In this study, the physical properties (oven-dry density, basic density, volumetric shrinkage, and swelling) and structural components (cellulose, lignin, and extractives content) of three wild almond wood species from southwestern Iran, namely Amygdalus arabica, Amygdalus eburna, and Amygdalus scoparia, were investigated. Wild almond is a valuable wood species in the Zagros forests of Iran, but there is a lack of data on their wood properties. Three adult trees of each species were chosen, and samples were prepared from the breast height diameter to measure the focal properties. Results of analysis of variance (ANOVA) showed that the wood species had a significant effect on the wood density and volumetric shrinkage. Maximum oven-dry density and volumetric shrinkage of wood were identified in Amygdalus scoparia. The highest and lowest content of structural components were found in Amygdalus scoparia and Amygdalus arabica wood species, respectively. A deep understanding of the almond wood characteristics will provide a fresh insight into the relationship between the properties and conservation of these special, as well as applications of their wood.

The use of wood in outdoor conditions is of great importance for the service life of wood, and the process of thermal modification (TM) directly affects the effective value of wood products. This paper presents theoretical and experimental studies of the parameters influencing TM of wood on the changes of its physical and mechanical properties. Experimental studies were performed on thermally modified wood samples for different values of the influential parameters of thermal modification: T (°C), t (h) and ρ (g·cm–3), while the tensile strength was obtained as the output parameter. The obtained experimental data were stochastically modelled and compared with the model obtained by genetic programming. The optimization of processing parameters was performed by classical mathematical analysis and compared with the results obtained by optimization with genetic algorithm. The results of the optimal design parameters obtained by different approaches to optimization were compared and based on that the analysis of the characteristics of the presented techniques was conducted.

This study aims to investigate the influence of thermal modification (TM) on the physical and mechanical properties of wood. For this purpose, the experimental part focused on selected influential parameters, namely temperature, residence time, and density, while the four-point bending strength is obtained as the output parameter. The obtained experimental data are stochastically modeled and compared with the model created by genetic programming (GP). The classical mathematical analysis obtained treatment parameters in relation to the maximum bending strength (T = 187 °C, t = 125 min = 0.780 g/cm3) and compared with the results obtained by genetic algorithm (GA) (T = 208 °C, t = 122 min, and = 0.728 g/cm3). It is possible to obtain models that describe experimental results well with stochastic modeling and evolutionary algorithms.

In the production of expanded polystyrene, the standards are very high in terms of thermal, fire, dimensional, and mechanical characteristics, because each of the characteristics is a condition for achieving quality that allows competitiveness in the market. To ensure high-quality products, it is necessary to achieve optimal performance and product quality through carefully adjusted input parameters of production. Since the production of expanded polystyrene is specific in several ways, an experimental study was conducted in which the basic parameters affecting product quality were detected and through which a series of experiments were performed to prove product quality. Experimental research for this work was conducted on three types of expanded polystyrene samples whose purpose is to insulate floors exposed to pressure. The samples were made of the same material of different densities and aging times for which the pressure stress at a deformation of 10% was tested. After the experimental phase, the modeling of the output parameters was performed. Modeling involved the development of a model that describes a given problem and the obtained modeled values were analyzed and compared with the experimental one. The modeling method used genetic programming using the GPdotNET software package. The goal of modeling with the GpdotNET tool is to obtain a realistic model that would give the value of the compression stress at a deformation of 10% as an output variable in materials made of expanded polystyrene.

Wood is one of the most important construction materials in Europe and its use in building applications has increased in the recent decades. To enable even more extensive and reliable use of wood, this article aimed to determine the effect of thermal modification on mechanical properties of fir wood (lat. Abies sp.), linden wood (lat. Tilia sp.), and beech wood (lat. Fagus sp.). The thermal modification was conducted in a laboratory oven at five different temperatures of 170, 180, 195, 210, 220 °C and processed with a different maximum duration of the process of 78, 120, 180, 240, 276 minutes. Mechanical properties of treated wood have shown statistically insignificant fluctuations at lower temperatures compared to control samples. On the other hand, raising the temperature to 210 °C significantly affected the strength of all the species. The results revealed that thermal modification at high temperatures and longer exposure causes a decrease in the maximum force of the three wood species.

Nitrate is one of the focal water quality indices in aquatic systems. However, proper estimation of nitrate concentration is a complicated task. In this article, capabilities of deep neural networks (DNN) and conventional artificial neural networks (ANN) to model and predict nitrate concentration in the Klokot River, Bosnia and Herzegovina were investigated. The measured data includes nitrate and pH values. Different scenarios were considered as the potential models for DNN and ANN structuring. The result showed DNN and ANN networks are incapable of precisely modeling the nitrate concentration in the Klokot River based on the designed scenarios. In addition, DNN was slightly superior to ANN in different terms of estimation accuracy.

Abstract The paper presents the development of the mathematical model of joint parameters impact, during solid wood lengthening with a mini tooth, on the joint strength. Impact parameters are wood density (ρ), ratio of wood element width and thickness (b/h) and geometrical size of mini tooth expressed as ratio of length and distance between width of the tooth (l/p). This paper presents an example of how the process of joining solid wood can be optimized prior to the production process to avoid exploitation testing and increased costs of the process. Based on the obtained model (F), force optimization was performed as a function of the maximum tensile force. The results of the conducted research indicate that mathematical modelling and optimization can be successfully used to define the tensile break force and technological parameters of solid wood elements lengthened by finger joint.

ABSTRACT This paper presented the quality of the treated surface, which is most often followed by roughness representing micro-geometric irregularities of the treated surface. The processing influence parameters behaviour of on the roughness of the treated wood surface was considered, where the results of measurements of the average deviation of the roughness profile of the surface of beech and maple wood from the area of Bosnia and Herzegovina after processing with orthogonal cutting on a four-sided shaper was studied, at different feed speed and cutting depth . The mathematical model of the influence parameters on the treated surface roughness of solid wood was also presented. The paper presented a sample example of how a process can be predicted before it goes into production, avoiding exploitation testing and increased costs of the process itself.