Board of the Swiss Federal Institutes of Technology

Abstract Global environmental changes generate novel communities. Some species adapt to novel community contexts by demonstrating interaction fidelity, or consistently interacting with the same species, while others demonstrate interaction flexibility, or interacting with whichever partners are available (i.e., rewiring). However, the drivers and benefits of fidelity are unclear. Here, we use data from a large-scale mesocosm experiment to determine whether plant characteristics (e.g., provenance, functional group) and community contexts impact plant interaction fidelity to belowground mutualist and antagonist partners (i.e., root fungi and bacteria), and how this fidelity relates to plant performance. We found that the plant-antagonist relationships exhibited higher fidelity than plant-mutualist relationships with the strength of fidelity depending on a combination of plant characteristics and community context. Conversely, plant-mutualist fidelity impacted plant performance with competing effects depending on plant provenance and aboveground herbivore presence. Our study advances understanding of how species interactions influence plant performance in a changing world. Statement of authorship AMM, JDT, JMT and DBS designed the concept. All authors contributed to refining the ideas. AM coded the analysis to produce the results with contributions from JDT, DBS, and BBM. HRL and DBS helped AMM with the statistical analysis. LPW, WJA, IAD, and JMT designed the experiment while LPW and WJA conducted the experiment. AMM wrote the first draft of the manuscript with assistance from all authors. IAD, JMT, and JDT obtained funding. All authors contributed to further editing of the manuscript.

Abstract Thermally induced errors critically limit precision in machine tools (MT), affecting dimensional accuracy in high-precision manufacturing. Physics-based models offer a sustainable solution and can do without training measurements, unlike data-driven models, but are often unsuitable for real-time thermal error compensation due to computational demands and boundary condition uncertainties. This paper presents an enhanced ensemble Kalman filter approach to accurately and efficiently reconstruct the thermal state from limited sensor data. Utilizing a reduced finite element model generated via Krylov modal subspace techniques, the proposed method integrates real-time sensor measurements with an ensemble of perturbed models to robustly estimate thermal states and mechanical deformations. Validation experiments on a thermal test bench with 10 out of 40 temperature sensors observed demonstrate significant improvements in accuracy achieving an RMSE of $$1.5\,^{\circ }$$ 1.5 ∘ C. The computational speed is over 45 $$\times $$ × faster than real-time allowing the implementation of the digital twin for any MT. The reduction of the volumetric thermal error by consistently more than 70% confirm the potential of this digital twin-based approach for real-time thermal error compensation in precision manufacturing.

DEAD-box proteins such as Dhh1 play essential roles in RNA metabolism and the formation of biomolecular condensates, with a modular architecture comprising folded domains and disordered regions. To elucidate how this architecture shapes conformational dynamics in solution, we combined solution scattering experiments and multi-scale simulations on core and full-length constructs. Enhanced-sampling simulations captured a dynamic ensemble of core conformations stabilized by transient interdomain contacts that underpin functional regulation. Coarse-grained modelling revealed that disordered tails behave as independent modules exerting minimal influence on core dynamics. This integrated approach reveals a modular organization balancing structural heterogeneity and functional specificity, providing a framework for studying DEAD-box proteins in phase separation.

Laser-driven dynamics in polyatomic molecules poses a complex many-body problem. Understanding intense light-matter interaction is crucial for steering intramolecular quantum dynamical processes. Here, we record time-resolved x-ray diffraction images of molecules during and after their interaction with intense near-infrared fields, giving direct access to structural changes of the molecules and their fragmentation in real time. Tuning the intensity of the excitation pulses, we uncover a transition from a weak-field regime of excited but stable molecules to a high-field regime dominated by Coulomb explosion. In the transition region, the molecules expand by up to 50% of their initial size within just 140 fs, with major fragmentation only setting in afterward. This work demonstrates that x-ray diffractive imaging is capable of retrieving time-resolved structural information of large molecules reshaped by intense laser fields. Laser-driven fragmentation is a first step toward observing molecular processes modified by laser fields of increasing intensity.

Humidity haloes—regions of enhanced water vapour surrounding trade wind shallow cumulus clouds—can significantly influence downward longwave radiation due to their elevated moisture content compared to clear-sky conditions. Using data from the Bar

The paper explore the possibility of recycling post-consumer polymers from the Building & Construction industry (B&C), using Large Scale Robotic 3D Printing (LSR3DP) to produce bespoke building facade components. A case study is developed, in which a stock of Flexible Polyolefine (FPO) - resulting from the recycling of a roof waterproofing membrane - is transformed into a set of highly crafted façade/roofing shingles. The paper describes: 1) the material recycling process, 2) the material testing for LSR3DP, and 3) the fabrication of a 1:1 demonstrator.

Dynamic tariff adoption is considered to be an important driver of demand response, enabling more sustainable and reliable power systems.However, first studies have shown that a high share of households subscribing to dynamic tariffs can lead to so-called "avalanche effects" on the distribution grid level, wherein load profiles align across households.Avalanche effects can create new demand peaks that necessitate costly grid reinforcement measures.Here, we analyze the impacts of policy options for grid charge and solar photovoltaic (PV) feed-in remuneration on grid reinforcement costs given increasing shares of dynamic tariff adoption.The analysis framework is open-source and uses empirical data from real households.We find that the widely proliferated regulatory scenario with volumetric grid charges and PV feed-in-tariffs leads to heavy reinforcement needs.We show that novel policy options, such as rotating or segmented grid charges, can alleviate grid reinforcement needs.