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07 January 2025 | Story Gerda-Marie van Rooyen | Photo Supplied
KovsieX
KovsieX offers a comprehensive digital experience through podcasts, video content, and social media. This initiative is set to transform the student experience, creating a strong sense of belonging and collaboration across campuses.

Optimising student experience while providing students with multimedia training using state-of-the-art equipment and aligning with Vision 130, KovsieX is set to become a great asset to the university, its students, and the community. 

This initiative, approved by the UFS Rectorate on 29 November 2023, combines various student media brands on the Bloemfontein and Qwaqwa campuses (KovsieFM, Q-Lit, KovsieTV, KovsieCAST) into a unified brand consisting of three student-driven sub-departments. This includes audio (radio and podcasts), video (long and short form), and social media (including TikTok, Instagram, WhatsApp, and YouTube). 

An all-digital approach 

Gerben van Niekerk, Head of Student Experience (KovsieX), explains: “This all-digital approach leverages digital radio, podcasts, and social media platforms to create a sense of belonging among students by reflecting on and leading student life across the campuses.” KovsieX has achieved remarkable success, reaching an audience of more than 1,2 million in the first semester alone, with multiple TikTok videos surpassing 100 000 views. 

“Recognising the evolving radio landscape, our approach integrates a comprehensive digital strategy to adapt to changing media consumption preferences and provide students with hands-on experience on emerging platforms, strengthening their market relevance. KovsieX (previously KovsieFM) moves away from traditional FM broadcasting and has enabled the students to cover a wider range of topics that affect the Kovsie community,” says Van Niekerk. He adds, “The essence of KovsieX can be summarised in our one-word slogan: IMAGINE.”  

KovsieX supports Vision 130, as it leverages emerging technologies to enrich academic and non-academic student experiences. Furthermore, it also provides students with the opportunity to gain on-the-job and leadership experience in the KovsieX executive committee (KovsieXco), comprising a small group of ‘dynamic and highly talented students’, with their first objective: to decide on a brand name and setting on KovsieX – with the ‘X’ referring to experience. 

A mobile app provides students with easier access to KovsieX’s content. This initiative is set to increase students’ experience even more, as possible partnerships are in the pipeline to deliver a year-long dialogue series on themes pertinent to students. “This initiative will engage students on key issues such as leadership, mental health, heritage, and anti-discrimination through a blend of digital content – including interviews, social media posts, and expert discussions – and live on-campus events.”  

State-of-the-art facilities 

The construction of the KovsieX Pod on the Bloemfontein Campus allows students to produce content in a state-of-the-art podcast and video studio with Apple Mac workstations and a meeting room. A similar space in the current Student Media Building on the Qwaqwa Campus, named the KovsieX Q-Pod, is on the cards, as is the integration of KovsieX across the Bloemfontein and Qwaqwa campuses. “KovsieX will be broadcast from two locations and will, therefore, allow students from both campuses to interact with one another live on air. Both radio studios will be rebuilt to allow students to stream directly on YouTube, Instagram, and TikTok from both campuses simultaneously. This is made possible by cutting edge cloud-based software – popular in Europe – but KovsieX will be the first to leverage this technology in the country,” shares Van Niekerk.

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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