This research focuses on the development of a model for evaluating the human impact that password authentication issues are having on the security of information systems. Through observational analysis, organizational policy, and retrospective analysis, researchers created a model for predicting the vulnerability that a particular set of conditions will have on the likelihood of error in an information system. The methodology for the experiment and analysis of the results are presented. The findings indicate that human error associated with password authentication can be significantly reduced through the use of passwords which are comprised of meaningful data for the user and which meet the information technology community requirement for strength of password. The details of this study are provided as well as the human factors implications in information security.

The University of Central Florida (UCF) was contracted by the Florida Center for Solid and Hazardous Waste Management (FCSHWM) to develop a well-defined methodology for conducting municipal solid waste composition studies. This methodology must account for the statistical variations in waste composition, be economical and practical in implementation, and build on a consensus of waste management professionals. This paper identifies possible sources of bias in waste composition study results and provides guidance for future planning of local waste stream composition analysis. To accomplish this objective, a composition study was designed and implemented for Marion County, FL, in fall 1996. The potential sources of concern investigated in detail were sample weight and contamination.The methodology developed by UCF is statistically valid and if widely implemented would provide a better representation of the waste stream. Lack of contamination adjustment is a major contributor to error in the waste stream analysis and should be accounted for in the methodology. For sample sorts using a large number of categories, sample size may be a contributor to bias. This likelihood for bias can be reduced by increasing the sample weight to at least IMPLICATIONS Because of the highly variable nature of municipal solid waste, characterization studies are essential to proper waste management. Waste composition studies are frequently performed without adequate study preparation and planning. The research presented in this paper clearly demonstrates the need to consider many factors in planning a study to ensure high-quality results.

This research evaluates the human impact that password authentication issues have on the security of information systems within organizations. This research resulted in the creation of password guidelines for authentication with passwords based on Miller's (1956) and Cowan's (2001) chunking theory research and a model for predicting the vulnerability that a particular set of conditions have on the likelihood of error in an information system. The findings indicate that human error associated with password authentication can be significantly reduced through the use of passwords that are composed of meaningful data for the user and that meet technical requirements for strong passwords.

This paper presents Part I in a two-phase research project to develop a fuzzy-linguistic expert system for quantifying and predicting the risk of occupational injury, specifically, cumulative trauma disorders (CTD's) of the forearm and hand. This aspect of the research focuses on the development and representation of linguistic variables to qualify risk levels. These variables are then quantified using fuzzy-set theory, thus allowing the model to evaluate qualitative and quantitative data. These linguistic risk variables may be applied to other potentially hazardous environments. The three phases of the knowledge acquisition and variable development are covered, as well as the feasibility of the linguistic variables.

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