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Summary Coarse roots represent a globally important belowground carbon pool, but the factors controlling coarse root decomposition rates remain poorly understood relative to other plant biomass components. We compiled the most comprehensive dataset of coarse root decomposition data including 148 observations from 60 woody species, and linked coarse root decomposition rates to plant traits, phylogeny and climate to address questions of the dominant controls on coarse root decomposition.We found that decomposition rates increased with mean annual temperature, root nitrogen and phosphorus concentrations. Coarse root decomposition was slower for ectomycorrhizal than arbuscular mycorrhizal associated species, and angiosperm species decomposed faster than gymnosperms. Coarse root decomposition rates and calcium concentrations showed a strong phylogenetic signal.Our findings suggest that categorical traits like mycorrhizal association and phylogenetic group, in conjunction with root quality and climate, collectively serve as the optimal predictors of coarse root decomposition rates.Our findings propose a paradigm of the dominant controls on coarse decomposition, with mycorrhizal association and phylogeny acting as critical roles on coarse root decomposition, necessitating their explicit consideration in Earth‐system models and ultimately improving confidence in projected carbon cycle–climate feedbacks. 

This work unlocks an opportunity to rapidly construct fused heterocyclic cations for efficient screening of single-molecular white-light-emitting materials and pure red-light-emitting materials. 

Energy dissipation around a propagating crack is the primary mechanism for the enhanced fracture toughness in viscoelastic solids. Such dissipation is spatially non-uniform and is highly coupled to the crack propagation process due to the history-dependent nature of viscoelasticity. We present an experimental approach to map the dissipation field during crack propagation in soft viscoelastic solid. Specifically, we track randomly distributed tracer particles to measure the evolving deformation field. The measured deformation field is then put into a nonlinear constitutive model to determine the dissipation field. Our methodology was used to investigate the deformation and dissipation fields around a propagating crack in a Polyampholyte (PA) hydrogel. The deformation field measurements allowed us to assess whether the commonly assumed translational invariance in viscoelastic fracture theories holds true in practical experiments. Furthermore, by combining the obtained deformation fields with a nonlinear viscoelastic model, we captured the complete history of the dissipation field during crack propagation. We found that dissipation occurred even at material points that are a few millimeters away from the crack tip. The mapped dissipation field also enabled the separate determination of the intrinsic and dissipative components of fracture toughness for the viscoelastic hydrogel. 

We demonstrate that contrastive representation learning is a computationally efficient and flexible method to incorporate physical constraints, especially those defined by equalities, in machine-learning-based density functional design. 

Abstract Copula is a popular method for modeling the dependence among marginal distributions in multivariate censored data. As many copula models are available, it is essential to check if the chosen copula model fits the data well for analysis. Existing approaches to testing the fitness of copula models are mainly for complete or right-censored data. No formal goodness-of-fit (GOF) test exists for interval-censored or recurrent events data. We develop a general GOF test for copula-based survival models using the information ratio (IR) to address this research gap. It can be applied to any copula family with a parametric form, such as the frequently used Archimedean, Gaussian, and D-vine families. The test statistic is easy to calculate, and the test procedure is straightforward to implement. We establish the asymptotic properties of the test statistic. The simulation results show that the proposed test controls the type-I error well and achieves adequate power when the dependence strength is moderate to high. Finally, we apply our method to test various copula models in analyzing multiple real datasets. Our method consistently separates different copula models for all these datasets in terms of model fitness.