Learning organizations: Empirically investigating metaphors

Learning organizations are a special form of organization where enhancing learning is a strategy to increase intellectual capital. Developing learning organizations has become an imperative for many managers, since an organization's learning methods and rate may be the only source of sustainable competitive advantage. However, learning organization theory tends to be prescriptive and rhetorical, with empirical research still relatively new. This paper contributes to the literature by reporting case-study research in progress based on four Australian organizations. In the organizations studied, use of the learning organization metaphor was coupled with an emergent metaphor: organization as `family". By employing structure mapping of metaphor within analytical induction, both established methods but not combined before, this paper shows how theory might be developed from metaphor.

System for foreign exchange trading using genetic algorithms and reinforcement learning

Foreign exchange trading has emerged recently as a significant activity in many countries. As with most forms of trading, the activity is influenced by many random parameters so that the creation of a system that effectively emulates the trading process will be very helpful. A major issue for traders in the deregulated Foreign Exchange Market is when to sell and when to buy a particular currency in order to maximize profit. This paper presents novel trading strategies based on the machine learning methods of genetic algorithms and reinforcement learning.

Genes for the majority of group A streptococcal virulence factors and extracellular surface proteins do not confer an increased propensity to cause invasive disease

Background. The factors behind the reemergence of severe, invasive group A streptococcal (GAS) diseases are unclear, but it could be caused by altered genetic endowment in these organisms. However, data from previous studies assessing the association between single genetic factors and invasive disease are often conflicting, suggesting that other, as-yet unidentified factors are necessary for the development of this class of disease. Methods. In this study, we used a targeted GAS virulence microarray containing 226 GAS genes to determine the virulence gene repertoires of 68 GAS isolates (42 associated with invasive disease and 28 associated with noninvasive disease) collected in a defined geographic location during a contiguous time period. We then employed 3 advanced machine learning methods (genetic algorithm neural network, support vector machines, and classification trees) to identify genes with an increased association with invasive disease. Results. Virulence gene profiles of individual GAS isolates varied extensively among these geographically and temporally related strains. Using genetic algorithm neural network analysis, we identified 3 genes with a marginal overrepresentation in invasive disease isolates. Significantly, 2 of these genes, ssa and mf4, encoded superantigens but were only present in a restricted set of GAS M-types. The third gene, spa, was found in variable distributions in all M-types in the study. Conclusions. Our comprehensive analysis of GAS virulence profiles provides strong evidence for the incongruent relationships among any of the 226 genes represented on the array and the overall propensity of GAS to cause invasive disease, underscoring the pathogenic complexity of these diseases, as well as the importance of multiple bacteria and/ or host factors.

Prediction of protein continuum secondary structure with probabilistic models based on NMR solved structures

Background: The structure of proteins may change as a result of the inherent flexibility of some protein regions. We develop and explore probabilistic machine learning methods for predicting a continuum secondary structure, i.e. assigning probabilities to the conformational states of a residue. We train our methods using data derived from high-quality NMR models. Results: Several probabilistic models not only successfully estimate the continuum secondary structure, but also provide a categorical output on par with models directly trained on categorical data. Importantly, models trained on the continuum secondary structure are also better than their categorical counterparts at identifying the conformational state for structurally ambivalent residues. Conclusion: Cascaded probabilistic neural networks trained on the continuum secondary structure exhibit better accuracy in structurally ambivalent regions of proteins, while sustaining an overall classification accuracy on par with standard, categorical prediction methods.

Rule extraction from technology IPOs in the US stock market

Machine learning techniques for prediction and rule extraction from artificial neural network methods are used. The hypothesis that market sentiment and IPO specific attributes are equally responsible for first-day IPO returns in the US stock market is tested. Machine learning methods used are Bayesian classifications, support vector machines, decision tree techniques, rule learners and artificial neural networks. The outcomes of the research are predictions and rules associated With first-day returns of technology IPOs. The hypothesis that first-day returns of technology IPOs are equally determined by IPO specific and market sentiment is rejected. Instead lower yielding IPOs are determined by IPO specific and market sentiment attributes, while higher yielding IPOs are largely dependent on IPO specific attributes.

A shift from passive teaching at medical conferences to more interactive methods improves physician learning

Conferences that deliver interactive sessions designed to enhance physician participation, such as role play, small discussion groups, workshops, hands-on training, problem- or case-based learning and individualised training sessions, are effective for physician education.

Special issue on methods and learning in functional MRI: Introductory remarks

This special issue represents a further exploration of some issues raised at a symposium entitled “Functional magnetic resonance imaging: From methods to madness” presented during the 15th annual Theoretical and Experimental Neuropsychology (TENNET XV) meeting in Montreal, Canada in June, 2004. The special issue’s theme is methods and learning in functional magnetic resonance imaging (fMRI), and it comprises 6 articles (3 reviews and 3 empirical studies). The first (Amaro and Barker) provides a beginners guide to fMRI and the BOLD effect (perhaps an alternative title might have been “fMRI for dummies”). While fMRI is now commonplace, there are still researchers who have yet to employ it as an experimental method and need some basic questions answered before they venture into new territory. This article should serve them well. A key issue of interest at the symposium was how fMRI could be used to elucidate cerebral mechanisms responsible for new learning. The next 4 articles address this directly, with the first (Little and Thulborn) an overview of data from fMRI studies of category-learning, and the second from the same laboratory (Little, Shin, Siscol, and Thulborn) an empirical investigation of changes in brain activity occurring across different stages of learning. While a role for medial temporal lobe (MTL) structures in episodic memory encoding has been acknowledged for some time, the different experimental tasks and stimuli employed across neuroimaging studies have not surprisingly produced conflicting data in terms of the precise subregion(s) involved. The next paper (Parsons, Haut, Lemieux, Moran, and Leach) addresses this by examining effects of stimulus modality during verbal memory encoding. Typically, BOLD fMRI studies of learning are conducted over short time scales, however, the fourth paper in this series (Olson, Rao, Moore, Wang, Detre, and Aguirre) describes an empirical investigation of learning occurring over a longer than usual period, achieving this by employing a relatively novel technique called perfusion fMRI. This technique shows considerable promise for future studies. The final article in this special issue (de Zubicaray) represents a departure from the more familiar cognitive neuroscience applications of fMRI, instead describing how neuroimaging studies might be conducted to both inform and constrain information processing models of cognition.

Extending teaching and learning initiatives in the cross-disciplinary field of biotechnology

Our AUTC Biotechnology study (Phases 1 and 2) identified a range of areas that could benefit from a common approach by universities nationally. A national network of biotechnology educators needs to be solidified through more regular communication, biennial meetings, and development of methods for sharing effective teaching practices and industry placement strategies, for example. Our aims in this proposed study are to: a. Revisit the state of undergraduate biotechnology degree programs nationally to determine their rate of change in content, growth or shrinkage in student numbers (as the biotech industry has had its ups and downs in recent years), and sustainability within their institutions in light of career movements of key personnel, tightening budgets, and governmental funding priorities. b. Explore the feasibility of a range of initiatives to benefit university biotechnology education to determine factors such as how practical each one is, how much buy-in could be gained from potentially participating universities and industry counterparts, and how sustainable such efforts are. One of many such initiatives arising in our AUTC Biotech study was a national register of industry placements for final-year students. c. During scoping and feasibility study, to involve our colleagues who are teaching in biotechnology – and contributing disciplines. Their involvement is meant to yield not only meaningful insight into how to strengthen biotechnology teaching and learning but also to generate ‘buy-in’ on any initiatives that result from this effort.

A randomized trial of a problem-based learning approach for teaching epidemiology

Purpose. To conduct a controlled trial of traditional and problem-based learning (PBL) methods of teaching epidemiology. Method. All second-year medical students (n = 136) at The University of Western Australia Medical School were offered the chance to participate in a randomized controlled trial of teaching methods fur an epidemiology course. Students who consented to participate (n = 80) were randomly assigned to either a PBL or a traditional course. Students who did not consent or did not return the consent form (n = 56) were assigned to the traditional course, Students in both streams took identical quizzes and exams. These scores, a collection of semi-quantitative feedback from all students, and a qualitative analysis of interviews with a convenience sample of six students from each stream were compared. Results. There was no significant difference in performances on quizzes or exams between PBL and traditional students. Students using PBL reported a stronger grasp of epidemiologic principles, enjoyed working with a group, and, at the end of the course, were more enthusiastic about epidemiology and its professional relevance to them than were students in the traditional course. PBL students worked more steadily during the semester but spent only marginally more time on the epidemiology course overall. Interviews corroborated these findings. Non-consenting students were older (p < 0.02) and more likely to come from non-English-speaking backgrounds (p < 0.005). Conclusions. PBL provides an academically equivalent but personally far richer learning experience. The adoption of PBL approaches to medical education makes it important to study whether PBL presents particular challenges for students whose first language is not the language of instruction.

Visualization of learning in multilayer perceptron networks using principal component analysis

This paper is concerned with the use of scientific visualization methods for the analysis of feedforward neural networks (NNs). Inevitably, the kinds of data associated with the design and implementation of neural networks are of very high dimensionality, presenting a major challenge for visualization. A method is described using the well-known statistical technique of principal component analysis (PCA). This is found to be an effective and useful method of visualizing the learning trajectories of many learning algorithms such as back-propagation and can also be used to provide insight into the learning process and the nature of the error surface.

Memory awareness and schematization: Learning in the university context

Following the application of the remember/know paradigm to student learning by Conway et al. (1997), this study examined changes in learning and memory awareness of university students in a lecture course and a research methods course. The proposed shift from a dominance of 'remember' awareness in early learning to a dominance of 'know' awareness as learning progresses and schematization occurs was evident for the methods course but not for the lecture course. The patterns of remember and know awareness and proposed associated levels of schematization were supported by a separate measure of the quality of student learning using the SOLO (Structure of Observed Learning Outcomes) Taxonomy. As found by previous research, the remember-to-know shift and schematization of knowledge is dependent upon type of course and level of achievement. Findings are discussed in terms of the utility of the methodology used, the theoretical implications and the applications to educational practice. Copyright (C) 2001 John Wiley & Sons, Ltd.

Techniques to determine ignition, flame stability and burnout of blended coals in p.f. power station boilers

The blending of coals has become popular to improve the performance of coals, to meet specifications of power plants and, to reduce the cost of coals, This article reviews the results and provides new information on ignition, flame stability, and carbon burnout studies of blended coals. The reviewed studies were conducted in laboratory-, pilot-, and full-scale facilities. The new information was taken in pilot-scale studies. The results generally show that blending a high-volatile coal with a low-volatile coal or anthracite can improve the ignition, flame stability and burnout of the blends. This paper discusses two general methods to predict the performance of blended coals: (1) experiment; and (2) indices. Laboratory- and pilot-scale tests, at least, provide a relative ranking of the combustion performance of coal/blends in power station boilers. Several indices, volatile matter content, heating value and a maceral index, can be used to predict the relative ranking of ignitability and flame stability of coals and blends. The maceral index, fuel ratio, and vitrinite reflectance can also be used to predict the absolute carbon burnout of coal and blends within limits. (C) 2000 Elsevier Science Ltd. All rights reserved.

On learning context-free and context-sensitive languages

The long short-term memory (LSTM) is not the only neural network which learns a context sensitive language. Second-order sequential cascaded networks (SCNs) are able to induce means from a finite fragment of a context-sensitive language for processing strings outside the training set. The dynamical behavior of the SCN is qualitatively distinct from that observed in LSTM networks. Differences in performance and dynamics are discussed.