International Scientific and Academic Research Publisher

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.

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.

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 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 work proposes the implementation of an intelligent system based on Wireless Sensor Networks (WSN) and the Internet of Things (IoT) for real-time water consumption monitoring using a digital water meter. The research is framed within a qualitative, empirical, and descriptive approach, aiming to analyze the potential of emerging technologies in improving water resource management, reducing losses, and increasing the efficiency of measurement processes. The proposed system uses the Arduino Uno microcontroller as a processing unit, due to its accessibility, flexibility, low cost, and compatibility with different computing platforms, including Windows and Linux operating systems. The solution aims to overcome limitations of traditional measurement models, such as reading errors, malfunctions, and equipment inaccuracies, allowing for the continuous collection, processing, and transmission of data on water consumption. The integration between IoT, smart sensors, and digital water meters represents an innovative alternative to support decision-making, promote the sustainable use of water, and contribute to the digital transformation of water supply systems.