A locking-free meshfree method for the simulation of shear-deformable plates based on a mixed variational formulation

in Computer Methods in Applied Mechanics and Engineering (2012), 241-244

The problem of shear-locking in the thin-plate limit is a well known issue that must be overcome when discretising the Reissner-Mindlin plate equations. In this paper we present a shear-locking-free ... [more ▼]

The problem of shear-locking in the thin-plate limit is a well known issue that must be overcome when discretising the Reissner-Mindlin plate equations. In this paper we present a shear-locking-free method utilising meshfree maximum-entropy basis functions and rotated Raviart-Thomas-Nédélec elements within a mixed variational formulation. The formulation draws upon well known techniques in the finite element literature. Due to the inherent properties of the maximum-entropy basis functions our method allows for the direct imposition of Dirichlet (essential) boundary conditions, in contrast to methods based on moving least squares basis functions. We present benchmark problems that demonstrate the accuracy and performance of the proposed method. © 2012. [less ▲]

Lost in legal data visualization? A legal scholar’s view on the ECB dedicated section in EUR-Lex

in European Central Bank (Ed.) ESCB Legal Conference 2022 (2022, December)

Legal data visualisation and legal knowledge visualisation are increasingly attracting the attention of the legal community, given their alleged positive impact on legal teaching, access to and ... [more ▼]

Legal data visualisation and legal knowledge visualisation are increasingly attracting the attention of the legal community, given their alleged positive impact on legal teaching, access to and understanding of the law by citizens and/or litigants, or the production of new legal products or services (e.g. Stanford Legal Design Lab). On the other hand, very few legal research studies are based on and integrate into their analysis the visualization of legal data, legal information, or of legal knowledge. Even fewer studies examine the impact of these tools on legal research methodology. Legal scholars are no less skilled and familiar with the use of IT tools, from preparing their lectures (e.g. ppt presentations) to consulting online databases or using expert systems to review articles. Is the lack of interest in legal data visualisation tools an expression of any technological difficulty? To use Friedrich Lachmayer's metaphor, "Legal informatics, which includes the visualisation of legal data, is about building a bridge between law and informatics. But in the design of visualisation tools, informatics scholars expect too much from legal scholars, then the latter give up and the bridge collapses. Or does the problem stem from a specific feature of the law, namely the ontological formalism of legal norms, which would prevent the dissemination of these tools - or render them useless? Rather than exploring this issue from a theoretical perspective, this study will examine the relevance of legal data visualization tools through the analysis of a concrete case, namely the updating that the present author is to carry out of the "Euro" section, published in the French Répertoire Dalloz. This section, originally written in 2002 by Professor Jean-Victor Louis, analyses the monetary law of the Union. In addition to updating the references to the relevant legal acts and instruments in force, this new version should also highlight changes to the content of the lex monetae and related legislation (protection of banknotes and coins, anti-counterfeiting measures, conversion and rounding procedures, etc.). As part of the updating process, the research methodology will be based on the visualisation tools made available by EUR-lex. The study will therefore examine the extent to which these tools have supported the research and facilitated the identification of new insights. Or, on the contrary, to what extent they proved to be inappropriate or insufficient and, if so, for what reasons? [less ▲]

Luxembourg’s free public transport sounds great. It isn't

Article for general public (2019)

This is a reproduction of Carr/Hesse's 2019 "Luxembourg’s free public transport sounds great, but it won’t help people get from A to B" published Jan 16, 2019, in The Conversation. Available at:https ... [more ▼]

This is a reproduction of Carr/Hesse's 2019 "Luxembourg’s free public transport sounds great, but it won’t help people get from A to B" published Jan 16, 2019, in The Conversation. Available at:https://theconversation.com/luxembourgs-free-public-transport-sounds-great-but-it-wont-help-people-get-from-a-to-b-109695 [less ▲]

Machine learning applied to higher order functional representations of omics data reveals biological pathways associated with Parkinson‘s Disease

Poster (2022, September 18)

Background: Despite the increasing prevalence of Parkinson’s Disease (PD) and research efforts to understand its underlying molecular pathogenesis, early diagnosis of PD remains a challenge. Machine ... [more ▼]

Background: Despite the increasing prevalence of Parkinson’s Disease (PD) and research efforts to understand its underlying molecular pathogenesis, early diagnosis of PD remains a challenge. Machine learning analysis of blood-based omics data is a promising non-invasive approach to finding molecular fingerprints associated with PD that may enable an early and accurate diagnosis. Description: We applied several machine learning classification methods to public omics data from PD case/control studies. We used aggregation statistics and Pathifier’s pathway deregulation scores to generate higher order functional representations of the data such as pathway-level features. The models’ performance and most relevant predictive features were compared with individual feature level predictors. The resulting diagnostic models from individual features and Pathifier’s pathway deregulation scores achieve significant Area Under the Curve (AUC, a receiver operating characteristic curve) scores for both cross-validation and external testing. Furthermore, we identify plausible biological pathways associated with PD diagnosis. Conclusions: We have successfully built machine learning models at pathway-level and single-feature level to study blood-based omics data for PD diagnosis. Plausible biological pathway associations were identified. Furthermore, we show that pathway deregulation scores can serve as robust and biologically interpretable predictors for PD. [less ▲]

Machine Learning to Geographically Enrich Understudied Sources: A Conceptual Approach

in Rocha, Ana; Steels, Luc; van den Herik, Jaap (Eds.) Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 1: ARTIDIGH (2020)

This paper discusses the added value of applying machine learning (ML) to contextually enrich digital collections. In this study, we employed ML as a method to geographically enrich historical datasets ... [more ▼]

This paper discusses the added value of applying machine learning (ML) to contextually enrich digital collections. In this study, we employed ML as a method to geographically enrich historical datasets. Specifically, we used a sequence tagging tool (Riedl and Padó 2018) which implements TensorFlow to perform NER on a corpus of historical immigrant newspapers. Afterwards, the entities were extracted and geocoded. The aim was to prepare large quantities of unstructured data for a conceptual historical analysis of geographical references. The intention was to develop a method that would assist researchers working in spatial humanities, a recently emerged interdisciplinary field focused on geographic and conceptual space. Here we describe the ML methodology and the geocoding phase of the project, focussing on the advantages and challenges of this approach, particularly for humanities scholars. We also argue that, by choosing to use largely neglected sources such as immigrant newspapers (a lso known as ethnic newspapers), this study contributes to the debate about diversity representation and archival biases in digital practices. [less ▲]

Maintenance location routing for rolling stock under line and fleet planning uncertainty

in Transportation Science (2019), 53(5), 1252-1270

Making the Case for Evidence-based Standardization of Data Privacy and Data Protection Visual Indicators

in Journal of Open Access to Law (2020), 8(1),

Lately, icons have witnessed a growing wave of interest in the view of enhancing transparency and clarity of data processing practices in mandated disclosures. Although benefits in terms of ... [more ▼]

Lately, icons have witnessed a growing wave of interest in the view of enhancing transparency and clarity of data processing practices in mandated disclosures. Although benefits in terms of comprehensibility, noticeability, navigability of the information and user’s attention and memorization can be expected, they should also be supported by decisive empirical evidence about the efficacy of the icons in specific contexts. Misrepresentation, oversimplification, and improper salience of certain aspects over others are omnipresent risks that can drive data subjects to wrong conclusions. Cross-domain and international standardization of visual means also poses a serious challenge: if on the one hand developing standards is necessary to ensure widespread recognition and comprehension, each domain and application presents unique features that can be hardly established, and imposed, in a top-down manner. This article critically discusses the above issues and identifies relevant open questions for scientific research. It also provides concrete examples and practical suggestions for researchers and practitioners that aim to implement transparency-enhancing icons in the spirit of the General Data Protection Regulation (GDPR). [less ▲]

A Markov chain dynamic model for trip generation and distribution based on CDR

in Periodica Polytechnica Transportation Engineering (2015)

A Markov Chain Monte Carlo Approach for Estimating Daily Activity Patterns

Poster (2019, January)

Determining the purpose of trips brings is a fundamental information to evaluate travel demand during the day and to predict longer-term impacts on the population’s travel behavior. The concept of tours ... [more ▼]

Determining the purpose of trips brings is a fundamental information to evaluate travel demand during the day and to predict longer-term impacts on the population’s travel behavior. The concept of tours is the most suited to consider the value of a daily scheduling of individuals and travel interdependencies. However, the meticulous care required for both collecting data of high quality and interpret results of advanced demand models are frequently considered as major drawbacks. The objective of this study is to incorporate into a standard trip-based model some inherent concepts of activity-based models in order to enhance the representation of travel behavior. The main focus of this work is to infer, employing utility theory, the trip purpose of a population, at a zonal level. Making use of Markov Chain Monte Carlo, a set of parameters is estimated in order to retrieve tour-based primitives of the demand. The main advantage of this methodology is the low requirements in terms of data, as no individual information are used, and the good interpretation of the model. Estimated parameters of the priors set a utility-based probability function for departure time, which allows to have a dynamic overview of the demand. In order to account for the tour consistency of travel decisions, a duration constraint is added to the model. The proposed model is applied to the region of Luxembourg city and the results show the potential of the methodologies for dividing an observed demand based on the activity at destination. [less ▲]

A mass conservative Kalman filter algorithm for thermo-computational fluid dynamics

in Materials (n.d.)

Computational fluid-dynamics (CFD) is of wide relevance in engineering and science, due to its capability of simulating the three-dimensional flow at various scales. However, the suitability of a given ... [more ▼]

Computational fluid-dynamics (CFD) is of wide relevance in engineering and science, due to its capability of simulating the three-dimensional flow at various scales. However, the suitability of a given model depends on the actual scenarios which are encountered in practice. This challenge of model suitability and calibration could be overcome by a dynamic integration of measured data into the simulation. This paradigm is known as data-driven assimilation (DDA). In this paper, the study is devoted to Kalman filtering, a Bayesian approach, applied to Reynolds-Averaged Navier-Stokes (RANS) equations for turbulent flow. The integration of the Kalman estimator into the PISO segregated scheme was recently investigated by (1). In this work, this approach is extended to the PIMPLE segregated method and to the ther- modynamic analysis of turbulent flow, with the addition of a sub-stepping procedure that ensures mass conservation at each time step and the com- patibility among the unknowns involved. The accuracy of the algorithm is verified with respect to the heated lid-driven cavity benchmark, incorporat- ing also temperature observations, comparing the augmented prediction of the Kalman filter with the CFD solution obtained on a very fine grid. [less ▲]