The main objective of our study was to investigate the possible differences in the chemical composition of extractives from the bark of silver fir (Abies alba) with respect to the location of the bark sample on the tree, viz. differences in extract composition between stem bark and branch bark samples. Extractives in the bark samples from branches, depending on the distance of the sample from the trunk, were also analysed, and the stem bark samples were analysed with respect to their inner and outer parts. The results of the chemical analysis of extractives were supported by information about their antifungal and antioxidant effects. After felling and sampling silver fir trees, the collected bark samples were ground and freeze-dried. Extraction of bark samples was followed by a system of accelerated extraction using only water as a solvent. The extracts were analysed chemically using gravimetry, spectrophotometry and chromatography. Free-radical-scavenging activity was measured using the DPPH method, and the antifungal effect towards three moulds and three wood-decaying fungi was investigated with antifungal assay using the agar well diffusion method. It was found that the moisture content in bark samples decreased intensively just after the bark samples were peeled off the stem. Detailed chromatographic analysis showed that the bark extracts contained 14 compounds, among which phenolic acids, flavonoids and lignans were found to be the characteristic ones. The content of hydrophilic extractives in the branch bark samples decreased with increasing distance of the sample location from the tree stem. The largest amounts of phenolic extractives were measured in stem bark, followed by branch bark sampled at the point at which the branch entered the tree. Analysis of the separated parts of the bark showed that the outer layers of stem bark contained larger amounts of phenolic extractives, as well catechin and epicatechin, compared to the inner layers. Concentrated extracts of branch bark showed the largest free-radical-scavenging activity among the investigated samples, while strong antifungal effects of the bark extract were not found.

Decision-making trial and evaluation laboratory (DEMATEL) is one of the multicriteria decision-making methods based on asymmetric linguistic comparison matrices that has received a great deal of attention, and it is a widely used method in various fields. One of the drawbacks of DEMATEL is a convergence problem that may occur when the infinite sum of normalized influences does not converge. Based on the observations of some examples, the new concept of DEMATEL, the DEMATEL of a finite sum of influences (FSI DEMATEL), is proposed. Instead of an infinite sum, a finite sum of influences is used in FSI DEMATEL so that the convergence problem is avoided. The advantage is that FSI DEMATEL can handle more decision-making problems than the DEMATEL. It can also be used for fuzzy evaluations. FSI DEMATEL can be used as the multicriteria decision-making method to evaluate the relationships between the factors in many different fields.

Nowadays the multi-criteria decision making is very complicated due to uncertainty, vagueness, limited sources, knowledge and time. The Decision-making Trial and Evaluation Laboratory (DEMATEL) method is a widely used multi-criteria decision-making method to analyze the structure of a complex system. It is useful in analysing the cause and effect relationships between the components of the system. Fuzzy sets can be used to include uncertainty in multi-criteria decision making. Linguistic assessments of decision makers can be translated into fuzzy numbers. In this study, fuzzy numbers, intuitionistic fuzzy numbers and neutrosophic fuzzy numbers were used for the decision makers evaluations in the DEMATEL method. The aim of this study was to evaluate how different types of fuzzy numbers affect the final results. An application of risk in construction projects was selected from the literature, where seven experts used a linguistic scale to evaluate different criteria. The results showed that there are only slight differences between the weights of the criteria with regard to the type of fuzzy numbers.