http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/5 Books 2026-11-03T05:48:01Z http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/32029 Title: ADAPTIVERANGE-BASEDVOLATILITY MODELLINGOF OIL AND GAS STOCK RETURNS IN NIGERIA Authors: Tanimu, Mohammed; Isah, Audu; Yakubu, Yisa; Adetutu, Olayiwola Matthew Abstract: In this article, a range based volatility model that integrated robust hybrid range-based estimators with exogenous variables was proposed for oil and gas sector. The study employed historical daily data on opening, closing, high, and low prices of stocks from Chevron, Conoil, Oando Plc, and Total Energies, spanning 1 January, 2012 to 12 September, 2025, alongside daily Brent crude oil prices as an exogenous variable. The model and exogenous influences on volatility was examined. The study showed that Range-Based Generalised Autoregressive Conditional Heteroskedasticity with Exogenous variable (RB-GARCH-X) models, especially the non-zero drift version, captured volatility in Nigeria’s oil and gas sector effectively, while the adaptive RB-GARCH-X model that adapt to both drift-free and drift-present conditions performed best for highly volatile stocks. The results also revealed that range-based estimation techniques improved model robustness against microstructure noise and estimation errors in both drift-free and drift-present conditions. Parameter estimates indicated strong volatility persistence (β: 0.71–0.99), moderate short-term effects (α: 0.03–0.21), and significant influence of crude oil prices (γ: ≈2.0E-07 to -1.25E-07). The adaptive model provided a more effective balance between short- and long-term effects, demonstrating its robustness for stocks characterised by heightened instability like the Nigeria Oil and Gas stock. Description: The study developed a range-based volatility model incorporating robust hybrid estimators and crude oil prices as an exogenous variable to analyse stock market volatility in Nigeria’s oil and gas sector from 2012 to 2025. The analysis covered Chevron, Conoil, Oando Plc, and TotalEnergies, using daily opening, closing, high, and low stock prices alongside Brent crude oil prices. The results showed that the Range-Based GARCH-X model, particularly its adaptive version, effectively captured volatility and performed well under both drift-free and non-zero drift conditions. The findings revealed strong volatility persistence, moderate short-term effects, and a significant influence of crude oil prices on stock volatility. The study concluded that the adaptive RB-GARCH-X model provides a robust and reliable approach for volatility estimation and risk management in Nigeria’s oil and gas stock market. 2025-08-09T00:00:00Z http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/32028 Title: On Retention and Growth Patterns of Abuja Electricity Distribution Company Metered Customers through Times Series Forecasting. Authors: ADETUTU, OLAYIWOLA MATTHEW Abstract: This study investigates the long term growth dynamics of metered customers within the Abuja Electricity Distribution Company (AEDC) from 2015 to the first quarter of 2024, using descriptive analytics, trend modelling, and time series forecasting. The descriptive analysis shows a sustained upward trajectory in metering coverage, characterized by policy driven surges during key roll out periods. A linear trend model reveals a highly significant positive slope β = 4, 946.69 and strong explanatory power (R2 = 0.94), confirming that time is a dominant driver of metering expansion. Annual retention rates further highlight substantial growth in several years, with values exceeding 110% during intensive metering campaigns. In order to forecast future metering performance, multiple ARIMA models were compared, and ARIMA (1, 1, 1) emerged as the optimal specification based on lowest AIC, BIC, RMSE, and MAPE values. The model’s Ljung-Box p-value of 1 indicates that residual autocorrelation is fully addressed which validate its suitability for forecasting. Projections for 2024–2028 indicate continued growth under current operational and policy conditions. Overall, the findings demonstrate strong historical progress and provide a statistically sound basis for future planning and metering investment strategies within AEDC. Description: he study examines the retention and growth patterns of metered customers of the Abuja Electricity Distribution Company (AEDC) from 2015 to the first quarter of 2024 using descriptive statistics, trend analysis, and time series forecasting. The results reveal a sustained increase in metered customers, with the linear trend model showing a significant positive growth rate of 4,946.69 customers per time period and a strong explanatory power of 94%. Among the ARIMA models evaluated, ARIMA (1,1,1) was identified as the most suitable forecasting model based on its information criteria and prediction error measures. The model projects continued growth in metered customers from 2024 to 2028, while its Ljung–Box test result indicates no evidence of residual autocorrelation. The study concludes that the findings provide a reliable statistical foundation for AEDC's future metering investment, operational planning, and electricity service expansion strategies. 2026-03-18T00:00:00Z http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/32000 Title: Revolutionizing Library Services: Exploring the Potential of Artificial Intelligence-Based Research Tools Authors: Muhammed, Musa; Madu, Everest C.; Abduldayan, Fatimah Jibril; Aminu, Enesi F. Abstract: The advent of ICTs has revolutionised nd the rise of artificial intelligence (AI) is a tension of this trend. This review article analyses how AI-based tools are changing the standard for effective and efficient library service delivery, examining their capabilities to improve user experience and operational efficiency. The article categorises Al- based research tools by function: topic discovery (Perplexity, Semantic Scholar, Elicit, ChatGPT), writing and proofreading (Grammarly, Ginger AI, Word Wordvice AI, ProWritingAid) research conduct (Aithor, Google Bard, Research Rabbit, Connected Papers). It highlights benefits including improved user satisfaction, efficiency and decision-making, alongside challenges such as limited awareness and skills, data-quality concerns, ethical and privacy issues, and resistance to change among librarians and users. The study recommends that academic libraries embrace AI as a vital service-delivery tool, supported by staff training and development. 2026-08-10T00:00:00Z http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/31904 Title: Digital Molecular Magnetic Resonance Imaging Authors: Awojoyogbe, Bamidele; Dada, Michael Abstract: This book pushes the limits of conventional MRI visualization methods by completely changing the medical imaging landscape and leads to innovations that will help patients and healthcare providers alike. It enhances the capabilities of MRI anatomical visualization to a level that has never before been possible for researchers and clinicians. The computational and digital algorithms developed can enable a more thorough understanding of the intricate structures found within the human body, surpassing the constraints of traditional 2D methods. The Physics-informed Neural Networks as presented can enhance three-dimensional rendering for deeper understanding of the spatial relationships and subtle abnormalities of anatomical features and sets the stage for upcoming advancements that could impact a wider range of digital heath modalities. This book opens the door to ultra-powerful digital molecular MRI powered by quantum computing that can perform calculations that would take supercomputers millions of years. Description: https://link.springer.com/book/10.1007/978-981-97-6370-2 2024-08-24T00:00:00Z