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The photocatalytic degradation of Crystal Violet (CV) using ZnO-based nanomaterials presents a promising solution for addressing water pollution caused by synthetic dyes. This review highlights the exceptional efficiency of ZnO and its modified forms—such as doped, composite, and heterostructured variants—in degrading CV under both ultraviolet (UV) and solar irradiation. Key advancements include strategic bandgap engineering through doping (e.g., Cd, Mn, Co), innovative heterojunction designs (e.g., n-ZnO/p-Cu2O, g-C3N4/ZnO), and composite formations with graphene oxide, which collectively enhance visible-light absorption and minimize charge recombination. The degradation mechanism, primarily driven by hydroxyl and superoxide radicals, leads to the complete mineralization of CV into non-toxic byproducts. Furthermore, this review emphasizes the emerging role of Artificial Neural Networks (ANNs) as superior tools for optimizing degradation parameters, demonstrating higher predictive accuracy and scalability compared to traditional methods like Response Surface Methodology (RSM). Potential operational challenges and future directions—including machine learning-driven optimization, real-effluent testing potential, and the development of solar-active catalysts—are further discussed. This work not only consolidates recent breakthroughs in ZnO-based photocatalysis but also provides a forward-looking perspective on sustainable wastewater treatment strategies.

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.

This study explores residents’ perceptions of tourism development with a particular emphasis on the economic dimension of sustainability, focusing on how economic benefits, costs, and related factors shape local support in Serbia, Kazakhstan, and Hungary. By analyzing perceived advantages and disadvantages, the study aims to assess the extent of local support for tourism and the moderating effects of travel frequency and contact with tourists. In parallel, tourist arrival forecasts for 2025–2030 provide context on the anticipated dynamics of tourism growth, with Hungary showing the highest projected increase. Using advanced statistical techniques, including Multi-Group Analysis (MGA), structural equation modeling (SEM), and machine learning methods, key factors driving tourism support were identified. Positive perceptions of economic benefits and cultural identification significantly enhance support for tourism, while perceived costs act as inhibitors. The application of Random Forest and XGBoost (version 1.7.x) models improved predictive accuracy, while K-means clustering and principal component analysis (PCA) clarified relationships among constructs. The findings provide actionable insights for developing sustainable tourism strategies that prioritize economic outcomes and community engagement, particularly in culturally and economically diverse settings.

The evolution of daily extreme temperature and precipitation from 1961 to 2010 in Serbia was investigated. Trends of five temperature indices, three precipitation indices, and four combined indices were calculated at ten temperature and ten precipitation stations located within the study area, and their corresponding significances were tested using the Student’s t test. Obtained results suggest that the time periods of extremely hot weather last longer, while the periods of extremely cold weather are shortened. Trends of duration of extreme temperature conditions were most pronounced in summer season. Periods of mild weather conditions are extended. Amount and intensity of precipitation had statistically significant increase only during autumn and were most pronounced in the northern and western parts of the country. On an average, there was no significant decrease in the maximum number of consecutive dry days or increase in the wet days (except in autumn). The investigation of four combined temperature-precipitation regimes showed the domination of “dry” regimes over “wet,” increasing trend of “warm” regimes and decreasing trend of “cold” regimes. The correlation between the examined extreme indices and the large-scale circulation patterns showed that EA and NAO had significant influence on duration of winter warm periods, while their influence on duration of cold periods cannot be confirmed with certainty.

Atmospheric properties have a significant influence on electromagnetic (EM) waves, including the propagation of EM signals used for remote sensing. For this reason, changes in the received amplitudes and phases of these signals can be used for the detection of the atmospheric disturbances and, consequently, for their investigation. Some of the most important sources of the temporal and space variations in the atmospheric parameters come from the outer space. Although the solar radiation dominates in these processes, radiation coming out of the solar system also can induces enough intensive disturbance in the atmosphere to provide deflections in the EM signal propagation paths. The aim of this issue is to present the latest research linking events and processes in outer space with changes in the propagation of the satellite and ground-based signals used in remote sensing.

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.

Background Aspergillus spp liver abscess is a relatively rare entity and thus far no systematic review has been performed examining patients’ demographics, clinical manifestations, diagnosis, management, and outcome. Methods We performed a systematic review of the literature using MEDLINE and LILACS databases. We searched for articles published in the period from January 1990 to December 24, 2022, to identify patients who developed liver abscesses due to Aspergillus spp. Results Our search yielded 21 patients all of whom had invasive aspergillosis confirmed on liver biopsy. Of these patients 81% were adults, and 60% were males. The majority (86%) of patients were immunocompromised and 95% had symptomatic disease at the time of diagnosis. The most common symptoms were fever (79%), abdominal pain (47%), and constitutional symptoms (weight loss, chills, night sweats, fatigue) (38%). Liver enzymes were elevated in 50%, serum galactomannan was positive in 57%, and fungal blood cultures were positive in only 11%. Co-infection with other pathogens preceded development of apsergillosis in one-third of patients, and the majority of the abscesses (43%) were cryptogenic. In the remaining patients with known source, 28% of patients developed liver abscess through dissemination from the lungs, 19% through the portal vein system, and in 10% liver abscess developed through contiguous spread. The most common imaging modality was abdominal computerized tomography done in 86% of patients. Solitary abscess was present in 52% of patients while 48% had multiple abscesses. Inadequate initial empiric therapy was prescribed in 60% of patients and in 44% of patients definite treatment included combination therapy with two or more antifungal agents. Percutaneous drainage of the abscesses was done in 40% of patients, while 20% required liver resection for the treatment of the abscess. Overall mortality was very high at 38%. Conclusion Further studies are urgently needed for a better understanding of pathophysiology of liver aspergillosis and for developement of newer blood markers in order to expedite diagnosis and decrease mortality.

Despite the fact that the topic of the influence of stereotypes and prejudices on the loyalty and willingness of tourists to visit a destination again is increasingly common in world publications, researchers have yet to examine this relationship. The aim of this research is precisely to assess the influence of stereotypes and prejudices (S&P) in the system of other attractors of tourist visits, on the loyalty and willingness of foreign visitors to revisit Serbia as a tourist destination. It is known that Serbia possesses large natural and anthropogenic resources as a basis for tourism development, but it is also known for numerous prejudices and stereotypes, which can potentially dictate loyalty to the destination and the behavior of tourists. The results obtained by the Path analysis show that stereotypes and prejudices, among 892 randomly selected foreign tourists, have a more significant direct influence on the dimensions of loyalty and revisiting Serbia compared to other factors. Further analysis showed that foreign tourists consider the Serbian people to be arrogant, with expressed nationalism and a preserved traditional social approach, as well as that middle-aged tourists show the highest loyalty and willingness to visit again after evaluating all the aforementioned factors. Limitations of the research were related to a lack of understanding of the language and non-cooperation on the part of the respondents. The results of the research have theoretical and applied importance as information for future research in Serbia and beyond, but also for improving the management strategy of tourism development.

Exposure to ambient particulate matter (PM) is one of the leading health risks globally. Several European regions experience high PM concentrations due to the burning of fossil fuels for domestic heating. Accordingly, Serbia ranks among the countries with the highest levels of air pollution. The annual mean concentrations are the most common indicator in exposure studies. However, this study uses station data in Serbia to indicate the concentrations to which the population is exposed during the heating season (October–April) based on daily PM10 and PM2.5 concentrations from 2011 to 2022. In addition, events with concentrations above WHO-recommended daily upper limits (45 μg/m3 for PM10 and 15 μg/m3 for PM2.5) are classified by duration, intensity, and the cumulative sum of excess concentrations. The highest daily mean PM10 concentration in the heating season is 87.1 μg/m3, and for PM2.5, the highest concentration is 65.6 μg/m3 (up to three to four times more than in the rest of the year in both cases). During the most extreme events, mean daily PM10 and PM2.5 concentrations were in the ranges of 150–200 μg/m3 and 100–150 μg/m3, respectively. The cumulative sum of excess concentration in the most extreme events reached up to ~7600 μg/m3 for PM10 and ~5000 μg/m3 for PM2.5. To better understand the problem, the share of occupied dwellings with heating installations and prevailing types of fuels were explored on the municipal level. In general, in most municipalities in Serbia, the share of dwellings with heating installations is up to 60%. Among those, dwellings with district heating dominate, while only a small number of them have a significant share of central heating. When it comes to fuel types, wood is primarily used in dwellings with district heating or without heating installations. These findings imply directions for the development of air quality planning and management policies.

PurposeDuring the pandemic, two types of fear were identified that occur significantly in all groups or profiles of people. The aim of this paper is to determine which of the two types of fears exist in certain psychological groups, and which of the fears strongly influence the decision to travel.Design/methodology/approachThe VALS 2 method and standardized questionnaire were used for the segmentation of the tourist market or for the determination of the psychographic profiles of the consumers, and three additional questions were joined to it as they were vital for the research of the type of fear and its impact on the decision for traveling. When the reliability of the questionnaire and the validity of the sample were determined, the data were further processed using a computer program package IMB AMOS SPSS 21.00, and then, based on the theoretical suppositions and hypotheses, the SEM structural model was created.FindingsThe paper indicates the existence of established types of fears in humans, when it comes to pandemics and similar crisis situations. People are most afraid of infection during travel, and lack of funds and job loss during the critical period of the pandemic. The research conducted confirms that all groups of people, who are determined by the psychological technique VALS 2, react with a certain dose of fear and make decisions under the pressure of fears.Research limitations/implicationsThe research had limitations in terms of contact with people and conducting live surveys. The measures during the pandemic, which were carried out by the state, included social distance and limited movement of people.Practical implicationsThe work can contribute to the community, along with similar research. The results of the research will be available, and it will be possible to see the behavior of people during crisis situations, and the impact of fears on making decisions, both travel decisions and decisions related to other areas of life.Originality/valueThe paper provides research results on a large sample of respondents, and can serve as a basis for further research in the field of tourism, psychology and similar fields. It is crucial to consider the type of fears, and the strength of the impact of these fears on the decision to travel, during crisis situations.