This research examined the water quality in the Asese/Mowe Industrial Area of Ogun State, Nigeria. Twenty (20) water samples comprising both surface and groundwater sources were obtained from industrial zones and subjected to comprehensive physicochemical analysis using standard procedures. The analytical parameters examined encompassed pH levels, electrical conductivity (EC), total dissolved solids (TDS), dissolved oxygen (DO), biological oxygen demand (BOD), chemical oxygen demand (COD), and principal anionic constituents (chloride (Cl⁻), sulphate (SO42⁻), and nitrate (NO3⁻)). Surface and groundwater quality analysis was evaluated using water quality index (WQI). 14 out of the evaluated 20 samples had pH below the minimum acceptable limit of 6.5, indicating that the water in many locations is slightly acidic. The COD values varied between 3.99 and 12.04 mg/l, with a mean value of 7.09 (± 1.88) mg/l. SW1 exhibits a COD level of 12.04 mg/L, which fails to comply with the required COD standards and consequently renders it unsuitable for drinking water consumption based on COD criteria. Results show that the WQI values for the 20 samples ranged between 6.83 and 106.73 reflecting noticeable variation in water quality across different sampling points. 45
Recent advances in machine and deep learning have paved the way for automated quality control in injection molding. However, existing approaches still face challenges such as insufficient performance evaluations, scarce training data, and limited generalization. In this study, a comparative evaluation of commonly used pretrained Convolutional Neural Networks, including InceptionV3, ResNet-101, ResNet-50, and VGG16, for defect detection was carried out. It was shown that InceptionV3 outperformed the other models in test accuracy and convergence behavior, under consistent hyperparameter settings and fine-tuning strategies. Building on this, Squeeze-and-Excitation (SE) and Convolutional Block Attention Module (CBAM) were integrated into InceptionV3, forming InceptionV3-SE-CBAM, to enhance feature representation. The proposed framework was tested on three diverse injection molding datasets to demonstrate generalized application and industrial adaptability. Compared to the baseline InceptionV3, the InceptionV3-SE-CBAM achieved accuracy gains in excess of 23.9
PURPOSE:The health and well-being of young adult building artisans are germane because of their critical role in infrastructure development. There is a perceived increase and paucity of studies regarding young adult building artisans' involvement in illicit substances, which threatens the achievement of Sustainable Development Goals (SDGs) 3 and 8. This study investigated the underlying causes and recommended feasible measures to prevent or mitigate the use of illicit substances among young adult artisans on construction sites in Nigeria, and, by extension, to improve the achievement of Goals 3 and 8. DESIGN/METHODOLOGY/APPROACH:This research employs a face-to-face interview to collect data in Lagos and Abuja, Nigeria. FINDINGS:Findings reveal that there is a prevalent issue of increasing young adult building artisans' illicit substance intake on building sites. This poses a significant threat to occupational health (SDG 3) and workplace safety (SDG 8). Findings also identified 17 underlying causes, including a lack of awareness among artisans, a low educational background and lax safety and management policies. ORIGINALITY/VALUE:This research contributes to the existing literature on preventing or mitigating illicit substance use among young adult building artisans. It also recognises that preventive or mitigative measures can be useful for mental health stability and, by extension, for achieving the SDGs.
Circular wastewater management is increasingly recognized as a critical lever for climate resilience, water security, and the recovery of nutrients, energy, and strategic materials. Yet conventional treatment infrastructures remain constrained by limited selectivity, high energy demand, operational inflexibility, and weak coupling between treatment performance and resource valorization. Although nanotechnology has demonstrated substantial potential to address these bottlenecks, real-world deployment remains fragmented due to fouling, regeneration burdens, scale-up uncertainty, and unresolved safety and governance challenges. This review advances a roadmap that moves beyond material-centric assessments toward a decision-oriented, scale-aware framework for integrating nanotechnology into circular wastewater systems. Drawing on recent laboratory advances, pilot studies, and early demonstrations across municipal, industrial, and agro-food contexts, we situate nano-enabled adsorbents, catalysts, membranes, bio–nano hybrids, and nanosensors within integrated treatment–recovery–reuse platforms, rather than isolated unit operations. Techno-economic and life-cycle evidence is synthesized to identify conditions under which nano-enabled process trains deliver net circular value relative to incumbent technologies. The roadmap explicitly couples nanotechnology with digital intelligence, including nanosensing, AI-enabled monitoring, digital twins, and adaptive control, to translate nanoscale functionality into robust system-level performance under variable influent conditions. To support actionable decision-making, we introduce a pollutant-to-valorization decision matrix, a readiness–impact scorecard, and a 2030 research and standards agenda emphasizing safe-by-design materials, scalable regeneration, antifouling interfaces, hybrid bio–nano reactors, and harmonized risk assessment. By integrating materials science, digital process control, and governance, this roadmap positions nanotechnology as a systems enabler for circular wastewater infrastructure rather than a standalone fix.
Nigerian cities are expanding faster than the infrastructure systems that are meant to service them, and two of the most consequential deficits are the progressive erosion of urban green cover and the persistence of an unreliable, fossil-dependent electricity supply. These two deficits are conventionally treated as separate policy problems, handled by separate ministries and financed through separate budget lines. This paper argues that they are structurally coupled and should be planned together. Using a structured desk-based synthesis of peer-reviewed evidence published between 2016 and 2025, together with national policy instruments and energy transition datasets, the study assesses the extent to which green infrastructure and renewable energy systems are currently integrated in Nigerian urban development practice, identifies the mechanisms through which integration produces compound benefits, and diagnoses the institutional barriers that keep the two agendas apart. Evidence from Nigerian and comparable tropical contexts shows that vegetated surfaces measurably reduce ambient and surface temperature, that cooling load is a dominant and rising component of urban electricity demand, and that reductions in cooling demand improve the technical and financial performance of distributed solar systems. The analysis finds that integration in Nigeria remains largely rhetorical: national instruments acknowledge both agendas but assign them to institutions with no shared spatial plan, no shared performance indicators, and no shared budget envelope. The paper proposes an integration framework organised around four coupling points, namely thermal load reduction, stormwater and asset protection, land use co-location, and community-level co-governance, and sets out an implementation pathway that is realistic for the fiscal and administrative capacity of Nigerian states. The contribution is a diagnostic and planning framework rather than a new primary measurement, and the limitations of a synthesis-based design are stated explicitly.
