In-situ growing of MnS and FeS nanoclusters at the interlayer of Al-pillared bentonite

Introduction Since formation of metal nanoclusters is thermodynamically unstable and difficult to control, in this work it has been explored the in-situ growing of either MnS or FeS nanoclusters in the interlayer space of a bentonite by means of a pretty short process taking only around 12 h. The interlayered polynuclear sulfidized metal clusters were prepared by cationic exchange of either Mn 2+ or Fe 2+ on the bentonite previously interlayered/pillared with aluminium under different conditions. These metal sulfidized nanomaterials have attracted substantial interests due to their unique optical and electrical properties and wide variety of potential applications in electroluminescence 1 and nonlinear optical devices 2. Since the main physical and optical properties of such metal sulfides primarily depend on their shape and size, the immobilization of metal sulfide nanoparticles in a spatially confined environment is a way to control the photo-physical and photo-chemical properties which result in very interesting strategy of morphological control

Three-dimensional modelling as a novel interactive tool for preoperative planning for complex perianal fistulas in Crohn's disease

Crohn disease; Artificial intelligence; Three-dimensional imagingEnfermedad de Crohn; Inteligencia artificial; Imagen tridimensionalMalaltia de Crohn; Intel·ligència artificial; Imatge tridimensionalAim The aim of this study is to demonstrate the added value of three-dimensional (3D) reconstruction models and artificial intelligence for preoperative planning in complex perianal Crohn's disease. MRI is the gold standard for diagnosis of complex perianal fistulas and abscess due to its high sensitivity, but it lacks high specificity values. This creates the need for better diagnostic models such as 3D image processing and reconstruction (3D-IPR) with artificial intelligence (AI) algorithms. Method This is a prospective study evaluating the utility of 3D reconstruction models from MRI in four patients with perineal Crohn's disease (pCD). Results Four pCD patients had 3D reconstruction models made from pelvic MRI. This provided a more visual representation of perianal disease and made possible location of the internal fistula orifice, seton placement in fistula tracts and abscess drainage. Conclusion Three-dimensional reconstruction in CD-associated complex perianal fistulas can facilitate disease interpretation, anatomy and surgical strategy, potentially improving preoperative planning as well as intraoperative assistance. This could probably result in better surgical outcomes to control perianal sepsis and reduce the number of surgical procedures required in these patients

Hydrogeological characterization of heterogeneous volcanic aquifers in the Canary Islands using recession analysis of deep water gallery discharge

Aquifers constitute the main freshwater supply of oceanic islands. Maintaining groundwater quantity and quality is of critical concern as demographic and climatic changes place additional pressures on already fragile water resources systems. Islands with heterogeneous volcanic aquifers pose additional difficulties in assessing their water resources. This work proposes an approach for improving the hydrogeological characterization of heterogeneous volcanic aquifer systems by making use of recession coefficients from deep water gallery discharge. To demonstrate the usefulness of this approach, hydrographs and recession curves from groundwater discharge of 30 water galleries on La Palma (Canary Islands) were evaluated. This analysis allowed us to obtain the main hydrogeological parameters of a volcanic aquifer system, in terms of hydraulic diffusivity. A Maillet-Boussinesq model with an exponential decay law was adopted, according to field observations of drainage discharge. The alpha coefficients of recession values ranged between 10−3 and 4·10−4 day−1 and showed significant spatial correlation with insular geology. Additionally, hydraulic diffusivity values of island hydrogeological domains were obtained from recession coefficients using the Rorabaugh-Singh method. Weighted storage coefficients for volcanic materials were in the range of 3% to 7%, with an average transmissivity in the range of 15 to 150 m2·day−1. The methodology proposed has demonstrated its usefulness in coping with local uncertainty in hydraulic characterization of insular aquifers associated with volcanic heterogeneity. This is an improvement compared to standard pumping tests, thus providing hydraulic parameters prior to numerical analysis for water management planning.This research was partially supported by the European Union's Horizon 2020 research and innovation programme under grant agreement 101037424, project ARSINOE (Climate resilient regions through systemic solutions and innovations), and the Spanish Research Agency (project SAGE4CAN PID2020-114218RA-100)

Comparative Hydrodynamic Analysis by Using Two-Dimensional Models and Application to a New Bridge

This document develops a methodology that evaluates the impact on the environment of the rivers produced by the creation of civil infrastructures. The methodology is based on the two-dimensional hydrodynamic calculation by using shallow water equations both in the conditions prior to the creation of the infrastructure, and in the new conditions after the infrastructure is created. Subsequently, several characteristics, such as water depth and velocity, among others, are compared between the initial and final conditions, and a two-dimensional zoning of the changes observed is obtained. The methodology herein presented is useful to verify the impact that the implantation of different infrastructures around the river currents could produce. In addition, it is also relevant for carrying out a study taking into account different infrastructure options related to river currents, as well as for selecting the most suitable one. By using the methodology presented, changes on the regime of the currents caused by the infrastructures can be deduced, including a qualitative and quantitative zoning of the changes, with a special emphasis on some characteristics, such as depth and velocity. The methodology is applied in a case study for the creation of a road bridge over the Jalon River in Spain

Efficient training of energy-based models via spin-glass control

[EN] We introduce a new family of energy-based probabilistic graphical models for efficient unsupervised learning. Its definition is motivated by the control of the spin-glass properties of the Ising model described by the weights of Boltzmann machines. We use it to learn the Bars and Stripes dataset of various sizes and the MNIST dataset, and show how they quickly achieve the performance offered by standard methods for unsupervised learning. Our results indicate that the standard initialization of Boltzmann machines with random weights equivalent to spin-glass models is an unnecessary bottleneck in the process of training. Furthermore, this new family allows for very easy access to low-energy configurations, which points to new, efficient training algorithms. The simplest variant of such algorithms approximates the negative phase of the log-likelihood gradient with no Markov chain Monte Carlo sampling costs at all, and with an accuracy sufficient to achieve good learning and generalization.ML and AA groups acknowledge the Spanish Ministry MINECO and State Research Agency AEI (FIDEUA PID2019-106901GBI00/10.13039/501100011033, Severo Ochoa Grant Nos. SEV-2015-0522 and CEX2019-000910-S, FPI), the European Social Fund, Fundacio Cellex, Fundacio Mir-Puig, Generalitat de Catalunya (AGAUR Grant Nos. 2017 SGR 1341 and SGR 1381, CERCA program, QuantumCAT U16-011424, co-funded by ERDF Operational Program of Catalonia 2014-2020), ERC AdG NOQIA and CERQUTE, EU FEDER, MINECO-EU QUANTERA MAQS (funded by the State Research Agency AEI PCI2019-111828-2/10.13039/501100011033), the National Science Centre, Poland-Symfonia Grant No. 2016/20/W/ST4/00314 and the AXA Chair in Quantum Information Science. A P-K acknowledges funding from Fundacio Obra Socialla Caixa' (LCF/BQ/ES15/10360001) and the European Union's Horizon 2020 research and innovation programme-Grant Agreement No. 648913. G M-G acknowledges funding from Fundacio Obra Social 'la Caixa' (LCF-ICFO grant). M A G-M acknowledges funding from the Spanish Ministry of Education and Vocational Training (MEFP) through the Beatriz Galindo program 2018 (BEAGAL18/00203).Pozas-Kerstjens, A.; Muñoz-Gil, G.; Piñol, E.; Garcia March, MA.; Acín, A.; Lewenstein, M.; Grzybowski, PR. (2021). Efficient training of energy-based models via spin-glass control. Machine Learning: Science and Technology. 2(2). https://doi.org/10.1088/2632-2153/abe8070250262

A Study of the Near-Ultraviolet Spectrum of Vega

UV, optical, and near-IR spectra of Vega have been combined to test our understanding of stellar atmospheric opacities, and to examine the possibility of constraining chemical abundances from low-resolution UV fluxes. We have carried out a detailed analysis assuming Local Thermodynamic Equilibrium (LTE) to identify the most important contributors to the UV continuous opacity: H, H$^{-}$, C I, and Si II. Our analysis also assumes that Vega is spherically symmetric and its atmosphere is well described with the plane parallel approximation. Comparing observations and computed fluxes we have been able to discriminate between two different flux scales that have been proposed, the IUE-INES and the HST scales, favoring the latter. The effective temperature and angular diameter derived from the analysis of observed optical and near-UV spectra are in very good agreement with previous determinations based on different techniques. The silicon abundance is poorly constrained by the UV observations of the continuum and strong lines, but the situation is more favorable for carbon and the abundances inferred from the UV continuum and optical absorption lines are in good agreement. Some spectral intervals in the UV spectrum of Vega that the calculations do not reproduce well are likely affected by deviations from LTE, but we conclude that our understanding of UV atmospheric opacities is fairly complete for early A-type stars.Comment: 13 pages, 9 figures, to be published in Ap

Genetic variation and heritability estimates of Ulmus minor and Ulmus pumila hybrids for budburst, growth and tolerance to Ophiostoma novo-ulmi

Seedlings obtained by crossing Ulmus minor and U. minor × U. pumila clones were assessed for flowering, bark beetle damage, vegetative budburst, height growth and resistance to Ophiostoma novo-ulmi. Ramets and open pollinated seedlings obtained from the parent trees were assessed for the same traits. Most progenies had similar traits to their parents, but some presented heterosis in annual growth or resistance to O. novo-ulmi. Leaf wilting was significantly lower in progenies with U. minor × U. pumila rather than U. minor as female parent (21.5 and 30.6%, respectively; P<0.05). Resistance to O. novoulmi increased significantly as a function of increased amounts of U. pumila germplasm from the female parent, suggesting that resistance to Dutch elm disease is primarily transmitted from the mother. Budburst occurred earlier in seedlings with low rather than high growth rates (P=0.0007) and percentage of wilting was negatively related to early budburst (P<0.0001). Other phenotypic relations included percentage of flowering trees and annual height growth (rp=0.44; P=0.0042), percentage of flowering trees and vegetative budburst (rp=-0.53; P=0.0004) and percentage of beetle-affected trees and annual height growth (rp=0.60; P<0.0001). Heritability estimates obtained from the regression and variance components methods ranged from 0.06 ± 0.04 to 0.64 ± 0.18, 0.10 ± 0.05 to 0.69 ± 0.17, and 0.13 ± 0.32 to 0.71 ± 0.22 for budburst, growth and tolerance to O. novo-ulmi, respectively. Broad- and narrowsense heritability values were higher when estimated 60 days post inoculation (dpi) than 15, 30 or 120 dpi. Heritability estimates and genetic gains reported indicate a high degree of additive genetic control and show the effectiveness of selection for Dutch elm disease resistance and rapid tree growth

3DCNN Performance in Hand Gesture Recognition Applied to Robot Arm Interaction

In the past, methods for hand sign recognition have been successfully tested in Human Robot Interaction (HRI) using traditional methodologies based on static image features and machine learning. However, the recognition of gestures in video sequences is a problem still open, because current detection methods achieve low scores when the background is undefined or in unstructured scenarios. Deep learning techniques are being applied to approach a solution for this problem in recent years. In this paper, we present a study in which we analyse the performance of a 3DCNN architecture for hand gesture recognition in an unstructured scenario. The system yields a score of 73% in both accuracy and F1. The aim of the work is the implementation of a system for commanding robots with gestures recorded by video in real scenarios.This work was funded by the Ministry of Economy, Industry and Competitiveness from the Spanish Government through the DPI2015-68087-R and the pre-doctoral grant BES-2016-078290, by the European Commission and FEDER funds through the project COMMANDIA (SOE2/P1/F0638), action supported by Interreg-V Sudoe

Intramuscular EMG-driven Musculoskeletal Modelling: Towards Implanted Muscle Interfacing in Spinal Cord Injury Patients

Objective: Surface EMG-driven modelling has been proposed as a means to control assistive devices by estimating joint torques. Implanted EMG sensors have several advantages over wearable sensors but provide a more localized information on muscle activity, which may impact torque estimates. Here, we tested and compared the use of surface and intramuscular EMG measurements for the estimation of required assistive joint torques using EMG driven modelling. Methods: Four healthy subjects and three incomplete spinal cord injury (SCI) patients performed walking trials at varying speeds. Motion capture marker trajectories, surface and intramuscular EMG, and ground reaction forces were measured concurrently. Subject-specific musculoskeletal models were developed for all subjects, and inverse dynamics analysis was performed for all individual trials. EMG-driven modelling based joint torque estimates were obtained from surface and intramuscular EMG. Results: The correlation between the experimental and predicted joint torques was similar when using intramuscular or surface EMG as input to the EMG-driven modelling estimator in both healthy individuals and patients. Conclusion: We have provided the first comparison of non-invasive and implanted EMG sensors as input signals for torque estimates in healthy individuals and SCI patients. Significance: Implanted EMG sensors have the potential to be used as a reliable input for assistive exoskeleton joint torque actuation