A 30-Day-Ahead Forecast Model for Grass Pollen in North London, United Kingdom

A 30-day ahead forecast method has been developed for grass pollen at north London. The total period of the grass pollen season is covered by eight multiple regression models, each covering a 10-day period running consecutively from 21st May to 8th August. This means that three models were used for each 30-day forecast. The forecast models were produced using grass pollen and environmental data from 1961-1999 and tested on data from 2000 and 2002. Model accuracy was judged in two ways: the number of times the forecast model was able to successfully predict the severity (relative to the 1961-1999 dataset as a whole) of grass pollen counts in each of the eight forecast periods on a scale of one to four; and the number of times the forecast model was able to predict whether grass pollen counts were higher or lower than the mean. The models achieved 62.5% accuracy in both assessment years when predicting the relative severity of grass pollen counts on a scale of one to four, which equates to six of the eight 10-day periods being forecast correctly. The models attained 87.5% and 100% accuracy in 2000 and 2002 respectively when predicting whether grass pollen counts would be higher or lower than the mean. Attempting to predict pollen counts during distinct 10-day periods throughout the grass pollen season is a novel approach. The models also employed original methodology in the use of winter averages of the North Atlantic Oscillation to forecast 10-day means of allergenic pollen counts.

Constructing a 7-day Ahead Forecast Model for Grass Pollen at North London, United Kingdom

A number of media outlets now issue medium-range (~7 day) weather forecasts on a regular basis. It is therefore logical that aerobiologists should attempt to produce medium-range forecasts for allergenic pollen that cover the same time period as the weather forecasts. The objective of this study is to construct a medium-range (< 7 day) forecast model for grass pollen at north London. The forecast models were produced using regression analysis based on grass pollen and meteorological data from 1990-1999 and tested on data from 2000 and 2002. The modelling process was improved by dividing the grass pollen season into three periods; the pre-peak, peak and post peak periods of grass pollen release. The forecast consisted of five regression models. Two simple linear regression models predicting the start and end date of the peak period, and three multiple regression models forecasting daily average grass pollen counts in the pre-peak, peak and post-peak periods. Overall the forecast models achieved 62% accuracy in 2000 and 47% in 2002, reflecting the fact that the 2002 grass pollen season was of a higher magnitude than any of the other seasons included in the analysis. This study has the potential to make a notable contribution to the field of aerobiology. Winter averages of the North Atlantic Oscillation were used to predict certain characteristics of the grass pollen season, which presents an important advance in aerobiological work. The ability to predict allergenic pollen counts for a period between five and seven days will benefit allergy sufferers. Furthermore, medium-range forecasts for allergenic pollen will be of assistance to the medical profession, including allergists planning treatment and physicians scheduling clinical trials.

Virtual Worlds for Learning

This chapter examines four papers that have been influential in the use of virtual worlds for learning, but also draws on a range of other research and literature in order to locate virtual world learning across the landscape of higher education. Whilst there is sometimes a misconception that research into learning in virtual worlds is very new, the field began to develop in the late 1990’s and has continued since then. Typical examples of the first iterations of virtual worlds include Second Life, Active Worlds, and Kaneva, which have been available for up to 20 years. The second generation is currently being developed, examples being High Fidelity and Project Sansar. The chapter reviews the literature in this field and suggests central themes that emerge are: Socialisation; Presence and immersion in virtual world learning; Learning collaboratively and Trajectories of participation

Pedagogies in Virtual Worlds

This chapter explores some of the central issues and dilemmas that have emerged from recent research into the pedagogical uses, impact and innovation in virtual worlds. It will begin by discussing the most popular pedagogical approaches employed within the popular virtual world Second Life, noting key trends and identifying areas of potential future growth. It will then consider the ways in which teaching, learning and assessment for Second Life are shaped by and embedded within spatial practices and proxemics, drawing partially on data from two studies undertaken by the authors. The chapter concludes by suggesting that the opportunity to do things differently when designing for disciplinary learning within these new environments, forces a reconsideration of how (virtual) learning spaces might be constituted and experienced by individual users.

Using Social Worlds Theory to Explore Influences on Community Nurses’ Experiences of Providing Out of Hours Paediatric Palliative Care

Abstract Background: Paediatric oncology palliative care in the community is rare and nationally there is a lack of standardisation of out of hours nursing service provision. Objectives: This paper seeks to explore influences on the experiences of paediatric nurses providing out of hours palliative care within the family home to children with cancer. The study used social worlds theory to aid identification and demonstration of the findings. Methods: Twelve community-based palliative cases were purposively selected from children with cancer treated at one regional centre. Tape-recorded interviews were undertaken with 54 health professionals (general practitioners, community nurses and allied health professionals) involved in providing their palliative care and five facilitated case discussions completed. Data analysis followed a grounded theory approach; chronological comparative data analysis identifying generated themes. Social worlds theory was used as a framework to examine the data. Results: Nurses’ experiences are shaped by their social world and those of the nursing team,child and family and the inter-professional team providing the care. The lack of a formalised service, sub-optimal inter-professional working and impact of social worlds influence the experience of the nurse. Conclusions: Social worlds theory provided a new perspective in understanding these experiences based within a paediatric palliative care setting, knowledge that can be used to inform service provision.