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Context matters: why we must consider resources and context when implementing artificial intelligence tools in the teaching and learning of mathematics in South Africa
(AOSIS, 2025) Govender, Rajendran
South African schools face stark inequalities in infrastructure, connectivity, language, and teacher preparedness. These contextual factors profoundly shape what artificial intelligence (AI) can and cannot do for mathematics teaching and learning. This article synthesizes recent peer-reviewed scholarship, policy texts, and book chapters to argue that AI adoption must be context-responsive: aligned to local resource constraints, multilingual realities, professional development ecosystems, and regulatory frameworks (notably POPIA). This article emphasizes that without attention to connectivity, electricity, devices, teacher TPACK, multilingual pedagogy, and data protection, AI may amplify—rather than reduce—existing inequities. In mitigation, this article provides practical, evidence-based principles for context-aware AI implementation in South African mathematics education.
Exploring one-point statistics in HERA phase I data: effects of foregrounds and systematics on measuring one-point statistics
(American Astronomical Society, 2026) Bull, Phillip; Kim, Honggeun; Hewitt, Jacqueline N.
The epoch of reionization (EoR) marks a pivotal phase in cosmic evolution, transitioning the intergalactic medium (IGM) from mostly neutral to ionized. Studying this transition offers key insights into the formation of the first luminous sources. A primary observable from the EoR is the redshifted 21 cm line emission, originating from hyperfine splitting in ground-state neutral hydrogen. Radio interferometers, designed to detect these faint signals, are crucial for characterizing the EoR. In recent years, various radio interferometers have undertaken extensive efforts to probe the power spectrum of 21 cm fluctuations during the EoR, leading to several upper limits. These include the Giant Meter Wave Radio Telescope (GMRT; G. Paciga et al. 2013), the Murchison Widefield Array (MWA; S. J. Tingay et al. 2013; J. S. Dillon et al. 2014; A. P. Beardsley et al. 2016; N. Barry et al. 2019; C. M. Trott et al. 2020), the Donald C. Backer Precision Array for Probing the Epoch of Reionization (PAPER; A. R. Parsons et al. 2010; C. Cheng et al. 2018; M. Kolopanis et al. 2019), the Low Frequency Array (LOFAR; van Haarlem et al. 2013; A. H. Patil et al. 2017; F. G. Mertens et al. 2020; R. Ghara et al. 2025), and the Hydrogen Epoch of Reionization Array (HERA; J. S. Dillon & A. R. Parsons 2016; D. R. DeBoer et al. 2017; L. M. Berkhout et al. 2024). More recently, The HERA Collaboration et al. (2022a, hereafter H22a) and The HERA Collaboration et al. (2023, hereafter H23) reported upper limits on the 21 cm power spectrum. The HERA Collaboration et al. (2022b, hereafter H22b) and H23 explored the astrophysical parameter space, using these limits to constrain IGM X-ray heating and disfavor inefficient heating by z ∼ 8.
Exploring medicinal plants used by South African bush doctor communities for wound healing purposes: a follow-up investigation
(Elsevier, 2026) Ndlovu, Banele; Fisher, Farzana
Traditional medicine is an essential component of healthcare in South Africa, where medicinal plants serve as a primary therapeutic source. Despite the region's rich biodiversity and long history of use, the indigenous knowledge of a small group of traditional “bush doctors” in the Western Cape (WC), including their methods for treating wounds, is poorly documented and at risk of being lost. As a follow-up to previous research, this study aimed to identify, document and analyse the medicinal plants used by practising bush doctors for wound care. The data was analysed to assess plant diversity, dominant families and frequency of use, providing a foundation for preserving indigenous knowledge and supporting future pharmacological validation. Given the small size of this practitioner population, a snowball sampling approach was used to recruit 20 bush doctors in the WC from this established cohort of 32 healers, using a modified protocol. The investigation documented 35 medicinal plant species representing 21 botanical families. The Asphodelaceae family was the most represented, confirming its importance in the regional pharmacopoeia. Species with the highest frequency of citation (FC), reflecting strong clinical relevance, included Aloe ferox (FC=16) and Eucomis autumnalis (FC=13). In contrast, species such as Galenia africana and Xerophyta retinervis had low FC values among participants and remain unexplored in the literature. These findings add great insight into ethnomedicinal knowledge preserved by bush doctors for wound treatment and help safeguard an important record of this endangered cultural heritage. Moreover, this study provides a framework for evaluating the pharmacological potential of both popularly cited and less common plant species used by bush doctors. This targeted approach can accelerate the development of new, evidence-based wound therapies.
Real-time electricity load forecasting in South Africa using SOM-enriched deep learning ensembles
(Aimspress, 2026) Makatjane, Katleho; Sigauke, Caston; Shoko, Claris; Moroke, Ntebogang
Accurate short-term electrical demand forecasting is critical for maintaining operational efficiency and energy security, especially in power-constrained systems like South Africa's Eskom. Statistical methods like autoregressive integrated moving average (ARIMA) and exponential smoothing often fail to represent nonlinear and regime-dependent trends in power demand. This study presents a dynamic ensemble that combines deep neural networks (DNN) and long short-term memory (LSTM) architectures, which are both augmented by self-organising maps (SOM)-based clustering. The proposed method divides historical hourly load data from the Drakensberg generation plant into discrete temporal regimes using SOM, then trains the DNN and LSTM architectures within each regime, and dynamically combines their predictions. Shapley additive explanations (SHAP) are used to improve the interpretability of the impact of each cluster and time hierarchies, while mean square error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) measures are used to assess prediction performance. The ensemble architecture delivers a higher accuracy, lowering MAPE to 2.20% while consistently outperforming individual benchmark architectures. The deployment on Amazon Web Services (AWS) proves the model's scalability and appropriateness for real-time applications. Although performance degrades in irregular demand clusters, adaptive re-clustering may alleviate this constraint. Overall, the combined DNN-LSTM-SOM strategy is a reliable, interpretable, and scalable solution for short-term load forecasting, enabling better operational planning and grid dependability in developing energy systems.
Determinants of viral suppression among young people living with HIV and on antiretroviral therapy in Eswatini: A cross-sectional study
(Taylor& Francis Group, 2025) Simelane, Thabile P.; Okonji, Emeka F.; van Wyk, Brian E.
Background: HIV treatment, adherence, and retention in care are fundamental in achieving viral load suppression (VLS) and fighting the HIV pandemic. The Government of Eswatini has decentralised antiretroviral treatment (ART) services and introduced differentiated HIV service delivery models to improve treatment outcomes for all people living with HIV, including young people. It is imperative to investigate the success of these initiatives in improving VLS in young people living with HIV (YPLHIV) in Eswatini. Objective: To determine the factors associated with VLS amongst YPLHIV (15 to 24 years) who are on ART in Eswatini. Methodology: A retrospective cohort analysis of 4 334 YPLHIV on ART in Eswatini between January 2018 and December 2021 was conducted. The Statistical Package for Social Scientists version 25 was used to determine the factors associated with viral suppression through bivariate and logistic regression analysis. Results: The prevalence of viral suppression (< 1 000 copies/mL) was 92.9%. After adjusting for all covariates, the likelihood of viral suppression remained significantly higher among YPLHIV who were female (AOR = 2.06; CI 1.04-4.11), had enrolled on the fast-track ART care model (AOR = 2.14; CI 1.09-4.20), and were receiving 4 to 5 months’ (AOR = 5.88; CI 3.14-11.02) and 6+ months’ (AOR = 3.55; CI 1.67-7.53) of medication. Conclusion: The national HIV treatment program in Eswatini exceeded the UNAIDS target of 90% virologic suppression for YPLHIV who are enrolled for ART for more than six months. However, to meet the revised 95% target, tailored interventions are required to strengthen long-term adherence and retention in care for YPLHIV on ART.