Proper management and safe disposal of medical waste (MW) is vital in the reduction of infection or illness through contact with discarded material and in the prevention of environmental contamination in hospital facilities. The management practices for MW in selected healthcare facilities in Lagos, Nigeria were assessed. The cross-sectional study involved the use of questionnaires, in-depth interviews, focused group discussions and participant observation strategies. It also involved the collection, segregation, identification and weighing of waste types from wards and units in the representative facilities in Lagos, Nigeria, for qualitative and quantitative analysis of the MW streams. The findings indicated that the selected Nigerian healthcare facilities were lacking in the adoption of sound MW management (MWM) practices. The average MW ranged from 0.01 kg/bed/day to 3.98 kg/bed/day. Moreover, about 30% of the domestic waste from the healthcare facilities consisted of MW due to inappropriate co-disposal practices. Multiple linear regression was applied to predict the volume of waste generated giving a correlation coefficient (R(2)) value of 0.99 confirming a good fit of the data. This study revealed that the current MWM practices and strategies in Lagos are weak, and suggests an urgent need for review to achieve vital reversals in the current trends.
This investigation aimed to examine the load carrying capacity of model piles embedded in sand soil and to develop a predictive model to simulate pile settlement using a new artificial neural network (ANN) approach. A series of experimental pile load tests were carried out on model concrete piles, comprised of three piles with slenderness ratios of 12, 17 and 25. This was to provide an initial dataset to establish the ANN model, in attempt at making current, in situ pile-load test methods unnecessary. Evolutionary Levenberg-Marquardt (LM) MATLAB algorithms, enhanced by T-tests and F-tests, were developed and applied in this process. The model piles were embedded in a calibration chamber in three densities of sand; loose, medium and dense. According to the statistical analysis and the relative importance study, pile lengths, applied load, pile flexural rigidity, pile aspects ratio, and sand-pile friction angle were found to play a key role in pile settlement at different contribution levels, following the order: P > δ > lc/d > lc > EA. The results revealed that the optimum model of the LM training algorithm can be used to characterize pile settlement with good degree of accuracy. There was also close agreement between the experimental and predicted data with a root mean square error, (RMSE) and correlation coefficient (R) of 0.0025192 and 0.988, respectively.
This study was implemented to examine pile load-settlement response and to develop a rapid, highly efficient predictive intelligent model, using a new computational intelligence (CI) algorithm. To achieve this aim, a series of experimental pile load tests were performed on steel, closed-ended pile models consisting of three piles with aspect ratios of 25, 17, and 12 in an attempt to make site in-situ pile-load tests unnecessary. An optimised, evolutionary, supervised Levenberg-Marquardt (LM) training algorithm was used for this process due to its remarkably robust performance. The model piles were penetrated and tested in three sand relative densities; dense, medium, and loose. Applied load (P), pile effective length (lc), pile flexural rigidity (EA), pile slenderness ratio (lc/d) and interface friction angle (δ) were identified, based on a comprehensive statistical analysis, as these parameters play a key role in governing pile settlement. To evaluate the efficiency and the generalisation ability of the proposed algorithm, graphical comparisons were made between the proposed algorithm and the experimental results with further comparisons made with conventional prediction approaches. The results revealed outstanding agreement between the targeted and predicted pile-load settlement with a coefficient of correlation of 0.985 and a Pearson's correlation coefficient, P = 2.22 x10-32 and root mean square error (RMSE) of 0.059 respectively. This, in parallel with a non-significant mean square error level (MSE) of 0.002, validates the feasibility of the proposed method and its potential in future applications.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.