Adverse drug reactions (ADRs) represent one of the main health and economic problems in the world. With increasing data on ADRs, there is an increased need for software tools capable of organizing and storing the information on drug-ADR associations in a form that is easy to use and understand. Here we present a step by step computational procedure capable of extracting drug-ADR frequency data from the large collection of patient safety reports stored in the Federal Drug Administration database. Our procedure is the first of its type capable of generating population specific drug-ADR frequencies. The drug-ADR data generated by our method can be made specific to a single patient population group (such as gender or age) or a single therapy characteristic (such as drug dosage, duration of therapy) or any combination of such.

In 2002, we first brought High Performance Computing (HPC) methods to the college classroom as a way to enrich Computational Science education. Through the years, we have continued to facilitate college faculty in science, technology, engineering, and mathematics (STEM) disciplines to stay current with HPC methodologies. We have accomplished this by designing and delivering faculty workshops, hosted in a variety of lab settings, as well as by developing tools supporting the technical infrastructure necessary for HPC education, all this without requiring access to traditional HPC computing platforms. In all, we have so far presented 16 professional development workshops for close to 400 predominantly undergraduate STEM faculty. This paper presents the result of internal formative evaluation by workshop instructors and the materials and tools developed during that process.We did this work as part of the National Computational Science Institute (NCSI) and in collaboration with the following groups:

In today's high-performance computational environments communication substrates often stand out as the major limiting factor to performance, accessibility, and stability. Such platforms are naturally well-suited for high-performance network foundation built upon Gigabit Ethernet, or "GigE" technologies. Gigabit Ethernet hardware continues to become more attractive in price and availability, making it a viable choice for situations where large bandwidth is demanded. And copper-based Gigabit Ethernet holds additional advantages when considering the additional cost associated with rewiring for fiber technologies. This report characterizes the current capabilities of copper-based gigabit solutions and identifies requisite components for optimal performance. These aspects are compared under various Linux and Windows 2000 configurations as they relate to latency, throughput, and stability for a variety of hardware configurations.

Clustering the rules in a knowledge-based system (KBS) on the basis of their static distance (lexical information) reduces the burden of understanding in the mind of the maintainer. This paper explores a clustering algorithm based on the Hopfield neural net algorithm that clusters automatically using lexical similarity. Using that algorithm, this paper presents a tool that can aid the maintainer in maintaining a KBS. The tool is the Rule Base Clusterizer (RBC) which structures the KBS rule base to make it appear easy to understand for the maintainer. This paper shows by using entropy and Miller's number that the RBC finds the best clustering that produces both an adequate amount of information and acceptable sized clusters. The paper also presents three examples of running the RBC on real-world rule bases. © 1998 John Wiley & Sons, Ltd.

In 1978, a Delphi forecast was undertaken on future developments in personal computers (Nilles et al., 1980). The forecasts are compared with the actual outcomes and indicators available in 1982. The comparison shows the consensus reached by the Delphi panel to be almost completely accurate.

An interdisciplinary research program of the University of Southern California, Los Angeles, has investigated the potential impact of substituting telecommunications and computer technology for certain forms of urban transportation [1]. Emphasis has been placed upon the use of these technologies to alleviate the massive twice-daily peak commuting loads imposed upon most major American cities. The primary means by which these technologies will be used is to allow "information industry" workers to perform their jobs at work centers scattered throughout each city, close to their homes, rather than commuting long distances to work. A basic premise is made that this reorientation of urban work patterns will not occur unless the technological substitutes can be shown to be both economical and effective. The results of a study of a major insurance firm located in the Los Angeles central business district are presented. The study evaluated the present transportation patterns, population distribution, and business contact requirements of a major administrative division of this firm. These commuting and communicating patterns were then used as the basis for assessing the feasibility of the use of various levels of telecommunications and computer technology for allowing the substitution for commuting by "telecommuting." Alternative telecommuting network designs were formulated and evaluated with respect to their ability to facilitate current work functions. Costs of network installation and operation were compared with the present costs of commuting. It is concluded that present communications and computer technology, in terms of "off-the-shelf" hardware and services, can offer an economical substitute for commuting.