Niger Delta University

Uterine fibroids represent the most common benign gynecological tumors globally, yet management pathways differ substantially across health systems. Observational evidence demonstrates marked divergence between the United Kingdom (UK) and Sub-Saharan Africa (SSA), particularly Nigeria, in treatment access, surgical thresholds, and fertility-preserving strategies. To synthesize biological, structural, and health-system determinants underpinning divergent fibroid management pathways between the UK and SSA, and to construct a multi-domain explanatory framework with equity implications. A comparative narrative analysis was conducted using SANRA quality standards to guide critical appraisal and reporting. Elements of the PRISMA 2020 checklist informed transparent reporting of the search strategy. Databases were searched for epidemiological, clinical, policy, and systems-level literature comparing fibroid prevalence, genetic predisposition, treatment modalities, and institutional frameworks across regions. SSA populations demonstrate earlier age of onset, larger tumor burden at presentation, and higher surgical rates. In contrast, UK management is characterized by guideline-directed conservative pathways, wider access to MRI, uterine artery embolization, minimally invasive surgery, and fertility-preserving pharmacotherapy. Determinants of divergence include genetic susceptibility, delayed presentation, financing constraints, imaging availability, workforce distribution, and national guideline enforcement. The review identifies an emerging pattern of reverse reproductive medical tourism, wherein UK-resident African-ancestry women seek cross-border surgical intervention due to system-level unmet need. Divergent fibroid management reflects not merely biological variation but structural inequities embedded within health systems. Addressing disparities requires integrated policy reform, workforce investment, patient-stratified clinical decision-making, and context-adapted guideline implementation. Future prospective comparative studies are warranted to quantify outcome differentials and inform equity-driven global gynecological practice.

Water quality degradation in data-scarce and pollution-prone regions such as the Niger Delta poses serious health and ecological risks. Traditional monitoring methods are limited by cost, temporal gaps, and lack of interpretability. This study develops a hybrid artificial intelligence (AI) framework integrating Long Short-Term Memory (LSTM), Extreme Gradient Boosting (XGBoost), and K-Means clustering for interpretable water-quality prediction and pattern discovery in under-monitored environments. The framework addresses the scarcity of temporal datasets by adapting LSTM to static physicochemical data through pseudo-sequential encoding, while XGBoost enhances regression and classification accuracy in small, heterogeneous samples. K-Means provides unsupervised insight into latent contamination clusters, complemented by Principal Component Analysis (PCA) for gradient-based visualization. Using 50 georeferenced samples from Yenagoa, Nigeria, ten key parameters were analyzed to compute the Water Quality Index (WQI). Results show that XGBoost achieved the highest predictive performance (R² = 0.95, AUC = 0.96), identifying iron, nitrate, and electrical conductivity as dominant drivers of poor water quality. Three chemically distinct clusters revealed spatial coherence with industrial and residential pollution zones, underscoring the region’s environmental vulnerability. The study demonstrates that hybridizing ensemble learning, deep networks, and clustering enhances both accuracy and interpretability in low-data contexts. By coupling supervised and unsupervised AI components, the proposed framework supports scalable, data-driven decision-making for water-resource management. Its transferability offers practical value for other developing regions facing similar data and infrastructure limitations, contributing to global Sustainable Development Goal 6 on clean water and sanitation. Key findings from this study include: • XGBoost was the most accurate model for both regression (R² = 0.95) and classification (AUC = 0.96). • Over 70

Point bars, essential geomorphic elements in meandering rivers, influence sediment dynamics and ecological processes but face escalating threats from climate variability and human activities in the Niger Delta. This study investigates the 50-year evolution (1974–2024) of point bars along the Niger River to assess their spatiotemporal changes and driving mechanisms using remote sensing and machine learning. Using Landsat and Sentinel-2 satellite imagery, digital elevation models (DEMs), and rainfall data, we applied Object-Based Image Analysis (OBIA) and Support Vector Machines (SVMs) for automated classification and mapping of river features. Temporal trends were evaluated through decadal statistical metrics, spatial autocorrelation (Moran’s Index), and correlation analyses between point bar morphology, rainfall, and elevation. Results reveal a fluctuating Niger River area, declining from 46,376.54 km² (1974) to 44,796.47 km² (2024), with intermittent expansion (49,601.2 km² in 2014). Point bar area surged from 1,945.63 km² (1974) to 8,087.89 km² (2024), peaking at 7,026.33 km² (2004) before contraction, linked to sediment supply shifts. Morphological analysis highlighted elongated bars (aspect ratio 54.22) indicating lateral accretion, contrasting with rounded forms (ratio 20.8) in depositional zones. Spatial autocorrelation confirmed clustering with elevation (Moran’s Index = 1.06, p < 0.001), while rainfall exhibited a dominant correlation (R² = 0.9921) with bar dynamics. These patterns mirror global systems like the Mississippi and Amazon, underscoring synergistic climatic and anthropogenic impacts. The study provides a framework for adaptive management of the Niger Delta and analogous fluvial environments, emphasizing the critical role of hydrological and sedimentological monitoring. Fluctuating point bar areas (1,945.63 km² in 1974 to 8,087.89 km² in 2024) highlight dynamic sediment deposition-erosion cycles, influenced by seasonal rainfall and sand mining. Landsat imagery (1974–2024) quantified river area shifts (46,376.54 km² to 44,796.47 km²), linking variability to upstream dams. Automated detection revealed unstable point bar metrics (mean area decline from 780.7 to 622.15 km²,2004–2024), underscoring spatial heterogeneity. Rainfall (R²=0.9921) and elevation clustering (Moran’s Index = 1.06) drove morphology, while human interventions exacerbated instability.

Flooding is one of the most recurrent and destructive hydro-environmental hazards in Nigeria Niger Delta, exacerbated by climate variability, rapid land use change, and geomorphological vulnerability. This study integrates multi-temporal Sentinel-1 Synthetic Aperture Radar (SAR), Sentinel-2 multispectral imagery, geospatial analysis, and machine learning (ML) techniques to map flood inundation and analyze land use/land cover (LULC) dynamics in Ahoada West Local Government Area, Rivers State, Nigeria, from 2018 to 2023. Flood inundation extent and severity were derived using DEM-based flood depth modeling, while LULC classification was performed using five supervised ML algorithms: Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), and Decision Tree (DT). Model performance was evaluated using overall accuracy, Kappa coefficient, and class-based metrics. Results show that flood extent increased non-linearly with water level, with the highest inundation recorded in 2022, affecting approximately 98.93 km² (22.5

The study investigated the relationship between financial literacy and investment decisions of students in Bayelsa State, focusing on trainee accountants from three universities: Niger Delta University (NDU), Federal University Otuoke (FUO), and University of Africa (UAT). Specifically, it examined the relationship between debt management, savings literacy, and investment decisions. A cross-sectional survey design was employed, with a population of 1,473 trainee accountants. A sample of 315 students was determined using Taro Yamane’s formula, and Bowley’s statistics was used to determine the proportional representation of each institution. A simple random sampling technique was employed to select respondents. Data were collected through structured questionnaires and analyzed with the aid of SPSS version 23. Descriptive statistics and Spearman’s rank correlation coefficient explored the relationships between financial literacy dimensions and investment decisions. The findings revealed positive and significant relationships between financial literacy dimensions: debt management and savings literacy, and investment decisions. The study concludes that there is a positive and significant relationship between financial literacy and investment decisions of student in Bayelsa State. Based on the findings, the study recommends that educational institutions organize workshops and courses on debt management, covering topics like interest rates and repayment strategies, and incorporate savings literacy into the curriculum through practical lessons on building emergency funds, understanding saving techniques, and financial planning to enhance students' investment decision-making skills.