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Recent Submissions

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Mechanical property comparison of AISI 5120 steel produced by LENS and DMD systems

(2026-06) Sibisi, TH; Mathoho, Ipfi; Shongwe, BS; Tshabalala, Lerato C; Skhosane, Besabakhe S; Motau, R; Mndebele, N; Vithi, N

This study presents a comparative investigation of the microstructure and mechanical properties of AISI 5120 low-alloy steel fabricated using two Directed Energy Deposition (DED) systems: Laser Engineered Net Shaping (LENS) and robotic Direct Metal Deposition (DMD). The objective was to evaluate process–structure–property relationships under optimized operating conditions representative of each system. Microstructural characterization was performed using optical microscopy and scanning electron microscopy (SEM), while tensile strength, microhardness, and Charpy impact toughness were evaluated according to ASTM standards. The LENS-fabricated samples exhibited a predominantly ferrite–pearlite microstructure and demonstrated higher surface hardness (217 HV) and superior impact energy (137 J). In contrast, the DMD specimens displayed refined microstructural features with bainitic-like characteristics inferred from SEM morphology and achieved significantly higher tensile properties, including an ultimate tensile strength of 754.3 MPa, yield strength of 675.97 MPa, and elongation of 17.63%. Fractographic analysis indicated ductile failure modes in both systems; however, LENS samples showed a higher qualitative presence of porosity. The improved tensile performance of the DMD system is attributed primarily to reduced porosity and enhanced interlayer bonding resulting from higher energy input and improved melt pool stability. While LENS provided enhanced surface hardness and impact resistance, DMD demonstrated superior overall mechanical integrity. The findings highlight the importance of system-level process optimization when selecting DED platforms for load-bearing or wear-critical industrial applications. Further investigation including fatigue testing, quantitative porosity analysis, and phase confirmation using diffraction techniques is recommended.

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Lasers in health care - How light is revolutionising medicine

(2026) Thwala, Nomcebo L; Ramokolo, Lesiba R

Once confined to science fiction movies and high-tech laboratories, lasers are now playing an increasingly important role in healthcare. From precise surgeries to advanced diagnostic tools, laser technology is reshaping how we diagnose, treat and even prevent diseases.

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Workforce skills gaps and human-AI collaboration in adaptive factories

(2026-05) Nelufule, Nthatheni; Siphambili, Nokuthaba; Shadung, Lesiba D; Senamela, Pertunia M

The transition from Industry 4.0 to Industry 5.0 has repositioned humans at the center of adaptive, resilient, and sustainable manufacturing systems. It has been projected that by then end of November 2025, over 68 % of global manufacturers will report lack of critical workforce skills that also impede full adoption of AI-enabled adaptive factories. In this article, a survey results on the nature, magnitude, and evolution of the lack of critical workforce skills, and the emerging paradigms of human-AI collaboration are presented. A PRISMA framework was used to synthesize peerreviewed articles between 2020 to 2026 to examine the existing dominant themes, ranging from technical deficiencies in AI literacy and data science to socio-emotional and creative skills required for effective robot interaction. The main research contribution in this article is the Human-AI Synergy Competency Framework, which is a multilevel, dynamic model that maps required competencies, assesses maturity, and prescribes personalized reskilling pathways using generative AI tutors and digital twins. This research has also revealed that current AI tutoring technologies have demonstrated faster upskilling of about 57 % and 28–54 % of productivity gains based on the simulated data. This article has also recommended the adoption of regulatory mandates particularly for the lifelong learning credits and enterprise adoption of Human-AI Synergy Competency Framework frameworks to reduce the projected global manufacturing talent shortfall of 8.5 million workers, by 2030.

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Removal of pharmaceuticals and personal care products in conventional and advanced wastewater treatment processes

(2026-10) Kaium, A; Nocanda, Xolani W; Fick, JB

Water scarcity and contamination of surface waters with chemicals and pathogens pose significant challenges to global public health. Effective wastewater treatment is essential to safeguard water quality for reuse and to protect the environment. Here, we analyzed influent and effluent samples from seven wastewater treatment plants in Durban, South Africa, employing conventional and tertiary treatment processes. Using advanced analytical methods, we quantified concentrations of 140 pharmaceuticals and personal care products, detecting 75 compounds in influents at elevated levels, including antibiotics and antivirals linked to regional health burdens. Average measured concentrations in the influents ranged from 19,000 ng l-1 to 6100 ng l-1, caffeine had the highest measured value (1,600,000 ng l-1). Removal efficiencies varied widely, between >95% to 30%, with tertiary treatments such as membrane filtration and advanced oxidation achieving superior reductions compared to conventional methods. These findings underscore the importance of advanced treatment technologies in mitigating pharmaceutical and personal care products pollution in wastewater effluents, informing strategies to enhance water reuse safety and addressing emerging contaminants in water-scarce regions.

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PrivSev: Privacy-Preserving artificial intelligence in 6G Open Radio Access Networks: A survey

(2026-05) Nelufule, Nthatheni; Siphambili, Nokuthaba; Shadung, Lesiba D

The disaggregated, multi-vendor architecture of Open Radio Access Networks (O-RAN) in 6G, promises an unprecedented flexibility through the AI-native intelligence, and cost efficiency. However, these benefits also introduce challenges such as severe privacy and security risks which include the model inversion, data poisoning, and unauthorized access across distributed edge nodes; mainly Open Radio Unit (O-RU), Open Distributed Unit (O-DU), Open Centralized Unit (O-CU), and RAN Intelligent Controllers (RICs). In this paper, a systematic review based on the PRISMA framework was used to synthesize 42 peer-reviewed articles published between 2020 and 2026, particularly on the privacy-preserving AI techniques, Federated Learning (FL), Differential Privacy (DP), Secure Multi-Party Computation (SMPC), and emerging hybrid technologies applied to 6G O-RAN environments. The key research findings revealed that the combination of the Zero trust Architecture (ZTA) and FL can achieve up to 32% energy savings and Near-RT compliance, while the combination of DP and FL helps to secure the RIC and FBMP, and the Intrusion Detection System (IDS) helps to enable lightweight Multi-Party Computation (MPC). The notion of introducing a three-way FL, DP and SMPC integration for O-RAN remains unexplored, and this work bridges this gap, by introducing Privacy-Preserving (PrivSev), which is a novel layered hybrid framework that applies lightweight DP at the edge clients and threshold SMPC at the Non-RT RIC. The projected performance of the proposed framework tested against the reviewed benchmarks promises a 92% baseline accuracy retention.