scholarly journals Variable Magnitude and Intensity of Strombolian Explosions: Focus on the Eruptive Processes for a First Classification Scheme for Stromboli Volcano (Italy)

2021 ◽  
Vol 13 (5) ◽  
pp. 944
Author(s):  
Sonia Calvari ◽  
Flora Giudicepietro ◽  
Federico Di Traglia ◽  
Alessandro Bonaccorso ◽  
Giovanni Macedonio ◽  
...  
Keyword(s):  
Ground Deformation ◽  
Local Population ◽  
High Energy ◽  
Explosive Activity ◽  
Stromboli Volcano ◽  
Strombolian Activity ◽  
Quantitative Classification ◽  
Strombolian Explosions ◽  
Explosive Events

Strombolian activity varies in magnitude and intensity and may evolve into a threat for the local populations living on volcanoes with persistent or semi-persistent activity. A key example comes from the activity of Stromboli volcano (Italy). The “ordinary” Strombolian activity, consisting in intermittent ejection of bombs and lapilli around the eruptive vents, is sometimes interrupted by high-energy explosive events (locally called major or paroxysmal explosions), which can affect very large areas. Recently, the 3 July 2019 explosive paroxysm at Stromboli volcano caused serious concerns in the local population and media, having killed one tourist while hiking on the volcano. Major explosions, albeit not endangering inhabited areas, often produce a fallout of bombs and lapilli in zones frequented by tourists. Despite this, the classification of Strombolian explosions on the basis of their intensity derives from measurements that are not always replicable (i.e., field surveys). Hence the need for a fast, objective and quantitative classification of explosive activity. Here, we use images of the monitoring camera network, seismicity and ground deformation data, to characterize and distinguish paroxysms, impacting the whole island, from major explosions, that affect the summit of the volcano above 500 m elevation, and from the persistent, mild explosive activity that normally has no impact on the local population. This analysis comprises 12 explosive events occurring at Stromboli after 25 June 2019 and is updated to 6 December 2020.

scholarly journals Classifying Major Explosions and Paroxysms at Stromboli Volcano (Italy) from Space

2021 ◽  
Vol 13 (20) ◽  
pp. 4080
Author(s):  
Claudia Corradino ◽  
Eleonora Amato ◽  
Federica Torrisi ◽  
Sonia Calvari ◽  
Ciro Del Negro
Keyword(s):  
Surface Temperature ◽  
Land Surface ◽  
High Energy ◽  
Temporal Variations ◽  
Stromboli Volcano ◽  
Plume Height ◽  
Volcanic Deposits ◽  
Ash Plume ◽  
Thermal Content ◽  
Explosive Events

Stromboli volcano has a persistent activity that is almost exclusively explosive. Predominated by low intensity events, this activity is occasionally interspersed with more powerful episodes, known as major explosions and paroxysms, which represent the main hazards for the inhabitants of the island. Here, we propose a machine learning approach to distinguish between paroxysms and major explosions by using satellite-derived measurements. We investigated the high energy explosive events occurring in the period January 2018–April 2021. Three distinguishing features are taken into account, namely (i) the temporal variations of surface temperature over the summit area, (ii) the magnitude of the explosive volcanic deposits emplaced during each explosion, and (iii) the height of the volcanic ash plume produced by the explosive events. We use optical satellite imagery to compute the land surface temperature (LST) and the ash plume height (PH). The magnitude of the explosive volcanic deposits (EVD) is estimated by using multi-temporal Synthetic Aperture Radar (SAR) intensity images. Once the input feature vectors were identified, we designed a k-means unsupervised classifier to group the explosive events at Stromboli volcano based on their similarities in two clusters: (1) paroxysms and (2) major explosions. The major explosions are identified by low/medium thermal content, i.e., LSTI around 1.4 °C, low plume height, i.e., PH around 420 m, and low production of explosive deposits, i.e., EVD around 2.5. The paroxysms are extreme events mainly characterized by medium/high thermal content, i.e., LSTI around 2.3 °C, medium/high plume height, i.e., PH around 3330 m, and high production of explosive deposits, i.e., EVD around 10.17. The centroids with coordinates (PH, EVD, LSTI) are: Cp (3330, 10.7, 2.3) for the paroxysms, and Cme (420, 2.5, 1.4) for the major explosions.

scholarly journals Machine learning algorithm improved automated droplet classification of ddPCR for detection of BRAF V600E in paraffin-embedded samples

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Gabriel A. Colozza-Gama ◽  
Fabiano Callegari ◽  
Nikola Bešič ◽  
Ana C. de J. Paviza ◽  
Janete M. Cerutti
Keyword(s):  
Machine Learning ◽  
Sanger Sequencing ◽  
Learning Algorithm ◽  
Absolute Quantification ◽  
Braf V600e Mutation ◽  
Braf V600e ◽  
Driver Genes ◽  
Quantitative Classification ◽  
Cancer Driver

AbstractSomatic mutations in cancer driver genes can help diagnosis, prognosis and treatment decisions. Formalin-fixed paraffin-embedded (FFPE) specimen is the main source of DNA for somatic mutation detection. To overcome constraints of DNA isolated from FFPE, we compared pyrosequencing and ddPCR analysis for absolute quantification of BRAF V600E mutation in the DNA extracted from FFPE specimens and compared the results to the qualitative detection information obtained by Sanger Sequencing. Sanger sequencing was able to detect BRAF V600E mutation only when it was present in more than 15% total alleles. Although the sensitivity of ddPCR is higher than that observed for Sanger, it was less consistent than pyrosequencing, likely due to droplet classification bias of FFPE-derived DNA. To address the droplet allocation bias in ddPCR analysis, we have compared different algorithms for automated droplet classification and next correlated these findings with those obtained from pyrosequencing. By examining the addition of non-classifiable droplets (rain) in ddPCR, it was possible to obtain better qualitative classification of droplets and better quantitative classification compared to no rain droplets, when considering pyrosequencing results. Notable, only the Machine learning k-NN algorithm was able to automatically classify the samples, surpassing manual classification based on no-template controls, which shows promise in clinical practice.

Textural and chemical features of a “soft” plug emitted during Strombolian explosions: A case study from Stromboli volcano

2021 ◽  
Vol 559 ◽  
pp. 116761
Author(s):  
A. Caracciolo ◽  
L. Gurioli ◽  
P. Marianelli ◽  
J. Bernard ◽  
A. Harris
Keyword(s):  
Stromboli Volcano ◽  
Strombolian Explosions

Quantitative classification of near-fault ground motions selected by energy indicators

Structures ◽  
2022 ◽  
Vol 35 ◽  
pp. 780-791
Author(s):  
Dahai Zhao ◽  
Huiwei Wang ◽  
Ding Wang ◽  
Ruiguang Zhu ◽  
Jinghui Zhang
Keyword(s):  
Ground Motions ◽  
Quantitative Classification ◽  
Near Fault ◽  
Energy Indicators

scholarly journals Hip Fracture Types in Canadian Men and Women Change Differently with Age: A Population-Level Analysis

2016 ◽  
Vol 9 ◽  
pp. CMAMD.S38531 ◽  
Author(s):  
Richard G. Crilly ◽  
Marita Kloseck ◽  
Selam Mequanint
Keyword(s):  
Hip Fracture ◽  
Relative Proportion ◽  
Local Population ◽  
Population Level ◽  
Community Dwelling ◽  
Administrative Database ◽  
Fracture Type ◽  
Discharge Abstract Database ◽  
Fracture Types

Background We have previously reported a gender difference in the occurrence of hip fracture type with age in our local population. In the current report, we have explored this phenomenon in a Canadian population using five years of data from a national administrative database. We have compared community-dwelling and institutionalized individuals to determine if frailty is important and has a differential effect on the type of hip fracture experienced. Methods Hospitalization records from 2005 to 2009, in which the most responsible diagnosis, that is the diagnosis causing the admission to hospital, was a hip fracture, were obtained from the Discharge Abstract Database of the Canadian Institute for Health Information. Hip fracture type was identified using the Canadian Classification of Health Interventions and the International Classification of Diseases 10th Revision, Canada (ICD-10-CA). Hip fracture proportions were calculated for the study period and stratified by age group and sex. Results The relative proportion of intertrochanteric fractures in women rose from 35% in the youngest group (55–59 years) to 51% in the oldest group (84+ years; P < 0.0001). In men, the proportions remain relatively stable (47% and 44%, respectively). Community and institutionalized patients showed the same pattern. Conclusions The change in the proportion of the two hip fracture types that occur in women but not men may point to differences in the etiology and consequently the approaches to prevention for the two fracture types. Level of frailty did not seem to be important.

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