Data-driven fuzzy analysis in quantitative mineral resource assessment

The integration of geo-information from multiple sources and of diverse nature in developing mineral favourability indexes (MFIs) is a well-known problem in mineral exploration and mineral resource assessment. Fuzzy set theory provides a convenient framework to combine and analyse qualitative and quantitative data independently of their source or characteristics. A novel, data-driven formulation for calculating MFIs based on fuzzy analysis is developed in this paper. Different geo-variables are considered fuzzy sets and their appropriate membership functions are defined and modelled. A new weighted average-type aggregation operator is then introduced to generate a new fuzzy set representing mineral favourability. The membership grades of the new fuzzy set are considered as the MFI. The weights for the aggregation operation combine the individual membership functions of the geo-variables, and are derived using information from training areas and L, regression. The technique is demonstrated in a case study of skarn tin deposits and is used to integrate geological, geochemical and magnetic data. The study area covers a total of 22.5 km(2) and is divided into 349 cells, which include nine control cells. Nine geo-variables are considered in this study. Depending on the nature of the various geo-variables, four different types of membership functions are used to model the fuzzy membership of the geo-variables involved. (C) 2002 Elsevier Science Ltd. All rights reserved.

Unfreezing the frame: Functional fram analysis for journalists and journalism students

This paper presents two case studies that suggest, in different but complementary ways, that the critical tool of frame analysis (Entman, 2002) has a place not only in the analytical environments of critical media research and media studies classes, where it is commonly found, but also in the media-production oriented environments of skills-based journalism training and even the newsroom. The expectations and constraints of both the latter environments, however, necessitate forms of frame analysis that are quick and simple. While commercial pressures mean newsrooms and skills-based journalism-training environments are likely to allow only an oversimplified approach to frame analysis, we argue that even a simple understanding and analysis at the production end could help to shift framing in ways that not only improve the quality and depth of Australasian newspapers' news coverage, but increase reader satisfaction with media output.

Pitch jump probability measures for the analysis of snoring sounds in apnea

Obstructive sleep apnea (OSA) is a highly prevalent disease in which upper airways are collapsed during sleep, leading to serious consequences. The gold standard of diagnosis, called polysomnography (PSG), requires a full-night hospital stay connected to over ten channels of measurements requiring physical contact with sensors. PSG is inconvenient, expensive and unsuited for community screening. Snoring is the earliest symptom of OSA, but its potential in clinical diagnosis is not fully recognized yet. Diagnostic systems intent on using snore-related sounds (SRS) face the tough problem of how to define a snore. In this paper, we present a working definition of a snore, and propose algorithms to segment SRS into classes of pure breathing, silence and voiced/unvoiced snores. We propose a novel feature termed the 'intra-snore-pitch-jump' (ISPJ) to diagnose OSA. Working on clinical data, we show that ISPJ delivers OSA detection sensitivities of 86-100% while holding specificity at 50-80%. These numbers indicate that snore sounds and the ISPJ have the potential to be good candidates for a take-home device for OSA screening. Snore sounds have the significant advantage in that they can be conveniently acquired with low-cost non-contact equipment. The segmentation results presented in this paper have been derived using data from eight patients as the training set and another eight patients as the testing set. ISPJ-based OSA detection results have been derived using training data from 16 subjects and testing data from 29 subjects.

An examination of the dynamic relationship between self-efficacy and performance across levels of analysis and levels of specificity

This research used resource allocation theory to generate predictions regarding dynamic relationships between self-efficacy and task performance from 2 levels of analysis and specificity. Participants were given multiple trials of practice on an air traffic control task. Measures of task-specific self-efficacy and performance were taken at repeated intervals. The authors used multilevel analysis to demonstrate differential and dynamic effects. As predicted, task-specific self-efficacy was negatively associated with task performance at the within-person level. On the other hand, average levels of task-specific self-efficacy were positively related to performance at the between-persons level and mediated the effect of general self-efficacy. The key findings from this research relate to dynamic effects - these results show that self-efficacy effects can change over time, but it depends on the level of analysis and specificity at which self-efficacy is conceptualized. These novel findings emphasize the importance of conceptualizing self-efficacy within a multilevel and multispecificity framework and make a significant contribution to understanding the way this construct relates to task performance.

Training emotional intelligence: Presenting the results of an experimental study

This presentation outlines the results of an eighteen month study examining the effect of an emotions focused training intervention on the emotional intelligence of employees from a large public sector organisation. Utilising an experimental methodology, 280 staff attended a two-day program focused on training emotional intelligence skills and abilities. These interventions were created around Mayer and Salovey’s four-branch model of emotional intelligence (awareness, understanding, facilitation and management of emotions). The experimental group’s emotional intelligence was tested pre and post training using the Workgroup Emotional Intelligence Profile (WEIP). In addition, a control group from the same organisation also completed the same measure at three points during the same eighteen month period. Analysis of the control and experimental group data were conducted, and whilst no changes were found in the control group, the experimental group’s overall emotional intelligence significantly improved post training. To further strengthen these findings, a measure of effect size using Cohen’s d was also conducted to assess the magnitude of the training intervention’s overall effect. Full results will be presented during the presentation, with feedback on the study and methods utilised encouraged from participants.

The status and relevance of Vietnamese journalism education: An empirical analysis

Based primarily on data from indepth interviews with senior journalists and journalism educators as well as a content analysis of journalism curricula, this paper sets out to provide an overview of the demand, overall provision structure, teaching materials and methods of Vietnamese journalism education. It first shows that with a fast expansion in both size and substance, the Vietnamese media system is beginning to feel the urgent need for formal journalism education. However, the country's major journalism programs have been criticised for producing hundreds of unqualified journalism graduates a year. In general, the most deplorable aspects of Vietnamese journalism education include its body of outdated and awkward teaching material, its undue focus on theories and politics at the expense of practical training, its lack of qualified teaching staff and its inadequate teaching resources.

Training selective use of a complex categorisation strategy

The present study aims to encourage selective use of a complex categorisation strategy. More specifically, participants will be trained to use a two dimensional strategy in one region of category space and a more complex three-dimensional strategy in another region of category space. In the 2–3 conditions, participants will be presented with stimuli requiring the two-dimensional strategy in the first phase of training and the three-dimensional strategy in the second phase of training. In the 3-2 conditions, participants will be presented with stimuli requiring the three-dimensional strategy in the first phase of training and the two-dimensional strategy in the second phase of training. The main dependent measure will be performance on exceptions to the two-dimensional strategy. If participants learn to selectively use the three-dimensional strategy, then we expect them to correctly classify novel exceptions that occur in the three-dimensional region of the category space and incorrectly classify novel exceptions that occur in the two-dimensional region of the category space.

Virtual training environments to improve train driver's crisis decision making

This paper reports on a current research project in which virtual reality simulators are being investigated as a means of simulating hazardous Rail work conditions in order to allow train drivers to practice decision-making under stress. When working under high stress conditions train drivers need to move beyond procedural responses into a response activated through their own problem-solving and decision-making skills. This study focuses on the use of stress inoculation training which aims to build driver’s confidence in the use of new decision-making skills by being repeatedly required to respond to hazardous driving conditions. In particular, the study makes use of a train cab driving simulator to reproduce potentially stress inducing real-world scenarios. Initial pilot research has been undertaken in which drivers have experienced the training simulation and subsequently completed surveys on the level of immersion experienced. Concurrently drivers have also participated in a velocity perception experiment designed to objectively measure the fidelity of the virtual training environment. Baseline data, against which decision-making skills post training will be measured, is being gathered via cognitive task analysis designed to identify primary decision requirements for specific rail events. While considerable efforts have been invested in improving Virtual Reality technology, little is known about how to best use this technology for training personnel to respond to workplace conditions in the Rail Industry. To enable the best use of simulators for training in the Rail context the project aims to identify those factors within virtual reality that support required learning outcomes and use this information to design training simulations that reliably and safely train staff in required workplace accident response skills.