In modern agriculture today, the cultivation of agricultural products cannot be imagined without greenhouses. This paper presents an energy optimization of a solar greenhouse with a photovoltaic system (PV) and a ground-source heat pump (GSHP). The PV system generates electricity, while the GSHP is used for heating and cooling. A greenhouse is designed with an Open Studio plug-in in the Google SketchUp environment, the EnergyPlus software (8.7.1 version) was used for energy simulation, and the GenOpt software (2.0.0 version) was used for optimization of the azimuth angle and PV cell efficiency. Results for different solar greenhouse orientations and different photovoltaic module efficiency are presented in the paper. The obtained optimal azimuth angle of the solar greenhouse was −8°. With the installation of a PV array with higher module efficiency (20–24%), it is possible to achieve annual energy savings of 6.87–101.77%. Also, with the PV module efficiency of 23.94%, a concept of zero-net-energy solar greenhouses (ZNEG) is achieved at optimal azimuth and slope angle. Through the environmental analysis of different greenhouses, CO₂ emissions of PV and GSHP are calculated and compared with electricity usage. Saved CO₂ emission for a zero-net-energy greenhouse is 6626 kg CO₂/year. An economic analysis of installed renewable energy systems was carried out: with the total investment of 19,326 € for ZNEG, the payback period is 8.63 years.

The tribological properties of dental materials, such as wear and friction, are crucial for ensuring their long-term reliability and performance. Traditional experimental approaches, while accurate, are often resource intensive and time consuming, prompting a need for efficient computational methods. This study explores the application of artificial neural networks (ANNs) to predict the tribological behavior of dental ceramic lithium disilicate (IPS e.max Cad). A genetic algorithm (GA) was used to optimize the ANN’s hyperparameters, improving its ability to model complex, nonlinear relationships between input variables, including normal load and velocity and output properties such as wear rate and friction coefficients. By integrating experimental data with an ANN, this study identifies key factors influencing tribological performance, reducing the dependency on extensive experimental testing. The results demonstrate that the optimized ANN model accurately predicts tribological behavior, offering a robust framework for material optimization. These findings emphasize the potential of combining ANNs and GAs to enhance the understanding and design of dental materials, accelerating innovation while addressing the challenges of traditional evaluation methods. This research underscores the transformative role of advanced computational approaches in tribology and material science.

Thanks to the outstanding combination of construction and special features such as high resistance and stiffness, increased wear resistance to fatigue, higher values of critical speeds, lower mass, long lifetime, and so on, today a growing number of shafts, especially in the automotive industry, are made of composite materials. The optimal values of these parameters can be obtained by selecting the appropriate material. The selection of materials in the process of product design requires efficient decision-making. Incorporating hybrid materials, in addition to providing the potential for improved performance, can make the decision-making process in the selection of material difficult. In this sense, the use of multi-criteria decision making (MCDM) was used as an adequate tool and support for making optimum decisions. This method has the potential to improve practically all areas of decision making in engineering from design to product.

The study analyzes the influence of material type, shaft wall thickness, and fiber orientation angle on the natural frequencies of a hybrid aluminum/composite driveshaft for a Nissan vehicle. The composite layers were made of carbon, glass, and aramid fibers combined with epoxy resin. The analysis was conducted using the Finite Element Method (FEM) within the FEMAP 2021.2 software package. The results showed that reducing the shaft wall thickness leads to an increase in the natural frequency values. It was also concluded that hybrid shafts combining aluminum and carbon fiber/epoxy composites exhibit the highest natural frequencies, while increasing the fiber orientation angle in the layers reduces the natural frequency values for all analyzed material types