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Item The Settlement-mesozone geo-frame: An evolving spatial data framework facilitating data integration in support of South Africa’s development planning(2025) Arnold, Kathryn A; Maritz, Johan; Mans, Gerbrand GIn the development planning domain, there is a critical need for enhanced understanding of the complex spatial dynamics of social, economic and environmental patterns. Data are a critical resource for evidence-based planning and decision-making, and the 2030 Agenda for Sustainable Development has underscored the need for harmonised data of increasingly high quality, accuracy, currency, timeliness and granularity to effectively measure and monitor progress towards the Sustainable Development Goals at global, regional, national and subnational levels. Integrating geospatial and statistical data is one of the most effective ways to maximise its value, and while data integration methods are well documented and widely applied, less attention has been given to determining the most suitable spatial units for publicly useable, policy-informing data. A key challenge lies in the varying units of analysis and geographical scales employed across sectors and scientific disciplines. In South Africa, the Settlement-Mesozone Geo-Frame was developed and has evolved over 20 years of focused research and development to address issues inherent in the geographically arbitrary administrative areas according to which official data are collected and reported. The geo-frame provides a robust foundation for integrating spatially misaligned national datasets, enabling better profiling, mapping, analysis and monitoring of places over space and time.Item Black carbon emissions generally underestimated in the global south as revealed by globally distributed measurements(2025-07) Ren, Y; Oxford, CR; Zhang, D; Liu, X; Zhu, H; Dillner, AM; White, WH; Chakrabarty, RK; Garland, Rebecca M; Naidoo, MogeshCharacterizing black carbon (BC) on a fine scale globally is essential for understanding its climate and health impacts. However, sparse BC mass measurements in different parts of the world and coarse model resolution have inhibited evaluation of global BC emission inventories. Here, we apply globally distributed BC mass measurements from the Surface Particulate Matter Network (SPARTAN) and complementary measurement networks to evaluate contemporary BC emission inventories. We use a global chemical transport model (GEOS-Chem) in its high-performance configuration (GCHP) for high-resolution simulations to relate BC emissions to ambient concentrations for comparison with measurements. Here we find that simulations using the Community Emissions Data System (CEDS) emission inventory exhibit skill (r2 = 0.73) in representing variability in SPARTAN measurements across primarily developed regions with low BC concentrations but exhibit pronounced discrepancy (r2= 0.00019) across high-BC regions in the Global South, underestimating BC by 38%. Alternative inventories (EDGAR, HTAP) yield similar results. These findings motivate renewed attention to the challenging task of characterizing BC emissions from low- and middle-income countries.Item Effects of encapsulation and in vitro digestion on anthocyanin composition and antioxidant activity of raspberry juice powder(2025-07) Mokale, MJ; Kesavan Pillai, Sreejarani; Sivakumar, DMicrobeads of raspberry extract were produced using encapsulation matrices alginate + pea protein isolate + psyllium mucilage, alginate + pea protein isolate + psyllium mucilage + okra, and alginate + pea protein isolate + psyllium mucilage + Aloe ferox gel + gallic acid using freeze-drying method. The microbeads were characterised and assessed for their effectiveness on the release, bioaccessibility, of anthocyanin components and antioxidant activities during in vitro digestion. Alginate + pea protein isolate + psyllium mucilage + Aloe ferox gel + gallic acid matrix showed the highest encapsulation efficiency of 91.60% while the lowest encapsulation efficiency was observed in alginate + pea protein isolate + psyllium mucilage + okra (69.94%). Scanning electron microscope images revealed spherical shapes and varying surface morphologies for different encapsulation matrices. Despite the differences observed in Fourier transform infrared spectra, microbeads showed similar thermal degradation patterns. X-ray diffractograms showed amorphous structures for different encapsulation matrices. Comparatively, alginate+ pea protein isolate + psyllium mucilage + Aloe ferox gel + gallic acid microbeads exhibited the highest bioaccessibility for total phenols (93.14%), cyanidin-3-O-sophoroside (54.61%), and cyanidin-3-O-glucoside (55.30%). The encapsulation matrices of different biopolymer combinations (alginate+ pea protein isolate+ psyllium mucilage, alginate + pea protein isolate + psyllium mucilage + okra, and alginate + pea protein isolate + psyllium mucilage + Aloe ferox gel + gallic acid) enhanced anthocyanin stability and protected it against in vitro degradation of bioactive compounds.Item Advances and challenges in betulinic acid therapeutics and delivery systems for breast cancer prevention and treatment(2025-09) Selepe, Cyril T; Dhlamini, Khanyisile S; Tshweu, Lesego L; Kwezi, Lusisizwe; Ramalapa, Bathabile E; Ray, Suprakas SBreast cancer (BC) is the leading cause of cancer-related death among women worldwide. Due to limited treatment options for patients with advanced BC, preventive and innovative therapeutic strategies are essential to combat this disease. Therefore, finding safe and effective anticancer treatments remains a significant challenge in the 21st century. Plant-derived triterpenoids, widely used for medicinal purposes, exhibit various biological activities. Most triterpenoids are cytotoxic to multiple tumor cells and demonstrate anticancer effects in preclinical animal models. One example is betulinic acid (BA), a natural product mainly extracted from the bark of birch trees. BA is a promising anti-tumor compound with numerous pharmacological properties. However, its poor water solubility limits its optimal therapeutic potential. Additionally, the low BA content in plants hampers large-scale production from these sources. To address these issues, extensive research has focused on producing BA through chemical synthesis and biotransformation. Furthermore, several BA derivatives have been developed through structural modifications, and various delivery systems have been created to improve solubility and enhance therapeutic efficacy. This review discusses recent advances and challenges related to BA and its derivatives in preventing and treating breast tumors, as well as the potential obstacles and future directions for improving delivery systems in BC therapy.Item A review of smart crop technologies for resource constrained environments: Leveraging multimodal data fusion, edge-to-cloud computing, and IoT virtualization(2025-10) Olatinwo, DD; Myburgh, HC; De Freitas, A; Abu-Mahfouz, Adnan MISmart 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.Item Fire-retardant wood polymer composite to be used as building materials for South African formal and informal dwellings — A review(2025-02) Maake, T; Asante, JKO; Mhike, W; Mwakikunga, Bonex WA demand to replace an easily combustible wood with wood–plastic–rubber composite with better thermal performance than wood is at its peak globally. Wood-based composite materials in the form of wood–polymer composite (WPC) have emerged as new materials that can replace wood to produce wood products for various use. The use of recycled polymers as biodegradable polymer blended with fiber particles, waste tire powder, and other substances to manufacture new products known as wood–rubber–plastics composite (WRPC) for building construction and other different applications, has piqued the interest of numerous researchers. High flammability and weak combustibility parameters are a setback for many wood-based composites because of the flammability of these composites. Fabricated WRPC based on non-toxic fire retardants and other additives used to modify the flame-resistant quality of these composites, the fabrication techniques, and mechanical characteristics are herein reviewed. It is hoped that better composite in the form of WRPC can be used as building materials for informal and formal dwellings.Item An empirical evaluation of the main factors of a cybersecurity culture in South African E-health institutions using multiple linear regression(2025-08) Mwim, NE; Mtsweni, Jabu S; Chimbo, BE-health institutions are prominent targets for cybercriminals due to their reliance on information technology systems and issues related to the users have been identified as the biggest security weakest. Hence, while cybersecurity culture (CSC) research emphasizes the necessity of the human factor, limited empirical work has been done in the context of e-health in Africa. Therefore, an empirical evaluation was conducted to identify how preparedness, responsibility, management, technology and environment influence cybersecurity in South African e-health institutions. This quantitative research studied e-health institutions in the Mpumalanga province of South Africa. Various methods were used to investigate the multiple linear regression effects of the main factors of CSC and the results show that although the preparedness (Beta = 0.281; p-value < 0.05) and environment (Beta = 0.500; p-value < 0.05) factors had the greatest influence, management, technology and environment had a positive effect on CSC. These factors contributed 48.2 % to the variance (R-Squared). The study seems to be the first empirical study that combines the human factor domain framework (HFD) with other theoretical frameworks to identify critical factors of CSC. Furthermore, the impact of technology on CSC was empirically tested. The study is significant as it identified key factors that contributed to the institution’s CSC and quantified their impact. These results can enable e-health institutions to make decisions based on evidence regarding their cybersecurity interventions, strategy and practices. However, the empirical evaluation was limited to one context, namely the Mpumalanga province in South Africa and at two hospitals selected based on easy access (convenience) and purposive sampling with criteria based on work experience and knowledge of CSC limited the number of participants eligible to participate.Item Meteorological Drought Trend Analysis and Forecasting Using a Hybrid SG-CEEMDAN-ARIMA-LSTM Model Based on SPI from Rain Gauge Data(2026-01) Sibiya, S; Ramroop, S; Melesse, S; Mbatha, Nkanyiso BMeteorological drought presents considerable challenges to water supplies, agriculture, and socio-economic stability, especially in areas heavily reliant on precipitation. The Standardized Precipitation Index (SPI) is esteemed for its efficacy in drought monitoring, owing to its straightforwardness and applicability across many time scales. This study examines meteorological drought dynamics in the uMkhanyakude district using the Standardized Precipitation Index (SPI) at 6-, 9-, and 12-month timescales. Trend analysis was conducted using Mann–Kendall (MK), Modified Mann–Kendall (MMK), and Innovative Trend Analysis (ITA) methods. The study also proposes a hybrid model that integrates the Savitzky–Golay (SG) filter, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Autoregressive Integrated Moving Average (ARIMA), and Long Short-Term Memory (LSTM) networks, referred to as SG-CEEMDAN-ARIMA-LSTM, for forecasting of the SPI time series. Analysis of SPI trends and variability revealed statistically significant declining trends at five monitoring stations, characterized by negative Z-scores and p-values, showing a marked downward trajectory across several SPI scales. On the other hand, the forecasting results demonstrate that the SG-CEEMDAN-ARIMA-LSTM methodology outperformed benchmark models across all temporal scales, achieving high prediction accuracy with R2 values of 0.9839 (SPI-6), 0.9892 (SPI-9), and 0.9990 (SPI-12). These findings highlight the effectiveness of decomposition techniques (SG, CEEMDAN) in enhancing model performance and confirm the suitability of the hybrid model for both short-term and long-term drought forecasting. This study merges robust trend analysis with advanced hybrid forecasting techniques, providing a reliable framework for early warning systems and sustainable water resource management in drought-prone regions.Item Hybrid perovskite solar cells: A disruptive technology for hydrogen production through photocatalytic water splitting(2025-08) Akin Olaleru, S; Palaniyandy, N; Mamba, BB; Mwakikunga, Bonex WPerovskite solar cells (PSCs) have recently emerged as a viable technology for photovoltaic applications, offering high efficiency and cost-effective manufacturing. Beyond generating electricity, PSCs can also facilitate hydrogen production through water splitting. This article provides a comprehensive review of current research on PSCs for hydrogen production, highlighting their potential as a transformative technology in this field. The challenges and opportunities associated with using PSCs for hydrogen production are discussed, including their stability and efficiency under various operating conditions. The impact of device design, system integration, and materials engineering on PSC performance for hydrogen production is also examined. Furthermore, an overview of hydrogen demand is provided and how PSCs can be integrated with other renewable energy sources to contribute to a sustainable energy future through green hydrogen production is explored. The analysis suggests that hydrogen production using PSCs has the potential to become a groundbreaking technology, significantly impacting the energy sector and the transition to low-carbon energy.Item A novel hybrid path loss prediction model for 5G midband networks using empirical, machine learning, and feature prioritization techniques(2025-12) Shaibu, FE; Onwuka, EN; Salawu, N; Oyewobi, SS; Abu Mahfouz, Adnan MIAccurate path loss prediction is vital for 5G deployment, especially at midband frequencies where signal degradation is significant. This paper presents a hybrid model that integrates an optimized COST-231 Hata model with a random forest algorithm to improve prediction accuracy at 3.5 GHz. Recursive feature elimination identified eleven key features from eighteen multidimensional parameters, including novel environmental attributes, to prioritize factors influencing urban path loss. Validation against measurement and simulation datasets showed strong alignment with observed results, achieving lower errors (MAE = 1.82 dB, RMSE = 2.05 dB, and MAPE = 2.4%) compared to existing models. Additionally, cross-band validation at 1.6 GHz further demonstrated the model’s robustness, though retraining or fine-tuning is recommended for optimal performance at lower frequencies. Future research may expand the dataset to enhance generalizability.Item Colorimetric detection and removal of copper(II) ions from wastewater using a Griess reagent and cellulose nanofibers supported with mesoporous silica nanoparticles: An ImageJ and CIELAB colour space-based analytical approach(2025-12) Ninela, AM; Shange, SF; Mtibe, Asanda; Andrew, Jerome E , Jerome E Mokhothu; Mokhothu, THThis study presents a green, cost-effective, and dual-function approach for the colorimetric detection and removal of copper ions (Cu(II)) in wastewater, utilizing a cellulose nanofiber–mesoporous silica nanoparticle (CNF-MSN) composite in conjunction with the Griess reagent. The CNF-MSN composite was synthesized via a sol–gel process using cellulose nanofibers derived from natural biomass. Comprehensive characterization using FTIR, SEM, XRD, BET, TEM, and TGA confirmed the successful integration of CNF and MSN, with TEM revealing a web-like nanofiber structure (∼33 nm) and SEM showing mesoporous silica nanoparticles (2–50 nm). Hydrogen bonding between CNF hydroxyl groups and MSN silanol groups was indicated by O–H stretching shifts. For Cu(II) detection, the CNF-MSN composite produced a visible purple color change upon reaction with the Griess reagent across 1–5 mg/L Cu(II) standards. Color intensity and RGB values were quantified using ImageJ, converted to CIEXYZ and CIELAB (Lab) values, resulting in a linear response (R² = 0.9956) over the range of 0.01–5 mg/L, with a detection limit of 0.001521 mg/L. The UV–Vis spectrophotometric method validated the ImageJ approach, yielding an R² value of 0.9993 and a detection limit of 0.006253 mg/L. For Cu(II) adsorption, CNF-MSN removed nearly 100 % of Cu(II) within 45 min at pH 4–6, outperforming individual CNF and MSN with an adsorption capacity of 0.0978 mg/g and 97.85 % removal efficiency. In real samples, removal efficiencies ranged from 94.1 % to 99.1 %, with a maximum adsorption capacity of 38.9 mg/g. The adsorption data fit the Dubinin–Radushkevich isotherm (R² = 0.980) and the pseudo-second-order kinetics (R² = 0.999). Overall, the CNF-MSN composite offers a sustainable and efficient material for detecting and remediating Cu(II) in water systems.Item Case Study: Evaluation of stress concentration factors in shaft keyways through FE analysis(2025-09) Jordaan, Johannes PThe finite element method (FEM) is utilised in evaluating stresses in keyways of shafts loaded in torsion. These stress values are divided by their corresponding nominal stresses to arrive at so-called stress concentrationfactors, which are compared against published charts and experimental results for specific reference cases. FEM has become ubiquitous in the analysis and design of mechanical systems. While simple and well-known formulas for analytical solutions are employed to calculate nominal stresses in static design, dynamic or fatigue design is typically concerned with higher-than-nominal stresses that are associated with localised geometric stress raisers present in the system. These higher stresses are derived from nominal stresses by multiplication with an appropriate stress concentration factor. At present, though, the application of a multiplier to a nominal value seems somewhat redundant since the complete stress distribution – which includes the maximum stresses in areas of stress concentration – is a direct result from a finite element analysis (FEA). In this paper it is shown that FEA results not only compare favourably with available known results for commonly encountered stress raisers such as fillets and keyways but provide resolution to the stress distribution and paves the way for analysis and design of mechanical devices exhibiting uncommonly encountered stress raisers for which charts and formulas are not available.Item Synthesis of chitosan-modified poly (lactic-co-glycolic acid) microparticles with pH-dependent controlled-release kinetics to enhance the delivery of potential antidiarrheal medicinal plant extract to the lower gastrointestinal track(2025-12) Shatri, AMN; Lemmer, Yolandy; Kalombo, L; Mandiwana, Vusani; Mumbengegwi, RPhytotherapy has been used to treat gastroenteritis in many African countries, with medicinal plant extracts from Grewia tenax, Corchorus tridens, and Lantana camara showing strong antibacterial properties against bacteria that cause gastroenteritis. However, issues such as uncontrolled metabolism by gastric juices and instability in the gastrointestinal tract due to varying pH levels reduce the effectiveness of these phytomedicines. This has limited their use as an alternative or complementary treatment for gastroenteritis. To address this, nanotechnology has been employed to improve the pharmacokinetic and pharmacodynamic properties of phytomedicines. This study aimed to develop biodegradable, plant-based, chitosan-modified poly(lactic-co-glycolic acid) (CMPLGA) microparticles for targeted release in the lower gastrointestinal tract. Nanoparticles were created by mixing 12. 5 mg/ml of polymers with 120 mg/ml of antibacterial extracts from G. tenax, C. tridens. and L. camara using a modified double emulsion (W 1/O/W 2) and solvent evaporation method. The size and zeta potential of the nanoparticles were measured using photon correlation spectroscopy and electrophoretic laser Doppler anemometry. Scanning Electron Microscopy was used to examine morphology, and the encapsulation efficiency was determined via UV- vis spectroscopy. In vitro, the release kinetics of the plant extracts from the nanoparticles were investigated using sample separation techniques in simulated gastric and intestinal fluids, without the presence of enzymes. The plant-based CMPLGA nanoparticles were spherical, with sizes ranging from 524 ± 18 nm. 92 nm to 2582 ± 123 nm, and zeta potential from 2. 68 ± 0. 08 mV to 44. 2 ± 0. 100 mV; encapsulation efficiency was greater than 89.8 %. The release of phytomedicine from the nanoparticles depended on pH, with <2 % release at pH 1. 2 and over 50 % release at pH 7. 7.4. These CMPLGA nanoparticles improved the stability of the antibacterial phytomedicine in acidic conditions similar to those in the upper GI tract. They may serve as an effective vehicle for future drug delivery targeting gastrointestinal pathogens in the lower GI tract.Item Phenomenological and mechanistic insights into potential dietary nucleotide – probiotic synergies in layer chickens: A review(2025-05) Dibakoane, SR; Mhlongo, G; Moonsamy, Ghaneshree; Wokadala, OC , SR Mhlongo; Mnisi, CM; Mlambo, VDespite their growing popularity as alternatives to antibiotic growth promoters (AGPs), the individual effects of nucleotides and probiotics on poultry gut functionality remain poorly understood. In addition, inconsistent outcomes are quite common in studies where these two additives have been used separately to modify gut function and related parameters in birds. These inconsistencies, which have limited the potential of probiotics and nucleotides as AGP replacements, stem from various factors and need to be addressed. Combining probiotics and nucleotides could potentially enhance their effectiveness and lead to more consistent outcomes in layer chickens. Since their mechanisms of action complement each other, some level of synergy is expected when used together. Both additives have been shown to support gut health, boost immune function, and improve performance in chickens when used individually. However, no studies have investigated the possible synergistic effects of nucleotides and probiotics in poultry. This review makes the case for combined use of probiotics and nucleotides in layer chickens by providing phenomenological and mechanistic insights into hypothetical synergistic effects. This paper highlights the need for AGP alternatives and reviews studies on the effects and mechanisms of probiotics and nucleotides in layer chickens when used individually. We then propose potential mechanisms for their synergistic effects on gut health, performance, and egg quality based on logical deductions from observed biological responses. These proposed mechanisms are hypothetical and require experimental validation. Finally, the review explores how this synergy could lead to more consistent outcomes and enhance the feasibility of AGP-free egg production.Item SNEL-DFF: Android malware detection using Siamese networks with ensemble learning(2025-09) Zaidi, AR; Abbas, T; Ramay, SA; Shahzad, T; Qaisar, ZH; Khan, MA; Abu Mahfouz, Adnan MI; Beheshti, AThis paper proposes a new model simply known as Siamese Networks of Optimal Ensemble Learning with Deep Forest Feature (SNEL-DFF). The proposed model has the Deep Forest Feature extraction feature because of the complexity that is present in the data and to enhance the proficiency of the detection system. The feature vectors used in this study includes 215 attributes in android applications which are derived from samples sourced from Drebin dataset. Some of the performance evaluation results have been highlighted revealing that the proposed model yielded an accuracy of 0.99. The precision of 0.98 shows its ability to avoid miss-identification of negatives and the recall of 0.99 proves the effectiveness of using it for detection of the real malware samples. The F1 score is 0.99 and ROC-AUC value of 0.99 indicating the model has achieved 99% accuracy which points to the fact that it is balanced and provides a superior performance. These findings vindicate the hypothesis that SNEL-DFF has strong predictive accuracy as compared to the conventional machine learning algorithms. The proposed technique utilizes Siamese networks, deep forest feature enhancement, and ensemble learning, which makes it perform better than its competitors in terms of various evaluation criteria.Item Effect of freeze-dried cellulose Nanocrystals on the morphological and thermal properties of polyvinyl alcohol bioplastic Films(2025-11) Fagbemi, Olajumoke D; Gbadeyan, OJ; Andrew, Jerome E; Sithole, BThis study investigates the influence of incorporating freeze-dried cellulose nanocrystals (CNCs) on the morphological and thermal properties of polyvinyl alcohol (PVOH) biofilm. The CNC-reinforced PVOH films were developed using solvent casting techniques. The thermal stability and material degradation, functional groups, crystallinity, textural characteristics, and microstructure properties of PVOH reinforced with different concentrations of CNC (0.1, 0.3, 0.5, 0.7, and 1 wt%) were analyzed. An increase in thermal stability of bioplastic films corresponded with an increase in CNC loading and biofilm, with 1 wt% exhibiting the supreme properties, which was over 68% higher than pure PVOH’s thermal property. The crystalline index (CI) of the CNC and the biofilms ranges between 67.59% and 83.37%, respectively, with the highest CI found in the biofilms that contain 1% CNC. Scanning electron microscopy (SEM) images showed homogeneous dispersion of CNC in the PVOH/CNC matrix and indicated good compatibility between the filler and the polymeric blend matrix. The textural analysis of the bioplastic film image shows equal value for all the specified orientations and distances; the results range between 0.0007 and 0.0858, with the highest correlation seen at angle 45° and the lowest at angle 135°, which is related to the biofilm image and homogeneity. The most prominent FTIR peak suggests an interaction between carbon single-bonded oxygen stretching and single-bonded hydroxyl bending in carboxylic acids, the primary functional group of the developed bioplastic film. The result follows x-ray diffraction patterns and DTG data analysis. The improved properties of developed biofilm suggest a material for drug delivery and food packaging.Item Chitosan‑supported graphite as an anodic counter electrode for stable organic solar cell applications: Insight from first‑principles studies(2025-11) Idisi, DO; Benecha, EM; Asante, JKO; Mwakikunga, Bonex WThe course for improving the stability and electronic transport properties of electrode materials is crucial for obtaining highperformance organic solar cells and warrants attention. The current study explores the potential of graphite as an anode-based counter electrode material for organic solar cell applications using a first-principles calculations approach. The study focuses on the effect of chitosan molecules on the charge transfers and optical response properties of graphite. The adsorption of chitosan onto graphite showed a negligible lattice mismatch and decreased cohesive energies, suggesting improved stability. The increased density of states of graphite with chitosan incorporation suggests the presence of delocalized electronic states near the Fermi level. The optical response properties show increased absorption with chitosan adsorption on graphite surface, suggesting the introduction of surface dipoles and light absorption. The variation of the refractive index of graphite ( 1.23 → 1.45 ) with chitosan adsorption suggests significant interfacial charge transfers. The bulk of the charge transfer behaviour can be attributed to the π-π and n-π transitions. Hence, chitosan-supported graphite heterostructures can act as potential anode electrode materials for organic solar cells and other optoelectronic applications. Methods All computations were performed using density functional theory (DFT) as implemented in the CASTEP code, the DMol package, and the adsorption locator tool. The geometric structures were optimized using the generalized gradient approximation (GGA) with the Perdew–Burke–Ernzerhof (PBE) exchange-correlation functional. The electronic and optical properties were studied using the same norm-conserving pseudopotentials of the CASTEP code.Item Effects of induced structural modification on properties of V+ ion-implanted RF—magnetron sputtering deposited ZnO thin films of thickness 120 nm on borosilicate glass substrates(2025-02) Oluwaleye, O; Mwakikunga, Bonex W; Asante, JKOThe influence of structural modifications on the thermal stability, chemical bonds, and optical properties of zinc oxide (ZnO) thin films (120 nm thick) for optoelectronic devices (solar cells, LEDs) and energy nanodevices was investigated. The films, synthesized via rf-magnetron sputtering, were implanted with V+ ions at 170 keV with varying fluences. Optical properties, including bandgap, transmittance, and absorbance, were analyzed using UV–Vis spectroscopy, XRD, AFM, and FTIR. Structural changes such as strain, lattice constant, surface roughness, and crystallite size significantly influenced the optical properties. Increased surface roughness led to a higher optical bandgap (up to 4.10 eV) and transmittance (82.34%), with reduced absorbance (0.12 nm). Crystallite size exhibited similar effects. At an ion fluence of 1 × 1016 ions/cm2, the bandgap and transmittance increased, while absorbance slightly decreased. Thermal stability and chemical bond analysis supported these findings. The study demonstrates that V+ ion-induced modifications enhance ZnO thin films’ properties, highlighting their potential for advanced optoelectronic and energy nanodevice applications.Item BEV-CAM3D: A unified bird’s-eye view architecture for autonomous driving with monocular cameras and 3D point clouds(2025-04) Oladele, DA; Markus, ED; Abu Mahfouz, Adnan MIThree-dimensional (3D) visual perception is pivotal for understanding surrounding environments in applications such as autonomous driving and mobile robotics. While LiDAR-based models dominate due to accurate depth sensing, their cost and sparse outputs have driven interest in camera-based systems. However, challenges like cross-domain degradation and depth estimation inaccuracies persist. This paper introduces BEVCAM3D, a unified bird’s-eye view (BEV) architecture that fuses monocular cameras and LiDAR point clouds to overcome single-sensor limitations. BEVCAM3D integrates a deformable cross-modality attention module for feature alignment and a fast ground segmentation algorithm to reduce computational overhead by 40%. Evaluated on the nuScenes dataset, BEVCAM3D achieves state-of-the-art performance, with a 73.9% mAP and a 76.2% NDS, outperforming existing LiDAR-camera fusion methods like SparseFusion (72.0% mAP) and IS-Fusion (73.0% mAP). Notably, it excels in detecting pedestrians (91.0% AP) and traffic cones (89.9% AP), addressing the class imbalance in autonomous driving scenarios. The framework supports real-time inference at 11.2 FPS with an EfficientDet-B3 backbone and demonstrates robustness under low-light conditions (62.3% nighttime mAP).Item Improving the thermal stability of fly ash‑based geopolymer materials through cellulose nanocrystal reinforcement(2025-11) Roopchund, R; Fajimi, L, L Seedat; Seedat, N; Andrew, Jerome EPrior to this study, the thermal stability of fly ash-based geopolymer (FAG) construction materials as a function of cellulose nanocrystal (CNC) concentration and the optimal CNC dosage leading to thermal stability have not been investigated. This study investigates the influence of CNC reinforcement and curing duration on the thermal stability of FAG geopolymer. A series of samples incorporating CNC dosages ranging from 0 to 1.86 wt.% were prepared and subjected to 24- and 48-h curing regimes to evaluate their thermal degradation behaviors. Thermogravimetric analysis (TGA) revealed that 24-h-cured samples exhibited steeper weight losses compared to 48-h-cured ones, particularly in the 100–600 ℃ range. This trend was attributed to incomplete stabilization of organics in shorter curing times. Among all dosages, the 48-h-cured 1.7 wt.% CNC sample demonstrated the lowest total weight loss (~ 9.6% lower than the control), indicating enhanced thermal resistance. Derivative weight analysis further confirmed this, showing the lowest peak weight change rate (0.105%/℃) for the 1.7 wt.% CNC sample cured for 48 h, compared to 0.304%/℃ for the unreinforced control. Additionally, differential scanning calorimetry (DSC) indicated reduced exothermic heat flow in 48-h-cured samples, especially in the 1.7 wt.% CNC formulation, suggesting minimal phase transitions and improved thermal reliability. The novelty of this work lies in demonstrating the synergistic enhancement of thermal resistance through CNC addition and extended curing. Unlike prior studies that primarily focused on mechanical reinforcement, this research establishes an optimal CNC dosage (1.7 wt%) that minimizes thermal degradation, offering critical insights for thermally stable, bio-reinforced geopolymer development. These findings support the application of CNC–geopolymer composites in fire-resistant, sustainable construction materials.