Nowadays, sustainable development is considered a key concept and solution in creating a promising and prosperous future for human societies. Nevertheless, there are some predicted and unpredicted problems that epidemic diseases are real and complex problems. Hence, in this research work, a serious challenge in the sustainable development process was investigated using the classification of confirmed cases of COVID-19 (new version of Coronavirus) as one of the epidemic diseases. Hence, binary classification modeling was used by the group method of data handling (GMDH) type of neural network as one of the artificial intelligence methods. For this purpose, the Hubei province in China was selected as a case study to construct the proposed model, and some important factors, namely maximum, minimum, and average daily temperature, the density of a city, relative humidity, and wind speed, were considered as the input dataset, and the number of confirmed cases was selected as the output dataset for 30 days. The proposed binary classification model provides higher performance capacity in predicting the confirmed cases. In addition, regression analysis has been done and the trend of confirmed cases compared with the fluctuations of daily weather parameters (wind, humidity, and average temperature). The results demonstrated that the relative humidity and maximum daily temperature had the highest impact on the confirmed cases. The relative humidity in the main case study, with an average of 77.9%, affected positively, and maximum daily temperature, with an average of 15.4 • C, affected negatively, the confirmed cases.

Sustainable development has been a controversial global topic, and as a complex concept in recent years, it plays a key role in creating a favorable future for societies. Meanwhile, there are several problems in the process of implementing this approach, like epidemic diseases. Hence, in this study, the impact of climate and urban factors on confirmed cases of COVID-19 (a new type of coronavirus) with the trend and multivariate linear regression (MLR) has been investigated to propose a more accurate prediction model. For this propose, some important climate parameters, including daily average temperature, relative humidity, and wind speed, in addition to urban parameters such as population density, were considered, and their impacts on confirmed cases of COVID-19 were analyzed. The analysis was performed for three case studies in Italy, and the application of the proposed method has been investigated. The impacts of parameters have been considered with a delay time from one to nine days to find out the most suitable combination. The result of the analysis demonstrates the effectiveness of the proposed model and the impact of climate parameters on the trend of confirmed cases. The research hypothesis approved by the MLR model and the present assessment method could be applied by considering several variables that exhibit the exact delay of them to new confirmed cases of COVID-19.

Buildings portion in global energy consumption is 40%, and in the building envelope, the roof is a crucial point for improving indoor temperature, especially in the last and second last floors. Studies show that green roofs can be applied to moderate roof temperature and affect the indoor temperature in summer and winter. However, the performance of green roofs depends on several parameters such as climate, irrigation, layer materials, and thickness. In this context, the present research deals with a comprehensive experimental analysis of different thermal impacts of green roofs in summer and winter in a Mediterranean climate. Measurements carried out in one year in three different types of green roofs with different thicknesses, layers, and with and without the insulation layer. The analysis determined the possible period that indoor cooling or heating might be required with and without green roofs and demonstrated the positive impact of green roofs in moderating the roof temperature and temperature fluctuations, which in summer was remarkable. In conclusion, since in the Mediterranean climate, the thermal differences between green roofs and conventional roofs in summer are much higher than winter, it seems that the green roof without an insulation layer would show better performance.

Nowadays, an infectious disease outbreak is considered one of the most destructive effects in the sustainable development process. The outbreak of new coronavirus (COVID-19) as an infectious disease showed that it has undesirable social, environmental, and economic impacts, and leads to serious challenges and threats. Additionally, investigating the prioritization parameters is of vital importance to reducing the negative impacts of this global crisis. Hence, the main aim of this study is to prioritize and analyze the role of certain environmental parameters. For this purpose, four cities in Italy were selected as a case study and some notable climate parameters—such as daily average temperature, relative humidity, wind speed—and an urban parameter, population density, were considered as input data set, with confirmed cases of COVID-19 being the output dataset. In this paper, two artificial intelligence techniques, including an artificial neural network (ANN) based on particle swarm optimization (PSO) algorithm and differential evolution (DE) algorithm, were used for prioritizing climate and urban parameters. The analysis is based on the feature selection process and then the obtained results from the proposed models compared to select the best one. Finally, the difference in cost function was about 0.0001 between the performances of the two models, hence, the two methods were not different in cost function, however, ANN-PSO was found to be better, because it reached to the desired precision level in lesser iterations than ANN-DE. In addition, the priority of two variables, urban parameter, and relative humidity, were the highest to predict the confirmed cases of COVID-19.

The rapid outbreak of the new Coronavirus (COVID-19) pandemic and the spread of the virus worldwide, especially in the Northern Hemisphere, have prompted various investigations about the impact of environmental factors on the rate of development of this epidemic. Different studies have called the attention to various parameters that may have influenced the spread of the virus, and in particular, the impact of climatic parameters has been emphasized. The main purpose of this study is to investigate the correlation between the average daily temperature and the rate of coronavirus epidemic growth in the infected regions. The main hypothesis object of our research is that between regions exhibiting a significant difference in the mean daily temperature, a significant difference is also observed in the average cumulative daily rate of confirmed cases, and that this does not happen if there is no significant difference in mean daily temperature. To test this research hypothesis, we carried on the case study of three regions in each of five countries and analyzed the correlation through F-test, and Independent-Samples T-Test. In all five selected countries, we found that when there is a significant difference in the daily mean temperature between two regions of a country, a significant difference exists also in the average cumulative daily rate of confirmed cases. Conversely, if there are no significant differences in the mean daily temperature of two regions in the same country, no significant difference is observed in the average cumulative daily rate of confirmed cases for these regions.

The simulation of the ventilation and the heating, ventilation, and air conditioning (HVAC) systems of vehicles could be used in the energy demand management of vehicles besides improving the air quality inside their cabins. Moreover, traveling by public transport during a pandemic is a concerning factor, and analysis of the vehicle’s cabin environments could demonstrate how to decrease the risk and create a safer journey for passengers. Therefore, this article presents airflow analysis, air changes per hour (ACH), and respiration aerosols’ trajectory inside three vehicles, including a typical car, bus, and airplane. In this regard, three vehicles’ cabin environment boundary conditions and the HVAC systems of the selected vehicles were determined, and three-dimensional numerical simulations were performed using computational fluid dynamic (CFD) modeling. The analysis of the airflow patterns and aerosol trajectories in the selected vehicles demonstrate the critical impact of inflow, outflow, and passenger’s locations in the cabins. The CFD model results exhibited that the lowest risk could be in the airplane and the highest in the bus because of the location of airflows and outflows. The discrete CFD model analysis determined the ACH for a typical car of about 4.3, a typical bus of about 7.5, and in a typical airplane of about 8.5, which were all less than the standard protocol of infection prevention, 12 ACH. According to the results, opening windows in the cars could decrease the aerosol loads and improve the low ACH by the HVAC systems. However, for the buses, a new design for the outflow location or an increase in the number of outflows appeared necessary. In the case of airplanes, the airflow paths were suitable, and by increasing the airflow speed, the required ACH might be achieved. Finally, in the closed (recirculating) systems, the role of filters in decreasing the risk appeared critical.

The role of the industrial sector in total greenhouse gas (GHG) emissions and resource consumption is well-known, and many industrial activities may have a negative environmental impact. The solution to decreasing the negative effects cannot be effective without the consideration of sustainable development. There are several methods for sustainability evaluation, such as tools based on products, processes, or plants besides supply chain or life cycle analysis, and there are different rating systems suggesting 80, 140, or more indicators for assessment. The critical point is the limits such as required techniques and budget in using all indicators for all factories in the beginning. Moreover, the weight of each indicator might change based on the selected alternative that it is not a fixed value and could change in a new case study. In this regard, to determine the impact and weight of different indicators in sustainable factories, a multi-layer Triangular Fuzzy Analytic Hierarchy Process (TFAHP) approach was developed, and the application of the method was described and verified. The defined layers are six; for each layer, the pairwise comparison matrix was developed, and the total aggregated score concerning the sustainability goal for each alternative was calculated that shows the Relative Importance Coefficient (RIC). The method is formulated in a way that allows adding the new indicators in all layers as the verification shows, and thus, there are no limits for using any green rating systems. Therefore, the presented approach by TFAHP would provide an additional tool toward the sustainable development of factories.

The outbreak of the new Coronavirus (COVID-19) pandemic has prompted investigations on various aspects. This research aims to study the possible correlation between the numbers of swab tests and the trend of confirmed cases of infection, while paying particular attention to the sickness level. The study is carried out in relation to the Italian case, but the result is of more general importance, particularly for countries with limited ICU (intensive care units) availability. The statistical analysis showed that, by increasing the number of tests, the trend of home isolation cases was positive. However, the trend of mild cases admitted to hospitals, intensive case cases, and daily deaths were all negative. The result of the statistical analysis provided the basis for an AI study by ANN. In addition, the results were validated using a multivariate linear regression (MLR) approach. Our main result was to identify a significant statistical effect of a reduction of pressure on the health care system due to an increase in tests. The relevance of this result is not confined to the COVID-19 outbreak, because the high demand of hospitalizations and ICU treatments due to this pandemic has an indirect effect on the possibility of guaranteeing an adequate treatment for other high-fatality diseases, such as, e.g., cardiological and oncological ones. Our results show that swab testing may play a significant role in decreasing stress on the health system. Therefore, this case study is relevant, in particular, for plans to control the pandemic in countries with a limited capacity for admissions to ICU units.