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13 March 2019 | Story Zama Feni | Photo Zama Feni
Career fair
UFS BCom (Marketing) student, Thandokazi Kiviet, who works part time for fellow master’s student Refilwe Xaba’s hair-product company, Glolooks, shows off their products to visitors at the Annual Careers Fair last week.

Budding student entrepreneurs from the University of the Free State presented their creative displays during the first part of the university’s 2019 Annual Careers Fair at the Callie Human Hall last week.

The first leg of the fair was in the Faculty of Economic and Management Sciences, with more than 15 companies exhibiting their products and explaining to students their business operations, career prospects, and employment opportunities.

Students’ construction business gets off the ground

Three ladies, Mannini Setai (master’s in Law), Refilwe Mogole (PhD in Chemistry), and Nthabiseng Molejane (honours in the Humanities) registered their company, Ahang Amalmagate Trading, in 2016 and have been operating since late 2017.

Mogole said they are currently operating from a backyard in Parys, but they have a manager on site who deals with the technical aspects of their business and runs the daily operations. 

“We managed to buy a brick-making machine, which enabled us to make up to 1 000 bricks per day; at this stage, we provide bricks to private homeowners,” she said.

The ladies said winning three competitions last year gave them a financial boost that aided them greatly; these included the Nampak Entrepreneurship Competition, the Free State Department of Economic, Small Business Development, Tourism and Environmental Affairs Tabalaza Pitching Programme, as well as the UFS Directorate for Research Development’s business pitching competition.

“As a result of these competitions, we managed to save some cash to buy ourselves a brick-making machine,” said Setai, adding that they are using social networks to market their product.

Hair-product business gives hope

Another student business stall was that of Glolooks – an emerging hair-products company established by UFS student, Refilwe Xaba, who has just finished her master’s programme in Entrepreneurship at the UFS. “The biggest challenge is access to the markets, but my business is doing fairly well here in Bloemfontein; our use of online media and social networks to market our products is keeping us there,” said Ms Xaba. She said she has just opened an ethnic hair salon in Westdene.

Taking it slowly, but surely

Another UFS student was Anet Matakala of Nettah Organics (Law degree) who makes organic products such as green tea, bath salts, chocolate coffee, cannabis butter, etc., from food-based ingredients. “It’s not an easy road, but step by step, we are getting there,” she said.

The last student was Keagan Nkwaira, who started a clothing company named ‘Weather’ last year. “What drove me to starting this venture was a passion for design and a need to raise cash. Business hasn’t been good so far, but I will have to find marketing initiatives that will get my work to the potential customers,” he said.


Career fairs benefit students

Head of Career Services, Belinda Janeke, said there will be four more career fairs catering for the Faculties of Law and Natural and Agricultural Sciences during the course of the year, and two general career fairs in May and August.

“The career fairs help to connect our students with the world of work, it helps to broaden the horizons for students because they can enquire about the products or services provided by the respective companies, and it can also create job and internship opportunities,” she said.  

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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