Прегледај по Аутор "Ðurić, Tihomir"

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    Experimental Testing of Combustion Parameters and Emissions of Waste Motor Oil and Its Diesel Mixtures
    (MDPI, 2021) Ðorđić, Dragiša; Milotić, Milan; Ćurguz, Zoran; Ðurić, Slavko; Ðurić, Tihomir
    The production of hydrocarbon fuel from waste engine oil is an excellent way to produce alternative fuels. The aim of the research in this paper is obtaining fuel with a mixture of waste engine oil (WMO) and diesel fuel that can be used as an alternative fuel for internal combustion engines and low power heat generators. With this goal in mind, tests were conducted to estimate the combustion parameters and emissions at a low heat output of 40 kW.Waste motor oils (WMO) and four of its diesel mixtures were used, varying in weight from 20% WMO to 50% WMO. Test results were analysed and compared with diesel fuel. Higher NO, CO and CO2 emissions were determined for WMO and its mixtures compared to diesel fuel. The flue gas temperature in the kiln was high for all WMO and diesel blends, which indicates the efficiency of the input energy. The absorption of flue gases in the scrubber with distilled water showed higher presence of sulphates, sulphides, nitrates and nitrites compared to allowable values.
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    Model for Sustainable Evaluation of the Impact of the Total Number of Centers for Technical Inspections of Motor Vehicles on the Occurrence and Consequences of Traffic Accidents in an Area
    (MDPI, 2022) Vranješ, Đorđe; Marić, Bojan; Ðurić, Tihomir; Jovanov, Goran; Vasiljević, Jovica; Jovanov, Nemanja; Radović Stojčić, Dunja
    Along with the development of capacities for the improvement of traffic safety, this work creates a model that analyzes the impact of the total number of centers for technical inspections of motor vehicles on the occurrence and consequences of accidents in a selected area. By using the statistical program SPSS26 and applying standard multiple regression, an analysis of the statistical correlations between a continuous dependent variable (number of accidents caused by technical malfunctions of vehicles) and two independent variables (number of centers for technical inspections of vehicles and number of vehicles registered by police jurisdictions) was performed. The aim of this work was to determine the influence of a part of the dependent variable’s variance that was explained by the variance of independent variables. The research results showed that the total number of technical inspections in relation to the number of registered vehicles in the selected area included a much larger part of the dependent variable’s variance, which referred to the total number of accidents caused by vehicles’ technical inspections. The results also showed a negative correlation value, i.e., that a larger number of technical inspections in relation to the number of registered vehicles did not have a positive impact on the increase in the number of accidents and consequences where the cause was technical malfunction, as well as that the number of centers and the number of registered vehicles had different influences on the occurrence of accidents and their consequences
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    Predicting Road Traffic Accidents—Artificial Neural Network Approach
    (MDPI, 2023) Gatarić, Dragan; Ruškić, Nenad; Aleksić, Branko; Ðurić, Tihomir; Pezo, Lato; Lončar, Biljana; Pezo, Milada
    Road traffic accidents are a significant public health issue, accounting for almost 1.3 million deaths worldwide annually, with millions more experiencing non-fatal injuries. A variety of subjective and objective factors contribute to the occurrence of traffic accidents, making it difficult to predict and prevent them on new road sections. Artificial neural networks (ANN) have demonstrated their effectiveness in predicting traffic accidents using limited data sets. This study presents two ANN models to predict traffic accidents on common roads in the Republic of Serbia and the Republic of Srpska (Bosnia and Herzegovina) using objective factors that can be easily determined, such as road length, terrain type, road width, average daily traffic volume, and speed limit. The models predict the number of traffic accidents, as well as the severity of their consequences, including fatalities, injuries and property damage. The developed optimal neural network models showed good generalization capabilities for the collected data foresee, and could be used to accurately predict the observed outputs, based on the input parameters. The highest values of r2 for developed models ANN1 and ANN2 were 0.986, 0.988, and 0.977, and 0.990, 0.969, and 0.990, accordingly, for training, testing and validation cycles. Identifying the most influential factors can assist in improving road safety and reducing the number of accidents. Overall, this research highlights the potential of ANN in predicting traffic accidents and supporting decision-making in transportation planning.