Niger Delta University (NDU) is in Wilberforce Island, Bayelsa State in southern part of Nigeria. It was established in 2000. It is a Bayelsa state government-funded university. In 2002, It was established by Chief DSP Alamieyeseigha, then governor of Bayelsa state. It has two main campuses, one in the state capital, Yenagoa, which contains the law faculty, and the other in Amassoma. It also has its teaching hospital known as Niger Delta University Teaching Hospital (NDUTH) in Okolobiri.Niger Delta University has come a long way since its establishment, @ESTABLISHMENTestablishment |access-date=2022-03-09@ Its main campus in Amassoma is in a temporary @temporarytemporary |access-date=2022-03-09@site, with work on the permanent site ongoing.The university offers education at Bachelor, Masters and PhD levels. It is a member of the Association of Commonwealth Universities.
Digital transformation is reshaping microbiology from specimen processing and molecular diagnostics to population-scale biosurveillance. This chapter examines digital One Health biosurveillance as an integrated framework that connects microbial, clinical, veterinary, plant, food, wastewater, environmental, genomic, geospatial, and sensor data across human, animal, plant, and ecosystem interfaces. It focuses on smart laboratory automation, laboratory information systems, sequencing and metagenomics, GIS, remote sensing, Internet of Things platforms, machine learning, and AI-assisted early warning. Applications in zoonoses, antimicrobial resistance, food- and waterborne infections, wastewater surveillance, and climate-sensitive microbial risks are critically reviewed. Emphasis is placed on interoperability, data quality, validation, privacy, cybersecurity, equity, and human oversight. The chapter argues that digital One Health systems are most effective when technology is embedded in robust laboratory networks, standards-based data exchange, and actionable public-health workflows
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
Jean-Jacques Rousseau’s educational philosophy argues that virtue cannot be fostered by reason alone, as human nature—though shaped by reason—remains inherently natural. He rejects rigid discipline, artificial schooling, and excessive social control, asserting that such “shackles” stifle children’s maturity, confidence, and self-sufficiency. Instead, education should nurture innate potential by aligning with the child’s natural stages of development, with teachers acting as guides rather than impositors. This study explores three questions central to Rousseau’s thought: What defines education in his philosophy? What are its core aims? And what role should teachers occupy? By analyzing his critique of artificial, society-constructed educational models, the research clarifies his vision of education as a natural, child-centered process. Employing analytical and expository methods, it seeks to deepen understanding of Rousseau’s ideas for students, teachers, and researchers in educational philosophy. In essence, the paper highlights Rousseau’s advocacy for education that honors the child’s natural growth, making his insights enduringly relevant for rethinking educational practice.
Predicting volume of shale and porosity in structurally complex deltaic environments like the Niger Delta remains challenging for conventional inversion techniques. This study applies machine learning regression models to integrate multi-attribute 3D seismic data and well logs for improved prediction of reservoir properties in the Akaso Field, eastern Niger Delta. Petrophysical evaluation of Sand Unit C from six wells yielded porosity values of 0.11–0.34, shale volume of 1–11
