FRAMEWORK

The convergence of Artificial Intelligence (AI) with Digital One Health systems, which integrate human, animal, and environmental health data into unified platforms, has introduced unprecedented cybersecurity challenges that conventional frameworks fail to sufficiently address. This project proposes the design, implementation, and evaluation of an adaptive AI- driven security framework tailored specifically to safeguard sensitive cross-sector health data within complex hybrid cloud and on-premise infrastructures. The framework leverages foundational cybersecurity principles, such as Zero Trust Architecture and layered defensein-depth, combined with advanced AI-powered anomaly detection models including Long Short-Term Memory (LSTM) Autoencoders and Isolation Forest algorithms to enable realtime identification and mitigation of emerging threats. Employing open-source technologies integral to the system architecture; OPNsense for perimeter firewall and VPN, WireGuard for encrypted communication, Suricata IDS/IPS for intrusion detection and prevention, Wazuh Security Information and Event Management (SIEM) for centralized log aggregation, Keycloak for Multi-Factor Authentication (MFA), and Debezium for Database Activity Monitoring (DAM), the framework demonstrates a holistic approach to securing Digital One Health ecosystems. Central to this effort is the development of a robust AI-powered security dashboard based on the ELK stack (Elasticsearch, Logstash, Kibana), which provides unified visualization, real-time alerting, and compliance monitoring. The research adopts a design science approach, systematically assessing security vulnerabilities unique to hybrid Digital One Health infrastructures and implementing layered controls to address gaps in cross-sector interoperability and AI-specific risks such as adversarial attacks and data poisoning. Validation is performed using comprehensive threat simulations that mimic real-world attack vectors including SQL injection, lateral movement, privilege escalation, and Distributed Denial-of-Service (DDoS) scenarios. Quantitative performance metrics reveal a high detection rate (approximately 97%), significant reductions in both mean time to detect (MTTD) threats by 70% and false positive alerts by 58%, thereby illustrating improved operational efficiency and accuracy compared to traditional static security measures. Despite promising results, challenges remain around optimizing AI model training data quality, managing performance overhead in resource-constrained settings, and balancing stringent security controls with healthcare delivery imperatives. The study advocates for future enhancements including federated learning models to preserve data privacy, lightweight AI implementations for broader accessibility, automated compliance frameworks to navigate complex regulatory environments, and expanded user training programs to address human-centric security gaps. This project contributes a scalable, ethically aligned cybersecurity framework specifically engineered for AI-powered Digital One Health systems, addressing the multifaceted demands of securing sensitive, interconnected health data across diverse environments. By merging advanced AI capabilities with proven cybersecurity practices and a centralized management platform, it advances the resilience and trustworthiness of modern healthcare ecosystems, fostering innovation while safeguarding critical digital health infrastructure globally.

This study examines urban regeneration and slum upgrading in Ogida District, Benin City, with the aim of developing a sustainable framework that can guide the improvement of living conditions in the area. Ogida is one of the older residential districts in Benin City and over time has experienced significant physical deterioration, inadequate infrastructure, and growing socio- economic challenges that have contributed to its classification as a blighted inner-city neighbourhood. The study was motivated by the absence of a coordinated, community-driven regeneration strategy for the district, despite its well-documented challenges including chronic flooding linked to clay-heavy soils and blocked drainage, deteriorating housing stock, insecure land tenure, and the progressive degradation of the ancient Benin Moat, a UNESCO-listed heritage site that passes through the area. Previous urban renewal attempts in Nigeria, particularly in Lagos and Abuja, have largely relied on demolition and forced relocation, producing displacement without sustainable improvement. This study takes a different position, arguing for in-situ upgrading as the more appropriate and humane approach. A mixed-method research design was adopted. Structured questionnaires were administered to one hundred residents of Ogida District, with a 100% response rate. Direct physical observation of the study area was also carried out alongside a review of relevant documents, policies, and empirical literature. Data were analysed using descriptive statistical methods including frequency tables, percentages, and pie charts. The findings reveal that Ogida has a predominantly young, low-income population, the majority of whom rely on informal economic activities for their livelihood. Housing conditions across the district are poor, with most buildings showing visible signs of deterioration and overcrowding. Basic infrastructure is inadequate, particularly drainage systems, roads, and waste management facilities. Flooding during the rainy season was identified as one of the most serious environmental problems affecting residents. Community participation in development planning is extremely limited, with over 74% of respondents reporting that they have never been consulted about development projects in their area, even though the overwhelming majority expressed willingness to be involved. Based on these findings, the study proposes a sustainable urban regeneration framework for Ogida District built around five pillars: infrastructure and basic services improvement, community participation and indigenous governance, environmental resilience, anti- displacement and social equity safeguards, and phased implementation with community ownership. The framework is designed as a replicable model that can be adapted for similar inner-city settlements across Benin City and Edo State. The study concludes that meaningful regeneration of Ogida requires a coordinated effort involving government agencies, urban planners, traditional community leaders, and residents themselves. Without genuine community involvement and government support, physical improvements alone will not be sustainable. Keywords: Urban Regeneration, Slum Upgrading, Ogida District, Benin City, Sustainable Framework, Community Participation, In-situ Upgrading, Flood Resilience

A total of ten (10) beds exposed at the Ekosodin/Evbuomore were obtained and studied. These beds belong to the Benin Formation of the Niger Delta Basin. Sieve analyses were carried out on them, with the results plotted and the mean, standard deviation, skewness and kurtosis calculated. These calculations were used to interpret the provenance, depositional process and environment of deposition. The beds mainly contain quartz, feldspar and detrital fragments revealing their igneous and metamorphic origin. The results indicate that the grain size were fine to medium to very coarse sand; poor to very moderately well sorted. The results of the mineral assemblage based on the percentage of quartz, feldspar and lithic fragment reveal that the beds are quartz arenite, subarkose, arkose and lithic arkose. Grain sizes are mostly medium to coarse, with some fine grains in Bed 5. Grain shape is mostly angular and subrounded, with few rounded and sub angular. The values obtained from the standard deviation for the beds suggest near moderately well sorted to poorly sort. The sorting show that the maturity of the bed samples ranges from immature (BED 3, 4 and 8) to submature (BED 1, 5 and 7) to mature (BED 2, 6, 9 and 10). The Bivariant plot of inclusive skewness against inclusive standard deviation indicates that the depositional process that led to the deposition of the beds is fluvial (river systems) suggesting a low energy of transportation of the sediments as well as beach sediments. Economic importance the sands serve as the main aquifer in Benin Formation. The abundance of quartz makes it a potential for use in glass making. The sands are mined/excavated and used in construction viz; concrete making, moulding of blocks, etc. My suggestions for further studies are, thus: More detailed study should be done with morphoscopic and petrographic analysis to ascertain the minerals present in traces. Environmental studies should be done in the area under study to ascertain the presence of any heavy minerals that is a potential health hazard to the subsurface water.