β-decay half-lives and β-delayed neutron emission probabilities for several isotopes of Au, Hg, Tl, Pb, and Bi, beyond N = 126

Background: There have been measurements on roughly 230 nuclei that are β-delayed neutron emitters. They range from 8 He up to 150La. Apart from 210Tl, with a branching ratio of only 0.007%, no other neutron emitter has been measured beyond A = 150. Therefore, new data are needed, particularly in the region of heavy nuclei around N = 126, in order to guide theoretical models and help understand the formation of the third r-process peak at A ∼ 195. Purpose: To measure both β-decay half-lives and neutron branching ratios of several neutron-rich Au, Hg, Tl, Pb, and Bi isotopes beyond N = 126. Method: Ions of interest were produced by fragmentation of a 238U beam, selected and identified via the GSI-FRS fragment separator. A stack of segmented silicon detectors (SIMBA) was used to measure ion implants and β decays. An array of 30 3 He tubes embedded in a polyethylene matrix (BELEN) was used to detect neutrons with high efficiency and selectivity. A self-triggered digital system is employed to acquire data and to enable time correlations. The latter were analyzed with an analytical model and results for the half-lives and neutron-branching ratios were derived by using the binned maximum-likelihood method. Results: Twenty new β-decay half-lives are reported for 204−206Au, 208–211Hg, 211–216Tl, 215–218Pb, and 218–220Bi, nine of them for the first time. Neutron emission probabilities are reported for 210,211Hg and 211–216Tl. Conclusions: The new β-decay half-lives are in good agreement with previous measurements on nuclei in this region. The measured neutron emission probabilities are comparable to or smaller than values predicted by global models such as relativistic Hartree Bogoliubov plus the relativistic quasi-particle random phase approximation (RHB + RQRPA).Spanish Ministerio de Economía y Competitividad-FPA2011- 28770-C03-03, FPA2008-04972-C03-3, AIC-D2011-0705, FPA2011-24553, FPA2008-6419, FPA2010-17142, FPA2014-52823-C2-1-P, FPA2014- 52823-C2-2-P, and CPAN CSD-2007-00042 (Ingenio2010)Program Severo Ochoa-SEV-2014-0398German Helmholtz Association (Young Investigators)-VH-NG 627 (LISA-Lifetime Spectroscopy for Astrophysics)Nuclear Astrophysics Virtual Institute-VH-VI-417German Bundesministerium für Bildung und Forschung-06MT7178 / 05P12WOFNFSpanish Nuclear Security Council (CSN)-Catedra ArgosUK Science & Technology Facilities Council (STFC)-ST/F012012/

Energy Efficiency Indicators for Assessing Construction Systems Storing Renewable Energy: Application to Phase Change Material-Bearing Façades

Assessing the performance or energy efficiency of a single construction element by itself is often a futile exercise. That is not the case, however, when an element is designed, among others, to improve building energy performance by harnessing renewable energy in a process that requires a source of external energy. Harnessing renewable energy is acquiring growing interest in Mediterranean climates as a strategy for reducing the energy consumed by buildings. When such reduction is oriented to lowering demand, the strategy consists in reducing the building’s energy needs with the use of construction elements able to passively absorb, dissipate, or accumulate energy. When reduction is pursued through M&E services, renewable energy enhances building performance. The efficiency of construction systems that use renewable energy but require a supplementary power supply to operate can be assessed by likening these systems to regenerative heat exchangers built into the building. The indicators needed for this purpose are particularly useful for designers, for they can be used to compare the efficiency or performance to deliver an optimal design for each building. This article proposes a series of indicators developed to that end and describes their application to façades bearing phase change materials (PCMs)

Alternative spliced isoforms of Kν10.1 potassium channels modulate channel properties and can activate cyclin-dependent kinase in Xenopus oocytes

K(V)10.1 is a voltage-gated potassium channel expressed selectively in the mammalian brain but also aberrantly in cancer cells. In this study we identified short splice variants of K(V)10.1 resulting from exon-skipping events (E65 and E70) in human brain and cancer cell lines. The presence of the variants was confirmed by Northern blot and RNase protection assays. Both variants completely lacked the transmembrane domains of the channel and produced cytoplasmic proteins without channel function. In a reconstituted system, both variants co-precipitated with the full-length channel and induced a robust down-regulation of K(V)10.1 current when co-expressed with the full-length form, but their effect was mechanistically different. E65 required a tetramerization domain and induced a reduction in the overall expression of full-length K(V)10.1, whereas E70 mainly affected its glycosylation pattern. E65 triggered the activation of cyclin-dependent kinases in Xenopus laevis oocytes, suggesting a role in cell cycle control. Our observations highlight the relevance of noncanonical functions for the oncogenicity of K(V)10.1, which need to be considered when ion channels are targeted for cancer therapy

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

Effectiveness of a telerehabilitation intervention using ReCOVery APP of long COVID patients: a randomized, 3-month follow-up clinical trial

The main objective of this study is to analyze the clinical efficacy of telerehabilitation in the recovery of Long COVID patients through ReCOVery APP for 3 months, administered in the Primary Health Care context. The second objective is to identify significant models associated with an improvement in the study variables. An open-label randomized clinical trial was conducted using two parallel groups of a total of 100 Long COVID patients. The first group follows the treatment as usual methods established by their general practitioner (control group) and the second follows the same methods and also uses ReCOVery APP (intervention group). After the intervention, no significant differences were found in favour of the group intervention. Regarding adherence, 25% of the participants made significant use of the APP. Linear regression model establishes that the time of use of ReCOVery APP predicts an improvement in physical function (b = 0.001; p = 0.005) and community social support (b = 0.004; p = 0.021). In addition, an increase in self-efficacy and health literacy also contribute to improving cognitive function (b = 0.346; p = 0.001) and reducing the number of symptoms (b = 0.226; p = 0.002), respectively. In conclusion, the significant use of ReCOVery APP can contribute to the recovery of Long COVID patients. Trial Registration No.: ISRCTN91104012

Bioavailability, mobility and leaching of phosphorus in a Mediterranean agricultural soil (ne Spain) amended with different doses of biosolids

The precipitation of sparingly soluble calcium phosphate in calcareous soils decreases the bioavailability of macronutrients, which makes their addition by way of fertilisers necessary. Sludge resulting from treating urban wastewater does not only provide significant amounts of phosphorus, but also helps lower the pH, thus increasing its bioavailability. The loss of part of soil nutrients due to irrigation or rain can contaminate groundwater. In order to assess the movement of phosphorus, a experiment was conducted on percolation columns, to which different doses of wastes were applied. The pH decreased by as much as 0.89 units, as well as the assimilable and soluble P, in intervals of 20 cm of depth, obtaining maximum values of 254 mg P kg-1 and 1455 lg P kg-1 respectively, and the P present in the leached water collected, which did not surpass 95 lg PL-1. The intent was to learn which was the majoritarian inorganic formed crystalline phase that immobilised the movement of phosphorus through the percolation column. The results obtained by the diffraction of X-rays are not conclusive, although they point to the formation of octacalcium phosphate. The diffractograms of the studied samples have similar diffraction lines to those of apatites

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Adapting and validating the Hospital Survey on Patient Safety Culture (HSOPS) for nursing students (HSOPS-NS): A new measure of Patient Safety Climate

Background: Patient Safety Culture and Patient Safety Climate (PSC) are different factors. PSC is the shared perception that is held within a hospital''s area or unit at a specific moment in time. This measure is necessary for designing activities for promoting and improving safety. It must include the perception of all the agents involved, including future nurses throughout their patient safety education. Objectives: The aim was to adapt and validate a new version of the Hospital Survey on Patient Safety Culture (HSOPS), targeted specifically at nursing students. It provides a new comprehensive and more complete measure of PSC that contributes to improving patient safety. Methods: Data were obtained from 654 undergraduate and postgraduate nursing students. PSC was tested using factor analyses and structural equation modeling. In order to facilitate the improvement of PSC, we examined differences in climate strength across different academic groups using the Rwg(j) and ICC measures of inter-rater agreement. Results: Factor analyses confirmed a five-factor solution that explained between 52.45% and 54.75% of the variance. The model was found to have adequate fit ¿ 2 (5) = 14.333, p =.014; CFI = 0.99; RMSEA = 0.05. Cronbach''s alphas for PSC were between 0.74 and 0.77. “Teamwork within units” was the highest rated dimension, and “Staffing” the lowest rated. Medium-to-high scores were obtained for PSC. The median of Rwg (j) was high in the five dimensions of the PSC survey, supporting the idea of shared climate perceptions (0.81–0.96) among undergraduate and postgraduate nursing students. Conclusions: HSOPS-NS is a useful and versatile tool for measuring the level and strength of PSC. It screens knowledge regarding patient safety in clinical practice placements and compares nursing students’ perceptions of the strength of PSC. Weaknesses perceived in relation to PSC help implement changes in patient safety learning