Tillage greatly modifies soil structure, yet existing approaches to modelling tillage-induced soil structural change remain largely qualitative or over-simplified. Here, we present a quantitative, energy-based reformulation of the classical tillage equation that predicts soil fragmentation from the initial soil state and the applied energy. The new model partitions tillage energy into surface creation through fragmentation, displacement of existing soil fragments, and plastic deformation of the soil. Soil moisture and mechanical properties control the energy partitioning among these processes. As fragmentation progresses, an increasing proportion of tillage energy is dissipated through the displacement of existing fragments, whereas at elevated soil water contents energy is consumed by plastic deformation. Soil fragmentation resulting in the creation of new fragment surface area scales with the remaining energy. Literature-derived soil fragmentation data from drop-shatter tests and tillage experiments were used for model parameterisation. The model was subsequently evaluated against separate, independent literature datasets not used for parameterisation, covering tillage-induced soil fragmentation across a range of soil conditions. Illustrative applications demonstrate the model’s ability to capture (i) the texture-dependent soil workability range and (ii) the diminishing effectiveness of repeated tillage operations. We outline how the model-derived fragment surface area can be linked to fragment size distributions under simplifying assumptions. Assuming spherical fragment geometry and a Weibull distribution of fragment sizes allows an estimation of the tillage-induced pore size distribution. Altogether, our physically grounded model provides a basis for predicting tillage-induced soil structural change and can be incorporated into agroecosystem models. Further model refinement would benefit from datasets that jointly quantify tillage energy input, soil water status, as well as pre- and post-tillage soil structure and hydraulic properties, enabling a more mechanistic description of the transition from brittle fragmentation to plastic deformation.
The pantograph–catenary interface is essential for ensuring uninterrupted and reliable power delivery in electrified rail systems. However, electrical arcing at this interface poses serious risks, including accelerated wear of contact components, degraded system performance, and potential service disruptions. Detecting arcing events at the pantograph–catenary interface is challenging due to their transient nature, noisy operating environment, data scarcity, and the difficulty of distinguishing arcs from other similar transient phenomena. To address these challenges, we propose a novel multimodal framework that combines high-resolution image data with force measurements to more accurately and robustly detect arcing events. First, we construct two arcing detection datasets comprising synchronized visual and force measurements. One dataset is built from data provided by the Swiss Federal Railways (SBB), and the other is derived from publicly available videos of arcing events in different railway systems and synthetic force data that mimic the characteristics observed in the real dataset. Leveraging these datasets, we propose MultiDeepSAD, an extension of the DeepSAD algorithm for multiple modalities with a new loss formulation. Additionally, we introduce tailored pseudo-anomaly generation techniques specific to each data type, such as synthetic arc-like artifacts in images and simulated force irregularities, to augment training data and improve the discriminative ability of the model. Through extensive experiments and ablation studies, we demonstrate that our framework significantly outperforms baseline approaches, exhibiting enhanced sensitivity to real arcing events even under domain shifts and limited availability of real arcing observations. To the best of our knowledge, this is the first method and publicly available dataset that integrates image and force data for pantograph–catenary arcing event detection. The proposed framework offers a practical solution for real-time monitoring of arcing events in pantograph–catenary systems, ultimately contributing to safer and more reliable railway operations. Our source code and dataset are at https://github.com/EPFL-IMOS/Multimodal-Arcing.
Wood is a unique natural resource widely utilized for various applications, and its processing has a significant role in enabling its sustainable development. Laser technology has emerged as a powerful and precise tool for the functionalization and processing of wood materials, offering flexibility and sustainability benefits. This article explores the principles of laser–wood interactions, covering photothermal, photochemical, and photomechanical processes. We identified key parameters that influence laser processing efficiency, such as laser pulse, laser wavelength, and wood material composition. It highlights novel applications of laser processing, from enhancing permeability through laser cutting or drilling, to enabling it with new properties or functions via surface laser treatment technologies such as laser-induced graphene. Furthermore, the review discusses future perspectives of laser-assisted wood engineering, highlighting its critical role in driving sustainable practices and innovations in wood materials technology.
Nitrous oxide fluxes from urine patches (F(N2O)urine) of grazing livestock are variable over time due to fluctuations in driving parameters, such as soil temperature, water-filled pore space (WFPS), and availability of the source substrates ammonium and nitrate. Therefore, the frequency and timing of flux measurements after urine application are important when determining cumulative F(N2O)urine. In this study, F(N2O)urine was measured in eight experiments at high temporal frequency using an automatic chamber system in a pasture located in Switzerland. A driver analysis using random forest identified the time since urine application as most important predictor for F(N2O)urine. The exponential decay in F(N2O)urine after urine addition was in parallel to decreasing soil ammonium but anticorrelated to nitrate concentration, suggesting that nitrification and nitrifier denitrification are major source processes. Since nitrate showed elevated concentrations up to 122 days after application, bacterial denitrification is most likely responsible for late F(N2O)urine peaks following an increase of WFPS. The isotopic composition of the emitted N2O indicates that nitrification dominates the N2O production immediately after urine application, while bacterial denitrification and nitrifier denitrification become more important with increasing time since urine application. The observed high-frequency emission time series were also used to simulate typical low-frequent manual chamber sampling schedules. They led to considerable deviations (up to ± 30
Controlling quantum matter with light offers a promising route to dynamically tune its many-body properties, ranging from band topology1,2 to superconductivity3. However, achieving such optical control for strongly correlated electron systems in the steady state has remained elusive. Here we demonstrate optical switching of the spin-valley degree of freedom of itinerant ferromagnets in twisted MoTe2 (t-MoTe2) homobilayers. This system uniquely features flat valley-contrasting Chern bands and exhibits a range of strongly correlated phases at various moiré lattice fillings, including Chern insulators and ferromagnetic metals4-7. We show that the spin-valley orientation of all of these phases can be dynamically reversed by resonantly exciting the exciton-polaron8 transitions with circularly polarized light. These findings not only provide direct evidence for non-thermal optical switching of a ferromagnetic spin state at zero magnetic field but also demonstrate the possibility of dynamical control over a topological order parameter, paving the way for optical generation of chiral edge modes and topological quantum circuits.
