Process and machine system development for the forming of miniature/micro sheet metal products

This paper reports on the current development of the process for the forming of thin sheet-metal micro-parts (t < 50µm) and the corresponding machine system which is part of the research and technological development of an EU funded integrated project - MASMICRO ("Integration of Manufacturing Systems for the Mass-Manufacture of Miniature/Micro-Products" (/www.masmicro.net/). The process development started with qualification of the fundamentals related to the forming of thin sheet-metals in industrial environment, for which a testing machine and several sets of the testing tools were developed. The process was further optimised, followed by new tool designs. Based on the experience gained during the process development, a new forming press which is suitable for industrial, mass-customised production, has been designed

A Finite Element based Deep Learning solver for parametric PDEs

We introduce a dynamic Deep Learning (DL) architecture based on the Finite Element Method (FEM) to solve linear parametric Partial Differential Equations(PDEs). The connections between neurons in the architecture mimic the Finite Element connectivity graph when applying mesh refinements. We select and discuss several losses employing preconditioners and different norms to enhance convergence. For simplicity, we implement the resulting Deep-FEM in one spatial domain (1D), although its extension to 2D and 3D problems is straightforward. Extensive numerical experiments show in general good approximations for both symmetric positive definite (SPD) and indefinite problems in parametric and non-parametric problems. However, in some cases, lack of convexity prevents us from obtaining high-accuracy solutions.This work has received funding from: the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 777778 (MATHROCKS); 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 projects with references PID2019-108111RB-I00 (FEDER/AEI) and PDC2021-121093-I00, the "BCAM Severo Ochoa" accreditation of excellence (SEV-2017-0718); and the Basque Government through the BERC 2018-2021 program, the three Elkartek projects 3KIA (KK-2020/00049), EXPERTIA (KK-2021/00048), and SIGZE (KK-2021/00095), and the Consolidated Research Group MATHMODE (IT1294-19) given by the Department of Education

Analysis of instabilities in the Basque Coast Geopark coastal cliffs for its environmentally friendly management (Basque-Cantabrian basin, northern Spain)

Coastal cliffs provide a high landscape value to many natural sites around the world. This means that an ever-increasing number of people are attracted to them. At this point, there is a growing need to manage these spaces from the safety of visitors, but with a view to preserving the environment. With this aim, this paper presents an approach to analyze and manage instabilities in these environments, particularly those subjected to significant anthropic activity, which has been implemented in the cliffs of the Basque Coast Geopark. The starting point is a detailed topographic information, obtained from UAV flights, and the identification on site of unstable elements, including their typology, active source areas, dynamics and reach. From this information, the simulation of rockfall processes, which basically correspond to toppling and infinite slope instabilities favored by differential erosion along the coastline, is approached in two and three dimensions. Results allow the design of precise actions by sectors, according to the energy, height and reach of the detached blocks, including barriers, middle slope actions, ditches and information strategies, depending on the different uses of the sectors. Therefore, this approach leads to a more detailed and environmentally friendly management of these environments. © 2021This study has been carried out by the UPV/EHU Research GroupIT-1029/16 (Government of the Basque Country) in the framework of the strategic project ?Analysis of instabilities in coastal environments of the Basque Country? PES-18/97 (University of the Basque Country) and the collaboration of the Basque Coast Geopark (Geoparkea). Finally, the authors are grateful to the reviewers and the handling editor, for the valuable comments that highly improved the paper

The sound of waves in the Mutriku wave energy plant

Peer Reviewe

Use of the self-organising map network (SOMNet) as a decision support system for regional mental health planning

Background: Decision-making in mental health systems should be supported by the evidence-informed knowledge transfer of data. Since mental health systems are inherently complex, involving interactions between its structures, processes and outcomes, decision support systems (DSS) need to be developed using advanced computational methods and visual tools to allow full system analysis, whilst incorporating domain experts in the analysis process. In this study, we use a DSS model developed for interactive data mining and domain expert collaboration in the analysis of complex mental health systems to improve system knowledge and evidence-informed policy planning. Methods: We combine an interactive visual data mining approach, the self-organising map network (SOMNet), with an operational expert knowledge approach, expert-based collaborative analysis (EbCA), to develop a DSS model. The SOMNet was applied to the analysis of healthcare patterns and indicators of three different regional mental health systems in Spain, comprising 106 small catchment areas and providing healthcare for over 9 million inhabitants. Based on the EbCA, the domain experts in the development team guided and evaluated the analytical processes and results. Another group of 13 domain experts in mental health systems planning and research evaluated the model based on the analytical information of the SOMNet approach for processing information and discovering knowledge in a real-world context. Through the evaluation, the domain experts assessed the feasibility and technology readiness level (TRL) of the DSS model. Results: The SOMNet, combined with the EbCA, effectively processed evidence-based information when analysing system outliers, explaining global and local patterns, and refining key performance indicators with their analytical interpretations. The evaluation results showed that the DSS model was feasible by the domain experts and reached level 7 of the TRL (system prototype demonstration in operational environment). Conclusions: This study supports the benefits of combining health systems engineering (SOMNet) and expert knowledge (EbCA) to analyse the complexity of health systems research. The use of the SOMNet approach contributes to the demonstration of DSS for mental health planning in practice

Use of a novel sharp-tip, J-shaped guidewire to facilitate transseptal catheterization

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Plantmetabolomics.org: mass spectrometry-based Arabidopsis metabolomics—database and tools update

The PlantMetabolomics (PM) database (http://www.plantmetabolomics.org) contains comprehensive targeted and untargeted mass spectrum metabolomics data for Arabidopsis mutants across a variety of metabolomics platforms. The database allows users to generate hypotheses about the changes in metabolism for mutants with genes of unknown function. Version 2.0 of PlantMetabolomics.org currently contains data for 140 mutant lines along with the morphological data. A web-based data analysis wizard allows researchers to select preprocessing and data-mining procedures to discover differences between mutants. This community resource enables researchers to formulate models of the metabolic network of Arabidopsis and enhances the research community's ability to formulate testable hypotheses concerning gene functions. PM features new web-based tools for data-mining analysis, visualization tools and enhanced cross links to other databases. The database is publicly available. PM aims to provide a hypothesis building platform for the researchers interested in any of the mutant lines or metabolites