Women in engineering in Turkey – a large scale quantitative and qualitative examination

The underrepresentation of women in engineering is well known and unresolved. However, Turkey has witnessed a shift in trend from virtually no female participation in engineering to across-the-board proportions that dominate other industrialised countries within the 76 years of the founding of the Turkish Republic. This paper describes the largest known direct cross-sectional study of women in engineering in Turkey with over 800 participants. The methods include survey and facilitated focus groups. The study shows that women in Turkey choose engineering mainly because they enjoy the underlying mathematics and science. There is no gender bias on the part of teachers or fellow students; however, women students believe that they have fewer opportunities than male peers and acutely feel the lack of role models. Working professionals in industry or government perceive that women assume a more indirect, supporting role; however, women overall strongly affirm their selection of engineering despite some negative factors.     

A successful cross-training experience of reference service at UCLA

This paper describes a successful cross-training and reference librarian exchange experience involving two librarians at UCLA, one based at the Louise M. Darling Biomedical Library and the other based at the College Library, which primarily serves undergraduates. The experience has increased the reference skills and expertise of both librarians by introducing them to new sets of reference tools and databases, and has broadened their network of colleagues within the UCLA Library system. The participating libraries have also benefited from the exchange, and the program is expanding to include other UCLA libraries.     

Machine and deep learning for sport-specific movement recognition: a systematic review of model development and performance

Objective assessment of an athlete’s performance is of importance in elite sports to facilitate detailed analysis. The implementation of automated detection and recognition of sport-specific movements overcomes the limitations associated with manual performance analysis methods. The object of this study was to systematically review the literature on machine and deep learning for sport-specific movement recognition using inertial measurement unit (IMU) and, or computer vision data inputs. A search of multiple databases was undertaken. Included studies must have investigated a sport-specific movement and analysed via machine or deep learning methods for model development. A total of 52 studies met the inclusion and exclusion criteria. Data pre-processing, processing, model development and evaluation methods varied across the studies. Model development for movement recognition were predominantly undertaken using supervised classification approaches. A kernel form of the Support Vector Machine algorithm was used in 53% of IMU and 50% of vision-based studies. Twelve studies used a deep learning method as a form of Convolutional Neural Network algorithm and one study also adopted a Long Short Term Memory architecture in their model. The adaptation of experimental set-up, data pre-processing, and model development methods are best considered in relation to the characteristics of the targeted sports movement(s).     

Editorial Introduction

ABSTRACT

Eighteenth-century England saw a close association between personal decency and the condition, cleanliness and quality of underwear – this shirt or shift, usually made from linen, was used to judge status and respectability. Novels and a pawnbroker’s book reveal that inadequate underwear shamed its owner. Bodily intimacy had the potential to create emotional attachments, illustrated by the poignant Derwentwater sheet. However quantitative analysis of wills and textiles from the Foundling Hospital, which continues as the children’s charity Coram, indicate that bodily intimacy did not automatically create emotional meaning and economic value could have primacy over emotional attachment.