Kwara State Polytechnic

In this article, we discuss three new extensions of the Leonardo numbers in a generalized way, which we call the generalized Silver, Bronze, and Copper Leonardo numbers that converge to the Silver, Bronze, and Copper ratios, unifying existing metallic Leonardo sequences. We investigate their fundamental algebraic properties, including recurrence relations, limiting ratios, Binet-type formulas, explicit expressions, and partial sums. Furthermore, we explore their ordinary and exponential generating functions. Finally, we illuminate the inherent relationships between these generalized metallic Leonardo numbers and the well-established metallic Fibonacci and Lucas numbers, revealing their interconnectedness within a broader mathematical structure.

The long-term reliability and performance of photovoltaic (PV) modules largely depend on the thermal stability and durability of encapsulation materials that protect solar cells from environmental and thermal degradation. Ethylene–vinyl acetate (EVA) is widely used as a solar cell encapsulant due to its excellent optical and mechanical properties; however, its thermal stability and resistance to degradation remain critical challenges under prolonged operating conditions. Although EVA-based nanocomposites have been investigated for solar cell encapsulation, limited studies have systematically examined how different nanoclay fillers and processing conditions influence the thermal stability and encapsulation efficiency of EVA materials. This study aims to optimize the thermal stability of EVA nanocomposites by incorporating different inorganic fillers mica, montmorillonite (MMT), and vermiculite, at varying concentrations and milling cycles. An 8% EVA solution was prepared and blended with these fillers to evaluate their effects on the thermal and structural properties of the nanocomposite materials. Thermal characterization using Differential Scanning Calorimetry (DSC) and Thermogravimetric Analysis (TGA) revealed noticeable changes in melting temperature, glass transition temperature, and thermal degradation behavior. The incorporation of nanofillers improved the thermal stability of the EVA matrix and influenced its crystallinity and mechanical properties. The optimized EVA nanocomposite demonstrated enhanced thermal resistance and improved durability compared with neat EVA, although a slight reduction in encapsulation efficiency was observed. These findings provide valuable insights into the formulation and optimization of EVA nanocomposites for solar cell encapsulation, contributing to the development of more thermally stable and durable encapsulation materials for sustainable photovoltaic applications.

The Nigerian banking sector faces escalating challenges from non-performing loans, which surged from 13.2% in 2021 prediction to 16.2% in 2022, necessitating advanced credit risk assessment methods. This study develops an ensemble machine learning model integrating Logistic Regression, Support Vector Machine (SVM), and XGBoost to improve the of loan defaults. Using Kaggle’s Credit Risk Dataset, comprising 14,092 approved credits from 32,581 applications, the model was trained after preprocessing to address missing values, scale features, and encode categorical variables. The ensemble achieved 92% accuracy, 92% precision, 98% recall, and 95% F1 score, demonstrating its effectiveness in identifying potential defaulters. These results suggest that machine learning ensembles can enhance lending decisions, reduce financial losses, and improve banking stability in Nigeria. However, the use of a non-Nigeria-specific dataset limits direct applicability. Future research should prioritize localized datasets to tailor models to Nigeria’s economic context.

A comprehensive analysis of the mitochondrial genomes of Japetella diaphana and Amphitretus pelagicus was conducted to investigate their genomic composition, gene size, sequence characteristics, and phylogenetic positioning within the Amphitretidae family. A rigorous phylogenetic analysis was performed utilizing a dataset comprising 13 protein-coding genes, two ribosomal RNAs, and 22 transfer RNAs derived from 26 cephalopod mitochondrial genomes, representing 25 species across seven families, Vampyroteuthidae, Tremoctopodidae, Octopodidae, Enteroctopodidae, Bolitaenidae, Argonautidae, and Amphitretidae, along with outgroup Nautilus macromphalus. Notably, both focal species demonstrated a pronounced adenine-thymine bias in their mitochondrial genomes, with A. pelagicus exhibiting gene rearrangements and two extensive non-coding regions. The analysis, employing both the maximum likelihood and Bayesian inference methodologies, revealed a monophyletic relationship between Bolitaenidae and Vitreledonellidae, as well as a sister taxon relationship between Amphitretidae and Tremoctopodidae. The majority of species were classified into the Amphitretidae and Bolitaenidae clades, with numerous species exhibiting close phylogenetic relationships. This study provides novel insights into the evolutionary relationships within Octopodiformes, underscoring the significance of mitochondrial genome data in resolving phylogenetic relationships among cephalopods. The findings contribute to our understanding of the evolutionary history of octopi and pose implications for their classification and conservation. Furthermore, the results underscore the necessity for continued research into the evolutionary relationships among cephalopod taxa.

Ship maneuverability remains a cornerstone of maritime safety and operational efficiency, particularly under the influence of environmental stressors such as wind and ocean currents. This study presents a comprehensive analysis of how these forces, individually and in combination, impact vessel handling across diverse operational contexts, with focused case studies from Nigerian ports: Lagos (Apapa), Bonny River, and Calabar. Utilizing a mixed- methods approach, including literature synthesis, incident analysis, expert survey (n=59), and theoretical modelling, the research identifies critical vulnerabilities in current navigational practices and evaluates the efficacy of technological and operational countermeasures. Findings reveal that crosswinds exceeding 25knots and tidal currents above 3knots significantly compromise lateral control, especially for vessels with high windage areas (e.g., container ships) or deep drafts (e.g., tankers). The compounding effect of simultaneous wind and current forces increases navigational risk by up to 70% in confined channels. While tug assistance and pilot expertise remain highly effective (rated 4.7/5), adoption of advanced technologies like Dynamic Positioning Systems (DPS) remains low (12% of surveyed vessels). Real-time environmental data integration is inconsistently available (41%), and simulation- based training is underutilized (29%). The study proposes a three-pillar framework for improvement: (i) Enhanced crew training using scenario-based simulators, (ii) Strategic deployment of DPS and AI-enhanced predictive routing tools, and (iii) Infrastructure and policy upgrades in high-risk ports. Recommendations include standardized “green window” scheduling during slack tides, mandatory environmental briefings pre-berthing, and regulatory incentives for DPS adoption. This research contributes actionable, regionally contextualized insights to bridge the gap between theoretical hydrodynamics and real-world ship handling, supporting global efforts to enhance maritime resilience in an era of climate volatility and increasing vessel scale.

This study assessed structural inequity, exclusion, and discrimination in urban recreational parks in Northern Nigeria and their effects on access, participation, management, and general user satisfaction. The study tested the hypotheses that inequity in facility distribution does not significantly affect access to parks (H₀₁) and that exclusion does not significantly influence participation among women and persons with disabilities (H₀₂). A convergent parallel mixed-methods design was employed, collecting quantitative data from 320 park users via a structured 4-point Likert scale questionnaire and qualitative data from 20 key informants, including park managers, staff, and community leaders, through semi-structured interviews. The parks studied included Yankari Game Reserve, Jos Wildlife Park, Dagona Birds Sanctuary, Gashaka-Gumti National Park, Millennium Park Abuja, and Gorilla Park. Quantitative data were analyzed using descriptive statistics and independent sample t-tests, while qualitative data underwent thematic analysis. Findings revealed that inequity in facility distribution and exclusion significantly affected access and participation, contradicting both H₀₁ and H₀₂. Females and persons with disabilities reported poorer access, limited inclusion, and negative experiences due to uneven facility provision, inadequate accessibility, cultural and safety barriers. Integration of quantitative and qualitative results confirmed that structural inequity, exclusion, and unethical management substantially reduce park participation and satisfaction, particularly among marginalized groups. The study recommends equitable distribution of facilities across all urban recreational parks, improved physical accessibility including ramps and seating for persons with disabilities, regular maintenance and upgrades to ensure safe, functional, and inclusive environments for all users.

This study compares five key data science domains: data structures, data analytics, big data, data mining, and database management systems (DBMS). As data-driven technologies have an increasing impact on corporate intelligence, automation, and academic research, it is vital to analyze their changing importance. A scoring framework was used using simulated yet realistic data to measure both current relevance and future development potential across fields. These trends were highlighted using grouped bar charts as visualizations. According to the report, while core areas such as data structures and DBMS remain vital, technologies such as big data and data analytics have the potential for significant growth. Data mining has also gained increased relevance as a result of its interaction with machine learning. These findings provide actionable insights for educators, academics, and industry experts who want to connect their skills and investments with future technological demands

Purpose The purpose of this study is to examine the convergence of infrastructure development within the regional economic communities of 52 African countries between 2005 and 2022. Design/methodology/approach The modeling of transition paths to convergence and club convergence was tested using Phillips and Sul's econometric convergence approach. Following the Programme for Infrastructure Development in Africa (PIDA) regional classification, we classified the panel data into seven regional economic groups. Energy, transportation, telecommunications, water, and sanitation are the four indicators that make up the African Infrastructure Development Index (AIDI), from which the infrastructure development index data were collected. Findings The results suggest that panel convergence is not present across all regions at the full sample size. Nonetheless, there is evidence of a trend toward convergence at the conclusion of the research period in all regions. The club convergence results could not be extrapolated to other locations because both divergent and convergent clubs existed. The findings additionally demonstrated that only the nations in the COMESA and SADC regions' final club classes are rapidly adjusting to the same level of infrastructure development. Research limitations/implications This paper's most significant policy implication is that to address the lack of convergence in infrastructure development across regions, African countries should prioritize regional infrastructure integration initiatives. Originality/value This study contributes to the current literature by examining the convergence of infrastructure development in the African context and assessing the club convergence/divergence of African countries in infrastructure development using the Phillips and Sul (2007, 2009) technique.

Regional crisis management in West Africa has increasingly drawn scholarly attention, particularly regarding the role of dominant states within regional institutions. Nigeria’s position as a hegemonic actor within the Economic Community of West African States (ECOWAS) has been widely acknowledged; however, existing literature has not sufficiently examined how Nigeria’s influence operates through institutional behaviour to shape crisis management outcomes. This study addresses this gap by examining the nature and extent of Nigeria’s hegemonic influence within ECOWAS and critically assessing how institutional rules, norms, and enforcement mechanisms mediate responses to political crises, including coups and democratic breakdowns. An explorative and descriptive qualitative research design was adopted, drawing data from high-ranking peer-reviewed journal articles, academic books, and authoritative regional and international reports. All materials were subjected to rigorous thematic analysis to ensure analytical credibility and authenticity. The analysis is guided by Hegemonic Stability Theory, which provides insight into how a dominant state contributes to regional order through leadership, coordination, and the provision of collective goods. The study finds that Nigeria’s hegemonic role remains central to ECOWAS’s crisis response capacity, yet its effectiveness is constrained by institutional weaknesses, inconsistent enforcement, and fragmented political will among member states. The study recommends strengthening institutional cohesion, enhancing compliance mechanisms, and improving collective burden-sharing. Consequently, this research contributes to international relations, regional integration studies, African studies, and security studies, while offering policy-relevant insights for regional organisations and decision-makers. Future research should explore comparative hegemonic dynamics across other African regional institutions.