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This paper introduces an improved convolutional neural network based on the conventional U-Net for simulating built-up land expansion. The proposed method hires a pixel-wise semantic segmentation approach considering the spatial drivers affecting urbanization as data cubes. Independent variables including altitude, slope, and distance from barren, crop, greenery, roads, and urban areas for 1998, 2008, and 2018 were considered as covariates for the simulation of built-up land expansion in Tehran and Karaj regions in Iran. The proposed method was compared with the random forest (RF) algorithm as the baseline model. Evaluation using the area under the total operating characteristic indicated the superiority of our modified U-Net (0.87) over the RF (0.82) algorithm. Furthermore, evaluation using the percent correct metric indicated that our proposed model is capable of learning neighborhood effects effectively leading to simulate built-up land expansion accurately, independent from applying a cellular automata (CA) model. Therefore, the modified U-Net independent from the CA which can consider the neighborhood effects is recommended for the simulation of built-up land expansion precisely. [ABSTRACT FROM AUTHOR]

Introduction: The COVID-19 pandemic posed significant risks to children worldwide. This study aimed to assess the COVID-19 protection status of children and explored the relationship between household socio-economic status and COVID-19 morbidity and preventive measures, including vaccination and mask-wearing, in two cities in Iran. Method: A population-based cross-sectional study was conducted from July to October 2022 among 7 to 18-year-old children and their families in Tehran and Karaj. A total of 3,022 samples were selected using stratified multistage cluster sampling. Data were collected through interviews with children and adults, using questionnaires and was analyzed with Stata software version 14. Results: The analysis focused on 2,878 children with a median age of 12. Over half (54%) reported that the pandemic negatively affected their family's financial status, with 45% describing its impact on children's needs as negative or very negative. Just under 50% of respondents consistently wore masks during the study period, and around 54% had received at least one dose of the COVID-19 vaccine. Reasons for not getting vaccinated included concerns about side effects, ineligibility for the target age group, and overcrowding at vaccination sites. The odds of not getting vaccinated were significantly lower for children aged 15–18, with boys more likely to refuse vaccination than girls. Conclusion: The financial impact of the pandemic in Iran affected families' ability to meet their children's needs. Moreover, low vaccination acceptance rates increased children's vulnerability to health problems and contributed to COVID-19 infections. Efforts should be made to increase vaccination acceptance, particularly among immigrant populations. [ABSTRACT FROM AUTHOR]

This paper couples a Forward Feature Selection algorithm with Random Forest (FFS-RF) to create a transition index map, which then guides the spatial allocation for the extrapolation of urban growth using a Cellular Automata model. We used Landsat imagery to generate land cover maps at the years 1998, 2008, and 2018 for the Tehran-Karaj Region (TKR) in Iran. The FFS-RF considered the independent variables of slope, altitude, and distances from urban, crop, greenery, barren, and roads. The FFS-RF revealed temporal non-stationary of drivers from 1998–2008 to 2008–2018. The FFS-RF detected that altitude and distance from greenery were the most important drivers of urban growth during 1998–2008, then distances from crop and barren were the most important drivers during 2008–2018. We used the Total Operating Characteristic to evaluate the transition index maps. Validation during 2008–2018 showed that FFS-RF produced a transition index map that had predictive power no better than an allocation of urban growth near existing urban. Simulation to 2060 extrapolated that Tehran, Karaj, and their adjacent cities will interconnect spatially to form a gigantic city-region. [Display omitted] • A Forward Feature Selection - Random Forest (FFS-RF) model describes urban growth. • FFS-RF found temporal non-stationarity of drivers in Iran's Tehran-Karaj region. • The Total Operating Characteristic evaluated the transition index map (TIM). • FFS-RF produced a TIM that has predictive power no better than a baseline TIM. • FFS-RF and Cellular Automata extrapolated urban growth to the year 2060. [ABSTRACT FROM AUTHOR]