174 research outputs found
A machine learning method to infer clusters of galaxies mass radial profiles from mock Sunyaev-Zel’dovich maps with The Three Hundred clusters
No full textOur study introduces a new machine learning algorithm for estimating 3D cumulative radial profiles of total and gas mass in galaxy clusters from thermal Sunyaev-Zel’dovich (SZ) effect maps. We generate mock images from 2522 simulated clusters, employing an autoencoder and random forest in our approach. Notably, our model makes no prior assumptions about hydrostatic equilibrium. Our results indicate that the model successfully reconstructs unbiased total and gas mass profiles, with a scatter of approximately 10%. We analyse clusters in various dynamical states and mass ranges, finding that our method’s accuracy and precision are consistent. We verify the capabilities of our model by comparing it with the hydrostatic equilibrium technique, showing that it accurately recovers total mass profiles without any bias
The three hundred project. A machine learning method to infer clusters of galaxy mass radial profiles from mock Sunyaev–Zel’dovich maps
Get PDFWe develop a machine learning algorithm to infer the three-dimensional cumulative radial profiles of total and gas masses in galaxy clusters from thermal Sunyaev–Zel’dovich effect maps. We generate around 73 000 mock images along various lines of sight using 2522 simulated clusters from THE THREE HUNDRED project at redshift z < 0.12 and train a model that combines an auto-encoder and a random forest. Without making any prior assumptions about the hydrostatic equilibrium of the clusters, the model is capable of reconstructing the total mass profile as well as the gas mass profile, which is responsible for the Sunyaev–Zel’dovich effect. We show that the recovered profiles are unbiased with a scatter of about 10 per cent, slightly increasing towards the core and the outskirts of the cluster. We selected clusters in the mass range of 1013.5 ≤ M200/(h−1 M) ≤ 1015.5, spanning different dynamical states, from relaxed to disturbed haloes. We verify that both the accuracy and precision of this method show a slight dependence on the dynamical state, but not on the cluster mass. To further verify the consistency of our model, we fit the inferred total mass profiles with a Navarro–Frenk–White model and contrast the concentration values with those of the true profiles. We note that the inferred profiles are unbiased for higher concentration values, reproducing a trustworthy mass–concentration relation. The comparison with a widely used mass estimation technique, such as hydrostatic equilibrium, demonstrates that our method recovers the total mass that is not biased by non-thermal motions of the gas
Simulating the ideal geometrical and biomechanical parameters of the pulmonary autograft to prevent failure in the Ross operation
Get PDFOBJECTIVES: Reinforcements for the pulmonary autograft (PA) in the Ross operation have been introduced to avoid the drawback of conduit
expansion and failure. With the aid of an in silico simulation, the biomechanical boundaries applied to a healthy PA during the operation
were studied to tailor the best implant technique to prevent reoperation.
METHODS: Follow-up echocardiograms of 66 Ross procedures were reviewed. Changes in the dimensions and geometry of reinforced
and non-reinforced PAs were evaluated. Miniroot and subcoronary implantation techniques were used in this series. Mechanical stress
tests were performed on 36 human pulmonary and aortic roots explanted from donor hearts. Finite element analysis was applied to obtain
high-fidelity simulation under static and dynamic conditions of the biomechanical properties and applied stresses on the PA root and leaflet
and the similar components of the native aorta.
RESULTS: The non-reinforced group showed increases in the percentages of the mean diameter that were significantly higher than those
in the reinforced group at the level of the Valsalva sinuses (3.9%) and the annulus (12.1%). The mechanical simulation confirmed geometrical
and dimensional changes detected by clinical imaging and demonstrated the non-linear biomechanical behaviour of the PA anastomosed
to the aorta, a stiffer behaviour of the aortic root in relation to the PA and similar qualitative and quantitative behaviours of leaflets
of the 2 tissues. The annulus was the most significant constraint to dilation and affected the distribution of stress and strain within the entire
complex, with particular strain on the sutured regions. The PA was able to evenly absorb mechanical stresses but was less adaptable to
circumferential stresses, potentially explaining its known dilatation tendency over time.
CONCLUSIONS: The absence of reinforcement leads to a more marked increase in the diameter of the PA. Preservation of the native geometry
of the PA root is crucial; the miniroot technique with external reinforcement is the most suitable strategy in this context
A deep learning approach to infer galaxy cluster masses from Planck Compton-y parameter maps
No full textGalaxy clusters are useful laboratories to investigate the evolution of the Universe, and accurate measurement of their total masses allows us to constrain important cosmological parameters. However, estimating mass from observations that use different methods and spectral bands introduces various systematic errors. Here we evaluate the use of a convolutional neural network (CNN) to reliably and accurately infer the masses of galaxy clusters from the Compton-y parameter maps provided by the Planck satellite. The CNN is trained with mock images generated from hydrodynamic simulations of galaxy clusters, with Planck’s observational limitations taken into account. We observe that the CNN approach is not subject to the usual observational assumptions, and therefore is not affected by the same biases. By applying the trained CNNs to the real Planck maps, we find cluster masses compatible with Planck measurements within a 15% bias. Finally, we show that this mass bias can be explained by the well-known hydrostatic equilibrium assumption in Planck masses, and the different parameters in the integrated Compton-y signal and the mass scaling laws. This work highlights that CNNs, supported by hydrodynamic simulations, are a promising and independent tool for estimating cluster masses with high accuracy, which can be extended to other surveys as well as to observations in other bands
Analyse de la sécheresse hydrologique en milieu continental tempéré et en milieu méditerranéen en Italie, pendant la période 1981-2010
No full textAnalysis of hydrological drought in continental temperate and Mediterranean environment during the period 1981-2010. A thirty-year (1981-2010) study of the hydrological drought trend is realised in two Italian regions, Piedmont and Sardinia, with different climatic features (Temperate continental climate and Mediterranean climate). For this purpose, we have examined the daily data of 13 meteorological stations uniformly installed in the two areas, and the trends of the SPI (Standardized Precipitation Index) and SPEI (Standardized Precipitation Evapotranspiration Index) have been also evaluated. The similarities and differences between the indices of the two Regions were then considered. In most stations of both zones, there is a statistically significant trend with an increase in the SPI index and a decrease in the SPEI index. Nevertheless, the mean values trend of the two indices is not significant in any of the two environmental areas considered.Une étude de trente ans (1981-2010) sur la tendance de la sécheresse hydrologique a été conduite dans deux régions italiennes, Piémont et Sardaigne, avec différentes caractéristiques climatiques (climat continental tempéré et méditerranéen). A cet effet, nous avons examiné les données journalières de 13 stations thermo-pluviométriques installées uniformément dans les zones examinées, et nous avons aussi évalué les tendances des indices dérivés SPI (Standardized
Precipitation Index) et SPEI (Standardized Precipitation Evapotranspiration Index). On a ensuite considéré les similitudes et les différences entre les indices des deux régions analysées. Dans les deux zones, il y a dans la plupart des stations une tendance statistiquement significative à l’augmentation de l’indice SPI et à la diminution de l'indice SPEI, la tendance des valeurs moyennes des deux indices n’est par contre significative dans aucun des deux milieux climatiques considérés
Cloud-based mobile system for biometrics authentication
No full textThis paper introduces a new framework to perform handwritten password authentication as an Internet service based on the cloud computing technology. Using the proposed cloud-based authentication platform, we would be able to apply several advanced practical algorithms, such as k-nearest neighbor, and artificial neural network classifier used for large-scale character recognition. The classifier algorithm uses parallel classifier combination method in order to achieve satisfying accuracy for both recognition and error rate. 2013 IEEE.Scopu
Cloud-Ready Biometric System for Mobile Security Access
No full textIn this contribution, we introduce an application that allows a mobile phone to be used as a biometric-capture device for secure access to the cloud. In this application, the biometric capture and recognition are performed during a standard web session, using JQuery, a new mobile web framework which provides the technology needed to build web pages that act more like mobile applications rather than traditional web pages. The mobile user obtains service catalog through an interface developed for the Android system. We have used Hadoop, an open source cloud computing environment, to establish the connection between mobile user and server in the cloud via Ethernet, WiFi or 3G. Springer-Verlag Berlin Heidelberg 2012.Scopu
Assessment of Artificial Recharge Efficiency Against Groundwater Stress in the El Khairat Aquifer
No full textDistributed collaborative reasoning for HAR in smart homes
No full textInternational audienceDistributed Human Activity Recognition (D-HAR) is an active research issue for pervasive computing that aims to identify human activities in smart homes. This paper proposes a fully distributed multi-agent reasoning approach where agents, with diverse classifiers, observe sensor data, make local predictions and collaborate to identify current activities. Experimental tests on Aruba dataset indicate an enhancement in terms of accuracy and F-measure metrics compared either to a centralized approach or a distributed approach from the literatur
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