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Sustainable traffic system management under conditions of uncertainty and inappropriate road infrastructure is a responsible and complex task. In Bosnia and Herzegovina (BiH), there is a large number of level crossings which represent potentially risky places in traffic. The current state of level crossings in BiH is a problem of the greatest interest for the railway and a generator of accidents. Accordingly, it is necessary to identify the places that are currently a priority for the adoption of measures and traffic control in order to achieve sustainability of the whole system. In this paper, the Šamac–Doboj railway section and passive level crossings have been considered. Fifteen different criteria were formed and divided into three main groups: safety criteria, road exploitation characteristics, and railway exploitation characteristics. A novel integrated fuzzy FUCOM (full consistency method)—fuzzy PIPRECIA (pivot pairwise relative criteria importance assessment) model was formed to determine the significance of the criteria. When calculating the weight values of the main criteria, the fuzzy Heronian mean operator was used for their averaging. The evaluation of level crossings was performed using fuzzy MARCOS (measurement of alternatives and ranking according to compromise solution). An original integrated fuzzy FUCOM–Fuzzy PIPRECIA–Fuzzy MARCOS model was created as the main contribution of the paper. The results showed that level crossings 42 + 690 (LC4) and LC8 (82 + 291) are the safest considering all 15 criteria. The verification of the results was performed through four phases of sensitivity analysis: resizing of an initial fuzzy matrix, comparative analysis with other fuzzy approaches, simulations of criterion weight values, and calculation of Spearman’s correlation coefficient (SCC). Finally, measures for the sustainable performance of the railway system were proposed.

In this work, the yields of resinoid and potassium were obtained from aerial parts of white lady's bedstraw (Galium mollugo L.) by maceration, reflux extraction and ultrasound-assisted extraction using aqueous ethanol solutions as solvents. The main goal was to define the influence of the extraction technique and the ethanol concentration on the resinoid and potassium yields. The resinoid and potassium yields were determined by the solvent evaporation from the liquid extracts to constant weight and the AAS emission method, respectively. The dependence of resinoid and potassium yields on the ethanol concentration was described by linear and quadratic polynomial models, respectively. The best potassium extraction selectivity of 0.077 g K/g of dry extract was achieved by maceration at the ethanol concentrations of 10 g/100 g. The artificial neural network (ANN) was successfully applied to estimate the resinoid and potassium yields based on the ethanol concentration in the extracting solvent and the time duration for all three extraction techniques employed. The response surface methodology was also used to present the dependence of ANN results on the operating factors. The extraction process was optimized using the ANN model coupled with genetic algorithm. The maximum predicted resinoid and potassium yields of 30.4 and 1.67 g/100 g of dry plant were obtained by the ultrasonic extraction (80 min) using the 10 g/100 g aqueous ethanol solution.

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