Vehicle classification is a crucial task owing to vehicles' diverse and intricate features, such as edges, colors, shadows, corners, and textures. The accurate classification of vehicles enables their detection and identification on roads and facilitates the development of an electronic tollcollection system for smart cities. Furthermore, vehicle classification is useful for traffic signal control strategy. However, achieving accurate vehicle classification poses significant challenges due to the limited processing time for real-time applications, image resolution, illumination variations in the video, and other interferences. This study proposes a method for automated automobile detection, recognition, and classification using statistics derived from approximately 11,000 images. We employ SURF-based detection and different classifiers to categorize vehicles into three groups. The Traffic Management System (TMS) is crucial for studying mobility and smart cities. Our study achieves a high automobile classification rate of 91% with the medium Gaussian Support Vector Machine (SVM) classifier. The paper's main objective is to analyze five object classifiers for vehicle recognition: Decision Tree, Discriminant Analysis, SVM, K-Nearest Neighbor Classifier (KNN), and Ensemble Classifier. In the discussion section, we present the limitations of our work and provide insights into future research directions.

The main goal of this research is to examine thermal comfort in the central area of Belgrade (Serbia), over a period of 30 years (1991-2020). The Universal Thermal Climate Index (UTCI) was used as a measure for evaluating outdoor thermal comfort (OTC). The obtained results were considered separately for each season, as well as at the annual level. The analysis was carried out on the basis of an extensive database, which included hourly values (7h, 14h, 21h CET) of meteorological parameters, as well as their average daily, minimum, and maximum values.

The obtained values of UTCIs show a positive growth trend during all four seasons. A significant increase in the annual values of UTCIs was also recorded. Four of five years with the highest average UTCIs were recorded in the last decade of the survey, more precisely in the period 2015-2020. The years that stand out for the frequency of record spring, autumn and winter UTCIs values are 2017, 2018, 2019 and 2020. On an annual level, minimum UTCI value has rising trend of 0.099°C/year, while at maximum UTCI value, that trend is 0.081°C/year.

The aim of this study was to examine the effect of different fertilizers on the yield and antioxidant capacity of two lettuce genotypes “Santoro RZ” and “Kiribati RZ”.

Lettuce genotypes are fertilized with organic fertilizer (Slavol) and organic-inorganic NPK fertilizer (Fitofert hemisuper plus ) during the vegetation. The analyzed parameters were root length and head weight of lettuce, total phenols, and flavonoids, as well as antioxidant capacity. Lettuce genotypes “Santoro RZ” and “Kiribati RZ” fertilized with organic fertilization showed the highest content of total phenols (358.13 ± 1.30 mg RU/100 g of fresh sample), the total content of flavonoids (114.22 ± 0.3 mg RE/100 g of fresh sample) and antioxidant capacity (neutralization of DPHH radicals 58.72 ± 1.88%).

The results revealed that the yield and antioxidant capacity of lettuce can be improved by using organic fertilizers.