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We present two spatially distributed computing systems that operate in a building and provide intelligent navigation services to people for evacuation purposes. These systems adapt to changing conditions by monitoring the building and using local communication and computation for determining the best evacuation paths. The first system, called distributed evacuation system (DES), comprises a network of decision nodes (DNs) positioned at specific locations inside the building. DNs provide people with directions regarding the best available exit. The second system, called opportunistic emergency support system (OESS), consists of mobile communication nodes (CNs) carried by people. CNs form an opportunistic network in order to exchange information regarding the hazard and to direct the evacuees towards the safest exit. Both DES and OESS employ sensor nodes deployed at fixed locations for monitoring the hazard. We evaluate the spatial systems using simulation experiments with a purpose-built emergency simulator called DBES. We show how parameters such as the frequency of information exchange and communication range affect the system performance and evacuation outcome. [ABSTRACT FROM AUTHOR]

We examine a generalisation of the M/G/1 queue. The arrival and service processes are governed by a Markov chain which determines the rate of arrival and the service time distribution from a finite set. This Markov chain is assumed to vary "slowly", so that we are able to derive analytical results for the stationary distribution of the queue length using an approach based on decomposability. The practical interest of this model stems from the numerous applications where the parameters of queueing systems are time varying, such as inventory models, telephone systems, time-sharing systems, computer networks with bursty traffic, etc. We also show how this approach can be extended to arbitrary networks of queues and in particular to those with product form solution. [ABSTRACT FROM AUTHOR]

This guest editorial introduces a special issue of the periodical "IEEE Transactions on Software Engineering" which focuses on neural network software and systems. It outlines the history of artificial neural networks and their role in the origin of the theory of computing. The special issue addresses aspects of neural computation that are closely related to software engineering. The main problem at hand is the development of paradigms to deal effectively with the issues related to neural network programming. The papers in the issue highlight increasingly important issues of artificial neural network programming tools and systems, and suggest applications of neural networks to the diverse and difficult problems of software engineering.