98%
921
2 minutes
20
This work highlights the importance of the Geogenic Radon Potential (GRP) component originated by degassing processes in fault zones. This Tectonically Enhanced Radon (TER) can increase radon concentration in soil gas and the inflow of radon in the buildings (Indoor Radon Concentrations, IRC). Although tectonically related radon enhancement is known in areas characterised by active faults, few studies have investigated radon migration processes in non-active fault zones. The Pusteria Valley (Bolzano, north-eastern Italy) represents an ideal geological setting to study the role of a non-seismic fault system in enhancing the geogenic radon. Here, most of the municipalities are characterised by high IRC. We performed soil gas surveys in three of these municipalities located along a wide section of the non-seismic Pusteria fault system characterised by a dense network of faults and fractures. Results highlight the presence of high Rn concentrations (up to 800 kBq·m) with anisotropic spatial patterns oriented along the main strike of the fault system. We calculated a Radon Activity Index (RAI) along north-south profiles across the Pusteria fault system and found that TER is linked to high fault geochemical activities. This evidence confirms that TER constitutes a significant component of GRP also along non-seismic faults.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9751298 | PMC |
http://dx.doi.org/10.1038/s41598-022-26124-y | DOI Listing |
PLoS One
September 2025
Department of Maths and Computer Science, Faculty of Science, University of Kinshasa, Kinshasa, The Democratic Republic of the Congo.
Reliable and timely fault diagnosis is critical for the safe and efficient operation of industrial systems. However, conventional diagnostic methods often struggle to handle uncertainties, vague data, and interdependent multi-criteria parameters, which can lead to incomplete or inaccurate results. Existing techniques are limited in their ability to manage hierarchical decision structures and overlapping information under real-world conditions.
View Article and Find Full Text PDFIEEE Trans Neural Netw Learn Syst
September 2025
Class incremental learning (CIL) offers a promising framework for continuous fault diagnosis (CFD), allowing networks to accumulate knowledge from streaming industrial data and recognize new fault classes. However, current CIL methods assume a balanced data stream, which does not align with the long-tail distribution of fault classes in real industrial scenarios. To fill this gap, this article investigates the impact of long-tail bias in the data stream on the CIL training process through the experimental analysis.
View Article and Find Full Text PDFThis paper presents a novel multiscale signal processing framework for power quality disturbance (PQD) and cyber intrusion detection in smart grids, combining Non-Subsampled Contourlet Transform (NSCT), Split Augmented Lagrangian Shrinkage Algorithm (SALSA), and Morphological Component Analysis (MCA). A key innovation lies in an adaptive weighting mechanism within NSCT's directional sub bands, enabling dynamic energy redistribution and enhanced representation of both low-frequency anomalies (e.g.
View Article and Find Full Text PDFSmall
September 2025
Department of Materials Science, Key Laboratory of Automobile Materials, MOE and State Key Laboratory of High Pressure and Superhard Materials, International Center of Future Science, Jilin University, Changchun, 130012, China.
Molybdenum disulfide (MoS) exhibits excellent lubrication capacity rooted in its layered structure, but it suffers significant structural and functional deterioration due to oxidation in ambient environments, limiting its applications. Concerted efforts are focused on enhancing the antioxidation ability of MoS, but challenges remain. This work conceptualizes and demonstrates a contrarian design of MoS-based film via metal incorporation and oxidation based on consideration of key fundamental principles of thermodynamics, chemistry, and physical mechanics.
View Article and Find Full Text PDFScience
September 2025
Dipartimento di Scienze della Terra, dell'Ambiente e delle Risorse, Universita`degli Studi di Napoli Federico II, Napoli, Italy.
We used a retrained machine learning workflow to enhance the performance of the seismic monitoring network at Campi Flegrei caldera (CFc) for improved tracking of the evolution of volcanic unrest. We analyzed the recent (1/21/22 - 03/20/25) continuous seismic data, which showed a sharp increase in seismicity at the highly populated CFc. Our analysis expanded the seismicity catalog from around 12,000 to over 54,000 earthquakes.
View Article and Find Full Text PDF