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Research Publications (Engineering and Built Environment)

Permanent URI for this collectionhttp://ir-dev.dut.ac.za/handle/10321/215

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    Gender and gender mainstreaming In engineering education in Africa
    (Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP, 2020) Fomunyam, Kehdinga George; Matola, Noluthando; Moyo, Sibusiso
    In Africa, a lot of debates on the issues of gender gap and gender inequality has raised concerns in engineering education (EE) and engineering workforce. Thus, gender inequality and equity are significant in realizing Sustainable Development Goals (SDGs), and in recent years much has been done to address gender gaps, yet women are still excluded, underrepresented, segregated and relegated inengineering profession and academia. With much sensitization on gender equality, Africa is still far from addressing gender gaps in EE; hence the crux of this paper. This paper was guided by Liberal Feminism theory, focusing on women’s freedom as an autonomy to be free from coercive interference, due to‘gender system’ or patriarchal nature of inherited traditions and institutions. This paper takes a broad look at the concepts of gender and gender mainstreaming in EE in Africa. Specifically, it explores gender and inequality in EE and how gender mainstreaming canbe enacted to address gender gaps in EE, as well as its implications in Africa. Thus, to address these gaps, recommendations such as developing gendersensitive curriculum for EE, adopting policies in facilitating women’s access to training and employment opportunities as well as creating gender-sensitive career counselling were advocated
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    Using computational intelligence
    (FIMS, 2014-12) Singh, Navin Runjit; Peters-Futre, Edith M.
    The aim of this study was to assess the efficacy of using artificial neural networks (ANNs) to classify hydration status and predict the fluid requirements of endurance athletes. Hydration classification models were built using a total of 237 data sets obtained from 148 participants (106 males,42 females) in field-and laboratory studies involving running or cycling. 116 data sets obtained from athletes who completed endurance events euhydrated (plasma osmolality: 275-295 mmol.kg-1) following ad libitum replenishment of fluid intake was used to design prediction models. A filtering algorithm was used to determine the optimal inputs to the models from a selection of 13 anthropometric, exercise performance, fluid intake and environmental factors. The combination of gender, body mass, exercise intensity and environmental stress index in the prediction model generated a root mean square error of 0.24 L.h-1 and a correlation of 0.90 between predicted and actual drinking rates of the euhydrated participants. Additional inclusion of actual fluid intake resulted in the design of a model that was 89% accurate in classifying the post-exercise hydration status of athletes. These findings suggest that the ANN modelling technique has merit in the prediction of fluid requirements and as a supplement to ad libitum fluid intake practices.