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04 March 2020
What does the bursary cover?

• Accommodation
• Transport (up to 40 km from institution) 
• Meal allowance (including incidental allowance)
• Book allowance 
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• Tuition
• Funded students with disabilities need to submit claims for assistive devices and human support directly to the university’s Centre for Universal Access and Disability Support (CUADS)/Financial Aid office.

Who qualifies for NSFAS allowances?

STUDENTS STAYING IN A RESIDENCE:
• Books up to a maximum of R5 200
• Actual accommodation cost
• Meals (including incidental allowance) up to a maximum of R15 000
 
STUDENTS LIVING OFF CAMPUS IN FAMILY ACCOMMODATION

• Books up to a maximum of R5 200
• Meals (including incidental allowance) up to a maximum of R15 000
• Transport up to a maximum of R7 500

STUDENTS LIVING OFF CAMPUS IN ACCREDITED and NON-ACCREDITED ACCOMMODATION: 

• Books up to a maximum of R5 200
• Private accommodation amount to a maximum of R34 400
• Meals (including incidental allowance) up to a maximum of R15 000

DISTANCE-LEARNING STUDENTS:

• Books based on the number of modules registered, up to a maximum of R5 200

Please note that students who were registered for the first time at a tertiary institution before 2018 are subject to a maximum NSFAS amount for the year.  The maximum NSFAS amount for 2020 is R93 400.
According to NSFAS policy, payments must be made in the following order of priority if your qualifying NSFAS costs exceed the maximum amount:
1.  Tuition
2.  Books
3.  Accommodation
4.  Meals
5.  Transport
This means that the amount by which you exceeded the maximum NSFAS amount must be deducted from your allowances, starting with the transport and meal allowances.  Therefore, you might not receive the full allowances.

How will NSFAS allowances be paid?

NSFAS allowances will be paid in cash to the student via the Fundi system.  Once the allowances are debited to your student class-fee account, you will receive an SMS message from Fundi to upload your banking details.  Fundi will confirm your banking details and payment will follow.

Please note that no payments will be made to a third party.
You only need to upload your banking details once.  If you experience any problems with uploading your banking details, please contact Fundi at 086 055 5544.

When will I receive my NSFAS allowances?

NSFAS allowances will be paid during the first week of each month over a period of 10 months.  Please note that due to several variables, a specific date for payment cannot be provided.

How do I apply for NSFAS private accommodation?

Please visit the UFS website for a complete guide:
Students
Financial Aid

When will I receive my private accommodation payment?

You must apply online for your private accommodation.  It is compulsory to upload your rental agreement and proof of home address.  If your private accommodation application is approved by the 25th of a month, you will receive payment from your move-in date up to date during the first week of the following month, and thereafter you will receive your monthly payments until November.

How will I know if my private accommodation application status has changed?

You will immediately receive an email on your ufs4life email address when your status changes.  

What should I do if my private accommodation application is incomplete?

Please log in on your Student Self-Service.  The reasons for your incomplete application will be listed under your private accommodation application.  Please correct  the application and resubmit.  Please do not resubmit if the application was not corrected.
Please visit the website for clear explanations on the reasons for incomplete applications if you are unsure of what is expected of you. 

Please note that no payment will be made before your private accommodation application is approved.

When is the closing date for NSFAS private accommodation applications?
The closing date for private accommodation applications is 11 September 2020.  Please note that no extension will be granted.

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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