摘要： 身处一个信息爆炸的时代,数据已经成为企业发展的重要资源,在这个背景下,财务信息化建设显得尤为重要,不仅是企业提升管理效率、优化资源配置的关键,更是推动现代企业转型升级的重要力量.

关键词： Nanoclay  

摘要： The risk of long-term global food insecurity is being exacerbated by climate change, as well as by a range of other socio-economic and political factors. Conventional agriculture has been heavily dependent on agrochemical use for decades, and although this has increased food production in a way that has benefited billions of citizens, the subsequent damage to terrestrial and aquatic ecosystems has been significant. This is in part due to agrochemical delivery and use efficiency levels that are often 30% or less, leading to over-application to achieve acceptable yield but that results in a significant negative environmental footprint of agriculture. Recent advances in agrochemical delivery have sought to mimic nanodrug delivery systems in health, where materials design and delivery is engineered to be efficient, precise and safe. Consequently, a number of agrochemical delivery strategies have been described in the literature, offering equivalent or enhanced efficacy with reduced environmental impact. Several recent studies have extended this work to targeting specific biotic and abiotic plant stresses, with the goal of developing more sophisticated nanostimulants and nanofertilizers that promote overall climate resilience in agriculture. This paper will review and analyze the vast potential of these agrochemical delivery systems, including an assessment of future directions that could validate the widespread application of nano-enabled agriculture as a critical tool in combatting global food insecurity in a changing climate.

摘要： Spiking Neural Networks (SNNs) have emerged as a promising tool for event-based optical flow estimation tasks due to their ability to leverage spatio-temporal information and low-power capabilities. However, the performance of SNN models is often constrained, limiting their application in real-world scenarios. In this work, we address this gap by proposing a novel neural network architecture, ST-FlowNet, specifically tailored for optical flow estimation from event-based data. The ST-FlowNet architecture integrates ConvGRU modules to facilitate cross-modal feature augmentation and temporal alignment of the predicted optical flow, improving the network's ability to capture complex motion dynamics. Additionally, to overcome the challenges associated with training SNNs, we introduce a novel approach to derive SNN models from pre-trained artificial neural networks (ANNs) through ANN-to-SNN conversion or our proposed BISNN method. Notably, the BISNN method alleviates the complexities involved in biological parameter selection, further enhancing the robustness of SNNs in optical flow estimation tasks. Extensive evaluations on three benchmark event-based datasets demonstrate that the SNN-based ST-FlowNet model outperforms state-of-the-art methods, delivering superior performance in accurate optical flow estimation across a diverse range of dynamic visual scenes. Furthermore, the inherent energy efficiency of SNN models is highlighted, establishing a compelling advantage for their practical deployment. Overall, our work presents a novel framework for optical flow estimation using SNNs and event-based data, contributing to the advancement of neuromorphic vision applications.

关键词： Motion estimation  

摘要： Spiking Neural Networks (SNNs) have emerged as a promising tool for event-based optical flow estimation tasks due to their ability to leverage spatio-temporal information and low-power capabilities. However, the performance of SNN models is often constrained, limiting their application in real-world scenarios. In this work, we address this gap by proposing a novel neural network architecture, ST-FlowNet, specifically tailored for optical flow estimation from event-based data. The ST-FlowNet architecture integrates ConvGRU modules to facilitate cross-modal feature augmentation and temporal alignment of the predicted optical flow, improving the network’s ability to capture complex motion dynamics. Additionally, to overcome the challenges associated with training SNNs, we introduce a novel approach to derive SNN models from pre-trained artificial neural networks (ANNs) through ANN-to-SNN conversion or our proposed BISNN method. Notably, the BISNN method alleviates the complexities involved in biological parameter selection, further enhancing the robustness of SNNs in optical flow estimation tasks. Extensive evaluations on three benchmark event-based datasets demonstrate that the SNN-based ST-FlowNet model outperforms state-of-the-art methods, delivering superior performance in accurate optical flow estimation across a diverse range of dynamic visual scenes. Furthermore, the inherent energy efficiency of SNN models is highlighted, establishing a compelling advantage for their practical deployment. Overall, our work presents a novel framework for optical flow estimation using SNNs and event-based data, contributing to the advancement of neuromorphic vision applications. Copyright © 2025, The Authors. All rights reserved.