Anomalous Seepage Flows and Piping in Oje-Owode earthdam: Granular filter-drain media as controlling measure

There was loss of water at Oje-Owode dam embankment through seepage. Evidence of some degrees of fracturing and seepage at the dam toe been reported. Previous results showed that the dam embankment was permeable and anomalous seepage occurred at the toe. This study, therefore attempted the application of locally sourced granular filters, precisely, stone dust from quarry, to model the control of seepage and piping at the toe of the dam embankment. Coarse soil samples were collected from quarry site as selected granular filters. Granite stones of sizes ½ - ¾ inch were selected as the drain samples. These were subjected to particle size analysis, compaction tests, specific gravity; and constant head permeability test. Numerical analyses were also carried out to generate flow lines, seepage rates and velocity vectors of the dam. The results of the simulated flow net showed a seepage value of 3.8066 x 10-8 m3/s per width, while total seepage at maximum phreatic level and at full length (896m) of dam axis was 3.4107 x 10-5 m3/s. This indicated loss of water from dam toe through seepage. The velocity vector contours showed flow directions and maximum velocity magnitude of 3.6 x 10-8m/s in the direction of decreasing heads. The modelled filter-drain installed at dam toe controlled the anomalous seepage water and prevented piping as though it were a horizontal drain. The flow lines were controlled at coordinate points (36.25m, 0.56m) and remained horizontal through the filter media, until it exited the dam at the toe at coordinate points (41.88m, 0.59m), which is relatively a save point for collection. The modelled filter-drain media installed at dam toe controlled the anomalous seepage water and prevented piping. Keywords: Oje- Owode dam, Piping, Granular Sand filter, Numerical analyses, Seepage

Genetic Programming: Principles, Applications and Opportunities for Hydrological Modelling

Hydrological modelling plays a crucial role in the planning and management of water resources, most especially in water stressed regions where the need to effectively manage the available water resources is of critical importance. However, due to the complex, nonlinear and dynamic behaviour of hydro-climatic interactions, achieving reliable modelling of water resource systems and accurate projection of hydrological parameters are extremely challenging. Although a significant number of modelling techniques (process-based and data-driven) have been developed and adopted in that regard, the field of hydrological modelling is still considered as one that has sluggishly progressed over the past decades. This is majorly as a result of the identification of some degree of uncertainty in the methodologies and results of techniques adopted. In recent times, evolutionary computation (EC) techniques have been developed and introduced in response to the search for efficient and reliable means of providing accurate solutions to hydrological related problems. This paper presents a comprehensive review of the underlying principles, methodological needs and applications of a promising evolutionary computation modelling technique – genetic programming (GP). It examines the specific characteristics of the technique which makes it suitable to solving hydrological modelling problems. It discusses the opportunities inherent in the application of GP in water related-studies such as rainfall estimation, rainfall-runoff modelling, streamflow forecasting, sediment transport modelling, water quality modelling and groundwater modelling among others. Furthermore, the means by which such opportunities could be harnessed in the near future are discussed. In all, a case for total embracement of GP and its variants in hydrological modelling studies is made so as to put in place strategies that would translate into achieving meaningful progress as it relates to modelling of water resource systems, and also positively influence decision-making by relevant stakeholders

An Investigation of Generalized Differential Evolution Metaheuristic for Multiobjective Optimal Crop-Mix Planning Decision

This paper presents an annual multiobjective crop-mix planning as a problem of concurrent maximization of net profit and maximization of crop production to determine an optimal cropping pattern. The optimal crop production in a particular planting season is a crucial decision making task from the perspectives of economic management and sustainable agriculture. A multiobjective optimal crop-mix problem is formulated and solved using the generalized differential evolution 3 (GDE3) metaheuristic to generate a globally optimal solution. The performance of the GDE3 metaheuristic is investigated by comparing its results with the results obtained using epsilon constrained and nondominated sorting genetic algorithms—being two representatives of state-of-the-art in evolutionary optimization. The performance metrics of additive epsilon, generational distance, inverted generational distance, and spacing are considered to establish the comparability. In addition, a graphical comparison with respect to the true Pareto front for the multiobjective optimal crop-mix planning problem is presented. Empirical results generally show GDE3 to be a viable alternative tool for solving a multiobjective optimal crop-mix planning problem

Dimethyl fumarate in patients admitted to hospital with COVID-19 (RECOVERY): a randomised, controlled, open-label, platform trial

Dimethyl fumarate (DMF) inhibits inflammasome-mediated inflammation and has been proposed as a treatment for patients hospitalised with COVID-19. This randomised, controlled, open-label platform trial (Randomised Evaluation of COVID-19 Therapy [RECOVERY]), is assessing multiple treatments in patients hospitalised for COVID-19 (NCT04381936, ISRCTN50189673). In this assessment of DMF performed at 27 UK hospitals, adults were randomly allocated (1:1) to either usual standard of care alone or usual standard of care plus DMF. The primary outcome was clinical status on day 5 measured on a seven-point ordinal scale. Secondary outcomes were time to sustained improvement in clinical status, time to discharge, day 5 peripheral blood oxygenation, day 5 C-reactive protein, and improvement in day 10 clinical status. Between 2 March 2021 and 18 November 2021, 713 patients were enroled in the DMF evaluation, of whom 356 were randomly allocated to receive usual care plus DMF, and 357 to usual care alone. 95% of patients received corticosteroids as part of routine care. There was no evidence of a beneficial effect of DMF on clinical status at day 5 (common odds ratio of unfavourable outcome 1.12; 95% CI 0.86-1.47; p = 0.40). There was no significant effect of DMF on any secondary outcome

Dimethyl fumarate in patients admitted to hospital with COVID-19 (RECOVERY): a randomised, controlled, open-label, platform trial

Dimethyl fumarate (DMF) inhibits inflammasome-mediated inflammation and has been proposed as a treatment for patients hospitalised with COVID-19. This randomised, controlled, open-label platform trial (Randomised Evaluation of COVID-19 Therapy [RECOVERY]), is assessing multiple treatments in patients hospitalised for COVID-19 (NCT04381936, ISRCTN50189673). In this assessment of DMF performed at 27 UK hospitals, adults were randomly allocated (1:1) to either usual standard of care alone or usual standard of care plus DMF. The primary outcome was clinical status on day 5 measured on a seven-point ordinal scale. Secondary outcomes were time to sustained improvement in clinical status, time to discharge, day 5 peripheral blood oxygenation, day 5 C-reactive protein, and improvement in day 10 clinical status. Between 2 March 2021 and 18 November 2021, 713 patients were enroled in the DMF evaluation, of whom 356 were randomly allocated to receive usual care plus DMF, and 357 to usual care alone. 95% of patients received corticosteroids as part of routine care. There was no evidence of a beneficial effect of DMF on clinical status at day 5 (common odds ratio of unfavourable outcome 1.12; 95% CI 0.86-1.47; p = 0.40). There was no significant effect of DMF on any secondary outcome

Review of hybrid evolutionary algorithms for optimizing a reservoir

Many studies present hybrid algorithms to solve multiobjective uses of a reservoir. The reservoirs presented in this review are used for flood control, hydropower generation, ecological flow requirement and water distribution systems. While flood control and hydropower are main function of a reservoir, ecological flow requirement are shown to be an important part that should be incorporated into reservoir operation models. Evolutionary algorithms are shown to be capable of solving complex reservoir operation models with fast convergence rate. A review of different algorithms in solving different reservoir operation problems is presented. The results generated by these algorithms are effective, competitive, comparable and applicable. The algorithms present the solutions to the computationally expensive models in an efficient way. Systematic ways of solving different reservoir operation models are presented. Many models reviewed involve reservoirs operated in single objective, multiobjectives, single reservoir and multireservoir. Real time operation is shown to be superior to normal operation. The results generated by the evolutionary algorithms presented show that the algorithms are capable of solving complex and multidimensional problem of water resources. The non-dominated solutions generated are many and spread widely on the Pareto-optimal front. Keywords: Reservoir operation, Flood control, Ecological flow, Water distribution, Evolutionary algorithm, Hybri

Differential evolution algorithm for solving multi-objective crop planning model

This study presents four strategies of a novel evolutionary algorithm, multi-objective differential evolution algorithm (MDEA). The four strategies namely, MDEA1, MDEA2, MDEA3 and MDEA4 are adapted to solve the multi-objective crop planning model with multiple constraints in a farmland in the Vaalharts irrigation scheme (VIS) in South Africa. The three objectives of the model are to minimize the total irrigation water (m2) and to maximize both the total net income in South African Rand (ZAR) from farming and the total agricultural output in tons. The total area of the farm is 771,000m2 and supplied with 704,694m2 of irrigation water annually. Numerical results produce non-dominated solutions which converge to Pareto optimal fronts. MDEA1 and MDEA2 strategies with binomial crossover method are better for solving the crop planning problem presented than MDEA3 and MDEA4 strategies with exponential crossover method. MDEA1 found a solution with the highest total net income of ZAR 1,304,600 with the corresponding total agricultural output, total irrigation water and total planting areas of 316.26tons, 702,000m3 and 725,000m2, respectively. The planting areas for the crops in the solution are 73,463m2 for maize, 551,660m2 for groundnut, 50,000m2 for Lucerne and 50,000m2 for Peacan nut. It can be concluded that MDEA is a good algorithm for solving crop planning problem especially in water deficient areas like South Africa.MDEA Differential evolution Multi-objective Irrigation Evolutionary algorithm