Probabilistic modelling of CO2 corrosion laboratory data using neural networks

The present paper addresses two major concerns that were identified when developing neural network based prediction models and which can limit their wider applicability in the industry. The first problem is that it appears neural network models are not readily available to a corrosion engineer. Therefore the first part of this paper describes a neural network model of CO2 corrosion which was created using a standard commercial software package and simple modelling strategies. It was found that such a model was able to capture practically all of the trends noticed in the experimental data with acceptable accuracy. This exercise has proven that a corrosion engineer could readily develop a neural network model such as the one described below for any problem at hand, given that sufficient experimental data exist. This applies even in the cases when the understanding of the underlying processes is poor. The second problem arises from cases when all the required inputs for a model are not known or can be estimated with a limited degree of accuracy. It seems advantageous to have models that can take as input a range rather than a single value. One such model, based on the so-called Monte Carlo approach, is presented. A number of comparisons are shown which have illustrated how a corrosion engineer might use this approach to rapidly test the sensitivity of a model to the uncertainities associated with the input parameters. (C) 2001 Elsevier Science Ltd. All rights reserved.

Is emotional intelligence training for leaders justified?

In this paper, we present the results of a qualitative study of subordinate perceptions of leaders. The study represents a preliminary test of a model based on Affective Events Theory, which posits that leaders who are seen to be effective shape the affective events that determine employees' attitudes and behaviours in the workplace. Within this framework, we argue that effective leaders ameliorate employees' hassles by providing frequent, small emotional uplifts. The resulting positive affective states are then proposed to lead to more positive employee attitudes and behaviours, and more positive regard for the leader. Importantly, leaders who demonstrate these ameliorating behaviours are likely to require high levels of emotional intelligence, defined in terms of the ability to recognise, understand, and manage emotions in self and others. To investigate this model, we conducted interviews and focus groups with 10 leaders and 24 employees. Results confirmed that these processes do indeed exist in the workplace. In particular, leaders who were seen by employees to provide continuous small emotional uplifts were consistently held to be the most effective. Study participants were especially affected by negative events (or hassles). Leaders who failed to deal with hassles or, worse still, were the source of hassles, were consistently seen to be less effective. We conclude with a discussion of implications for practicing managers, and suggest that our exploratory findings provide justification for emotional intelligence training as a means to improve leader perceptions and effectiveness. [Abstract from author]

Document retrieval for e-mail search and discovery using Formal Concept Analysis

This paper discusses a document discovery tool based on Conceptual Clustering by Formal Concept Analysis. The program allows users to navigate e-mail using a visual lattice metaphor rather than a tree. It implements a virtual. le structure over e-mail where files and entire directories can appear in multiple positions. The content and shape of the lattice formed by the conceptual ontology can assist in e-mail discovery. The system described provides more flexibility in retrieving stored e-mails than what is normally available in e-mail clients. The paper discusses how conceptual ontologies can leverage traditional document retrieval systems and aid knowledge discovery in document collections.