Direct Research Journal of Engineering and Information Technology
Direct Research Journal of Engineering and Information Technology (DRJEIT) (ISSN 2354-4155) is an international open-access journal. The journal is currently accepting papers for publication in its monthly issue in English in all areas of Engineering, physical science, and Information Technology. We are looking forward to strict compliance with the modern age standards in all these fields. Submit your manuscript using the submission link Submit Manuscript ARTICLE PROCESSING COST = 655USD FOR AUTHOR OTHER COUNTRIES OR 55000 NAIRA FOR NIGERIA. AUTHORS CAN APPLY FOR PARTIAL WAIVER TO THE JOURNAL EDITORIAL AT editorialoffice@directresearchpublisher.org Indexed in JISC Scilit
Design and Implementation of a Smart Solar-powered Microgrid Monitoring and Fault Detection System for Sustainable Energy Management
Decentralized solar microgrids are key to sustainable electrification, but they require robust monitoring and rapid fault detection to ensure reliability and protect equipment. This paper describes the design and implementation of a smart, low-cost microgrid monitoring and fault detection system using an ESP32 microcontroller, INA219 current/voltage sensors, voltage dividers for higher voltages, data acquisition modules, and edge/cloud analytics architecture. The system implements real-time monitoring, local rule-based fault detection covering overcurrent, islanding, and battery undervoltage conditions, and machine-learning-ready feature extraction for fault classification. A prototype was developed and tested on a small solar microgrid comprising a photovoltaic (PV) array, battery bank, inverter, and connected loads. Results demonstrate effective early detection of common faults and clear visualization via a web and Android dashboard. The system provides a replicable framework for rural and campus microgrids, enabling efficient monitoring and maintenance that enhances reliability and reduces downtime in similar energy systems.
Design and Implementation of a Smart Multi Tank Water Filling and Monitoring System with Android APP Integration for Sustainable Water Resource Management
Water scarcity and inefficient domestic/institutional water management remain major problems in many urban and peri-urban communities. This paper presents the design, implementation, and evaluation of a low-cost smart multi-tank water filling and monitoring system using an ESP32 microcontroller, ultrasonic and float sensors, flow measurement, relayed pump control, and an Android mobile application. The system supports prioritized tank filling, automatic pump control with anti-dry-run protection, remote monitoring via Wi-Fi/GSM, and historical consumption logs on a cloud backend. A prototype was built and tested in a campus environment. Results show accurate tank level detection (±2 cm), reliable automatic switching between tanks, reduced overflow incidents, and pump run-time through optimized control logic. The system contributes to sustainable water-resource management by reducing wastage and enabling data-driven conservation measures. Key contributions are: (1) a modular hardware and software architecture for multi-tank control; (2) practical algorithms for prioritized and demand-driven filling; and (3) an Android UI for real-time control and historical analytics. Relevant sensor choices, energy budgeting, and cybersecurity considerations are discussed.
Smart Water System, Multi-tank Control, Electronic Valves, ESP32, Internet of things, Android App and Sustainable Water Management
Artificial Intelligence and Managerial Decision-Making in Manufacturing Firms in Delta State
This study examined the impact of artificial intelligence (AI) on managerial decision-making in manufacturing firms in Delta State, Nigeria, focusing on machine learning and robotic process automation (RPA). A quantitative cross-sectional survey design was adopted, and data were collected from 234 managers and supervisors using structured questionnaires. The data were analyzed using descriptive statistics, Pearson correlation, and multiple regression analysis. Findings revealed that machine learning significantly enhances managerial decision-making by improving data analysis, forecasting accuracy, and decision quality. Similarly, RPA positively influences decision-making by automating routine tasks, ensuring data accuracy, and providing real-time operational insights. The regression results indicated that both variables significantly predict managerial decision-making effectiveness. The study concludes that AI technologies are critical for improving decision speed, accuracy, and strategic responsiveness in manufacturing firms. It recommends that firms invest in AI infrastructure and integrate AI tools into managerial processes to enhance performance and competitiveness. This study contributes to knowledge by providing empirical evidence on AI-driven decision-making in the Nigerian manufacturing sector.
Copyright (c) 2026 Direct Research Journal of Engineering and Information Technology
Design and Development of an AI-Driven Mobile Application for Smart Menstrual Cycle Tracking Using Agile Methodology
In developing countries such as Nigeria, the unavailability of secure and user-friendly menstrual tracking tool presents an all-pervasive problem for young women. Stable menstrual health condition significantly contributes to maintaining public composure and coordinated mental interactions among the women folk. Mobile app-based smart menstrual tracker represents a cutting-edge innovation for enhancing menstrual health. This study developed a model of mobile app that leverages user-friendly interfaces, advanced artificial intelligence algorithms to monitor, analyze and predict menstrual cycles. The app offers personalized insights into cycle patterns, ovulation and fertility windows, as well as track symptoms such as cramps, mood changes and energy level. The study adopted agile methodology software development process to facilitate integration of predictive algorithm for identifying cycle patterns and forecasting likely future occurrence using previous data. This approach harnessed critical hardware and software tools such as visual studio code editor, React.js with Vite, Node.js with Express and MongoDB to develop and implement both the frontend and backend functionalities of the app. It explored AI models to accurately identify menstrual cycle phases using last short-time memory (LSTM) algorithm to advance privacy-preserving reproductive health monitoring. The app was tested using locally accessed real user data assigned pseudo-names, summary results were presented as test-case I, test-case II and test-case III. The results showed optimal performance where the date-based computation model provided accurate outputs that matched the expected results. This study contributes to women’s health by enhancing flexible user-centric and personalized menstrual health information management system for improved, secure and precision oriented cycle tracking.
Design, Fabrication, and Experimental Validation of a Low-cost Neuromuscular Electrical Stimulator for the Prevention of Skeletal Muscle Atrophy
Individuals with limited mobility are at a significantly higher risk of muscle atrophy due to prolonged sedentary states. In rural Nigeria, the inconsistent electrical supply often renders contemporary electrical muscle stimulators (EMS) redundant. To address this, the current study aims to design and fabricate and experimentally validate a low cost, battery-powered muscle stimulator that ensures readily available therapy in resource-constrained environments. A user-centered design approach was adopted, involving the construction of a portable power management unit and a muscle stimulation circuit. The system’s output parameters were verified using Proteus 8 simulation software to ensure voltage and current ranges aligned with established clinical standards. The hardware features an LCD interface that displays real-time frequency and stimulation duration. To evaluate clinical efficacy, handgrip strength gains were measured using a dynamometer and compared against a standard commercial EMS unit. The test was approved by the ethics committee of the Federal University of Science and Technology, Owerri. Nigeria. A paired-sample t-test conducted on 30 young adults between ages 18 – 30 revealed that the mean strength gain for the fabricated system (3.995 ± 0.483) did not differ significantly from the standardized EMS (3.947 ± 0.782), with t (29) = 0.516 and p = 0.610 (two-tailed). Furthermore, a strong positive correlation (r = 0.7899) indicated highly similar response patterns between the two modalities. These results demonstrate that the low-cost, battery-operated stimulator is a viable, high-performance alternative for muscle rehabilitation in areas lacking stable power infrastructure.
Muscle atrophy, muscle stimulator, battery-powered
Modeling and Dynamic Analysis of a 9 MW DFIG-Based Grid-Connected Wind Energy Conversion System
The global shift toward low-carbon energy sources has elevated the role of wind power in sustainable electricity generation. This study presents the design and MATLAB/Simulink-based simulation of a 9 MW grid-connected doubly fed induction generator (DFIG) wind energy conversion system. The system comprises a horizontal-axis wind turbine, back-to-back converters, and a DC-link, with decoupled vector control used to regulate active and reactive power. The wind farm is connected to a 120 kV point of common coupling and interfaced with a 575 kV transmission network through step-up transformer stages. System performance is evaluated under normal operating conditions and symmetrical grid faults, with emphasis on transient and steady-state response as well as fault ride-through capability. Sensitivity and correlation analyses identify grid frequency and converter switching frequency as the most influential parameters affecting system performance. The results demonstrate stable operation, effective power quality control, and satisfactory dynamic response, confirming the suitability of DFIG-based wind energy systems for large-scale grid integration. The findings provide practical guidance for optimizing wind power systems and support the deployment of reliable and resilient renewable energy infrastructure in developing regions.
An Empirical Assessment of Cloud Computing Adoption in University Libraries in Niger State, Nigeria
Cloud computing has emerged as a transformative force in libraries, offering scalable, on-demand access to digital resources and enabling operational efficiency. The study examined the adoption of cloud computing for service delivery in academic libraries in Niger State. Three objectives were formulated to guide the study. The study adopted descriptive survey design and structured questionnaire was used to collect data for the study. Data collected was analyzed and presented in tables, frequencies, percentage and mean. The results of the findings revealed high level of agreement on cloud computing improves access to library materials from anywhere (4.39), allows remote and convenient access to services (4.36) and enhances speed and efficiency of service delivery (4.34). However, the finding also identified challenges affecting adoption of cloud computing which included poor internet connectivity, financial constraints and erratic power supply. The study concluded that cloud computing offers substantial benefits in enhancing library services, however, its success in adoption depends on addressing internet connectivity, financial, erratic power supply and other challenges identified.
Academic Libraries, Cloud Computing, Cloud Adoption, Niger State, University Libraries, Nigeria
Improved Human-Centric Cyber security Framework for Protecting Home users from Social Engineering Attacks
Social engineering attacks remain a major cybersecurity threat, especially among home users who do not have the well-developed security policies typically found in organizations. Despite the fact that current countermeasures are primarily focused on technical defences, there is increasing evidence that the strategies are no longer adequate in the light of attacks that rely on human cognition, emotion, and behaviour. The problem addressed in this study is the inefficiency of existing cybersecurity measures in preventing social engineering attacks against home users, necessitating the development of an improved cybersecurity approach to address gaps identified in the literature. The qualitative research design was chosen and semi-structured interviews with cybersecurity professionals were carried out using the purposive sampling method. Thematic analysis was used to identify existing threats, anticipated future risks, and mitigation strategies. Results indicate that phishing, vishing, and smishing are the most prevalent attack vectors affecting home users, and their sophistication has been increasing due to personalisation, artificial intelligence, and impersonation techniques. Experts argued that technical solutions alone are insufficient; continuous user awareness, behavioural adaptation, and contextual understanding are therefore imperative to improve resilience. The research develops a more human-centred cybersecurity framework that integrates cognitive, behavioural, psychological, and contextual variables with applicable technological controls. The framework does this by positioning home users as stakeholders in the fight against cybersecurity offences and by establishing a culture of adaptive cybersecurity, which provides a less inherently unsustainable means of addressing evolving social engineering challenges. Although the framework remains conceptual and has not yet been empirically validated in a home setting, it provides a systematic basis for further research and for the practice of home-user cybersecurity. It is suggested that future research be conducted to empirically validate the framework in large populations and in new threat situations.
Social engineering; Human-centric Cybersecurity; Home Users; Phishing; Vishing; Smishing; User awareness; Cybersecurity framework; Behavioral Security
Design and Implementation of an Internet of Things (IoT) Based Automatic Fire Fighting Sprinkler System
The frequent incidence of fire accidents in homes, work places, industrial buildings and the limitations of conventional fire suppressor system demand the development of intelligent and responsive fire-fighting solutions. This research work, design and Implementation of an IoT based automatic fire fighting sprinkler system is aimed at improving response time, reliability, and remote monitoring compared to conventional fire protection systems. The system employs a combination of temperature, smoke and flame sensor interfaced with a microcontroller (ESP8266) to continuously monitor environmental conditions. When fire indicators exceed preset safety thresholds of above 600C, the controller automatically triggers a sprinkler mechanism through a relay- driven solenoid valve to suppress the fire. In addition, the system transmits real-time alerts and sensor data to a cloud platform (Bylink) through a wireless communication module, allowing users to monitor conditions and receive notifications from remote locations. The prototype was developed using avoidable and energy efficient components and evaluated under controlled fire simulation. The result shows that the system responds promptly to fire events which activates the sprinkler effectively, and provides reliable real- time monitoring. The constructed IoT based fire- fighting sprinkler system demonstrates improved efficiency, scalability, and suitability for home and industrial fire safety applications.
Fire detection, Internet of things (IoT), Automatic fire sprinkler system, embedded system, GSM alert system
Multi doped Hydroxyapatite-Urea Nano fertilizer: Synthesis and Controlled Micronutrient Release
Hydroxyapatite HAp-urea composite based nano fertilizer constitutes a novel approach of incorporating variety of ionic species into HAp hexagonal lattice structure with control release capabilities. Evidently, it is a veritable and sustainable alternative to commercial fertilizer in enhancing plant yield in nutrient deficient soil. In this research, HAp doped with multi ionic species of Mg2+, Si4+ and Cu2+ (Ca10-w/2-x-ySiw+MgxCuy(PO4)6-w(OH)2-w) with nutrient and subsequently form a HAp-urea nano fertilizer composite via chemical precipitation route. The following HAp to nano-urea matrix-filler ratio 100 mL: 0.15 g was adopted. The crystallinity, chemical constituent and nanoparticle sizes was investigated using X-ray diffractometry (XRD), X-ray fluorescence (XRF), Transmission Electron Microscope (TEM), Brunauer–Emmett–Teller (BET) and Scanning Electron Microscopy (SEM). Micronutrient ion release of Ca2+, P5+, Mg2+ and Cu2+ was examined using Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES) in water sample and XRF in soil sample. Monitored plant foliage activities was analyzed by applying 10g of the produced HAp-urea nanofertilizer composite in comparison to 10g of commercial fertilizer on selected plants. Ultimately, the multidoped HAp-urea composite based nanofertilizer showed superior plant growth height (PGH) of HU: 54, HUMS: 58 and HUMSC: 56 cm as compared to commercial fertilizer with PGH of 36 cm.
Hydroxyapatite, urea, nanofertilizer, controlled release, nanocomposite, sustainable agriculture, ICP-OES
Multi-Objective Generation Expansion Planning with Renewable Integration and Uncertainty Modelling
The global energy landscape confronts unprecedented challenges in generation expansion planning, characterized by increasing renewable energy integration, complex technological uncertainties, and the critical need to simultaneously optimize economic, environmental, and reliability objectives. Traditional deterministic planning approaches have proven inadequate for capturing the dynamic and stochastic nature of modern power systems. This research aims to develop a comprehensive multi-objective generation expansion planning framework capable of effectively modelling renewable energy integration, quantifying deep uncertainties, and providing robust decision support mechanisms for strategic infrastructure planning. The study employs a hybrid stochastic-robust optimization approach, integrating advanced techniques such as Monte Carlo scenario generation, econometric load forecasting models, spatial-temporal correlation modelling, multi-criteria decision analysis, and sophisticated mathematical optimization algorithms. The proposed framework generated 147 non-dominated Pareto solutions, validated through extensive simulation and benchmarking on the IEEE 118-bus test system. Key achievements include revealing non-linear cost-emission trade-offs and identified critical renewable integration thresholds. Performance was measured using the hyper volume indicator (a metric that quantifies the extent of the objective space covered by Pareto-optimal solutions), achieving a value of 0.847, which demonstrates superior coverage compared to traditional models. Ultimately, the research presents a transformative approach to generation expansion planning, offering a sophisticated methodology that bridges technological complexity, uncertainty management, and strategic decision-making in the evolving global energy landscape.
Copyright (c) 2025 Direct Research Journal of Engineering and Information Technology
Multi-Criteria Evaluation Framework for Deep Learning Architectures in Medical Image Segmentation
The rapid expansion of deep learning architectures for medical image segmentation presents a major challenge to selecting optimal model to implement in practice. While several traditional evaluation methods have been proposed to solving this selection problem, they cannot use criteria interdependencies and expert judgment to select deep learning architectures for medical image segmentation. This study, therefore, presents a multi-stage multi-criteria decision-making framework to address this knowledge gap. The framework contains an improved DEMATEL (Decision Making Trial and Evaluation Laboratory), intuitionistic fuzzy AHP (Analytic Hierarchy Process), and adaptive VIKOR (VIekriterijumsko Optimizacija Kompromisno Resenje). This study evaluated framework performance using six deep learning architectures and thirteen criteria. The DEMATEL-fuzzy AHP results showed sensitivity, specificity, and accuracy the most important criteria for evaluate the deep learning architectures for medical image segmentation. The enhanced VIKOR model identified Swin-Unet and nnU-Net as the optimal compromise and best solution, respectively, for the optimum compromise. Based on sensitivity analysis that was conducted, the VIKOR ranked architecture as UNETR >> nnU-Net >> Attention U-Net >> TransUNet >> Swin-Unet >> classic U-Net. This study’s findings have showed that the proposed framework can be used to support deep learning architectures for medical image segmentation decisions.
Deep learning architecture selection, medical image segmentation, multi-criteria decision-making, clinical decision support
Decentralization of Electronic Record Systems for Improved School Record Management in Nigeria
In Nigeria, particularly in Katsina State, implementing electronic record-keeping systems (ERKS) has been slow and uneven. While these systems offer benefits for information management and data retrieval, public schools face challenges including limited technology, inadequate training, and resistance to change. This study employed a mixed-methods approach combining qualitative interviews and quantitative surveys. The evaluation assessed ERKS decentralization's impact on record-keeping, while the survey collected data from 364 participants using stratified random sampling across 34 Local Government Areas. Questionnaires were administered online as well the interviews were conducted online to maximize response rates and data quality. Quantitative data were analyzed using descriptive statistics and inferential tests (Chi-square and log-likelihood ratio). Both tests showed statistical significance (p < 0.001), confirming that decentralized e-record management and improved information sharing significantly enhance record management efficiency. The Likert scale ranged from 1 (Strongly Disagree) to 5 (Strongly Agree). Findings revealed respondents occasionally use computers for work (mean = 2.77), but rarely use smartphones (2.07) or tablets (2.38), primarily due to limited device availability and infrastructure constraints. Despite these limitations, most respondents who had exposure to or training in electronic systems strongly agreed ERKS would improve work efficiency (4.35), accuracy (4.39), and time savings (4.45). Qualitative interviews with 19 stakeholders confirmed electronic records improve efficiency, accuracy, and productivity. The study concludes that ERKS decentralization and enhanced information sharing significantly improve school record management, enabling faster data access, better decision-making, and improved administrative outcomes. However, successful implementation requires substantial investment in infrastructure (electricity, internet connectivity) and capacity building. It is recommended that the Ministry of Education and State Universal Basic Education Board encourage phased ERKS adoption in all schools, prioritizing infrastructure development, and develop clear policies on data privacy, security, and compliance.
Electronic record-keeping systems, Decentralization, School record management, Digital transformation, Katsina State, Public Schools, Nigeria, Educational technology
Comparative Analysis of PCA-Transformed Soil Data and ML Models for Maize Yield Prediction in Nigeria
This study predicts maize yield using soil data from long-term trials by the International Institute of Tropical Agriculture (IITA) in Ibadan, Nigeria. Multi-year measurements from experimental and farmer-managed fields covered pH, organic matter, nitrogen, phosphorus, exchangeable cations, texture, and micronutrients. To manage multi-collinearity, variables were standardized and analysed using principal component analysis (PCA). Six principal components (PCs) explained over 80% of variance, capturing fertility and texture gradients. These PCs was used as predictors in three machine-learning models: Random Forest (RF), Gradient Boosting Machine (GBM), and Artificial Neural Network (ANN). RF achieved the highest accuracy (R² ≈ 0.89; RMSE ≈ 0.59), outperforming GBM (R² ≈ 0.50) and ANN (R² ≈ 0.36). PCA loadings and RF feature importance identified soil organic matter, nitrogen, cation exchange capacity, and texture as major yield drivers. Results confirm PCA improves data efficiency and interpretability, while RF provides robust, reliable predictions for maize yield, supporting precision agriculture in tropical systems.
Soil Fertility, Agronomic Modeling, Principal Component Analysis (PCA), Random Forest (RF, Artificial Neural Network (ANN), Precision Agriculture, Gradient Boosting Machine (GBM)
Awareness and Practices on Smartphone Battery Drainage: A Cross-Sectional Study in Dodoma City Higher Education Institutions, Tanzania
Smartphone battery drainage remains a remarkable challenge that negatively affects device performance and users’ daily activities. The challenge is more severe mainly to students who rely heavily on mobile devices for academic and social purposes. This study examined the level of awareness and practices related to smartphone battery drainage among students in higher education institutions (HEIs) in Dodoma City, Tanzania. A cross-sectional study design was employed, using a structured self-administered questionnaire distributed to 384 students selected through systematic sampling. Of these, 381 completed questionnaires were valid and included in the analysis. Data were analyzed using descriptive statistics and multiple linear regression analysis to determine the relationship between students’ awareness, charging behaviors, usage patterns, and experiences of smartphone battery drainage. The findings revealed a moderate level of awareness regarding the causes of battery drainage and battery-saving practices among students. However, inappropriate behaviors such as prolonged use of background applications, excessive screen brightness, and irregular charging patterns were prevalent and significantly associated with increased battery drainage. The multiple regression results further indicated that awareness level was a statistically significant predictor of effective battery drainage management practices (p < 0.05). These findings underscore the need for targeted awareness and educational programs within HEIs to promote optimal smartphone usage and charging practices, thereby enhancing battery longevity and improving overall device efficiency among students.
Smartphone battery drainage, user awareness, charging practices, higher education institutions, Dodoma city, Tanzania
Effect of Coconut Shell Aggregate on Shrinkage Cracking in Concrete using Modified Ring Test
This study investigates the effect of coconut shell aggregate (CSA) as partial replacement for natural coarse aggregate on the restrained shrinkage cracking behavior of concrete. Concrete specimens with 0%, 10%, 20%, and 30% CSA were cast to simulate restrained drying shrinkage conditions in accordance with AASHTO PP34-99. Crack initiation, width and length were monitored for 70 days. Results show that increasing CSA content delayed crack initiation and reduced crack severity. Crack width and length reduced from 0.2mm and 87mm in control concrete to 0.15mm and 72mm respectively when 10% CSA was added. Notably, at 20% and 30% CSA, no shrinkage cracks were observed, indicating a significant improvement in cracking resistance. Split tensile strength decreased by 8.5%, 13.4% and 17.1% for 10%, 20%, and 30% CSA respectively. These findings demonstrate the potential of CSA as a sustainable alternative aggregate that enhances concrete durability under restrained shrinkage conditions.
Coconut Shell Aggregate, Drying Shrinkage, Crack Behavior, Restrained Shrinkage, Sustainable Concrete, Lightweight Aggregate
Strength Properties Model of Modified Hot Mix Asphalt
Researchers have extensively investigated the use of various materials and industrial wastes as modifiers and additives in concrete and hot mix asphalt production. Reusing and recycling industrial wastes has become more appealing, steering away from their disposal in landfills and dumpsites. This particular study aimed to enhance asphaltic concrete properties by incorporating Katsina Steel Rolling Furnace Dust (SRFD), a waste product estimated at 15-20 kg per ton of steel produced, as an additive. The SRFD was introduced into the asphalt mix at different concentrations (0%, 2%, 4%, 8%, and 10%) relative to the volume of binder. The study found the optimal bitumen content to be 5.4%, meeting the General Specification of Nigeria Roads and Bridges, 2016. Key properties such as stability, flow, VMA (voids in mineral aggregate), VFB (voids filled with bitumen), and Pa (air voids) fell within specified ranges. Notably, the modified asphalt with SRFD exhibited improved stability (6.61kN compared to 5.4kN in the control asphalt) and a flow of 3.9mm. This change suggested increased bulk density, potentially reinforcing road pavement against damage from traffic loads. Statistical analysis using one-way ANOVA indicated that SRFD did not significantly impact stability, flow, and percentage of air voids (P-values were 0.517292, 0.06201, and 0.22976, respectively, with F-calculated values lower than F-critical values). However, for unit weight, VMA, and VFB, SRFD did have a significant role in the variance of the measured properties. Regression analysis is used to develop a relationship that could be applied in similar circumstance. The models exhibited good prediction capacity with coefficient of determination (R2 values) within the range of 76% to 99%.
Marshal Characteristics of Hot Mix Asphalt Concrete Modified with Steel Rolling Furnace Dust (SRFD)
The study aimed to enhance asphaltic concrete properties by utilizing Katsina Steel Rolling Furnace Dust (SRFD) waste, estimated at 15-20 kg per ton of produced steel, as an additive. SRFD was incorporated into the asphalt mix at varying concentrations (0%, 2%, 4%, 8%, and 10%) by volume of binder. Optimal bitumen content was established at 5.4%, meeting the General Specification of Nigeria Roads and Bridges, 2016. Properties like stability, flow, VMA, VFB, and Pa fell within the specified range. The modified asphalt, containing SRFD, exhibited improved stability (6.61kN compared to 5.4kN in the control asphalt) and a flow of 3.9mm. This alteration led to increased bulk density, suggesting potential structural reinforcement against road pavement distress caused by traffic loads. Results from the one-way ANOVA test indicated that SRFD didn't significantly impact stability, flow, or % air voids (p-values > 0.05 and F cal. < F crit.). However, SRFD did play a significant role in unit weight, VMA, and VFB variance. Marshal Quotient values were determined for different SRFD-modified asphalt mixes, with the highest value of 2.55 indicating enhanced stability and reduced flow, potentially offering high resistance to rutting. Analysing SEM ImageJ data revealed that SRFD-modified HMA exhibited a greater spread in particle distances, with Particle Mean length and area measuring 7,825.87 micrometres and 964,775.7 sq. micrometre, respectively, and an S.D. of 2,615.615 micrometre. In contrast, the control HMA showed closer particle distance deviation, with Particle Mean length and area of 235.659 micrometre and 259.09 sq. micrometres, respectively, and an S.D. of 234.2 micrometre
Marshal Properties Assessment of Hot Mix Asphal Containing Katsina Steel Rolling Furnace Dust as an Additive
Significant investments have been directed towards road infrastructure development, resulting in a vast network spanning approximately 194,000 kilometers. Responsibility for this network is divided among different government levels, with the Federal Government overseeing 17%, State Governments 16%, and local Governments 67% (Federal Ministry of Works Nigeria, 2013). In the steel industry, by-products like mill scale and oily sludge are generated during the cooling and rolling processes, with an estimated global production of nearly 5 million tons. The European steel industry alone produces around 500,000 tons annually, with over 30% of this remaining unutilized (Dana et al., 2017). Research incorporating Steel Dust Residue (SRFD) as an additive in asphalt mixes revealed promising results. Optimal bitumen content was identified at 5.4%, meeting the specified requirements outlined in the General Specification of Nigeria Roads and Bridges, 2016. This modified asphalt showcased improved stability (6.61kN versus 5.4kN in control asphalt) and exhibited desirable flow properties (3.9mm). XRF analysis identified the elemental composition of the steel dust used in the research, primarily comprising ferric oxide (Fe2O3 = 31%) and zinc oxide (ZnO = 28%). Experimental findings indicated that Marshal stability, unit weight, voids filled with bitumen (VFB), and stiffness initially increased and then decreased with higher SRFD content in asphalt mixtures. Notably, a 4% SRFD content by volume of binder met the specified Marshal properties for asphalt mixtures.
The Ethics of Chemical Waste Disposal: Balancing Technological Progress with Environmental Responsibility
Chemical waste disposal presents a complex ethical and environmental challenge in the face of rapid technological advancement. This paper explores the multifaceted relationship between industrial progress and environmental responsibility, emphasizing the urgent need for sustainable chemical waste management. It begins by categorizing chemical waste into hazardous, non-hazardous, universal, organic, and sink-safe types, each with distinct regulatory and environmental implications. The environmental impacts soil contamination, water pollution, air degradation, and biodiversity loss are explored alongside serious health consequences, particularly for vulnerable populations exposed to toxic substances. Technological advancements have intensified chemical waste generation, but they also offer solutions through green chemistry, recycling, and waste-to-energy innovations. The paper highlights how industries can leverage these technologies to reduce harmful byproducts while maintaining productivity. Ethical frameworks such as utilitarianism, deontology, virtue ethics, the precautionary principle, and the "polluter pays" principle are applied to assess the moral obligations of corporations and governments in managing chemical waste responsibly. Current disposal practices including landfilling, incineration, and advanced treatment methods are critically evaluated for their effectiveness and long-term sustainability. The study concludes that balancing technological progress with ethical waste disposal requires a collaborative effort between industries, policymakers, and communities to ensure environmental protection and public health. By integrating ethical principles with innovative waste management strategies, society can move toward a more sustainable future.
Chemical waste disposal, Environmental ethics, Technological progress, Sustainable practices, Public health
The Impact of Organic and Conventional Farming Methods on Soil Physicochemical Composition and Bioaccumulation of Heavy Metals and Arsenic on Pearl Millet (Pennisetumglaucum) Grown in Kaura Namoda, Zamfara State, Nigeria
The impact of organic and conventional farming practices at four planting fields was carried out to determine the effect on physicochemical properties and heavy metal accumulation on Pearl millet. Pearl millet is used for processing of food products such as tuwo, fura, and kunun- zaki, Cuscus, burabusko, thin and thick porridges, alcoholic, non-alcoholic beverages and some winning foods. Food processors and consumers need to understand what they consume when they adopt any farming system for the production of this important crop. This study was carried out between the month of June and September, 2024 (15weeks). Sasakawa planting method was applied in planting the millet seeds and application of organic and inorganic fertilizers. Results obtained were statistically determined using Minitab software in order to arrive at level of significant difference among the parameters. The outcome of the result of physical parameters showed that conventional farming system produced less leaf size in terms of, width (1.3 to 2.25 cm), length (25.00 to 39.60cm), stalk length (49.00 to 77.00cm) and seed weight (10.50 to12.22g) compared to those of organic farming system. The result of proximate analysis showed that there was significant variation on ash, protein and carbohydrate content of conventional farming seeds (P ≤ 0.05) when compared with organic farming. Heavy metals analysis in pearl millet seeds indicate that the use of synthetic fertilizer did not lead to seed contamination with heavy metals, phosphorus, nitrogen and iron containing compounds. The outcome of this study shows that the type of farming system did not significantly (p ≤ 0.05) affects the quality and safety of the agricultural soil and plant seeds during practices. Farmers can leverage on this data to improve on conventional and organic system for better production of pearl millet to improve food security.
Trends in the Application of Blockchain Technology to the Internet of Medical Things (IoMT)
The integration of blockchain technology with the Internet of Medical Things (IoMT) has seen many changes and evolutions, with the last couple of years really witnessing an upward trajectory in growth and innovation. This paper offers a detailed review of the most recent trends in this area, with a focus on how blockchain technology is solving some of the long-standing problems of IoMT like data privacy, interoperability, security, and scalability. Some key trends discussed are lightweight blockchain architecture, integration of federated learning with blockchain, off-chain and distributed storage systems based on IPFS, and emerging post-quantum cryptographic tools for enhanced security. The review also captures current research trends in academia and industry, including healthcare edge computing, energy-efficient consensus, and smart contract automation. This paper offers a timeline for development, thematic analysis of trends, and areas for productive investigation into the realization of secure, scalable, and interoperable IoMT systems.
Blockchain, Internet of Medical Things, IoMT Security, Federated Learning, Smart Contracts, Post-Quantum Cryptography
Leke Joseph Oloruntoba Leke Joseph Oloruntoba
Gravimetric Performance Evaluation of Wood Vinegar Liquor and Wood Vinegar Liquor Nano-fluid Corrosion Inhibitors
This research investigated the performances of Wood Vinegar Liquor (WVL) and Wood Vinegar Liquor Nanofluid (WVL+NP) as corrosion inhibitors on micro-alloyed steel immersed in 1.0 M HCl solution. Gravimetric method was used to determine effectiveness of the inhibitors by monitoring corrosion rates and evaluating inhibition efficiencies of inhibited and control (1.0 M HCl) solutions. The micro-alloyed steel samples were immersed in 1.0 M HCl, WVL-inhibited 1.0 M HCl and WVL+NP-inhibited 1.0 M HCl solutions respectively, for periods ranging 24 – 168 hours at a step of 24 hours. Results obtained indicates that from 24 - 72 hours, samples immersed in 1.0 M HCl, WVL-inhibited 1.0 M HCl and WVL+NP-inhibited 1.0 M HCl solutions experienced rises in corrosion rates, for which samples immersed in 1.0 M HCl recorded highest corrosion rate values within these periods. However, samples immersed in 1.0 M HCl showed decline in corrosion rate after reaching a maximum of 36.19 mmpy at 72 hours, to 12.29 mmpy at 168 hours. Whereas, Samples immersed in WVL-inhibited 1.0 M HCl and WVL+NP-inhibited 1.0 M HCl solutions continued to experience increased corrosion rates beyond 72 hours, attaining their maximum values of 30.44 and 30.79 mmpy respectively at 96 hours. Corrosion inhibition efficiencies evaluated showed that WVL inhibited corrosion of micro-alloyed steel in 1.0 M HCl by recording higher values in the range of 53.85 – 12.47% between 24 – 96 hours of immersion. When these inhibitors are used for period beyond 96 hours, they are like to worsen corrosive effect of 1.0 M HCl.
Nanofluid, Wood vinegar Liquor, Corrosion Inhibition, Weight Loss, Gasification Bye-product
Compressive Strength of Marketed Hollow Blocks Produced in Sokoto, Nigeria
This paper investigates the strength characteristics of hollow blocks in the Sokoto State metropolis. Five commercial block industries were selected, and soil samples were obtained from each manufacturer and transported to the Civil Engineering Laboratory at Umaru Ali Shinkafi Polytechnic, Sokoto. Compressive strength tests were conducted on the blocks, and particle size distribution tests were conducted on the soil samples. The test results show that the aggregates used are good for block making. The blocks produced were found to be unsuitable for use as either load-bearing or non-load-bearing walls as their strength ranges fall below the minimum values of between 2.5 N/mm2 and 3.45 N/mm2 as recommended by NIS 87:2000. Proper selection of constituent materials and curing were suggested to improve the quality of hollow blocks.
Light Gradient Boosting Machine (LGBM) for Credit Card Fraud Detection in Financial Institution
The upsurge in fraudulent credit card deed is raising rancorous alarms on daily basis; as its globally causing multi-billion dollars’ losses and instigates derogatory imprint which affects both financial institutions and customers. To these effects, many methods have been offered by scholars in diverse applications for curbs; that yielded near perfection results. Due to Machine learning (ML) approaches challenges and imbalance class distribution in most dataset imbibed for credit card fraud detection. The approach pervades over fitting and under-fitting results, that leads to poor generalization of outcomes. Because the classifier tends to predicts only majority class. To address this issue, an optimized performance metrics designed to handle data balancing either by using re-sampling methods or data augmentation approach is seemly. In this paper, accuracy score, Matthews’s correlation coefficient (MCC), Cohen’s Kappa and F1-Scoreareselected as the evaluation metrics to analyze the outcomes of four distinct ML model classifiers of LR, RF, Isolation Forest, and three DL models of MLP, ANN and CNN in addition to the proposed LGBM organized for credit card fraud detection. Two experiments were steered in this study on the imbalance kaggle dataset utilized. While, exploiting the Google Colab integrated Jupyter notebook cloud infrastructure as the program development settings with Python programming language for modelling. The foremost experiment is on baseline model probing LR and RF. The RF model excel in performance against the LR model was selected for the second experiment on balancing class models absorbing SMOTE oversampling methods. It is inferred that the proposed LGBM offered surpass performance across seven aspects of the evaluation metrics; out of the eleven instances deployed. The model renders an accuracy scores of (96%), least error rate of (0.4%), Recall (95%), prevalence (47%), Cohen Kappa (45%), F1-score (96%) and MCC (93%) against other ML and DL models betrothed respectively.
Development of a Multifunctional Oven using Agricultural Waste as a Heat Source
This study was conducted to design, manufacture, and evaluate the performance of a multifunctional oven that uses agricultural waste combustion as a heat source for drying, smoking agricultural products, and baking bread. The machine was developed and built to properly dry pepper, smoke fish, bake bread, and grill with locally accessible materials. The machine described comprises an agricultural waste combustion chamber designed specifically for burning agricultural waste material. This is accompanied by a drying chamber dedicated to the drying of biomaterials. The two chambers are connected by a heat transfer duct, which serves the purpose of transferring the heat generated during the combustion process. The utilization of agricultural waste material as a fuel source is particularly advantageous due to its widespread availability in many regions. This not only presents an opportunity to reduce environmental pollution but also provides a cost-effective and sustainable solution for farmers. By repurposing agricultural waste in this manner, the machine offers a practical and environmentally friendly approach to addressing the energy needs of agricultural operations. The fabrication process entails the meticulous design and construction of a combustion chamber specifically engineered to effectively burn agricultural waste. This combustion process is meticulously calibrated to generate sufficient heat to facilitate the drying of pepper, smoking of fish, and baking of bread. The performance of the dryer machine is rigorously evaluated, with a keen focus on its drying rate, drying capacity, and affordability. Through this comprehensive evaluation, recommendations for potential improvements are meticulously outlined, with a view towards enhancing the overall efficiency and effectiveness of the system. The obtained drying rate of 90.9% and drying capacity of 727 kg/day at a temperature of 150°C, along with a baking capacity for 480 kg of dough at 180°C for 20 minutes, demonstrate the promising potential of this new technology. The development of this equipment represents a significant advancement in ensuring food security and economic stability in a sustainable and cost-effective manner. Furthermore, it has the capability to address the challenges of nutritional loss and high energy consumption commonly associated with most drying technologies. This innovation holds great promise for enhancing food preservation processes and contributing to the overall efficiency and sustainability of food production.
Internet of Thing (IoT) Privacy and Security Issues
The Internet of Things (IoT) is a network that connects various devices to the Internet using specific protocols, allowing for the exchange of information and communication to enable smart functionalities such as recognition, positioning, tracing, monitoring, and administration. This paper provides a brief overview of IoT, its enabling technologies, architecture, characteristics, security threats, potential remedies, and applications. It also discusses the functional view of IoT and future challenges. The main advantage of IoT is its significant impact in creating a new dimension in the world. Key features necessary for implementing a large-scale IoT include low-cost sensors, high-speed and error-tolerant data communications, intelligent computations, and a wide range of applications. This research is presented in four main sections: an overview of IoT technology, a summary of related surveys, a review of main IoT applications, and a section on security challenges, potential attacks, and solutions for IoT.
Energy Audit in BUA Cement Plant Okpella Edo State using ETAP and MATLAB Software for Improved Performance
This research work depicts the study of Electrical energy audit analysis of a cement plant, using BUA cement plant Okpella line one as a case study. A survey of single line diagram distribution network of 11/0.415kv was analyzed using Electrical Transient Analyzer Program ETAP 12.6 and MATLAB Simulink/PSAT R2018a software, for modeling and simulation to perform a load flow analysis so as to provide optimal voltage profile for the purpose of optimizing energy efficiency, minimizing reactive losses, and to improve the Power Factor to a desired value. The sizes of the capacitor bank required to compensate the load reactive power was analyzed. The critical voltage Buses on the feeders were improved with placement of shunt capacitor using optimal placement capacitor tool in ETAP 12.6 and MATLAB Simulink/PSAT software. The application of ETAP and MATLAB Simulink/PSAT// software with the integration of shunt capacitor improved the real power and reactive power from 0.374476 p.u to 0.431572 p.u and 0.223837 p.u to 0.327201 p.u respectively. It is shown that there is a great reduction of power losses along the lines with the network compensation. This reduced the total amount of losses on the lines from (0.00276+ j0.00763) p.u to (0.0023+ j0.0035) p.u. The distribution system achieved an optimized power system that becomes reliable sustainable for production in BUA cement plant. The total optimal sizing capacitor bank with the capacitor of 909KVAR was used to compensate the entire network to improve the condition of the 11kv distribution network of BUA cement plant Okpella. The simulation results obtained gave a 0% violation on both Buses profile.
Chemical, Ultimate and Proximate Analyses of Mixed Wood Sawdust from Igbinedion Sawmill, Okada, Edo State
Sawdust, also known as wood dust, is a significant by-product of the wood processing industry. While it has the potential to cause environmental pollution, it also holds practical value. In the context of modern interest, sawdust, as a form of biomass, can be converted into clean fuels, thereby mitigating environmental pollution. Its utilization as a feed in thermochemical processes not only addresses waste management concerns but also contributes to the improvement of the country’s energy-mix. The characteristics of the biomass play a crucial role in its conversion into bio-energy. Through biomass analyses, we can enhance our understanding of the economic and environmental implications of the biomass conversion process. Proximate analysis reveals moisture, ash, volatile matter, and fixed carbon contents of 6.93%, 2.03%, 38.5%, and 52.54% by weight, respectively. Ultimate analysis indicates carbon, oxygen, nitrogen, hydrogen, and sulfur contents of 61.5%, 29.52%, 0.67%, 7.68%, and 0.63% by weight, respectively. Furthermore, chemical analysis demonstrates cellulose, hemicellulose, and lignin contents of 68.7%, 27.3%, and 4.0% by weight, respectively.
Effects of Precipitation Hardening Heat Treatment on Mechanical Properties of Recycled Aluminium, Silicon Alloy
The objective of this research was to determine the effect of precipitation heat treatment on mechanical properties of recycled Aluminium- Silicon alloy. Aluminium block engine scrap was melted and cast via sand casting method the samples were machined into standard specifications of hardness, tensile and impact with lathe machine. The samples where solution treated at 500°C, 515°Cand 530°C in a heat treatment furnace to obtain solid solution and quenched in water at room temperature. The samples where aged at 200°C, 175°C and 150°C for 3 hours each to obtain precipitation hardening. The samples where then subjected to tensile strength test, impact test and hardness test before and after heat treatment to study the effect of precipitation hardening heat treatment on the mechanical properties. From the results of the control sample, solution temperatures and ageing temperatures, the control sample has a UTS, impact energy, and Vickers hardness of (135.36 Mpa, 1.04J, and 96.9 HV) respectively, and at 500°C &200°C (180.32Mpa, 1.04J and 130.7HV) respectively, at 515°C &175°C (201.18Mpa, 1.13J and 154.6HV) respectively and at 530°C &150°C (185.19Mpa, 1.08J and 113.2HV) respectively. It was observed an increase in the tensile strength with UTS of 201.18Mpa, impact energy of 1.13Joule and hardness of 154.6 HV values. This shows that 175°C ageing temperature has the best evaluation of tensile strength, impact strength and hardness at room temperature.