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Navigating Disruptive Technological innovations in African Libraries: the Adaptive leadership Imperative .
(Lead City Journal of Library and information science, 2026-01-01) Abdulakeem sodeek sulyman; Yakub Olayinka Ahmed,Phd; Olarongbe Agboola Shuaib,Phd; Ozoh Uchenna loveline
Digital Literacy and Reading Skills as Determinants of Digital Reading Behaviour among Library and Information Science Undergraduates in Kwara State,
(Bayero Journal of Library and Information Science,, 2026-04-03) Shuaib A; Maryam Jummai; Uchenna L. Ozoh
Digital reading is gaining prominence among students due to the rapid evolution of digital resources and tools. This study investigated digital literacy and reading skills as determinants of digital reading behaviour among 400-level Library and Information Science undergraduates in three universities in Kwara State, Nigeria. A descriptive correlational survey design was employed, and the entire population of 520 students was studied using total enumeration. Data were collected using a validated, researcher-designed questionnaire with reliability coefficients of 0.848 (digital literacy), 0.717 (reading skills), and 0.832 (digital reading). Out of the distributed instruments, 387 (73.53%) were duly completed and analysed using Pearson Product-Moment Correlation and multiple regression. Findings revealed a very strong, significant positive relationship between digital literacy and digital reading (r = 0.876, p = .000), leading to the rejection of Ho1. Similarly, reading skills showed a strong, significant relationship with digital reading (r = 0.793, p = .000), thereby rejecting Ho2. Jointly, digital literacy and reading skills demonstrated a strong predictive relationship with digital reading (R = 0.876; R² = 0.768), indicating that 76.8% of the variance in digital reading behaviour is explained by the two predictors. The regression model was statistically significant (F = 634.739, p = .000), thus rejecting Ho3. Coefficient results further showed digital literacy as the strongest predictor (B = 1.171, β = .954, p = .000), while reading skills had a non-significant negative contribution (B = –0.128, β = –.085, p = .176). The study concludes that digital literacy is the most critical determinant of digital reading behaviour, underscoring the need for enhanced digital skills training within LIS curricula.
Pebble Morphometric Studies of Part of Bida Formation, Northern Bida Basin, North Central Nigeria
(International Journal of Advances in Scientific Research and Engineering (ijasre), 2020-02-05) Abdullahi M.; Goro A.I; Suleiman T.M; Folorunsho O.W; Abubakar M
The Bida formation exposed in Doko locality, Bida town appeared Massive, poorly sorted with localized sedimentary structures. The formation shows three lithofacies: mf, ssf and sf respectively. The formation grades into more matured upward, the pebbles are found within the sf unit as lenses and as scattered pebbles. Vernier caliper was used to measure the Longest, intermediate and shortest axes that are mutually perpendicular for each pebble. These values were used to compute different morphometric parameters as a single variable including MPSI, OPI, FR, ER, R and CF. These parameters were also used as dependent variables in bivariate plots to characterize the depositional process. The average values of MPSI, OPI, FR, ER, and CF are: 0.64, 3.01,0.41, 0.63, 44.5 and 40.73 respectively. The ternary plot of the ratios of the three-axis showed that most samples fall within Bladed(B) field while some other few on Elongate(E) Field, the plot of MPSI versus OPI, FI versus MPSI and R versus ER. Both independent and dependent parameters showed that the Bida formation was deposited by both river and beach processes. Its therefore deduced that the fluvial deposits were later modified by marine activities, possibly during the transgression period.
Deep Learning enabled electricity consumption prediction system for households in Offa using smart meter Data
(The scale An Interdisciplinary Journal of The ACADEMIC STAFF UNION OF POLYTECHNICS - ZONE D, 2026-05-30) Jimoh, A.A. et. al.
Modern electrical power systems in developing sub-Saharan urban areas face critical structural vulnerabilities, characterized by severe distribution bottlenecks, rampant transformer overloads, and unmanaged demand-side dynamics. This research addresses these acute electrical power system challenges within the municipal grid of Offa, Kwara State, Nigeria, where rapid urbanization, localized commercial expansion, and a dense concentration of academic institutions have induced highly volatile domestic load profiles. To mitigate localized grid instability and optimize distribution asset utilization, this paper proposes a deep learning-enabled residential power consumption prediction system utilizing high-resolution advanced metering infrastructure (AMI) telemetry. The proposed framework integrates time-series electrical feature extraction, multi-layered deep artificial neural network (DNN) architectures, and Gaussian-form probabilistic modeling to deliver robust, multi-step ahead active power (kW) forecasts at the individual household level. By identifying temporal loading signatures and mapping localized power system uncertainties, this interdisciplinary approach bridges power systems engineering, telemetry processing, and artificial intelligence. The resulting predictive model provides distribution network operators (DNOs) with actionable telemetry insights required to execute proactive peak load shedding, prevent distribution transformer failures, and formulate data-driven demand-side management (DSM) strategies tailored to the specific constraints of the Offa municipal grid
Deep Learning enabled electricity consumption prediction system for households in Offa using smart meter Data
(The scale An Interdisciplinary Journal of The ACADEMIC STAFF UNION OF POLYTECHNICS - ZONE D, 2026-05-30) Jimoh, A.A. et. al.
Modern electrical power systems in developing sub-Saharan urban areas face critical structural vulnerabilities, characterized by severe distribution bottlenecks, rampant transformer overloads, and unmanaged demand-side dynamics. This research addresses these acute electrical power system challenges within the municipal grid of Offa, Kwara State, Nigeria, where rapid urbanization, localized commercial expansion, and a dense concentration of academic institutions have induced highly volatile domestic load profiles. To mitigate localized grid instability and optimize distribution asset utilization, this paper proposes a deep learning-enabled residential power consumption prediction system utilizing high-resolution advanced metering infrastructure (AMI) telemetry. The proposed framework integrates time-series electrical feature extraction, multi-layered deep artificial neural network (DNN) architectures, and Gaussian-form probabilistic modeling to deliver robust, multi-step ahead active power (kW) forecasts at the individual household level. By identifying temporal loading signatures and mapping localized power system uncertainties, this interdisciplinary approach bridges power systems engineering, telemetry processing, and artificial intelligence. The resulting predictive model provides distribution network operators (DNOs) with actionable telemetry insights required to execute proactive peak load shedding, prevent distribution transformer failures, and formulate data-driven demand-side management (DSM) strategies tailored to the specific constraints of the Offa municipal grid

The Federal Polytechnic, Offa Institutional Repository

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