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IAM-based zero trust architecture for IoT: Securing non-human identities in a connected world

(2025-11) Mthethwa, S; Jembere, E; Dlamini, Thandokuhle M

The widespread adoption of IoT devices has fundamentally altered digital connectivity, facilitating real-time data exchange and autonomous interactions worldwide. While this transformation offers significant operational advantages, it also introduces critical security challenges, particularly concerning the Identity and Access Management (IAM) of non-human identities such as sensors, devices, machine agents, and service accounts. Traditional perimeter-based security models, which rely on static trust boundaries and implicit trust for internal actors, have been used over the years for human identities. However, they are inadequate for non-human identities. Their limitations have led to a growing interest in Zero Trust Architecture (ZTA), a cuttingedge security concept, built upon the foundational rule of "never trust, always verify". This paper explores the application of ZTA in securing IoT ecosystems, with a specific focus on managing nonhuman identities. It investigates ZTA’s core tenets, like least privilege, micro-segmentation, continuous monitoring, and identity-centric access control—and analyses how these can be effectively implemented in resource-constrained IoT environments. The study identifies key implementation challenges and considerations for the use of ZTA in IoT. The findings of this paper highlight that ZTA, when properly implemented, offers a robust framework for mitigating cyber risks inherent in IoT ecosystems. Finally, the paper outlines future research directions, aimed at integrating ZTA into IoT environments. Ultimately, this work contributes to the growing body of knowledge advocating for Zero Trust as a foundational approach to modern IoT security.

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Investigating gender bias using artificial intelligence classification models on RAVDESS dataset

(2025-08) Sefara,Tshephisho J; Khosa, Marshal V; Kisten, Melvin

Artificial intelligence (AI) classification models are increasingly being deployed across a wide array of sectors, becoming fundamental tools in decision-making processes that impact individuals and society. These models are utilised in critical applications such as healthcare diagnostics, financial risk assessment, criminal justice systems, and educational admissions, demonstrating their widespread influence. However, a significant challenge arises from the susceptibility of these models to biases, which can lead to outcomes that are unfair, discriminatory, and ultimately harmful to individuals and specific demographic groups. Artificial intelligence bias refers to systematic errors that occur within decision-making processes, ultimately leading to outcomes that are unfair or inequitable. This can manifest as skewed results stemming from human biases that have influenced the original data used to train the AI model, resulting in distorted outputs with potentially harmful results. In this paper, we mitigate the gender bias that occurred during data selection for a classification model. This research experiment was conducted on RAVDESS emotion recognition dataset. The experiments showed improvement in model accuracy by 6% after bias mitigation.

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Phosphite inhibits Phytophthora cinnamomi by downregulating oxidoreductases and disrupting energy metabolism

(2025) Prabhu, SA; Naicker, P; Duong, TA; Govender, Ireshyn S; Engelbrecht, J; Backer, R; Stoychev, SH; Van den Berg, N

Phytophthora root rot caused by the hemibiotrophic oomycete, Phytophthora cinnamomi is a major biotic hindrance in meeting the ever-increasing demand for avocados. In addition, the pathogen is a global menace to agriculture, horticulture and forestry. Phosphite trunk injections and foliar sprays remain the most effective chemical management strategy used in commercial avocado orchards against the pathogen. Phosphite is known to counter P. cinnamomi both directly and indirectly through fortification of host defense. However, phosphite's direct mode of action is still not understood completely. This study identified a P. cinnamomi isolate GKB4 sensitive to phosphite (EC50 of 27.9 μg/mL) and investigated the direct impact of phosphite on this isolate through label-free quantitative SWATH-MS. Proteomics data analysis of untreated vs. phosphite-treated samples revealed that the xenobiotic affects the pathogen's growth by targeting the oxidoreductases whose abundance is significantly reduced. Further, perturbations in the energy metabolism and membrane/transmembrane proteins and transporters, and oxidative stress contribute to growth inhibition. The current study also identified increased putrescine biosynthesis, a polyamine, that when present at non-optimal concentrations could be cytostatic/cytotoxic. The differential expression of enzymes involved in the biosynthesis of secondary metabolites and the intermediates/precursors involved in their biosynthesis is an interesting finding that needs further investigation to ascertain their role in phosphite-induced stress. The pathogen's attempt to counter phosphite's growth-inhibitory effects—through upregulation of alternate bioenergetics pathways (amino acid catabolism and β-oxidation of fatty acids), mitochondrial translation and translocation machinery, peroxisomal proteins, and antioxidants—appears ineffective. This research furthers our limited understanding of the direct in vitro effects of phosphite on P. cinnamomi and has identified potential candidates for molecular functional investigation.

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Laser metal deposited Ti4822 hollow pipe: Experimental and computational modelling study

(2025) Raji, SA; Tlotleng, M; Skhosane, Besabakhe S; Kanyane, LR; Popoola, API; Pityana, Sisa L

Generally, manufacturing valuable parts from General Electric's titanium aluminide (Ti-48Al-2Cr-2Nb; Ti4822) is extremely difficult. The difficulty is due to the alloy's poor room temperature castable and machining properties. To achieve functional parts from Ti4822 via cast and selective laser melting, built parts are hot isostatically pressed. Unfortunately, direct energy deposited Ti4822 parts are yet to be reported in open literature and hence this article wherein a 100 mm long pipe is laser metal deposited and investigated for use as a heat exchanger. Before printing, the theoretical model is used to predict geometric defects (tapering) and heat distribution. The results conclude tapering at around 30 mm and rapid increase in heat inputs (HIs). The microstructural analyses corroborated this observation, particularly the fracture analyses which indicated internal cracking, impurities and pores at moderate HI, and isotropic microstructure with elongation formed dimple grains at high HI position. Fracture is said to be transgranular, cleavage-like, and ductile-brittle overall. Nanoindentation results conclude that the pipe would withstand high stress due to high toughness. Pipe inner surface roughness is 5.190 μm, indicating acceptable pressure drops and therefore usable as a heat exchanger.

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Challenges and risks of digitalizing health systems- A review for South African eHealth

(2025-11) Ntshangase, Cynthia S; Mahlasela, Oyena N; Mtshali, Mamello L; Manari, Unarine J; Ledwaba, Lehlogonolo PI; Marengwa, Matshidiso S

This study represents a comprehensive literature review aimed at identifying the challenges and risks associated with the digitalization of health systems. The objective was to assess which healthcare systems are utilized globally and in South Africa. The key digital health systems identified include healthcare management systems, telemedicine, mHealth, electronic health records, cloud, data analytics, wearables, and emerging technologies such as 3D bioprinting, artificial intelligence, and robotics. Recommendations were formulated to promote a balanced approach to health systems digitalization, emphasizing maximizing benefits while mitigating risks and addressing challenges. The recommendations were developed following a review of the identified technologies, focusing on associated risks, challenges, and mitigation measures. Although these technologies enhance healthcare services, they encounter challenges including security issues, limited access, and regulatory compliance. The recommendations propose the enhancement of regulations for compliance and security, with future work concentrating on aligning technologies with South African standards to ensure secure adoption. The findings of this study can assist policymakers and system developers within the Department of Health in South Africa in devising healthcare solutions with minimized risks and challenges.