Mechanisms of biodiversity loss under nitrogen enrichment: unveiling a shift from light competition to cation toxicity.

The primary mechanisms contributing to nitrogen (N) addition induced grassland biodiversity loss, namely light competition and soil cation toxicity, are often examined separately in various studies. However, their relative significance in governing biodiversity loss along N addition gradient remains unclear. We conducted a 4-yr field experiment with five N addition rates (0, 2, 10, 20, and 50 g N m-2 yr-1) and performed a meta-analysis using global data from 239 observations in N-fertilized grassland ecosystems. Results from our field experiment and meta-analysis indicate that both light competition and soil cation (e.g. Mn2+ and Al3+) toxicity contribute to plant diversity loss under N enrichment. The relative importance of these mechanisms varied with N enrichment intensity. Light competition played a more significant role in influencing species richness under low N addition (≤ 10 g m-2 yr-1), while cation toxicity became increasingly dominant in reducing biodiversity under high N addition (>10 g m-2 yr-1). Therefore, a transition from light competition to cation toxicity occurs with increasing N availability. These findings imply that the biodiversity loss along the N gradient is regulated by distinct mechanisms, necessitating the adoption of differential management strategies to mitigate diversity loss under varying intensities of N enrichment.

Analysis of continuous laser-irradiation resistance of liquid-crystal optical switch based on sapphire-substrate GaN.

Developing high-power laser technology and its applications necessitates improvements in the laser-irradiation resistance of liquid-crystal modulation devices. In this study, the thermal characteristics of substrate and electrode materials, including sapphire-substrate indium tin oxide (ITO) electrodes, K9 glass-substrate ITO electrodes, sapphire-substrate gallium nitride (GaN) electrodes, and liquid-crystal optical switches, are investigated using simulation and experimental methods. Results show that the sapphire-substrate GaN electrode demonstrates the best heat dissipation and that the maximum temperature at the center of the spot under 75 W laser irradiation is 319 K, 52 K lower than that of an equally thick sapphire-substrate ITO electrode and 225 K lower than that of an equally thick K9 glass-substrate ITO electrode (steady state and test time >2min). Additionally, the experimental results show that the liquid-crystal optical switch, comprising a sapphire substrate and GaN electrode, can endure continuous laser irradiation up to 18 W with a switching ratio of approximately 20:1. The optical switch with GaN electrodes on a sapphire substrate can endure a power density of 156W/c m 2, much higher than that (21W/c m 2, steady state and test time >2min) tolerable by the liquid-crystal optical switch with ITO transparent electrodes and K9 glass substrates.

A novel lightweight deep learning approach for simultaneous optic cup and optic disc segmentation in glaucoma detection.

Glaucoma is a chronic neurodegenerative disease that can result in irreversible vision loss if not treated in its early stages. The cup-to-disc ratio is a key criterion for glaucoma screening and diagnosis, and it is determined by dividing the area of the optic cup (OC) by that of the optic disc (OD) in fundus images. Consequently, the automatic and accurate segmentation of the OC and OD is a pivotal step in glaucoma detection. In recent years, numerous methods have resulted in great success on this task. However, most existing methods either have unsatisfactory segmentation accuracy or high time costs. In this paper, we propose a lightweight deep-learning architecture for the simultaneous segmentation of the OC and OD, where we have adopted fuzzy learning and a multi-layer perceptron to simplify the learning complexity and improve segmentation accuracy. Experimental results demonstrate the superiority of our proposed method as compared to most state-of-the-art approaches in terms of both training time and segmentation accuracy.