Obafemi Awolowo University

Experiences of extreme confinement, identity loss and fear characterize the lived realities of many Beninese women migrant domestic workers in Osogbo, Nigeria. This study employs a descriptive qualitative design to explore persistent violations of their fundamental rights and the broader structural conditions that sustain these abuses. Data were drawn from 32 interviews with migrant workers, police officers and a human rights advocate, providing both victim and institutional perspectives. Findings reveal multiple, overlapping forms of victimization, including physical abuse, unpaid labour, food deprivation, medical neglect and forced renaming that erases cultural identity and personal autonomy. Employers exploit the women’s undocumented status to enforce excessive workloads, surveillance and isolation, while sexual harassment and threats of deportation sustain a climate of silence and dependency. Most participants were unaware of their rights, which deepens their vulnerability and discourages reporting. Despite Nigeria’s Labour Migration Policy and its commitment to international conventions, weak enforcement and public indifference perpetuate systemic neglect. The study demonstrates how economic desperation, gendered subordination and institutional failure intersect to normalize exploitation within private households. It concludes by advocating for stronger monitoring mechanisms, gender-sensitive human-rights-based migration governance and community sensitization programmes to ensure dignity, protection and lasting justice for women migrant domestic workers in Nigeria.

This paper explores the relationship between knowledge and practices of menstrual hygiene management among female students and teachers in Ile-Ife. A descriptive cross-sectional design was used for the study. A simple random sampling method selected 280 respondents. A researcher-designed questionnaire was employed to collect data. The instrument was validated by experts, and a reliability coefficient of 0.82 was achieved using Cronbach’s Alpha. Data were analyzed using frequency, percentage, charts, and chi-square tests. The results show that 86% of respondents have good knowledge of menstrual hygiene management. Additionally, 84% of teachers have poor practices during their students’ menstrual periods. There is no significant association (χ2=1.523, P > 0.05) between teachers’ knowledge of menstrual hygiene management and their practices during their students’ menstrual periods. Therefore, it was concluded that there is no significant link between teachers’ knowledge and their practices regarding menstrual hygiene management based on these findings. Keywords: Menstrual, Hygiene, Management, Knowledge, Practices, Adolescents, Teachers

Swarm robotics and cooperative AI represent a transformative approach in robotics and artificial intelligence, drawing inspiration from collective behaviors observed in natural systems such as ant colonies, bird flocks, and fish schools. By leveraging distributed control, decentralized decision-making, and local communication among multiple agents, swarm robotics enables scalable, robust, and flexible multi-agent systems capable of tackling complex tasks. Cooperative AI integrates algorithms for coordination, task allocation, and collaborative learning to optimize group performance while maintaining individual autonomy. Applications span autonomous exploration, search and rescue, environmental monitoring, logistics, agriculture, and defense. This article explores the principles, architectures, coordination strategies, learning mechanisms, and real-world applications of swarm robotics and cooperative AI. It also addresses challenges including scalability, communication constraints, fault tolerance, ethical considerations, and human-swarm interaction. Future directions involve integrating advanced AI techniques such as reinforcement learning, neuro-symbolic reasoning, and edge intelligence to enhance adaptability, resilience, and efficiency in collective robotic systems. Swarm robotics and cooperative AI promise to redefine automation, multi-agent intelligence, and collective problem-solving in dynamic and uncertain environments.

Titanium matrix composites (TMCs) offer low density, high specific strength and good thermal stability, yet their deployment in certification-critical aerospace, automotive, energy and biomedical components remain constrained by incomplete understanding of strength–toughness–durability trade-offs. This work provides a mechanistic, meta-analytical review of reinforced TMCs, spanning ceramic, intermetallic, metallic, nanocarbon and laminate or hybrid architectures, with emphasis on how reinforcement chemistry, interface design and hierarchical microstructure jointly control static and time-dependent performance. A quantitative property landscape is compiled for major reinforcement families, enabling side-by-side comparison of tensile strength, stiffness, ductility, fracture toughness, fatigue strength, creep resistance and environmental stability, and revealing how ceramic-rich systems occupy a strong-but-brittle corner of design space, whereas laminate, hybrid and nanocarbon-reinforced systems offer more balanced toughness and durability windows. Building on this analysis, the review introduces a tri-coupled framework that treats interface engineering, hierarchical reinforcement architecture and multiscale validation as interdependent design levers for next-generation TMCs. Within this framework, advanced characterization, digital-twin concepts and machine-learning-assisted models are integrated with mechanical testing to generate more predictive process–structure–property maps and to identify microstructural signatures of long-term damage tolerance. The review further outlines key translation barriers—including microstructural reproducibility in advanced processing routes, the lack of standardized multi-environment fatigue and creep protocols, data limitations for mechanistic and data-driven modeling, and sustainability constraints—and proposes an agenda in which targeted durability testing, hetero-deformation-based strengthening models and data-rich digital workflows are systematically combined to accelerate qualification of titanium-based composite systems for reliable, certifiable service in demanding structural applications.

The rapid deployment of fifth-generation (5G) networks and the adoption of network slicing have significantly enhanced connectivity and service customization, but they have also expanded the cyber-attack surface of telecommunications infrastructure. At the same time, emerging quantum computing capabilities threaten the security of widely used classical cryptographic schemes such as RSA and Elliptic Curve Cryptography. Despite growing concerns, existing security frameworks for 5G slicing rarely integrate post-quantum cryptography with intelligent threat detection mechanisms. This study addresses this gap by developing a quantum-resistant security architecture for 5G network slicing that combines post-quantum cryptography and artificial intelligence–based anomaly detection. The framework incorporates NIST-standardized algorithms including CRYSTALS‑Kyber for secure key exchange and CRYSTALS‑Dilithium and FALCON for authentication. A hybrid post-quantum/classical TLS scheme was implemented to support secure key establishment with minimal latency overhead of 1.65 ms. Additionally, an AI-driven anomaly detection model using a stacked ensemble of Isolation Forest and XGBoost monitored slice orchestration behavior to detect cyber threats. Simulation results demonstrated high detection performance with a macro F1-score of 0.962 and AUC-ROC of 0.985. The proposed framework offers a practical and scalable approach for securing next-generation telecommunications infrastructure against both current cyber threats and future quantum attacks.

This study investigates construction professionals’ perceptions of locally sourced building materials in Lagos State, Nigeria. A quantitative cross-sectional survey design was adopted, targeting registered architects, engineers, builders, and quantity surveyors. Data were collected through structured Likert-scale questionnaires, and 63 valid responses were analyzed using SPSS with descriptive statistics, Relative Level of Agreement (RLA), and Spearman’s rank-order correlation. Findings show strong agreement that poor-quality local materials contribute to building defects (RLA = 0.81) and collapse (RLA = 0.88). While materials such as sandcrete blocks are viewed as moderately reliable (RLA = 0.57), concerns persist regarding inconsistent quality and weak regulatory oversight (RLA = 0.49). Professionals also identified poor workmanship (RLA = 0.90) and unregulated production (RLA = 0.86) as major factors exacerbating failures. A significant relationship was established between professionals’ perceptions and their material selection and supervision practices. Respondents recommended collaborative policy frameworks among government, professional bodies, and manufacturers (RLA = 0.96), continuous quality-control training (RLA = 0.93), and stricter enforcement against uncertified materials (RLA = 0.93). The study concludes that strengthening regulatory and quality assurance systems is critical to improving confidence in locally sourced materials and reducing building failures in Lagos State.

Climate change poses significant threats to human health, ecosystems, and socioeconomic stability, particularly in low- and middle-income countries such as Nigeria. Using a Planetary Health lens, this study explores public perceptions of climate change and its health implications in Nigeria, drawing on nationally representative data from Afrobarometer Round 9. It investigates how awareness, and health-related perception differ across region, hence shaping climate-responsive policies. A cross-sectional design was applied using secondary data from Afrobarometer’s Nigeria survey. The dataset includes geocoded responses capturing climate perceptions, experiences of extreme weather events, and perceived health effects. Descriptive statistics summarized demographic characteristics, while binary logistic regression examined predictors of climate change awareness and perceived health impacts. Spatial analysis using GIS tools in R mapped climate awareness across Nigeria’s geopolitical zones. Spatial autocorrelation (Moran’s I) and hotspot analysis identified clusters of awareness. Findings show substantial demographic and regional disparities. Mean age and gender composition varied across states. Education levels peaked in Imo (3.34) but were lowest in Kebbi (1.50), Katsina (1.63). Climate change awareness ranged from 0 to 87.5%, highest in southern urbanized states and lowest in northern states. Perceptions of climate-related health impacts including heat illnesses, vector-borne diseases, respiratory disorders, and malnutrition ranged from 1.90 to 4.71 on a Likert scale. Logistic regression confirmed significant predictors of awareness to include gender with regional effects more pronounced in some states like Abuja, Benue and markedly lower in Zamfara. The model showed excellent fit (χ2 = 311.1, df = 39, p < 0.001; AIC = 1647.1). The study highlights key gaps in climate knowledge that hinder resilience. It recommends inclusive, education-focused, and gender-responsive climate communication strategies aligned with Planetary Health principles to advance sustainable and equitable climate action in Nigeria.