The nexus between digital skills/competences and work: A bibliometric study

The widespread use of computers and other new information and communication technologies (ICT) in every realm of society has increased the demand for specific skills and competences for people at any age and stage of life to use and work with ICT effectively. Summarised under the terms "digital skills" and "digital competences" by the European Commission in 2018, these concepts still lack clarity and are characterised by some ambiguity though much research has been devoted to them. Given that these two concepts are of high topicality with regard to current labour market developments, like skills mismatch, the digital divide or the design and implementation of occupational retraining programmes, the main purpose of this paper is to contribute to a more clear-cut understanding of the nexus between digital skills/competences and work. To accomplish this goal, we carry out a bibliometric study consisting of both quantitative and qualitative analysis. Our main findings are that research on the nexus between digital skills/competences and work is evolving and this research field is anchored in many different scientific disciplines and shares thematic overlaps with various other areas such as higher education research. The qualitative part of our analysis reveals that this research field is defined by six building blocks with one motor theme on "digital literacy". Furthermore, employment or employability as well as the effects of changing technologies at the workplace are the most crucial topics addressed in this research field, reflecting the high value attributed to digital skills/competences in determining the employability of the current and future workforce

Digital inequality in Austria: Empirical evidence from the survey of the OECD "Programme for the International Assessment of Adult Competencies"

Digitisation and rapidly emerging new technologies are transforming many aspects of life such as education, work, and leisure. These changes lead to a growing demand for new skills related to ICT use, computer literacy, internet use, or technical digital skills. However, the extensive literature on digital inequality provides evidence for significant differences in computer skills along the usual dimensions of social inequality. Due to the omnipresence of digital technologies in everyday life, it is all the more important to know the extent of digital inequality to be able to take appropriate measures to ensure that social participation does not degenerate into a question of social stratification in the Digital Age. In this paper, we provide empirical evidence for socio-economic digital inequality in Austria using survey data from the “Programme for the International Assessment of Adult Competencies” (PIAAC) conducted in 2011/2012.1 We show, for Austria, that higher socio-economic background is positively related to digital problem-solving while being female is negatively correlated. However, when controlling for ICT engagement in everyday life, the positive effect of the socio-economic background only remains significant for groups of people with a very high socio-economic background while the effect of gender becomes statistically insignificant. Furthermore, based on Eurostat data we cannot identify a uniform trend towards a decline of digital inequality since 2012. Our results indicate that disadvantaged population groups in Austria should be encouraged and enabled to integrate ICT usage in their everyday life to reduce digital inequality

Finite strain porohyperelasticity: An asymptotic multiscale ALE-FSI approach supported by ANNs

The governing equations and numerical solution strategy to solve porohyperelstic problems as multiscale multiphysics media are provided in this contribution. The problem starts from formulating and non-dimensionalising a Fluid-Solid Interaction (FSI) problem using Arbitrary Lagrangian-Eulerian (ALE) technique at the pore level. The resultant ALE-FSI coupled systems of PDEs are expanded and analysed using the asymptotic homogenisation technique which yields three partially novel systems of PDEs, one governing the macroscopic/effective problem supplied by two microscale problems (fluid and solid). The latter two provide the microscopic response fields whose average value is required in real-time/online form to determine the macroscale response. This is possible efficiently by training an Artificial Neural Network (ANN) as a surrogate for the Direct Numerical Solution (DNS) of the microscale solid problem. The present methodology allows to solve finite strain (multiscale) porohyperelastic problems accurately using direct derivative of the strain energy, for the first time. Furthermore, a simple real-time output density check is introduced to achieve an optimal and reliable training dataset from DNS. A Representative Volume Element (RVE) is adopted which is followed by performing a microscale (RVE) sensitivity analysis and a multiscale confined consolidation simulation showing the importance of employing the present method when dealing with finite strain poroelastic/porohyperelastic problems

A hybrid MGA-MSGD ANN training approach for approximate solution of linear elliptic PDEs

We introduce a hybrid "Modified Genetic Algorithm-Multilevel Stochastic Gradient Descent" (MGA-MSGD) training algorithm that considerably improves accuracy and efficiency of solving 3D mechanical problems described, in strong-form, by PDEs via ANNs (Artificial Neural Networks). This presented approach allows the selection of a number of locations of interest at which the state variables are expected to fulfil the governing equations associated with a physical problem. Unlike classical PDE approximation methods such as finite differences or the finite element method, there is no need to establish and reconstruct the physical field quantity throughout the computational domain in order to predict the mechanical response at specific locations of interest. The basic idea of MGA-MSGD is the manipulation of the learnable parameters' components responsible for the error explosion so that we can train the network with relatively larger learning rates which avoids trapping in local minima. The proposed training approach is less sensitive to the learning rate value, training points density and distribution, and the random initial parameters. The distance function to minimise is where we introduce the PDEs including any physical laws and conditions (so-called, Physics Informed ANN). The Genetic algorithm is modified to be suitable for this type of ANN in which a Coarse-level Stochastic Gradient Descent (CSGD) is exploited to make the decision of the offspring qualification. Employing the presented approach, a considerable improvement in both accuracy and efficiency, compared with standard training algorithms such as classical SGD and Adam optimiser, is observed. The local displacement accuracy is studied and ensured by introducing the results of Finite Element Method (FEM) at sufficiently fine mesh as the reference displacements. A slightly more complex problem is solved ensuring its feasibility

ANN-aided incremental multiscale-remodelling-based finite strain poroelasticity

Mechanical modelling of poroelastic media under finite strain is usually carried out via phenomenological models neglecting complex micro-macro scales interdependency. One reason is that the mathematical two-scale analysis is only straightforward assuming infinitesimal strain theory. Exploiting the potential of ANNs for fast and reliable upscaling and localisation procedures, we propose an incremental numerical approach that considers rearrangement of the cell properties based on its current deformation, which leads to the remodelling of the macroscopic model after each time increment. This computational framework is valid for finite strain and large deformation problems while it ensures infinitesimal strain increments within time steps. The full effects of the interdependency between the properties and response of macro and micro scales are considered for the first time providing more accurate predictive analysis of fluid-saturated porous media which is studied via a numerical consolidation example. Furthermore, the (nonlinear) deviation from Darcy's law is captured in fluid filtration numerical analyses. Finally, the brain tissue mechanical response under uniaxial cyclic test is simulated and studied

The enriched space–time finite element method (EST) for simultaneous solution of fluid–structure interaction

International audienceThe paper introduces a weighted residual-based approach for the numerical investigation of the interaction of fluid flow and thin flexible structures. The presented method enables one to treat strongly coupled systems involving large structural motion and deformation of multiple-flow-immersed solid objects. The fluid flow is described by the incompressible Navier–Stokes equations. The current configuration of the thin structure of linear elastic material with non-linear kinematics is mapped to the flow using the zero iso-contour of an updated level set function. The formulation of fluid, structure and coupling conditions uniformly uses velocities as unknowns. The integration of the weak form is performed on a space–time finite element discretization of the domain. Interfacial constraints of the multi-field problem are ensured by distributed Lagrange multipliers. The proposed formulation and discretization techniques lead to a monolithic algebraic system, well suited for strongly coupled fluid–structure systems. Embedding a thin structure into a flow results in non-smooth fields for the fluid. Based on the concept of the extended finite element method, the space–time approximations of fluid pressure and velocity are properly enriched to capture weakly and strongly discontinuous solutions. This leads to the present enriched space–time (EST) method. Numerical examples of fluid–structure interaction show the eligibility of the developed numerical approach in order to describe the behavior of such coupled systems. The test cases demonstrate the application of the proposed technique to problems where mesh moving strategies often fail

Finite element method for strongly-coupled systems of fluid-structure interaction with application to granular flow in silos

A monolithic approach to fluid-structure interactions based on the space-time finite element method (STFEM) is presented. The method is applied to the investigation of stress states in silos filled with granular material during discharge. The thin-walled siloshell is modeled in a continuum approach as elastic solid material, whereas the flowing granular material is described by an enhanced viscoplastic non-Newtonian fluid model. The weak forms of the governing equations are discretized by STFEM for both solid and fluid domain. To adapt the matching mesh nodes of the fluid domain to the structural deformations, a mesh-moving scheme using a neo-Hookean pseudo-solid is applied. The finite element approximation of non-smooth solution characteristics is enhanced by the extended finite element method (XFEM). The proposed methodology is applied to the 4D (space-time) investigation of deformation-dependent loading conditions during silo discharge

Toward fluid-structure-piezoelectric simulations applied to flow-induced energy harvesters

The subject deals with the simulation of flow-induced energy harvesters. We focus in particular on the modelling of autonomous piezo-ceramic power generators to convert ambient fluid-flow energy into electrical energy. The vibrations of an immersed electromechanical structure with large amplitude have to be taken into account in that case. One challenge consists in modelling and predicting the nonlinear coupled dynamic behaviour for the improved design of such devices. The set of governing equations is expressed in integral form, using the method of weighted residuals, and discretized with finite elements using the open source package FEniCS. Preliminary results of separated problems using FEniCS will be detailed and discussed (e.g. Navier-Stokes with or without moving meshes, nonlinear elasticity, aeroelasticity and electromechanical coupling). The objective is to validate each problem independently before coupling all the phenomena in a monolithic framework. Those simulations involve nonlinearities at many levels of modeling. The perspective of using reduced order models to limit the computational cost (in time and memory) will be discussed in an outlook to this work