UKZN to host Postgraduate Research and Innovation Symposium (PRIS)

The University of KwaZulu-Natal (UKZN) will showcase top student research projects at the annual College of Agriculture, Engineering and Science (CAES) Postgraduate Research and Innovation Symposium (PRIS) on 10 & 11 December 2020.

Professor Salim Abdool-Karim, Director of the Centre for the AIDS Programme of Research in South Africa (CAPRISA) and co-chair of the South African Covid-19 Ministerial Advisory Committee will present the keynote address at the event:

Title: “Covid-19: Looking ahead at 2021”

Time of keynote address: 09h15 – 09h45

The media and members of the public are invited to join the two-day event online Register@Zoom & for further information visit https://pris.ukzn.ac.za/.

Professor Abdool Karim will describe the Covid-19 epidemic trends in South Africa, explore how the meaning of science has changed in the era of Covid-19 and reflect on what can be anticipated in 2021 and beyond. He will also highlight the importance of “following the science” and being wary of “some poor-quality science and snake oil treatments”. He will also discuss the threat of a second wave and the importance of remaining vigilant and adapting to a “new normal”.

ABOUT THE SYMPOSIUM :

Around 182 Postgraduate students in the Schools of Agricultural, Earth and Environmental Science, Chemistry and Physics, Engineering, Life Sciences, and Mathematics, Statistics and Computer Science will present their innovative and exciting research.

Below are Highlights of top presentations tomorrow Thursday, 10 December:

1. Development of a universal water quality index and water quality variability model for South African river catchments

                        Presentation time – 10h10-10h30

PhD student in Engineering, Mr Talent Banda, has designed an Artificial Intelligence (AI) water quality index model that has a universal application. With the increasing importance of evaluating water quality in South Africa and around the world, water quality monitoring tools have proliferated. Most commonly and increasingly used are water quality index (WQI) models; scientifically based devices that analyse water quality parameters and provide a single-digit score representing the contamination level. This research seeks to recommend three WQI models and a water quality variability model for evaluating South African river catchments, and makes use of innovative artificial intelligence, conventional methods, and proxy water quality variables to do so, suggesting models that demonstrate accuracy and stable performance for assessing water quality in South Africa’s critical river catchments.

Mr Talent Banda can be contacted for media interviews: diotrefetb@yahoo.co.uk,  217080686@stu.ukzn.ac.za, cell: 072 559 0403. Banda studied at the Howard College campus in Durban and resides in Limpopo.

• The development of a near-real time lightning warning system for rural communities in South Africa:

Presentation time – 10h50 – 11h10

PhD student in Agrometeorology, Ms Maqsooda Mahomed is contributing to the development of an early lightning warning system for rural communities.

As the climate changes, projections of increased lightning activity in lightning prone countries like South Africa are a cause for concern, particularly for rural communities that are made more vulnerable by the compounding factors of poverty, lack of education and awareness. These communities also do not benefit from national lightning networks, lightning alerts and warnings. This research developed a community-based near real-time lightning warning system (NRT-LWS) in the community of Swayimane in KwaZulu-Natal that makes use of field meters and flash sensors to disseminate warnings via audible and visible on-site alarms, as well as SMS and email alerts formulated in a simple, comprehensible way. Research on the system also involved a systematic evaluation using the South African Lightning Detection Network to enhance the system’s performance, and to explore the benefits of using lightning jumps to predict severe storm reports as a precursor for tornado events in South Africa.

Ms Maqsooda Mahomed can be contacted for media interviews: maqsoodamahomed@gmail.com, cell: 078 6722 820. She studied at the Pietermaritzburg campus and lives in Northdale, PMB.

• Targeted Gene Delivery to Cervical Cancer cells in vitro using Green synthesised Copper Oxide Nanoparticles

Presentation time: 12h20 – 12h40

Masters student in Biochemistry, Mr Keelan Jagaran has completed research on alternative treatment for cervical cancer, that is more affordable and less invasive. His research is novel in that it focuses on the combination of the use of copper oxide nanoparticles and syringa leaves which have both shown to be beneficial. The treatment is intended to specifically target the cervical cancer cells. This approach would help prevent unnecessary damage to other cells.

Cervical cancer is a global health crisis, the fourth most frequent cancer affecting women and, according to the World Health Organisation, responsible for 90% of female deaths in low- to middle- income countries. While current treatment approaches are effective, they can cause deleterious effects to healthy tissue and are so applied short-term. Recent technological advances have improved diagnostics, therapeutics and monitoring, while collaborative work in medicine and nanotechnology has brought us nanomedicine to revolutionise traditional treatments. Making use of nanomedicine techniques, this research explores the use of a novel copper oxide nanoparticle to enhance diagnostic and therapeutic strategies against cervical cancer by biologically synthesising the nanoparticles using extracts from Syringa leaves, an approach that boasts an array of benefits including cell specificity, lowered toxicity, good biocompatibility, antibacterial activity and low cost.

Mr Keelan Jagaran can be contacted for media interviews on: keelan243@gmail.com,  082 646 2435. He studied at the Westville campus and his home town is Pietermaritzburg.

• Irenbus – A Real-Time Machine Learning-Based Public Transport Management System

Presentation time – 12h40 – 13h00

Master’s student in Computer Science, Mr Menzi Skhosana has developed a prototype app for commuters to receive public transport information, e.g.  bus arrival times and bus operators can also use this app to track and collate data on the numbers of commuters boarding their buses. This would help them update schedules and predict the number of buses required for a particular route.

“Data is the new oil” – with the advent of Big Data and the Internet of Things, developing countries need to take hold of the solutions offered by these advances and apply them to real-world problems, and data can be used to tackle one of their major challenges: the transportation sector. By collecting appropriate data and applying predictive analytics, many daily problems faced in public transportation can be resolved or mitigated. This research used a cloud-based system to address two issues: the unavailability of real-time information to commuters about the current status of a given bus or travel route, and the inability of bus operators to efficiently assign available buses to routes for a given day based on demand. The novel system comprises a mobile application for commuters, a mobile application for drivers, and a web-based application for bus operators to provide a holistic, scalable, cost-efficient, technological solution to public transport problems.

Mr Menzi Skhosana can be contacted for media interviews: 216032734@stu.ukzn.ac.za / 072 679 0055. Menzi lives in Umlazi.

• Precipitation prediction from cloud properties using machine learning approach

                   Presentation time: 14h40-15h00

PhD student in Physics, Mr Abdulaziz Yakubu has conducted research focused on precipitation prediction. The accuracy and consistency of forecast models is of global interest for weather prediction and climate projections, and a critical component of this is precipitation. This component is surrounded by huge uncertainties, but it is vital to be able to predict precipitation for effective water resources management to sustain life. Machine learning techniques are data-driven, robust and require moderate resources, and they have shown promising results to address the challenge of predicting precipitation. This research made use of two algorithms to develop a model for daily precipitation prediction, based on cloud properties like optical thickness, their effective radius, temperature, pressure and more, and produced results with promising levels of accuracy.

With machine learning techniques Yakubu is looking to make it possible for more accurate predictions that are area specific e.g. weather predictions for a particular community / area within a city.  

Mr Abdulaziz Yakubu can be contacted for media interviews: yakubu.at@gmail.com, cell: 061 130 8677. He studied at the PMB campus and is currently in his home country of Nigeria.

Issued by:

Corporate Relations Division

University of KwaZulu-Natal