Engineering Headway

Preface

Wed, 11 Mar 2026 00:00:00 +0100

Publication date: 11 March 2026
Source: Engineering Headway Vol. 37

Analysis of Aircraft Accidents and Serious Incidents in Nigeria (2000-2020): Causes, Consequences and Safety Recommendations

Wed, 11 Mar 2026 00:00:00 +0100

Publication date: 11 March 2026
Source: Engineering Headway Vol. 37
Author(s): Abdussalam El-Suleiman, Kofoworola Deborah Asabi, Muyideen Omuya Momoh, Mohammed Shariff Lawal, Mathias Usman Bonet, Ameer Mohammed
This research aimed to analyze aircraft incidents and accidents in Nigeria during the specified period, identify and categorize their attributes, delineate trends, and evaluate preventive recommendations. Data were collected from the Nigerian Safety Investigation Bureau (NSIB) and the Aviation Safety Network (ASN). The Key findings were as follows: accidents accounted for 38% of recorded occurrences, while serious incidents comprised 62%. A total of 613 fatalities were recorded. Fixed-wing aircraft were involved in 79% of occurrences, with rotary-wing aircraft accounting for 21%. The landing phase was the most occurrence-prone, and passenger flights had the highest number of recorded incidents. Non-fatal occurrences were more common than fatal ones, with human and organizational factors being predominant causes. Lagos, Abuja, Port Harcourt, and Zaria were identified as high-frequency locations. Most recommendations focused on improving organizational procedures and regulations.

An Investigation of Airport Apron Capacity and its Impact on Aircraft Operations at Nnamdi Azikiwe International Airport

Wed, 11 Mar 2026 00:00:00 +0100

Publication date: 11 March 2026
Source: Engineering Headway Vol. 37
Author(s): Martins Dele Ivienagbor, Temitayo S. Ogedengbe
This study analyzed apron congestion and operational inefficiencies at Nnamdi Azikiwe International Airport (NAIA), Abuja, using regression and time-series analyses of aircraft and passenger movement data. The research aimed to identify factors influencing apron capacity, assess their impact on operations, and propose improvement strategies. Results showed that peak traffic volume strongly contributes to delays (β = 0.608), indicating that increasing aircraft movements strain the limited apron capacity. Domestic narrow-body aircraft significantly reduced delays (p = 0.001) due to quicker turnaround times, while other variables like domestic and international aircraft movements had indirect but notable effects. Time-series analysis (2013–2024) revealed seasonal peaks in January, July, and August, coinciding with heavy travel demand. Projections for 2025–2030 (CAGR = 3.24%) predict worsening congestion without infrastructure upgrades. Operational inefficiencies such as refueling and baggage handling delays further exacerbate congestion. Survey responses indicated that 68.3% of participants reported inadequate apron capacity, and 45% experienced daily congestion. The study concludes that improving efficiency and capacity requires apron expansion, better ground handling coordination, staggered flight scheduling, and enhanced stakeholder collaboration to reduce delays, support future traffic growth, and improve passenger satisfaction.

Unlocking the Potential of Associated Gas in Nigeria: Flaring Reduction, and Monetization

Wed, 11 Mar 2026 00:00:00 +0100

Publication date: 11 March 2026
Source: Engineering Headway Vol. 37
Author(s): Ajiri Otedheke, Wandi Blaise Kumfa, Abdullahi Suleiman Bah Gimba, Valentine Obumeyan, Blessing Alade, Imeh Onukak
Nigeria remains one of the world’s largest Gas flaring countries, with over 2.5 billion standard cubic feet of associated Gas flared daily, resulting in significant economic loss and severe environmental and health impacts. This paper critically examines Nigeria’s associated Gas landscape, emphasizing the environmental consequences of flaring and the lost economic opportunities from unutilized Gas. It identifies key barriers to effective Gas utilization, including inadequate infrastructure, policy enforcement weaknesses, technological and financial constraints. The study further evaluates emerging monetization pathways such as Floating Liquefied Natural Gas (FLNG), mini-LNG, Compressed Natural Gas (CNG) for transportation, and Gas-to-Liquids (GTL), highlighting their potential to transform Nigeria’s Gas sector. Finally, policy recommendations and the roles of diverse stakeholders are proposed to accelerate associated Gas commercialization, reduce flaring, and support Nigeria’s energy transition goals. By addressing these challenges with integrated solutions, Nigeria can convert its associated Gas liabilities into valuable assets, fostering sustainable development and environmental stewardship.

An Integrated Socioeconomic and Quality Assessment of Water Security in Chanchaga, Niger State

Wed, 11 Mar 2026 00:00:00 +0100

Publication date: 11 March 2026
Source: Engineering Headway Vol. 37
Author(s): Ibrahim Wali, Temitayo S. Ogedengbe, Ifeyinwa Obianyo
The study investigates urban water security in Chanchaga Local Government Area, Niger State, Nigeria, focusing on how household socioeconomic factors influence water accessibility and management. Using both laboratory analyses and structured surveys, the research found that water sources in the area exhibit poor quality, with turbidity, sulfate, nitrate, lead, and cadmium levels exceeding WHO and NSDWQ standards, posing health risks. Survey results from 250 respondents showed that most are young (58% under 30 years), male (75.6%), and educated, yet 82% experience limited water access despite 74% depending on boreholes. A regression analysis (R=0.758, R²=0.534) confirmed that demographic variables significantly affect willingness to pay for improved water services. The study concludes that urgent policy action and infrastructure investment are needed to enhance water quality, distribution, and accessibility in Chanchaga and similar urban areas.

AI-Powered Tools for Legal Research and Case Analysis: An Emperical Impact

Wed, 11 Mar 2026 00:00:00 +0100

Publication date: 11 March 2026
Source: Engineering Headway Vol. 37
Author(s): Donatus O. Njioku, Stanley A. Okolie, Juliet N. Odii, Janfrances Ebere Jibiri, Francisca O. Nwokoma, Somkelechi Emmanuel Ubalatu
Artificial Intelligence (AI) is rapidly transforming numerous sectors, and the legal industry is no exception. This paper explores the significant impact AI has had on legal research and case analysis, highlighting how it enhances efficiency, accuracy, and decision-making processes within the legal domain. Traditional legal research often involves manually sifting through vast databases of legal texts, case laws, and statutes—a time-consuming and sometimes error-prone task. However, AI technologies such as natural language processing (NLP), machine learning algorithms, and predictive analytics are now being employed to automate and streamline these processes. This paper examines the role of AI-powered legal tools like ROSS Intelligence, LexisNexis, and CaseText, which are capable of analyzing case law, identifying relevant precedents, and even predicting case outcomes based on historical data. Moreover, the study investigates how AI assists legal professionals in uncovering patterns, drafting legal documents, and conducting due diligence with increased speed and precision. While the adoption of AI offers several benefits, the paper also considers its limitations, such as concerns about data privacy, the potential for algorithmic bias, and the ethical implications of relying on machine-driven insights in judicial contexts. The research draws upon existing literature, case studies, and expert opinions to present a balanced analysis. It also explores the current trends and future potential of AI in reshaping legal workflows. Ultimately, the findings suggest that while AI will not replace legal professionals, it will significantly augment their capabilities, paving the way for a more data-driven and accessible legal system.

A Hybrid Based CNN-RNN Model for Improved Mining Techniques for Twetter Social Media Data and Analytics

Wed, 11 Mar 2026 00:00:00 +0100

Publication date: 11 March 2026
Source: Engineering Headway Vol. 37
Author(s): Donatus O. Njioku, Juliet N. Odii, Christopher I. Ofoegbu, Francisca O. Nwokoma, Uchenna C. Onyemauche, Innocent Harvey H. Ajunwa
This fast and quick growth of social media platforms have created vast repositories of unstructured data, necessitating advanced techniques to extract actionable insights. This study addresses the challenge of analyzing large-scale social media data by developing and evaluating deep learning models for sentiment classification. A preprocessing pipeline incorporating emoticon replacement, text normalization, tokenization, and stemming was applied to a dataset of 160,000 tweets. Two neural network architectures—a baseline Recurrent Neural Network (RNN) and a hybrid Convolutional Neural Network-Recurrent Neural Network (CNN-RNN)—were trained and compared. The hybrid CNN-RNN model demonstrated superior performance, achieving 76% accuracy, compared to the RNN’s 50%, underscoring the importance of combining local feature extraction with sequential dependency modeling. Temporal and lexical analyses further revealed trends in user engagement and sentiment expression. These findings highlight the effectiveness of hybrid deep learning architectures for social media analytics and provide a framework for future research in real-time sentiment monitoring.

Artificial Intelligence in Gas Operations: Emerging Trends and New Frontiers in Exploration and Production

Wed, 11 Mar 2026 00:00:00 +0100

Publication date: 11 March 2026
Source: Engineering Headway Vol. 37
Author(s): Obumeyan Valentine, Ajiri Otedheke, Abdullahi Suleiman Bah Gimba, Yemisi Chrisiana Alegbe, Ikechukwu Stanley Okafor, Petrus Nzerem
The integration of Artificial Intelligence (AI) into gas exploration and production (E&P) is reshaping the operational landscape by driving efficiency, safety, and sustainability. This review critically examines the applications, impacts, and future prospects of AI in the gas industry. Key AI technologies—such as deep learning, machine learning, computer vision, and natural language processing—have demonstrated significant improvements in seismic data interpretation, reservoir modeling, drilling optimization, predictive maintenance, production enhancement, and environmental compliance. Notably, AI-driven seismic interpretation has reduced analysis time by up to 50%, while predictive maintenance strategies have lowered equipment downtime by 30% and maintenance costs by 25–30%. Reservoir modeling informed by machine learning has led to 15–20% increases in recovery efficiency. Environmental monitoring systems utilizing AI have achieved up to 60% reductions in methane emissions. The paper also discusses industry-wide case studies including BP’s deployment of digital twins in the Khazzan gas fields, Shell’s AI-enhanced subsurface modeling, and Chevron’s predictive maintenance initiatives, all yielding tangible operational gains. Despite these advancements, challenges persist, including data integration complexities, cybersecurity vulnerabilities, and ethical concerns surrounding algorithmic decision-making. The review concludes with strategic recommendations focused on workforce upskilling, data governance, regulatory frameworks, and cross-sector collaboration.

Mobile-Optimised Deep Learning Architecture for Multi-Crop Disease Detection Using CNN and SVM

Wed, 11 Mar 2026 00:00:00 +0100

Publication date: 11 March 2026
Source: Engineering Headway Vol. 37
Author(s): Yusuf Ibrahim, Muhammed Bashir Mu'azu, Mustapha Abdullahi Jimoh, Abdullahi Yunus
In this paper, we propose a deep learning pipeline for real-time crop disease classification on mobile devices. Our system employs a custom Convolutional Neural Network (CNN) trained on publicly available crop disease datasets (Maize, Tomato, Potato, Rice). In addition, two transfer-learning models; ResNet-50 and MobileNet are used as fixed feature extractors, with their output features classified by a multi-class Support Vector Machine (SVM) with Radial Basis Function (RBF) kernel. We compare the models’ performance across all crop datasets and evaluate inference latency and model size. Experimental results show that the ResNet50-SVM hybrid attains near-perfect accuracy (≈100% for Maize, Tomato, Potato; 99.96% for Rice) on plant disease classification, far exceeding both the custom CNN and MobileNet-SVM approaches. The MobileNet-SVM pipeline is notably faster (≈23–66 ms per image) and compact (~8.7 MB) than ResNet50+SVM (≈108–192 ms, ~90 MB), making it well-suited for on-device deployment. The final model is converted to TensorFlow Lite for mobile inference; on a typical smartphone CPU it processes an input image in ~0.15–0.19s on average, enabling practical field use. These results demonstrate an efficient mobile AI solution for crop disease detection that balances accuracy with resource constraints. The proposed system can empower farmers with timely, in-field disease diagnosis, helping to mitigate yield losses and improve crop management through accessible AI-driven tools.

Mirai Botnet DDoS Attack Simulation Using Build Your Own Botnet (BYOB)

Wed, 11 Mar 2026 00:00:00 +0100

Publication date: 11 March 2026
Source: Engineering Headway Vol. 37
Author(s): Stanley Olumayowa Imadu, Braimoh Abdullahi Ikharo, Bello O. Lawal, Sunday Aigbiniode Lawani
The Mirai botnet malware creates a botnet by compromising Internet of Things (IoT) devices, including cameras and digital video recorders that are linked to a network. Individually, IoT devices lack significant processing power; however, when a vast number of these devices are utilized together, it becomes feasible to commence a high-powered attack. Mirai mutations are generated daily and continue to proliferate, inflict damages resulting in distributed denial-of-service (DDoS) attack which maliciously attempt to disrupt a server service or network traffic by flooding the target infrastructure with excessive amount of Internet traffic, using intrusion methods as the original malware is indicative of IoT device vendors’ chronic neglect in applying even basic security practices. To understand how Mirai operates, we used the Build Your Own Botnet (BYOB) variant to simulate a botnet attack. The BYOB source code was setup and deployed in a controlled lab environment to simulate botnet-driven DDoS attacks against our IoT devices communicating over AMQP using RabbitMQ sever. The simulation resulted in memory usage increase from 682M/5.79G before attack to 1.71G/5.79G during attack. This indicates system stress exerted on the IoT ecosystem by bots from the Mirai botnet attack, thereby reducing the performance of the IoT devices and making it unresponsive.

Empirical Data from the Case Study on the State of the Art of Cybercrime in Nigeria

Wed, 11 Mar 2026 00:00:00 +0100

Publication date: 11 March 2026
Source: Engineering Headway Vol. 37
Author(s): Afolabi David Adedoyin, Oluseun Damilola Oyeleke, Olabanji Daniel, Funmilola Ikeolu Fagbola, Lucy Tochukwu Ikonne, Ezekiel Kehinde Adediji
The review of literature reveals cybercrime as one of the fastest-growing global security threats, and in emerging economies like Nigeria, the evolution is intricately configured to the rapid growth of internet accessibility, weak regulatory policies, and socio-economic factors. This review analyses the problem of cybercrime in Nigeria with empirical evidence drawn from a case study that attempts to illustrate the complicated growth pattern of cyberspace in Nigeria. The analysis covers three distinct evolution phases: the initial phase of internet penetration through cybercafés (2000 to 2001) and the proliferation of mobile and GSM technology (2001 to 2011), both precursors to the broadband-enabled digital era, which presumably spans 2011 and onwards. These shifts correspond to changes in the level of sophistication of cybercrime which evolved from simple email hoaxes to identity theft and advanced fraud schemes. While adopting these technologies, other issues such as lack of adequate public awareness, regulatory policies, and limited capacity of cybersecurity approaches are equally pressing. Based on these findings, this research introduced the PRAP (Proactive, Reactive, Active, Persuasive)Framework, which can better contextualize Nigeria's cybersecurity needs. The holistic PRAP framework addresses prevention, incident response, threat assessment, prosecution and adapts to Nigeria's social and technical context. This policy is crucial for public and private sector stakeholders concerned with the fight against cybercrime in developing countries.

An EfficientNet-Based Framework for Real-Time and Reliable Intrusion Detection in Vehicular Networks

Wed, 11 Mar 2026 00:00:00 +0100

Publication date: 11 March 2026
Source: Engineering Headway Vol. 37
Author(s): Idris Shehu Musa, Muhammed Bashir Mu'azu, Yusuf Ibrahim
The Internet of Vehicles (IoV) has transformed transportation through seamless communication and intelligent coordination among connected vehicles. This advancement, however, has introduced a broader spectrum of cyber risks, necessitating intelligent and efficient threat detection strategies. This paper introduces a lightweight intrusion detection framework tailored for connected vehicle ecosystems, employing EfficientNet for deep feature extraction and a Particle Swarm Optimization (PSO)-tuned Random Forest (RF) classifier for classification. Transfer learning was utilized to enhance feature compactness and relevance, while PSO refined the RF parameters to maximize detection accuracy. Experimental validation on two publicly available benchmark datasets demonstrated superior performance, achieving perfect classification on the Car-Hacking dataset and 99.89% accuracy on CICIDS2017. The model also sustained high levels of detection precision, sensitivity, and F1 measure across multiple intrusion categories. With an inference latency of just 0.0173 milliseconds per sample, the system processes over 57,000 flows per second—confirming its viability for deployment in real-time, resource-limited vehicular environments.

Optimized Malware Detection Framework Using Principal Components Analysis

Wed, 11 Mar 2026 00:00:00 +0100

Publication date: 11 March 2026
Source: Engineering Headway Vol. 37
Author(s): Funmilayo Jumoke Akinshola-Awe, Afolayan Obiniyi, Oluwakayode Owa
Software that can damage an information system asset is considered a malware, such information systems have been rendered to several destructive attacks mainly due to the emergence of the Internet. Conventional Antimalware software is not effective at eliminating malware due to its many evasion techniques such as polymorphism and code obfuscation. Antimalware software is ineffectual and defenseless against zero-day attacks as it can only eliminate malware for which it has signatures. K Nearest Neighbor, Decision Tree and Support Vector Machine are some of the leading classifiers that has successfully detect and classify Malware but optimal accuracy of detection has not been achieved, in addition, false positives and false negatives persists because the hyperparameters of these classifiers were not optimized and noise was not filtered out of the datasets using feature selection technique. The aim of this research is to develop an optimized malware detection and classification framework employing Principal Components Analysis to mitigate the curse of dimensionality while utilizing optimal hyperparameters of chosen classifiers to boost accuracy of malware detection and classification as well as reduction of false positives and false negatives. This research employed K Nearest Neighbor, Decision Tree, and Support Vector Machine to detect and classify malware with CICMalmem dataset to train the model. Grid search optimization was combined with K-fold cross-validation to optimize the hyperparameters of the selected classifiers in order to boost the model's performance and achieve high detection accuracy as well as low false positives and low false negatives. Machine learning performance metrics such as the F1 Score, Precision, Recall, and Confusion Matrix were used to evaluate the Research Model. K Nearest Neighbor generated Zero False Positives while KNN, Decision Tree and Support Vector Machine achieved Accuracy of 99%, 98.64, and 100% respectively.

Investigating the Efficacy of Deep Learning Techniques in Detecting Advanced Persistent Threats (APTs)

Wed, 11 Mar 2026 00:00:00 +0100

Publication date: 11 March 2026
Source: Engineering Headway Vol. 37
Author(s): Christopher Ubaka Ebelogu, Israel Musa, Emmanuel Abbah, Donatus Onyedikachi Njoku, Ohunene Rahama Obadaki
Advanced Persistent Threats (APTs) pose significant challenges to cybersecurity due to their stealthy, multi-stage nature. Traditional detection methods struggle to identify such complex behaviors. This study investigates the effectiveness of deep learning (DL) techniques in detecting APTs. Two research questions guide the study: (i) How effective is a custom Transformer-based model compared to existing DL models in detecting APTs? (ii) To what extent have DL models contributed to APT detection between 2020 and 2025? A two-fold methodology was adopted: a meta-analysis of 30 peer-reviewed studies and an empirical evaluation of a Transformer model trained on the CIC-IDS2018 dataset. The model achieved 99.71% accuracy with macro-averaged F1-score, precision, and recall values above 0.70, demonstrating strong overall performance but revealing challenges in classifying rare attack types. The meta-analysis further identified key research gaps, including limited use of multi-class classification, underutilization of Transformers, and a need for real-world datasets. Findings show that Transformer-based architectures are not only viable but good at modeling complex feature dependencies and detecting sophisticated APT behaviours. This research highlights both the strengths and limitations of DL for cybersecurity and suggests future directions for improving robustness and scalability in real-world deployment.

Virtual Reality for Mechanical and Mechatronic Engineering Education in Africa: A Review of Applications and Future Prospects

Wed, 11 Mar 2026 00:00:00 +0100

Publication date: 11 March 2026
Source: Engineering Headway Vol. 37
Author(s): Timothy Adeyi, Samuel Enochoghene, John Adeyemi, Shade Ademokoya, Sunday Jegede, Adrian Cheok
This review investigates the transformative potential of Virtual Reality (VR) in mechanical and mechatronic engineering education within African settings, focusing on its applications in hands – on training, laboratory simulations and expanding access to quality instruction. By analyzing case studies and empirical data, we demonstrate that VR-based training reduces task completion times by 75 % compared to traditional methods while improving spatial understanding, learner engagement and knowledge retention. Key applications include immersive virtual labs for torsion testing, universal testing machines and safety training for high–risk scenarios. Despite challenges such as hardware costs limited infrastructure and curriculum integration, VR offers scalable and inclusive solutions that democratize access to high – quality engineering education. This work highlights VR’s role as a critical enabler of next-generation pedagogy, in resource–constrained environments, urging educators and institutions in African settings to adopt immersive technologies to bridge the gap between theoretical instruction and industry demands.

Enhancing Language Acquisition for Beginners: A Data-Driven Approach

Wed, 11 Mar 2026 00:00:00 +0100

Publication date: 11 March 2026
Source: Engineering Headway Vol. 37
Author(s): Osezua Ejodame, Francis Anyebe Oteikwu, Norman Osa-Uwagbue
This paper examines the integration of Artificial Intelligence (AI) and Machine Learning (ML) tools in language learning for novice learners, with a focus on grammar correction, vocabulary acquisition, speech recognition, and enhancing motivation for continued learning. A survey to justify the selection of AI-based tools such as Large Language Models (LLMs), reinforcement learning, adaptive learning systems, and automatic speech recognition (ASR) was performed. Results indicated that LLMs significantly improved grammar accuracy, while reinforcement learning-based vocabulary tools enhance retention. Transformer-based architectures outperform traditional models in contextual language understanding and speech recognition, reducing word error rates. Additionally, AI-driven chatbots and gamification platforms improved engagement. Despite these advancements, challenges such as AI over-reliance, lack of deep personalization, and accent bias in ASR models remain. The research follows a comparative and mixed-methods research design approach with machine learning-based model evaluation, using structured and unstructured datasets. Model performance will be assessed through accuracy, precision, and recall metrics.

Design and Implementation of a Cooperative Management System

Wed, 11 Mar 2026 00:00:00 +0100

Publication date: 11 March 2026
Source: Engineering Headway Vol. 37
Author(s): Austin Olom Ogar, David Ayomikun Akintunde, Godsfavour Ikoedem Akpan, Miracle Effiong Johnson, Perfect Uganeojo Benjamin-Maji, Malvin Nduka Mpi
Cooperative societies at educational institutions often rely on labor-intensive manual processes that lead to transcription errors, delayed loan processing, and limited member visibility into financial activities. This paper presents the design and implementation of a cross-platform mobile Cooperative Management System (CMS) for Nile University’s cooperative society, developed with Flutter and backed by Firebase services (Authentication, Firestore, Cloud Functions, and Storage). Following the Waterfall model, the development began with a comprehensive requirements analysis to identify core functional modules (member registration, share purchases, savings tracking, loan application and disbursement, notifications) and nonfunctional criteria (scalability, offline-sync, security). System design employed Unified Modelling Language diagrams and wireframes to guide implementation of a responsive UI featuring role-based access control and offline synchronization. The backend leverages Firebase for real-time data updates and integrated notifications. Through functional, performance, and usability testing, the system demonstrated a faster loan-approval cycle and reduced data-entry errors compared to the manual process. The proposed CMS enhances transparency, efficiency, and member engagement, offering a scalable template for similar cooperative societies in educational settings.

Emergent Model for Accelerating University-Industry Collaborations and Enhancing Innovation among Universities in Nigeria

Wed, 11 Mar 2026 00:00:00 +0100

Publication date: 11 March 2026
Source: Engineering Headway Vol. 37
Author(s): Abubakar Yusuf Dutse, Kabiru Ibrahim Musa, Jibrin Mohammed, Mohammed Musa Bayero
University-Industry Collaboration (UIC) is critical for universities to fulfill their role in providing solutions to societal development challenges and generating scientific knowledge, a function that is significantly improved by government participation, as indicated by the traditional triple-helix model. Though, Nigeria currently demonstrates a low level of performance in university-industry research collaborations. Acknowledging the limitations of existing models and the need for a more robust framework, this study adopted a descriptive research design involving primary data collection from respondents across universities in Nigeria. The research comprehensively assessed the constraints inhibiting sustained and robust UIC, the intensity of current UIC activities, the innovativeness of Nigerian universities, and the strategies employed for accelerating UIC and innovation. Based on the analysis of collected data and a synthesis of previous models, this work proposes an improved model that advocates for universities to engage with a broad range of companies, pursue global corporate-sponsored research, establish clear Intellectual Property Rights (IPR) agreements, involve industry experts in research, seek guidance on practical technology application, and integrate new technology-associated skills into curricula. The enhanced model offers a comprehensive framework aimed at improving UIC and innovativeness in Nigeria, addressing the existing fragmented practices and provides a practical guide for stakeholders and policymakers to foster more effective collaborations, thereby contributing to national development.