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UFS Modular Lecture Building gets national recognition

11 May 2023 | Story Samkelo Fetile | Photo iFlair Photography

The University of the Free State’s (UFS) Modular Lecture Building on its Bloemfontein Campus recently received a National Merit Award from the South African Institute of Architects (SAIA). The awards were announced at the 2021/2022 Corobrik SAIA Awards of Merit and Awards for Excellence ceremony in Johannesburg.

The multi-functional Modular Lecture Building, considered a hub for innovative learning, was designed by Roodt Architects in partnership with GXY Architects.

The adjudication panel received a total of 42 architectural projects from around the country, including infrastructure developments in the public and private sector. The SAIA Awards programme is structured over a two-year period and is conducted in two stages. In stage one regional awards for architecture are presented by the nine regional institutes affiliated to SAIA. In stage two the winning regional projects that are consequently entered into the national awards receive either a Commendation, an Award of Merit, and/or an Award for Excellence, which recognises exceptional achievement in the field of architecture.

In their citation the adjudicators noted that the Modular Lecture Building sets a benchmark for rational planning and technical efficiency and helps complete the campus urban framework through its placing and material choices.

Multi-functional spaces for students

Nico Janse van Rensburg, Senior Director at UFS University Estates, said the recognition is a testament to the UFS’s aspirations to renew, rejuvenate, regenerate, and revisit facilities and infrastructure.

“This award proves that excellence can be achieved with a reasonable set budget,” Janse van Rensburg said. “Energy efficiency and green building principles can be achieved by careful planning and teamwork.”

The Modular Lecture Building offers a variety of much-needed flexible teaching and learning spaces. “I have been using the facilities in this building for two years now, and I can say the building is much more spacious and conducive to studying,” said Hymne Spies, a third-year BSc student majoring in biochemistry and genetics. “The many plugs make it more efficient for studying, as one can plug in his or her laptop. There is also a nice computer lab for us to make use of.”

The UFS is proud that the construction of this facility forms part of a bigger endeavour – to create a cohesive campus identity that improves core business and to further extend its innovation and excellence as per its Vision 130.

Take a tour of the new Modular Lecturing Space and Assessment Centre Building:

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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