UNIABUJA Journal of Engineering and Technology (UJET)
UNIABUJA Journal of Engineering and Technology (UJET) - ISSN: 2714-3236 (Online); 2714-3228 (Print) - is a biannual multidisciplinary journal. Its focus is on the publication of current scholarly paper manuscripts within the context of international best practices in all fields of engineering and technology. UJET is published by the Faculty of Engineering of University of Abuja, Abuja, Nigeria. The Journal is currently indexed in Google Scholar.
Faculty of Engineering, University of Abuja, Abuja, Nigeria
Factors Influencing the Perception of Residents on Bioclimatic Design in Residential Buildings in Ilorin Metropolis
This study investigates the factors that influence how residents perceive climate-responsive design strategies in residential buildings within Ilorin Metropolis, Nigeria. Integrating indoor environmental comfort, architectural characteristics, and socio-demographic variables, the research adopts a quantitative approach using a structured questionnaire administered across high-, medium-, and low-density residential areas. A total of 597 questionnaires were distributed, of which 484 valid responses (81 % response rate) were analyzed using descriptive statistics and Chi-square tests at p < 0.05. Findings reveal significant associations between residential density and several environmental perception variables, particularly temperature perception (morning and evening), night-time ventilation, air freshness, lighting conditions, and proximity to major roads. High-density areas exhibited greater heat retention, reduced night-time air movement, and lower perceived air freshness, indicating microclimatic and urban morphology effects. The overall importance ratings show that thermal comfort ranked highest with a Mean Weighted Value (MWV) of 4.1983, followed by noise level (MWV = 4.0785) and light intensity (MWV = 4.0620). Ventilation and air quality were also highly valued, whereas acoustic comfort ranked lowest, though it still remained above the neutral midpoint. The study concludes that thermal and acoustic-related factors are the dominant determinants shaping residents’ evaluation of bioclimatic design, highlighting the need for climate-responsive housing strategies tailored to urban density contexts.
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Experimental Study on the Development of Particle Board Using Local Materials
The escalating demand for sustainable materials necessitates the development of high-performance bio-composites derived from agricultural wastes. This study explores the effects of varying proportions of melon husk, groundnut shell powder, and bone powder on the mechanical and water absorption properties of bio-composites using melamine formaldehyde as the binding matrix. Nine formulated samples (S1 - S9) were analyzed for mechanical strength, most importantly tensile strength and flexural strength, and also, water absorption. Results showed that Sample S6 containing 100 % melon husk hada lower tensile strength of 21.62 MPa and flexural strength of 36.89 MPa while Sample S7 made with 100 % groundnut husk had a tensile strength of 14.23 MPa and flexural strength of 30.31 MPa. However, Sample S5 with a hybrid formulation of 20 % melon husk, 70 % groundnut husk and 15 % bone powder gave the highest tensile strength of 28.96 MPa and flexural strength of 46.66 MPa. Using Grey Relational Analysis over the nine samples, considering nine properties in all, S5 was found to have the optimal composition with a Grey Relational Grade (GRG) of 7.32. All samples recorded high mechanical values well above the required threshold for woodwork and furniture and therefore, may be explored as alternative materials for structural applications.
Impact of Reverse Logistics Activities on Construction Project Management Cost Performance: Evidence from Abuja, Nigeria
Reverse logistics (RL) in construction can improve value recovery and reduce waste, yet it may also introduce additional logistics costs that affect project cost performance. This study evaluates the cost implications of key RL activities on construction project management in building projects within the Abuja Municipal Area Council, Nigeria. Primary data were collected from 25 construction professionals at active building project sites using a structured mixed-method questionnaire. Descriptive statistics were used to profile cost intensity across five RL activity dimensions: transportation, warehousing, material handling, time management, and cost management. Results indicate that transportation is the most cost-intensive RL activity (60% of responses in the high-cost category of ₦1 million and above), followed by warehousing and material handling (48% each). Cost management also shows notable intensity (44% high-cost), while time management is largely perceived as low-cost (64% low-cost; 8% high-cost). The findings indicate that RL cost exposure in Abuja projects is driven primarily by the physical logistics requirements of reverse flows, movement, consolidation, storage, and handling, rather than administrative effort alone. The study concludes that better reverse-flow planning, routing, consolidation, and fit-for-purpose storage and handling systems can strengthen cost control and improve cost performance in construction project management.
Design and Construction of an Automated Multipurpose Avian Deterrent System
This study focused on the development and integration of a bimodal bird scaring system, comprising multiple subsystems: bird detection, power and control, audio and laser, and a robotic arm. Visual data was collected using cameras, while avian vocalisations were sourced from the Federal University of Agriculture, Abeokuta (FUNAAB). An ornithologist analysed the collected data, differentiating birds from other objects and identifying distress and predator calls. The bird detection subsystem was crafted with YOLOv5 software, the audio subsystem with Audacity, and the control subsystem used microcontroller tools and UART protocols. Power modelling was conducted with Proteus, and manipulator modelling was done in SOLIDWORKS. Integration of subsystems involved serial communication protocols for synchronised operation. The efficacy of the Bimodal Bird Scaring system was evaluated by tracking bird invasions pre- and post-implementation, alongside assessing response accuracy. The bird detection model exhibited a precision range of 0.84208 to 0.84978 and a maximum recall of 0.82708, reflecting improved detection of real birds. Moreover, the mean Average Precision (mAP) rose from 0.70455 to 0.82104, confirming the model's effectiveness. Performance metrics showed a high true positive rate (0.89) and a low false positive rate (0.01). The aggregate deterrence system efficiently processed 10 commands per second, highlighting operational competence. Daily repulsion rates of the system at varying frequencies (1 kHz to 14 kHz) indicated increasing effectiveness in deterrence—62.00%, 70.00%, 72.20%, and 75.54% over three experiments. The findings concluded that using multiple strategies in an integrated management plan significantly boosted the ability to deter birds from rice farms.
Evaluation of Maintenance Condition of Buildings of Public Universities in Kebbi State, Nigeria
The maintenance of office buildings is critical to the effective functioning of universities. Yet, many public institutions in Nigeria struggle with deteriorating facilities due to inadequate maintenance management practices. This case is not different from that of public universities in Kebbi State, Nigeria. This study assesses the maintenance condition of office buildings in public universities buildings in Kebbi State with a view to determining the current operational condition of their buildings. A condition survey approach was adopted as method of data collection for the study. A physical inspection of both academic and administrative staff offices of the selected public universities was conducted. Data collected was analyzed using Building Condition Index (BCI). The result revealed that the current condition of the administrative building in university of Agriculture Zuru and Kebbi State University were better than that of the academic Staff blocks of offices with administrative blocks condition status being good with BCI of 0.75 and 0.79 while that of academic staff were fair with BCI of 0.57 and 0.70. In Federal University Birnin-Kebbi, both academic staff offices and administrative staff offices have equal condition of fair with BCI of 0.53 and 0.70. The study concluded that the condition of public university offices in Kebbi State were fair. It was recommended that preventive maintenance method should be adopted for public universities in Kebbi State.
Deep Learning-Driven Sentiment Analysis for Electoral Outcome Prediction
Social media platforms offer a unique, real-time window into public opinion, creating novel opportunities for measuring electoral sentiment. This study employs a mixed-methods approach to critically evaluate the predictive power of X (formerly Twitter) sentiment analysis for electoral forecasting, using Nigeria’s 2023 presidential election as a case study. We analysed a dataset of 136,500 tweets and conducted semi-structured interviews with 15 domain experts. Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) was benchmarked against traditional classifiers, Naïve Bayes, and Support Vector Machines (SVM). The optimised LSTM-RNN model achieved superior performance (93.8% accuracy, 95.0% F1-score). However, a moderately strong yet statistically insignificant correlation with official election results underscores the limitations of sentiment analysis as a standalone predictive tool. Qualitative insights contextualize this finding, highlighting the volatility of online discourse, challenges of multilingualism, and critical issues of representativeness, including urban bias and the influence of misinformation. While demonstrating the considerable promise of deep learning, our results reveal significant pitfalls, advocating for hybrid predictive frameworks that integrate real-time sentiment tracking with demographic weighting and multimodal data validation. Beyond forecasting, this research underscores the utility of social media analysis for promoting governance and participatory democracy in multilingual and resource-constrained contexts.
Design and Fabrication of a Steam Generation System for Fruit Preservation
Steam-based thermal processing is widely applied in fruit preservation due to its effectiveness in enzyme inactivation and microbial control. This study presents the design, fabrication, and performance evaluation of a natural circulation water-tube steam generation system developed for small- to medium-scale fruit processing. The system incorporates a steam drum (0.95 m height, 0.45 m diameter), a furnace chamber (0.80 m height, 0.13 m³ volume), and 32 mm outer diameter riser tubes to ensure efficient steam–water circulation. An automatic control unit integrating temperature, water-level, and emission sensors enhances operational safety and stability. The design was guided by thermodynamic and heat transfer analyses, including heat duty estimation and logarithmic mean temperature difference evaluation. The measured thermal efficiency was 67 %, which indicates that the system effectively converted thermal energy from fuel combustion into useful steam energy, minimizing losses. Fabrication was carried out using locally available materials to ensure durability and food safety compliance. Performance results indicate that the developed system is energy-efficient, cost-effective, and well-suited for fruit preservation applications in resource-limited agro-processing environments.
Low-Cost Quadruped Robot: Design, Kinematic Modelling, and Performance Evaluation
Quadruped robotic systems have gained increasing attention due to their superior mobility and stability in complex and unstructured environments. However, the high cost and system complexity of existing quadruped platforms significantly limit their accessibility for educational, domestic, and small-scale service applications, particularly in resource-constrained settings. This study presents the development and performance evaluation of a low-cost quadruped robotic designed using affordable materials, while maintaining reliable locomotion and task execution capabilities. The robotic platform integrates an ESP32 microcontroller, high-torque servo actuators, ultrasonic sensors, and an inertial measurement unit (IMU) within a lightweight 3D-printed chassis. Kinematic modelling of the robot leg was conducted using the Denavit–Hartenberg (DH) convention, and both forward and inverse kinematics were derived to enable accurate motion control. Performance evaluation was carried out through systematic experiments assessing mobility, task execution efficiency, sensor reliability, and energy consumption across different terrains and operational scenarios. Results indicate that the developed robot achieved an average walking speed of 0.75 m/s on flat terrain, with high stability and an overall task execution success rate exceeding 84%. Sensor evaluation revealed reliable obstacle detection at short to medium ranges, though performance declined at longer distances. A comparative cost analysis demonstrated that the proposed system, with an estimated cost of $450, offers a substantial reduction compared to commercial quadruped robots while retaining essential functional capabilities. The findings validate the feasibility of developing affordable quadruped robots with acceptable performance, making the system suitable for educational use, prototyping, and basic service applications.
Appraising the Impact of Granite Quarrying on the Magashanu Community in Kaduna State, Nigeria
This study is aimed at appraising the environmental and socio-economic impacts of granite quarrying on Magashanu community in Kaduna State, North-western Nigeria. Representative samples of soil and water were collected from within the community, alongside rock samples obtained from Tutu and Datum quarries, and subjected to laboratory analysis. Atomic Absorption Spectrometry (AAS) was employed to evaluate heavy metal concentrations in water and soil, while X-Ray Fluorescence (XRF) was used to determine the lithochemical composition of rock samples. Data from field surveys, including questionnaires, oral interviews, and visual assessments, were complemented by secondary sources and analyzed. Findings revealed that most heavy metal concentrations, including Fe, Co, Cu, Cr, Cd, Zn, and Mn, were within the World Health Organisation (WHO) permissible limits in soil and water samples. However, lead (Pb) concentrations in soil averaged 0.58 ppm, exceeding the WHO threshold of 0.15 ppm, indicating potential long-term environmental and health risks. Silicon content in quarry rock samples ranged from 50% - 58%, suggesting a significant risk of silica dust inhalation with prolonged exposure. Beyond chemical contamination, community feedback and field observations highlighted persistent environmental nuisances, including dust emissions, noise pollution, ground vibrations, and structural damage to buildings. The study highlights growing community concerns over environmental degradation and inadequate corporate social responsibility (CSR) from quarry operators. Hence, improved environmental management, enhanced regulatory enforcement, and strengthened stakeholder engagement are critical for mitigating quarry-related impacts and fostering sustainable coexistence between the industry and host communities.
Co-Pyrolysis of Cassava Peel and Sheep Manure Through Fixed-Bed Pyrolytic Unit for Biochar Application
The pyrolysis of agricultural and livestock residues through thermochemical conversion offers a sustainable pathway for waste management and biochar production. This study investigates the co-pyrolysis of cassava peel (CP) and sheep manure (SM) for biochar yield optimization using a fixed-bed pyrolytic reactor. A central composite design (CCD) under response surface methodology was employed to evaluate the effects of pyrolysis temperature (237.87–662.13 °C) and residence time (26.89–48.10 min) on biochar yield for three feedstock ratios: CP/SM (0/100), CP/SM (100/0), and CP/SM (50/50). The physicochemical characterization of the biomass through proximate and ultimate analyses confirmed their suitability for thermochemical conversion. Quadratic models were developed to describe the relationship between process parameters and biochar yield. The statistical evaluation using analysis of variance indicated that the developed models were highly significant (p < 0.0001) with strong predictive capability, yielding coefficients of determination (R²) of 0.9924, 0.9797, and 0.9863 for CP/SM (0/100), CP/SM (100/0), and CP/SM (50/50), respectively. Among the operating parameters, pyrolysis temperature was identified as the most influential factor affecting biochar yield, whereas residence time exhibited a comparatively minor effect. The experimental results revealed that sheep manure produced higher biochar yields due to its higher fixed carbon and ash content, while cassava peel generated relatively lower yields because of its elevated volatile matter fraction. The co-pyrolysis of CP and SM (50/50) demonstrated stable char yields, suggesting beneficial interactions between the two feedstocks. The findings highlight the potential of integrating agricultural residues and animal manure through co-pyrolysis for sustainable biochar production and efficient biomass waste management.
Development of a 4G Radio Frequency Signal Booster for Improved Mobile Communication Coverage
The construction of high rise building or the distance between mobile station and base station causes signal strength losses on mobile devices. This signal loss always led to wireless communication problems, therefore the need for a booster at the receiver. This article presents the development of a 4G Radio Frequency (RF) signal booster prototype aimed at improving indoor mobile network performance. The initial signal strength surveys for GLO and MTN networks between frequency bands of1800 MHz and 2100 MHz was first conducted with an Agilent FieldFox N9912A RF Analyzer. A 4G dual-component signal booster prototype was proposed, consisting of an indoor and outdoor antenna fabricated with aluminum elements and coaxial cable, coupled with a RF amplification module utilizing BFR93A transistors, ceramic capacitors, and resistors. This work compared the initial and the obtained signal strength from the proposed booster for GLO and MTN. The result shows an enhancement in the received signal strength measurement ranging from 2 dBm to 4 dBm approximately with a reduced uplink average gain of 1.2 dBm. This signal booster can be employed in mobile users to improve network qualities and coverage for better user service experience.
Development of an AI-Based YOLOv8 System for Microcrack Detection in Aircraft Jet Engine Components using Borescope Images
The timely detection of microcracks in jet engines is critical for aircraft safety, yet manual inspections are prone to human error, and existing AI models face limitations in accuracy and reliability. This study addresses these challenges by developing a novel deep learning framework for the detection and classification of microcracks in aircraft jet engines using the YOLO v8 algorithm. Through rigorous development and testing, the research demonstrates significant advancements over traditional computer vision techniques and previous deep learning approaches. The research utilized dataset of 27,708 high-quality borescope inspection images collected from three major Nigerian airports. The model achieved perfect precision (1.000), 87.5% recall, 100% specificity, 90.9% accuracy, and an F1 score of 0.933. The mean Average Precision reached 98.9% at IoU threshold 0.5 and 95.2% across the IoU range 0.5:0.95, confirming exceptional detection performance. Training and validation metrics showed effective model convergence without overfitting, while confusion matrix analysis revealed robust classification capabilities with no false positives. The system successfully detects various crack morphologies across different lighting conditions and surface textures, as demonstrated in visual results. A dedicated Windows application provides a practical interface for maintenance technicians to integrate this technology into existing workflows. This research directly addresses aviation safety challenges by enabling earlier and more reliable detection of potential failures in jet engine components, such as, compressor blades, fan blades, compressor casing, turbine blades, turbine disks, turbine vanes/nozzles, bearings and bearing housing, nozzle guide vanes, exhaust cone and ducting thereby enhancing maintenance efficiency and reducing operational risks. The methodology established provides a foundation for developing comprehensive AI-assisted inspection tools for the aviation industry.
Innovative Processing Techniques for Boosting Light End Fractions in Crude Oil Distillation
Processing heavy Antan crude oil in conventional refineries is constrained by its high viscosity, low API gravity, and elevated sulphur content, which result in poor light distillate recovery and excessive residue formation, limiting its economic viability. This study investigates the upgrading of Antan crude through blending with Oso condensate using a newly assembled, innovative distillation column. Various blend ratios were experimentally evaluated, with a 40:60% (Antan crude oil + Oso condensate) composition selected for detailed analysis. In parallel, a MATLAB-based design and optimization framework was developed to enhance yield prediction and process efficiency. Atmospheric distillation of pure Antan crude produced negligible light fractions. In contrast, the 40:60 blend significantly improved product recovery, yielding 25.60% naphtha, 15.37% kerosene, 12.49% diesel, and 45.50% residue, demonstrating the effectiveness of condensate-assisted upgrading. Computational optimization further enhanced performance, increasing yields to 34.11% naphtha, 24.26% kerosene, and 18.87% diesel, while reducing residue to 22.38%. This represents gains of 8.51%, 8.89%, and 6.38% in naphtha, kerosene, and diesel, respectively, alongside a substantial reduction in residuum. The model accurately captured process behaviour and outperformed conventional experimental approaches. Economic analysis shows that the optimized blend improves Gross Product Worth (97.2 $/bbl) and Net Refinery Margin (7.7 $/bbl) relative to Antan crude alone (89.9 $/bbl GPW; 1.9 $/bbl NRM). Profitability indicators confirm strong economic viability, with annual profit reaching 254.1 M$, positive NPV (+962.4 M$), and IRR of 18 – 20%, compared to negative returns for unblended Antan crude. The integration of blending strategies with computational optimization provides a robust pathway for upgrading heavy Nigerian crude oils. This approach enhances light product recovery, reduces processing risk, and improves refinery profitability, offering a practical and scalable solution for maximizing the value of challenging feedstocks.
Modelling Mechanical Properties of Self-Compacting Rice Husk Ash Concrete Using Artificial Neural Network
This study investigates the effects of Rice Husk Ash (RHA) on the mechanical properties and workability of Self-Compacting Concrete (SCC), focusing on compressive and flexural strengths. The mixes incorporated Hydroplast 260GR, a high-range water-reducing admixture (super-plasticizer), at dosages of up to 2.0% of the cement weight to achieve the high flowability required for self-compacting concrete (SCC) while minimizing overall water demand. Using a dataset of 56 concrete mix proportions developed using simplex lattice design method (N[6,3]) to systematically access the effect of varying Rice Hush Ash (RHA) content on the investigated performance parameters which include compressive strength, flexural strength and workability. The experimental analysis revealed a decline in compressive strength from 20 MPa to 11 MPa and flexural strength from 3.0 MPa to 2.2 MPa as RHA increased, though low RHA levels (5%–10%) occasionally matched the control due to pozzolanic effects. Workability remained stable (slump values 550–650 mm) up to 20% RHA, indicating minimal impact on flowability. Predictive models, including Linear Regression (LR) and Artificial Neural Network (ANN), were developed to estimate mechanical properties, with the dataset split into 80% training and 20% testing. The LR model outperformed the ANN, achieving RMSE values of 0.103 for flexural strength and 1.240 for compressive strength, compared to the ANN’s 0.374 (R² -0.89) and 2.074 (R² 0.09), respectively, highlighting the LR’s suitability for RHA-SCC predictions given the dataset’s linear tendencies and size constraints. Correlation analysis showed strong relationships between cement, water, and RHA, with RHA negatively affecting strength (r ≈ -0.48 to -0.51). The study recommends limiting RHA to 5–10% for practical applications, refining the ANN with a larger dataset, and exploring non-linear models to improve compressive strength predictions, providing a foundation for sustainable SCC design.
Development of a Recirculating Flood and Drain Aquaponic System Using Palm Kernel Shells as Growth and Biofiltration Media
A less complex, flood and drain Recirculatory Aquaponic System (RAPS) was developed. The growth medium for the system was palm kernel shells instead of the more commonly used materials like gravel, quartz and pebble balls. This paper reports the development considerations, materials used and the effectiveness of the system, based on water analysis, fish growth rate and plant growth rate. The system consists mainly the culture tank, media bed, 0.5hp pump, twin timer and storage tank. These components were developed based on specific parameters which include: capacity of culture tank, stocking density, fish biomass, daily feed need, area of media bed, hydraulic loading rate, hydraulic retention time and bulk density of the growth medium (PKS). The system was operated for 6 hours (11am-5pm) daily between the periods of 45 days under controlled condition. A total of 40 African catfish (Clarias gariepinus) of average initial weight of 148 g were stocked in two culture tanks (20 fish in each tank) at 15 kg/m3 density. Twelve days old healthy Amaranthus cruentus seedlings were transplanted to the grow bed after stocking of the fish. The efficacy of the system was determined based on quality of treated water compared with WHO standard for aquatic life. The average gained in weight per fish was 109.83 g and average feed consumed was 188.26g throughout the experiment, an average FCR of 1.71. For the control tank, the corresponding values were 103.8 g, 187.43 g, and 1.81; respectively. The survival rates of fish in the aquaponic system and control tank were 100% and 95%, respectively. A higher plant growth rate was observed in the aquaponic system (1.06 cm/day) than in traditional method (1.01 cm/day).
Enhancing the Performance of Mabushi Solar Power Plant through Machine Learning Forecasting Models: Generation and Demand Sides Management
This study explores enhancing the performance of Mabushi Solar Power plant through machine learning forecasting models: Generation and Demand Side Management. The research aims to develop models that can accurately forecast daily solar power output using environmental and operational data. A dataset of 108 daily records within 2023 to part of 2024 from the Mabushi solar power plant was used, incorporating variables such as solar power output (as dependent variable), irradiance, temperature, wind speed, humidity as (independent variables), and energy contributions from batteries, generators, and the grid. The methods adopted are data collection and processing, model development and training. Three machine learning models Random Forest, Gradient Boosting, and Prophet were developed and evaluated. Among these, the Gradient Boosting model proved most effective in optimal forecasting, achieving a Mean Absolute Error (MAE) of 0.006 MW, Root Mean Square Error (RMSE) of 0.008 MW, and a high R² score of 0.999. Its residual errors were tightly distributed around zero, indicating strong reliability and accuracy. The model was then used to forecast solar power generation into the future, with average daily predicted power output of 0.677 MW, with values ranging between 0.390 MW and 1.002 MW, culminating in a total predicted annual generation of approximately 247.04 MW. By applying ML algorithms to 365 days of data, this research produced accurate and interpretable forecasts of daily solar power generation. The results provided actionable insights for load scheduling, maintenance planning, and energy storage management. The forecast makes the plant predictive and not reactive, showcasing its practical relevance for energy planning and operational decision-making through demand side management.
Performance Evaluation of Azadirachta indica as a Corrosion Inhibitor on Mild Steel
This study explores the performance evaluation of Azadirachta indica (Neem) leaf extract as a corrosion inhibitor for mild steel in 1 M H2SO4. The Azadirachta indica (Neem) leaves were sourced at the University of Nigeria, Nsukka. The leaves were prepared using extraction method. The leaf extract was subjected to metallurgical analysis such as phytochemical , pH where the values were determined , Fourier Transform Infrared Spectroscopy (FTIR) analysis was used to determine the functional groups that are associacted the extract. The Spark Analytical instrument was used to determine the chemical composition of the mild steel. The mild steel samples were subjected to further analyses through the use of scanning electron microscope (SEM) attached Energy Dispersive Spectroscopy (EDX). This analytical technique was used to deterimne the morphology and elemental wt% in the samplee. The X-ray diffraction technique was used to determine the crystal structures. The weight loss mesurment experiment was performed for 240hours because it gives a clear , measurable corrosion signal, reflects realisitc industrial exposure, and aligns with standard pratices in the field, making the results both reliable and comparable to existing research. The results revealed that the extract significantly decreases corrosion, with inhibition efficiencies of up to 64.58% at 10 ml and 98.46% at 30 ml of H2SO4 concentration. The extract shows high inhibition efficiency, reaching up to 98.46% at a H2SO4 concentration of 30 ml. The Fourier Transform Infrared Spectroscopy (FTIR) analysis reveals the presence of Hydroxyl (OH) groups, Carbonyl (C=O), Amines (N-H) groups, and Aromatic (C=C) in the protective film formed by the leaf extract. The crystalline structure revealed by X-ray Diffraction (XRD) analysis includes peaks that correspond to many substances, including Fe(MgSO4), FeS2, Fe(ZnSO4), CaCO3, Fe2O3, and other compounds. According to the Scanning Electron Microscopy (SEM) with Energy Dispersive Spectroscopy (EDX) analysis, the examined samples' shape and elemental weight (%) concentration are revealed. The presence of some compounds, such as Alkaloids (0.6%), flavonoids (5.8%), saponins (1.5%), tannins (3.6%), and phenols (7.8%) was confirmed by the phytochemical analysis. The leaf extract and 1M of H2SO4 were tested for acidity using a pH meter. According to the study, the leaf extract of Azadirachta indica shows promise as an eco-friendly corrosion inhibitor for mild steel in acidic environments, providing a possible industrial solution. The phytochemicals present provided the protective coating that forms on the mild steel surface, as the extract functions as a mixed-type inhibitor. The study shows how Azadirachta indica leaf extract can be used to prevent mild steel from corroding in 1M H2SO4 with respect to time and increasing concentration; the extract's inhibitory action improves greatly and better; the 30 ml concentration showed the maximum inhibition efficiency (%IE). According to these results, the leaf extract of Azadirachta indica shows promise as a natural corrosion inhibitor for mild steel in acidic conditions.
Strength Characterization of Clay Deposits at College of Engineering, FUNAAB Abeokuta and Environs
Most clay deposits in Abeokuta, Southwest, Nigeria, do not have comprehensive data on their general properties, making it difficult to utilize them in any engineering application. In order to establish whether the clay deposits at the college of engineering (COLENG), FUNAAB, Abeokuta and environs were suitable for use in ovens or furnaces, some of their physico-mechanical and thermal properties were examined. Clay deposits from the location were collected, pre-calcined to 120oC and mixed with up to 20 wt% Portland cement, as binders. The samples were used to produce several bricks following recommendations from standard codes. Important physio-mechanical and thermal properties: bulk density (BDc), apparent porosity (APc), linear shrinkage (LSc), compressive strength (CSc), refractoriness (R), thermal shock resistance (TSRc), thermal conductivity(TC) of the produced bricks were determined. The results for BDc, APc, LSc, CSc, R, TSRc and TC of the developed bricks ranged from 1.46– 1.67 g/cm3, 14.0 – 21.4%, 10.5 – 15.0%, 6.0 – 6.8 MPa, 800 – 1010oC, 4.1 - 5.8 cycles and 0.185 – 0.221 W/mK, respectively. These results are close to those of the control sample which for the same parameters, were 1.60 g/cm3, 20.0%, 10.0 %, 7.0 MPa, 1050oC, 5.9 cycles and 1.000 W/mK, respectively. Sample B (90 wt% pre-calcined clay, 10% OPC) had the best combined properties evaluated, close to the control samples. The study showed that the clay deposits at COLENG, FUNAAB, Abeokuta and environs can be employed in ovens operating with temperature lower than 1000.
Evaluating Cultural Synthesis as a Catalyst for Pure and Sustainable Architecture: A case Study of the Dominican Chapel, Ibadan
Pure architecture echoes traditional and cultural practices with features reflecting functional and aesthetical sustainability, framed by context-sensitivity and environmentally responsible designs. Studies have shown that sustainable architecture embodies indigenous cultural identity of a people, valued by their cultural capital and hinged on their traditional heritage. Therefore, the effects of cultural neglect in place-making cannot be overemphasized. Indeed, the current concern on the erosion of cultural principles in contemporary works of architecture appears to account for nonorganic growth in African place-making with attributable consequences. This paper, therefore, examined the impact of cultural heritage on sustainable architectural development using the purity of architecture from the exploration of cultural synthesis. A case study of the Dominican Chapel at Ibadan, Nigeria was adopted with a qualitative approach to evaluate the impact of cultural forms and layouts on the functional aesthetics and sustainability of the Chapel. Field observations, interviews with the project architect, and other requisite observers in conjunction with content analysis provided relevant data which pointed attention to the value of cultural integration in the production of pure and sustainable architecture. The study revealed that cultural identity plays a pivotal role in shaping both the aesthetic and functional aspects of architecture, contributing significantly to sustainable postcolonial place-making. It advocates for the integration of indigenous architectural forms with practical, hands-on design approaches to foster originality and reduce reliance on Western paradigms. However, it encourages a shift to cultural synthesis in architectural expression and presents a clarification for both students and practicing architects towards adopting cultural forms in place-making for originality and sustainability choices.
Performance Optimization of 5G Wireless Networks in Nigeria Using Low-Power Massive MIMO and Intelligent Reflecting Surfaces
This paper investigates energy-conscious, performance-optimized strategies for enhancing 5G wireless networks in Nigeria, aiming to address the critical challenges of high operational energy costs and unstable grid infrastructure. The study employed a rigorous methodology using MATLAB simulations to model Massive MIMO systems, Intelligent Reflecting Surfaces (IRS), and adaptive reinforcement learning controllers. The primary objectives were to evaluate power-saving techniques through antenna switching and beam-shaping, assess the trade-offs of low-resolution quantization, and design intelligent resource management systems for hybrid energy environments. The simulation results revealed that energy efficiency in Massive MIMO systems does not scale linearly with hardware size; specifically, a configuration of 20 active antennas was identified as the optimal "sweet spot," achieving a peak efficiency of before diminishing returns set in. Furthermore, the analysis of Analog-to-Digital Converters (ADCs) demonstrated that power consumption increases by for every additional bit of resolution, establishing that a mid-range resolution of 4 to 8 bits provides the ideal balance between power economy and data throughput. The study also confirmed that IRS technology could enhance spectral efficiency from to using low-precision (3-4 bit) phase shifters, offering a viable solution for extending coverage in shadowed urban and rural areas. Additionally, the reinforcement learning controller successfully minimized reliance on diesel generators by optimizing the usage of grid and battery resources during power fluctuations. The research concludes that sustainable 5G deployment in Nigeria requires a hybrid approach combining dynamic hardware scaling with intelligent, adaptive power control. Consequently, it is recommended that network operators adopt dynamic antenna switching, utilize low-resolution ADCs, and integrate AI-driven power management systems to significantly reduce Operational Expenditure (OPEX) and ensure consistent service quality.
Development and Performance Evaluation of a Solar-Powered Domestic Water Pump Using Locally Available Materials
Access to reliable domestic water supply remains a major challenge in many developing regions due to high energy costs, unreliable electricity supply, and environmental concerns associated with fossil-fuel-based pumping systems. Conventional electric and diesel-powered water pumps contribute significantly to operational expenses, greenhouse gas emissions, and system unreliability, particularly in rural and semi-urban areas. This study addresses these challenges by developing and evaluating the performance of a solar-powered domestic water pumping system using locally available materials. The aim of the study is to design a cost-effective, environmentally sustainable, and reliable alternative water pumping solution suitable for domestic applications. The methodology involved the conceptual design, component sizing, fabrication, and experimental testing of a photovoltaic-powered pumping system comprising a monocrystalline solar panel, charge controller, inverter, battery storage unit, and a centrifugal water pump. Design calculations were carried out for pump selection, hydraulic power, battery sizing, inverter capacity, and solar panel requirements. The system was fabricated using locally sourced materials to enhance affordability and ease of maintenance. Performance evaluation was conducted through field testing under actual operating conditions, with voltage, flow rate, and efficiency measurements recorded. Results indicate that the system successfully pumped water to an elevated storage tank at a flow rate of 0.35 L/s, achieving an overall pump efficiency of approximately 58.33%. The findings demonstrate that solar-powered pumping systems can provide a reliable and clean alternative to conventional systems while reducing energy costs and environmental impact. The study concludes that solar-powered water pumping systems using locally available materials are technically feasible, economically viable, and environmentally sustainable for domestic water supply applications, especially in off-grid and energy-constrained regions.
Thermodynamic Performance Enhancement of a Natural Gas-Fired Power Plant Using Gas Path Diagnostics and Joule Thomson-Based Intake Air Cooling
Gas turbine performance decreases significantly under elevated ambient temperatures due to reduced compressor inlet air density and increased thermodynamic losses along the gas path. This work presents a thermodynamic performance assessment and enhancement analysis of a utility-scale natural-gas-fired gas turbine using actual operational data. Baseline evaluation shows progressive reductions in output and efficiency with increasing ambient temperature. Gas-path analysis based on Brayton-cycle relations identified increased compression work associated with elevated inlet temperatures. To mitigate these losses, cooling potential generated during natural-gas pressure reduction was utilized through a Joule–Thomson-assisted intake air cooling arrangement. Ambient air temperatures of 25.84–30.88 °C were reduced to 15.67–20.11 °C, producing inlet temperature reductions of 9–11 °C. Net power output increased from 137.14–147.86 MW to 151.63–161.61 MW (8.65–10.57%), while heat rate decreased by 2.3–2.7%, specific fuel consumption reduced by about 4.55%, and thermal efficiency improved by up to 2.8%. The combined gas-path diagnostic and JT-assisted cooling approach demonstrates a practical low-energy retrofit option for improving gas turbine performance under high-temperature operating conditions without additional water or auxiliary power consumption. The methodology involved baseline performance evaluation using operational data, Brayton-cycle-based gas-path diagnostics, and modeling of Joule–Thomson-assisted intake air cooling using literature-adopted thermodynamic parameters.
Production of Sustainable Paver Blocks from Metakaolin and Clayey Lateritic Sand
Sustainable alternatives to significantly reduced CO2 emissions and resource depletion associated with conventional concrete is in high demand in concrete production. In this backdrop, this study presents a production of sustainable paver blocks from metakaolin and clayey lateritic sand. The mechanical performance and statistical variability of this concrete paver blocks is achieved by partially replacing natural river sand with clayey lateritic sand and ordinary Portland cement (OPC) with metakaolin (MK), respectively. Six (6) concrete mix designs experimental specimens were cast and evaluated. These comprised a control mix (100% OPC and natural sand) and five modified mixes with 10%, 15%, 20%, and 25% laterite substitution for sand, each combined with 10% metakaolin replacement of cement. Concrete testing was undertaken at 7, 28, and 56 days, followed by statistical analysis of performance. The study revealed the 15% Laterite-Metakaolin composite (SLMK) replacement level as the optimum formulation for paver blocks, demonstrating the best balance of mechanical performance and consistency. The Control Mix achieved the highest overall strengths of 35.41 N/mm2 in compressive strength and 2.53 N/mm2 in split tensile strength at 56 days. Among the modified mixes, the 15% SLMK mix consistently had the highest strengths, with a 56-day compressive strength of 17.73 N/mm2 and a split tensile strength of 1.77 N/mm2. The study also revealed that strength development declined at lower replacement levels. Statistical analysis revealed that the 15% SLMK mix had the highest mean strength among modified mixes with the lowest Coefficient of Variation, indicating superior reliability and consistency. These findings demonstrate the potential of using 15% SLMK to produce structurally acceptable paver blocks while meeting sustainability goals.