Data science and machine learning (ML) techniques are employed to shed light into the molecular mechanisms that affect fluid-transport properties at the nanoscale. Viscosity and thermal conductivity values of four basic monoatomic elements, namely, argon, krypton, nitrogen, and oxygen, are gathered from experimental and simulation data in the literature and constitute a primary database for further investigation. The data refers to a wide pressure–temperature (P-T) phase space, covering fluid states from gas to liquid and supercritical. The database is enriched with new simulation data extracted from our equilibrium molecular dynamics (MD) simulations. A machine learning (ML) framework with ensemble, classical, kernel-based, and stacked algorithmic techniques is also constructed to function in parallel with the MD model, trained by existing data and predicting the values of new phase space points. In terms of algorithmic performance, it is shown that the stacked and tree-based ML models have given the most accurate results for all elements and can be excellent choices for small to medium-sized datasets. In such a way, a twofold computational scheme is constructed, functioning as a computationally inexpensive route that achieves high accuracy, aiming to replace costly experiments and simulations, when feasible.

The teaching method of flipped classroom (FC), gives students the opportunity to participate actively during class, while the teacher’s role is supportive than intrusive. The application of FC improves teaching and learning process, especially in applied sciences and technology, as literature study reveals. The current study aims to investigate the application of Unified Theory of Acceptance and Use of Technology (UTAUT) methodology, by applied sciences and technology teachers, who combine FC to improve learning results and better meet their teaching goals. To this end, we apply quantitative research, exploit, and discuss our data findings. A total number of 65 high school teachers, around the area of Central Greece, in the region of Thessaly, took part in our research. Our data is analyzed via Structural Equation Models (SEMs)which belong to a family of multivariable statistical analysis that refer to linear correlations among quantitative mainly variables, with confirmatory role. The results of our study reveal that social influence is significant for the prediction of teachers’ intention (current of future) to apply flipped classroom model approach. Additionally, our study claims to support that flipped classroom model plays a crucial role in updating educational demands of the 21st century, especially in guaranteeing the use of technology tools in equally during teaching and learning process. Finally, this study may assist teachers to adjust and reinforce flipped classroom’s approach, by distinguishing the technology acceptance factors inside a contemporary classroom.

Students often find it difficult to understand phenomena of everyday life that occur in the natural world, such as the phenomenon of expansion and contraction of liquids, in which there are many misinterpretations. To address this difficulty, this paper suggests another perspective on the didactic approach based on Mixed Reality, the attunement of the two worlds, the virtual and the real, and examines its effectiveness in teaching the phenomenon of fluid expansion. The main pillar of the educational process is teachers and the practices they use in the teaching process. For this reason, the sample of this survey consisted of 54 University students and prospective elementary education teachers. The mixed research approach was followed to collect the data. Through a Mixed Reality environment, using HoloLens 2, students followed a well-structured scenario and worksheets and observations to investigate the phenomenon of expansion/contraction of different fluids. The results demonstrate that through the interaction and direct feedback of the Mixed Reality environment, students gained a deeper understanding of the phenomenon. At the same time, the device was characterized as a particularly useful educational tool that should be accessible to all students for a better familiarization with complex scientific data.