Changes in the Content and Allocation of Carbon and Nitrogen during Forage Regrowth

Regrowth after cutting or grazing perennial grasslands sustains the production potential of forage and the persistence of grassland species. The changes in nature, content and allocation of compounds within plant parts are fundamentally correlated to the forage regrowth process (Lambers et al. 2008). These compounds are sourced from reserves and new assimilates. Carbohydrates and proteins stored mainly in the stem base and the root play an important role at the early stages of regrowth (Meuriot et al. 2004). The newly assimilated compounds include carbon from photosynthesis for the residual leaf and stem, and nitrogen absorbed by the roots from which amino acids and proteins will be produced (Dhont et al. 2003). Assimilates change as the forage regrows, playing a key role in the later stages. This mini-review summarises the changes in the content and allocation of carbon and nitrogen after cutting or grazing

Uncertainty Quantification of Phase Transition Problems with an Injection Boundary

We develop an enthalpy-based modeling and computational framework to quantify uncertainty in Stefan problems with an injection boundary. Inspired by airfoil icing studies, we consider a system featuring an injection boundary inducing domain changes and a free boundary separating phases, resulting in two types of moving boundaries. Our proposed enthalpy-based formulation seamlessly integrates thermal diffusion across the domain with energy fluxes at the boundaries, addressing a modified injection condition for boundary movement. Uncertainty then stems from random variations in the injection boundary. The primary focus of our Uncertainty Quantification (UQ) centers on investigating the effects of uncertainty on free boundary propagation. Through mapping to a reference domain, we derive an enthalpy-based numerical scheme tailored to the transformed coordinate system, facilitating a simple and efficient simulation. Numerical and UQ studies in one and two dimensions validate the proposed model and the extended enthalpy method. They offer intriguing insights into ice accretion and other multiphysics processes involving phase transitions

Indenter Shape Dependent Dislocation Actives and Stress Distributions of Single Crystal Nickel during Nanoindentation: A Molecular Dynamics Simulation

The influences of indenter shape on dislocation actives and stress distributions during nanoindentation were studied by using molecular dynamics (MD) simulation. The load-displacement curves, indentation-induced stress fields, and dislocation activities were analyzed by using rectangular, spherical, and Berkovich indenters on single crystal nickel. For the rectangular and spherical indenters, the load-displacement curves have a linear dependence, but the elastic stage produced by the spherical indenter does not last longer than that produced by the rectangular indenter. For a Berkovich indenter, there is almost no linear elastic regime, and an amorphous region appears directly below the indenter tip, which is related to the extremely singular stress field around the indenter tip. In three indenters cases, the prismatic dislocation loops are observed on the {111} planes, and there is a sudden increase in stress near the indenter for the Berkovich indenter. The stress distributions are smooth with no sudden irregularities at low-indentation depths; and the stress increases and a sudden irregularity appears with the increasing indentation depths for the rectangular and spherical indenters. Moreover, the rectangular indenter has the most complex dislocation activities and the spherical indenter is next, while very few dislocations occur in the Berkovich indenter case

Adaptive Guidance Learning for Camouflaged Object Detection

Camouflaged object detection (COD) aims to segment objects visually embedded in their surroundings, which is a very challenging task due to the high similarity between the objects and the background. To address it, most methods often incorporate additional information (e.g., boundary, texture, and frequency clues) to guide feature learning for better detecting camouflaged objects from the background. Although progress has been made, these methods are basically individually tailored to specific auxiliary cues, thus lacking adaptability and not consistently achieving high segmentation performance. To this end, this paper proposes an adaptive guidance learning network, dubbed \textit{AGLNet}, which is a unified end-to-end learnable model for exploring and adapting different additional cues in CNN models to guide accurate camouflaged feature learning. Specifically, we first design a straightforward additional information generation (AIG) module to learn additional camouflaged object cues, which can be adapted for the exploration of effective camouflaged features. Then we present a hierarchical feature combination (HFC) module to deeply integrate additional cues and image features to guide camouflaged feature learning in a multi-level fusion manner.Followed by a recalibration decoder (RD), different features are further aggregated and refined for accurate object prediction. Extensive experiments on three widely used COD benchmark datasets demonstrate that the proposed method achieves significant performance improvements under different additional cues, and outperforms the recent 20 state-of-the-art methods by a large margin. Our code will be made publicly available at: \textcolor{blue}{{https://github.com/ZNan-Chen/AGLNet}}

Investigating the integrate and fire model as the limit of a random discharge model: a stochastic analysis perspective

In the mean field integrate-and-fire model, the dynamics of a typical neuron within a large network is modeled as a diffusion-jump stochastic process whose jump takes place once the voltage reaches a threshold. In this work, the main goal is to establish the convergence relationship between the regularized process and the original one where in the regularized process, the jump mechanism is replaced by a Poisson dynamic, and jump intensity within the classically forbidden domain goes to infinity as the regularization parameter vanishes. On the macroscopic level, the Fokker-Planck equation for the process with random discharges (i.e. Poisson jumps) are defined on the whole space, while the equation for the limit process is on the half space. However, with the iteration scheme, the difficulty due to the domain differences has been greatly mitigated and the convergence for the stochastic process and the firing rates can be established. Moreover, we find a polynomial-order convergence for the distribution by a re-normalization argument in probability theory. Finally, by numerical experiments, we quantitatively explore the rate and the asymptotic behavior of the convergence for both linear and nonlinear models

Pyrolysis characteristics of waste tire particles in fixed-bed reactor with internals

This study investigated the characteristics of pyrolysis for waste tire particles in the newly developed fixed-bed reactor with internals that are a central gas collection channel mounted inside reactor. And a few metallic plates vertically welded on the internal wall of the reactors and extending to the region closing their central gas collection pipe walls. Experiments were conducted in two laboratory fixed bed reactors with or without the internals. The results shown that employing internals produced more light oil at externally heating temperatures above 700 °C due to the inhibited secondary reactions in the reactor. The oil from the reactor with internals contained more aliphatic hydrocarbons and fewer aromatic hydrocarbons, leading to its higher H/C atomic ratios as for crude petroleum oil. The char yield was relatively stable for two beds and showed the higher heating values (HHVs) of about 23 MJ/kg. The gaseous product of pyrolysis mainly consisted of H2 and CH4, but the use of internals led to less pyrolysis gas through its promotion of oil production. Keywords: Pyrolysis, Waste tire, Fixed bed, Internals, Secondary reaction