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Publications associées (31)

Analytical Model of Single-Sided Linear Induction Motors for High-Speed Applications

Mario Paolone, André Hodder, Simone Rametti, Lucien André Félicien Pierrejean

This article describes a field-based analytical model of single-sided linear induction motors (SLIMs) that explicitly considers the following effects altogether: finite motor length, magnetomotive force mmf space harmonics, slot effect, edge effect, and ta ...

IEEE2024

3D printed large amplitude torsional microactuators powered by ultrasound

Mahmut Selman Sakar, Amit Yedidia Dolev, Mehdi Ali Gadiri, Junsun Hwang

Here, we introduce a design, fabrication, and control methodology for large amplitude torsional microactuators powered by ultrasound. The microactuators are 3D printed from two polymers with drastically different elastic moduli as a monolithic compliant me ...

2024

Artificial intelligence of things for synergizing smarter eco-city brain, metabolism, and platform: Pioneering data-driven environmental governance

Jeffrey Huang, Simon Elias Bibri

Emerging smarter eco-cities, inherently intertwined with environmental governance, function as experimental sites for testing novel technological solutions and implementing environmental reforms aimed at addressing complex challenges. However, despite sign ...

Elsevier2024

Benchmarking machine-readable vectors of chemical reactions on computed activation barriers

Anne-Clémence Corminboeuf, Puck Elisabeth van Gerwen, Ksenia Briling, Yannick Calvino Alonso

In recent years, there has been a surge of interest in predicting computed activation barriers, to enable the acceleration of the automated exploration of reaction networks. Consequently, various predictive approaches have emerged, ranging from graph-based ...

Royal Soc Chemistry2024

Augmented Memory: Sample-Efficient Generative Molecular Design with Reinforcement Learning

Philippe Schwaller, Jeff Guo

Sample efficiency is a fundamental challenge in de novo molecular design. Ideally, molecular generative models should learn to satisfy a desired objective under minimal calls to oracles (computational property predictors). This problem becomes more apparen ...

Amer Chemical Soc2024

Electromagnetic Radiation of Implantable Antennas

Mingxiang Gao

In the development of implantable bioelectronics, the establishment of efficient wireless RF links between implants and external nodes is crucial, providing substantial contributions to the advancement of medical diagnosis, therapies, and basic science. Im ...

EPFL2024

System for predicting a prognosis of the neuropsychological and/or neuropsychiatric status in a subject

Olaf Blanke, Fosco Bernasconi

The present invention relates to systems and methods for predicting a prognosis of the neuropsychological and/or neuropsychiatric status in a subject based on reports of Minor Hallucination (MH) events in combination with electrophysiological data of the s ...

2024

Student Answer Forecasting: Transformer-Driven Answer Choice Prediction for Language Learning

Tanja Christina Käser Jacober, Paola Mejia Domenzain, Vinitra Swamy, Jibril Albachir Frej, Tommaso Martorella, Elena Grazia Gado

Intelligent Tutoring Systems (ITS) enhance personalized learning by predicting student answers to provide immediate and customized instruction. However, recent research has primarily focused on the correctness of the answer rather than the student's perfor ...

2024

Out of their minds? Externalist challenges for using AI in forensic psychiatry

Marcello Ienca

Harnessing the power of machine learning (ML) and other Artificial Intelligence (AI) techniques promises substantial improvements across forensic psychiatry, supposedly offering more objective evaluations and predictions. However, AI-based predictions abou ...

FRONTIERS MEDIA SA2023

Data-enabled Predictive Control for Empty Vehicle Rebalancing

Nikolaos Geroliminis, Giancarlo Ferrari Trecate, Pengbo Zhu

A critical operational challenge in Mobility-on-demand systems is the problem of imbalance between vehicle supply and passenger demand. However, conventional model-based methods require accurate parametric system models with complex nonlinear dynamics that ...

IEEE2023

Validation of D-T fusion power prediction capability against 2021 JET D-T experiments

Javier García Hernández, Mikhail Maslov, Michele Marin

JET experiments using the fuel mixture envisaged for fusion power plants, deuterium and tritium (D-T), provide a unique opportunity to validate existing D-T fusion power prediction capabilities in support of future device design and operation preparation. ...

IOP Publishing Ltd2023

Toward a common standard for data and specimen provenance in life sciences

Marco Mattavelli, Anna Marsano

Open and practical exchange, dissemination, and reuse of specimens and data have become a fundamental requirement for life sciences research. The quality of the data obtained and thus the findings and knowledge derived is thus significantly influenced by t ...

WILEY2023

Predicting Survey Response with Quotation-based Modeling: A Case Study on Favorability towards the United States

Alireza Amirshahi, Saleh Baghersalimi

The acquisition of survey responses is a crucial component in conducting research aimed at comprehending public opinion. However, survey data collection can be arduous, time-consuming, and expensive, with no assurance of an adequate response rate. In this ...

IEEE COMPUTER SOC2023

Behavioral outcome of very preterm children at 5 years of age: Prognostic utility of brain tissue volumes at term-equivalent-age, perinatal, and environmental factors

Djalel Eddine Meskaldji, Serafeim Loukas

ObjectivePrematurity is associated with a high risk of long-term behavioral problems. This study aimed to assess the prognostic utility of volumetric brain data at term-equivalent-age (TEA), clinical perinatal factors, and parental social economic risk in ...

WILEY2023

BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

Pascal Fua, Sean Lewis Hill, Jiancheng Yang, Amos Sironi, Jian Yang

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in m ...

Nature Publishing Group2023

Data-driven modeling of beam loss in the LHC

Tatiana Pieloni, Michael Schenk, Ekaterina Krymova, Loïc Thomas Davies Coyle

In the Large Hadron Collider, the beam losses are continuously measured for machine protection. By design, most of the particle losses occur in the collimation system, where the particles with high oscilla-tion amplitudes or large momentum error are scrape ...

FRONTIERS MEDIA SA2023

MIXING FOR GENERIC ROUGH SHEAR FLOWS

Lucio Galeati

We study mixing and diffusion properties of passive scalars driven by generic rough shear flows. Genericity is here understood in the sense of prevalence, and (ir)regularity is measured in the Besov-Nikolskii scale B\alpha 1,\infty, \alpha \in (0,1). We pr ...

Siam Publications2023

Snap buckling of bistable beams under combined mechanical and magnetic loading

Pedro Miguel Nunes Pereira de Almeida Reis, Dong Yan, Arefeh Abbasi, Tomohiko Sano

We investigate the mechanics of bistable, hard-magnetic, elastic beams, combining experiments, finite-element modelling (FEM) and a reduced-order theory. The beam is made of a hard magneto-rheological elastomer, comprising two segments with antiparallel ma ...

ROYAL SOC2023

Compliance-matching vascular grafts with personalization potential

Georgios Rovas

Aortic diseases are characterized by dire prognosis and inadequate diagnosis, owing to their insidious yet lethal nature. Aortopathies, namely aortic aneurysms and dissections, along with certain congenital diseases, might necessitate surgical replacement ...

EPFL2023

Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations

Ntorina Thanou, Nicola Parolini, Benjamin Bejar Haro, Daniel Rodrigo Lopez, Ekaterina Krymova, Andrea Kraus, David Kraus

Background:Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise the predictive p ...

eLIFE SCIENCES PUBL LTD2023