role in the future. However, there is little detailed understanding of the nature of this particular rail market, especially in terms its current operating efficiency. This paper examines container train service provision to/from the four main ports, based on analysis of a representative survey of more than 500 container trains between February and August 2007. The extent to which the existing capacity is utilised is presented, and scenarios by which the number of containers carried could be increased without requiring additional train service provision are modelled, to identify the theoretical potential for greater rail volumes. Finally, the paper identifies the challenges involved in achieving higher load factors, emphasising the importance both of wider supply chain considerations and government policy decision-making.

Distributed Agent-Based Load Balancer for Cloud Computing

In this paper we present a concept of an agent-based strategy to allocate services on a Cloud system without overloading nodes and maintaining the system stability with minimum cost. To provide a base for our research we specify an abstract model of cloud resources utilization, including multiple types of resources as well as considerations for the service migration costs. We also present an early version of simulation environment and a prototype of agent-based load balancer implemented in functional language Scala and Akka framework.

The immediate and short-term chemosensory impacts of coffee and caffeine on cardiovascular activity

The immediate and short-term chemosensory impacts of coffee and caffeine on cardiovascular activity. Introduction: Caffeine is detected by 5 of the 25 gustatory bitter taste receptors (hTAS2Rs) as well as by intestinal STC-1 cell lines. Thus there is a possibility that caffeine may elicit reflex autonomic responses via chemosensory stimulation. Methods: The cardiovascular impacts of double-espresso coffee, regular (130 mg caffeine) and decaffeinated, and encapsulated caffeine (134 mg) were compared with a placebocontrol capsule. Measures of four post-ingestion phases were extracted from a continuous recording of cardiovascular parameters and contrasted with pre-ingestion measures. Participants (12 women) were seated in all but the last phase when they were standing. Results: Both coffees increased heart rate immediately after ingestion by decreasing both the diastolic interval and ejection time. The increases in heart rate following the ingestion of regular coffee extended for 30 min. Encapsulated caffeine decreased arterial compliance and increased diastolic pressure when present in the gut and later in the standing posture. Discussion: These divergent findings indicate that during ingestion the caffeine in coffee can elicit autonomic arousal via the chemosensory stimulation of the gustatory receptors which extends for at least 30 min. In contrast, encapsulated caffeine can stimulate gastrointestinal receptors and elicit vascular responses involving digestion. Conclusion: Research findings on caffeine are not directly applicable to coffee and vice versa. The increase of heart rate resulting from coffee drinking is a plausible pharmacological explanation for the observation that coffee increases risk for coronary heart disease in the hour after ingestion.

A Meta-Heuristic Load Balancer for Cloud Computing Systems

This paper introduces a strategy to allocate services on a cloud system without overloading the nodes and maintaining the system stability with minimum cost. We specify an abstract model of cloud resources utilization, including multiple types of resources as well as considerations for the service migration costs. A prototype meta-heuristic load balancer is demonstrated and experimental results are presented and discussed. We also propose a novel genetic algorithm, where population is seeded with the outputs of other meta-heuristic algorithms.