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.
Impact of Annealing and Doping on the Structural and Optical Characteristics of Copper Oxide (CuO) Nanopowders Synthesized by Sol –gel Method.
In this study, pure copper oxide (CuO) and zinc oxide (ZnO) doped CuO nanopowders were prepared using the sol-gel chemical preparation method and Copper chloride dihydrate (CuCl2, 2H2O) used as precursor. To study the structural and morphological properties of the prepared powders, X-ray diffraction (XRD), Scanning electron microscopy (SEM) and infrared spectroscopy (IR) were used, while to study the optical properties, a UV-visible spectrophotometer was employed. XRD analysis showed the presence of the monoclinic structure of CuO and the wurtzite (hexagonal) structure of ZnO for powders doped with 7% and 10% ZnO. It was also observed that the grain size increases with the increase in annealing temperature and increase in ZnO concentration. Moreover, the study of the optical properties of pure CuO powder revealed that the band gap values range between 3.88 eV and 3.89 eV.
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Renewable Energy Optimisation For Electricity Generation In Nigeria: Lessons From Leading Countries
Nigeria is faced with a persistent electricity crisis that has crippled most sectors of the economy. This paper reviewed the strategies deployed by five leading countries in renewables, the status of renewable energy in Nigeria and ways to optimise it to generate a large amount of electricity in Nigeria and other developing countries. Relative Importance Index (RII) was used to analyse the collected data to achieve the study objectives. Wind power, solar power and hydropower were ranked the highest sources of renewable energy in China, the USA, India, Germany and Brazil. The major findings are (i) the various strategies deployed to develop the renewable energy industry in leading countries will also be effective in Nigeria; (ii) solar energy is considered to have the highest potential for Nigeria, and hydropower is the next potential. This study recommends that the Nigeria government should (i) target electricity generation capacity of 200 GW by the year 2035 and target fifty percent of the capacity i.e. 100 GW from renewable energy; (ii) adopt combined strategies used by leading countries which includes diversification of renewable sources, investment in research and development, international cooperation, financial incentive, subsidies and ambitious targets to optimise the development of renewable energy; (iii) focus on solar and small hydropower to kick start increase in electricity generation capacity from renewable sources; (iv) focus on building both large-scale plants and many small-scale plants.
Multipath effect of MIMO-enabled device in constrained environments
The demand for wireless throughput has increased immensely, and high data rate wireless communications is of significance in emerging wireless local area networks (WLANs) and home multimedia networks. Massive data consumption being aided by emerging technologies is helping the demand for wireless throughput to experience exponential growth. Massive data consumption results from high-definition video streaming, automatic software updates, cloud backups, social media autoplay, and background app syncing. Wireless transmission in constrained environments such as a highly obstructed and large network environment is crucial. Thus, designing a very high-speed wireless link that offers good quality-of-service (QoS), the QoS metric such as delay, throughput, latency, packet dropped and jitter, and range capability in NLOS (Non-Line-of-Sight) environments constitutes a significant technical challenge. MIMO (Multiple-Input Multiple-Output) enhances Non-Line-of-Sight (NLOS) communication by using multipath propagation to improve signal reliability, range, and throughput. MIMO-based systems effectively manage the interference and signal attenuation common in NLOS environments, enabling robust communications where single-antenna systems fail. It increases signal reliability through diversity, and boosts data throughput via spatial multiplexing. The coverage is extended by combining reflected signals. MIMO uses reflections off buildings and other objects to create multiple paths, and allows the receiver to reconstruct a stronger signal. Transmitting multiple copies of data across various antennas, MIMO increases signal reliability and mitigates signal drops in constrained environments. The study investigates the effect(s) of multipath propagation on QoS in a MIMO system, using unlicensed spectrum. Research findings indicated that MIMO-enabled device, using multipath effect in the line of transmission, is the best wireless transmission option for a highly obstructed and large network environment as it provides better throughput, high data rate, reliable delivery of data, low delay, wider coverage and easier mobility of users.
The Significance of Compliance with Law(s) and Code of Ethics in Engineering Practice in Nigeria
Persistent unprofessional conduct among Nigerian engineers continues to generate legal disputes between practitioners, clients, and regulatory bodies. This has resulted in severe consequences including reprimands, suspension, or erasure from the professional register. Despite a clear legal mandate compelling compliance with professional statutes and codes of ethics, the gap between regulatory requirements and actual practice remains significant. This study examines the significance of regulatory compliance in Nigerian engineering practice through doctrinal legal analysis of the core statutes governing the profession. The study analyses seven primary legal instruments constituting Nigeria's engineering regulatory framework, alongside multiple judicial decisions from Nigerian superior courts that have interpreted and applied these provisions. The study further synthesises findings from over 40 scholarly publications to contextualise compliance patterns within the broader discourse on professional ethics. The theoretical foundation relies on John Rawls' strict compliance doctrine, which demands exact adherence to legal requirements without deviation. Analysis of disciplinary proceedings reveals that registered engineers face sanctions under Section 16 of the COREN Act 2018, including erasure from the register, suspension, warning, or reprimand. Notable enforcement actions include a twelve-month suspension for unauthorised sale of construction materials and six months suspension for construction without statutory approvals. Findings indicate that while 100% of the necessary legal framework exists, compliance remains inadequate, with practitioner negligence and insufficient regulatory awareness identified as primary contributing factors. The study concludes that strict adherence to engineering laws and ethical codes is indispensable for maintaining professional integrity and ensuring public safety among Nigeria's estimated 50,000+ registered engineers. The study recommends targeted continuing professional development programmes to enhance regulatory knowledge and foster voluntary compliance.
Hybrid Approach for Evaluation and Optimization of 5G Mobile Network Coverage Predictions
Data usage continues to surge as 5G provides the foundation for massive Internet of Things (IoT) connectivity, ultra-low latency, and high-speed data transfer. For the fact that the demand for robust and faster network connectivity and data speeds is very crucial, fixed wireless access (FWA) is bridging the gap for high-speed internet in areas like rural or under-served areas, as well as urban/suburban environments where fiber is not available, utilizing 5G networks to offer fiber-like speeds. Nevertheless, environmental factors and network load affect signal performance, thus, on 5G coverage predictions. Though 5G offers higher speeds and lower latency compared to its predecessors. Notwithstanding, its dependence on higher-frequency radio waves makes it susceptible to degradation from physical obstructions, atmospheric conditions, and high user density. Heavy rain, snow, and dense fog absorb and scatter 5G radio waves while trees and buildings obstruct. 5G technology is a significant advancement that promises unparalleled speeds, reduced latency, and enhanced connectivity crucial for modern applications. However, the current situation reveals persistent challenges in achieving these ideals across diverse environments such as environments with complex terrains, high user density, and varying user demands. There are disparities between predicted and actual network performance metrics such as throughput, latency, coverage gaps, and inconsistencies in QoS (quality of service). The study adopts a hybrid approach that combines simulation-based analysis and real-world drive test measurements for optimization of 5G network coverage predictions. The paper presents the findings, analyzing existing 5G coverage predictions and modelling a similar network scenario on MATLAB to optimize 5G beamforming antenna coverage, and offering recommendations to have better coverage and optimal performance, providing users with more reliable and higher-quality connectivity of the 5G network.
Improving Power Quality in a Nigerian Distribution Feeder Using PV- D-STATCOM: Modeling and Simulation in DIgSILENT PowerFactory
ABSTRACT This study investigates the integration of photovoltaic-based Distribution Static Compensators (PV D-STATCOMs) to improve voltage stability, reduce losses, and mitigate harmonics in an 11 kV distribution feeder in Ughelli, Nigeria, operated by the Benin Electricity Distribution Company (BEDC). The feeder was modeled in DIgSILENT PowerFactory using actual network data under normal, medium, and heavy loading, as well as dynamic and harmonic conditions. The PV D-STATCOM was configured to supply active power while providing dynamic reactive power support in response to varying loads. Results showed that the minimum voltage under heavy loading improved from 0.637 p.u. to 0.992 p.u., while real power losses decreased by up to 91.4%. Total Harmonic Distortion (THD) at critical nodes reduced from 19.9% to about 12.5%. During sudden load increases of up to 700%, the compensators maintained voltages close to 1.0 p.u., demonstrating strong dynamic voltage support. The study confirms that PV inverters can perform dual functions, delivering renewable energy and providing grid support. The integration of PV D-STATCOM offers a cost-effective solution to enhance voltage stability, reduce technical losses, and improve power quality in Nigerian distribution networks. These results indicate that PV D-STATCOMs can complement existing grid infrastructure, contributing to improved reliability and resilience in developing country power systems.
Optimizing Electrochemical Turbidity Removal from Water Using Response Surface Methodology
Water turbidity, caused by suspended particulate matter, is a primary indicator of water quality and poses significant challenges for potable water production. This study investigates the optimization of the electrochemical coagulation (EC) process for removing kaolinite-induced turbidity from synthetic wastewater. By applying steel electrodes the impacts of 4 significant operational parameters (applied voltage (V), inter-electrode electrode distance (D), initial solution pH, and initial turbidity concentration (Co)) on turbidity removal efficiency were investigated carefully. The process was modeled and optimized using Response Surface Methodology (RSM) based on a factorial design. The analysis of variance (ANOVA) identified that voltage, pH and their quadratic terms V², pH²), whereas the interaction between voltage and distance (V*D) as the most significant factors affecting turbidity removal (p < 0.05). The fitted quadratic model showed a high value of coefficient of determination (R² = 98.04%), indicating good predictive quality of the model. The model also estimated that the maximum turbidity removal of 98.9% was obtained under conditions of applied voltage of 25 V, electrode distance of 2 cm, pH (7 ± 1) and an initial turbidity value of 200 NTU. The results demonstrated that EC is an excellent process for turbidity reduction, and RSM is a powerful tool to optimize complex intersystem, multi-variable interactions associated with it, giving a strong basis for efficient design and operation of water treatment plants.
Artificial Intelligence for Physical Layer Security in 6G Wireless Networks: Challenges and Opportunities- A Systematic Review
Since the introduction of 5G systems, the Sixth Generation (6G) network has quickly become a key area of research interest. It is anticipated that 6G, which is envisioned as an integrated communication architecture spanning space, air, ground, and sea, will accommodate a variety of applications with demanding security and quality-of-service specifications. However, 6G has never-before-seen security flaws because of its open wireless channels, dynamic topology distributed architecture, and strong reliance on machine learning (ML), artificial intelligence (AI), and cutting-edge technologies like terahertz and quantum communications. These issues are difficult for traditional cryptography-based security procedures to handle, especially in low-cost, power-constrained Internet of Things (IoT) devices and large-scale computing environments. In the past years, physical layer security (PLS) has been studied and indicated as a possible way to emancipate networks from classical, complexity-based security approaches. Multiple white papers on the vision for 6G incorporate PLS, as well as the IEEE International Network Generations Roadmap (INGR) 1st and 2nd Editions. Motivated by the above, a key point of this paper is to showcase how PLS and, in general, security controls at the PHY level can be exploited towards securing future networks. A supplementary method for protecting 6G systems is Physical Layer Security (PLS) approaches, which take advantage of intrinsic hardware flaws and the stochastic nature of wireless channels. Intelligent PLS solutions have proven to perform better than traditional security methods due to the quick developments in AI and ML. In this paper, we present a comprehensive review of machine learning-enabled PLS strategies for 6G. We begin by outlining the vision of 6G, its key applications, enabling radio technologies, security threats, and security requirements. We then discuss prominent ML techniques that motivate the introduction of intelligent PLS mechanisms in 6G. Subsequently, we examine state-of-the-art scholarly contributions on ML-driven PLS across critical 6G radio technologies, including ambient backscatter communication, cell-free massive MIMO, terahertz communication, visible light communication, configurable intelligent surfaces (CIS), and molecular communication. Furthermore, we provide an in-depth analysis of ML-enabled PLS advancements such as intelligent attack detection, anti-jamming strategies, secure wireless transmission, physical layer authentication, and key generation. Finally, we highlight open challenges and future research opportunities to foster the development of intelligent, robust, and secure 6G systems. This review aims to serve as a reference point for researchers and practitioners while inspiring further exploration at the intersection of machine learning and physical layer security in 6G networks.
Numerical Investigation of Heat Transfer in Straight Pipe of Elliptic Cross-section Rotating in Orthogonal Mode
Efficient heat removal in rotating cooling channels remains a critical challenge in thermal systems such as blades of gas turbine and rotating heat exchangers, where complex rotational forces significantly influence flow and temperature distributions. However, the combined effects of pipe eccentricity and orthogonal rotation on turbulent heat transfer in non-circular ducts are not yet fully quantified. This study numerically investigates the heat transfer characteristics of fluid flow in a straight pipe with an elliptical cross-section that rotates in an orthogonal mode. The objectives are to evaluate the influence of eccentricity, Rossby number, and Prandtl number on convective heat transfer. A three-dimensional computational fluid dynamics (CFD) model which is based on the finite volume method was developed using ANSYS Fluent. Simulations were conducted at a Reynolds number of 17,000 with a constant wall heat flux for three eccentricities (0.433, 0.866, and 0.916) and Prandtl numbers ranging from 0.73 to 7. Mesh independence was achieved at approximately 1.07 × 10? computational nodes. The results showed that the Nusselt number increased with both Rossby and Prandtl numbers, reaching a maximum value of approximately 340 at Pr = 7 and Ro = 0.1. The pipe with eccentricity 0.916 exhibited the highest heat transfer performance. The findings provide practical design guidance for cooling channels in rotating machinery such as gas turbines, heat exchangers, and rotating electrical systems.
The The Relationship Between Ambient CO2 Levels and Vehicle Emissions in Promoting Sustainable Campus Transportation
Atmospheric CO2 concentrations continue to increase, exacerbating the risk of global climate change and contributing to the deterioration of public health. This condition is primarily driven by escalating anthropogenic activities, particularly in the transportation sector, which relies on combustion engines, as observed in Indonesia. However, the relationship between vehicle emissions and ambient CO2 concentrations remains insufficiently characterised. This research examined the association between vehicle emissions and ambient CO2 concentrations at three monitoring locations within a campus in Indonesia. Measurements were conducted over 10 weekdays, and simultaneous full traffic counts and calibrated CO2 monitoring were performed. Emission intensity was estimated using national emission factors standardised per unit distance. Regression analysis and analysis of covariance (ANCOVA) incorporating wind speed as a covariate were applied. Results showed a statistically positive relationship between vehicle emissions and CO2 concentrations across all locations (p consistently < 0.001 with model R2 ranging from 67.4% to 70.8%). Wind speeds also had a significant influence (p ranging from 0.020 to 0.001). Based on ANCOVA, motorcycles consistently exhibited the strongest influence relative to other vehicles (p < 0.001). These findings underscore the need for policies to regulate motorcycle traffic within campus to reduce emissions, improve air quality, and safeguard public health.
Golden Jackal Optimization Algorithm For Superior Dynamic Performance Control Of Dc Motors Using Pid And Fopid Controllers
Direct Current (DC) motors are integral to industrial drive systems due to their efficiency, controllability, and favorable speed-torque characteristics. Achieving optimal speed regulation with PID and FOPID controllers remains challenging, particularly under demanding transient and precision requirements. This study introduces a novel metaheuristic approach, the Golden Jackal Optimizer (GJO), inspired by the cooperative hunting behavior of golden jackals, for the optimal tuning of PID and FOPID controllers. The Integral Time Absolute Error (ITAE) criterion is employed to ensure superior dynamic performance. The proposed GJO-PID and GJO-FOPID controllers were rigorously benchmarked against ten advanced metaheuristic optimization algorithms, including IGWO, PSO, HHO, LHHO, and GOA, across multiple metrics, such as rise time, settling time, overshoot, bandwidth, and phase margin. Results reveal that the GJO controllers surpasses LHHO-FOPID by 6.98%, 4.10%, and 0.56%, and GOA-FOPID by 3.87%, 3.42%, and 0.2022% in faster rise time, shorter settling time, and lower overshoot, respectively. Furthermore, comprehensive sensitivity analyses under varying motor parameters confirm the robustness and reliability of the proposed method. These findings demonstrate the GJO algorithm’s ability to deliver superior transient response, stability, and performance, highlighting its potential as a highly effective and generalizable solution for advanced DC motor control in industrial applications.
Comparative Evaluation of Path-Loss Models in a Typical African Terrain using Homogenous Grid Outdoor Foot Measurement Technique
Several works had been carried out on the comparative analysis of mobile signal propagation path loss at various locations in Africa. The assessments of relevant literature reveal that many, if not all, give no consideration to the peculiar African road networks, irregular building orientations, and patterns that usually prevent the driving of motor cars within when taking the signal strength measurements. This article presents a comprehensive analysis of mobile signal propagation path loss within the Ilorin metropolis. The method adopted for measurement is the homogeneous grid outdoor foot measurement technique; it gives consideration to the peculiar African road networks and irregular building orientations and patterns. GSM signal strengths were measured at various study locations within the Ilorin metropolis and the data obtained were used for path loss calculations. These results were then compared with several widely used and well-established empirical models that are commonly applied in contemporary research and practice. The findings revealed the measured path loss values at all the study locations swing between the COST-231-Hata and Eglis models forecast values. COST-231-Hata model may be adjusted to ensure precise forecasting of radio signals for cellular transmission in Ilorin and identical terrains.
Smart Irrigation System with Indigenous Multilingual Mobile Application
African farmers produce about 80 percent of the food consumed in the African continent yet a large percentage of them are cut off from agricultural innovation due to illiteracy and language barriers. Previous studies have developed mobile applications for smart irrigation systems in English language. This work develops a multilingual mobile App in three major indigenous Nigerian languages Hausa, Yoruba, and Igbo to make the smart irrigation innovation accessible and adaptable to indigenous farmers. The mobile App was developed using Flutter while the English version was translated to indigenous languages. A microcontroller-based laboratory-scale smart irrigation test bed was also developed to which the App was interfaced for testing. Performance evaluation revealed the ability of the App to perform remote control of the irrigation process and display soil moisture, temperature, and humidity of the farm environment on its graphical user interface in English and indigenous languages of Hausa, Yoruba, and Igbo. Further tests under Wi-Fi and Internet network revealed the Wi-Fi network was stable up to 50 meters, beyond which stability declined. The Internet connection remained stable up to 400 km except at certain weak or unavailable coverage points (45 km, 105 km, 210 km). The unique contribution of this work is that while previous studies developed mobile App for smart irrigation system in English language, the multilingual mobile App bridges communication gap and will make smart irrigation technology accessible and adaptable to indigenous farmers in their mother tongues to enhance food security.
An Integrated Ward-Level Analysis of Environmental Exposure and Health Vulnerability in the Federal Capital Territory, Nigeria
Nigeria’s Federal Capital Territory has experienced urban expansion, notably spurred by rural-urban migration, resulting in growing health vulnerabilities in underserved communities. The problem of growing environmental hazards and the associated health risks has not been properly researched and dimensioned in the FCT. This study investigated environmental exposure and urban health vulnerability across informal settlements in 15 Wards of Abuja Municipal, Bwari, and Gwagwalada Area Councils of the Federal Capital Territory (FCT). The research employed a quantitative design, combining household data from 1,137 questionnaires administered to residents on exposure to ceiling materials, solid waste management methods, and drinking water quality. In addition to the household data, field environmental samples (air, noise, and water) were collected and tested both on-site and in the laboratory to determine constituent concentrations. A Composite Vulnerability Index (CVI) was constructed from the six datasets, consolidating all variables into a single dataset for each ward within the study area. The findings show that values across the study area range from 0.32 to 0.91, with Zuba identified as the most vulnerable ward due to significant levels of air, water, and noise pollutants. The study recommends using health hazard communication tools for low-literacy populations, as well as community safety audits and local health hazard awareness index cards in highly vulnerable wards, to reduce health risks and improve residents' well-being.
Phased Analysis of Community Participation in the Resettlement of Flood-Prone Communities in North Central Nigeria: An Integrated Technological and Social Approach
: Resettlement housing projects frequently encounter challenges related to community acceptance and long-term sustainability. Community participation has been recognized as a critical determinant of project success. This study employs a phased analytical framework to evaluate the impact of community participation during the decision-making, design, and implementation stages of resettlement housing in flood-prone communities across North Central Nigeria. A quantitative methodology was adopted, utilizing structured surveys distributed to 300 households across three resettlement sites. Data were analyzed using descriptive statistics, including frequency distributions, percentages, and mean scores. Findings reveal significant dissatisfaction across all phases: decision-making (mean score: 2.86), design (mean score: 2.62), and implementation (mean score: 2.31). Notably, community involvement in construction processes and resource allocation was minimal, and the perceived quality of construction was rated as fair (mean: 2.86). The study concludes that meaningful, technology-facilitated community engagement across all project phases is essential for sustainable resettlement outcomes. Recommendations include the integration of digital participatory tools, Building Information Modeling (BIM) for collaborative design, and real-time monitoring systems to enhance transparency, accountability, and stakeholder satisfaction.
OPTIMIZATION OF OUTDOOR EDUCATIONAL LANDSCAPES IN SELECTED NIGERIAN PUBLIC PRIMARY SCHOOLS: A DESIGN AND SYSTEMS PERSPECTIVE
Outdoor school landscapes are critical infrastructural systems that provide essential behavioral affordances for children’s holistic development. However, in many developing contexts such as Nigeria, these environments remain under-designed and pedagogically underutilized. This study presents a systematic, multi-method appraisal of the composition and utilization of outdoor landscapes in public primary schools across North-Central Nigeria. Employing field observations across twelve schools and semi-structured interviews with sixteen educational stakeholders, we evaluated landscape provision against functional, social, and pedagogical criteria. Findings reveal a dominant design paradigm prioritizing security and circulation, with severe deficiencies in intentional learning-oriented spaces such as experimental gardens, sensory zones, and diverse play equipment. Statistical analysis indicated no significant regional variation in general landscape composition (F(3, 48) = 0.33, p=.776), but significant disparities in play equipment provision (F(3, 60) = 4.00, p=.015), highlighting inequitable resource allocation. Thematic analysis uncovered a strong latent demand among educators for multifunctional, ecologically integrated designs that support experiential learning. Synthesizing affordance theory, ecological systems theory, and landscape architectural principles, this study advances an integrated framework for schoolyard optimization. We conclude with evidence-based design and policy recommendations aimed at transforming these underutilized spaces into engineered, child-centered learning ecosystems that support sustainable development goals, offering a replicable model for assessment and participatory redesign in resource-constrained contexts.
Dam Break Analysis Using HEC-RAS Software for Oyan River Dam Abeokuta, Nigeria
This study presents a thorough dam break analysis of Oyan Dam, Nigeria, using the Hydrologic Engineering Center-River Analysis System (HEC-RAS) 6.6 software. The analysis simulated two hypothetical failure scenarios: overtopping and piping, to evaluate hydraulic and flood risks associated with potential failure of the dam. Breach characteristics were calculated using Froehlich’s analytical formulas, and unsteady flow simulations were conducted for an 11 km downstream reach. Key hydraulic parameters, including peak discharge, stage elevation, flow velocity and flood wave travel time, were computed. Results revealed that the overtopping scenario produced a higher peak discharge of 22,556.47 m³/s at the dam and greater flood velocities of about 10.24 m/s, whereas the piping failure produced a lower peak discharge of 17,140.02m³/s. In both scenarios, flood wave propagation was characterized by deeper inundation near the dam and progressively reduced flood depths downstream. Floodplain mapping using HEC-RAS Mapper revealed extensive inundation of downstream areas, including critical infrastructure, settlements and natural reserves. Simulations indicated that overtopping poses an immediate and severe flood hazard, making it the critical failure mode. Evaluation of possible downstream flood hazards identified at-risk areas: Arakanga nature reserve, Awyan, Iwofin and potential flooding of Lagos State through the Ogun River tributary system. This study highlights the critical importance of implementing flood risk reduction measures, construction of levees, development of auxiliary retention dams, emergency evacuation planning and enhanced dam monitoring systems. This analysis should improve dam safety, disaster preparedness and regional flood management planning, especially for coastal communities like Oyan and communities downstream of the dam.
Optimizing sisal fibre-reinforced self-compacting mortar with glass powder for enhanced mechanical properties
Sustainable alternatives are needed to reduce cement consumption while maintaining self-compacting behavior and enhancing toughness, ductility, and durability. Sisal fiber, with its high tensile strength and crack-bridging ability, improves ductility, while recycled glass powder (GP), due to its pozzolanic properties, reduces cement use and densifies the matrix. However, their combined effects in self-compacting mortar (SCM) remain underexplored. In this study, a Central Composite Design (CCD), implemented within a Design of Experiments (DoE) framework using Design-Expert software, was employed to statistically model and optimize the effects of GP and sisal fiber. GP replaced cement at 0–22.5%, while sisal fiber (SF) was incorporated at 0–1% by volume. Binder content, sand (14.3?kg), and water (4948?ml) were kept constant. Specimens (50?mm cubes and 40?×?40?×?160?mm beams) were vibrated, cured at room temperature for 24?hours, submerged in water at ~27?°C, and tested at 7, 28, 56, and 90 days. Results show that SF enhances compressive and flexural strength, especially when combined with GP. Microstructural analysis revealed an increased amorphous hydration matrix associated with GP incorporation and suggested enhanced pozzolanic-related phases in SF-modified mixes. The optimal mix (7.5% GP, 0.5% SF) achieved a balance of strength, supporting its potential use in eco-friendly construction.
Enhancing The Mechanical and Physical Properties of Polymethyl Methacrylate (PMMA) Denture Bases Using Ceramic Nanoparticle
Despite Polymethyl Methacrylate’s (PMMA) biocompatibility and aesthetic advantages, concerns remain about its poor mechanical properties when used to fabricate synthetic dentures. A thorough review of the mechanical and physical properties enhancement of PMMA dentures using ceramic nanoparticles needs to be carried out to understand and optimize PMMA-based dentures' strength and durability in the oral environment. Thus, this systematic review is aimed at presenting a comprehensive review of the effects of ceramic nanoparticle reinforcements on the mechanical and physical properties of PMMA denture bases, based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Using the keywords "PMMA denture reinforcement" and "ceramic nanoparticles," many search engines, including Scopus, PubMed, Wiley Online Library, and Google Scholar, were used to find academic publications published between 1997 and 2025. A total of 250 articles were found using these search parameters. The inclusion principle was then used to reduce the gathered results to 145 citations for this analysis. The mechanical and physical properties of PMMA dentures reinforced with various ceramic nanoparticles are reviewed, with a focus on zirconia nanoparticle-reinforced PMMA denture bases. Flexural strength, flexural modulus, impact strength, hardness, fracture toughness, elastic modulus, and transverse strength were reviewed. The in-vitro studies reviewed showed that the mechanical properties of PMMA denture composites were significantly improved by ceramic nanoparticles, demonstrating their effectiveness as reinforcing fillers. 3D-printed dentures, which exemplify how digital technology is transforming dental prosthetics, were reviewed. Hybrid reinforcement systems that leverage the synergistic effects of different filler types to improve denture performance were also highlighted.
Recycled Waste Polypropylene in Asphalt Pavement: An Experimental Research of Adopting Two Modification Processes
Incorporating waste materials in road construction will not only improve pavement performance but also significantly reduce road material cost and address the environmental challenges posed by hazardous waste disposal. This study explores the potential of using Recycled Waste Polypropylene (RWPP) as an asphalt modifier and an artificial aggregate to enhance the characteristics of the asphalt mixture. New recycling techniques are adopted in this work to produce RWPP with controlled dimensions. In addition, two modification processes were applied to prepare RWPP-modified mixtures: Wet Process (WP) and Semi-Wet Process (S-WP). The RWPP with a particle size of 0.075 mm was blended with base asphalt in WP at different ratios of 0.15%, 0.25%, and 0.35% by total mixture weight. Whereas, in S-WP, RWPP was used as a partial replacement of fine aggregate at three sizes of 2.36, 0.3, and 0.075 mm and dosages of 1%, 3% and 5% by the weight of the asphalt mixture. The asphalt tests, which included softening point, ductility, and penetration, were conducted to examine the tensile properties, consistency, and temperature sensitivity of base and RWPP-modified binders. Marshall and tensile strength tests were performed to evaluate the RWPP-modified mixtures' behaviour, and the storage stability test was performed to measure the segregation phase between RWPP and base binder. The outcome showed that the addition of RWPP significantly enhances the mechanical characteristics and resistance against moisture damage for the asphalt mixture. It is recommended to use 0.15% and 3% of RWPP for WP and S-WP, respectively, to achieve the optimal performance. Finally, S-WP is more effective than WP in terms of avoiding storage stability issues, incorporating large amounts of plastic in road pavement field, and reducing the carbon emissions and energy consumption during mixing and paving.
Investigation of Interlock Geometry Sizes Effect on the Shear Capacity of Dry-Joint Interlocking Core Block Masonry
Interlocking compressed earth block structure is a dry-stacked system that relies on geometric interlocking rather than mortar. The shear behavior of these structures is linked to the interlock geometry, especially the thickness and depth of the interlocking mechanism. This study examines the in-plane shear behavior of dry-stacked interlocking core block using a finite element triplet shear simulation model in Abaqus, focusing on how interlock thickness and depth influence shear strength, peak displacement, and failure modes. An experimental test was performed with the optimum interlock sizes obtained from the numerical simulation. The results indicate that the shear strength increases with interlock thickness but drops beyond 35mm (11.6% of the block length) due to a significant reduction in the residual soil interface on the female (depression) part. The findings also reveal that changes in interlock thickness have a greater effect on both strength and deformation capacity than changes in interlock depth. Shear strength ranges from 0.08 to 0.25 MPa, representing a 3.1-fold improvement (210% increase) from the thicknesses of 5mm to 35mm (2%-11.6% of the block length). In a similar range of depth, shear strength varies from 0.24 to 0.36 MPa, a 1.5-fold (50% increase) increase. These findings indicate the important role of interlock geometry in the shear performance of dry-stacked joints. The failure mode was characterized by early crushing of the interlock protrusions at thinner thicknesses, whereas at thicker thicknesses, both the interlock protrusions and the residual soil interface on the female face sustained significant damage. These findings emphasize the potential of core interlocking masonry in regions vulnerable to wind and seismic activity
A A Segmentation-Based Hybrid Chain Ladder–XGBoost Framework for Incurred But Not Reported Claims Estimation
Accurate estimation of Incurred But Not Reported (IBNR) claims is critical in non-life insurance, particularly for portfolios characterized by heterogeneous claim sizes and nonlinear development patterns. Existing hybrid reserving models improve predictive accuracy but typically apply multiple models uniformly to the same claim population, implicitly assuming homogeneous claim behavior. Under heterogeneous portfolios, large and volatile claims can simultaneously distort Chain Ladder development factors and destabilize machine-learning training. This study addresses this limitation by proposing a segmentation-based hybrid Chain Ladder (CL)–XGBoost framework that models claim according to their volatility characteristics rather than treating all claims identically. Claims are partitioned into small and large segments using a data-driven 65th percentile threshold (claim amount €32.49). The threshold is selected through sensitivity analysis by minimizing validation-set Mean Absolute Error (MAE) prior to test evaluation, ensuring that it is not arbitrary or tuned to the test data. Small claims, which exhibit stable development patterns, are modeled using the classical CL method, whereas large high-variance claims are modeled using XGBoost, and segment-level predictions are aggregated to obtain cumulative IBNR estimates. Using motor insurance claims data from 2015–2019, the proposed framework demonstrates substantial improvements over benchmark models. Compared with the classical CL baseline (MAE €1,774.51), the hybrid model reduces MAE to €819.28 (54% reduction), RMSE from €2,026.08 to €819.39 (60% reduction), and MAPE from 0.92% to 0.60%, relative to an average claim size of approximately €387. Bootstrap resampling confirms that the reductions are statistically significant at the 5% level. The proposed model also outperforms standalone XGBoost, Uniform Hybrid and Neural-Network–CL hybrid across all evaluation metrics. The results show that performance gains arise primarily from heterogeneity-aware segmentation rather than hybridization alone. The framework therefore, provides a robust and interpretable reserving approach that stabilizes development estimation for small claims while allowing flexible nonlinear learning for large claims
Waste-Derived Magnetically Recoverable CaO/Metakaolinite/Fe?O? Nanocomposite Catalyst for Efficient Biodiesel Production from Neem Oil
The development of sustainable, low-cost, and recyclable heterogeneous catalysts remains a major challenge for large-scale biodiesel production from non-edible oils. In this study, a novel waste-derived, magnetically recoverable CaO/metakaolinite/Fe?O? nanocomposite catalyst was synthesized using eggshell waste, Kutigi kaolin clay, and plant-mediated magnetite through combined impregnation and green synthesis routes. Structural and textural characterization by XRD, HRSEM, HRTEM, BET, FTIR, and DLS confirmed the formation of a crystalline nanoscale composite with enhanced surface area, uniform dispersion of basic CaO active sites, and strong magnetic responsiveness. The catalyst exhibited appreciable basicity (0.5 mmol g?¹; pH 8.1–8.7), enabling efficient transesterification of high free-fatty-acid neem oil. Process optimization using Box–Behnken response surface methodology demonstrated excellent model adequacy (R² = 0.98; Adj-R² = 0.96), achieving biodiesel conversion above 90 wt.% under optimal reaction conditions. Gas chromatography–mass spectrometry confirmed the formation of major fatty acid methyl esters. The produced biodiesel satisfied ASTM D6751 fuel specifications, with density of 890 kg m?³, kinematic viscosity of 4.6 mm² s?¹, flash point of 126 °C, and cetane number of 52. Reusability studies revealed that the catalyst retained approximately 62 wt.% of its initial activity after seven successive cycles, facilitated by efficient magnetic recovery and structural stability. Engine performance evaluation indicated improved brake thermal efficiency for blends up to B20, along with reduced CO and NO? emissions relative to conventional diesel. Overall, the developed magnetic nanocomposite catalyst provides a scalable, environmentally benign, and cost-effective platform for sustainable biodiesel production from non-edible feedstocks.
Time Series Forecasting Model of the Ionospheric f_o F2 Using Facebook Prophet
The study of ionospheric behaviour is critical for maintaining the reliability of space-based technologies, including HF communication links and Global Navigation Satellite Systems (GNSS). Ionospheric delays can significantly degrade the performance and accuracy of trans-ionospheric signals; thus, robust forecasting of the parameter is essential. This study uses a long-term dataset (2009 to 2016 for training) of observations from the Jicamarca Observatory in Peru to implement and evaluate the Facebook Prophet (FB-Prophet) time-series forecasting technique. For the years 2017 to 2019, the FB-Prophet model was used to forecast. Its performance was verified using independent observational test data and accepted empirical standards, namely the IRI-2020 URSI and CCIR sub-options. The study results indicate that FB-Prophet effectively reproduces the diurnal and semi-annual variations in the ionosphere. During the initial forecast period (2017 to 2018), FB-Prophet demonstrated superior alignment with observational fluctuations compared to empirical models, achieving a Root Mean Square Error (RMSE) of 0.71 MHz and a Mean Absolute Error (MAE) of 0.57 MHz. However, as the forecast horizon extended into the deep solar minimum of 2019, the CCIR model exhibited higher precision with a minimum RMSE of 0.15 MHz, while FB-Prophet's uncertainty interval expanded as the lead time increased and the solar environment transitioned into a significantly quieter regime. These findings demonstrate that FB-Prophet’s time-series decomposition is a highly effective tool for multi-year ionospheric forecasting at equatorial latitudes. The study highlights that while empirical models remain reliable for long-term climatological averages, the FB-Prophet approach offers enhanced predictive accuracy for capturing non-linear variations during periods of moderate solar activity (107.68 ± 33.81 sfu).
Computational Fluid Dynamics Investigation of Thermal Performance in Helical Tube Radiators Using Ethanol Coolant under Variable Outlet
This study investigates the thermal–hydraulic performance of a helical tube automotive radiator using ethanol as a coolant under varying outlet pressures. A three-dimensional CFD model was developed using ANSYS CFX 2021 R1 and validated using Dittus–Boelter and Darcy–Weisbach correlations. The analysis was conducted under steady-state turbulent conditions with a mass flow rate of 2.3 kg/s and inlet temperature of 371.75 K. The results indicate that outlet pressure significantly affects thermal performance. The maximum temperature drop of 12.58 K was achieved at 2.0 bar, while the minimum of 9.63 K occurred at 0.5 bar. However, the improvement from baseline (0.25 bar) to optimal condition is only 1.4%, indicating limited practical significance. Pressure drop remained nearly constant (~8.93 bar), leading to a pumping power requirement of approximately 6.5 kW. A comparative thermophysical analysis shows that although ethanol offers lower viscosity and reduced pumping resistance; its thermal conductivity and specific heat capacity are significantly lower than water-based coolants. This limits its intrinsic heat transfer capability. The study concludes that while ethanol can be used under specific conditions, its performance is strongly dependent on flow rate and system configuration, and it does not outperform conventional water-glycol coolants under identical operating conditions.
OPTIMISATION OF CONCRETE REINFORCED WITH RECYCLED POLYPROPYLENE FIBRE FROM CEMENT BAGS AND FLY ASH USING SCHEFFE METHOD
Recent developments in sustainable concrete technology emphasise reducing cement usage and integrating waste-derived materials. This study examines the combined effects of fly ash and recycled polypropylene fibre (RPPF) obtained from cement bags on grade 20 concrete, utilising the Scheffé (6,2) simplex lattice method for modelling and optimisation. Twenty-one concrete mixtures were prepared with water, cement, river sand, crushed granite, fly ash, and RPPF. Fly ash replaced cement at proportions from 0% to 25%, and RPPF was added at 0% to 2.5% by cement weight. Cube and beam specimens were cast, cured, and tested for strength at 7 and 28 days. The 28-day compressive strengths ranged from 6.3 to 19.7 N/mm², while flexural strengths varied from 0.42 to 3.04 N/mm². Optimal mechanical properties were observed with moderate fly ash substitution and low RPPF content. The Scheffé model demonstrated high predictive accuracy, with R² values of 99.98% for compressive strength and 99.97% for flexural strength. The optimal mix ratio, 0.50:0.95:2.00:0.05:0.01:3.00 (Water: Cement: Fine Aggregate: Fly Ash: RPPF: Coarse Aggregate), yielded 28-day strengths of 19.17?N/mm² (compressive) and 2.59?N/mm² (flexural), thereby advancing sustainability goals while ensuring adequate mechanical performance.
A Review of Geometric Deep Learning Technique for Emotional Detection Systems in the Context of Assistive Learning
The study of Emotional Detection Systems (EDS) has gained significant attention in recent years due to its importance in understanding human reactions during interactions with both other humans and machines. EDS involves the use of computer systems to identify human emotions, which can be expressed through various modalities such as facial expressions, text, and speech. While humans can naturally recognize emotions, enabling computer systems to do so remains a complex challenge. Although emotion detection technologies have been in development for decades, there is a continuous need for research to enhance their accuracy and effectiveness, as many existing systems still fall short of optimal performance. Recently, Geometric Deep Learning (GDL) techniques have emerged as a promising alternative to traditional machine learning approaches. By considering the geometric properties of data, GDL enables more efficient learning beyond conventional Euclidean spaces. In every human being, emotion varies from individual even when he or she have the same intonation and facial expression but which may not correspond to their internal state. Also, current EDS may find it difficult to detect the mind or state of mind such as culture, situation, intent, emotion and suppression. More so, some emotion categories are under-represented which makes it hard for models to generalize and also, many EDS are trained on the data sets which may not provide enough sufficiently diverse in culture, age, gender or rely on spontaneous emotions. However, its application to emotion detection remains relatively unexplored. This paper provides a critical review of various modalities used in emotion detection systems. It also analyzes different techniques and approaches, with a particular focus on assessing whether GDL techniques have been integrated into emotion detection models. Additionally, existing emotion detection datasets and performance results from recent studies are discussed. The report clearly states that the adoption of GDL is still partial and not yet universal across all commonalities and there is room for maturation in term of graph-construction best practices. The paper concludes with insights on current challenges and directions for future research. The future direction of GDL in EDS will integrate GDL’s structural intelligence with multimodal, interpretable, and privacy-preserving machine learning paradigms. Such integration will enable adaptive, human-centric systems capable of perceiving and responding to emotional states with better accuracy, empathy, and ethical responsibility.
Optimization of Geotechnical Enhanced Lateritic Soil stabilized with Bentonite and Ceramic Dust for landfill Liner application.
The rapid increase in Municipal Solid Waste (MSW) in urbanizing areas like Minna, Nigeria, necessitates effective landfill liners to protect soil and groundwater. This study optimized a mixture of bentonite and ceramic dust to enhance lateritic soil, focusing on hydraulic conductivity (K), unconfined compressive strength (UCS), and volumetric shrinkage (Vsh). Using Response Surface Methodology (RSM), mixtures of 0–12% bentonite and 0–10% ceramic dust were prepared under three compaction efforts at 13–23% moisture. The mixtures that met regulatory standards are: British Standard Heavy (BSH) compaction at 18% moisture, blends with 12% bentonite + 10% ceramic dust (K=7.9×10?¹? m/s, UCS=208.1 kN/m², Vsh=2.7%). Inaddition, Mixtures under West African Standard (WAS) and British Standard Light (BSL) at 18% moisture, particularly 12% bentonite + 10% ceramic dust, also achieved the required parameters. RSM optimization identified an optimum mix of 2.79% bentonite and 5% ceramic dust at 18% moisture. However, its Vsh (6.61%) slightly exceeded the threshold, indicating a need for further refinement to reduce shrinkage while maintaining permeability and strength. The study confirms that combining bentonite and ceramic dust is a viable, sustainable method for improving lateritic soil in landfill liner applications.
A Dynamical Study of Stability and Bifurcation in an In-Vitro Bovine Mastitis System Incorporating Nonlinear Incidence and Therapeutic Functions
A mathematical model was developed to study the transmission dynamics of Bovine Mastitis, a prevalent dairy cattle disease caused by bacterial infection, leading to significant economic losses and reduced milk quality. The model incorporated nonlinear transmission and treatment functions, and its epidemiological well-posedness was established. Stability analysis revealed that the disease-free equilibrium is stable if the basic reproduction number Rm is less than unity. However, the model exhibited backward bifurcation, indicating that Rm < 1 is necessary but not sufficient for effective control, and that initial population sizes and disease prevalence also play a crucial role. Sensitivity analysis identified key parameters influencing Rm, including per capita recruitment rate , transmission rates and , and maximum treatment rate . Numerical simulations demonstrated that targeted interventions, such as optimizing recruitment, reducing transmission, and maximizing treatment rates, can effectively control infectious populations and mitigate mastitis incidence, but require careful consideration of the complex disease dynamics.
Techno-Economic and Dynamic Performance Optimization of a Hybrid PV–Wind–BESS Microgrid for Rural Electrification
The escalating global energy demand and reliance on renewables highlight the need for sustainable and reliable power solutions. This study investigates the feasibility of a hybrid PV-Wind-BESS microgrid to provide clean electricity for underserved communities, using Olowo-Oko in Kwara State, Nigeria, as a case study. Average daily loads of 19.72 kWh (rainy season) and 22.95 kWh (dry season) were recorded using a Fluke 432-II Power Analyzer. Solar irradiance and wind speed data were sourced from NASA. The microgrid was modelled and simulated in MATLAB Simulink, incorporating advanced control strategies for voltage and frequency regulation. Techniques such as Triangular and Sinusoidal Pulse Width Modulation (TPWM and SPWM), combined with passive filters, effectively mitigated harmonics and enhanced power quality. Simulation results showed stable dynamic performance, minimal ripple, and efficient renewable integration. Further validation using HOMER Pro enabled techno-economic optimisation, achieving a 96.8% renewable fraction, a levelized cost of energy (LCOE) of $0.033/kWh, and a payback period of 8 years and 10 days. Sensitivity analysis identified battery cost and inverter performance as key factors influencing system viability. The proposed dual mode microgrid, validated through both dynamic modelling and financial simulation, demonstrates technical robustness and economic feasibility for decentralized rural electrification in Sub-Saharan Africa. This integrated framework offers valuable insights for energy developers, researchers, and policymakers aiming to scale up low-carbon, community-based energy systems. By advancing hybrid microgrid modelling, adaptive control techniques, and cost optimization strategies, the study contributes to the practical realization of Sustainable Development Goal (SDG) 7, supporting universal access to affordable, reliable, and clean energy.
OPTIMIZATION OF BINDER CONCENTRATION AND PARTICLE SIZE FOR ELEPHANT GRASS AND SAWDUST
The increasing demand for sustainable energy alternatives has renewed interest in the utilization of agricultural and forestry residues for solid biofuel production. In this study, elephant grass and sawdust were evaluated as feedstocks for briquette production using Cissus populnea (okoho) as a natural binder. Both biomass types, considered waste resources, were milled and sieved into four particle size fractions (1000, 500, 300, and 212 µm). Briquettes were produced with binder concentrations ranging from 0 to 35% and subsequently subjected to proximate analysis, density determination, compressive strength, shatter resistance, tumbling resistance, and higher heating value tests. Response surface methodology was employed to generate predictive models, while ANOVA and regression analyses were used to assess factor significance. Results showed that binder concentration and particle size significantly influenced briquette quality parameters (p<0.05), with higher binder levels and smaller particle sizes generally improving density and mechanical strength. Both feedstocks demonstrated good performance; however, sawdust briquettes exhibited superior overall properties, indicating their greater suitability for high-quality briquette production. These findings underscore the potential of locally available waste biomass and natural binders in the development of renewable energy solutions.