REVIEW 1 cited by
Training Future Engineers to Be Ghostbusters: Hunting for the Spectral Environmental Radioactivity
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Although environmental radioactivity is all around us, the collective public imagination often associates a negative feeling to this natural phenomenon. To increase the familiarity with this phenomenon we have designed, implemented, and tested an interdisciplinary educational activity for pre-collegiate students in which nuclear engineering and computer science are ancillary to the comprehension of basic physics concepts. Teaching and training experiences are performed by using a 4" x 4" NaI(Tl) detector for in-situ and laboratory {\gamma}-ray spectroscopy measurements. Students are asked to directly assemble the experimental setup and to manage the data-taking with a dedicated Android app, which exploits a client-server system that is based on the Bluetooth communication protocol. The acquired {\gamma}-ray spectra and the experimental results are analyzed using a multiple-platform software environment and they are finally shared on an open access Web-GIS service. These all-round activities combining theoretical background, hands-on setup operations, data analysis, and critical synthesis of the results were demonstrated to be effective in increasing students' awareness in quantitatively investigating environmental radioactivity. Supporting information to the basic physics concepts provided in this article can be found at http://www.fe.infn.it/radioactivity/educational.
Forward citations
Cited by 1 Pith paper
-
Quantum Powered Credit Risk Assessment: A Novel Approach using hybrid Quantum-Classical Deep Neural Network for Row-Type Dependent Predictive Analysis
A hybrid quantum-classical deep network, trained separately on agricultural and personal loan categories, reaches 81-83% accuracy on an imbalanced bank dataset, but without classical baselines or uncertainty estimates...
Discussion (0). Continue with ORCID to comment.