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Abstract Exploring why species of different plant growth forms can coexist in the same forest is critical for understanding the long-term community stability, but is poorly studied from root ecological strategies. The aim of this study was to explore the variation of root functional traits among different growth forms and their distribution patterns in root economics space to clarify how plant growth forms affect the root resource acquisition strategies of co-occurring species in a forest community. We sampled 115 co-occurring species with five growth forms (i.e., trees, shrubs, lianas, herbs and ferns) from a mega-plot (>50 ha) in temperate forest and measured seven root functional traits, including root morphological, anatomical and chemical traits, that are closely associated with root resource foraging and conservation strategies. We found that root specific length (SRL) and tissue density (RTD) showed wider variations than other traits among the five growth forms. Moreover, compared with clade and mycorrhizal type, variations of SRL and RTD were largely attributed to growth forms. Importantly, 115 co-occurring species were separately aggregated by growth forms along the trade-off dimension of SRL and RTD in root economics space, suggesting the diversity in root resource acquisition strategies at a local forest community is linked to plant growth forms. In particular, herbs were concentrated towards the side of high SRL and RN, by contrast, trees, shrubs and ferns were positioned at the side of high RTD and carbon/nitrogen, and lianas were located towards the middle. Diverse root resource acquisition strategies in plant growth forms allow them to occupy specific belowground ecological niches, thereby relieving the competition for the common resource. These findings advance our understanding of the mechanism for maintaining community species coexistence from a below-ground perspective. 

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. 

Abstract Neuromorphic computing shows promise for advancing computing efficiency and capabilities of AI applications using brain-inspired principles. However, the neuromorphic research field currently lacks standardized benchmarks, making it difficult to accurately measure technological advancements, compare performance with conventional methods, and identify promising future research directions. This article presents NeuroBench, a benchmark framework for neuromorphic algorithms and systems, which is collaboratively designed from an open community of researchers across industry and academia. NeuroBench introduces a common set of tools and systematic methodology for inclusive benchmark measurement, delivering an objective reference framework for quantifying neuromorphic approaches in both hardware-independent and hardware-dependent settings. For latest project updates, visit the project website (neurobench.ai). 

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.