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The success of the Eco-Vehicle Programme and future steps

12 June 2024 | Story Leonie Bolleurs | Photo Sonia Small

Eco Vehicle Race 2024 — This year's Eco-Vehicle Skills Programme saw remarkable participation. A total of 148 students completed the programme successfully.

For the past seven years, the University of the Free State’s (UFS) Kovsie ACT has proudly hosted the successful Eco-Vehicle Race. This event has grown into a major highlight, thanks to the significant support from MerSETA (Manufacturing, Engineering and Related Services), which has enabled the development of a comprehensive skills programme focused on sustainable energy and eco-vehicle technology.

In 2020, MerSETA's funding allowed Kovsie ACT to create a detailed skills initiative culminating in the exciting 2021 eco-vehicle race. Over nine months, 150 students received extensive training in eco-vehicle technology. This programme provided students with both theoretical knowledge and practical experience, preparing them not only for the competition but also for real-world applications of sustainable energy solutions.

Dr WP Wahl, Director of Student Life, emphasises the value of this initiative, saying, “This effort provides students with a set of skills that will help position them in the labour market. They are equipped with basic knowledge and abilities in sustainable energy, enabling them not only to compete in the eco-vehicle race but also to comprehend the inner workings of the vehicle.”

CUT Team 4: Overall winner of Kovsie ACT’s Eco-Vehicle Race 2024

According to Teddy Sibiya from the Kovsie ACT office, this year's Eco-Vehicle Skills Programme saw remarkable participation and achievements. A total of 148 students - 118 from the UFS and 30 from the Central University of Technology (CUT) - completed the programme successfully. Additionally, 10 engineering mediators completed the Mediated Learning Experience course, providing mentorship essential to the students.

In the 2024 Kovsie ACT Eco-Vehicle Race, CUT Team 4 emerged as the overall winner. Kovsie Q secured second place and East College took third place. North College won the Spirit Cup and was announced as the pitstop winner alongside East College.

In the Obstacle Race, which tested teams' control over their cars through various challenges, CUT Team 4 claimed the winning title. They also came in first place in the Endurance Race, where the objective was to complete as many laps as possible using the least amount of energy in 45 minutes.

The race took place at the UFS’s Bloemfontein Campus on Akademie Avenue, next to the George du Toit Administration Building, with spectators watching from the Red Square parking area.

Eco-Vehicle Sustainable Skills Programme 2.0 introduced

Sibiya announced the next phase of the journey - the Eco-Vehicle Sustainable Skills Programme 2.0. “With continued support from MerSETA, we have expanded our partnerships to include Nelson Mandela University and will continue to involve students from the Central University of Technology.”

“In the next phase, the focus is on developing a new eco-vehicle prototype and creating an advanced skills programme around it,” adds Sibiya. “We aim to debut and race this new eco-vehicle by 2025, continuing our commitment to innovation and sustainable energy education.”

Dr Wahl elaborates, “Students will be taught the same skills, but the learning experience will be deepened. The skills programme consists of five cycles. In cycle one, the students build a race car on a small scale that includes a charging station and a small solar panel. In cycle two, students learn to programme the small-scale racing car from their cell phones or laptops. In cycles three and four, they build the larger race cars with battery packs and solar panels. All of these come together in cycle five during the Eco-Vehicle race when the energy conservation of the cars is tested.

Support from sponsors

Several sponsors were involved in this year’s Eco-Vehicle Race. OFS Fire supported the race with equipment and certified training for all the participating students. Several of the teams also secured sponsorships: East College from Deluxe Grills, South Campus from SA Truck Bodies, West College from Mpeki Tsh Trading and Project, and the CUT Teams from the South African Institute of Electrical Engineers (SAIEE). Haval also exhibited a car at the event.

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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