LAUTECH Journal of Engineering and Technology

Co- Digestion of Neem (Azadirachta indica) Shoot Biomass with Poultry Droppings: Effect of Pretreatment Methods on Biogas Production

O. S. Oladejo, A. T. Bello, O. S. Olaniyan — Thu, 10 Jul 2025 00:00:00 +0000

Inadequate energy supply, environmental pollution, and declining soil fertility are major challenges in developing nations like Nigeria. Despite the abundance of biomass, much of it ends up as unmanaged solid waste. This study evaluated the effects of pretreatment on biogas yield from the co-digestion of Neem (Azadirachta indica) shoot biomass with poultry waste and cow rumen (inoculum). Materials were sourced from LAUTECH and prepared by washing, blending (mechanical pretreatment). The blended neem shoot was pretreated in a water bath to about 60 degrees Celsius for 1hr 20 minutes (thermal pretreatment). Chemical pretreatment was adopted to aid in the degradation of the lignin content. 4g of NaOH was dissolved in distilled water and then added to the thermally blended biomass. Two batches were prepared from the chemically treated Neem shoots, with poultry waste (batch A) and cow rumen (batch B), and put into airtight biodigesters. Physicochemical parameters (pH, TN, TP, TC, BOD, COD, MC, TS, C/N, FS) of both slurry and digestate were analyzed using standard methods. Biogas production, pH, and temperature were monitored over 30 days, and gas composition was determined via Gas Chromatography-Mass Spectrometry. Batch A showed biogas yields of 0.1016–0.0326 L/day, pH 8.39–8.41, and 35.7–35.8°C; Batch B yielded 0.1628–0.0488 L/day, pH 8.36–8.39, and 35.7–35.8°C. Methane content was 61.29% in Batch A and 63.29% in Batch B. ANOVA indicated significant differences in yields (p = 0.0256 for A, p = 0.0200 for B). Results showed that co-digestion, particularly with cow rumen, improved methane output. The produced methane is suitable for use in cooking, heating, and electricity generation, offering a sustainable solution for waste-to-energy conversion in Nigeria.

Solid Wastes and Greenhouse Gas Emissions Management for Energy Derivation: Case Study of LAUTECH Ogbomoso and Environs

O. S. Oladejo, A. O. Abdulazeez, A. S. Akeredolu — Thu, 10 Jul 2025 00:00:00 +0000

Final disposal of solid wastes at Ladoke Akintola University of Technology (LAUTECH) Ogbomoso, and its environs is by scavenging, dumping sites and open-air burning. This research aimed at studying the solid waste generation and greenhouse gas emissions management for energy derivation at LAUTECH and environs. The university was divided into sixteen zones based on Faculties and other prevailing activities on campus. Waste samples were obtained from bins and dumping sites, for 5 days (Monday, Tuesday, Wednesday, Thursday and Friday) in three years (2021, 2023 and 2024) for waste composition data. Sorted waste samples were taken to the laboratory to carry out moisture and energy content analyses. Methane (CH?) and Carbon dioxide (CO?) emissions from dumping sites and farm areas within LAUTECH and its environs were also measured using gas detectors. The collected primary data was analyzed statistically and discussed. Estimated waste generation in LAUTECH was 6161.47 kg/day, resulting in a daily waste generation rate of about 187 g per head, considering a university population of 33,000. The Energy content of daily wastes was 107.19 MJ, implying an electricity generation up to 0.02977 MWh (approx. 29.77 kWh) from daily steam production. Methane (CH?) levels range from 75 ppm (Rabbit Unit) to 2,107 ppm (layer birds, Abogunde Farms) and CO? concentrations vary between 400 ppm and 470 ppm, across farms. However, methane levels recorded peak values e.g., 11,169 ppm at AA Rano, 8,763 ppm at college, and 6,900 ppm at ALICE. CO? is highest at college (1,171 ppm) and AA Rano (1169 ppm). TVOC and HCHO values remain low at farm sites, while elevated at dumpsites. Considering the high material recyclability, reusability and energy recovery potentials from solid wastes generated from LAUTECH Ogbomoso and environs, there is an urgent need for emissions control in high-risk dumpsites through methods such as methane capture and air quality filtration. These actions are critical for environmental protection and safeguarding public health.

Beneficiation Consequence on the Influence of Potential Hydrogen Variation in the Froth Flotation of Farin-Lamba (Plateau State) Cassiterite

Thu, 10 Jul 2025 00:00:00 +0000

This study explores the influence of pH variation on the froth flotation performance of cassiterite ore obtained from the alluvial deposits of Farin-Lamba, Plateau State. Bulk ore samples were acquired via random sampling across the active mining site and subsequently homogenized to ensure uniformity. A 20 kg head sample was prepared, from which a representative 5 kg sub-sample was subjected to comminution and sieving. The processed material underwent chemical analysis, particle size distribution assessment, and beneficiation through froth flotation under controlled pH conditions. Initial analysis showed a tin oxide (SnO₂) grade of 20.22%. Sieve analysis across various mesh sizes identified -180+125 µm fraction as the optimal liberation size, recording the highest assay value of 23.28% SnO₂. Accordingly, sieve fractions -250+180, -180+125, and -125+90 µm were selected for flotation experiments conducted using a Denver D-12 mechanical agitator at 1200 rpm and gas flow of 0.5-1.0 m²/h. Experiments were carried out at pH 5, 7, and 9 to determine the best condition Farin-Lamba cassiterite to be beneficiated. Post-flotation analysis revealed that the highest SnO₂ concentration of 65.62% was achieved at pH 9 within the -180+125 µm size fraction, indicating this condition as optimal for tin beneficiation from Farin-Lamba cassiterite.

Optimization of Polypropylene Dosage for Improved Rheological, Physical and Mechanical Properties of Agbabu Bitumen

Thu, 10 Jul 2025 00:00:00 +0000

The vast majority of road infrastructure deformations are irreversible. They shorten the lifespan of flexible pavements and add to road safety concerns. Viscoelasticity is required for natural bitumen to function as a binder in pavements. However, when exposed to climate and heavy loads, natural bitumen's ability to undergo elastic deformation reduces. Consequently, road researchers have focused on modifying bitumen using polymers and nanomaterials to enhance pavement performance. Polymeric bitumen is extremely sensitive to Polypropylene (PP) dosage. Excessive use of PP leads to high viscosity. This study examines the impact of PP doses on the physical, rheological, and mechanical properties of Agbabu natural bitumen. The bitumen was dehydrated and analyzed for conventional characteristics using established techniques. The purified material was treated with PP at varying dosages (1.5 - 6 wt percent), and a binary mixture of bitumen and PP was optimized using D-Optima experimental design and response surface approach. The result shows that the mechanical properties, flash, and softening points of the raw bitumen were enhanced after modification. However, the penetration point of the modified bitumen decreases while viscosity increases as PP dosage increased from 2 to 3.75 wt percent. Therefore, the optimum dosage of 2.75 wt percent PP is recommended.

Evaluation of the Environmental and Social Benefits of Conversion Process of Open Cycle to Combined Cycle Gas Power Plant

O. Fadare, O. Ilori, T. Oyewusi, F. Adeyemi, O. Sole-Adeoye — Mon, 04 Aug 2025 00:00:00 +0000

Worldwide concern on reducing global warming consequences and combating energy crisis has motivated the development of power generation technologies to move towards sustainable energy production with higher efficiency and low environmental impacts. This study evaluated the environmental and social benefits of converting open cycle to combined cycle gas power plants in electric power generating system in Nigeria. All the current operational open and combined cycle gas power plants were considered. Green House Gas (GHG) emission data were collected for both open and combined cycle plants. The results showed that after conversion from open cycle to combined cycle, society bears a lesser cost of generating electricity as there is a minimum difference of 3.78 N/kWh (Calabar NIPP), which is about 23.34% change in cost and a maximum of 4.00 N/kWh (Omotosho Pacific Energy plant), which is about 25.20% change in cost for a minimum range of emission cost (40USD/tCO₂e). There is a minimum difference of 8.54 N/kWh (Calabar NIPP), which is about 28.57% change in cost and a maximum of 8.76 N/kWh (Omotosho Pacific Energy plant) which is about 29.64% change in cost for a maximum emission cost (100USD/tCO₂e). The study concluded that it costs less to reduce GHG and air pollution damage during the process of conversion from open cycle to combined cycle gas. Also, it is more beneficial to generate electricity using combined gas turbine and the society bears less cost for a higher electricity generation by a combined cycle when compared with an open cycle.

Pozzolanic Potential of Anacardium Occidentale Nutshell Ash (Aonsa) and Its Impact on the Mechanical Properties of Concrete

Mon, 04 Aug 2025 00:00:00 +0000

Investigating agricultural residue as pozzolans offers a two-fold benefits. It mitigates indiscriminate disposal what appears as a waste to some end users and promotes the sustainability of concrete. Tons of empty shells from Anacardium Occidentale Nutshells (AON), a by-product of the Anacardium Occidentale processing industry, are disposed indiscriminately in the environment where they eventually become nuisance. However, there is a notable paucity of all-inclusive studies exploring the pozzolanic potential and the impact of Anacardium Occidentale nutshell ash (AONSA) on the mechanical properties of concrete.

This study aims to contribute to the available knowledge base on the pozzolanic potential of AON being an agricultural residue, which serves as an outlet for AON. Mechanical properties of AONSA incorporated concrete was investigated. AONSA was obtained from the incineration of Anacardium Occidentale nutshell sourced from local Anacardium Occidentale processing unit in Ogbomoso and subjected to air-drying after getting rid of the nuts. Open ignition under ambient air took place to obtain AONS ash and this was calcinated in an Engineering Laboratory in LAUTECH.

AONSA samples A and B were placed differently inside furnace at 800 for 5 hours and 500 for 7 hours for calcination. X-Ray Diffraction (XRD) analysis and X-Ray Fluorescence (XRF) analysis and Scanning Electron Microscopy (SEM) analysis, were employed to assess the pozzolanicity of AONSA while notable observations of the effects of time and elevated temperatures on AONSA were noted.

The percentage XRD result of Quartz, Muscovite, Glauconite, Osumilite, Illite and Albite present in samples A and B are 6.4 (2), 47.8 (9), 18.3 (6), 23.6 (7), 3.9 (7), 0 and 24.3 (10), 21.3 (9), 29.9 (11), 0, 19.8 (9), 4.7 (2) respectively. XRF has these results for MgO, SiO₂, P₂O₅, SO₃, K₂O, CaO, TiO₂, MnO, Fe₂O₃, NiO, CuO, ZnO and Y₂O₃ for samples A and B are 7.620, 2.849, 4.761, 1.868, 20.88, 4.309, 583.5ppm,0.119, 10.073, 4.3 ppm, 388.9 ppm, 4.3 ppm, 388.9 ppm, 0.443, 75.0 and 5.691, 2.788, 4.817, 1.750, 21.893, 425.3 ppm, 0.115, 9.718, 0.0, 387.8 ppm, 0.851, 7.6 ppm, respectively. The element in SEM for samples A and B are K, Fe, Mg, Ca, P, Na, S, Si, Al, Cl, Ti with these percentages: 39.04, 12.24, 18.95, 6.81, 6.98, 6.93, 2.81, 3.02, 2.04, 1.18, 0.0 and 45.34, 7.28, 20.90, 5.93, 6.08, 6.27, 2.25, 2.48, 2.46, 1.01, 0.00, respectively.

The presence of silicate, lime, and aluminum oxides oxide in AONSA contributes to its suitability as a pozzolan. The SEM analysis shows that with increase temperature at 5 hours, the atomic weight of Potassium got reduced compared to decrease in temperature at 7 hours. The availability of SiO₂and Y₂O₃ in AONSA is essentially the same as that of cement. Furthermore, availability of MgO in AONSA is roughly 5.6912%, which is lower than 6% specified for cement in IS: 12, 269 – 1987. The amount of lime (CaO) in AONSA is roughly one-third that of Ordinary Portland Cement, as compared to alternative cement substitutes like fly ash.

Determination of Some Selected Mechanical Properties of Recycled Aluminium Alloy Modified with Micro- And Nano-Sized Additives

T. A. Adeyeye, M. O Durowoju, T. B. Asafa — Sat, 09 Aug 2025 00:00:00 +0000

The growing demand for sustainable materials has intensified interest in recycled aluminium alloys, which present a more energy-efficient alternative to primary aluminium production. However, recycled aluminium typically suffers from diminished mechanical properties due to the presence of impurities and microstructural irregularities. This study explored the influence of nano- and micro-sized reinforcements (black powder, graphite, and waste glass) on the mechanical performance of recycled aluminium alloys. Aluminium was mixed with 6%, 8%, and 10% weight fractions of each reinforcement, and processed using the sand casting technique to produce composite samples. The composites were evaluated for their physical, metallurgical, and mechanical properties. Results indicate that nano-scale reinforcements enhanced hardness, impact strength, and tensile strength relative to their micro-scale counterparts. The highest hardness (84.36 HV) was achieved with 10% nano-glass (GL10), while 8% nano-black powder (BP8) yielded the highest impact strength (18.67 J). Additionally, 10% nano-graphite (G10) produced the highest tensile strength (103 MPa), surpassing the micro-reinforced equivalent (78 MPa). These findings confirm the potential of nano-reinforced recycled aluminium alloys for use in high-performance engineering applications.

Development of a Fingerprint-Based Gender Detection System Using an Optimized Convolutional Neural Network

J. O. Ogunyode — Mon, 04 Aug 2025 00:00:00 +0000

Biometrics is a technology that identifies or verifies individuals based on unique physical or behavioral traits, offering a reliable form of authentication in sectors like healthcare, law enforcement, and security. Existing gender detection systems using fingerprints face challenges due to poor image quality and complex ridge patterns, while Convolutional Neural Networks (CNNs), though promising, are hindered by issues like overfitting, slow convergence, and getting trapped in local minima. Therefore, this study developed a fingerprint-based gender detection system through the optimization of CNN with Whale Optimization Algorithm. A dataset of 2,200 gender-labelled fingerprint images (1,320 male and 880 female) was acquired from Kaggle.com. The images underwent preprocessing involving cropping, grayscale conversion, histogram equalization for enhancement, and edge detection filtering to eliminate noise. Optimized CNN model was formulated using Whale Optimization Algorithm (WOA) by tuning CNN hyperparameters: number of neurons and dropout rate. The resulting WOA-CNN was employed for feature extraction (edges, texture patterns, shapes) and detection of fingerprint images. The model was implemented in MATLAB R2023a. Performance was evaluated using accuracy, sensitivity, specificity, false positive rate, precision, and recognition time, with an 80-20% training-testing split. CNN achieved 95.86% accuracy, 96.44% sensitivity, 95.00% specificity, 5.00% false positive rate, 96.66% precision, and 99.90 s recognition time. WOA-CNN achieved 97.23% accuracy, 97.58% sensitivity, 96.70% specificity, 3.30% false positive rate, 97.80% precision, and 87.40 s recognition time. This research showed WOA-CNN outperformed CNN in all metrics. It is recommended for use in biometric authentication, security checkpoints, and forensic investigations.

Synthesis and Characterization of Laggera Aurita-Derived Acetic Acid-Activated Carbon (LAAC) and it’s Potential for Toxic Element (TE) Metal Removal from Water

U. Idriss — Mon, 04 Aug 2025 00:00:00 +0000

The increasing contamination of water resources by toxic heavy metals necessitates the development of cost-effective and sustainable adsorbents. This study investigates the synthesis, characterization, and adsorption potential of Laggera aurita-derived activated carbon (LAAC) for the removal of Pb(II), Cd(II), and Cu(II) from aqueous solutions. LAAC was prepared via acetic acid activation followed by pyrolysis at 500°C and characterized using FTIR, SEM, EDXRF, BET and XRD surface area analysis. FTIR confirmed the presence of functional groups (–OH, –COOH, –C?C–, and S–H) that facilitate toxic element (TE) adsorption through hydrogen bonding, ?-electron interactions, ion exchange, and chelation. SEM revealed a nanostructured surface (nanotubes and nanospheres) with high affinity for Pb²?, Cd²?, and Cr(VI) due to increased active sites. BET analysis indicated a microporous structure (334.6 m²/g), enhancing TE retention via ion trapping and complexation with –COOH/–OH groups. Horvath-Kawazoe (HK) analysis further demonstrated an ultramicropore volume (0.5939 cc/g), enabling molecular sieving and Pb²? capture through dehydration mechanisms. EDXRF revealed CaO, 5.334%, SiO?, 4.836%, CeO?, 5.009%, P?O?, 1.902%, and SO?, 2.2966%. CaO provides alkaline sites that enhance cation exchange for metals like Pb²?, Cd²?, and Cu²?. XRD confirmed the nanocrystalline nature (3.82 nm crystallite size), contributing to high surface reactivity. These findings highlight LAAC as a promising, sustainable adsorbent for heavy metal removal, with future research needed to optimize activation parameters and assess real-world applicability.

Development of a Coati-Optimized Convolutional Neural Network for infected citrus fruit detection and classification system

T. A. Omotoso — Mon, 04 Aug 2025 00:00:00 +0000

Pest and disease management plays a significant role in minimizing losses to crops, particularly in citrus fruit production. Traditional methods for detecting and classifying infected citrus fruits are complex and tasking, while Convolutional Neural Networks (CNNs) offer promising solutions but still face challenges such as high computational requirements and data dependency. Therefore, this study developed an improved convolution neural network for infected citrus fruit detection and classification system using Coati Optimization Algorithm (COA). A dataset of 1,790 citrus images, containing samples of black spot, greening spot, citrus canker, and healthy fruits, was acquired from www.kaggle.com. The images underwent preprocessing involving cropping to remove unwanted elements, conversion to grayscale to simplify processing, normalization to enhance data consistency and reduce redundancy, and filtering to minimize noise. An optimized CNN model was formulated using COA to tune the hyperparameters (weight and learning rate) of CNN to produce Coati Optimization Algorithm–based Convolutional Neural Network (COA-CNN). The preprocessed images serve as input to the COA-CNN model. The COA-CNN was used for the extraction of edges, corners, texture, patterns and shapes, and classification of citrus fruits as infected or healthy. The developed system was implemented using MATLAB R(2023a). The system’s performance was evaluated using accuracy, false positive rate, sensitivity, specificity, and recognition time. A comparative analysis of CNN and COA-CNN was also carried out. The accuracy, false positive rate, sensitivity, specificity, and recognition time for CNN were 95.83%, 6.02%, 96.63%, 93.98% and 202.17 s, respectively, while the corresponding values for COA-CNN were 96.92%, 4.22%, 97.41%, 95.78% and 136.86 s. This research showed that COA-CNN performed better and is recommended for citrus disease detection and classification systems.

Evaluation of Pedestrian Safety on Highway Infrastructure in Ibadan

F. O. Akintayo, A. I. Omoremi, J. I. Braimah — Sat, 23 Aug 2025 00:00:00 +0000

Pedestrian safety is becoming increasingly important in metropolitan settings, especially in developing nations like Nigeria. This study examines the pedestrian safety state in Ibadan, Nigeria, highlighting the main difficulties and hazards pedestrians confront. A mixed-methods approach was used, combining quantitative and qualitative data gathering and analysis techniques. Five hundred people shared their experiences. The study revealed that 48% of respondents rated the roads as fair, while 27% described them as poor and 6% as very poor, citing concerns such as poor road design, inadequate pedestrian facilities, and careless driver behaviour as major safety issues for walkers. Pedestrians are most worried about poor traffic control, a lack of pedestrian bridges, and inadequate lighting. The study identified particular Ibadan streets that desperately require improvements for pedestrian safety. The findings of this research emphasize the need for a comprehensive plan to tackle the challenging issues regarding pedestrian safety in Ibadan. Enhancing pedestrian safety should be regarded as a primary concern; this may include appropriate pedestrian infrastructure, better road design, and more effective traffic management, for policymakers, urban planners and other interested parties. The study contributes to the information already available on pedestrian safety in Nigeria and provides practical analysis for improving pedestrian safety

Blockchain Security Model for Minimizing Free-Riding in a Peer-to-Peer Network

I.K. Ogundoyin, O.E. Ojo , O.E. Ayilara-Adewale , Y.O. Olatunde , L.E. Akwagha — Wed, 13 Aug 2025 00:00:00 +0000

Peer-to-Peer (P2P) networks are unique innovations utilised in file sharing applications to accomplish better execution and evade a weak link. They can be centralised and decentralised, structured and unstructured. The prevalence of the P2P network has drawn in various attacks, including Free Riding, which decreases the adequacy of the network. Although numerous moderation strategies have been proposed to diminish the effect of such attacks, minimising free-riding is still a major challenge on the Internet. A P2P-based mechanism called blockchain technology is a promising tool capable of addressing this challenge due to its distinct features, such as transparency, provision of a highly secure platform, immunity to attackers, and cost-effectiveness. This research proposes a blockchain-based security structure for P2P networks to address free-riding and is modelled using an incentive approach. The proposed framework uses blockchain hash value to connect each peer with the associated super peer that grants an upload or a download, validating the peer and forming an immutable block in the network. The system was tested within a peer-to-peer file-sharing system simulation scenario and implemented using Java programming language. The simulation results show improved performance in identifying and countering free riders in the network with a fairness index of 0.8 to 0.9, blocking approximately 400 peers, and creating more than 4500 validated blocks at 2500 peers.

Nutritional And Antinutritional Evaluation of Gluten-Free Pasta from Cocoyam Starch and Lima Beans Flour

A. A. Amanyunose, B. F. Olanipekun, O. E. Adelakun — Sat, 23 Aug 2025 00:00:00 +0000

Pasta is a wheat-based food product which has gained universal popularity in recent years due to its versatility, low cost, ease of preparation and nutritional quality. It is traditionally produced from wheat that is largely imported in Nigeria, placing a heavy burden on the dwindling financial resources of the nation. Wheat also contains gluten, which triggers a multi-system autoimmune disorder called celiac disease in genetically predisposed individuals. Treatment for celiac disease is complete exclusion of dietary gluten. Efforts are currently being made to develop gluten-free pasta from indigenous crops in order to reduce wheat importation and provide alternatives to consumers affected by celiac disease. Cocoyam and lima beans are gluten-free, underutilised indigenous tropical crops with a rich nutritional profile, which can be used to replace wheat flour in pasta production. This study, therefore, aims to develop gluten-free pasta from lima bean flour and cocoyam starch. Composite blends of pregelatinized cocoyam starch, germinated lima beans flour and xanthum gum (binder) were formulated in the following ratios: (100:0, 87.5:12:5, 75:25, 62.5:37.5 and 50:50) and used to produce pasta. The protein, moisture and carbohydrate contents of the gluten-free pasta ranged from 6.54 -18.82%, 9.06-10.56% and 59.29-74.30% respectively. Mineral content of gluten-free pasta ranged from 61.54-108.20 mg/100g for calcium and 0.94-6.09 mg/100g for iron. Lysine values increased from 2.63 (100% Wheat Flour Pasta) to 3.85 g/100 g protein (50% PCS, 50% GLBF & xanthum gum) while total amino acids ranged from 53.66-83.11 g/100 g protein. The antinutritional factors cyanide, oxalate and phytic acid ranged from 0.191-1.199mg/100g, 10.251-85.064 mg/100g and 0.01-1.66% respectively. The study concluded that pregelatinized cocoyam starch and germinated lima beans flour blends are effective in developing nutritious gluten-free pasta.

Sentiment Analysis of Movie Reviews using Word Embeddings and Machine Learning Techniques

I. O. Ipadeola, J. A. Ojo, I. G. Adebayo — Sat, 23 Aug 2025 00:00:00 +0000

In this study, sentiment analysis of movie reviews was carried out using word embeddings and machine learning techniques. Sentiment analysis, as an opinion mining technique, involves using feature extraction methods to understand the opinions and emotions expressed in text—particularly in domains such as movie reviews, where public sentiment plays a strong role in shaping consumer decisions. For sentiment analysis to be effective, text must be converted into a form that a computer can process. This involves transforming words or documents into vectors using word embedding techniques. Common techniques include Bag of Words, TF-IDF, and Word2Vec. In this study, TF-IDF and Bidirectional Encoder Representations from Transformers (BERT) were selected to compare their effectiveness in analyzing sentiment in movie reviews. The research used the IMDb dataset, which is widely recognized and commonly used in text mining tasks. Various machine learning models were applied, including Support Vector Machine (SVM), XGBoost, and Long Short-Term Memory (LSTM). Results showed that the combination of TF-IDF and SVM produced the highest accuracy, outperforming more complex models such as BERT with LSTM. The findings suggest that simpler word embedding techniques, when paired with effective classifiers, can give strong performance in sentiment analysis.

Review of N-Bundled Conductors on Right-of-Way in Transmission Network Reinforcement in Electrical Systems

S. O. Akinbode, A. S. Alayande, A. O. Balogun, S. O. Adetona — Sat, 23 Aug 2025 00:00:00 +0000

Transmission line expansion has become one of the critical network planning strategies to ensure the continuous evacuation of power. Right-of-way (ROW) has posed significant challenges due to an increase in population and rural-urban development. This research therefore reviews various methodologies of investigating the effect of N-bundled conductors on right-of-way and power system parameters such as voltage, current, power and load. The review of technical literature reveals that the effects of transmission lines bundling of conductors on the right-of-way were not considered in most cases of the literature reviewed. The changes in the electrical power system parameters with changes in conductor area and bundling of conductors were not examined in most cases of the papers reviewed. Even the relationship between transmission line expansion and bundling of conductors was not studied in most cases. To that effect, this study therefore proposed solutions to the above-mentioned shortcomings observed in the area of transmission network expansion.

Development of a Machine Learning-Based Cyber Threat Intelligence Dashboard System for Strategic Operations Centre

E. A. Gadzama, I. R. Saidu, J. K. Alhassan, P. O. Odion — Sat, 23 Aug 2025 00:00:00 +0000

Cyber Threat Intelligence (CTI) has become an essential element in the toolkit of Cybersecurity experts. In recent years, the significance of CTI has grown exponentially due to the increasing sophistication and frequency of cyber attacks. The incorporation of machine learning methodologies into CTI systems represents a substantial advancement in the domain. Conventional rule-based systems frequently fall short in identifying emerging threats and adjusting to the swiftly evolving strategies employed by cybercriminals. This paper presents a systematic appraisal of CTI dashboard systems that incorporate machine learning techniques to enhance strategic cybersecurity operations, which provide a user-friendly platform for real-time threat detection, analysis, and visualisation. At the core of this study is the utilisation of Gradient Boosting Trees (GBT) as the primary machine learning algorithm for threat detection and classification. The research only focused on the detection, analysis, and presentation of threat intelligence, leaving the specific response strategies at the discretion of the organisation implementing the system. The CTI dashboard system, which is the result of this work, showed strong performance, with a precision of 99.6%, a recall of 99.5%, and an F1-score of 99.97%. The system also showed an average response time of 3 minutes and 12 seconds, demonstrating its effectiveness in delivering timely and accurate threat intelligence.

Development of an Internet Of Things-Based Fingerprint Biometric Attendance System

E. T. Olawole, O. F. Adebayo, J. A. Adeshina, F. O. Ariba, R. A. Ajayi — Mon, 04 Aug 2025 00:00:00 +0000

This research explores the design and implementation of an Internet of Things (IoT) based fingerprint biometric attendance system. The traditional methods of attendance tracking often suffer from inaccuracies, time fraud, and significant administrative burdens. In response to these challenges, biometric systems have gained popularity for their ability to uniquely identify individuals through their physical or behavioural characteristics, offering a more reliable and secure approach to attendance management. The system proposed in this research utilizes fingerprint recognition, one of the most widely adopted biometric modalities, due to its high accuracy, ease of use, and cost-effectiveness. Integrating this system with the Internet of Things (IoT) expands its capabilities. The system comprises an ESP32 microcontroller, a fingerprint module, an OLED display, and a locally hosted Hypertext preprocessor (PHP)-based web interface. The OLED display serves as an immediate feedback mechanism for users, confirming whether their attendance has been successfully recorded by displaying the appropriate message. The web interface is designed for administrative use, allowing for the management of attendance records, user enrollment, and data exportation for further analysis. The results of this research demonstrate that the proposed IoT-based fingerprint biometric attendance system is a feasible and efficient solution. It offers a user-friendly interface for both students and administrators, significantly improving the accuracy and security of attendance tracking, verifying identities quickly under 1-2 seconds, with high accuracy. The system’s modular design and scalability also allow for future enhancements and adaptations to meet specific needs.

Radionuclide Assessment and Estimation of Radiological Indices in Groundwater Samples from Selected Mining Sites in Osun State, Southwestern Nigeria

Sat, 23 Aug 2025 00:00:00 +0000

The radiological contamination of groundwater sources near mining sites has become a significant environmental concern due to the potential risks posed by elevated levels of naturally occurring radioactive materials (NORMs). The study hereby assesses the radioactivity concentrations of 40K, 238U and 232Th in seventy-eight (78) groundwater samples collected from mining sites in the gold belt region of Osun State, Nigeria, using NaI(Tl) based gamma spectrometer and also estimates their associated radiological indices. The results indicated that the mean activity concentrations of all radionuclides exceeded the World Health Organisation's permissible limits of 1.0 Bq/L, raising significant concerns regarding the radiological safety of the water from the area under investigation. The radium equivalent values of 456.75 Bq/L and 272.87 Bq/L were obtained at Olowu and Iganga mining sites, respectively, where contamination levels were highest. The heightened exposure risks underscore the need for urgent interventions to protect public health and the environment in communities affected by mining activities.

Hybridization of Ring and Mesh Topologies in Fifth Generation (5g) Small Cells for Energy Optimisation of Millimetre-Wave Backhaul

F. A. Semire, I. A. Masud — Sat, 23 Aug 2025 00:00:00 +0000

Small cells are positioned as a complementary solution to the existing cellular infrastructure, rather than a complete replacement. However, densification of cells leads to an exponential rise in power consumption, especially in the backhaul segment connecting the small cells. Adopting mm Wave spectrum with a vast bandwidth for wireless backhaul links can provide multi-gigabit capacity through intelligent network design. Hence, the paper presents a hybrid backhaul architecture combining the reliability of ring topologies with the flexibility of mesh interconnects using mmWave technology. The methodology developed the hybrid ring-mesh (HRM) topology adaptation spanning the physical link and network layers. The optimisation problem formulation used a bio-inspired firefly algorithm and was embedded in MATLAB Simulink environment. The result showed that HRM topology has the highest throughput of 900Mbps and is fastest compared to Ring and Star topologies of 300Mbps and 500 Mbps, respectively. It maximises energy efficiency from 30Mbps/W and 50Mbps/W of Ring and Star topologies to 90Mbps/W and a latency of approximately 0.5ms. The study provides an alternative means for mm Wave backhauling of small cells as it maximises energy efficiency while ensuring stringent quality of service (QoS) and also offers considerable throughput and latency where there is few number of small cell base stations.

The Power System Stability Development and Application of Mathematical Model of Reactive Power Loss Index As A Measurement Of Voltage Stability in Nigerian Transmission System

N. A. Adeyemi; I. G. Adebayo — Sat, 23 Aug 2025 00:00:00 +0000

The Nigerian power system faces growing challenges in maintaining voltage stability due to increased demand, insufficient reactive power reserves, and the complexity of modern grid operations. This study presents the development and application of a mathematical model known as the Reactive Power Loss Index (RPLI) for identifying critical (weak) buses suitable for reactive power compensation in the Nigerian 52-bus transmission network. The RPLI model, derived using the bus admittance matrix method, serves as a voltage stability indicator by quantifying reactive power loss distribution across load buses. The model was implemented in MATLAB R2021a, and the voltage stability of the network was evaluated under contingency conditions. The performance of RPLI was compared with two existing methods: Relative Electrical Distance (RED) and Fast Voltage Stability Index (FVSI), based on voltage magnitude, maximum loadability, reactive power loss, and transmission line charges. Results showed that RPLI accurately identified vulnerable buses and achieved a reduction in reactive power loss by 6.78% and active power loss by 0.76% during contingencies compared to base contingency conditions. These outcomes validate the RPLI as an effective and computationally efficient tool for optimal placement of Static VAR Compensators (SVCs) and enhancing voltage stability in power systems.