Ezine and iRadio as knowledge creation metaphors for scaffolding learning in physical and virtual learning spaces

eZine and iRadio represent metaphors for multimedia communication on the Internet. Participating students experience a simulated Internet publishing environment in both their classroom and virtual learning environment. This chapter presents an autoethnographic account highlighting the voices of the learning designer and the teacher and provides evidence of the planning and implementation of two tertiary music elective courses over three iterations of each course. A blended learning environment was incorporated within each elective music course and a collaborative approach to development between lecturers, tutors, learning and technological designers using an iterative research design. The research suggests that learning design which provides real world examples and resources integrating authentic task design into their unit can provide meaningful and engaging experiences for students. The dialogue between learning designers and teachers and iterative review of the learning process and student outcomes, we believe, has engaged students meaningfully to achieve transferable learning outcomes.

The probability archive : essence, uncertainty, and the ‘Olduvai imperative.’

Starting from a local problem with finding an archival clip on YouTube, this paper expands to consider the nature of archives in general. It considers the technological, communicative and philosophical characteristics of archives over three historical periods: 1) Modern ‘essence archives’ – museums and galleries organised around the concept of objectivity and realism; 2) Postmodern mediation archives – broadcast TV systems, which I argue were also ‘essence archives,’ albeit a transitional form; and 3) Network or ‘probability archives’ – YouTube and the internet, which are organised around the concept of probability. The paper goes on to argue the case for introducing quantum uncertainty and other aspects of probability theory into the humanities, in order to understand the way knowledge is collected, conserved, curated and communicated in the era of the internet. It is illustrated throughout by reference to the original technological 'affordance' – the Olduvai stone chopping tool.

“A special kind of city knowledge” : innovative clusters, tacit knowledge and the ‘Creative City’

That the cultural industries are highly networked and operate in clusters is now well established. The notion of cluster is linked to the idea of place-based advantage with cultural industries gaining competitive advantage from mobilising the resources of places to compete in global markets. ‘Place’ in the cultural industries is frequently taken to be the city where city is seen as the key resource for cultural industry clusters and a primary point of intervention for cultural industry policy in creative city policy making. In this article I want to look at some of the implications of these moves for both academic research and policy discussion. The reasons for this emphasis on policy relates to some large questions of urban governance and cultural politics surrounding the proactive government of clusters which are raised by recent work on the cultural industries, notably by Alan Scott.

The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network

Sample complexity results from computational learning theory, when applied to neural network learning for pattern classification problems, suggest that for good generalization performance the number of training examples should grow at least linearly with the number of adjustable parameters in the network. Results in this paper show that if a large neural network is used for a pattern classification problem and the learning algorithm finds a network with small weights that has small squared error on the training patterns, then the generalization performance depends on the size of the weights rather than the number of weights. For example, consider a two-layer feedforward network of sigmoid units, in which the sum of the magnitudes of the weights associated with each unit is bounded by A and the input dimension is n. We show that the misclassification probability is no more than a certain error estimate (that is related to squared error on the training set) plus A3 √((log n)/m) (ignoring log A and log m factors), where m is the number of training patterns. This may explain the generalization performance of neural networks, particularly when the number of training examples is considerably smaller than the number of weights. It also supports heuristics (such as weight decay and early stopping) that attempt to keep the weights small during training. The proof techniques appear to be useful for the analysis of other pattern classifiers: when the input domain is a totally bounded metric space, we use the same approach to give upper bounds on misclassification probability for classifiers with decision boundaries that are far from the training examples.

Neural network learning:theoretical foundations

This important work describes recent theoretical advances in the study of artificial neural networks. It explores probabilistic models of supervised learning problems, and addresses the key statistical and computational questions. Chapters survey research on pattern classification with binary-output networks, including a discussion of the relevance of the Vapnik Chervonenkis dimension, and of estimates of the dimension for several neural network models. In addition, Anthony and Bartlett develop a model of classification by real-output networks, and demonstrate the usefulness of classification with a "large margin." The authors explain the role of scale-sensitive versions of the Vapnik Chervonenkis dimension in large margin classification, and in real prediction. Key chapters also discuss the computational complexity of neural network learning, describing a variety of hardness results, and outlining two efficient, constructive learning algorithms. The book is self-contained and accessible to researchers and graduate students in computer science, engineering, and mathematics

Overcoming the complexity of diagnostic problems due to sensor network architecture

In fault detection and diagnostics, limitations coming from the sensor network architecture are one of the main challenges in evaluating a system’s health status. Usually the design of the sensor network architecture is not solely based on diagnostic purposes, other factors like controls, financial constraints, and practical limitations are also involved. As a result, it quite common to have one sensor (or one set of sensors) monitoring the behaviour of two or more components. This can significantly extend the complexity of diagnostic problems. In this paper a systematic approach is presented to deal with such complexities. It is shown how the problem can be formulated as a Bayesian network based diagnostic mechanism with latent variables. The developed approach is also applied to the problem of fault diagnosis in HVAC systems, an application area with considerable modeling and measurement constraints.

Are low CO2 emitters in rural areas also socially excluded? An empirical investigation of travel diary data from rural Northern Ireland

The reduction of CO2 emissions and social exclusion are two key elements of UK transport strategy. Despite intensive research on each theme, little effort has so far been made linking the relationship between emissions and social exclusion. In addition, current knowledge on each theme is limited to urban areas; little research is available on these themes for rural areas. This research contributes to this gap in the literature by analysing 157 weekly activity-travel diary data collected from three case study areas with differential levels of area accessibility and area mobility options, located in rural Northern Ireland. Individual weekly CO2 emission levels from personal travel diaries (both hot exhaust emission and cold-start emission) were calculated using average speed models for different modes of transport. The socio-spatial patterns associated with CO2 emissions were identified using a general linear model whereas binary logistic regression analyses were conducted to identify mode choice behaviour and activity patterns. This research found groups that emitted a significantly lower level of CO2 included individuals living in an area with a higher level of accessibility and mobility, non-car, non-working, and low-income older people. However, evidence in this research also shows that although certain groups (e.g. those working, and residing in an area with a lower level of accessibility) emitted higher levels of CO2, their rate of participation in activities was however found to be significantly lower compared to their counterparts. Based on the study findings, this research highlights the need for both soft (e.g. teleworking) and physical (e.g. accessibility planning) policy measures in rural areas in order to meet government’s stated CO2 reduction targets while at the same time enhancing social inclusion.

Group maintenance scheduling : a case study for a pipeline network

This paper presents a group maintenance scheduling case study for a water distributed network. This water pipeline network presents the challenge of maintaining aging pipelines with the associated increases in annual maintenance costs. The case study focuses on developing an effective maintenance plan for the water utility. Current replacement planning is difficult as it needs to balance the replacement needs under limited budgets. A Maintenance Grouping Optimization (MGO) model based on a modified genetic algorithm was utilized to develop an optimum group maintenance schedule over a 20-year cycle. The adjacent geographical distribution of pipelines was used as a grouping criterion to control the searching space of the MGO model through a Judgment Matrix. Based on the optimum group maintenance schedule, the total cost was effectively reduced compared with the schedules without grouping maintenance jobs. This optimum result can be used as a guidance to optimize the current maintenance plan for the water utility.

Stochastic models for regulatory networks of the genetic toggle switch

Bistability arises within a wide range of biological systems from the λ phage switch in bacteria to cellular signal transduction pathways in mammalian cells. Changes in regulatory mechanisms may result in genetic switching in a bistable system. Recently, more and more experimental evidence in the form of bimodal population distributions indicates that noise plays a very important role in the switching of bistable systems. Although deterministic models have been used for studying the existence of bistability properties under various system conditions, these models cannot realize cell-to-cell fluctuations in genetic switching. However, there is a lag in the development of stochastic models for studying the impact of noise in bistable systems because of the lack of detailed knowledge of biochemical reactions, kinetic rates, and molecular numbers. In this work, we develop a previously undescribed general technique for developing quantitative stochastic models for large-scale genetic regulatory networks by introducing Poisson random variables into deterministic models described by ordinary differential equations. Two stochastic models have been proposed for the genetic toggle switch interfaced with either the SOS signaling pathway or a quorum-sensing signaling pathway, and we have successfully realized experimental results showing bimodal population distributions. Because the introduced stochastic models are based on widely used ordinary differential equation models, the success of this work suggests that this approach is a very promising one for studying noise in large-scale genetic regulatory networks.

Knowledge translation for effective consumers

With the emergence of patient-centered care, consumers are becoming more effective managers of their care—in other words, “effective consumers.” To support patients to become effective consumers, a number of strategies to translate knowledge to action (KTA) have been used with varying success. The use of a KTA framework can be helpful to researchers and implementers when framing, planning, and evaluating knowledge translation activities and can potentially lead to more successful activities. This article briefly describes the KTA framework and its use by a team based out of the University of Ottawa to translate evidence-based knowledge to consumers. Using the framework, tailored consumer summaries, decision aids, and a scale to measure consumer effectiveness were created in collaboration with consumers. Strategies to translate the products into action then were selected and implemented. Evaluation of the knowledge tools and products indicates that the products are useful to consumers. Current research is in place to monitor the use of these products, and future research is planned to evaluate the effect of using the knowledge on health outcomes. The KTA framework provides a useful and valuable approach to knowledge translation.