Implanted muon spin spectroscopy on 2-O-adamantane: a model system that mimics the liquid

The transition taking place between two metastable phases in 2-O-adamantane, namely the [Formula: see text] cubic, rotator phase and the lower temperature P21/c, Z  =  4 substitutionally disordered crystal is studied by means of muon spin rotation and relaxation techniques. Measurements carried out under zero, weak transverse and longitudinal fields reveal a temperature dependence of the relaxation parameters strikingly similar to those exhibited by structural glass[Formula: see text]liquid transitions (Bermejo et al 2004 Phys. Rev. B 70 214202; Cabrillo et al 2003 Phys. Rev. B 67 184201). The observed behaviour manifests itself as a square root singularity in the relaxation rates pointing towards some critical temperature which for amorphous systems is located some tens of degrees above that shown as the characteristic transition temperature if studied by thermodynamic means. The implications of such findings in the context of current theoretical approaches concerning the canonical liquid-glass transition are discussed.Postprint (author's final draft

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

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 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

GCIMS: An R package for untargeted gas chromatography - Ion mobility spectrometry data processing

Gas-Chromatography coupled to Ion Mobility Spectrometry (GC-IMS) based metabolomics is an emerging technique for obtaining fast, reliable untargeted metabolic fingerprints of biofluids. The generated raw data is highly dimensional and complex, suffers from baseline problems, misalignments, long peak tails and strong non-linearities that must be corrected to extract chemically relevant features from samples. In this work, we present our GCIMS R package, which includes spectra loading, metadata handling, denoising, baseline correction, spectral and chromatographic alignment, peak detection, integration, and peak clustering to produce a peak table ready for multivariate data analysis. We discuss package design decisions, and, for illustration purposes, we show a case study of sex discrimination on the basis of the volatile compounds in urine samples. The GCIMS package provides a user-friendly workflow for non-code developers to process their raw data samples

Sistemas de acceso venoso central (SAVC) en pacientes pediátricos. Experiencia de seis años

The need for an access to the venous system, in order to infuse chemotherapeutic treatments or parenteral nutrition, has increased the number of central venous access systems (CVAS) implanted in the past years. Between February 1985 and December 1990, 87 devices were implanted in 76 patients (from 11 months to 15 years of age), with a median function time of 349 days (range: 7 to 1887 days). The overall incidence of complications was 0.10 per 10 days of catheterization, with complication rates for infection and thrombosis of 0.02 and 0.03, respectively. Nineteen systems were removed because of complications and 11 because of completion of the treatment. Of the cases, 97.7% included a follow-up period. The present study confirms the advantages of these devices, with a long working life and a low complication rate, being a good alternative for chronically ill children requiring long-term and/or cyclic intravenous therapy

On the microscopic mechanism behind the purely orientational disorder-disorder transition in the plastic phase of 1-chloroadamantane

Globular molecules of 1-chloroadamantane form a plastic phase in which the molecules rotate in a restrained way, but with their centers of mass forming a crystalline ordered lattice. Plastic phases can be regarded as test cases for the study of disordered phases since, contrary to what happens in the liquid phase, there is a lack of stochastic translational degrees of freedom. When the temperature is increased, a hump in the specific heat curve is observed indicating a change in the energetic footprint of the dynamics of the molecules. This change takes place without a change in the symmetry of the crystalline lattice, i.e. no first-order transition is observed between temperatures below and above the calorimetric hump. This implies that subtle changes in the dynamics of the disordered plastic phase concerning purely orientational degrees of freedom should appear at the thermodynamic anomaly. Accordingly, we describe, for the first time, the microscopic mechanisms behind a disorder–disorder transition through the analysis of neutron diffraction and QENS experiments. The results evince a change in the molecular rotational dynamics accompanied by a continuous change in density.Peer ReviewedPreprin

Using portable X-Ray fluorescence to identify the K-Pg boundary in terrestrial records

Peer Reviewe