339,228 research outputs found

    Optimizing the Incorporated Amount of Chinese Milk Vetch (<i>Astragalus sinicus</i> L.) to Improve Rice Productivity without Increasing CH<sub>4</sub> and N<sub>2</sub>O Emissions

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    Chinese milk vetch (CMV) is a leguminous green manure that is commonly cultivated in paddy fields and can partially substitute synthetic nitrogen fertilizer. However, the impacts of incorporating CMV on CH4 and N2O emissions are still a subject of controversy. Therefore, we conducted a field experiment over three years to investigate emissions under different substitution ratios: urea only (CF); incorporating a traditional amount of CMV (MV); and with incorporation ratios of 1/3 (MV1/3), 2/3 (MV2/3), and 4/3 (MV4/3) of MV for partial urea substitution. Compared with CF, MV2/3, MV, and MV 4/3 resulted in increased yields. MV and MV4/3 reduced N2O emissions but increased CH4 emissions by 28.61% and 85.60% (2019), 32.38% and 103.19% (2020), and 28.86% and 102.98% (2021), respectively, resulting in an overall increase in total global warming potential (except for MV in 2021). MV2/3 exhibited a low greenhouse gas intensity value ranging from 0.46 to 0.47. Partial least-squares-path model results showed that CH4 and N2O emissions were influenced by substitution ratios, which indirectly regulated the gene abundances of mcrA and nosZ. Overall, the impact of CMV on CH4 and N2O emissions was determined by substitution ratios. MV2/3, which involved partial substitution of synthetic N fertilizer with 15.0 t ha−1 of CMV, resulted in improved rice productivity without increasing CH4 and N2O emissions, making it a recommended approach in the study area

    Riparian Vegetation Conversion to an Oil Tea Plantation: Impacts on Small Mammals at the Community, Population, and Individual Level

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    Riparian vegetation is crucial for maintaining terrestrial and aquatic biodiversity, but it is threatened by land-use activities. To assess the ecological impacts of riparian vegetation conversion to an oil tea (Camellia oleifera) plantation, we quantified the responses of small mammals in two natural habitats (mature forest and flood-meadow) and in Camellia forests at the community, population, and individual level. We found that the community diversity was similar between Camellia forests and mature forests, but higher than the flood-meadow. Meanwhile, the community composition differed across three habitats, with Camellia forests favoring habitat generalist species. At the population level, Camellia forests and flood-meadow had a similar population density, which were higher than mature forests. At the individual level, Rattus nitidus was less sensitive to this conversion, but the body condition index of Niviventer confucianus was higher in Camellia forests than in mature forests, and Apodemus agrarius in Camellia forests had more ectoparasite load than in the flood-meadow, indicating a species-specific response to the impacts of oil tea plantation. Our study highlights that the occurrence of habitat generalist species and high ectoparasite loads may threaten regional biodiversity and increase the risk of parasite transmission with enlarging the oil tea plantation area within riparian zones

    CFD Simulation on Pressure Profile for Direct Contact Condensation of Steam Jet in a Narrow Pipe

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    In the published experimental results, it has been observed that when high-speed steam spurt into the subcooled waterflow, the total pressure along the axial direction at trailing edge of the steam plume shows a pressure-lift. To reveal the mechanism behind this phenomenon, this study utilizes a particle model to investigate the pressure profile of steam jet condensation in subcooled water flow in a narrow pipe. A numerical model based on the Eulerian–Eulerian multiphase model has been developed to accurately simulate the characteristics of pressure profile along the axial direction. The model’s validity is established by comparing the steam plume shape and temperature profiles with the experimental data. By analyzing the total pressure profile of the axis and the contours of gas volume fraction, it is found that there exists a pressure-lift phenomenon at trailing edge of the steam plume. The dynamic pressure of the water also shows a pressure-lift at this position, so it can be inferred that the dynamic pressure of the water is the main factor of the total pressure-lift

    Spiking Neural Network for Ultra-low-latency and High-accurate Object Detection

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    Spiking Neural Networks (SNNs) have garnered widespread interest for their energy efficiency and brain-inspired event-driven properties. While recent methods like Spiking-YOLO have expanded the SNNs to more challenging object detection tasks, they often suffer from high latency and low detection accuracy, making them difficult to deploy on latency sensitive mobile platforms. Furthermore, the conversion method from Artificial Neural Networks (ANNs) to SNNs is hard to maintain the complete structure of the ANNs, resulting in poor feature representation and high conversion errors. To address these challenges, we propose two methods: timesteps compression and spike-time-dependent integrated (STDI) coding. The former reduces the timesteps required in ANN-SNN conversion by compressing information, while the latter sets a time-varying threshold to expand the information holding capacity. We also present a SNN-based ultra-low latency and high accurate object detection model (SUHD) that achieves state-of-the-art performance on nontrivial datasets like PASCAL VOC and MS COCO, with about remarkable 750x fewer timesteps and 30% mean average precision (mAP) improvement, compared to the Spiking-YOLO on MS COCO datasets. To the best of our knowledge, SUHD is the deepest spike-based object detection model to date that achieves ultra low timesteps to complete the lossless conversion.Comment: 14 pages, 10 figure

    Copy Recurrent Neural Network Structure Network

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    Electronic Health Record (EHR) coding involves automatically classifying EHRs into diagnostic codes. While most previous research treats this as a multi-label classification task, generating probabilities for each code and selecting those above a certain threshold as labels, these approaches often overlook the challenge of identifying complex diseases. In this study, our focus is on detecting complication diseases within EHRs. We propose a novel coarse-to-fine ICD path generation framework called the Copy Recurrent Neural Network Structure Network (CRNNet), which employs a Path Generator (PG) and a Path Discriminator (PD) for EHR coding. By using RNNs to generate sequential outputs and incorporating a copy module, we efficiently identify complication diseases. Our method achieves a 57.30\% ratio of complex diseases in predictions, outperforming state-of-the-art and previous approaches. Additionally, through an ablation study, we demonstrate that the copy mechanism plays a crucial role in detecting complex diseases.Comment: Need modificatio

    An Efficient Virtual Data Generation Method for Reducing Communication in Federated Learning

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    Communication overhead is one of the major challenges in Federated Learning(FL). A few classical schemes assume the server can extract the auxiliary information about training data of the participants from the local models to construct a central dummy dataset. The server uses the dummy dataset to finetune aggregated global model to achieve the target test accuracy in fewer communication rounds. In this paper, we summarize the above solutions into a data-based communication-efficient FL framework. The key of the proposed framework is to design an efficient extraction module(EM) which ensures the dummy dataset has a positive effect on finetuning aggregated global model. Different from the existing methods that use generator to design EM, our proposed method, FedINIBoost borrows the idea of gradient match to construct EM. Specifically, FedINIBoost builds a proxy dataset of the real dataset in two steps for each participant at each communication round. Then the server aggregates all the proxy datasets to form a central dummy dataset, which is used to finetune aggregated global model. Extensive experiments verify the superiority of our method compared with the existing classical method, FedAVG, FedProx, Moon and FedFTG. Moreover, FedINIBoost plays a significant role in finetuning the performance of aggregated global model at the initial stage of FL.Comment: There are errors in the experimental settings in our pape

    A nanogapped hysteresis-free field-effect transistor

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    We propose a semi-suspended device structure and construct nanogapped, hysteresis-free field-effect transistors (FETs), based on the van der Waals stacking technique. The structure, which features a semi-suspended channel above a submicron-long wedge-like nanogap, is fulfilled by transferring ultraclean BN-supported MoS2_2 channels directly onto dielectric-spaced vertical source/drain stacks. Electronic characterization and analyses reveal a high overall device quality, including ultraclean channel interfaces, negligible electrical scanning hysteresis, and Ohmic contacts in the structures. The unique hollow FET structure holds the potential for exploiting reliable electronics, as well as nanofluid and pressure sensors.Comment: 22 pages, 4 figures, with S
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