Similar to autoimmune diseases, circulating mDC precursors were significantly reduced in patients with CAD. The emergence of mDC precursors in vulnerable plaques suggests their recruitment into atheromata as a possible reason for their decrease in blood. In contrast, no significant association of circulating pDC precursors with atherosclerosis was observed.

The structure and vibrational density of states (VDOS) of polymer glasses are investigated using numerical simulations based on the classical Kremer-Grest bead-spring model. We focus on the roles of chain length and bending stiffness, the latter being set by imposing three-body angular potentials along chain backbones. Upon increasing the chain length and bending stiffness, structural reorganisation leads to volumetric expansion of the material and build-up of internal stresses. The VDOS has two dominant bands: a low frequency one corresponding to inter-and intra-chain nonbonding interactions and a high frequency one corresponding principally to vibrations of bonded beads that constitute skeletal chain backbones. Upon increasing the steepness of the angular potential, vibrational modes associated with chain bending gradually move from the low-frequency to the high-frequency band. This redistribution of modes is reflected in a reduction of the so-called Boson peak upon increasing chain stiffness. Remarkably, the finer structure and the peaks of the high-frequency band, and their variations with stiffness, can, for short chains, be explained using an analytical solution derived for a model triatomic molecule. For longer chains, the qualitative evolution of the VDOS with chain stiffness is similar, although the distinct peaks observed for short chains become increasingly smoothed-out. Our findings can be used to guide a systematic approach to interpretation of Brillouin and Raman scattering spectra of glassy polymers in future work, with applications in polymer processing diagnostics.

Purpose Approaches to solving sustainability problems require a specific problem-solving mode, encompassing the complexity, fuzziness and interdisciplinary nature of the problem. This paper aims to promote a complex systems’ view of addressing sustainability problems, in particular through the tool of network science, and provides an outline of an interdisciplinary training workshop. Design/methodology/approach The topic of the workshop is the analysis of the Sustainable Development Goals (SDGs) as a political action plan. The authors are interested in the synergies and trade-offs between the goals, which are investigated through the structure of the underlying network. The authors use a teaching approach aligned with sustainable education and transformative learning. Findings Methodologies from network science are experienced as valuable tools to familiarise students with complexity and to handle the proposed case study. Originality/value To the best of the authors’ knowledge, this is the first work which uses network terminology and approaches to teach sustainability problems. This work highlights the potential of network science in sustainability education and contributes to accessible material.

This study highlights new opportunities for optimal reaction route selection from large chemical databases brought about by the rapid digitalisation of chemical data.

Data-driven models are becoming increasingly popular in engineering, on their own or in combination with mechanistic models. Commonly, the trained models are subsequently used in model-based optimization of design and/or operation of processes. Thus, it is critical to ensure that data-driven models are not evaluated outside their validity domain during process optimization. We propose a method to learn this validity domain and encode it as constraints in process optimization. We first perform a topological data analysis using persistent homology identifying potential holes or separated clusters in the training data. In case clusters or holes are identified, we train a one-class classifier, i.e., a one-class support vector machine, on the training data domain and encode it as constraints in the subsequent process optimization. Otherwise, we construct the convex hull of the data and encode it as constraints. We finally perform deterministic global process optimization with the data-driven models subject to their respective validity constraints. To ensure computational tractability, we develop a reduced-space formulation for trained one-class support vector machines and show that our formulation outperforms common full-space formulations by a factor of over 3000, making it a viable tool for engineering applications. The method is ready-to-use and available open-source as part of our MeLOn toolbox (https://git.rwth-aachen.de/avt.svt/public/MeLOn).