Remote and In-Person Learning: Utility Versus Social Experience

The massive transition from in-person to remote teaching increased the impact of technology on the everyday life of the universities. Without the face-to-face component, learning and teaching became a completely different experience for students and teachers. Recording the attitudes and perceptions of the undergraduate students on the new situation became necessary for the faculties to support them effectively. This research collected quantitative and qualitative data from 336 students of all the years of studies. The students preferred in-person teaching and reported higher engagement, learning, and understanding during classroom teaching. More senior students, who had developed face-to-face ties with their colleagues before the pandemic, found it easier to continue their interactions remotely. They were interested in matching learning with the duties and needs at the particular period of their life, despite their beliefs concerning the effectiveness of in-person teaching. The first-year students found it challenging to develop relationships remotely, and they were the most frustrated. Overall, students in the first years of their studies perceived remote teaching as dissatisfactory compared to the more senior students. Similar to other publications, the respondents of this study challenged the effectiveness of remote teaching and the concomitant transition from in-person to remote social relationships.

Electromagnetic wave absorption properties of ternary poly(vinylidene fluoride)/magnetite nanocomposites with carbon nanotubes and graphene

Ternary nanocomposite systems of PVDF/Fe₃O₄/CNT and PVDF/Fe₃O₄/GN, prepared with twin screw compounding method, exhibit enhanced microwave absorption properties.

Automatic model-based tracing algorithm for vessel segmentation and diameter estimation

An automatic algorithm capable of segmenting the whole vessel tree and calculate vessel diameter and orientation in a digital ophthalmologic image is presented in this work. The algorithm is based on a parametric model of a vessel that can assume arbitrarily complex shape and a simple measure of match that quantifies how well the vessel model matches a given angiographic image. An automatic vessel tracing algorithm is described that exploits the geometric model and actively seeks vessel bifurcation, without user intervention. The proposed algorithm uses the geometric vessel model to determine the vessel diameter at each detected central axis pixel. For this reason, the algorithm is fine tuned using a subset of ophthalmologic images of the publically available DRIVE database, by maximizing vessel segmentation accuracy. The proposed algorithm is then applied to the remaining ophthalmological images of the DRIVE database. The segmentation results of the proposed algorithm compare favorably in terms of accuracy with six other well established vessel detection techniques, outperforming three of them in the majority of the available ophthalmologic images. The proposed algorithm achieves subpixel root mean square central axis positioning error that outperforms the non-expert based vessel segmentation, whereas the accuracy of vessel diameter estimation is comparable to that of the non-expert based vessel segmentation.