Community Councils online: a survey.

A survey of Scottish Community Councils found that only 22% have up-to-date online public presences. Only 4% of Community Councils have easily accessible online planning content. Most Community Councils websites communicate from Community Councils to citizen – only 10% use social media to host online discussion and opinion-gathering. Local Authority-hosted presences guarantee that Community Councils have presence but not that they are up to date. Such presences are also not content-rich. Community Councils’ own volition makes the difference between having no presence at all, mediocre presences and informative, content-rich presences that may serve citizens well. Suggestions to improve matters include broadening the scope of Local Authority-hosted presences and CCs mentoring each other.

Scottish Community Councils online: the 2014 survey.

This document reports the results of a survey, carried out in late spring 2014, of the public web presences of potentially over 1300 Scottish Community Councils (CCs). It follows on from similar survey in summer 2012. The report reviews content-types associated with up-to-date presences and examines Community Councils’ social media use, using three archetypes which may be used to derive models and examples of good practice, and create recommendations for Community Councils and their Local Authorities (LAs). The research found that there has little change overall since 2012, which combined with a high level of churn implies an increasing number of digitally disengaged Community Councils. A good way forward would be for to CCs emulate and adapt the examples of good practice identified, by publishing minutes, news, planning and local area information, limiting publication of other types of content, and using social media to engage with citizens. A number of broader recommendations are made to LAs, including that that they publish CC schemes on their websites, provide training in online methods and work together via their CCLOs and IT teams to support CCs

Prevalence of overweight and obesity among nurses in Scotland: A cross-sectional study using the Scottish Health Survey.

Background Increasing prevalence of overweight and obesity represents a global pandemic. As the largest occupational group in international healthcare systems nurses are at the forefront of health promotion to address this pandemic. However, nurses own health behaviours are known to influence the extent to which they engage in health promotion and the public's confidence in advice offered. Estimating the prevalence of overweight and obesity among nurses is therefore important. However, to date, prevalence estimates have been based on non-representative samples and internationally no studies have compared prevalence of overweight and obesity among nurses to other healthcare professionals using representative data. Objectives To estimate overweight and obesity prevalence among nurses in Scotland, and compare to other healthcare professionals and those working in non-heath related occupations. Design Cross-sectional study using a nationally representative sample of five aggregated annual rounds (2008-2012) of the Scottish Health Survey. Setting Scotland. Participants: 13,483 adults aged 17 to 65 indicating they had worked in the past 4 weeks, classified in four occupational groups: nurses (n = 411), other healthcare professionals (n = 320), unqualified care staff (n = 685), and individuals employed in non-health related occupations (n = 12,067). Main outcome measures: Prevalence of overweight and obesity defined as Body Mass Index ≥ 25.0. Methods Estimates of overweight and obesity prevalence in each occupational group were calculated with 95% confidence intervals (CI). A logistic regression model was then built to compare the odds of being overweight or obese with not being overweight or obese for nurses in comparison to the other occupational categories. Data were analysed using SAS 9.1.3. Results 69.1% (95% CI 64.6,73.6) of Scottish nurses were overweight or obese. Prevalence of overweight and obesity was higher in nurses than other healthcare professionals (51.3%, CI 45.8,56.7), unqualified care staff (68.5%, CI 65.0,72.0) and those in non-health related occupations (68.9%, CI 68.1,69.7). A logistic regression model adjusted for socio-demographic composition indicated that, compared to nurses, the odds of being overweight or obese was statistically significantly lower for other healthcare professionals (Odds Ratio [OR] 0.45, CI 0.33,0.61) and those in non-health related occupations (OR 0.78, CI 0.62,0.97). Conclusions Prevalence of overweight and obesity among Scottish nurses is worryingly high, and significantly higher than those in other healthcare professionals and non-health related occupations. High prevalence of overweight and obesity potentially harms nurses’ own health and hampers the effectiveness of nurses’ health promotion role. Interventions are therefore urgently required to address overweight and obesity among the Scottish nursing workforce.

Tactile Sensing for Mechatronics: A State of the Art Survey

Lee M.H. and Nicholls H.R., Tactile Sensing for Mechatronics: A State of the Art Survey, Mechatronics, 9, Jan 1999, pp1-31.

Evolving Controllers for Real Robots: A Survey of the Literature

Walker,J. and Garrett,S. and Wilson,M.S., 'Evolving Controllers for Real Robots: A Survey of the Literature', Adaptive Behavior, 2003, volume 11, number 3, pp 179--203, Sage

A study of information literacy initiatives between secondary schools and universities in the UK

Lonsdale, R. E. & Armstrong, C. (2006). A study of information literacy initiatives between secondary schools and universities in the UK. In A.B. Martins, A.P. Falcao, E. Conde, I. Andrade, M.B. Nunes, M.J. Vitorino (Eds.), Proceedings of 35th Annual conference of the International Association of School Librarianship, Lisboa (Portugal). The Multiple Faces of Literacy: Reading, Knowing, Doing: Selected papers from the 35th Annual Conference of IASL [CD-ROM: PDF version] Lisbon, Portugal 2006 Sponsorship: JISC

JUSTEIS (JISC Usage Surveys: Trends in Electronic Information Services) Strand A: survey of end users of all electronic information services (HE and FE), with Action research report. Final report 2002/2003 Cycle Four.

Urquhart, C. (editor for JUSTEIS team), Spink, S., Thomas, R., Yeoman, A., Durbin, J., Turner, J., Armstrong, A., Lonsdale, R. & Fenton, R. (2003). JUSTEIS (JISC Usage Surveys: Trends in Electronic Information Services) Strand A: survey of end users of all electronic information services (HE and FE), with Action research report. Final report 2002/2003 Cycle Four. Aberystwyth: Department of Information Studies, University of Wales Aberystwyth with Information Automation Ltd (CIQM). Sponsorship: JISC

Survey of Rough and Fuzzy Hybridization.

P. Lingras and R. Jensen, 'Survey of Rough and Fuzzy Hybridization,' Proceedings of the 16th International Conference on Fuzzy Systems (FUZZ-IEEE'07), pp. 125-130, 2007.

Using a prompt sheet to improve the reference interview in a health telephone helpline service

Price, T., Urquhart, C. & Cooper, J. (2007). Using a prompt sheet to improve the reference interview in a health telephone helpline service. Evidence Based Library and Information Practice, 2(3), 43-58 Open access journal

Folklore Studies and Popular Film and Television: A Necessary Critical Survey

Koven, M. (2003). Folklore Studies and Popular Film and Television: A Necessary Critical Survey. Journal of American Folklore. 116(460), pp.176-195. RAE2008

A survey of fingerprint classification Part I: taxonomies on feature extraction methods and learning models

This paper reviews the fingerprint classification literature looking at the problem from a double perspective. We first deal with feature extraction methods, including the different models considered for singular point detection and for orientation map extraction. Then, we focus on the different learning models considered to build the classifiers used to label new fingerprints. Taxonomies and classifications for the feature extraction, singular point detection, orientation extraction and learning methods are presented. A critical view of the existing literature have led us to present a discussion on the existing methods and their drawbacks such as difficulty in their reimplementation, lack of details or major differences in their evaluations procedures. On this account, an experimental analysis of the most relevant methods is carried out in the second part of this paper, and a new method based on their combination is presented.

A survey of fingerprint classification Part II: experimental analysis and ensemble proposal

In the first part of this paper we reviewed the fingerprint classification literature from two different perspectives: the feature extraction and the classifier learning. Aiming at answering the question of which among the reviewed methods would perform better in a real implementation we end up in a discussion which showed the difficulty in answering this question. No previous comparison exists in the literature and comparisons among papers are done with different experimental frameworks. Moreover, the difficulty in implementing published methods was stated due to the lack of details in their description, parameters and the fact that no source code is shared. For this reason, in this paper we will go through a deep experimental study following the proposed double perspective. In order to do so, we have carefully implemented some of the most relevant feature extraction methods according to the explanations found in the corresponding papers and we have tested their performance with different classifiers, including those specific proposals made by the authors. Our aim is to develop an objective experimental study in a common framework, which has not been done before and which can serve as a baseline for future works on the topic. This way, we will not only test their quality, but their reusability by other researchers and will be able to indicate which proposals could be considered for future developments. Furthermore, we will show that combining different feature extraction models in an ensemble can lead to a superior performance, significantly increasing the results obtained by individual models.