511 research outputs found

    Development and critical evaluation of a generic 2-D agro-hydrological model (SMCR_N) for the responses of crop yield and nitrogen composition to nitrogen fertilizer

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    Models play an important role in optimizing fertilizer use in agriculture to maintain sustainable crop production and to minimize the risk to the environment. In this study, we present a new Simulation Model for Crop Response to Nitrogen fertilizer (SMCR_N). The SMCR_N model, based on the recently developed model EU-Rotate_N for the N-economies of a wide range of crops and cropping systems, includes new modules for the estimation of N in the roots and an associated treatment of the recovery of soil mineral N by crops, for the reduction of growth rates by excessive fertilizer-N, and for the N mineralization from soil organic matter. The validity of the model was tested against the results from 32 multi-level fertilizer experiments on 16 different crop species. For this exercise none of the coefficients or parameters in the model was adjusted to improve the agreement between measurement and simulation. Over the practical range of fertilizer-N levels model predictions were, with few exceptions, in good agreement with measurements of crop dry weight (excluding fibrous roots) and its %N. The model considered that the entire reduction of soil inorganic N during growth was due to the sum of nitrate leaching, retention of N in fibrous roots and N uptake by the rest of the plant. The good agreement between the measured and simulated uptakes suggests that in this arable soil, losses of N from other soil processes were small. At high levels of fertilizer-N yields were dominated by the negative osmotic effect of fertilizer-N and model predictions for some crops were poor. However, the predictions were significantly improved by using a different value for the coefficient defining the osmotic effect for saline sensitive crops. The developed model SMCR_N uses generally readily available inputs, and is more mechanistic than most agronomic models and thus has the potential to be used as a tool for optimizing fertilizer practice

    Effect of Slow Solidification of Ultra-thick Continuous Casting Slab on Solidification Structure and Macrosegregation

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    The slow solidification method of ultra-thick slab is in the ascendancy, and the macrosegregation is an important parameter of slab quality. Besides, solidification structure is also a crucial indicator of slab, such asSecondary Dendrite Arm Spacing (SDAS). In this paper, the slice moving boundary model was selected and optimized, and the influence on SDAS and macro segregation under slow solidification condition are investigated. Researches show that the SDAS increases by increasing supercooling and cooling intensity. When the superheating increases from 20 K to 40 K, the SDAS increases from 156,8 μm to 158,9 μm. By using mid-strong cooling, the segregation ratio decreases from 1,4331 to 1,3836, and the segregation degree decreases from 0,3535 to 0,3196. According to the discussions, a new method of improving the final quality of slow solidification continuous casting slabs is provided, which also has a high development prospect in the production of large-section casting slabs

    Anticancer activity of a thymidine quinoxaline conjugate is modulated by cytosolic thymidine pathways

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    Background High levels of thymidine kinase 1 (TK1) and thymidine phosphorylase (TYMP) are key molecular targets by thymidine therapeutics in cancer treatment. The dual roles of TYMP as a tumor growth factor and a key activation enzyme of anticancer metabolites resulted in a mixed outcome in cancer patients. In this study, we investigated the roles of TK1 and TYMP on a thymidine quinoxaline conjugate to evaluate an alternative to circumvent the contradictive role of TYMP. Methods TK1 and TYMP levels in multiple liver cell lines were assessed along with the cytotoxicity of the thymidine conjugate. Cellular accumulation of the thymidine conjugate was determined with organelle-specific dyes. The impacts of TK1 and TYMP were evaluated with siRNA/shRNA suppression and pseudoviral overexpression. Immunohistochemical analysis was performed on both normal and tumor tissues. In vivo study was carried out with a subcutaneous liver tumor model. Results We found that the thymidine conjugate had varied activities in liver cancer cells with different levels of TK1 and TYMP. The conjugate mainly accumulated at endothelial reticulum and was consistent with cytosolic pathways. TK1 was responsible for the cytotoxicity yet high levels of TYMP counteracted such activities. Levels of TYMP and TK1 in the liver tumor tissues were significantly higher than those of normal liver tissues. Induced TK1 overexpression decreased the selectivity of dT-QX due to the concurring cytotoxicity in normal cells. In contrast, shRNA suppression of TYMP significantly enhanced the selective of the conjugate in vitro and reduced the tumor growth in vivo. Conclusions TK1 was responsible for anticancer activity of dT-QX while levels of TYMP counteracted such an activity. The counteraction by TYMP could be overcome with RNA silencing to significantly enhance the dT-QX selectivity in cancer cells

    Wheat stripe rust grading by deep learning with attention mechanism and images from mobile devices

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    Wheat stripe rust is one of the main wheat diseases worldwide, which has significantly adverse effects on wheat yield and quality, posing serious threats on food security. Disease severity grading plays a paramount role in stripe rust disease management including breeding disease-resistant wheat varieties. Manual inspection is time-consuming, labor-intensive and prone to human errors, therefore, there is a clearly urgent need to develop more effective and efficient disease grading strategy by using automated approaches. However, the differences between wheat leaves of different levels of stripe rust infection are usually tiny and subtle, and, as a result, ordinary deep learning networks fail to achieve satisfying performance. By formulating this challenge as a fine-grained image classification problem, this study proposes a novel deep learning network C-DenseNet which embeds Convolutional Block Attention Module (CBAM) in the densely connected convolutional network (DenseNet). The performance of C-DenseNet and its variants is demonstrated via a newly collected wheat stripe rust grading dataset (WSRgrading dataset) at Northwest A&F University, Shaanxi Province, China, which contains a total of 5,242 wheat leaf images with 6 levels of stripe rust infection. The dataset was collected by using various mobile devices in the natural field condition. Comparative experiments show that C-DenseNet with a test accuracy of 97.99% outperforms the classical DenseNet (92.53%) and ResNet (73.43%). GradCAM++ network visualization also shows that C-DenseNet is able to pay more attention to the key areas in making the decision. It is concluded that C-DenseNet with an attention mechanism is suitable for wheat stripe rust disease grading in field conditions

    High spatial and temporal resolution interrogation of fully distributed chirped fiber Bragg grating sensors

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    A novel interrogation technique for fully distributed linearly chirped fiber Bragg grating (LCFBG) strain sensors with simultaneous high temporal and spatial resolution based on optical time-stretch frequency-domain reflectometry (OTS-FDR) is proposed and experimentally demonstrated. LCFBGs is a promising candidate for fully distributed sensors thanks to its longer grating length and broader reflection bandwidth compared to normal uniform FBGs. In the proposed system, two identical LCFBGs are employed in a Michelson interferometer setup with one grating serving as the reference grating whereas the other serving as the sensing element. Broadband spectral interferogram is formed and the strain information is encoded into the wavelength-dependent free spectral range (FSR). Ultrafast interrogation is achieved based on dispersion-induced time stretch such that the target spectral interferogram is mapped to a temporal interference waveform that can be captured in real-Time using a single-pixel photodector. The distributed strain along the sensing grating can be reconstructed from the instantaneous RF frequency of the captured waveform. High-spatial resolution is also obtained due to high-speed data acquisition. In a proof-of-concept experiment, ultrafast real-Time interrogation of fully-distributed grating sensors with various strain distributions is experimentally demonstrated. An ultrarapid measurement speed of 50 MHz with a high spatial resolution of 31.5 μm over a gauge length of 25 mm and a strain resolution of 9.1 μϵ have been achieved

    Ultrafast Interrogation of Fully Distributed Chirped Fibre Bragg Grating Strain Sensor

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    A novel ultrafast and high spatial-resolution interrogation method for fully distributed chirped fibre Bragg grating sensors based on photonic time-stretch frequency-domain reflectometry is presented. Real-time interrogation at measurement speed of 50 MHz with a spatial resolution of 35 µm was experimentally demonstrated
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