Applying machine learning in the investigation of the link between the high-velocity streams of charged solar particles and precipitation-induced floods.

This study explores a possible link between solar activity and floods caused by precipitation. For this purpose, discrete blocks of data for 89 separate flood events in Europe in the period 2009-2018 were used. Solar activity parameters with a time lag of 0-11 days were used as input data of the model, while precipitation data in the 12 days preceding the flood were used as output data. The level of randomness of the input and output time series was determined by correlation analysis, while the potential causal relationship was established by applying machine learning classification predictive modeling. A total of 25 distinct machine-learning algorithms and four model ensembles were applied. It was shown that in 81% of cases, the designed model could explain the occurrence or absence of precipitation-induced floods 9 days in advance. Differential proton flux in the 0.068-0.115 MeV and integral proton flux > 2.5 MeV were found to be the most important factors for forecasting precipitation-induced floods. The study confirmed that machine learning is a valuable technique for establishing nonlinear relationships between solar activity parameters and the onset of floods induced by precipitation.

Evaluation of outdoor thermal comfort conditions: evidence from the Serbian major ski resort over the last 30 years.

The study aims to present reliable information about thermal conditions and their impacts on visitors to ski travel destinations. Mountain tourism areas are specific since high altitudes affect the ambient weather conditions which can affect different types of human activities. In this paper, the thermal comfort and its changes in Kopaonik Mountain, the most popular ski resort in Serbia over the last 30 years, have been evaluated. Information about thermal comfort is presented by using the Universal Thermal Climate Index (UTCI), physiologically equivalent temperature (PET), and modified physiologically equivalent temperature (mPET) in 3-h resolution for the period 1991-2020. The results indicate prevailing cold stress all year round. Days with moderate, strong, and very strong heat stress were not recorded. Strong and extreme cold stress prevailed during winter, while slight and moderate cold stress prevailed during summer. Transitional seasons were very cold, but autumn was more comfortable than spring. The occurrence of days with neutral and slightly warm/cool conditions is concentrated in the summer months. However, summer is not used enough for tourism because the choice of tourists to stay at Kopaonik is not primarily based on favorable bioclimatic conditions, but on resources for winter tourism. With global warming, the annual number of thermally favorable days has been increasing, while the number of days with extreme and strong cold stress is decreasing. Continuing this trend can significantly influence tourism in the future, and therefore, new strategies in ski resorts will be required to adapt to the changing climate.

Significance of the Work Environment and Personal Resources for Employees' Well-Being at Work in the Hospitality Sector.

The aim of the study was to investigate whether different elements of the work environment (manifested by job demands, job control, and social support) and personal resources were linked to employees' well-being at work. Based on data gathered from 574 employees in the hospitality industry in Serbia, it was also tested if personal resources, expressed through self-efficacy, hope, optimism, and resilience, could moderate the relationship between work environment and employees' well-being at work. Correlation analyses showed that high job demands had negative effects on employees' well-being, causing negative emotional reactions to their job, while job control and social support developed positive relationships with positive employees' well-being. The moderating effect analysis found that personal resources can fully moderate the relationship between job demands and well-being at work, and job control and well-being at work. On the other side, personal resources were not a significant moderator in the relationship between social support and well-being at work, indicating that even when employees have adequate personal resources, they are not enough to decrease the negative effects of lack of social support on employees' well-being at work. This shows how important the support of supervisors and colleagues is for employees in hospitality.

Health risks of extended exposure to low-level UV radiation - An analysis of ground-based and satellite-derived data.

This study aims to indicate the importance of revising current health recommendations concerning the duration of exposure and individual sensitivity of the skin to solar ultraviolet (UV) radiation. For this purpose, a 16-year data series (2005-2020) of erythemal radiant exposure (Her) and UV index (UVI) for Serbia was analyzed. The UV-related risk was estimated for lighter skin (skin phototypes I-IV) under prolonged exposure on days when maximum UVI was below the recommended protection threshold (UVIlow days, for UVI < 3). Risk assessment was performed for seasonal exposure using satellite-derived data (OMUVBd product) previously validated by ground-based measurements in Novi Sad. The assessment of harmful effects included an analysis of the relation between the daily maximum UVI and the corresponding daily Her, the occurrence of UVIlow days, the exceedance of minimal erythema dose (MED), and the minimum duration of exposure to induce erythema (tMED) for all lighter skin phototypes. It was found that the share of UVIlow days in the total number of days in Serbia increases with the latitude, with the highest percentage in winter (up to 69.454%) and the lowest in summer (up to 3.468%). The results show that the daily Her frequently exceeded the harmful threshold for lighter skin phototypes I-IV (on average by 91.521, 84.923, 70.556, and 56.515%, respectively) on UVIlow days. It was found that prolonged exposure on days with a maximum of UVI = 2 poses a significant risk of erythema for all lighter skin phototypes, even for a duration of 3 h in the middle of the day, as well as medium risk for UVI = 1, and an absence of risk for UVI = 0. The results suggest that health recommendations should be revised, especially in the mid-latitudes, where the share of UVIlow days is large, and in areas where the population is predominantly lighter-skinned.

Prediction of tropospheric ozone concentration using artificial neural networks at traffic and background urban locations in Novi Sad, Serbia.

In this paper, we described generation and performances of feedforward neural network models that could be used for a day ahead predictions of the daily maximum 1-h ozone concentration (1hO3) and 8-h average ozone concentration (8hO3) at one traffic and one background station in the urban area of Novi Sad, Serbia. The six meteorological variables for the day preceding the forecast and forecast day, ozone concentrations in the day preceding the forecast, the number of the day of the year, and the number of the weekday for which ozone prediction was performed were utilized as inputs. The three-layer perceptron neural network models with the best performance were chosen by testing with different numbers of neurons in the hidden layer and different activation functions. The mean bias error, mean absolute error, root mean squared error, correlation coefficient, and index of agreement or Willmott's Index for the validation data for 1hO3 forecasting were 0.005 µg m-3, 12.149 µg m-3, 15.926 µg m-3, 0.988, and 0.950, respectively, for the traffic station (Dnevnik), and - 0.565 µg m-3, 10.101 µg m-3, 12.962 µg m-3, 0.911, and 0.953, respectively, for the background station (Liman). For 8hO3 forecasting, statistical indicators were - 1.126 µg m-3, 10.614 µg m-3, 12.962 µg m-3, 0.910, and 0.948 respectively for the station Dnevnik and - 0.001 µg m-3, 8.574 µg m-3, 10.741 µg m-3, 0.936, and 0.966, respectively, for the station Liman. According to the Kolmogorov-Smirnov test, there is no significant difference between measured and predicted data. Models showed a good performance in forecasting days with the high values over a certain threshold.

Modeling and analysis of different scenarios for the spread of COVID-19 by using the modified multi-agent systems - Evidence from the selected countries.

Currently, there is a global pandemic of COVID-19. To assess its prevalence, it is necessary to have adequate models that allow real-time modeling of the impact of various quarantine measures by the state. The SIR model, which is implemented using a multi-agent system based on mobile cellular automata, was improved. The paper suggests ways to improve the rules of the interaction and behavior of agents. Methods of comparing the parameters of the SIR model with real geographical, social and medical indicators have been developed. That allows the modeling of the spatial distribution of COVID-19 as a single location and as the whole country consisting of individual regions that interact with each other by transport, taking into account factors such as public transport, supermarkets, schools, universities, gyms, churches, parks. The developed model also allows us to assess the impact of quarantine, restrictions on transport connections between regions, to take into account such factors as the incubation period, the mask regime, maintaining a safe distance between people, and so on. A number of experiments were conducted in the work, which made it possible to assess both the impact of individual measures to stop the pandemic and their comprehensive application. A method of comparing computer-time and dynamic parameters of the model with real data is proposed, which allowed assessing the effectiveness of the government in stopping the pandemic in the Chernivtsi region, Ukraine. A simulation of the pandemic spread in countries such as Slovakia, Turkey and Serbia was also conducted. The calculations showed the high-accuracy matching of the forecast model with real data.