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1680 Publications visible to you, out of a total of 1680
New mechanism for autoxidation of polyolefins: kinetic Monte Carlo modelling of the role of short-chain branches, molecular oxygen and unsaturated moieties

Abstract (Expand)

In this work, a bivariate kinetic Monte Carlo (kMC) model is constructed to study autoxidation, which is the degradation of polymers in the presence of oxygen. The use of computational methods for the … determination of rate coefficients as input for the model is illustrated. Focus is on the presence of short-chain branches (SCB) and unsaturated moieties and their role in the fate of alkyl, alkoxyl and alkylperoxyl radicals in the autoxidation mechanism. The autoxidation kinetics are studied for three model polymers, namely poly(ethylene) (reference case), poly(butadiene) (presence of allylic hydrogens), and poly(isobutylene) (presence of quaternary carbon atoms). Using the kMC model, reaction path analysis shows that the autoxidation mechanism for each of the polymer types follows a chain reaction mechanism, but that the presence of branches/unsaturated moieties influences the dominant reaction pathway in the autoxidation mechanism, and thus also the autoxidation rate. It is shown that the influence of varying oxygen concentration and initiation rate coefficient (e.g. to simulate variable ultraviolet (UV) light intensity) on the dominant pathway is small as their role is mainly situated in the first steps of the chain mechanism.

Authors: Lies De Keer, Paul Van Steenberge, Marie-Françoise Reyniers, Ganna Gryn'ova, Heather M. Aitken, Michelle L. Coote

Date Published: 2022

Publication Type: Journal

Learning Features via Transformer Networks for Cardiomyocyte Profiling

Abstract

Not specified

Authors: Jan Plier, Matthias Zisler, Jennifer Furkel, Maximilian Knoll, Alexander Marx, Alena Fischer, Kai Polsterer, Mathias H. Konstandin, Stefania Petra

Date Published: 2022

Publication Type: InProceedings

Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Difference

Abstract

Not specified

Authors: Shell Xu Hu, Da Li, Jan Stühmer, Minyoung Kim, Timothy M Hospedales

Date Published: 2022

Publication Type: InProceedings

Meta-learning richer priors for VAEs

Abstract

Not specified

Authors: Marcello Massimo Negri, Vincent Fortuin, Jan Stühmer

Date Published: 2022

Publication Type: InProceedings

TULIPS: A Tool for Understanding the Lives, Interiors, and Physics of Stars

Abstract (Expand)

Understanding the lives and interior structures of stellar objects is a fundamental objective of astrophysics. Research in this domain often relies on the visualization of astrophysical data, for … instance, the results of theoretical simulations. However, the diagrams commonly employed to this effect are usually static, complex, and can sometimes be non-intuitive or even counter-intuitive to newcomers in the field. To address some of these issues, this paper introduces TULIPS, a python package that generates novel diagrams and animations of the structure and evolution of stellar objects. TULIPS visualizes the output of one-dimensional physical simulations and is currently optimized for the MESA stellar evolution code. Utilizing the inherent spherical symmetry of such simulations, TULIPS represents the physical properties of stellar objects as the attributes of circles. This enables an intuitive representation of the evolution, energy generation and loss processes, composition, and interior properties of stellar objects, while retaining quantitative information. Users can interact with the output videos and diagrams. The capabilities of TULIPS are showcased by example applications that include a Sun-like star, a massive star, a low-metallicity star, and an accreting white dwarf. Diagrams generated with TULIPS are compared to the Hertzsprung-Russell diagram and to the Kippenhahn diagram, and their advantages and challenges are discussed. TULIPS is open source and free. Aside from being a research tool, it can be used for preparing teaching and public outreach material.

Author: E. Laplace

Date Published: 2022

Publication Type: Journal

Systems Biology in ELIXIR: modelling in the spotlight

Abstract (Expand)

In this white paper, we describe the founding of a new ELIXIR Community - the Systems Biology Community - and its proposed future contributions to both ELIXIR and the broader community of systems … biologists in Europe and worldwide. The Community believes that the infrastructure aspects of systems biology - databases, (modelling) tools and standards development, as well as training and access to cloud infrastructure - are not only appropriate components of the ELIXIR infrastructure, but will prove key components of ELIXIR’s future support of advanced biological applications and personalised medicine. By way of a series of meetings, the Community identified seven key areas for its future activities, reflecting both future needs and previous and current activities within ELIXIR Platforms and Communities. These are: overcoming barriers to the wider uptake of systems biology; linking new and existing data to systems biology models; interoperability of systems biology resources; further development and embedding of systems medicine; provisioning of modelling as a service; building and coordinating capacity building and training resources; and supporting industrial embedding of systems biology. A set of objectives for the Community has been identified under four main headline areas: Standardisation and Interoperability, Technology, Capacity Building and Training, and Industrial Embedding. These are grouped into short-term (3-year), mid-term (6-year) and long-term (10-year) objectives.

Authors: Vitor Martins dos Santos, Mihail Anton, Barbara Szomolay, Marek Ostaszewski, Ilja Arts, Rui Benfeitas, Victoria Dominguez Del Angel, Polonca Ferk, Dirk Fey, Carole Goble, Martin Golebiewski, Kristina Gruden, Katharina F. Heil, Henning Hermjakob, Pascal Kahlem, Maria I. Klapa, Jasper Koehorst, Alexey Kolodkin, Martina Kutmon, Brane Leskošek, Sébastien Moretti, Wolfgang Müller, Marco Pagni, Tadeja Rezen, Miguel Rocha, Damjana Rozman, David Šafránek, Rahuman S. Malik Sheriff, Maria Suarez Diez, Kristel Van Steen, Hans V Westerhoff, Ulrike Wittig, Katherine Wolstencroft, Anze Zupanic, Chris T. Evelo, John M. Hancock

Date Published: 2022

Publication Type: Journal

Aspects of spatial postprocessing for global temperature forecasts

Abstract

Not specified

Author: Kira Feldmann

Date Published: 2022

Publication Type: Doctoral Thesis

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