72 research outputs found

    Hybridised Artificial Neural Network model with Slime Mould Algorithm: A novel methodology for prediction urban stochastic water demand

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    Urban water demand prediction based on climate change is always challenging for water utilities because of the uncertainty which results from a sudden rise in water demand due to stochastic patterns of climatic factors. For this purpose, a novel combined methodology including, firstly, data pre-processing techniques were employed to decompose the time series of water and climatic factors by using Empirical Mode Decomposition and identifying the best model input via tolerance to avoid multi-collinearity. Second, the Artificial Neural Network (ANN) model was optimised by an up-to-date Slime Mould Algorithm (SMA-ANN) to predict the medium term of the stochastic signal of monthly urban water demand. Ten climatic factors over 16 years were used to simulate the stochastic signal of water demand. The results reveal that SMA outperforms Multi-Verse Optimiser and Backtracking Search Algorithm based on error scale. The performance of the hybrid model SMA-ANN is better than ANN (stand-alone) based on the range of statistical criteria. Generally, this methodology yields accurate results with a coefficient of determination of 0.9 and a mean absolute relative error of 0.001. This study can assist local water managers to efficiently manage the present water system and plan extensions to accommodate the increasing water demand

    Efficacy of Electrocoagulation Treatment for the abatement of Heavy Metals: An Overview of Critical Processing Factors, Kinetic Models and Cost Analysis

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    The electrocoagulation (EC) process introduces coagulants by electrochemical means, and is widely adopted for removing heavy metals, besides other contaminants, such as organic pollutants, suspended and dissolved solids, colloidal materials, etc. However, its capability can vary significantly, depending on the operating conditions. Although most of the investigations so far are limited at the laboratory level with artificially prepared solutions or industrial effluent lacking full- and field-scale studies, the success of the process depends a lot on optimizing the process variable. It has been found that the current density (typically 1–20 mA/cm2), type of electrode (generally aluminum or iron) and minimum electrolysis time are the key process parameters that influence performance. Furthermore, key mechanisms involved in the EC process, including charge neutralization, reduction-oxidation and precipitation/co-precipitation, are crucial for pollutant abatement. This review presents a detailed study undertaking all significant parameters that play a crucial role in the EC process, its mechanism, and improving the efficiency of this process by optimization of these parameters, along with suitable kinetic models

    Dental caries experience, oral health status and treatment needs of dental patients with autism

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    OBJECTIVES: Autism is a lifelong neurodevelopmental disorder. The aims of this study were to investigate whether children with autism have higher caries prevalence, higher periodontal problems, or more treatment needs than children of a control group of non-autistic patients, and to provide baseline data to enable comparison and future planning of dental services to autistic children. MATERIAL AND METHODS: 61 patients with autism aged 6-16 years (45 males and 16 females) attending Dubai and Sharjah Autism Centers were selected for the study. The control group consisted of 61 non-autistic patients chosen from relatives or friends of autistic patients in an attempt to have matched age, sex and socioeconomic status. Each patient received a complete oral and periodontal examination, assessment of caries prevalence, and caries severity. Other conditions assessed were dental plaque, gingivitis, restorations and treatment needs. Chi-square and Fisher's exact test of significance were used to compare groups. RESULTS: The autism group had a male-to-female ratio of 2.8:1. Compared to controls, children with autism had significantly higher decayed, missing or filled teeth than unaffected patients and significantly needed more restorative dental treatment. The restorative index (RI) and Met Need Index (MNI) for the autistic children were 0.02 and 0.3, respectively. The majority of the autistic children either having poor 59.0% (36/61) or fair 37.8% (23/61) oral hygiene compared with healthy control subjects. Likewise, 97.0% (59/61) of the autistic children had gingivitis. CONCLUSIONS: Children with autism exhibited a higher caries prevalence, poor oral hygiene and extensive unmet needs for dental treatment than non-autistic healthy control group. Thus oral health program that emphasizes prevention should be considered of particular importance for children and young people with autism
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