To design a residential or commercial building with high energy performance that would be economical at the same time, an analysis was performed that relates these two aspects of the problem. The first aspect is focused on evaluation of the thermal performance of a multi-layered wall in order to achieve the lowest energy consumption for heating and cooling. The second aspect of the analysis covered the choice of materials (type, thickness and price) so that the building has the lowest possible construction costs, but the best achieved thermal comfort. The three types of external walls with the same structure were analyzed in this paper. The lowest and highest values of the layer thickness offered by the manufacturer were chosen and their dynamic characteristics for the heat transfer were calculated. The following step was to perform optimization of the objective function, which was defined by the unit price of the material per mass of the material, that is, the economical aspect was provided. The genetic algorithm method was used to obtain the optimal thickness of the external wall layers that provided the best dynamic characteristics for the heat transfer in the defined conditions.

The heat produced in metal cutting process has negative influence on the cutting tool and the machined part in many aspects. This paper deals with measurement of cutting temperature during single-point dry machining of the AISI 4140 steel, using an infrared camera. Various combinations of cutting parameters, i.e. cutting speed, feed rate and depth of cut lead to different values of the measured cutting temperature. Analysis of the measured data should explain the trends in temperature changes depending on changes in the cutting regimes. Furthermore, the temperature data is modelled using response surface methodology and fuzzy logic. The models obtained should determine the influence of cutting regimes on cutting temperature. The main objective is the reduction of cutting temperature, i.e. enabling metal cutting process in optimum conditions.

This study explores the enhanced photocatalytic performance of boron-doped zinc oxide (ZnO) nanoparticles synthesized via a scalable mechanochemical route. Utilizing X-ray diffraction (XRD) and scanning electron microscopy with energy-dispersive spectroscopy (SEM-EDS), the structural and morphological properties of these nanoparticles were assessed. Specifically, nanoparticles with 1 wt%, 2.5 wt%, and 5 wt% boron doping were analyzed after calcination at temperatures of 500 °C, 600 °C, and 700 °C. The obtained results indicate that 1 wt% B-ZnO nanoparticles calcined at 700 °C show superior photocatalytic efficiency of 99.94% methyl orange degradation under UVA light—a significant improvement over undoped ZnO. Furthermore, the study introduces a predictive model using the artificial neural network (ANN) technique, developed in Python, which effectively forecasts photocatalytic performance based on experimental conditions with R² = 0.9810. This could further enhance wastewater treatment processes, such as heterogeneous photocatalysis, through ANN-guided optimization.

The analysis of influence of factors that depend on construction characteristics of the vibrosieves with circular vibrations on screening efficiency is presented in this paper. The dependence of the screening efficiency on the aperture size, length and inclination of the screen, as well as on vibration amplitude, is considered. Based on obtained results, one can see that the screening efficiency increases with vibration amplitude and the screen length increase. Further, increases of the screen inclination and aperture size are causing an initial increase of the screening efficiency, which is later decreasing.