Efficient learning process in the area of science, technology, engineering and mathematics depends on many factors. Motivation of students to learn is one of them and requires deployment of attractive learning objects which can captivate students' attention, involve them in solving problems and connect their experience with knowledge. Virtual reality applications represent modern technology which could offer new possibilities and meet these requirements. In this paper we present two virtual reality applications (games) we developed within international project Newton. These applications were included in pilot testing at a secondary school. Results from research study showed increased motivation of students to learn and positive knowledge gain.

The impact of technical and technological parameters on the quality of machining during milling of thermally modified pine wood (Pinus sylvestris L.) was studied. Experiments were conducted to evaluate the effects of tools (α = 30°, β = 45°, γ = 15°, 20°, and 30°), material (natural material, thermally treated at 160 °C, 180 °C, 210 °C, and 240 °C), and technological factors, such as cutting speed (20 m.min-1, 40 m.min-1, and 60 m.min-1) and feed rate (6 m.min-1, 10 m.min-1, and 15 m·min-1) on the quality of the machined surface (standard deviation of surface Ra). The roughness measurements were realized by a non-contact method using a laser. This paper aimed to highlight which one of the technological or tool factors had the greatest impact on the quality of the surface of heat-treated wood in face milling. The importance of the parameters impact on surface quality was in the following order: rake angle, feed rate, thermal treatment, and cutting speed.

Sparse light field imaging often limits the quality of 3D scene reconstruction due to insufficient viewpoint coverage, resulting in incomplete or inaccurate reconstructions. This work introduces a hybrid CNN–LSTM-based framework to address this issue by generating novel camera poses and the corresponding synthesized novel views, effectively densifying the light field representation. The CNN extracts spatial features from the sparse input views, while the LSTM predicts temporal and positional dependencies, enabling smooth interpolation of novel poses and views. The proposed method integrates these synthesized views with the original sparse dataset to produce a comprehensive set of images. Our approach was evaluated on several datasets, including challenging datasets. The inference capability of our method was tested extensively, and it showed good generalization across diverse datasets. The effectiveness of the framework was evaluated not only with local light field fusion (LLFF) but also with NeRF and 3D Gaussian Splatting, which are considered state-of-the-art reconstruction methods. Overall, the enriched dataset generated by our method led to consistent improvements in 3D reconstruction quality, including higher depth estimation accuracy, reduced artifacts, and enhanced structural consistency. Most importantly, LSTM-based approaches have so far attracted limited attention in the context of generating novel views. While LSTMs have been widely applied in sequential data domains such as natural language processing, their use for image generation conditioned on camera poses remains largely unexplored, which underscores the novelty and significance of the proposed work. This approach provides a scalable and generalizable solution to the sparsity problem in light fields, advancing the capabilities of computational imaging, photorealistic rendering, and immersive 3D scene reconstruction. The results firmly establish the proposed method as a robust and versatile tool for improving reconstruction quality in sparse-view settings.