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Prof Abdon Atangana highest-ranked UFS scientist in top 2% of global scientists

09 November 2023 | Story André Damons | Photo SUPPLIED

Prof Atangana — Prof Abdon Atangana, a professor of Applied Mathematics at the University of the Free State (UFS), is the highest-ranked UFS scientist included in Stanford University’s World’s Top 2% Scientists list.

A professor of Applied Mathematics at the University of the Free State (UFS) is again the highest-ranked scientist from the institution included in Stanford University’s annual ranking of the top 2% of scientists in the world.

Prof Abdon Atangana from the UFS’s Institute for Groundwater is ranked number one in applied mathematics, mathematical physics, mathematics, and statistics in the world, and number 260 in all of science, technology, and engineering in the Stanford University World’s Top 2% Scientists list. He is also ranked highest (5 620) of all the UFS scientists included in the career-long data set.

‘Africans in Africa can impact the world’

“The ranking provides us with the impact of our outputs, and it shows that Africans can contribute to the development of science, technology, engineering, and mathematics while still in Africa,” Prof Atangana said. “This also shows that Africans in Africa can have impact on the world. My motivation is to tell the next generation that Africans do not always need to graduate from the top universities of the global North to make a global impact.

“We must work hard to make our African universities reach the same level of those from the global North, such that a student from the global North will wish to enroll in our universities. The development of our continent does not rest on sport, music, and so forth alone, but on science, technology, engineering, and mathematics. Having the best scientists, mathematicians, and engineers in the world in Africa should be the strive of all Africans.”

Three of the UFS’s SARChI Research Chairs have also been included in this list: Prof Hendrik Swart, Chair: Solid-state Luminescent and Advanced Materials (Applied Physics, ranked 40 269 in the single-year dataset); Prof Melanie Walker, Chair: Higher Education and Human Development (ranked 68 337); and Prof Maryke Labuschagne, Chair: Disease Resistance and Quality in Field Crops (Plant Sciences, 165 780).

Other UFS scientists included in the single-year data set are: Prof John M. Carranza (Geology, 4 837); Prof Muhammad Altaf Khan ( Applied Mathematics, 6 366); Prof Maxim Finkelstein (Statistics/ Mathematical Statistics, 63 394); Prof Marianne Reid (School of Nursing, 72 861); Prof John Owen (Centre for Development Support, 103 368); Prof Brownhilder Neneh (Department of Business Management, 73 635); Prof Jorma Hölsä (Research Fellow: Department of Physics, 88 833); Prof Johann Beukes (Philosophy & Classics, 6 547 764); Rian Venter, (829 709); Dr Yuri Marusik (Zoology and Entomology, 553 619); Prof Robert Schall (Department of Mathematical Statistics and Actuarial Science, 276 681); Prof Deborah Posel (Department of Sociology, 275 535); Dr Vijay Kumar (Physics, 274 541); Dr Abhay Prakash Mishra (Pharmacology, 229 625); Prof RE Kroon (Physics, 226 554); Dr Krishnan Anand (Chemical Pathology, 235 300); Prof Andrew Marston (Chemistry, 147 147); Dr Seda Igret Araz (Applied Mathematics,125 824); Prof Jeanet Conradie (Chemistry, 106 521); Prof Louis Scott (Plant Sciences, 73 874); Prof Johan Grobbelaar (Plant Sciences, 97 722); Prof David Motaung (Physics, 53 553); Dr Samuel Nambile Cumber (Health Systems Research and Development, 555 563).

Career-long data set

The Stanford University rankings also include a list of the top 2% of world-class researchers based on citations over their full careers. Scientists are classified into 22 scientific fields and 174 sub-fields. Field- and subfield-specific percentiles are also provided for all scientists with at least five published papers. Career-long data is updated to the end of 2021, and single recent-year data pertain to citations received during calendar year 2021. The selection is based on the top 100 000 scientists by C-score (with and without self-citations) or a percentile rank of 2% or above in the sub-field.

The career-long data set includes the names of:

Prof Carranza (17 466); Prof Scott (55 882); Prof Reid (57 173); Prof Hölsä (64 402); Prof Grobbelaar (71 094); Prof Walker (78 239); Prof Andrew Marston (Chemistry, 84 484); Prof Schall (90 268); HA Snyman (Animal, Wildlife and Grassland Sciences, 96 374); Prof Swart (103 895); Robert WM Frater Cardiovascular Research Centre (111 896); Prof Frederick Kruger (Centre for Environmental Management,117 971); Prof Finkelstein (124 118); Prof Johan Visser (Geology, 125 331); Prof James C du Preez (Biotechnology, 168 841); Prof Posel (172 295); Prof Conradie (178 157); Prof Michael D MacNeil (Dairy and Animal Science, 184 193); Prof Khan (201 101); Prof Owen (262 897).

“The representation of our researchers from a variety of disciplinary domains in this prestigious ranking, is confirmation of their excellence, impact, and the global esteem they hold. UFS is proud to be a home to scholars in our midst who take us incrementally forward as an institution because of their cutting-edge research,” said Prof Vasu Reddy, UFS Deputy Vice-Chancellor: Research and Internationalisation.

Prof Atangana has also been shortlisted as one of the finalists for the prestigious Alkebulan Immigrants Impact Awards (AIIA) 2023, in the South African Flag Carrier category. Voting started on 1 November, and the award ceremony is set to take place on 23 November in Johannesburg.

Prof Melanie Walker, Chair: Higher Education and Human Development, is ranked 68 337 in the single-year data set.

Prof Brownhilder Neneh, from the Department of Business Management and Vice-Dean for Research Engagement and Internationalisation, is ranked 73 635.

Prof Marianne Reid from the School of Nursing is ranked 72 861.

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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