Nigerian Journal of Technological Development
The Nigerian Journal of Technological Development is a quarterly publication of the Faculty of Engineering & Technology, University of Ilorin, Ilorin, Nigeria. It publishes original high-quality articles focusing on all aspects of Engineering and Applied Sciences. Manuscripts are double-blind peer-reviewed and if found suitable, published according to the subject matter as a Research Paper, Review Paper or a Technical Note. It is indexed by AJOL, Scopus, Google Scholar, and DOAJ.
Faculty of Engineering and Technology, University of Ilorin, Nigeria.
In accordance with the Copyright Act of 1976, which became effective January 1, 1978, the following statement signed by each author must accompany the manuscript submitted: "I, the undersigned author, transfer all copyright ownership of the manuscript referenced above to the Nigerian Journal of Technological Development, in the event the work is published. I warrant that the article is original, does not infringe upon any copyright or other proprietary right of any third party, is not under consideration by another journal, and has not been published previously. I have reviewed and approved the submitted version of the manuscript and agree to its publication in the Nigerian Journal of Technological Development." A copyright transfer form can be downloaded from the NJTD Website (http://njtd.com.ng/index.php/njtd). Author(s) will be consulted, whenever possible, regarding republication of material. All authors must have access to the data presented, and the authors and sponsor (if applicable) must agree to share original data with the editor if requested.
Experimental Investigation of Flexural BehavioUr of Conventionally Reinforced Concrete Beams Strengthened with Near-surface-mounted Mild Steel Strips
Due to material degradation with age, design and construction inaccuracies, reinforced concrete (RC) beams may develop flexural strength deficiencies during service life and require strengthening. This study experimentally investigates the flexural performance of RC beams strengthened with near-surface-mounted (NSM) hot-rolled mild steel strips, considering the influence of pre-loading conditions on the effectiveness of the strengthening. Flexure-dominant beams were tested under four-point loading, and performance was evaluated in terms of peak load capacity, elastic stiffness, and energy absorption derived from load–deflection responses. Results showed that NSM strengthening increased flexural capacity by 30% and 53% for preloaded and directly strengthened specimens, respectively, compared to the control beam, although failure in strengthened beams was governed by shear rather than flexure, indicating a shift in the critical failure mode following strengthening. Elastic stiffness improved by 3% and 49% for preloaded and directly strengthened specimens, respectively, while energy absorption decreased by 65% and 63%, reflecting reduced ductility and a shift toward more brittle behaviour. Notably, NSM strengthening altered the governing failure mode from ductile flexural failure in the control beam to brittle shear failure in the strengthened beams, a significant behavioural shift with important design implications. A design equation to estimate the required area of steel strips for retrofitting was derived from EC2-based sectional analysis and modified to account for near-surface strips.
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A GIS-Based Transmissivity-Constrained Frequency Ratio Approach for Groundwater Potential Assessment in Basement Terrain
: Groundwater potential assessment in crystalline basement terrains is challenging due to heterogeneous subsurface conditions and limited recharge pathways. This study applied a transmissivity-constrained Frequency Ratio (FR) model within a Geographic Information System (GIS) framework to delineate groundwater potential zones (GPZs) in southwestern Nigeria. A total of 150 Vertical Electrical Sounding (VES) datasets were analyzed, of which 88 points with transmissivity >15 m²/d were considered hydraulically effective and used for model calibration. Twelve thematic layers—elevation, lithology, slope, drainage density, surface and subsurface lineament densities, geomorphology, curvature, topographic wetness index, soil texture, land use/land cover, and depth to fracture—were integrated to evaluate their influence on transmissivity. The FR analysis revealed that subsurface lineament density, lithology, and drainage density exerted the strongest control on transmissivity (FR > 2.5), indicating a high likelihood of groundwater occurrence in these classes. In contrast, steep slopes and high drainage density areas (FR < 1.0) showed limited groundwater potential. The resulting groundwater potential prediction map (GwPPM) classifies about 11.35% of the zone as very low potential, while 30.17%, 45.40% and 13.08% are categorized as low, moderate and high potential zones, respectively. Moderate-to-high potential zones, mainly around Igede-Ekiti and Iyin-Ekiti, correspond to highly fractured granitic terrains with gentle slopes and low drainage density. Validation with borehole yield data yielded ~80% accuracy, confirming model reliability. These results demonstrate that integrating transmissivity with FR-based GIS modeling provides an effective approach for groundwater assessment and supports sustainable exploration in basement terrains.
Development and Validation of a Landscape-Security Framework for Perceived Safety in Urban Recreational Parks: A Case Study of Lagos, Nigeria
Urban recreational parks in rapidly urbanizing cities of the Global South, such as Lagos State in Nigeria, encounter unique safety challenges that are distinct from those observed in Western contexts. This research established and validated an Integrated Landscape-Security Framework (ILSF) aimed at evaluating the connections between environmental design features and perceived safety within urban recreational parks. A descriptive quantitative cross-sectional methodology was utilized across 18 parks, from which 9 were purposefully selected for comprehensive spatial analysis. Data collection involved structured questionnaires distributed to park users (n = 423 valid responses), supplemented by expert evaluations and field observations. Reliability assessments indicated a high level of internal consistency across the measurement scales (Cronbach’s ? = 0.86–0.90). Two composite indicators were formulated: the Mean Crime Perception (MCP) index and the Weighted Crime Safety Index (WCSI), with WCSI values normalized through min–max scaling. Analytical methods included descriptive statistics, Chi-square tests, and Exploratory Factor Analysis (EFA). Parks that featured sufficient lighting, enhanced visibility, and proper upkeep reported higher levels of perceived safety (WCSI ? 0.68), while areas with dense vegetation and inadequate surveillance were linked to lower safety perceptions (WCSI ? 0.61). Notable associations were identified between perceived safety and factors such as natural surveillance (?² = 12.33), access control (?² = 14.23), territorial reinforcement (?² = 11.45), lighting (?² = 10.83), and maintenance (?² = 9.21) (p < 0.05). EFA (KMO = 0.82; 66.8% variance explained) revealed two underlying dimensions—Design Integration and Maintenance and Lighting—thus empirically substantiating the ILSF framework. This study illustrates that both spatial design and operational management play crucial roles in influencing perceived safety within resource-limited urban settings. The validated ILSF provides an evidence-based planning tool to improve park safety and support sustainable management of recreational spaces in the Global South.
Review Appraisal of Synthetic Aperture Radar Technique for Earth Deformation Monitoring: Applications and Insights for Nigeria
Interferometric Synthetic Aperture Radar (InSAR) is a geodetic remote sensing technique used to measure ground deformation with high precision and is widely applied in environmental monitoring and geodynamic studies. This review explored three main processing methods: Differential InSAR (D-InSAR), Persistent Scatterer Interferometry (PSI), and Small Baseline Subset (SBAS) technique. D-InSAR estimates deformation by comparing phase differences between two co-registered SAR images, PSI focuses on stable reflectors called persistent scatterers, and SBAS uses multiple acquisitions with short temporal and spatial baselines to minimize decorrelation and atmospheric effects. SBAS performance is improved through singular value decomposition (SVD), which helps resolve rank-deficient problems in interferometric processing. In Nigeria, recent studies indicate emerging susceptibility to low-to-moderate seismic activity despite its historical classification as an aseismic region. The country’s geology includes the Precambrian Basement Complex, Mesozoic-Cenozoic sedimentary basins, and minor volcanic zones, with ancient fault systems such as the Ifewara-Zungeru Shear Zone potentially reactivating due to natural and anthropogenic forces. InSAR can support monitoring of land subsidence, infrastructure instability, and seismic hazards in urban areas such as Lagos, Port Harcourt, and Abuja. However, its application is constrained by limited access to high-resolution satellite data, insufficient technical expertise, and signal decorrelation in vegetated regions. To fully realize InSAR’s potential for sustainable development and disaster risk reduction in Nigeria, stronger capacity building, improved data access, and integration with GIS, GPS, and ground-based observations are required. Collaboration among national institutions and international partners is essential for advancing geohazard monitoring and environmental management capabilities across Nigeria and beyond effectively.
Mechanical and Durability Performance of Lateritic Clay Soil Stabilized with Bitumen Emulsion and Iron Ore Tailings for Pavement Subbase Construction
Lateritic clay soils, prevalent across tropical sub-Saharan Africa, present significant challenges for pavement construction due to their high plasticity (liquid limit of 56%, plasticity index of 24%) and low bearing capacity (CBR of 18.5%), necessitating stabilisation for subbase use. This study explores a novel stabilisation approach combining cationic bitumen emulsion (K3-65) with iron ore tailings (IOT) to identify an optimal, cost-effective mix for tropical pavement subbase applications. A full factorial experimental design was implemented, testing 16 mix proportions with bitumen emulsion ranging from 2–10% and IOT from 10–30%. Mechanical performance was evaluated at 7, 14, and 28 days through California Bearing Ratio (CBR), Unconfined Compressive Strength (UCS), and Indirect Tensile Strength (ITS) tests, using a total of 432 specimens. Comprehensive durability assessments included wet-dry mass loss, retained UCS (Durability Index), hydraulic conductivity, and moisture susceptibility via the Tensile Strength Ratio (TSR). Material characterisation, Atterberg limits, grain size, XRF, and SEM-EDS were performed on raw materials to elucidate stabilisation mechanisms. Statistical analysis using one-way ANOVA (?=0.05) and Tukey's HSD test confirmed that mix proportions significantly influenced all performance metrics (p<0.001). The optimal formulation, identified as 4% bitumen emulsion with 20% IOT (Mix M5), achieved substantial improvements over the untreated soil: a 28-day CBR of 28.6% (+55%), UCS of 17.3 MPa (+182%), and ITS of 3.9 MPa. Crucially, this mix uniquely satisfied all durability thresholds, exhibiting a durability index of 88.1%, hydraulic conductivity of 6.4×10?? cm/s, and a TSR of 88.7%, surpassing the AASHTO T283 minimum of 80%. These findings establish the 4% bitumen emulsion + 20% IOT combination as a technically viable, lower-carbon alternative to conventional cement stabilisation. The study recommends this stabilised mix for subbase construction at a compacted thickness of 150–200 mm, with a minimum 14-day curing period before opening to traffic.
Secure Remote Attestation for IoT Device Swarms under Roving Malware Threats
Remote attestation for IoT swarms faces critical gaps: software-based methods suffer high latency and assume tamper-proof hardware, while hardware-based solutions are costly and scale poorly. No existing approach provides both scalability and physical security simultaneously. This paper introduces a PUF-enhanced parallel attestation protocol that partitions memory across swarm nodes to achieve fast verification while embedding hardware-based security. The protocol was implemented on Raspberry Pi3 platforms as IoT provers with a dedicated 256 KB golden firmware region, excluding device-specific data to prevent false positives. Each device's memory was partitioned into blocks, with one block assigned per device for parallel attestation; each device used its PUF response to seed random bit sampling from its assigned block to generate a compact checksum. The border router aggregated individual checksums into a cumulative response for the verifier, with each device adding its MAC using a pre-shared key to prevent tampering with the router. The protocol was evaluated against three state?of?the?art techniques (SWARNA, FeSA, and JANUS) under identical hardware and memory configurations, measuring end?to?end latency, communication overhead, and energy consumption across swarm sizes of 32, 64, 128, and 256 nodes. The proposed technique detected roving malware with 95% accuracy. Experimental measurements showed end-to-end latency of 0.046 s for 32 nodes, representing reductions of 97%, 95%, and 91% compared to SWARNA (1.5 s), FeSA (1.0 s), and JANUS (0.5 s), respectively. The latency decreased as swarm size increased due to parallelism, while per-device communication overhead remained constant at 128 bytes regardless of swarm population. Energy consumption was approximately 154 ?J per device per round, which is about 17× lower than that of SWARNA and 650× lower than that of JANUS. This work provides a scalable, physically secure, and energy-efficient attestation solution for IoT swarms, suitable for smart manufacturing, industrial IoT, and defense environments, deployable on commodity hardware without expensive trusted modules.
Climate-Driven Stochastic Modelling of Crimean-Congo Haemorrhagic Fever Transmission in Uganda
Crimean-Congo haemorrhagic fever (CCHF) is a climate-sensitive tick-borne zoonosis that remains a significant public health concern in Uganda, where temperature and vapour pressure deficit influence tick ecology and consequently disease transmission. This study aimed to develop and analyse a climate-driven stochastic model for CCHF transmission among ticks, livestock, and humans under environmental variability in Uganda. A compartmental transmission model was formulated and extended into a stochastic differential equation framework by incorporating multiplicative environmental noise. Climate-dependent tick recruitment, development, and mortality were parameterized using district-level temperature and vapour pressure deficit data to capture spatial heterogeneity in transmission risk. Theoretical analyses based on the next-generation matrix, Lyapunov methods, and stochastic stability theory were used to derive the basic reproduction number and establish positivity, boundedness, equilibrium stability, extinction conditions, and the existence of a stationary distribution and ergodicity. Numerical simulations were performed using the Milstein scheme over a three-year period to compare deterministic and stochastic dynamics under endemic and sub-threshold transmission scenarios, while district-level climate data were used to generate spatial risk maps and evaluate model performance against reported CCHF outbreaks. The model reproduced seasonal transmission patterns, predicted rapid disease extinction under sub-threshold conditions, and persistent endemic dynamics when Ro > 1. Spatial analyses revealed substantial geographical variation in transmission potential, and model validation achieved a classification accuracy of 79.6% against reported outbreak districts. The proposed framework provides a practical tool for climate-informed CCHF surveillance, spatial risk assessment, early warning, and prioritization of targeted intervention strategies in Uganda.
Effect of Recycled Pet-GFRP Hybrid Laminates on the Ultimate Moment Capacity of Reinforced Concrete Beams
Reinforced concrete (RC) structures increasingly require strengthening due to ageing, deterioration, and higher service demands. Conventional glass fibre-reinforced polymer (GFRP) strengthening systems effectively improve flexural strength but exhibit brittle failure and limited deformation capacity, while sustainable alternatives capable of balancing strength and ductility remain insufficiently investigated. The objective of this study was to evaluate the influence of recycled polyethylene terephthalate fibre-reinforced polymer (PET-FRP) and GFRP hybrid laminate configurations and stacking sequence on the flexural performance of RC beams. Ten under-reinforced RC beams (150 × 250 × 1200 mm) were cast using C30 concrete and divided into five groups comprising an unstrengthen control, PET-FRP, GFRP, PET-GFRP-PET, and GFRP-PET-GFRP strengthening configurations. Tensile coupon tests were conducted to determine the mechanical properties of the laminates before they were externally bonded to the beam soffits using Sikadur®-30 epoxy through the wet lay-up technique. All specimens were tested under four-point monotonic bending, and load-deflection response, ultimate moment capacity, displacement ductility, strain development, and failure modes were evaluated. The GFRP-PET-GFRP hybrid configuration achieved the highest ultimate moment capacity of 38.57 kN·m, representing a 57.6% increase over the control beam, while the PET-GFRP-PET hybrid attained 36.67 kN·m with superior hybrid ductility (? = 3.36). PET-FRP-only beams exhibited the greatest ductility (? = 3.96), whereas GFRP-only beams showed the lowest ductility because of brittle fibre rupture. Both hybrid laminates exhibited progressive failure mechanisms that enhanced structural performance compared with the single-material systems. These findings demonstrate that recycled PET-GFRP hybrid laminates provide an effective and sustainable strengthening solution for RC beams. The GFRP-PET-GFRP configuration is recommended where maximum flexural strength is required, whereas the PET-GFRP-PET configuration is more suitable for applications requiring greater deformation capacity.
Hybrid Multi-Agent Q-Learning and Intelligent Water Drops Framework for Adaptive Campus Bandwidth Allocation
Efficient bandwidth allocation in campus networks remains challenging because of dynamic traffic patterns, heterogeneous user demands, congestion, and fairness constraints. This study aims to develop and evaluate a hybrid Intelligent Water Drops Multi-Agent Q-Learning (IWDAMA) framework for adaptive bandwidth allocation in software-defined campus networks. The proposed framework integrates Multi-Agent Q-Learning with the Intelligent Water Drops algorithm to enhance exploration, convergence, and adaptive scheduling. Performance evaluation was conducted using MATLAB simulations in a software-defined campus network under five traffic scenarios involving 200–2000 users with a fixed link capacity of 2000 Mbps. The proposed model was compared with standalone Multi-Agent Q-Learning (MAQL), Q-learning, and First-Come-First-Served (FCFS) scheduling using throughput, delay, bandwidth utilization, fairness index, and packet loss as evaluation metrics. Experimental results showed that IWDAMA achieved throughput ranging from 392 Mbps to 1764 Mbps, bandwidth utilization of 0.99, fairness index of 0.995, packet loss between 0.0159 and 0.0600, and delay between 0.0636 ms and 0.2019 ms, consistently outperforming MAQL, Q-learning, and FCFS across all scenarios. The findings demonstrate that integrating heuristic optimization with cooperative reinforcement learning provides a scalable and robust solution for intelligent bandwidth management in campus and enterprise software-defined networks.
Scientometric Analysis of Research Trends and Emerging Frontiers in Facial Age Estimation
Facial age estimation (FAE) has become an important part of current computer vision systems and has applications in security, digital identity validation, healthcare, and human-computer interaction. Despite the high rate of methodological development, a holistic view of the research landscape, the patterns of collaboration, and emerging frontiers of FAE is still limited. This study presents a scientometric analysis of FAE research by using 378 publications retrieved from the Scopus database with the use of a keyword-based search strategy, by title, abstract and keywords, covering the time period 2008-2025. The dataset comprised journal articles and conference papers which were peer-reviewed. Following a structured screening process, a total of 328 documents were kept for detailed analysis using a software called VOSviewer. The publication trends, country contributions, institutional collaboration, author networks, and keyword co-occurrence and co-citation structures are analysed in the study. The result shows that China is leading the field in terms of number of publications, with 35.3% of all publications, followed by the United States of America and India. Keyword analysis shows that deep learning, facial recognition, and feature extraction are still important themes for research and that emerging trends lean towards attention mechanisms, domain generalisation, and multimodal learning. Beyond descriptive mapping, this research offers important information about the implications of the current research trends, such as dataset bias, model generalisation issues, and deployment constraints. The results provide a systematic basis for future studies and technical design of FAE.
Configurable FPGA-Based Watchdog Timer for Real-Time Fault Detection in SCADA Energy Systems
Supervisory Control and Data Acquisition (SCADA) systems play a critical role in modern energy infrastructures, where reliable real-time fault detection is essential for maintaining operational safety and system availability. However, software execution faults, timing violations, and communication disturbances can compromise the effectiveness of conventional monitoring mechanisms. This work presents a configurable Field-Programmable Gate Array (FPGA)-based dual-window watchdog timer for real-time fault detection in SCADA-based energy systems. The objective is to provide deterministic hardware-level supervision capable of detecting abnormal software execution patterns and initiating appropriate recovery actions. The proposed dual-window watchdog architecture employs configurable service and frame windows implemented in Verilog Hardware Description Language (HDL) and integrates dedicated fault detection, fault classification, and reset-control logic. The watchdog detects missed-service, out-of-window service, and double-service faults while providing a dual-stage response mechanism consisting of fault signaling followed by delayed system reset. The design was implemented on a Xilinx Artix-7 FPGA and validated through simulation, synthesis, timing analysis, and experimental hardware measurements. Results demonstrated correct detection and classification of all considered fault conditions. Out-of-window service and double-service faults were detected within a single System Clock (SYSCLK) cycle after occurrence, whereas missed-service faults were detected upon frame-window expiration. Timing analysis confirmed stable operation up to 149.992 MHz with a critical-path delay of 5.421 ns and positive timing slack. Resource utilization remained below 1% of the available FPGA resources, with no inferred BRAM or DSP blocks. These results demonstrate the suitability of the proposed dual-window watchdog timer as an efficient, lightweight, and deterministic hardware supervision mechanism for SCADA-based energy systems.
Evaluating the Mechanical Performance of Cold Mix Reclaimed Asphalt Pavement Incorporating Crumb Rubber
Cold mix asphalt (CMA), produced at ambient temperature, is less energy-intensive, cleaner and safer than hot mix asphalt, making it well-suited to rural low-volume roads. Durability is nevertheless limited by incomplete binder coating, high air voids and residual moisture, which promote moisture damage and rutting, while surface-treated crumb rubber has yet to be combined with cold-mix reclaimed asphalt pavement (RAP). This study therefore evaluated cold mix RAP incorporating NaOH-treated crumb rubber. Materials were characterised by physical, XRF, XRD and binder-recovery tests, and crumb rubber (2.00-0.075 mm) was NaOH-treated at 70 °C for 2 h, then oven-dried at 60 °C for 24 h. Three dense-graded mixes within the TRRL envelope were designed: virgin aggregate, 50:50 RAP, and RAP with rubber replacing the 0/6 mm fines at 0.5-3.0%. Initial emulsion and premix water contents followed the MS-14 formula and coating tests, while optimum contents came from second-order polynomial regression of dry density, indirect tensile strength (ITS) and air voids. Marshall specimens (50 blows, cured at 40 ± 1 °C for 72 h) were tested for dry and soaked ITS to determine the tensile strength ratio (TSR) and for rutting using the Hamburg wheel tracking test (50 °C, 705 N, 10,000 passes), and the results were compared using a t-test (? = 0.05). The RAP mix required 8.2% fluid and 6.2% emulsion, compared with 11.0% and 8.9% for the virgin mix, a 30% saving, with air voids of 9.6% versus 11.5% and TSR of 0.83 versus 0.86. At the optimum 1.8% rubber, air voids fell to 8.0%, TSR rose to 1.15 and mean rut depth fell from 2.74 ± 0.43 to 2.12 ± 0.22 mm, a significant 22.63% improvement (p = 0.043). Treated crumb rubber therefore mitigates both moisture damage and rutting, and 1.8% is recommended for cold mix RAP surfacing of low-volume rural roads carrying up to one million equivalent standard axles.
Bioactive Landscape of Desert Date (Balanites aegyptiaca): An Extensive Phytochemical Characterization, Spectroscopic Analysis and its Cytotoxic Properties
Balanites aegyptiaca is an economically and culturally important fruit-bearing tree widely distributed in Africa and the Middle East. Despite its traditional applications and potential medicinal value, it remains an underutilised plant species with limited comprehensive evaluation of its phytochemical composition, extraction efficiency and biological activities. This study aimed to evaluate the extraction efficiency, phytochemical constituents, chemical profile and acute toxicity of Balanites aegyptiaca extracts obtained using ethanol and methanol. The plant extracts were prepared using ethanol and methanol as solvents, and extraction yields were determined. Qualitative phytochemical screening was carried out to identify major secondary metabolites, while Fourier Transform Infrared (FT-IR) spectroscopy, Gas Chromatography-Mass Spectrometry (GC-MS) and UV-Visible spectroscopy were employed to characterise the chemical constituents and functional groups present in the extracts. Acute toxicity assessment was conducted using Drosophila melanogaster as a biological model to evaluate concentration-dependent effects on survival, locomotion and development. The extraction yields were 23.3% and 24.7% for ethanol and methanol extracts, respectively, indicating slightly higher extraction efficiency of methanol. Phytochemical analysis revealed the presence of saponins, tannins, alkaloids, flavonoids, phenolic compounds, steroids, terpenoids and cardiac glycosides in both extracts, with methanol showing greater affinity for terpenoids and steroids. FT-IR analysis confirmed the presence of functional groups associated with alcohols, alkanes, carbonyls and phenolic compounds, while GC-MS identified approximately 38 compounds comprising alkanes, fatty acids, phenolics and silyl derivatives. UV-Visible analysis showed a prominent absorption peak at 665 nm, suggesting the presence of chlorophyll-related compounds. Toxicity evaluation demonstrated dose-dependent cytotoxic effects, with higher concentrations (250–750 mg/10 g) causing increased mortality and developmental abnormalities, whereas lower concentrations (50–100 mg/10 g) showed no observable toxic effects. The findings indicate that Balanites aegyptiaca contains diverse bioactive compounds with potential pharmaceutical and nutraceutical applications; however, concentration-dependent toxicity should be considered in future utilisation and dosage optimisation studies.
Effect of Brine Pretreatment on the Thin-Layer Drying Characteristics and Moisture Diffusivity of Blue Whiting (Micromesistius poutassou) Fillets
Traditional handling and open-air drying of panla (blue whiting, Micromesistius poutassou) are often inefficient, resulting in prolonged drying times, uneven moisture removal, quality deterioration, and increased microbial risks. This study investigated the drying kinetics, mathematical modelling, and physicochemical changes of brined blue whiting fillets subjected to hot-air drying at 60–100 °C following immersion in sodium chloride solutions (0–200 g/L). Moisture reduction was monitored gravimetrically, and the drying parameters were fitted to six thin-layer models. The effective moisture diffusivity (De) was estimated from Fick’s second law, and colour changes were determined using the CIE Lab* system. Results show that drying time decreased with increasing temperature and salt concentration, with moisture diffusivity values for unbrined fillets ranging from 5.59 × 10?¹? to 5.29 × 10?? m²/s, while brined samples exhibited higher diffusivity up to 4.96 × 10?? m²/s at 200 g/L NaCl, with an activation energy of 17.94 kJ/mol. The Two-term Exponential model best described the drying kinetics (R² = 0.9987–0.9997; RMSE ? 0.0081), outperforming the other models. Brining significantly influenced lightness, chroma, and hue, with the most pronounced changes observed at 50–100 g/L NaCl. These findings demonstrate that brine pretreatment enhances drying efficiency, accelerates moisture transfer, and improves product quality, offering a cost-effective strategy for preserving panla as a marketable protein source.
Attention-Enhanced 2D CNN for Multi-Class Alzheimer’s Disease Classification with Grad-CAM Visualization
Early and accurate diagnosis of Alzheimer’s disease remains a critical challenge in clinical neuroscience, particularly during the mild and moderate stages, when timely intervention can still have meaningful impact. In this study, we propose a deep learning framework based on a 2D convolutional neural network (ResNet-18) enhanced with a Convolutional Block Attention Module (CBAM) for multi-class classification of Alzheimer’s disease stages from brain MRI slices. To address the pronounced class imbalance inherent in the dataset, a class-weighted loss function was combined with aggressive online data augmentation during training. The model was evaluated on a large public dataset comprising over 86,000 MRI slices spanning four diagnostic categories: Non-Demented, Very Mild Dementia, Mild Dementia, and Moderate Dementia. Quantitative evaluation demonstrated a slice-level classification accuracy of 93.7%, with robust performance across all classes, including the underrepresented stages. Interpretability was provided through Grad-CAM visualizations, which highlighted anatomically meaningful regions consistent with established Alzheimer’s disease biomarkers. While these results underscore the potential of attention-augmented 2D CNNs for automated staging of Alzheimer’s disease, the framework has only been validated on a single public dataset and has not yet been tested on independent cohorts. Consequently, this study should be considered a proof-of-concept, and further multi-center, out-of-distribution validation is needed before any clinical deployment can be contemplated.
Open-Automated Teller Machines for Dynamic Transactions and Data Access
The Automated Teller machine (ATM) is the workhorse of modern financial service delivery to end–users. The ATM is owned and deployed by a given financial service provider or bank. The bank’s account holders can access financial service providers via their ATM card. Individuals who operate accounts with other financial service providers can also use the ATM belonging to another financial service provider. However, this incurs an additional service charge. Individuals seek to execute more transactions on an ATM besides cash withdrawals. Some of these transactions are specific to a financial service provider and cannot be executed on the ATM of other financial service providers. This is challenging because financial service providers do not conduct significant cross–functional collaboration in ATM design and deployment. The discussion in this paper seeks to address this challenge and proposes the open–automated teller machine (O–ATM). The O–ATM incorporates software and hardware payload realized by the co–hosting of multiple financial services from different financial service providers. The discussion presents the proposed O–ATM, its entities and describes how the entities function and relate with each other. The role of the O–ATM in enhancing the access to data storage via cloud computing is also recognized. In addition, performance analysis shows that the use of the O-ATM enhances the ATM deployment density and cloud service reach by an average of 21.2%, and 63%, respectively.
Flexural Behavior of Rubberized Ferrocement Beams Reinforced by GFRP and Steel Wire Rope: A Comprehensive Review
Ferrocement is widely recognized for its lightweight characteristics and high tensile efficiency; however, it faces limitations related to durability and ductility, particularly under aggressive environmental and loading conditions. This study presents a comprehensive review of the flexural behavior of rubberized ferrocement beams reinforced with hybrid Glass Fiber Reinforced Polymer (GFRP) and Steel Wire Rope (SWR), aiming to address these challenges. The methodology is based on a systematic evaluation and synthesis of recent experimental, analytical, and finite element studies, focusing on material properties, bond behavior, reinforcement mechanisms, and structural performance of rubberized and hybrid-reinforced systems. The results indicate that the incorporation of crumb rubber significantly enhances ductility, energy absorption, and crack control, although it leads to moderate reductions in compressive and flexural strength. GFRP reinforcement provides high tensile strength and corrosion resistance but exhibits relatively brittle behavior, whereas SWR contributes superior ductility and post-cracking performance. Hybrid GFRP–SWR systems demonstrate improved overall behavior by combining strength, durability, and deformation capacity. Furthermore, the findings highlight that surface treatment, bond optimization, and proper reinforcement detailing are critical factors governing structural efficiency and serviceability. Based on the reviewed literature, it is recommended that future research focus on large-scale structural validation, long-term durability under environmental and cyclic loading, advanced bond modeling at the material interface, and comprehensive life-cycle assessment. These directions are essential to support the practical implementation and code development of rubberized ferrocement systems with hybrid reinforcement.
A Review on Quality Evaluation of Kunun-zaki in Nigeria
Kunun-zaki is a traditional non-alcoholic cereal-based beverage widely consumed in Nigeria. However, its large-scale production and commercialization remains limited by persistent challenges in product quality and processing. This review examines the overall quality and timeline status of Kunun-zaki with emphasis on the technological constraints especially in scaling up its artisanal production ways. Several past studies have primarily focused on improving individual aspects of product quality; however, a major research gap discovered is the lack of integrated process engineering approaches. These are the processes that would address the issues of heat transfer limitations, uncontrolled fermentation kinetics, inefficient solid–liquid separation, and inadequate packaging systems. The review captures findings from previous studies and analysis modern processing techniques, such as pasteurization, use of starter culture, carbonation, and hurdle technology. The trade-offs between microbial safety and nutrient retention during heat application, and the need for controlled fermentation systems to ensure product consistency was also addressed. Particular attention was also on the challenges of scaling up from artisanal to commercial production were explicitly discussed. Overall, the study stresses that the major limitation in Kunun-zaki production is not the absence of technological solutions, but the lack of scalable, cost-effective, and integrated processing systems. Future research should therefore focus on the development of production line that includes energy-efficient thermal processing units, low-cost fermentation systems, and hygienic packaging technologies specifically to small- and medium-scale producers. Focus on these gaps will be important in enhancing the shelf-life, safety, and commercial viability of Kunun-zaki in both local and international markets.
Development and Validation of OpenMC Model for Criticality Safety Analysis of an MNSR-type Research Reactor
The accurate prediction of criticality and neutronic safety parameters is essential for the safe operation, licensing, and continued utilization of research reactors. There is limited published work assessing the performance of the open-source Monte Carlo code OpenMC for compact-core Miniature Neutron Source Reactors (MNSR) such as the Nigeria Research Reactor-1 (NIRR-1). This study aimed to develop and validate a high-fidelity OpenMC model of the NIRR-1 for predicting key criticality and neutronic safety parameters. A detailed three-dimensional OpenMC model of the NIRR-1 was developed using the ENDF/B-VII.1 nuclear data library. The reactor geometry was explicitly modelled to include the active fuel pins, dummy pins, tie rods, control rod, beryllium reflectors, irradiation channels, fission chamber, reactor vessel, moderator, and pool water. Material compositions, geometry, simulation settings, and tally structures were implemented through the OpenMC Python API. Criticality calculations employed 100,000 neutron histories per batch over 100 batches, including 20 inactive batches to ensure source convergence. The validated model was used to determine the effective multiplication factor for fully withdrawn and fully inserted control rod positions, from which the core excess reactivity, control rod worth, shutdown margin, and effective delayed neutron fraction were calculated. The calculated core excess reactivity, control rod worth, shutdown margin, and effective delayed neutron fraction were 3.78 mk, 6.82 mk, 3.04 mk, and 7.99 mk, respectively. These values agreed with commissioning measurements within a deviation of less than 5% and satisfied the neutronic safety requirements for MNSRs. The study demonstrates that OpenMC provides an accurate and reliable open-source platform for predicting the neutronic safety characteristics of the NIRR-1 and supports its application in reactor safety analysis, operational support, and future studies of MNSR-type research reactors
On ?-asymmetric Geraghty Type Contractive Mappings in Metric Spaces
: Fixed point theory has become an essential tool in the study of nonlinear problems arising in mathematics, science and engineering. However, many of the existing contractive conditions are restrictive or insufficient for establishing the existence and uniqueness of solutions for certain classes of nonlinear problems. The paper aims to introduce a new class of asymmetric Geraghty-type contractive mappings in metric spaces and establish its theoretical properties together with selected practical applications. In order to achieve this aim, a novel asymmetric Geraghty-type contractive condition is formulated for self-mappings on complete metric spaces. The newly proposed contractive condition is then employed to derive sufficient conditions for the existence and uniqueness of fixed points by constructing an appropriate iterative sequence and analysing its convergence behaviour. The stability of the iterative sequence associated with the proposed contraction is further established under suitable assumptions. The applicability of the developed fixed-point theorem is demonstrated by proving the existence and uniqueness of solutions for a first-order nonlinear differential equation arising from a population growth model and the analysis of a mass-spring-damper system governed with second order- nonlinear differential equations, which has important applications in vehicle suspension systems, structural vibration control and machine design. The proposed asymmetric Geraghty-type contractive mapping guaranteeing unique solutions and stable iterative approximations for nonlinear problems and extends the applicability of fixed-point theory for analyzing practical problems encountered in science and engineering.
A Statistically Validated and Interpretable Machine Learning Framework for Breast Cancer Classification Using Shape Descriptors
Breast cancer (BC) remains a major global health priority, with nearly half a million invasive cases reported in 2024. Early diagnosis is critical, and Machine Learning (ML) offers significant potential for automated detection. This study evaluated ML models using tumour shape descriptors from the Wisconsin Diagnostic Breast Cancer (WDBC) dataset (569 cases: 357 benign, 212 malignant). Ten numerical descriptors, including radius, texture, and concave points, were extracted from digitized fine needle aspirates (FNA). The dataset was split 70:30 into training and test sets, with Chi-square feature selection, mutual information (MI), and ANOVA feature selection applied to reduce dimensionally and prevent data leakage. Three models: Artificial Neural Network (ANN), Classification and Regression Tree (CART), and Logistic Regression (LR) were trained using stratified cross-validation and evaluated on the independent test set. LR achieved the highest AUC (97.31%), significantly outperforming ANN (p = 0.009) and CART (p = 0.001) by DeLong’s test, while McNemar’s test revealed no significant differences in misclassification rates (p > 0.05). CART achieved the best F1-score, reflecting balanced sensitivity and specificity. These findings demonstrated that statistically validated and interpretable ML models provide robust diagnostic performance for BC classification
A Non-Iterative Basis Selection Algorithm for Linear Programming Using Directional Cosines
This paper presents a geometry-based non-iterative method for solving linear programming (LP) problems that have either three constraints or three decision variables. Instead of using iterative procedures like the simplex, Interior-point, and Criss-Cross methods, the method finds a basic feasible solution consistent with optimal results for the class of problems considered. The algorithm measures the directional cosines alignment between each constraint vector and the resource vector, picks the three most aligned constraints to form a 3×3 Dantzig matrix, and solves that small linear system for the decision variables and objective value. The algorithm was tested on standard textbook examples, and the results match conventional solvers exactly, but are obtained without pivoting or tableau updates, just one matrix inversion. The approach is fast and provides a very clear geometric visualization, making it useful as a pedagogical tool or as an initialization step in larger routines.
Hybrid Machine Learning Modelling for Rice Yield Forecasting under Climatic Variability in South Sudan
Reliable agricultural yield forecasting is crucial for strengthening food security planning in South Sudan, where rice yields remain highly sensitive to climatic variability and structural production constraints. This study evaluates classical statistical and ensemble-based machine learning models for forecasting national rice yield using key agronomic and climatic determinants: cultivated land area, historical yield trends, rainfall, temperature, and soil pH. The empirical analysis draws on annual time-series data spanning nearly five decades (1961–2010), with the dataset partitioned chronologically into training (n=38) and testing (n=10) subsets to enable disciplined out-of-sample validation. Four modelling approaches were examined: Multiple Linear Regression (MLR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and a Hybrid RF–XGBoost ensemble constructed via prediction aggregation. Model performance was assessed on the reserved test set using RMSE, MAE, R², and MAPE. Within the test sample (n=10), all models exhibited strong correlation (R² > 0.91). MLR achieved R²=0.9128 but exhibited higher RMSE (295.60 kg/ha) and MAPE (31.75%) relative to the ensemble approaches, suggesting limited flexibility in capturing nonlinear yield responses. Random Forest improved performance (R²=0.9647; RMSE=273.39; MAE=227.13; MAPE=28.26%), while XGBoost delivered R²=0.9360 with RMSE=289.25, MAE=182.05, and MAPE=30.59%. The Hybrid model attained the lowest RMSE (259.14 kg/ha), lowest MAE (174.29), lowest MAPE (26.91%), and highest R² (0.9725). Although improvements are moderate in magnitude, the results suggest that hybrid ensemble aggregation can provide incremental gains in prediction accuracy under data-constrained conditions. Given the limited sample size, findings should be interpreted cautiously; nonetheless, this study offers an applied comparative assessment to support evidence-informed agricultural planning in fragile production environments.