Прегледај по Аутор "Lončar, Biljana"

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    A Comparative Analysis of Thin-Layer Microwave and Microwave/Convective Dehydration of Chokeberry
    (MDPI, 2023) Petković, Marko; Filipović, Vladimir; Lončar, Biljana; Filipović, Jelena; Miletić, Nemanja; Malešević, Zoranka; Jevremović, Darko
    Due to high water content, chokeberries (Aronia melanocarpa L.) are perishable. Therefore, energy-saving, combined drying technologies have been explored to improve the chokeberry drying. The combined microwave and the traditional convective drying method (MCD) have significantly enhanced the drying effectiveness, efficiency, and energy utilization rate and improved product quality. The MCD method, which implies the microwave power (MD) of 900 W for 9 s and the convective dehydration (CD) at 230 C for 12 s, has the shortest dehydration time t (24 2 min), has the maximum coefficient of diffusion (Deff = 6.0768 10􀀀9 5.9815 10􀀀11 m2 s􀀀1), and represents the most energy effective for dehydration process (Emin = 0.382 0.036 kWh). A higher waterholding capacity (WHC) characterized the chokeberries obtained by the MCD method compared to the regular microwave method (MD). The mildest MCD (15 s of MD on 900 W, 7 s of CD on 180 C) could dehydrate chokeberries with the highest WHC (685.71 40.86 g H2O g􀀀1 d.m.) and the greatest evaluations for sensory attributes in terms of all properties. The results of this study provide the drying behavior of chokeberries that can help develop efficient drying methods and improve existing ones.
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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.