Engineering Headway Vol. 37

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.