Yazar "Ajibade S.M." seçeneğine göre listele

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    Comparative Assessment of Medical Waste Management in Multi-System and Selected Teaching Hospitals in Ekiti State, Nigeria
    (Technoscience Publications, 2023) Oyebode O.J.; Okpala C.C.; Ajibade S.M.; Ogarekpe N.M.; Afolalu S.A.; Coker A.O.; Udeagbara S.G.; Adeniyi A.T.
    Medical facilities, such as hospitals, clinics, and locations where diagnosis and treatment are administered, create dangerous waste that predisposes individuals to deadly infections. Medical waste management aims to improve health and prevent public health and environmental threats. Questionnaires, interviews, site visitations, and observations were utilized to determine the management strategies implemented in the three hospitals and evaluate the efficacy of waste management. The hospitals under review are Afe Babalola University Multi-system Hospital (AMSH), Ekiti State University Teaching Hospital (EKSUTH), and Federal Teaching Hospital Ido-Ekiti (FETHI). Statistical Package for the Social Sciences (SPSS) was utilized for the statistical analysis of the questionnaires, and the mean assessment was utilized to compute the waste per bed each day. The results revealed that the three hospitals’ sharp, infectious, and pharmaceutical waste is the most sorted. All hospitals burn their medical waste in incinerators but dispose of the ashes in dumpsites. The mean evaluation of all hospitals’ medical waste was weighed to establish the overall amount generated. The total amount of medical waste created at AMSH, EKSUTH, and FETHI is 31.5 kg, 53.6 kg, and 135.1 kg, respectively. The medical waste generated per bed per day in AMSH, EKSUTH, and FETHI is 0.61 kg, 0.74 kg, and 0.73 kg, respectively. It was determined that the proper management and disposal of waste is a critical obligation of healthcare facilities. There should be a provision for educating personnel about the consequences of inappropriately disposing of medical waste.
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    Strategic Monitoring of Groundwater Quality Around Olusosun Landfill in Lagos State for Pollution Reduction and Environmental Sustainability
    (Technoscience Publications, 2023) Oyebode O.J.; Jimoh F.O.; Ajibade S.M.; Afolalu S.A.; Oyebode F.A.
    As urbanization and population increase in the megacity, there is a need for engineering intervention and strategic monitoring of groundwater around landfills for environmental sustainability, pollution reduction and public health. This study evaluated water’s physical and chemical parameters in wells and boreholes near the Olusosun landfill in Lagos State to determine how they impact groundwater quality. An Atomic Absorption Spectrometer (AAS) was used to evaluate groundwater samples obtained from five locations within the dump site. Some water parameters, such as dissolved oxygen (DO), iron (Fe), lead (Pb), manganese (Mn), and magnesium (Mg), had concentrations that were higher than the WHO, NESREA, and Nigerian Standard for Drinking Water Quality (NSDWQ) standard limits in some sampling sites, with mean concentrations of 0.33 mg.L-1, 0.04 mg.L-1, 0.74 mg.L-1, and 0.74 mg.L-1, respectively. A small amount of lead was identified in the groundwater of the study area. A major source of air and groundwater pollution, the Olusosun landfill has a detrimental impact on the health of those who live there. Solid waste, groundwater interactions, and contaminated migration into the nearby neighbourhood were studied. It was observed that the degradation of waste products in dump sites releases harmful leachate into the groundwater. Even though some heavy metal concentrations in the study area are still within WHO, NESREA, and NSDWQ standard limits, investigations and further monitoring should be conducted regularly to assess the concentrations of heavy metals in groundwater.
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    Utilizing Logistic Map to Enhance the Population Diversity of PSO
    (Institute of Physics, 2022) Ajibade S.M.; Chweya R.; Ogunbolu M.O.; Fadipe S.R.
    Particle swarm optimization (PSO) is a high-quality, nature-inspired global optimization algorithm that can be applied to a variety of real-world optimization problems. PSO, on the other hand, has some flaws, such as slow convergence, premature convergence, and the ability to become stuck at local optimum solutions. This research aims to address the issue of population diversity in the PSO search process, which leads to premature convergence. As a result, in this study, a method is introduced to PSO in order to avoid early stagnation, which leads to premature convergence. A chaotic dynamic weight particle swarm optimization (CTPSOA) is proposed, in which a chaotic logistic map is delivered to increase the population range within the PSO search technique by editing the inertia weight of PSO to avoid premature convergence. This study also looks into the overall performance and viability of the proposed CTPSOA as a set of function selection rules for solving optimization issues. There are eight traditional benchmark functions that are used to assess the overall results and obtain the accuracy of the proposed (CTPSOA) algorithms when compared to a few other meta-heuristics optimization rules. The test results reveal that the CTPSOA outperforms other meta-heuristics algorithms in solving optimization problems by 85% and has established itself as a reliable and superior metaheuristics algorithm for feature selection.