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Item Quantum biosensing illuminates infected cells for timely disease diagnosis(2026) Mthunzi-Kufa, Patience; Mpofu, Kelvin TPhotonics has a profound impact on daily life in numerous ways, from communication and entertainment to manufacturing and healthcare. In Africa, photonics applications in point-ofcare diagnostics, the diagnosis of substandard drugs, and treatments for cancer, diabetic wounds, and other ailments promise significant improvements in healthcare and the quality of human life. Of particular interest is that quantum technologies can be used for new forms of sensing, imaging, and computing, with potential applications in diagnostics and drug discovery. The amalgamation of biological molecules, such as deoxyribonucleic acid (DNA), proteins, or enzymes, with quantum sensors (QS), which illuminate changes in light emissions during lightmatter interactions, introduces a game-changer in rapid, sensitive, and specific disease diagnostics. Here, we theoretically demonstrate how the integration of quantum phenomena into sensing mechanisms could enable the detection of minute variations in intracellular factors, such as refractive index, cell morphology, and pH shifts in infected versus uninfected cells. We propose a conceptually designed quantum biosensing experiment that can be conducted using nitrogen vacancy (NV) centres.Item Simplifying verifiable credential systems: A hybrid blockchain-based architecture for credential issuance and verification(2026-07) Kanjere, Julian S; Ford, Merryl; Mutemula, Edzani BVerifiable credentials (VCs) enable trusted issuers to provide cryptographically verifiable digital credentials to holders, which can later be presented to verifiers without relying on centralised authorities. Despite their conceptual advantages, real-world deployment of VC systems remains challenging due to infrastructure complexity, usability barriers, fragmented standards, and the need for specialised technical expertise. This paper presents a simplified hybrid credential system that supports credential issuance and verification on a blockchain through RESTful APIs. The system consists of a credential service that interacts with a smart contract deployed on the Polygon blockchain test network and a web-based credential management application that manages the lifecycle of issuing, holding, and verifying credentials. To improve usability, an issued credential is delivered to a holder as a PDF document via email. This PDF contains a record of the corresponding blockchain transaction and a QR code for verification. We describe the design and implementation of the system and present initial benchmarking results. Experimental evaluation shows average issuance and verification times of approximately 4 seconds and 0.4 seconds via the API workflow, and 5 seconds and 0.7 seconds via the web-based workflow, indicating practical performance for real-world deployments. Future work will focus on performance optimisation, privacy-preserving credential mechanisms, credential revocation and alignment with verifiable credential standards.Item Evaluating the reliability of LLMs in OSINT investigations: A friend or foe?(2026-06) Baloyi, Errol; Siphambili, Nokuthaba; Ntshangase, Ntomfuthi L; Letshwenyo, Mpho; Makharamedzha, Fhatuwani; Mmbodi, Rendani; Hlongwane, Ndabezinhle EThis study investigates the use of Large Language Models (LLMs), including GPT, Claude 3, Gemini, Meta, DeepSeek, Qwen 2.5, Mistral Large, and Grok, in Open-Source Intelligence (OSINT) investigations, focusing on their capabilities, limitations, and practical implications. Using a controlled fictional organization, the CtrlZ Society, to simulate a plausible online footprint, the LLMs were provided with a synthetic dataset purportedly linked to the organization. The dataset included social media posts, a Reddit thread, a Pastebin document, a GitHub repository, a blog post, a Telegram broadcast, a WHOIS record, and a news article. Based on this dataset, the models were evaluated on accuracy, timeline reconstruction, account attribution, evidence traceability, susceptibility to hallucination, and handling of ambiguity and incomplete information. Results revealed substantial variation among models: Claude 3, GPT, and Qwen 2.5 demonstrated strong analytical performance and reliable synthesis of investigative outputs, while Gemini and DeepSeek exhibited weaker capabilities. Some models, including Meta were also prone to forced narrative construction when prompted adversarially, highlighting risks of misinterpretation or overreach. Despite these limitations, all LLMs provided valuable support for structuring and summarising complex data, demonstrating their potential as efficiency multipliers in OSINT workflows. Based on these findings, the study provides recommendations for practitioners, including rigorous human oversight, multi-model validation, adherence to verification protocols, and careful evaluation of outputs to mitigate risks and maximise the reliability of LLM-assisted investigations.Item Improved diabetic classification using chaotic grey wolf random forest optimiser for hyperparameter tuning(2026-06) Chukwujekwu, CK; Bello-Salau, H; Mu’azu, MB; Aliyu, HA; Onumanyi, Adeiza J; Ibrahim, OMThe application of machine learning in medical diagnostics has significantly advanced the early detection and classification of diabetes. However, classifiers often suffer from challenges related to high-dimensional data, overfitting, and ineffective hyperparameter tuning, which compromise their predictive performance and generalisation capability. To address these limitations, this study proposes an improved diabetic classification framework using a chaotic Grey Wolf Optimisation (cGWO) algorithm for hyperparameter tuning of a Random Forest (RF) classifier. The cGWO enhances the standard GWO by improving its exploration and exploitation abilities, thus reducing the risk of local optima and premature convergence. Using the BRFSS dataset, the study evaluated the proposed cGWO-RF model and compared its performance against an RF and a standard RF-GWO model. The cGWO-RF outperformed the RF-GWO method, achieving a 0.03% increase in accuracy, 0.51% in sensitivity, 0.31% in precision, 0.51% in F1score, 0.2% in AUC, and 1.46% in average precision-recall because of the balance in exploration and exploitation introduced by the cGWO . These results show the efficacy of the cGWO in optimising RF hyperparameters, leading to more accurate, efficient, and generalisable models for diabetes classification. The study contributes an optimisation strategy that enhances predictive modelling in healthcare and supports more reliable decision-making for early diabetes diagnosis.Item Investigating different acoustic wall treatment materials for attenuation of noise and vibration in a supersonic wind tunnel(2025-11) Mogwera, A; Desai, DA; Fameso, F; Dikgale, Moyahabo SThe demand for high-performance testing environments with reduced noise levels has driven increased focus on the aerodynamics and acoustics of supersonic wind tunnels. Operating at supersonic speeds, these wind tunnels generate significant noise and vibrations that can interfere with measurement accuracy, cause structural degradation, and disrupt operations. Traditional approaches to attenuate these problems often focused on airflow dynamics, somehow neglecting the impact of compressor fan vibrations and dynamics. However, acoustic wall treatments placed where fans are commonly located in the wind tunnel, can minimize these sounds and vibrations but such investigations seem under researched. Hence, this study investigates the vibro-acoustic performance of three materials: ceramic fibre, mineral wool, and melamine foam using finite element analysis (FEA). The simulated results indicate that ceramic fibre achieved the lowest A-weighted SPL of 24.04 dBA, demonstrating its strong sound-absorbing properties compared to melamine foam and mineral wool, with SPLs of 48.687 dBA and 35.357 dBA, respectively. Analytical validation further confirmed the accuracy of the simulated harmonic response analysis results. These results highlight ceramic fibre as a highly effective material for acoustic wall treatments in minimizing compressor fan-induced noise and vibrations in supersonic wind tunnel applications such as those found at the CSIR. By providing a comparative analysis of material responses specific to compressor fan acoustics, this research contributes to the design of quieter, more efficient supersonic testing environments.Item Pioneering MEASNET accreditation in Africa: CSIR’s subsonic wind tunnel facilities(2026-06) Dikgale, Moyahabo SMEASNET is the only global authority on wind speed measurement standards. Currently, all MEASNET accredited facilities are in Europe and North America and ongoing wind measurements accuracy is critical. All wind energy projects require annual maintenance and recalibration of wind speed measurement devices to ensure reliable data, optimal turbine performance and regulatory compliance. South African wind energy projects face: 1) High costs; 2) Long lead times and 3) Logistical challenges for anemometer calibration. The CSIR offers the following solution: A local, accredited facility to serve South Africa and the continent’s wind energy sector.Item An overview: Applying blockchain technology to improve traceability in manufacturing(2026-05) Nzima, G; Pretorius, J-H; Erasmus, Louwrence DGlobal production and distribution speed bring a renewed focus on the safety, quality and validation of several important criteria in supply value chain. There are a growing number of issues related to safety and contamination risks that have established an immense need for effective traceability systems that act as an essential quality management tool ensuring adequate safety of manufacturing products. Traditional systems face challenges like centralized management, unclear information, and unsatisfactory data reliability. Basic IoT traceability systems offer some solutions but often rely on centralized models, making real-time tracking difficult. Thus, there is a call for a strong end-to-end track and trace system in manufacturing to improve product safety and reduce defects. The research proposes a Traceable Resource Unit (TRU) system using blockchain technology to enhance transparency and credibility. Blockchain can improve traceability by providing security and a clear transaction history, although its full advantages and development methods are still being explored.Item Impact of post weld heat treatment on structural and mechanical properties of dissimilar high strength steels(2026-02) Shoke, Lerato S; Mutombo, Kalenda; Olubambi, P; Zondi, MCThis study explores the influence of Submerged Arc Welding (SAW) parameters on the microstructural evolution, mechanical properties, and residual stresses in dissimilar welds of A516 Gr70 and A106 GrB high-strength carbon steels. Through a comprehensive experimental approach, this study delineates the direct relationship between the welding heat input, determined by the voltage, current, and speed, and its profound impact on the heat-affected zone (HAZ) size, microstructure, and mechanical properties, such as hardness and tensile strength. The investigation revealed that the optimal welding parameters play a critical role in controlling the bead geometry and HAZ size, which in tum significantly affect the mechanical integrity and performance of the welded joint. Notably, this study revealed that a higher heat input does not necessarily lead to a larger HAZ, challenging the conventional assumptions in welding practices. Furthermore, this study delves into the effects of post-weld heat treatment (PWHT) techniques, including normalizing, quenching, and annealing, on reversing the microstructural alterations induced by welding and restoring the initial mechanical properties of the base metals. Microstructural analyses employing stereo microscopy, optical microscopy, scanning electron microscopy, and X-ray diffraction techniques provide insight into the granular details of the morphological changes across different regions of the weld, highlighting the formation of martensitic lathes in the weld bead region and variations in the pearlite-ferrite matrix. Mechanical testing and hardness profiles substantiate these findings, illustrating the significant influence of cooling rates and heat treatment methods on the mechanical properties of the weldments. This study highlights the importance of controlled heat treatment in enhancing material performance, suggesting avenues for further research into parameter optimization, fatigue assessments, and advanced heat treatment techniques to refine welding processes for industrial applications.Item A comprehensive analysis of identity theft: Definitions, types, and impacts(2026-05) Ntshangase, Cynthia S; Steyn, ASIdentity theft is a complex and evolving phenomenon spanning physical, digital, algorithmic, and hybrid domains. This paper presents a systematic literature review following PRISMA guidelines to consolidate definitions, identify major and emerging subtypes, and assess impacts on individuals, organisations, and society. The study proposes a comprehensive definition that unifies fragmented and subtype‑specific descriptions in the literature and explicitly encompasses document‑based, child, offline, familial, criminal, synthetic, biometric, and algorithmic identity theft. We clarify that our bibliometric analysis reflects research attention rather than real‑world incidence and explain the practical implications of this distinction. To enhance applied value, we discuss Africa‑specific identity‑management challenges, including persistent identity document fraud, and the rise of synthetic identities, and outline how the taxonomy supports national ID authorities, policy makers, financial institutions, and fraud‑prevention bodies in South Africa and across the continent. Benefits include improved incident‑response categorisation, more comprehensive compliance checklists, enhanced risk assessments, and clearer cross‑sector communication. The review highlights a research focus bias toward financial theft and calls for further empirical work on underexplored subtypes (e.g., familial, child, algorithmic, deceased). We conclude with future research that triangulates bibliometric signals with incident statistics and operational data to inform resilient identity‑management frameworks.Item A lightweight IoT and blockchain-based cold chain track-and-trace prototype for medical materials in resource-constrained environments(2026-05) Kanjere, Julian S; Ford, Merryl; Dickens, John S; Goorun, Yurisha; Mutemula, Edzani BTransporting and storing temperature-sensitive medical materials requires strict adherence to controlled conditions across multi-actor cold chains to ensure safety and efficacy. This challenge is amplified in resource-constrained environments, where cost-effective and verifiable monitoring solutions are limited. This study presents the design and implementation of a low-cost cold chain track-and-trace system integrating IoT sensors, edge computing, and blockchain technology. Temperature and GPS data are captured via sensors connected to a Raspberry Pi Pico microcontroller, published over Wi-Fi to an MQTT broker, persisted to database and relayed via an API to a Node.js server for persistence on the Polygon blockchain. A web application enables stakeholders to initiate shipment, and track shipment conditions and custody. Evaluation results show average round trip (sensor - MQTT broker - Node.js - blockchain - transaction confirmation) transaction write times of 10 seconds on Ethereum Sepolia, 10.5 seconds on Polygon Amoy, and 0.08 seconds on Ganache. Average transaction fees are approximately 0.05 ETH (Sepolia) and 0.05 POL (Amoy), demonstrating feasibility with cost and latency trade-offs.Item A zero trust architecture for dynamic identity and access management of IoT devices(2026-07) Mthethwa, Sthembile N; Jembere, E; Dlamini, Thandokuhle MTraditional resource-intensive security protocols such as centralised Identity and Access Management (IAM) solutions are often impractical and cannot be used to protect Internet of Things (IoT) devices and the data that they frequently collect and transmit across distributed networks. This necessitates the design and development of lightweight security protocols that are tailored to resource-constrained IoT devices. Therefore, it is crucial to design a robust, yet lightweight framework that ensures secure and efficient IAM to safeguard such devices. In response to this challenge, this study proposes an adaptive and decentralised lightweight Zero Trust Architecture for IoT (ADZTA-IoT). This is a multilayered architecture that integrates Distributed Ledger Technologies (DLTs) and Self-Sovereign Identity (SSI) to enhance IAM of IoT devices. The ADZTA-IoT architecture provides a comprehensive and forward-looking approach to securing heterogeneous and resourceconstrained IoT devices. A sequence diagram demonstrates the feasibility of the proposed architecture.Item The burden of adverse drug reactions in Africa in the context of pharmacogenetics-based clinical guidelines(2026-05) Scholefield, Janine; Mazhindu, TA; Nagy, M; Twesigomwe, D; Agesa, G; Masimirembwa, CGenetic variation has a significant impact on patients’ response to medicines. Variations in important pharmacogenes have been evaluated in several studies and consequently led to international guidelines from clinical pharmacogenomics consortia. However, there are limited examples of these being implemented across the African continent despite the increased evidence of how these variants contribute to adverse drug reactions (ADRs) or ineffective treatments. Considering the vast genetic diversity in Africa, there are significant gaps in understanding how much of a negative effect this might be having across healthcare across the continent. We therefore sought to establish the extent of ADRs from a subset of medicines shown to be associated with pharmacogenetic biomarkers from the last 10 years across 47 countries on the African continent. In the absence of pharmacogenetic testing on the African continent, we used international guidelines to derive a subset of definitions associated with published drug- gene- interaction associated ADRs to infer the role of pharmacogenetic variation on the prevalence of ADRs. The data showed that African countries report only 1% of ADRs in the VigiBase database, indicative of weak pharmacovigilance programs on the continent. Our analysis revealed that ADRs were mainly caused by anti- infectives such as efavirenz. The inferred pharmacogenes associated with high prevalence of ADRs were CYP2B6, CYP2D6, and CYP2C19. Using limited data, this foundational analysis may serve as the basis for stakeholders to prioritize pharmacogenetic interventions across the African continent.Item The conceptualisation of a national malware intelligence laboratory for South Africa(2026-03) Gertenbach, Wian P; McDonald, Andre M; Masango, Mfundo G; Mukondeleli, Elekanyani; Buckinjohn, Ethan; Mmbodi, Rendani; Latakgomo, Molebogeng; Hlongwane, Ndabezinhle E; Veerasamy, NamoshaSouth Africa has become an increasingly attractive target for cybercriminals, with malware and ransomware attacks on critical national infrastructure and public institutions rising in both frequency and severity. Highprofile incidents, such as ransomware attacks on Transnet, the Department of Justice, and the Government Employees Pension Fund (GEPF) highlight the scale of the threat. These attacks result in severe operational disruption and financial losses, often due to inadequate readiness and response mechanisms. This paper presents the conceptualisation of a national malware intelligence laboratory (NMIL) for South Africa, designed to strengthen domestic readiness and response to malware-related threats. A literature study was carried out to determine the main gaps. This was complemented by a stakeholder interview and questionnaire. This led to a gap analysis, and the examination of existing models as reference for the proposed design of the NMIL. This process not only identifies systemic weaknesses in the national cyber defence capabilities of South Africa but also evaluates how an NMIL could be integrated into existing national cybersecurity processes. The gap analysis revealed limited coordination of malware intelligence sharing across sectoral computer security incident response teams (CSIRTs), the potential to improve the ability of the National Cybersecurity Hub (CSHUB) to aggregate and disseminate actionable threat data, and insufficient hands-on exposure to malware in current cybersecurity education and training programs. In response, the paper introduces a framework and reference model that defines NMIL functions and its manner of integration into the national cybersecurity ecosystem. Specifically, the laboratory would provide high-quality malware intelligence to the CSHUB, including sample analysis results, threat profiling, and advisory support on removal tools, to improve effective response coordination. Additionally, the laboratory would offer access to a sandboxed training environment to educational institutions, thereby adding greater depth to cybersecurity education and promoting national cybersecurity readiness. The framework and reference model is developed using a systems engineering approach, to detail the NMIL’s information flows, interfaces and functional domains. It is anticipated that the formal process resulting in conceptual laboratory provides a replicable approach for institutionalising national malware laboratories. This model offers both strategic and operational insights for South Africa as well as other developing countries.Item SANReN Connect Proof of Concept – keeping the research community connected(2026-05) Pillay, KasandraSANReN Connect Proof of Concept (POC) is a new VPN-based solution designed to provide students with zero-cost internet access. It creates a great opportunity for South African mobile network providers and the South African NREN to extend internet connectivity reach to the South African research and education community. The service is intended to reroute student traffic destined for the internet through zerorated SANReN Connect IP addresses, using reversed billing services funded by SANReN. Multiple IP addresses and ports selected for loading balancing, represent Virtual Private Networks for a university which have been zero rated and are used to connect to the internet. Students are authenticated via the Identity Federation for the South African research and higher education community, SAFIRE. The SANReN Connect POC service leverages a national mobile provider’s (in this case, Telkom’s) national network coverage so that it provides access to the internet throughout the country without having to be at a specific institution or hotspot location. This paper documents the progress of the implementation of the service. It is envisioned that the service may be of interest for other NRENs to consider as a way of extending their network connectivity reach. The POC service will be launched at the national Centre for High Performance Computing (CHPC) conference in December 2025. Results from initial testing have been positive - showing that the solution is viable, however more exposure is required to the intended target market to determine whether the service should be taken to production.Item Machine learning in optics: Comparative analysis using ANN, SVM, and decision trees(2026-03) Tsebesebe, Nkgaphe T; Mpofu, Kelvin T; Mthunzi-Kufa, PThe integration of machine learning (ML) and deep learning (DL) into optical science is rapidly transforming the design, optimization, and interpretation of optical systems. Traditional optical analysis relies on complex mathematical modelling and labour-intensive calibration, which can be time consuming and error prone. This study demonstrates the application of ML and DL algorithms to three optical domains: computational imaging, spectral analysis, and diffraction pattern interpretation. Convolutional neural networks (CNNs) enable efficient denoising and reconstruction of optical images, while support vector machines (SVMs) and random forests (RFs) enhance the classification of spectral signals. Artificial neural networks (ANNs) facilitate rapid interpretation of diffraction patterns. Quantitative evaluation demonstrates that ML/DL methods improve accuracy, processing speed, and robustness compared to conventional approaches, highlighting the potential for next-generation optical systems in Biophotonics and quantum optics.Item Machine learning-driven analysis of surface plasmon resonance imaging for automated biomolecular binding detection(2026-03) Tsebesebe, NkgapheT; Mpofu, Kelvin T; Mthunzi-Kufa, PSurface Plasmon Resonance Imaging (SPRI) enables label-free, real-time monitoring of biomolecular interactions across sensor surfaces, making it an essential tool for multiplexed diagnostics. However, high-dimensional SPRI image data introduces challenges for interpreting binding events, particularly in complex sample matrices. This study trains machine learning model based on Multinomial Naïve Bayes (MNB) classifier with SPRI images to automatically classify binding and non-binding events. Statistical feature analysis (mean intensity, standard deviation, entropy, gradient magnitude) revealed significant differences between the SPRI binding and non-binding images, leading to the model (with Laplace smoothing parameter α = 1.0) achieve 97.7% accuracy, 97.8% sensitivity, 97.6% specificity, and an area under the ROC curve (AUC) of 0.98. The approach holds a potential to automated and high-throughput diagnostics, reducing reliance on manual interpretation and potentially extending SPRI platforms diagnostic tools in clinical and research settings.Item Machine learning-enhanced LAMP-fluorescence assay for automated tuberculosis detection(2026-03) Tsebesebe, Nkgaphe T; Mpofu, Kelvin T; Sivarasu, S; Mthunzi-Kufa, PRapid and accurate disease detection is critical for effective clinical management and public health interventions, particularly in low-resource settings. Loop-Mediated Isothermal Amplification (LAMP) is a promising molecular diagnostic technique due to its simplicity, speed, and compatibility with real-time fluorescence detection. However, interpreting fluorescence images, especially in cases with low target concentrations or high background noise remains challenging and error-prone when performed manually or using basic threshold-based methods. This study proposes the integration of a Support Vector Machine (SVM) algorithm with the LAMP-fluorescence assay to automate and enhance the detection of tuberculosis (TB) at low concentrations. Using a dataset of fluorescence images, the model was trained with 5-fold cross-validation across 20 candidate configurations (100 total fits). The optimized model achieved a classification accuracy of 99.76%, effectively distinguishing true positives, false positives, and ambiguous amplification patterns. Diagnostic sensitivity and specificity improved to 95.0% and 97.0%, respectively, supporting earlier and more reliable detection. This enhanced approach demonstrates strong potential for developing robust, scalable, and fielddeployable diagnostic tools for TB and other infectious diseases.Item Proposed amendments to SACAA part 101 regulations progress and future outlook(2026-07) Chirwa, MaryPart 101 of the South African Civil Aviation Regulations governs unmanned aircraft systems (UAS) operations and was introduced early to ensure aviation safety. However, the framework was largely adapted from manned aviation regulations and applies a uniform, certification-driven approach to operations with very different risk profiles. As the UAS sector has evolved, concerns have emerged regarding whether the current regulatory structure continues to support innovation, accessibility, and compliance, particularly for low-risk operations, research activities, and emerging manufacturers. This paper presents a structured review of Part 101 and a proposal for targeted amendments developed through stakeholder consultation, expert engagement, and comparative benchmarking against international regulations and standards. The proposed framework introduces a proportionate, risk-based regulatory architecture, differentiated operational categories, and dedicated provisions for institutional and research operations whilst managing safety and security risks associated with unsecured airspace. The analysis demonstrates that a risk-based approach can reduce regulatory barriers and improve compliance while maintaining core aviation safety objectives.Item Digital governance in the generative AI era: A qualitative analysis of privacy and trust in South African organizations(2026-07) Mlambo, Tlangelani P; Zuva, T; Brown, A; Moletsane, RSouth African organizations are experiencing rapid digital transformation as digital technologies increasingly shape service delivery, data management, and interactions between the state and citizens. In this time where machines such as Generative Artificial Intelligence (GenAI) can generate answers from the user prompt, there’s a need to control their use in order not to harm others. This study examines digital governance in the generative AI era, focusing on how privacy and trust are managed within organizations in South Africa. The study adapts a qualitative research approach utilizing the traditional literature review and secondary data analysis. There’s a disconnect between the initiated policies and the implementation or regulation of these policies, therefore, there is a need to develop a comprehensive digital governance framework for South African organizations to help minimize any harm that can be caused by use of Generative Artificial Intelligence. The research shows that in the generative AI era, is necessary for digital governance to move from only technologycentric strategies to a comprehensive framework that will combine institutional, ethical and operational aspects. The study contributes theoretically and practically to a deeper understanding of governance challenges and opportunities in South Africa. The study is limited to South African organizations and on secondary data from existing literature. Future work can extend the scope of the study beyond South Africa and conduct empirical and longitudinal studies to gain more insights on digital governance in the generative AI era.Item Bridging the gap from development to implementation of tools for the South African Transport Department(2026-07) Chirwa, Mary; Mukwena, Shothodzo T; Roux, Michael P; Muronga, KhangweloThe South African government has invested significantly in developing digital decisionsupport systems to improve road infrastructure planning and maintenance. Despite their technical robustness, the impact of these tools, such as the Abnormal Loads System, Road Asset Management Systems (RAMS), the PotholeFixGP application, and the STRUMAN Bridge Management System, has been limited by institutional, governance, and funding challenges. This study examines whether these systems are being used as intended, and identifies the barriers between development, implementation, and sustained network-level use. Drawing on interviews with system developers and implementation stakeholders, the paper presents four case studies highlighting common implementation bottlenecks. Findings show that while technical design was sound across all systems, uptake was constrained by weak institutional ownership, inconsistent operational funding, and misalignment with budgeting and decision-making processes. The study concludes with recommendations for strengthening the long-term integration of digital tools into infrastructure governance frameworks.