Suchergebnisse

Abstract
Background
In densely populated urban centers, increased air temperature due to urban heat island (UHI) effect can undermine the thermal comfort and health of citizens. Research has shown that large urban parks can mitigate the effect of UHIs and improve thermal comfort, especially in the warmer months of the year when temperature changes are more noticeable. This study investigated the cooling effect intensity (CEI) of the Retiro Park in the center of Madrid at three different distances from its southern edge and the impact of this cooling effect on thermal comfort from physiological and psychological perspectives. This investigation was performed by measuring microclimate data and conducting a survey simultaneously during the summer days.

Results
The results showed that the CEI of the park varies with distance from its edge. Because of this effect, air temperature within the 130 m and 280 m distance of the park was, respectively, 1.6 °C and 0.9 °C lower than the temperature at the 520 m distance (the nearest heat island). After examining the effect of the park in terms of physiological equivalent temperature (PET), it was found that the PET at the 130 m and 280 m distance of the park was 9.3% and 5.4% less than the PET in the heat island domain. More than 81% of the respondents (in all three areas) had a mental image of the park as the place where they would experience the highest level of outdoor thermal comfort, and this rate was higher in the areas closer to the park. The analysis of citizens' responses about perceived thermal comfort (PTC) showed that citizens in areas with higher CEI had perceived a higher degree of thermal comfort from the psychological perspective.

Conclusion
This study demonstrates the significant role of large urban parks located in the core of the populated cities in providing thermal comfort for citizens from both physiological and psychological perspectives. Additionally, the results of this study demonstrated that among the environmental (natural and artificial) factors around the park (topography, urban structure, etc.), the aspect ratio has the greatest impact on thermal comfort.

AbstractThe longitudinal dispersion coefficient (LDC) of river pollutants is considered as one of the prominent water quality parameters. In this regard, numerous research studies have been conducted in recent years, and various equations have been extracted based on hydrodynamic and geometric elements. LDC's estimated values obtained using different equations reveal a significant uncertainty due to this phenomenon's complexity. In the present study, the crow search algorithm (CSA) is applied to increase the equation's precision by employing evolutionary polynomial regression (EPR) to model an extensive amount of geometrical and hydraulic data. The results indicate that the CSA improves the performance of EPR in terms of R2 (0.8), Willmott's index of agreement (0.93), Nash–Sutcliffe efficiency (0.77), and overall index (0.84). In addition, the reliability analysis of the proposed equation (i.e., CSA) reduced the failure probability (Pf) when the value of the failure state containing 50 to 600 m2/s is increasing for the Pf determination using the Monte Carlo simulation. The best-fitted function for correct failure probability prediction was the power with R2 = 0.98 compared with linear and exponential functions.

Extension of SIR type models has been reported in a number of publications in the mathematics community. But little is done on validation of these models to fit adequately with multiple clinical data of an infectious disease. In this paper, we introduce the SEIR-PAD model to assess susceptible, exposed, infected, recovered, super-spreader, asymptomatic infected, and deceased populations. SEIR-PAD model consists of 7-set of ordinary differential equations with 8 unknown coefficients which are solved numerically in MATLAB using an optimization algorithm. Four sets of COVID-19 clinical data consist of cumulative populations of infected, deceased, recovered, and susceptible are used from the start of the outbreak until 23rd June 2020 to fit with SEIR-PAD model results. Results for trends of COVID-19 in GCC countries indicate that the disease may be terminated after 200 to 300 days from the start of the outbreak depends on current measures and policies. SEIR-PAD model provides a robust and strong tool to predict trends of COVID-19 for better management and/or foreseeing effects of certain enforcing laws by governments, health organizations or policy makers.

On 30 July 2020, a total number of 301,530 diagnosed COVID-19 cases were reported in Iran, with 261,200 recovered and 16,569 dead. The COVID-19 pandemic started with 2 patients in Qom city in Iran on 20 February 2020. Accurate prediction of the end of the COVID-19 pandemic and the total number of populations affected is challenging. In this study, several widely used models, including Richards, Gompertz, Logistic, Ratkowsky, and SIRD models, are used to project dynamics of the COVID-19 pandemic in the future of Iran by fitting the present and the past clinical data. Iran is the only country facing a second wave of COVID-19 infections, which makes its data difficult to analyze. The present study's main contribution is to forecast the near-future of COVID-19 trends to allow non-pharmacological interventions (NPI) by public health authorities and/or government policymakers. We have divided the COVID-19 pandemic in Iran into two waves, Wave I, from February 20, 2020, to May 4, 2020, and Wave II from May 5, 2020, to the present. Two statistical methods, i.e., the Pearson correlation coefficient (R) and the coefficient of determination (R2), are used to assess the accuracy of studied models. Results for Wave I Logistic, Ratkowsky, and SIRD models have correctly fitted COVID-19 data in Iran. SIRD model has fitted the first peak of infection very closely on April 6, 2020, with 34,447 cases (The actual peak day was April 7, 2020, with 30,387 active infected patients) with the re-production number R0=3.95. Results of Wave II indicate that the SIRD model has precisely fitted with the second peak of infection, which was on June 20, 2020, with 19,088 active infected cases compared with the actual peak day on June 21, 2020, with 17,644 cases. In Wave II, the re-production number R0=1.45 is reduced, indicating a lower transmission rate. We aimed to provide even a rough project future trends of COVID-19 in Iran for NPI decisions. Between 180,000 to 250,000 infected cases and a death toll of between 6,000 to 65,000 cases are ...