Research Articles (Electrical, Electronic and Computer Engineering)

Permanent URI for this collectionhttp://hdl.handle.net/2263/1693

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Non-linear control of a fuel gas blending benchmark problem with added consumer dynamics
(Elsevier, 2025-10) Sibiya, M.D.; Wiid, Andries Johannes; Le Roux, Johan Derik; Craig, Ian Keith; ian.craig@up.ac.za
This paper contributes to existing literature on fuel gas control by providing a feasible control solution with improved economic performance for an existing fuel gas control benchmark problem. Improved economic performance is achieved by implementing a non-linear model predictive controller (NMPC) that uses state estimates provided by a moving horizon estimator (MHE) and extended Kalman filter (EKF) for the fuel gas composition and flame speed index (FSI) to provide continuous inputs for the controller. Furthermore, the original fuel gas benchmark model is expanded to include consumer dynamics affecting fuel gas demand due to changes in the fuel gas heating value, making the model more representative of real industrial plants. The behaviour of an NMPC that neglects consumer dynamics (NMPC1) was compared against an NMPC that includes consumer dynamics (NMPC2). The aim of the benchmark problem is to reduce the time-weighted average cost of fuel gas for three 46-hour cases, accounting for purchase costs and penalties for fuel gas specification violations. An optimal cost for each case is determined assuming ideal conditions and perfect control. The benchmark controller is a conventional multi-loop feedforward/feedback system and has an average cost for the three cases which is 38.5% higher than the optimal cost. The NMPC1 controller has an average cost which is 33.9% higher than the optimal cost and better than the benchmark controller. A new benchmark scenario was developed which includes the consumer dynamics. For the new scenario, NMPC1 could not find a feasible solution, resulting in oscillations and specification violations. The oscillations would result in site-wide instabilities for all equipment using fuel gas. NMPC2 was able to keep the process stable during these scenarios and maintain all specifications. This shows the necessity to include consumer dynamics for effective fuel gas blending control. HIGHLIGHTS • Show improved economic performance for an existing fuel gas control benchmark problem. • Provide continuous estimates of fuel gas composition and flame speed index to an NMPC. • Consumer dynamics are included, making the model more representative of industry. • An NMPC that neglects consumer dynamics is compared against one that does not. • Show the necessity to include consumer dynamics for effective fuel gas blending control.
A review of smart crop technologies for resource constrained environments : leveraging multimodal data fusion, edge-to-cloud computing, and IoT virtualization
(MDPI, 2025-10-09) Olatinwo, Damilola D.; Myburgh, Hermanus Carel; De Freitas, Allan; Abu-Mahfouz, Adnan Mohammed
Smart crop technologies offer promising solutions for enhancing agricultural productivity and sustainability, particularly in the face of global challenges such as resource scarcity and climate variability. However, their deployment in infrastructure-limited regions, especially across Africa, faces persistent barriers, including unreliable power supply, intermittent internet connectivity, and limited access to technical expertise. This study presents a PRISMA-guided systematic review of literature published between 2015 and 2025, sourced from the Scopus database including indexed content from ScienceDirect and IEEE Xplore. It focuses on key technological components including multimodal sensing, data fusion, IoT resource management, edge-cloud integration, and adaptive network design. The analysis of these references reveals a clear trend of increasing research volume and a major shift in focus from foundational unimodal sensing and cloud computing to more complex solutions involving machine learning post-2019. This review identifies critical gaps in existing research, particularly the lack of integrated frameworks for effective multimodal sensing, data fusion, and real-time decision support in low-resource agricultural contexts. To address this, we categorize multimodal sensing approaches and then provide a structured taxonomy of multimodal data fusion approaches for real-time monitoring and decision support. The review also evaluates the role of IoT virtualization as a pathway to scalable, adaptive sensing systems, and analyzes strategies for overcoming infrastructure constraints. This study contributes a comprehensive overview of smart crop technologies suited to infrastructure-limited agricultural contexts and offers strategic recommendations for deploying resilient smart agriculture solutions under connectivity and power constraints. These findings provide actionable insights for researchers, technologists, and policymakers aiming to develop sustainable and context-aware agricultural innovations in underserved regions.
A wideband circularly polarised magneto-electric dipole antenna array with a series sequential phase feed network
(Wiley, 2025-01) Coetzer, Elmien; Joubert, Johan; Odendaal, Johann Wilhelm; jjoubert@up.ac.za
A printed circularly polarised antenna array is presented that utilizes the inherent good bandwidth and stable gain of magneto-electric dipoles in combination with the wideband benefits of a sequential rotation feed technique. The proposed antenna has a simple geometry using two substrates and does not require any additional cavity or parasitic elements. The designed and simulated antenna has an impedance bandwidth of more than 75%, a 3 dB axial ratio bandwidth of 67% and a peak gain of 12.4 dBic, with less than 3 dB gain variation across the entire axial ratio bandwidth. The antenna provides a good combination of simple and compact geometry, wide bandwidth, good gain and stable radiation patterns when compared to previously published research. Simulated as well as measured results are presented for a protype antenna array.
Hybrid intelligent optimisation for onshore wind farm forecasting
(Springer, 2025-09) Gwabavu, Mandisi; Bansal, Ramesh C.; Bryce, Andrew
Accurate wind power forecasting is crucial for the dependable functioning and strategising of contemporary power systems, especially as the global integration of renewable energy escalates. This study introduces an innovative hybrid intelligent forecasting model that amalgamates Long Short-Term Memory (LSTM) neural networks with Complementary Ensemble Empirical Mode Decomposition (CEEMD) and a hybrid optimisation strategy that incorporates Ant Colony Optimisation (ACO), Genetic Algorithm (GA), and Particle Swarm Optimisation (PSO). The model was developed and evaluated utilising empirical data from a 138 MW wind farm consisting of 46 turbines, based on operational data from 2019. The proposed CEEMD-LSTM-ACO-GA-PSO model adeptly tackles the nonlinearity and intermittency of wind speed data through the decomposition of intricate signals, the enhancement of temporal learning, and the optimisation of model hyperparameters. The evaluation results indicated a substantial enhancement in forecasting precision relative to baseline models. The hybrid model attained a Root Mean Square Error (RMSE) of 0.142 and a Mean Absolute Percentage Error (MAPE) of 3.8% for 24-h forecasts, representing an enhancement of more than 35% compared to traditional LSTM models. It also exhibited strong performance over extended forecasting periods of up to 168 h. This study validates the effectiveness of a hybrid intelligent model in improving wind power forecasting while emphasising the limitations associated with computational complexity, sensitivity, feature importance and generalisation. Future research should incorporate uncertainty quantification, simplify models for real-time deployment, and adopt transformer-based architectures. The results endorse the application of intelligent optimisation in enhancing the reliability and sustainability of energy system operations.
A bi-cylindrical lens for a DRGH antenna
(Wiley, 2025-09) Roodt, Pieter; Odendaal, Johann Wilhelm; Joubert, Johan; Jacobs, Bennie; wimpie@up.ac.za
Broadband double-ridged guide horn (DRGH) antennas are extensively used in antenna measurement and electromagnetic compatibility and interference testing, especially the 1–18 GHz DRGH antenna, which is widely accepted as a standard for this band. Increasing the gain of the DRGH will result in higher field strengths for EMI testing and increased sensitivity in antenna testing facilities. In this paper, a complete wideband near-field E- and H-plane phase center analysis is performed with CST, at observation points over a region inside the flared section and also outside the aperture of the DRGH. A new plano-convex intersecting bi-cylindrical lens was designed using the two discrete phase centers corresponding to the statistical mode of samples from the population of phase centers obtained from the simulated phase distributions. This new lens is a practical implementation with both convex surfaces on the inside and a planar surface on the outside of the DRGH. This makes manufacturing and mounting the lens much easier without significantly increasing the size of the DRGH antenna. The bi-cylindrical lens significantly increases the boresight gain of the DRGH antenna, while simultaneously reducing the variation in 3 dB beamwidth over most of the operating band.
Hearing loss configurations in low- and middle-income countries
(Taylor and Francis, 2025-10) Newall, John; Kim, Rebecca; Dawes, Piers; Alnafjan, Fadwa; Vaughan, Glyn; Carkeet, Donna; Ghannoum, Heba; Mcpherson, Bradley; Patel, Nitish Ranjan; Sasidharan, Megha; Damam, Nitin K.; Goswami, S.P.; Chinnaraj, Geetha; Sartika, Dahlia Eka; Alhanbali, Sara; Bartlett, Rebecca A.; Ismail, Afzarini Hasnita; Smith, Mike C.F.; Ghimire, Anup; Shah, Shankar; Martinez, Norberto V.; Ramos, Hubert D.; Alparce, Ultima Angela; Tavartkiladze, George A.; Bakhshinyan, Vigen; Boboshko, Maria; Kasper, Annette; Pifeleti, Sione; Swanepoel, De Wet; Myburgh, Hermanus Carel; Frisby, Caitlin; Pitathawatchai, Pittayapon; Atas, Ahmet; Serbetcioglu, Bulent; Sennaroglu, Gonca; Konukseven, Ozlem; Yilmaz, Suna Tokgoz; Turkyilmaz, Meral Didem; Batuk, Merve; Kara, Eyyup; Senkaya, Duygu Hayir; Babaoglu, Gizem; Oruc, Yesim; Ozkan, Melek Basak; Cetinkaya, Merve Meral; Ceyhan, Aysenur Kucuk; Adali, Inci
OBJECTIVE : The majority of individuals with hearing loss worldwide reside in low- and middle-income countries (LMICs), but there is limited information regarding the characteristics of hearing loss in these regions. This descriptive study aims to address this knowledge gap by analysing audiogram patterns in LMIC populations. Greater knowledge about the properties of hearing loss in LMICs allows for improved planning of interventions. STUDY SAMPLE : Retrospective data from 23 collaborating centres across 16 LMICs were collected. All participants were adults seeking help for hearing problems. A machine learning approach was utilised to classify the hearing threshold data and identify representative profiles. The study comprised 5773 participants. RESULTS : The results revealed mildly sloping audiometric patterns with varying severity. The patterns differed from previous studies conducted in high-income regions which included more steeply sloping losses. The findings also indicated a higher proportion of more severe levels of hearing loss. CONCLUSIONS : These variations could be attributed to population-level differences in the causative mechanisms of hearing loss in LMICs, such as a higher prevalence of infectious disease-related hearing loss. The results may also reflect differences in health seeking behaviours. This study highlights the need for tailored, scalable, hearing interventions for LMICs.
Analyzing the impact of electric vehicles on the power network of the United Arab Emirates
(Wiley, 2025-06) Al-Arab, Suma; Bansal, Ramesh C.; Abo-Khalil, Ahmed G.
This study investigates the impact of increasing electric vehicle (EV) adoption on the power grid in the United Arab Emirates (UAE), focusing on grid performance, stability, and efficiency under different EV penetration scenarios. A mathematical model is developed to evaluate EV charging load profiles based on energy consumption, charging schedules, and station distribution. The results reveal that level 1 (120 V) charging stations generate a peak load of 93.6 kW, whereas level 2 (240 V) stations impose a significantly higher peak load of 187.2 kW. The study finds that while the existing power grid can support up to 40% EV penetration with level 1 charging, it risks exceeding capacity with level 2 infrastructure. By 2030, a 40% EV penetration with level 2 charging is projected to surpass the system’s margin capacity, increasing the likelihood of voltage instability and transformer overloads. This research is novel in its UAE-specific modeling of EV charging impacts, offering detailed insights into grid constraints under future EV growth. To mitigate these challenges, the study recommends dynamic pricing strategies and vehicle-to-grid (V2G) technology to optimize load distribution and enhance grid resilience. The findings provide essential guidance for policymakers, utilities, and industry stakeholders in developing a sustainable and efficient EV charging infrastructure.
Distribution network time-based framework for PV DG and BESSs sizing and integration
(Elsevier, 2025-02) Van der Merwe, Carel Aron; Naidoo, Raj; Bansal, Ramesh C.
Traditional distribution network designs are based on single-value (static) yearly maximum demands, and do not consider the time-based nature of load-side DR (PV DG and/or BESS) installations. The increasing presence of high-penetration, private-sector driven renewable generation and energy storage systems installed within internal networks necessitates quasi-dynamic analysis to modernise and advance network design procedures. Distribution network design parameters affected by the capacity, capability, load-to-generation balancing, and power management of high-penetration load-side/private integrated PV DG and BESSs must be re-evaluated for optimal combined DR system sizing and shared external network integration acceptability. These initial performance parameters were analysed within the two distinctive distribution network load profile forms in a quasi-dynamic sizing and impact study. Other variables include TOU tariff structures, load diversity, demands, load factors, PV DG and BESS parameters, the combined DR system power control, voltage profiles, utilisation factors, reactive power requirements, and fault levels. By identifying operational parameters for PV DG and BESSs, a symbiotic approach to DR utilisation through power control is defined for a permanently reduced load-side maximum demand with lowering peak tariff period demands; benefiting both end-users and shared external networks. This is achieved by limiting bi-directional power flows within the private/internal network and maximising the overall DR system's capability, utilisation, and operational synergy as governed by hierarchical control adapting to a varying load profile. The time-based analysis, integration methodology, quasi-dynamic DR penetration limits, and the developed power flow control algorithm provide planners and developers a baseline for including DR integration impacts within service agreements. The approach also offers an alternative strategy for securing development approvals within remote or overloaded networks that would otherwise have been rejected. HIGHLIGHTS • Modernises distribution network design procedures through quasi-dynamic analysis • Provides a guideline for optimal PV DG and BESS sizing and hierarchical operation • Develops a synergetic TOU power algorithm to combine and enhance DR capabilities • Reduces network demands with a simplified PV DG, BESS, and control methodology • Assesses the impacts of DR penetration on quasi-dynamic network parameters
Application of monoclonal anti-mycolate antibodies in serological diagnosis of tuberculosis
(MDPI, 2024-11-06) Truyts, Alma; Du Preez, Ilse; Maesela, Eldas M.; Scriba, Manfred R.; Baillie, Les; Jones, Arwyn T.; Land, Kevin J.; Verschoor, Jan Adrianus; Lemmer, Yolandy; jan.verschoor@up.ac.za
Patient loss to follow-up caused by centralised and expensive diagnostics that are reliant on sputum is a major obstacle in the fight to end tuberculosis. An affordable, non-sputum biomarker-based, point-of-care deployable test is needed to address this. Serum antibodies binding the mycobacterial cell wall lipids, mycolic acids, have shown promise as biomarkers for active tuberculosis. However, anti-lipid antibodies are of low affinity, making them difficult to detect in a lateral flow immunoassay—a technology widely deployed at the point-of-care. Previously, recombinant monoclonal anti-mycolate antibodies were developed and applied to characterise the antigenicity of mycolic acid. We now demonstrate that these anti-mycolate antibodies specifically detect hexane extracts of mycobacteria. Secondary antibody-mediated detection was applied to detect the displacement of the monoclonal mycolate antibodies by the anti-mycolic acid antibodies present in tuberculosis-positive guinea pig and human serum samples. These data establish proof-of-concept for a novel lateral flow immunoassay for tuberculosis provisionally named MALIA—mycolate antibody lateral flow immunoassay.
Exposure of the facial nerve within the facial canal
(Elsevier, 2024-11) Govender, Shavana; Hanekom, Tania; Human-Baron, Rene; tania.hanekom@up.ac.za
BACKGROUND : The facial canal lies in the petrous part of the temporal bone and contains the facial nerve. The facial canal and nerve are divided into three segments: the labyrinthine, tympanic and mastoid segments, which travel in different planes. These segments are closely related to the structures of the middle- and inner-ear, so pathology of the intracranial facial nerve is often evident in cochlear implant users. The facial canal and nerve are of great concern to otologists during electrode placement for a cochlear implant, as any damage to the nerve may result in untreatable facial paralysis. Few studies have been conducted on a cadaveric population, with most carried out on CT images of the cochlea and facial nerve. Thus, there is no standard or straightforward methodology to visualise the facial canal and nerve directly. We propose a detailed dissection technique to bridge this gap in research. METHOD : Four cadavers were used, and both the left and right facial canals were dissected. After the exposure of the cranial floor, the internal acoustic meatus and the facial canal were dissected out using drilling tools to remove the surrounding temporal bone and expose the facial nerve within the facial canal. RESULTS : This technique allowed for morphometric analyses and observations of the facial canal in relation to the middle- and inner-ear. CONCLUSION : Knowledge of the facial canal may assist otosurgeons in safely dissecting the region without injuring vital structures within this area.
Automatic self-similarity based form labelling of classical-period piano sonata movements from audio recordings
(Institute of Electrical and Electronics Engineers, 2025-08) Burger, Paul Alwyn Desmond; Jacobs, Jan Pieter
Musical form refers to the overall structure or organisation of a musical composition. It is a complex and high-level property of music that requires musical training to identify. A review of previous research in this field indicates that the focus has been on the task of detecting section boundaries and that automatic audio based form label recognition is a field of study that remains largely unexplored. This study explores the complex task of automatically determining musical form from audio. It demonstrates the ability of a novel methodology to label eight different form types that occur in the movements of Classical-period piano sonatas. The methodology makes use of self-similarity matrices, generated from features extracted from raw audio, as input to a convolutional neural network. The superiority of our approach was confirmed by evaluating it against a neural network model based on state-of-the-art features. We also report an evaluation of self-similarity matrices based on automatically transcribed piano rolls for the task of form recognition. Piano rolls are demonstrated to be superior for this application when compared to a range of other feature representations. Additionally, the performance of the model is shown to be robust in handling variations in performer choices. These range from different interpretations of the same score to actual deviations from the score where performers may elect to play or not to play notated repeats thus highlighting its ability to generalise across different performances of the same piece.
Enhancing solar irradiance estimation for pumped storage hydroelectric power plants using hybrid deep learning
(Springer, 2024-11) Konduru, Sudharshan; Naveen, C.; Bansal, Ramesh C.
This research article explores the potential of Pumped Storage Hydroelectric Power Plants across diverse locations, aiming to establish a sustainable electric grid system and reduce per-unit energy costs. A distinctive feature of the study involves forecasting solar irradiance on large-scale hydroelectric dam locations to identify optimal sites for a PV-integrated hydropower system. The research focuses on advancing the integration of floating solar power modules on water storage systems in eight selected regions across India, emphasizing precise solar irradiance estimation. The paper introduces a state-of-the-art hybrid intelligent deep learning model, combining time series analysis and deep learning through residual ensembling to address these challenges. The primary objective is to pinpoint the optimal location for a Power Storage System (PSS) with the highest solar irradiation for PV-integrated hydro system integration. A secondary goal involves minimizing errors within computational time constraints by the proposed model. The study also employs various optimization techniques to enhance its effectiveness and fine-tune the model’s performance, contributing to the advancement of sustainable energy solutions. The proposed model performs best with a Whale optimization algorithm with mean absolute error varying from 0.34 to 3.63 W/m2 and root mean square error from 0.75 to 9.51 W/m2 on PSS locations. The analysis also confirms average solar irradiance is high on PSS 7 with 221.0 W/ m2 followed by PSS 1 with 221.1 W/ m2 among the eight designated sites.
Reliability of using CBCT scans to derive the parameters of the facial canal
(Elsevier, 2025-09) Govender, Shavana; Hanekom, Tania; Human-Baron, Rene
PURPOSE : Studies have analysed the facial canal (FC) parameters using dissection or imaging technologies. To date, no studies have analysed the differences between these methods. The purpose of this study was to evaluate the reliability and accuracy of deriving the parameters from the FC on cone-beam computed tomography (CBCT) scans by comparing it to FC morphometric analyses of dissected heads. METHODS : Ten embalmed heads (n = 20) were CBCT scanned and analysed using ImageJ. Next, each FC segment was dissected. Measurements for both methods included the proximal and distal diameters and lengths for each segment, and angles of the first and second genua. RESULTS : The paired t-test indicated significant differences (p < 0.05) between the CBCT and dissected measurements for most FC segments measured. The respective dissection measurements were consistently higher than the CBCT measurements. However, the Bland-Altman plots showed agreement between the two modalities in measuring FC segments. Interobserver error values were 0.963 and 0.950 for the CBCT and dissection groups, respectively, indicating a high repeatability. CONCLUSIONS : Although the current study showed differences between the parameters of the FC derived from CBCT scans and dissected measurements, CBCT scans remain a valuable tool for non-invasive assessments. However, the differences have implications for modelling in that CBCT measurements underestimate the true size for the various segments of the FC. It is worth noting that a potential difference in segment sizes may exist between populations but will have no effect on using CBCT scans as a pre-operative assessment of the FC.
SPARCQ : enhancing scalability and adaptability of proactive edge caching through q-learning
(Institute of Electrical and Electronics Engineers, 2025-04) Lall, Shruti; De Clercq, Johan; Pillay, Nelishia; Maharaj, Bodhaswar Tikanath Jugpershad; shruti.lall@tuks.co.za
The exponential growth of network traffic and data-intensive applications demands innovative solutions to manage data efficiently and ensure high-quality user experiences. Proactive edge caching has become a crucial technique for enhancing network performance by predicting and pre-storing content closer to users before access. Accurate prediction models, such as Long Short-Term Memory (LSTM) networks, are crucial for effective proactive caching. However, these models rely on carefully tuned hyperparameters to maintain predictive accuracy, and manual tuning is impractical in dynamic and diverse network environments, limiting scalability and adaptability. To overcome these challenges, we propose a novel framework, SPARCQ, that leverages Q-learning, a reinforcement learning algorithm, to automate hyperparameter tuning for LSTM-based prediction models. By dynamically adjusting hyperparameters, our approach ensures accurate predictions, improving caching efficiency and adaptability. Using the MovieLens dataset, we achieve an average improvement of 8% in cache hit ratios compared to baseline models, including popularity-based and untuned models. Additionally, our framework demonstrates scalability and robustness across geographically distributed regions, consistently adapting to diverse and evolving data patterns.
Scalable Δ-AGC logic for enhanced electromechanical oscillation damping in modern power systems with utility-scale photovoltaic generators
(Institute of Electrical and Electronics Engineers, 2025-05) Ratnakumar, Rajan, Rajan; Venayagamoorthy, Ganesh Kumar
Power systems with utility-scale solar photovoltaic (PV) can significantly influence the operating points (OPs) of synchronous generators, particularly during periods of high solar PV generation. A sudden drop in solar PV output due to cloud cover or other transient conditions will alter the generation of synchronous generators shifting their OPs. These shifted OPs can become a challenge for stability as the system may operate closer to its stability limits. If a disturbance occurs while the system is operating at the shifted OP, with reduced stability margins, it will be more vulnerable to increased oscillations, loss of synchronism of its generator(s) and system instability. This study introduces a scalable Δ -automatic generation control ( Δ -AGC) logic method designed to address stability challenges arising from shifts in the OPs of synchronous generators during abrupt drops in PV generation. By temporarily adjusting the OPs of synchronous generators through modification of their participation factors (PFs) in the AGC logic dispatch, the proposed method enhances power system stability. The proposed Δ -AGC logic method focuses on the optimal determination of ΔPFs in power systems with large number of generators, using the concept of coherency and employing a hierarchical optimization strategy that includes both inter-coherent and intra-coherent group optimization. Additionally, a new electromechanical oscillation index (EMOI), integrating both time response analysis (TRA) and frequency response analysis (FRA), is utilized as an online situational awareness tool (SAT) for optimizing the system’s stability under various conditions. This online SAT has been implemented in a decentralized manner at the area level, limiting wide-area communication overheads and any cybersecurity concerns. The Δ -AGC logic method is illustrated on a modified IEEE 68 bus system, incorporating large utility-scale solar PV plants, and is validated through real-time simulation. Various cases, including high-loading conditions with and without power system stabilizers, conventional AGC logic, and Δ -AGC logic, are carried out to evaluate the effectiveness of the proposed Δ -AGC logic method. The results illustrate the performance and benefits of the Δ -AGC logic method, highlighting its potential to significantly enhance power system stability.
Blockchain-enhanced attribute-based encryption architecture with feasibility analysis
(Institute of Electrical and Electronics Engineers, 2025-03) Ferrer-Rojas, Agustin; Maharaj, Bodhaswar Tikanath Jugpershad; Hlophe, Mduduzi Comfort
In today’s digital landscape, data security is critical, particularly in the Internet of Things (IoT), where large volumes of sensitive data are exchanged. Traditional encryption methods like RSA and AES face challenges in balancing security and performance, exposing systems to advanced cyber threats. To address these issues, blockchain technology offers decentralized, tamper-resistant data protection that enhances trust and transparency. Attribute-Based Encryption (ABE) schemes have been developed, often combining asymmetric and symmetric encryption for efficiency and security. However, gaps remain in practical deployment due to underexplored network architectures and limited feasibility simulations. This study proposes an end-to-end security architecture integrating ABE with Linear Secret Sharing Scheme (LSSS) access policies and blockchain-based distributed key management. The system’s feasibility was evaluated using Network Simulator 3 (NS3) within a simulated IoT network. Results demonstrate a lightweight and scalable solution suitable for constrained environments. Numerical simulations showed consensus times as low as 0.25 seconds for key agreement and 0.7 seconds for message consensus, even in resource-constrained settings. For large networks, consensus times reached as low as 0.75 seconds. The system also achieved an average throughput of 0.3 transactions per second in low-resource environments. These outcomes highlight the system’s potential for secure, efficient data transmission in IoT and other distributed systems.
Alcohol degradation of anhydride-cured epoxy resin insulating materials containing SiO2 filler
(American Institute of Physics, 2025-06) Zhang, Xu; Li, Chengjie; Ye, Xianming; Zhang, Xiaoxing; Maluta, Eric; Wu, Yunjian
Please read abstract in the article.
Mental disorder assessment in IoT-enabled WBAN systems with dimensionality reduction and deep learning
(MDPI, 2025-06) Olatinwo, Damilola D.; Abu-Mahfouz, Adnan Mohammed; Myburgh, Hermanus Carel
Mental health is an important aspect of an individual’s overall well-being. Positive mental health is correlated with enhanced cognitive function, emotional regulation, and motivation, which, in turn, foster increased productivity and personal growth. Accurate and interpretable predictions of mental disorders are crucial for effective intervention. This study develops a hybrid deep learning model, integrating CNN and BiLSTM applied to EEG data, to address this need. To conduct a comprehensive analysis of mental disorders, we propose a two-tiered classification strategy. The first tier classifies the main disorder categories, while the second tier classifies the specific disorders within each main disorder category to provide detailed insights into classifying mental disorder. The methodology incorporates techniques to handle missing data (kNN imputation), class imbalance (SMOTE), and high dimensionality (PCA). To enhance clinical trust and understanding, the model’s predictions are explained using local interpretable model-agnostic explanations (LIME). Baseline methods and the proposed CNN–BiLSTM model were implemented and evaluated at both classification tiers using PSD and FC features. On unseen test data, our proposed model demonstrated a 3–9% improvement in prediction accuracy for main disorders and a 4–6% improvement for specific disorders, compared to existing methods. This approach offers the potential for more reliable and explainable diagnostic tools for mental disorder prediction.
Refurbishment and commissioning of a dual-band 23/31 GHz tipping radiometer at potential radio astronomical sites
(American Institute of Physics, 2025-04) Cuazoson, J.; Hiriart, D.; Stander, Tinus; Botha, R.; Contreras, J.; Ferrusca, D.; Ibarra-Medel, E.; Kurtz, S.; Neate, Reuben; Rojas, D.; Velázquez, M.
This paper reports on the refurbishment of a dual-band 23/31 GHz tipping radiometer previously used to measure atmospheric water vapor content at the Hartebeesthoek Radio Astronomy Observatory. We describe the motivation for the refurbishment, provide technical details of the modifications made to the instrument, and present preliminary test results of field measurements at the site of the High Energy Stereoscopic System Observatory in Namibia. Together with other radiometric measurements, this instrument will contribute to the site characterization of potential radio astronomical observatories in southern Africa.
Application of back propagation neural network in complex diagnostics and forecasting loss of life of cellulose paper insulation in oil-immersed transformers
(Nature Research, 2024-03-13) Ngwenyama, M.K.; Gitau, Michael Njoroge; u11265702@tuks.co.za
Oil-immersed transformers are expensive equipment in the electrical system, and their failure would lead to widespread blackouts and catastrophic economic losses. In this work, an elaborate diagnostic approach is proposed to evaluate twenty-six different transformers in-service to determine their operative status as per the IEC 60599:2022 standard and CIGRE brochure. The approach integrates dissolved gas analysis (DGA), transformer oil integrity analysis, visual inspections, and two Back Propagation Neural Network (BPNN) algorithms to predict the loss of life (LOL) of the transformers through condition monitoring of the cellulose paper. The first BPNN algorithm proposed is based on forecasting the degree of polymerization (DP) using 2-Furaldehyde (2FAL) concentration measured from oil samples using DGA, and the second BPNN algorithm proposed is based on forecasting transformer LOL using the 2FAL and DP data obtained from the first BPNN algorithm. The first algorithm produced a correlation coefficient of 0.970 when the DP was predicted using the 2FAL measured in oil and the second algorithm produced a correlation coefficient of 0.999 when the LOL was predicted using the 2FAL and DP output data obtained from the first algorithm. The results show that the BPNN can be utilized to forecast the DP and LOL of transformers in-service. Lastly, the results are used for hazard analysis and lifespan prediction based on the health index (HI) for each transformer to predict the expected years of service.

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