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Along with environmental pollution, urban planning has been connected to public health. The research indicates that the quality of built environments plays an important role in reducing mental disorders and overall health. The structure and shape of the city are considered as one of the factors influencing happiness and health in urban communities and the type of the daily activities of citizens. The aim of this study was to promote physical activity in the main structure of the city via urban design in a way that the main form and morphology of the city can encourage citizens to move around and have physical activity within the city. Functional, physical, cultural-social, and perceptual-visual features are regarded as the most important and effective criteria in increasing physical activities in urban spaces, based on literature review. The environmental quality of urban spaces and their role in the physical activities of citizens in urban spaces were assessed by using the questionnaire tool and analytical network process (ANP) of structural equation modeling. Further, the space syntax method was utilized to evaluate the role of the spatial integration of urban spaces on improving physical activities. Based on the results, consideration of functional diversity, spatial flexibility and integration, security, and the aesthetic and visual quality of urban spaces plays an important role in improving the physical health of citizens in urban spaces. Further, more physical activities, including motivation for walking and the sense of public health and happiness, were observed in the streets having higher linkage and space syntax indexes with their surrounding texture.
The production of a desired product needs an effective use of the experimental model. The present study proposes an extreme learning machine (ELM) and a support vector machine (SVM) integrated with the response surface methodology (RSM) to solve the complexity in optimization and prediction of the ethyl ester and methyl ester production process. The novel hybrid models of ELM-RSM and ELM-SVM are further used as a case study to estimate the yield of methyl and ethyl esters through a trans-esterification process from waste cooking oil (WCO) based on American Society for Testing and Materials (ASTM) standards. The results of the prediction phase were also compared with artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS), which were recently developed by the second author of this study. Based on the results, an ELM with a correlation coefficient of 0.9815 and 0.9863 for methyl and ethyl esters, respectively, had a high estimation capability compared with that for SVM, ANNs, and ANFIS. Accordingly, the maximum production yield was obtained in the case of using ELM-RSM of 96.86% for ethyl ester at a temperature of 68.48 °C, a catalyst value of 1.15 wt. %, mixing intensity of 650.07 rpm, and an alcohol to oil molar ratio (A/O) of 5.77; for methyl ester, the production yield was 98.46% at a temperature of 67.62 °C, a catalyst value of 1.1 wt. %, mixing intensity of 709.42 rpm, and an A/O of 6.09. Therefore, ELM-RSM increased the production yield by 3.6% for ethyl ester and 3.1% for methyl ester, compared with those for the experimental data.