ANALYSIS

The recruitment and use of children in armed conflict remains one of the most persistent violations of international humanitarian law and international human rights law. Despite the existence of an extensive normative framework prohibiting child soldiering, including the Geneva Conventions and their Additional Protocols, the Convention on the Rights of the Child and its Optional Protocol, and the Rome Statute of the International Criminal Court, the practice continues across both international and non-international armed conflicts. This study examines the effectiveness of international legal mechanisms designed to prevent the recruitment and use of children in hostilities, and the extent to which these mechanisms have been enforced in practice. Adopting a doctrinal research methodology, the study analyses international treaties, customary international humanitarian law, decisions of international and hybrid criminal tribunals, and relevant United Nations instruments. Through case studies drawn from Africa, the Middle East, and Eastern Europe, the research highlights the evolving nature of armed conflict, the blurred distinction between voluntary and forced recruitment, and the increasing involvement of non-state armed groups. The findings reveal that while the international legal framework has achieved normative clarity, enforcement remains uneven and largely dependent on political will, domestic incorporation, and international cooperation. The International Criminal Court and other accountability mechanisms have contributed to the development of jurisprudence on child soldiering, yet significant gaps persist in prevention, prosecution, and post-conflict reintegration. The study concludes that strengthening domestic implementation, enhancing international cooperation, and prioritising child-centred reintegration strategies are essential to closing the enforcement gap. By providing a comprehensive legal and theoretical analysis, this research contributes to ongoing scholarly and policy debates on child protection, accountability, and the future of international humanitarian law.

This study investigates the stylistic features of selected funeral dirges in the Ika dialect, with the aim of identifying the linguistic and artistic devices that characterize them as a unique form of oral poetry. The work explores how language is used in Ika dirges to express grief, convey communal beliefs about death, and preserve cultural identity. The study adopts a qualitative research design, drawing data from field recordings, oral interviews, and transcriptions of authentic funeral performances among native Ika speakers. The analysis is based on stylistic and ethnographic frameworks, which allow for the interpretation of the dirges within their linguistic, cultural, and situational contexts. Findings from the analysis reveal that Ika funeral dirges are rich in figurative language such as metaphor, repetition, personification, imagery, and symbolism. These stylistic features heighten emotional expression and strengthen the communal bond during mourning. The dirges also reflect the Ika worldview that regards death not as an end, but as a transition to the ancestral world. Moreover, the performance of dirges marked by call-and-response patterns, tonal variation, and rhythmic delivery demonstrates the aesthetic and participatory nature of Ika oral tradition. The study concludes that Ika funeral dirges serve both artistic and social purposes: they console the bereaved, immortalize the dead, and transmit moral and cultural values across generations. The research therefore recommends the preservation, documentation, and inclusion of Ika oral literature in educational and cultural programs to safeguard the linguistic and cultural heritage of the people.

This study examined the role of big data analytics in enhancing underwriting efficiency and risk assessment accuracy in the Nigerian insurance industry. Motivated by the growing relevance of data-driven decision-making in global financial services, the study sought to evaluate the extent of adoption, the benefits and challenges, and the potential of big data analytics in improving industry performance. A quantitative research design was employed, with data collected through structured questionnaires administered to underwriters, risk managers, and data analysts across selected insurance companies. Out of 200 distributed questionnaires, 176 were duly completed and returned, representing a response rate of 88 percent. Descriptive statistics were used to analyze demographic characteristics and summarize responses, while regression analysis was applied to test the hypotheses. The findings revealed that adoption of big data analytics is gradually increasing in the Nigerian insurance industry, with companies investing in tools and staff training, although integration into daily operations remains moderate. Respondents indicated that big data analytics has significantly improved underwriting efficiency by reducing processing time, minimizing errors, and enhancing the accuracy of customer profiling, thereby contributing to greater customer satisfaction. The study also identified key benefits, including cost savings, improved competitiveness, and better decision-making. However, challenges such as poor data quality, shortage of skilled personnel, and infrastructural limitations continue to hinderq its full implementation. Regression results further confirmed that big data analytics has a positive and statistically significant effect on underwriting efficiency and risk assessment accuracy, explaining 48 percent of the variation in performance. The study concludes that big data analytics is a critical driver of performance in the Nigerian insurance industry and has the potential to transform underwriting and risk management practices. It recommends increased investment in technology, capacity building, stronger data governance frameworks, regulatory support, and industry-wide standards to maximize its impact. While the findings provide useful insights, the study is limited by its focus on selected firms and its cross-sectional design. Future research is encouraged to adopt longitudinal and comparative approaches across different markets.

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 presented a comprehensive investigation into the failure mechanism and performance modelling of the combustion and emission characteristics of an industrial dualfired steam boiler operating on natural gas and diesel. The study was driven by the reoccurring failure on the third pass, operational inefficiencies and concerns over the environmental performance observed in the boiler system of a bottling company in Nigeria The failure investigation component of the study involved a systematic diagnostic assessment of the third-pass tubes including visual inspection, chemical composition analysis, micro- structural analysis and water chemistry analysis. For performance evaluation, the study employed experimental measurements and computational fluid dynamics (CFD) simulations using ANSYS Fluent to model combustion dynamics, temperature distribution, pollutant formation, and flue gas flow behaviour under various operating loads and fuel combinations. The models were validated against plant data, Furthermore, parametric studies were conducted to optimize key operational boiler control parameters such as air flow, load, variable frequency drive (VFD) and fuel flow rate using Taguchi methodology. Failure investigation results revealed key degradation phenomena due to due to thermal stresses, corrosive water chemistry, tube fouling, scaling, fouling-induced heat transfer impairment. Also, micro-structural examination showed thermal degradation, including decarburization, grain growth, and oxidation, alongside pitting corrosion, indicating high-temperature exposure and scale deposition weakened the metal. The water chemistry analysis revealed elevated levels of total dissolved solids, P-alkalinity, chloride, and silica in the boiler drum water, which contribute to scaling and localized overheating. Major failures was traced to inadequate water treatment and poor combustion control. CFD simulations revealed that scale layers significantly reduces heat transfer, leading to increased failure risk. An optimal water mass flow rate of 0.454kg/s was determined as the effective mass flow rate to attain the desired outlet steam temperature. The study revealed load as the most influential factor, leading to reduced emissions, improved combustion, operational stability and prolonged component life. The findings provide insights into enhancing boiler reliability, thermal efficiency, and environmental compliance.

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 explores the semiotic significance of Yoruba dressing styles, examining how traditional attire functions as a cultural symbol, a means of communication, and an expression of identity, spirituality, and social hierarchy. Using Ferdinand de Saussure’s theory of signs and Roland Barthes’ concepts of denotation and connotation, the research analyzes the deeper meanings embedded in Yoruba clothing, colors, fabrics, and accessories. The study reveals that Yoruba dressing styles are more than just fashion; they convey social status, religious beliefs, and ancestral connections. Key attires such as agbádá, aṣọ-òkè, gèlè, bùbá, and adìrẹ serve specific purposes in ceremonial, religious, and daily life contexts. The research also highlights the symbolism of colors—for example, white (funfun) signifies purity and spirituality, red (pupa) represents power and aggression, while gold (òwú) is associated with wealth and royalty. Additionally, Yoruba festivals such as Ojúde Ọba, Ẹyọ, Egúngún, and the Sàngó festival provide a platform for cultural preservation through dressings. Furthermore, the study examines the impact of modernization and globalization on Yoruba dressing, noting a shift towards hybrid fashion that blends traditional elements with contemporary styles. The method of data collections employs structure interview, photography and the analysis of the Yoruba fashion materia

This research provides an in-depth analysis of workplace discrimination against persons with disabilities (PWDs) in Nigeria, assessing the adequacy of the legal, institutional, and policy measures designed to protect their rights and foster inclusion. The study evaluates the extent to which Nigeria fulfils its obligations under the UN Convention on the Rights of Persons with Disabilities (CRPD) and the International Labour Organization (ILO) Convention No. 159. Using a doctrinal and comparative legal approach, it examines statutory provisions, case law, and the functions of enforcement institutions, notably the National Commission for Persons with Disabilities (NCPWD), the National Human Rights Commission (NHRC), Nigeria Social Insurance Trust Fund and the National Industrial Court of Nigeria (NICN). The findings indicate that although Nigeria’s legislative framework is progressive, implementation is undermined by weak institutional capacity, inadequate funding, low awareness, and enduring societal prejudice. Judicial responses, while occasionally advancing disability rights, face obstacles such as prolonged litigation, high legal costs, and limited judicial expertise. Persistent issues include discriminatory hiring, inadequate infrastructure accessibility, non-compliance with the statutory 5% public sector employment quota, and unclear reasonable accommodation guidelines. The study identifies legal ambiguities, enforcement weaknesses, and policy gaps, recommending reforms such as strengthening institutional mandates, creating sector-specific compliance frameworks, expanding public education on disability rights, and improving access to affordable legal remedies. The research concludes that realising workplace equality for PWDs in Nigeria requires more than legal provisions, it demands effective enforcement, structural reforms, and a cultural shift towards genuine inclusion, recognising disability rights as both a human rights priority and a driver of national development.

This research focuses on the analysis of the effectiveness of cybercrime laws in Nigeria: Challenges and solutions. Despite the enactment of the Cybercrimes (Prohibition, Prevention, etc.), Act, 2015, Nigeria continues to struggle with enforcing its cybercrime laws effectively. This paper critically analyzes the effectiveness of Nigeria’s cybercrime legal framework, identifies the systemic and operational challenges that hinder enforcement, and proposes viable solutions to improve the legal and institutional response to cyber threats. Drawing on scholarly sources, legal documents, and policy analysis, this study argues that while the legal framework is a significant step forward, its enforcement is undermined by weak institutional capacity, corruption, technological gaps, and low public awareness. The research concludes that the enactment of the Cybercrimes (Prohibition, Prevention, etc.), Act, 2015 marked a significant milestone in Nigeria’s fight against digital crime. However, its effectiveness is hindered by structural, legal, and operational shortcomings. Low public awareness, poor enforcement capacity, outdated legal provisions, and weak institutional coordination continue to plague Nigeria’s cybersecurity landscape. This study recommends that there should be periodic review and amendment of the Cybercrime Act to include modern cyber threats such as AI, cryptocurrency scams, and deep fakes. Sections that are vague, particularly those that threaten digital rights, should be redefined with clearer language; launch nationwide, multilingual cybercrime awareness programs via traditional media, social media, and grassroots outreach. Engage religious and community leaders to disseminate messages in rural areas; invest in the training of law enforcement agents, prosecutors, and judges on digital forensics, cyber law, and electronic evidence management. Establish cybercrime labs in collaboration with academia and the private sector and develop and adopt protocols for the admissibility of digital evidence in court, ensuring that data is collected and preserved in accordance with global best practices.

This fieldwork exercise was carried out in Igarra within the Nigerian Basement Complex terrain to provide practical training in geological mapping, rock identification, structural analysis, and field observation techniques. The exercise involved detailed study of various rock units, their mineralogical composition, textures, structures, modes of occurrence, and geological relationships. Rock types identified during the exercise include quartzite, schist, metaconglomerate, granite, pegmatite, aplite, hornfels, sandstone, shale, mudstone, claystone, siltstone, and fault breccia. Structural features such as foliations, folds, fractures, joints, shear zones, and intrusive contacts were also observed and interpreted. Evidence of regional metamorphism, contact metamorphism, brittle deformation, and igneous intrusion associated with the Pan-African Orogeny was recognized within the study area.The field exercise was supervised by lecturers from the Department of Geology, who guided students in structural measurements, rock description, geological interpretation, sedimentary logging, and geological mapping techniques. The exercise enhanced practical understanding of the geology, hydrogeology, engineering significance, and economic importance of rocks within the Nigerian Basement Complex