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24 May 2021 | Story Xolisa Mnukwa
The 2021 Kovsie ACT Eco-vehicle race puts students’ sustainable energy and critical thinking skills to the test.

The University of the Free State (UFS) Division of Student Affairs’ (DSA) Director of Student Life, Dr WP Wahl, believes the knowledge and skills that students have gained through participating in the 2021 Eco-vehicle project will position them more optimally in the future world of work. “We are also tremendously grateful for the project funding received from merSETA; without their support, none of this would have been possible,” he remarked.

The Kovsie ACT Eco-vehicle race, in conjunction with the overall programme, was established to encourage students to learn more about the technology and logic behind sustainable energy sources and how this can influence the future global society.

This year’s events witnessed students competing according to their UFS residence teams, with Sonnedou, Legatum, Kestell (SonLeTell); Soetdoring, Beyers Naude, Arista (Soetbeyrista); and Roosmaryn, Kagiso, Karee (Kar-is-myn) ending in first, second, and third place respectively, obtaining the highest scores for the races they competed in.

Anton Calitz, Electrical Engineer in University Estates who was the announcer on the day, described the event as one that exceeded his wildest expectations. “From a sustainable energy point of view, the eco-vehicle race results really turned the tables, with lower energy usage per lap being successfully recorded – as anticipated,” he further added.

Andre van Wyk, Client Liaison Officer of merSETA (Manufacturing, Engineering and Related Services Seta) for the Free State and Northern Cape – as one of the sponsors of the innovative programme – extended warm congratulations to the UFS for hosting an outstanding event. He further applauded the university for its resourcefulness in virtually adding electronic media broadcasts to extend the event to the entire UFS community.

“The Kovsie ACT Eco-vehicle programme was eye-opening and exposed me to the broad field of electronics. It definitely came as a challenge – one I had not anticipated on that level, because at times I couldn’t even see what all the building was leading to, but I just had to put my mind and hands to work – it pushed me to think critically and creatively. I was honoured to have been part of this entire experience and I’m grateful to Anton and his team, as well as the Kovsie ACT office, for being a constant support structure throughout the process, as it was not easy.” 

These were the humble words of Sinegugu Sibisi, a University of the Free State (UFS) student who was part of the 2021 Kovsie ACT Eco-vehicle race, where sustainable energy was at the order of the day.

For more information about the Kovsie ACT eco-vehicle skills programme, email ACT at ACT@ufs.ac.za
 

 

News Archive

Mathematical methods used to detect and classify breast cancer masses
2016-08-10

Description: Breast lesions Tags: Breast lesions

Examples of Acho’s breast mass
segmentation identification

Breast cancer is the leading cause of female mortality in developing countries. According to the World Health Organization (WHO), the low survival rates in developing countries are mainly due to the lack of early detection and adequate diagnosis programs.

Seeing the picture more clearly

Susan Acho from the University of the Free State’s Department of Medical Physics, breast cancer research focuses on using mathematical methods to delineate and classify breast masses. Advancements in medical research have led to remarkable progress in breast cancer detection, however, according to Acho, the methods of diagnosis currently available commercially, lack a detailed finesse in accurately identifying the boundaries of breast mass lesions.

Inspiration drawn from pioneer

Drawing inspiration from the Mammography Computer Aided Diagnosis Development and Implementation (CAADI) project, which was the brainchild Prof William Rae, Head of the department of Medical Physics, Acho’s MMedSc thesis titled ‘Segmentation and Quantitative Characterisation of Breast Masses Imaged using Digital Mammography’ investigates classical segmentation algorithms, texture features and classification of breast masses in mammography. It is a rare research topic in South Africa.

 Characterisation of breast masses, involves delineating and analysing the breast mass region on a mammogram in order to determine its shape, margin and texture composition. Computer-aided diagnosis (CAD) program detects the outline of the mass lesion, and uses this information together with its texture features to determine the clinical traits of the mass. CAD programs mark suspicious areas for second look or areas on a mammogram that the radiologist might have overlooked. It can act as an independent double reader of a mammogram in institutions where there is a shortage of trained mammogram readers. 

Light at the end of the tunnel

Breast cancer is one of the most common malignancies among females in South Africa. “The challenge is being able to apply these mathematical methods in the medical field to help find solutions to specific medical problems, and that’s what I hope my research will do,” she says.

By using mathematics, physics and digital imaging to understand breast masses on mammograms, her research bridges the gap between these fields to provide algorithms which are applicable in medical image interpretation.

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