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Item A comparison of various machine learning algorithms on ISAR image classification of complex targets with varying levels of Gaussian noise(2025-08) Stewart-Burger, CD; Ludick, DJ; Potgieter, MoniqueThis paper investigates the ability to classify complex targets using inverse synthetic aperture radar (ISAR) images with varying noise levels. ISAR is an imaging technique used to generate high resolution two-dimensional images of radar targets. ISAR images contain more information about targets than one-dimensional data, such as high- resolution range (HRR) profiles. ISAR images therefore have useful applications in target classification.Item Climate resilience in urban transport planning: A case study of Cosmo City’s wetland dilemma(2025-07) Kgoa, LeratoUrban transport planning in climate-sensitive regions requires balancing accessibility needs with environmental sustainability, particularly in informal settlements. This paper examines the dilemma surrounding an operational informal taxi rank located in an ecologically sensitive area near wetland in Cosmo City, Johannesburg. Ideally situated for community access, the rank serves as a vital transit hub for workers, scholars, and traders. However, its location raises concerns about climate resilience and ecological impact, as the area is prone to flooding and holds significant biodiversity value. While formalising the rank would enhance safety and infrastructure, relocation to a non-wetland area risks underutilisation, as seen with a nearby formal facility that remains unused due to its inconvenient location. Using available environmental, land use and transport data, this paper explores the trade offs between environmental risks and community benefits in maintaining the taxi rank’s current location. By weighing climate resilience considerations against transport accessibility needs, the study offers insights into adaptive strategies for transport infrastructure in vulnerable urban areas. This case presents a broader challenge for Southern African cities, emphasising the need for context-sensitive solutions that address both climate resilience and societal demands. The paper invites debate on sustainable urban transport planning in high-density, climate-sensitive regions.Item Operational readiness for e-governance in local government: A case study of the City of Mbombela with a focus on e-participation(2025-11) Thulare, Tumiso; Maremi, Keneilwe JThis paper assesses the operational readiness of the City of Mbombela for implementing e Governance, focusing specifically on e-Participation to improve service delivery. Using a qualitative case study design approach, the research involves document analysis, secondary data, and key informant interviews to evaluate four readiness areas: digital infrastructure, institutional capacity, legal frameworks, and stakeholder engagement. Findings reveal that while Mbombela demonstrates policy alignment and partial ICT adoption, challenges persist in bridging the digital divide, strengthening interdepartmental coordination, and promoting inclusive citizen participation. Comparative insights from global examples like Seoul, Nairobi, Reykjavik, Cape Town, and Estonia suggest adaptable strategies such as mobile-first platforms, community access points, participatory budgeting, and open data dashboards. Building on these lessons, the paper proposes a phased roadmap for Mbombela, including broadband expansion, pilot digital engagement programs, digital literacy initiatives, and multi-stakeholder partnerships. The study contributes to e-Governance scholarship by contextualising operational readiness within the realities of South African municipalities, offering both a conceptual framework and practical strategies for institutionalising inclusive e-Participation. The paper concludes that sustainable e-Governance in Mbombela requires bridging technical, social, and institutional gaps while embedding citizen engagement into governance structures.Item Characterisation of CSIR’s Open-Jet 2-meter wind tunnel for IEC 61400 MEASNET-compliant calibration of cup anemometers(2025-09) Ragimana, Phumudzo; Dikgale, Moyahabo S; Mabeko, Philimon KAccurate measurement of wind speed is essential for effective assessment of wind resources and evaluation of turbine performance within the wind energy industry. This paper details the characterization of the Council for Scientific and Industrial Research (CSIR) open-jet 2-meter wind tunnel, which is designed to facilitate MEASNET-compliant calibration of cup anemometers in line with IEC 61400. The test section underwent a rigorous evaluation to determine flow uniformity, turbulence intensity, and both axial and vertical flow alignment, utilizing a grid of calibrated pitot tubes. Measurements were taken across various horizontal and vertical planes to evaluate the spatial velocity distribution and confirm compliance with international calibration standards. The open-jet design presents distinct aerodynamic challenges, especially in maintaining low turbulence and uniform velocity profiles in the wind tunnel test section. Through a process of iterative flow conditioning and systematic testing, the tunnel achieved velocity deviations within ±0.03 m/s, thereby complying with the rigorous criteria for high-precision anemometer calibration. Repeatability tests conducted with a reference anemometer, along with assessments of environmental stability, further substantiated the wind tunnel's performance under different ambient conditions. The findings affirm the tunnel's capability as a national calibration facility, providing traceable and internationally recognised wind speed measurement capabilities. This characterisation establishes a basis for future improvements, including broader calibration ranges, automated testing rigs, and integrated uncertainty modelling to meet the increasing demands of the wind energy sector. This characterisation lays the groundwork for future upgrades, like wider calibration ranges, automated calibration systems, and better uncertainty modelling, to keep up with the growing needs of accurate measurements within the wind energy industry.Item R.A.I.S.E - A novel framework for evaluating foundational AI models in medical deployment: Moving beyond traditional metrics to real-world deployability(2025-12) Adendorff, JAE; Lourens, Roger L; Delport, R; Marivate, V; Gichoya, JWThe shift from “narrow” traditional deep learning models to more generalist foundation models represents a paradigm shift for AI in medicine with the emergence of unimodal and multimodal systems such as MedGemma, Biomedclip, DINO models, and MedImageInsight. While these generalist models promise broad capabilities, they demand large datasets and high computational resources for training, and carry risks such as hallucinations, which can be hazardous in clinical use. In medicine, whether a model can be securely incorporated into actual clinical workflows is more important than whether it passes standard ized tests. Current assessment techniques for foundation models are fre quently based on multiple choice questions and do not account for real world deployment scenarios. At a two-day datathon (16-17 July 2025), we explored deploying MedGemma for chest X-ray reporting in South Africa. We proposed a gradual, radiologist-guided integration focused on controlled, automatable tasks rather than full diagnostic use. Our three pronged evaluation framework creates a uniform readiness score and al lows for continuous real-world monitoring by combining tailored deploy ment paths and hierarchical decision making with Go/No-Go thresholds.Item Scoping the approaches of frugal innovation, micro frontends, and microservices to provide recommendations for the design and development of a South African electronic health record system(2025-08) Lourens, Roger L; Maremi, Keneilwe J; Van der Westhuizen, MelanieThis study provides a set of recommendations informed by the socio-economic context and existing literature to guide the design and development of an Electronic Health Record (EHR) system in South Africa. A comprehensive scoping review was conducted to identify critical recommendations essential for ensuring the effectiveness and efficiency of the EHR system’s design. The review utilised academic databases, including SpringerLink, IEEE Xplore, ScienceDirect, and the CSIRIS library. Additionally, supplementary searches were conducted using the Google search engine to identify relevant literature aligned with the study’s objectives. The findings indicate that adopting a system architecture based on micro frontends and microservices enhances development efficiency, reduces download times, and improves overall system performance. Moreover, the study emphasises the importance of frugal innovation, advocating for the strategic use of minimal resources during the design and development phases of the EHR system. Consequently, this paper recommends the integration of micro frontends and microservices as a fundamental approach to the design and development of an EHR system tailored to the South African context.Item Misinformation detection in COVID-19 news for low-resource languages: A Sesotho case study(2025-05) Mokoena, L; Rananga, S; Mbooi, Mahlatse SAs digitization in public healthcare increases, so does the spread of misinformation in health-related news. This presents significant challenges, particularly in low-resource communities such as those speaking Sesotho. This study focuses on enhancing and evaluating machine-learning models for detecting misinformation in Sesotho. The dataset used consisted of COVID-19-related misinformation from websites and social media and user engagement data translated from English to Sesotho. Two separate translation methods were performed to address the language barrier: one using Helsinki-NLP’s Opus-MT model and the other using Google Translate. Both translated datasets were then used to train different machine-learning models: Support Vector Machine (SVM), Logistic Regression, Decision Trees, Gradient Boosting, Random Forest, and Naive Bayes. The performance of these models was evaluated by using metrics such as accuracy, F1 score, precision, and recall. The results indicate that the quality of data has a substantial impact on the model's performance, with Helsinki-NLP’s translations generally outperforming Google Translate. This research addresses the limited availability of studies on automated misinformation detection in Sesotho and the lack of translated datasets. It also aims to improve the tools for protecting public health in similar language communities.Item DFT-based evaluation of Li2MnO3 as a promising cathode coating material for lithium-ion batteries(2025-07) Kgasago, M; Phoshoko, Katlego W; Ngoepe, P; Ledwaba, RAn ideal coating material should combine chemical stability, mechanical strength, and suitable conductivity to enhance cathode durability. Li2MnO3 has previously been used as a coating material due to its stabilizing effect on the core, but other beneficial properties it may offer as a coating material are still underexplored. In this study, these ideal coating properties of Li2MnO3 were investigated using Density Functional Theory (DFT). To enhance accuracy, spin configurations were also considered, and calculations were performed using the GGA+U functional. The findings show that Li2MnO3 is thermodynamically stable, mechanically robust, and a semiconductor with a band gap of 2.17 eV. These results affirm Li2MnO3 as a promising cathode coating material, possessing the key attributes which are thermodynamic, electronic, and mechanical stability needed to enable durable, high-performance lithium-ion battery systems.Item A bio-optical modelling study of inland waterbodies of the Western Cape, South Africa(2025-05) Pillay, H; Smith, Marie E; Lain, Lisl; O’Shea, R; Guild, L; Torres-Perez, J; Sharp, S; Gitari, WM; Mudzielwana, R; Pindihama, GRationale: Globally, high-quality in situ bio-optical measurements of optically complex aquatic environments remain limited, yet they are crucial for the parameterization and calibration of optical models and datasets essential for remote sensing applications in these contexts. During November 2023, the BioSCape campaign in the Western Cape, South Africa, facilitated the acquisition of a comprehensive array of synchronized biogeochemical data, inherent optical properties (IOP), and hyperspectral radiometric measurements across four biologically and optically diverse inland water sites. Study Aim: Evaluate different Hydrolight radiative transfer models, using a variety of input combinations that include both pre-existing IOP models and in situ measurements, to determine the most effective approach and model combinations for characterizing and replicating the environmental conditions of our studysites.Item Coupling radiative transfer models and machine learning for crop trait retrieval in dryland ecosystems(2025-07) Masemola, Cecilia R; Bonnet, W; Cho, Moses ARadiative Transfer Models (RTMs) such as PROSAIL, which integrates leaf-level (PROSPECT) and canopy-level (SAIL) reflectance simulations, are increasingly employed to support biophysical trait retrieval in crop monitoring applications. In this study, we assess and compare the performance of three PROSAIL configurations—PROSPECT-5 + SAIL, PROSPECT-D + SAIL, and PROSPECT-PRO + SAIL—for estimating Leaf Area Index (LAI) and Canopy Chlorophyll Content (CCC) in dryland maize systems using synthetic Sentinel-2 reflectance data. Results from synthetic test datasets indicate that the PROSPECT-PRO + SAIL configuration achieved superior performance, with LAI retrieved at an R² of 0.88 and RMSE of 0.35 m²/m², and CCC estimated at an R² of 0.83 and RMSE of 4.1 µg/cm². These outcomes highlight the advantage of using the enhanced biochemical and structural parameterizations in PROSPECT-PRO, especially under semi-arid cropping conditions. Comparative analysis confirms that this configuration consistently yielded the lowest normalized RMSE (nRMSE) for both LAI (9.5%) and CCC (10.7%) across the variants tested. The findings substantiate the added value of improved leaf optical modeling for accurate trait estimation and suggest that PROSPECT-PRO + SAIL provides a robust forward modeling basis for data-driven crop monitoring frameworks.Item Initial airborne experiment of South Africa’s first dedicated bistatic SAR receiver for µSTAR(2025-10) Dass, Reevelen; Nel, Willem AJ; Tema, Thabo H; Magaoga, Mpereke E; Mosito, Katlego E; Blaauw, Ciara; Botha, Louis; Van Zyl, Casper WThe µSTAR concept proposes a constellation of satellite illuminators and ground-based receivers to form a space-to-ground Bistatic Synthetic Aperture Radar (SAR). This approach lowers costs and latency compared to traditional spaceborne SAR by separating the transmitter and receiver. The Council for Scientific and Industrial Research (CSIR) has developed a first generation µSTAR receiver and conducted initial airborne tests, demonstrating the potential of this technology to provide high-resolution imagery for various applications.Item Harnessing Earth Observation in support of monitoring marine and aquatic ecosystem health in southern Africa(2025-09) Smith, Marié E; Molapo, Nkadimeng R; Ngulube, Mabuela; Sibolla, Bolelang H; Lufuno Vhengani, Lufuno MCoastal and estuarine systems are subject to escalating anthropogenic pressures, facing issues such as water quality deterioration and ecosystem degradation due to eutrophication and alterations in river discharge patterns. It is imperative to monitor the extent and implications of these pressures to facilitate effective resource management and strategicItem A satellite-based decision support service for the marine fisheries and aquaculture industry of Southern Africa(2025-12) Smith, Marié E; Molapo, Nkadimeng R; Ngulube, Mabuela; Sibolla, Bolelang H; Vhengani, Lufuno MThis present gives details regarding the development and application of a satellite-based decision support service designed specifically for the marine fisheries and aquaculture industry of southern Africa.Item Deep learning emulators for radiative transfer models: Accelerating crop monitoring in data-scarce regions(2025-07) Masemola, C; Bonnet, Wessel J; Cho, Moses AAccurate and timely crop monitoring remains a major challenge in data-scarce regions like South Africa’s maize belt, where field measurements are limited and conventional radiative transfer model inversion methods are too slow for operational use. This study presents a hybrid approach combining RTM-generated synthetic datasets with a 1D convolutional neural network emulator to retrieve Leaf Area Index and canopy chlorophyll content (CCC) from Sentinel-2 imagery. The deep learning emulator was trained on 150,000 synthetic spectra and fine-tuned with limited field data, then applied to a dryland maize region in Gauteng. Results show the emulator achieved strong agreement with ground-truth measurements (LAI: R2 = 0.91, nRMSE = 8.7%; CCC: R2 = 0.90, nRMSE = 9.3%), accurately capturing field-scale spatial variability. Processing time was reduced by over 10× compared to traditional LUT-based inversion, with full-scene biophysical maps produced in under two minutes. By leveraging the full Sentinel-2 spectral range, the emulator avoided saturation in dense canopies and proved robust across diverse maize conditions. This workflow enables scalable, near real-time crop monitoring with minimal dependence on ground surveys, supporting precision agriculture in resource-limited settings.Item Utilising mass timber to unlock the potential of adaptive reuse projects for sustainable human settlements(2025-07) Azar, M; Van Reenen, Coralie AThe paper discusses the potential of alternative building technologies, specifically mass timber (MT), to facilitate the adaptive reuse (AR) of empty office buildings in South African cities. Many South African inner city buildings have been vacated in preference for decentralised business districts [9], leaving vacant buildings that could be reused to meet the need for social housing close to economic opportunities and services. This potential was emphasised in the 2025 State of the Nation Address, in which the president of South Africa noted that underutilised urban buildings would be repurposed for housing closer to work and business opportunities [1]. AR refers to the repurposing existing buildings, offering benefits such as sustainability, circularity, and cost savings. Although this has been implemented in some instances, barriers, such as a lack of building plans, heritage legislation, service alterations, and lack of knowledge and technology [2] limit the realisation of the benefits. Innovative building technologies, such as mass timber, can potentially overcome these challenges.Item The detection of HIV using plasmonically active colloidal gold nanoparticles(2025-07) Lugongolo, Masixole Y; Mcoyi, MP; Mngwengwe, Luleka; Ombinda-Lemboumba, SaturninLocalized surface plasmon resonance (LSPR) phenomenon occurs when incident light of specific wavelength excites the free electrons on the gold nanoparticles surface, which then leads to the enhancement of the nanoparticle surface electromagnetic field. The enhanced electromagnetic field has a short decay length and is localized in LSPR as opposed to the surface plasmon resonance (SPR) where the activated surface plasmons propagate. The short electromagnetic field decay length in LSPR means that it is highly sensitive to the refractive index changes near the gold nnopaticles surface rather than the bulk refractive index in SPR. This makes this technique efficient particularly to changes induced by subtle interactions. In this work, LSPR was used to differentiate between samples with HIV and the ones with no HIV. A glass slide was treated with 1% APTES solution in ethanol before depositing a layer of gold nanoparticles. An anti-HIV-gp120 antibody was added as a biorecognition element prior to the addition of the HIV pseudovirus as the analyte. Thereafter the slide was analyzed on an LSPR system using a green LED light. The results showed that when using 60 nm gold nanoparticles, there was a clear distinction between a sample with the pseudovirus and the one without it as shown by the varying light transmission intensities between the negative sample and the sample with the virus. This denotes that LSPR is sensitive enough as a label free detection method for virus detection. This can be used for the development of simple and cost effective ways of detecting various diseases in developing countries.Item A comparison of two biosensing recognition elements using SPR for the detection of drug- resistant genes(2025-07) Chauke, Sipho H; Tjale, Mabotse A; Maphanga, Charles P; Felix Dube, F; Ombinda-Lemboumba, Saturnin; Mthunzi-Kufa, PThe burden of tuberculosis (TB) infections is disproportionately high in low-income and resource-limited settings. This disparity exacerbates the emergence of multidrug-resistant (MDR) and extensively drug-resistant (XDR) Mycobacterium tuberculosis (Mtb), the bacterium that causes TB. Early detection and treatment of TB remain key strategies to reduce the spread and disease progression, particularly for the detection of drug-resistant forms. Therefore, optical-based diagnostic devices could solve this problem. Surface plasmon resonance (SPR) biosensors offer various advantages, including rapid analysis, high specificity, and sensitivity, as well as requiring small amounts of samples for analysis. For this study, two multidrug-resistant genes, namely, catalase-peroxidase (KatG) and enoyl reductase (InhA), were detected using a custom-built surface plasmon resonance (SPR) setup. Biotinylated and thiolated deoxyribonucleic acid (DNA) probes, specific to the two genes (KatG and InhA), were used as biorecognition elements to capture KatG and InhA target DNA. The SPR setup was used for the analysis of the binding interactions occurring on the gold-coated slides. The SPR biosensor setup indicated binding interactions through the changes in reflected intensities. The reflected intensities indicated the differences in the resonance angle between each experimental test. This is the initial step to identifying the best characterization of DNA as biorecognition elements for detecting drug-resistant mutations using an SPR-based setup.Item How do AI technologies impact business models and strategies of startups? A systematic literature review(2025-09) Ntoyanto-Tyatyantsi, N; Malinga, Andries LArtificial intelligence (AI) technologies are transforming business models and strategies across industries and organisations. Numerous studies have investigated the impact of these disruptive technologies on business performance and design. To our knowledge, there is a lack of systematic reviews on the impact of AI on business models and strategies, especially with a focus on startups. In this paper, we addressed this gap by using a PRISMA framework and conducted a systematic literature review on this theme, utilising the Scopus database. We reviewed peer-reviewed articles and conference papers published in English and conducted a qualitative thematic analysis to identify key themes. The results indicated that implementing AI technologies in startups can improve efficiency and promote proficiency by influencing customer relationships, value propositions, key resources, activities and revenue streams. Moreover, adopting AI technologies in a startup can transform the business model from a product-based to a service-based one. Despite their direct impact on the value chain, these technologies also affect other business processes and functions, such as human resources. However, effectively leveraging these capabilities requires addressing associated challenges, including the need for human expertise, data security, and legal and ethical issues. These findings suggest that AI technologies have a ripple effect on business models and strategies. A change in one business strategy element impacts others. For example, introducing AI-driven task automation has an impact on human resources and capabilities. In addition, AI technologies can improve operational efficiency and proficiency through process optimisation, thereby enhancing performance across the startup value chain. AI adoption and implementation require a comprehensive approach from different levels of the organisation. The study highlights the need for further empirical research to deepen understanding of AI’s multifaceted effects on business models and strategies.Item Risk of microplastics in South African freshwater: Case study(2025-09) Komane, Wesley K; Moalusi-Mathye, Salamina M; Moloi, M; Tancu, Yolanda; Lehutso, Raisibe FMicroplastics (MPs) are polymeric materials of size below five millimeters (<5 mm). Anthropogenic activities have led to the abundance of MPs detected in all environmental compartments, indicating their ubiquity nature. Their small size and persistent nature allow them to disperse easily, leading to a growing environmental concern. Of particular, the interaction and/or accumulation of MPs with aquatic organisms has raised concerns since some organisms ingest MPs and can induce adverse effects such as disrupting digestive systems and reducing organisms’ growth and reproduction (Ding et al., 2021). While the potential hazard exposure posed by MPs are somewhat known, their pollution and risk extent in African water systems are unknown. Due to limited experimental data, models are used to predict the risk potential factoring in various MPs properties and exposure extent. Using a suite of complimentary models, the study estimate the MP pollution extent and risk.Item Fine-tuning a machine learning model to detect COVID-19 misinformation in Xitsonga(2025-05) Baloyi, MG; Rananga, S; Mbooi, Mahlatse SThis paper explores the effectiveness of models, specifically Multilingual Bidirectional Encoder Representations from Transformers (mBERT) and Random Forest, for detecting misinformation about Coronavirus Disease 2019 (COVID-19) in Xitsonga, a South African language. The focus is on evaluating how synonym replacement and translation techniques can enhance model performance for misinformation detection by comparing the mBERT and Random Forest models in terms of accuracy, precision, recall, and F1 score. The continuous spread of misinformation during the pandemic COVID-19 has highlighted the need for accurate and efficient classification systems, especially in low-resource languages. To address this challenge, we enhanced an English news dataset using two data augmentation techniques: synonym replacement and translation into Xitsonga using the Marian Machine Translation (MarianMT) model. We fine-tuned mBERT for sequence classification and compare its performance with Random Forest classification model using Term Frequency-Inverse Document Frequency (TF-IDF) features. The results demonstrate that mBERT achieves good accuracy, precision, recall, and F1 score compared to Random Forest. This paper contributes to the field of misinformation detection by introducing a multilingual approach and improving classification performance in low-resource languages.