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18 March 2025 | Story Litha Banjatwa | Photo Supplied
Fiesta winners 2025
Ons wag vir Godot shines at the 2025 kykNET Fiësta Awards, winning three major accolades and cementing UFS’s reputation for world-class theatre excellence.

Ons wag vir Godot, a groundbreaking stage production from the University of the Free State (UFS) Department of Drama and Theatre Arts, was one of the biggest winners at the 2025 kykNET Fiësta Awards, which celebrate the best of Afrikaans theatre.

The awards ceremony was held at the Kirstenbosch National Botanical Garden in Cape Town on 27 February 2025.

Ons wag vir Godot, an Afrikaans translation of Irish writer Samuel Beckett’s celebrated 1953 play Waiting for Godot, won three of its four nominations: Best Director for Dion van Niekerk, Best Translation for Naomi Morgan, and Best Supporting Actor for Gerben Kamper. This haul positioned Ons wag vir Godot as the second biggest winner of the evening, and marked an unprecedented achievement for a Free State production at the Fiësta Awards.

This success builds upon the play’s earlier triumphs at the Free State Arts Festival, where it received accolades for Best Director, Best Translation, Best Supporting Actor (Peter Taljaard), and Best Ensemble.

Director Dion van Niekerk said what set Ons wag vir Godot apart was its unique origin: it is the first Afrikaans translation of Beckett's masterpiece directly from the French original. Securing the translation rights was no small feat, requiring a special appeal to the notoriously selective Samuel Beckett Estate.

“The production’s greatest challenge lay in making the play accessible to a South African audience,” Van Niekerk said. “We aimed to find a stage language with visual imagery that would situate the play within a recognisable South African context."

This was achieved through Naomi Morgan’s “immaculate translation work, which captured the existential concerns of the play with precisely the right Afrikaans vocabulary and turns of phrase”. The production team further grounded the play in South African reality through the creation of characters, setting, and costuming that evoked the stark beauty of the Karoo landscape.

The success of Ons wag vir Godot has profound implications for the UFS Department of Drama and Theatre Arts. It firmly establishes the department among the nation’s leading drama institutions, showcasing its ability to contribute high-quality, meaningful work to the South African artistic landscape. “This production highlights the importance of performing translated classics,” Van Niekerk said. “Works like Waiting for Godot are part of the canon of great international theatrical works. South Africa was banned from producing this play during apartheid, and it has been rarely seen since, predominantly in English.” This production, therefore, offers Afrikaans-speaking South Africans and others a unique opportunity to engage with Beckett’s timeless work.

The impact of this success extends to the department’s students. Sibabalwe Jokani, a student cast member, shared in the nominations for Best Ensemble at both the Free State Festival and Fiësta Awards. Jokani said the play’s success has inspired the student body and reaffirmed the department’s commitment to high standards and industry access.

When asked about the future of Afrikaans theatre, Van Niekerk said, “This production will hopefully inspire others to continue to reconsider the value that great theatrical works that have been created in other languages might have in a contemporary Afrikaans context.”

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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