This study evaluated the effectiveness of the ICT-LEAP (ICT-Led Enhancement and Advancement Program) intervention designed to improve the ICT competencies of Technology and Livelihood Education (TLE) teachers at Agusan National High School. Anchored on curriculum alignment and innovation goals, the program began with a training needs assessment, focus group discussions, and identification of ICT skills gaps, which revealed limited experience in digital content creation and productivity tools among respondents. Using a quantitative descriptive-evaluative research design, the study involved ten TLE teachers who underwent structured ICT-LEAP training modules. Data were collected through pre-test and post-test evaluation and training surveys. Statistical analysis was employed to assess competency development. Findings revealed a significant improvement in ICT competency levels after the intervention. Mean scores increased by 28.3 with high statistical significance (p < 0.001), and a very large effect size (Cohen’s d = 4.78) was recorded. Respondents progressed from intermediate to advanced competency levels. Additionally, teacher satisfaction was exceptionally high, with 100% completion and a 5/5 satisfaction rating. Qualitative feedback highlighted increased confidence in using digital tools such as Canva and improved instructional readiness. The results demonstrate that targeted, needs-based ICT training programs effectively enhance teacher competencies, instructional preparedness, and technology integration in TLE education. The study recommends program expansion, institutional partnerships, continued module development, and ICT-related initiatives to sustain impact and promote innovation in teaching practices.

This study proposes a Student-t Mixture Density Network (St-MDN) for multivariate probabilistic density forecasting of Ghana Cedi exchange rates against the US Dollar, British Pound, and Euro. Using 222 monthly observations spanning January 2005 to June 2025, we demonstrate that the monthly log-returns of all three currency pairs exhibit severe leptokurtosis (excess kurtosis 9.55–14.65) and significant negative skewness, rendering the standard Gaussian assumption untenable. The St-MDN maps eleven macroeconomic covariates, including inflation, money supply (M2), interest rates, and commodity prices through a two-hidden-layer feedforward network to the parameters of a K-component mixture of Student-t distributions, jointly estimating location, scale, and degrees-of-freedom parameters for each component. Maximum likelihood estimation under the Student-t mixture likelihood yields AIC improvements of 101.7–284.8 units over the Gaussian benchmark. Out-of-sample evaluation confirms that the St-MDN delivers superior probabilistic calibration, with CRPS reductions of 28–39% relative to Gaussian mixture networks, and empirical 90% prediction interval coverage of 90.8–91.4%, near the nominal level. Feature importance analysis identifies M2 growth, the interest rate differential, and inflation as the dominant drivers of exchange rate dynamics. These findings establish the St-MDN as a principled and practically superior framework for exchange rate density forecasting in emerging market economies characterized by heavy-tailed financial data.

This study models a distressed bank under Central Bank of Nigeria (CBN) regulatory pressure where baseline valuation decays exponentially with multiplicative seasonality effect. Three analytical paths and theoretical expected means were simulated showing up and down horizontal trend lines instead of exponential growth. Sensitivity analysis for volatility and drift shows both plots look alike with up and down horizontal movements, unlike growth banks where volatility dominates. Results show distressed pattern trapped sideways oscillation with slow decay, indicating that both growth rate and volatility cannot rescue the bank under regulatory overhang. Implication is range-bound trading rather than buy-and-hold. This system is valuable for regulators and risk managers.

Nigeria's textile and apparel industries possess employment and value-chain potential for youths, yet the sector continues to develop weak industrial capacity, face imported competition with foreign firms, finance constraints, skills mismatches, infrastructure deficits, also limited integration between creative talent youth and productive manufacturing textile industries. This study examines how the creative capabilities of Nigerian youths can be linked to textile and fashion productions, digital commerce, design, cultural heritage, circular production, as well as export-oriented enterprises development in Nigeria. The study adopted an empirical secondary-data research design, combining labour-market evidence, trade statistics, policy documents, and documented programme result from the National Bureau of Statistics, International Labour Organization, Central Bank of Nigeria, World Bank, African Development Bank, UN Trade and Development, as well as World Trade Organization-related trade databases. Evidence is analysed thematically and comparatively to identify employment opportunities, binding constraints, and policy mechanisms capable of converting youth creativity into productive textile-sector growth. Findings indicated that the opportunity is not limited to conventional factory employment; it lies in an integrated youth textile ecosystem covering design, garment production, textile surface development, digital pattern-making, branding, merchandising, e-commerce, content creation, repair, upcycling, recycling, logistics, quality assurance, and export services. The study argues that skills training alone is insufficient without access to finance, productive equipment, reliable power, market information, industrial clusters, mentorship, intellectual-property protection and domestic and regional market access. It recommends a youth-focused textile industrial strategy linking technical and creative education as included in the curriculum of some universities and Polytechnics offering textile courses in Nigeria, with enterprise finance, apprenticeships, local procurement, cotton-to-cloth development, circular production, and AfCFTA-oriented export support. Such an approach can strengthen decent employment, entrepreneurship, industrial competitiveness, and inclusive economic growth in Nigeria.

Artificial Intelligence (AI), Data Science and Automation are transforming organisations, labour markets and public administration, yet their adoption raises interrelated concerns about ethics, data governance, privacy, cybersecurity and workforce displacement. This paper examines these dimensions together and proposes a framework for responsible, human-centered AI-driven automation, with particular attention to Nigeria. The study combines an integrative review of multidisciplinary literature with qualitative document analysis of legal and policy instruments and a comparative analysis of documented cases across ten jurisdictions: Nigeria, Ghana, Kenya, Rwanda, the United States, the United Kingdom, Spain, France, Germany and China. AI principles have converged internationally but remain difficult to operationalise; data governance is the foundation on which fairness, privacy and security depend; AI introduces distinctive attack surfaces such as data poisoning, model extraction and membership inference; and automation transforms tasks more often than it eliminates whole occupations, making upskilling decisive. Nigeria exhibits rapid deployment of data-intensive systems alongside a governance lag, although regulatory enforcement under the Nigeria Data Protection Act 2023 is strengthening. The paper proposes the Integrated Responsible Automation Framework (IRAF), which positions data governance as the foundation for three pillars (responsible and ethical AI, privacy and security by design, and workforce and organisational readiness), maps controls to the AI lifecycle, and derives four testable propositions. It offers actionable guidance for policymakers, organisations and educators, especially in emerging economies.

For more than seven decades, the dominant ambition of computing research has been artificial intelligence (AI): machines that perform tasks which ordinarily require human intelligence. The rapid progress of deep learning, transformer-based foundation models and large-scale reinforcement learning since 2012 has moved that ambition from laboratory curiosity to everyday infrastructure. This paper argues that the next technological turn of events is not "more AI" in the incremental sense, but the possible emergence of superintelligence (ASI): systems whose cognitive performance greatly exceeds that of the best human minds across virtually all domains of interest. Using an integrative literature review of peer-reviewed scholarship, expert surveys, technical reports and policy instruments published between 1950 and 2025, the paper (i) clarifies the conceptual distinction between narrow AI, artificial general intelligence (AGI) and superintelligence; (ii) traces the intellectual history of the idea from Turing and Good to contemporary scaling research; (iii) analyses six plausible pathways to superintelligence, namely compute and data scaling, algorithmic and architectural innovation, recursive self-improvement, whole brain emulation, biological and neural-interface enhancement, and networked collective intelligence; (iv) evaluates the evidence and counter-evidence on timelines and "takeoff" speeds; (v) maps the transformative opportunities and the systemic and existential risks; and (vi) reviews emerging governance responses at global, continental and national levels, with particular attention to Africa and Nigeria. The review finds that superintelligence differs from present-day AI not merely in degree but in kind: it shifts the central problem from building capable tools to aligning and governing autonomous agents more capable than their designers. The paper proposes a Superintelligence Readiness and Governance Framework (SRGF) built on five interlocking layers: foundational capacity, technical alignment and safety, institutional governance, socio-economic transition, and international coordination. It concludes with recommendations for researchers, policymakers, industry and developing economies, and identifies priority areas for future research.

The study area is part of the Niger Delta, comprising of Benin and its adjoining areas situated in the Gulf of Guinea. The area is located within latitudes 5°30′- 6°30′ N and longitudes 4°30′- 5°30′ E. Structural interpretation of the Aeromagnetic and Landsat Data of Benin and its Environs, Southern Nigeria was carried out to determine depth to the magnetic basement, delineate the basement morphology and relief, and delineate the structural features associated with the basin and to infer the effects of such structures to the general tectonic history and basin geodynamics of the study area. The aeromagnetic and Landsat data were subjected to various image and data enhancement and transformation routines using ILWIS 3.2 Academic and Erdas imaging softwares. Results of the study revealed that the dominant structural trend direction of the study area is in the NE-SW direction. Other lineament trend directions are in the NW-SE, N-S and E-W directions. The drainage pattern is dendritic which is indicative of lithological, structural and topographic differences and also indicative of alluvial rocks, which is typical of the geology of the area that consists mainly of sedimentary rocks. The lineament density map revealed the presence of high-density fracture zones around Atyere and Salawa-Odo areas. Results of the 2-D spectral analysis revealed a two-layer depth model.  The shallower magnetic source (D₁) has an average depth of 0.37 km while the deeper magnetic source bodies (D₂) have an average depth of 3.47 km. The shallower magnetic anomalies is as a result of basement rocks which intruded into the sedimentary rocks while, the deeper magnetic anomalies is associated with intra-basement discontinuities like faults. The average sediment thickness of 3.47 km obtained in this study is significant for accumulation and entrapment of hydrocarbons. However, there seem to be a correlation of the interpreted lineaments with areas of primary mineralization believed to be in the study area.

Coal resources in southeastern Nigeria occur across contrasting geological settings and exhibit substantial variation in rank, petrographic composition, mineral matter, moisture, volatile matter, ash yield and energy potential. This study presents an integrated geological, mineralogical and energy assessment of Ihioma lignite in the Orlu area of Imo State and the better-established sub-bituminous coal resources of the Enugu area, southeastern Nigeria. The assessment combines published geological and stratigraphic evidence with reported proximate, ultimate and mineralogical data for Ihioma lignite and published petrographic and physicochemical information for Enugu coal. Ihioma occurs within the Tertiary sedimentary succession of the Anambra Basin and represents a lower-rank coal resource than the Lower Maastrichtian coal of the Mamu Formation. Published analysis of an Ihioma lignite sample reported moisture of 6.0%, ash of 4.0%, volatile matter of 38.5% and fixed carbon of 51.5%, together with reported carbon, hydrogen, nitrogen and oxygen concentrations of 64.6%, 4.0%, 1.064% and 2.0%, respectively. Hematite was identified as the principal mineral phase, with quartz and albite among the subordinate minerals. The reported low sulphur content is potentially advantageous for thermal utilization, although broader sampling is required before regional conclusions can be made. Enugu coal, by contrast, belongs predominantly to the sub-bituminous range of the Lower Maastrichtian Mamu Formation. Earlier rank studies identified a progressive decrease in rank from Enugu toward northern coalfields, with Enugu samples reaching sub-bituminous A rank. Published studies further indicate that the Mamu Formation accumulated in a paralic to deltaic depositional system characterised by repeated alternation of sandstone, siltstone, mudstone, carbonaceous shale and coal. The contrasting rank and mineralogical characteristics of Ihioma and Enugu demonstrate that southeastern Nigerian coal resources cannot be treated as a homogeneous fuel. Ihioma lignite may have potential for low- to medium-temperature thermal applications, briquetting, industrial heat and selected non-fuel uses, whereas the higher-rank Enugu coal presents a stronger case for conventional thermal and metallurgical evaluation. Nevertheless, commercial development should be preceded by systematic drilling, proximate and ultimate analyses, petrography, trace-element characterisation, calorific-value determination, ash-fusion testing, washability studies and environmental baseline assessment. The study concludes that the greatest opportunity for southeastern Nigerian coal lies in resource-specific beneficiation and diversified utilization rather than indiscriminate combustion.