The 2015 outburst of the accreting millisecond pulsar IGR J17511-3057 as seen by INTEGRAL, Swift and XMM-Newton

We report on INTEGRAL, Swift and XMM-Newton observations of IGR J17511-3057 performed during the outburst that occurred between March 23 and April 25, 2015. The source reached a peak flux of 0.7(2)E-9 erg/cm$^2$/s and decayed to quiescence in approximately a month. The X-ray spectrum was dominated by a power-law with photon index between 1.6 and 1.8, which we interpreted as thermal Comptonization in an electron cloud with temperature > 20 keV . A broad ({\sigma} ~ 1 keV) emission line was detected at an energy (E = 6.9$^{+0.2}_{-0.3}$ keV) compatible with the K{\alpha} transition of ionized Fe, suggesting an origin in the inner regions of the accretion disk. The outburst flux and spectral properties shown during this outburst were remarkably similar to those observed during the previous accretion event detected from the source in 2009. Coherent pulsations at the pulsar spin period were detected in the XMM-Newton and INTEGRAL data, at a frequency compatible with the value observed in 2009. Assuming that the source spun up during the 2015 outburst at the same rate observed during the previous outburst, we derive a conservative upper limit on the spin down rate during quiescence of 3.5E-15 Hz/s. Interpreting this value in terms of electromagnetic spin down yields an upper limit of 3.6E26 G/cm$^3$ to the pulsar magnetic dipole (assuming a magnetic inclination angle of 30{\deg}). We also report on the detection of five type-I X-ray bursts (three in the XMM-Newton data, two in the INTEGRAL data), none of which indicated photospheric radius expansion.Comment: 10 pages, 7 figures, accepted for publication in A&

Deep Neural Network for damage detection in Infante Dom Henrique bridge using multi-sensor data

This paper proposes a data-driven approach to detect damage using monitoring data from the Infante Dom Henrique bridge in Porto. The main contribution of this work lies in exploiting the combination of raw measurements from local (inclinations and stresses) and global (eigenfrequencies) variables in a full-scale SHM application. We exhaustively analyze and compare the advantages and drawbacks of employing each variable type and explore the potential of combining them. An autoencoder-based Deep Neural Network is employed to properly reconstruct measurements under healthy conditions of the structure, which are influenced by environmental and operational variability. The damage-sensitive feature for outlier detection is the reconstruction error that measures the discrepancy between current and estimated measurements. Three autoencoder architectures are designed according to the input: local variables, global variables, and their combination. To test the performance of the methodology in detecting the presence of damage, we employ a Finite Element model to calculate the relative change in the structural response induced by damage at four locations. These relative variations between the healthy and damaged responses are employed to affect the experimental testing data, thus producing realistic time-domain damaged measurements. We analyze the Receiver Operating Curves and investigate the latent feature representation of the data provided by the autoencoder in the presence of damage. Results reveal the existence of synergies between the different variable types, producing almost perfect classifiers throughout the performed tests when combining the two available data sources. When damage occurs far from the instrumented sections, the area under the curve in the combined approach increases $50\%$ compared to using local variables only. The classificatoin metrics also demonstrate the enhancement of combining both sources of data in the damage detection task, reaching close to $97\%$ precission values for the four considered test damage scenarios. Finally, we also investigate the capability of local variables to localize the damage, demonstrating the potential of including these variables in the damage detection task.HAZITEK programme (ERROTAID project) and TCRINI project (KK-2023-0029) European Horizon (HE) with LIASON project (GA 101103698), and FUTURAL project (101083958

Deep learning enhanced principal component analysis for structural health monitoring

This paper proposes a Deep Learning enhanced Principal Component Analysis (PCA) approach for outlier detection to assess the structural condition of bridges. We employ a partially explainable autoencoder architecture to replicate and enhance the data compression and reconstruction ability of PCA. The particularity of the method lies in the addition of residual connections to account for nonlinearities. We apply the proposed method to monitoring data obtained from two bridges under real operation conditions and compare the results before and after adding the residual connections. Results show that the addition of residual connections enhances the outlier detection ability of the network, allowing to detect lighter damages.This work has received funding from: the European Union's Horizon 2020 research and innovation program under the grant agreement No 769373 (FORESEE project) and the Marie Sklodowska-Curie grant agreement No 777778 (MATHROCKS); the Base Funding - UIDB/04708/2020 of the CONSTRUCT - Instituto de I\&D em Estruturas e Construções - funded by national funds through the FCT/MCTES (PIDDAC); the European Regional Development Fund (ERDF) through the Interreg V-A Spain-France-Andorra program POCTEFA 2014-2020 Project PIXIL (EFA362/19); the Spanish Ministry of Science and Innovation with references PID2019-108111RB-I00 MCIN/AEI/10.13039/501100011033 (FEDER/AEI) and the “BCAM Severo Ochoa” accreditation of excellence (SEV-2017-0718); and the Basque Government through the BERC 2018-2021 program, the two Elkartek projects 3KIA (KK-2020/00049) and MATHEO (KK-2019-00085), the grant "Artificial Intelligence in BCAM number EXP. 2019/00432", and the Consolidated Research Group MATHMODE (IT1294-19) given by the Department of Education

Bridge damage identification under varying environmental and operational conditions combining Deep Learning and numerical simulations

This work proposes a novel supervised learning approach to identify damage in operating bridge structures. We propose a method to introduce the effect of environmental and operational conditions into the synthetic damage scenarios employed for training a Deep Neural Network, which is applicable to large-scale complex structures. We apply a clustering technique based on Gaussian Mixtures to effectively select Q representative measurements from a long-term monitoring dataset. We employ these measurements as the target response to solve various Finite Element Model Updating problems before generating different damage scenarios. The synthetic and experimental measurements feed two Deep Neural Networks that assess the structural health condition in terms of damage severity and location. We demonstrate the applicability of the proposed method with a real full-scale case study: the Infante Dom Henrique bridge in Porto. A comparative study reveals that neglecting different environmental and operational conditions during training detracts the damage identification task. By contrast, our method provides successful results during a synthetic validation

Bayesian variable selection and survival modeling: assessing the Most important comorbidities that impact lung and colorectal cancer survival in Spain

ancer survival represents one of the main indicators of interest in cancer epidemiology. However, the survival of cancer patients can be affected by several factors, such as comorbidities, that may interact with the cancer biology. Moreover, it is interesting to understand how different cancer sites and tumour stages are affected by different comorbidities. Identifying the comorbidities that affect cancer survival is thus of interest as it can be used to identify factors driving the survival of cancer patients. This information can also be used to identify vulnerable groups of patients with comorbidities that may lead to worst prognosis of cancer. We address these questions and propose a principled selection and evaluation of the effect of comorbidities on the overall survival of cancer patients. In the first step, we apply a Bayesian variable selection method that can be used to identify the comorbidities that predict overall survival. In the second step, we build a general Bayesian survival model that accounts for time-varying effects. In the third step, we derive several posterior predictive measures to quantify the effect of individual comorbidities on the population overall survival. We present applications to data on lung and colorectal cancers from two Spanish population-based cancer registries. The proposed methodology is implemented with a combination of the R-packages mombf and rstan. We provide the code for reproducibility at https://github.com/migariane/BayesVarImpComorbiCancer

An XMM-Newton and INTEGRAL view on the hard state of EXO 1745-248 during its 2015 outburst

CONTEXT - Transient low-mass X-ray binaries (LMXBs) often show outbursts lasting typically a few-weeks and characterized by a high X-ray luminosity ($L_{x} \approx 10^{36}-10^{38}$ erg/sec), while for most of the time they are found in X-ray quiescence ($L_X\approx10^{31} -10^{33}$ erg/sec). EXO 1745-248 is one of them. AIMS - The broad-band coverage, and the sensitivity of instrument on board of {\xmm} and {\igr}, offers the opportunity to characterize the hard X-ray spectrum during {\exo} outburst. METHODS - In this paper we report on quasi-simultaneous {\xmm} and {\igr} observations of the X-ray transient {\exo} located in the globular cluster Terzan 5, performed ten days after the beginning of the outburst (on 2015 March 16th) shown by the source between March and June 2015. The source was caught in a hard state, emitting a 0.8-100 keV luminosity of $\simeq10^{37}$~{\lumcgs}. RESULTS - The spectral continuum was dominated by thermal Comptonization of seed photons with temperature $kT_{in}\simeq1.3$ keV, by a cloud with moderate optical depth $\tau\simeq2$ and electron temperature $kT_e\simeq 40$ keV. A weaker soft thermal component at temperature $kT_{th}\simeq0.6$--0.7 keV and compatible with a fraction of the neutron star radius was also detected. A rich emission line spectrum was observed by the EPIC-pn on-board {\xmm}; features at energies compatible with K-$\alpha$ transitions of ionized sulfur, argon, calcium and iron were detected, with a broadness compatible with either thermal Compton broadening or Doppler broadening in the inner parts of an accretion disk truncated at $20\pm6$ gravitational radii from the neutron star. Strikingly, at least one narrow emission line ascribed to neutral or mildly ionized iron is needed to model the prominent emission complex detected between 5.5 and 7.5 keV. (Abridged)Comment: 14 pages, 6 figure, 2 tables. Accepted for publication on A&A (21/03/2017