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Examines the use of specification language approach in the analysis of discrete event simulation. Reduction of modeling costs using a model specification language; Illustration of language by a classical patrolling model; Objective of the model management system.

The quantum superposition principle allows massive particles to be delocalized over distant positions. Though quantum mechanics has proved adept at describing the microscopic world, quantum superposition runs counter to intuitive conceptions of reality and locality when extended to the macroscopic scale, as exemplified by the thought experiment of Schrödinger’s cat. Matter-wave interferometers, which split and recombine wave packets in order to observe interference, provide a way to probe the superposition principle on macroscopic scales and explore the transition to classical physics. In such experiments, large wave-packet separation is impeded by the need for long interaction times and large momentum beam splitters, which cause susceptibility to dephasing and decoherence. Here we use light-pulse atom interferometry to realize quantum interference with wave packets separated by up to 54 centimetres on a timescale of 1 second. These results push quantum superposition into a new macroscopic regime, demonstrating that quantum superposition remains possible at the distances and timescales of everyday life. The sub-nanokelvin temperatures of the atoms and a compensation of transverse optical forces enable a large separation while maintaining an interference contrast of 28 per cent. In addition to testing the superposition principle in a new regime, large quantum superposition states are vital to exploring gravity with atom interferometers in greater detail. We anticipate that these states could be used to increase sensitivity in tests of the equivalence principle, measure the gravitational Aharonov–Bohm effect, and eventually detect gravitational waves and phase shifts associated with general relativity.

In recent years, we have seen the introduction and use of many distance learning systems. Some of these systems are characterized as interactive, which means the dominant mode of instruction is live or synchronous using networked multimedia technology such as audio, video and shared workspaces. In this paper, we present the common features and essential elements that should be implemented in such systems. Throughout this paper, we will use as a model and case study the IRI-h system (for Interactive Remote Instruction-heterogeneous) that we have developed and implemented in Java and have used to support distance learning at Old Dominion University.

Although redundancy in model specification generally has negative connotations, we offer arguments for revising those convictions. Defining “representational redundancy” as the inclusion of any symbols not required to fulfill the study objectives, we cite several sources of redundancy, classified as accidental or intentional, that contribute positively to the model development tasks. Comparative benefits and detriments are discussed briefly. Focusing on the most interesting source of redundancy‐that which is intentionally induced by a modeling methodology—we demonstrate that automated elimination of redundancy can actually improve model execution time. Using four models drawn from the literature that are easily understood, but which represent some differences in size and complexity, the direct graphical representations shows improvements over a base case ranging from 27.3 percent to 68.1 percent in execution time. Further, increasing improvement is realized with increasing model size and complexity. These results are encouraging because they suggest that modeling methodologies with automated model diagnosis can significantly reduce both execution and developments time and cost.

SUMMARYThrough the integration of high speed computer networks and interactive multimedia workstations, the IRI (Interactive Remote Instruction) system creates a geographically dispersed virtual classroom. Each student participates in a lesson using a personal workstation which can be used to view multimedia lectures, to make presentations, to take notes in a multimedia notebook and to interact via audio/video and shared computer tools. We compare this approach with other modes of instruction and describe lessons learned from the instructor, student and technical perspectives.

This paper describes experiences with the use of the interactive remote instruction (IRI) system which was used to teach a computer science graduate course at Old Dominion University during the Fall 1995 semester. Through the use of high-speed networks and high performance workstations, IRI creates a virtual classroom so that geographically dispersed students can fully participate in a class. Central to this system is the availability of a personal workstation to each student through which the class is taken. Placing a workstation at each student's disposal opens the opportunity for student interaction and group collaboration. We compare this approach with other educational settings and describe lessons learned from instructor, student and technical perspectives. We also describe changes we are putting in place for an expanded usage in the coming fall semester using fiberoptic cable supporting a WAN internet protocol.

Presents letters to the editor referencing articles and topics discussed in previous issues. "Focus on Nov. 3," which focused on the prospects of Senator Barry Goldwater in California; "C'est Moi, de Gaulle," which focused on the foreign relations between the U.S. and France; "What Panama Wants Is Clear: It's the Canal," which focused on the Panama Canal.

An implementation of a double-ended priority queue is discussed. This data structure referred to as min–max–pair heap can be built in linear time; the operations Delete-min, Delete-max and Insert take O(log n) time, while Find-min and Find-max run in O(1) time. In contrast to the min-max heaps, it is shown that two min–max–pair heaps can be merged in sublinear time. More precisely, two min–max–pair heaps of sizes n and k can be merged in time O(log (n/k) * log k).