464 research outputs found

    Crystal structure of azilsartan methyl ester ethyl acetate hemisolvate

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    We gratefully acknowledge financial support from the NSFC (No. 21002009), the Scientific and Technological Project of Jiangsu Province (BY2014037–01), the Major Program for Natural Science Research of Jiangsu Colleges and Universities (12 K J A150002, 14 K J A150002) and the Qing Lan Project of Jiangsu Province.Peer reviewe

    Backpropagation-Based Cooperative Localization of Primary User for Avoiding Hidden-Node Problem in Cognitive Networks

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    Cognitive radio (CR) is a technology to implement opportunistic spectrum sharing to improve the spectrum utilization. However, there exists a hidden-node problem, which can be a big challenge to solve especially when the primary receiver is passive listening. We aim to provide a solution to the hidden-node problem for passive-listening receiver based on cooperation of multiple CRs. Specifically, we consider a cooperative GPS-enabled cognitive network. Once the existence of PU is detected, a localization algorithm will be employed to first estimate the path loss model for the environment based on backpropagation method and then to locate the position of PU. Finally, a disable region is identified taking into account the communication range of both the PU and the CR. The CRs within the disabled region are prohibited to transmit in order to avoid interfering with the primary receiver. Both analysis and simulation results are provided

    Fast Detection Method in Cooperative Cognitive Radio Networks

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    Cognitive Radio (CR) technology improves the utilization of spectrum highly via opportunistic spectrum sharing, which requests fast detection as the spectrum utilization is dynamic. Taking into consideration the characteristic of wireless channels, we propose a fast detection scheme for a cooperative cognitive radio network, which consists of multiple CRs and a central control office. Specifically, each CR makes individual detection decision using the sequential probability ratio test combined with Neyman Pearson detection with respect to a specific observation window length. The proposed method upper bounds the detection delay. In addition, a weighted K out of N fusion rule is also proposed for the central control office to reach fast global decision based on the information collected from CRs, with more weights assigned for CRs with good channel conditions. Simulation results show that the proposed scheme can achieve fast detection while maintaining the detection accuracy

    Synthesis And Characterization Of CaF2:Yb,Er(CORE) /CaF2(SHELL) Up-Conversion Nanoparticles

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    Master'sMASTER OF ENGINEERIN

    Forecast of cross-correlation of CSST cosmic shear tomography with AliCPT-1 CMB lensing

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    We present a forecast study on the cross-correlation between cosmic shear tomography from the Chinese Survey Space Telescope (CSST), and CMB lensing from Ali CMB Polarization Telescope (AliCPT-1) in Tibet. We generate the correlated galaxy lensing and CMB lensing signals from the Gaussian realizations based on the inputted auto- and cross-spectra. As for the error budget, we consider the CMB lensing reconstruction noise based on the AliCPT-1 lensing reconstruction pipeline; the shape noise of the galaxy lensing measurement; CSST photo-zz error; photo-zz bias; intrinsic alignment effect. The AliCPT-1 CMB lensing mock data are generated according to two experimental stages, namely the "4 modules*yr" and "48 modules*yr" cases. We estimate the cross-spectra in 4 tomographic bins according to the CSST photo-zz distribution in the range of z[0,4)z\in[0,4). After reconstructing the pseudo-cross-spectra from the realizations, we calculate the signal-to-noise ratio (SNR). By combining the 4 photo-z bins, the total cross-correlation SNR17\approx17 (AliCPT-1 "4 modules*yr") and SNR26\approx26 (AliCPT-1 "48 modules*yr"). Finally, we study the cosmological application of this cross-correlation signal. Due to the negative contribution to the galaxy lensing data, the exclusion of intrinsic alignment in the template fitting will lead to roughly a 0.6σ0.6\sigma increasement in σ8\sigma_8 but without changing the S8S_8 value. For AliCPT-1 first and second stages, the cross-correlation of CSST cosmic shear with CMB lensing give σ8=0.770±0.034\sigma_8=0.770\pm 0.034 and S8=0.797±0.028S_8=0.797\pm 0.028 and σ8=0.801±0.023\sigma_8=0.801\pm 0.023 and S8=0.813±0.016S_8=0.813\pm 0.016, respectively.Comment: 17 pages, 10 figure

    Effects of temperature on photosynthetic performance and nitrate reductase activity in vivo assay in Gracilariopsis lemaneiformis (Rhodophyta)

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    Gracilariopsis lemaneiformis is an economically-valued species and widely cultured in China at present. After being acclimated to different growth temperatures (15, 20, 25, and 30 degrees C) for 7 days, the relative growth rate (RGR), nitrate reductase activity, soluble protein content and chlorophyll a fluorescence of G. lemaneiformis were examined. Results show that RGR was markedly affected by temperature especially at 20 degrees C at which G. lemaneiformis exhibited the highest effective quantum yield of PSII [Y(II)] and light-saturated electron transport rate (ETRmax), but the lowest non-photochemical quenching. Irrespective of growth temperature, the nitrate reductase activity increased with the incubation temperature from 15 to 30 degrees C. In addition, the greatest nitrate reductase activity was found in the thalli grown at 20 degrees C. The value of temperature coefficient Q10 of alga cultured in 15 degrees C was the greatest among those of other temperatures tested. Results indicate that the optimum temperature for nitrate reductase synthesis was relatively lower than that for nitrate reductase activity, and the relationship among growth, photosynthesis, and nitrate reductase activity showed that the optimum temperature for activity of nitrate reductase in vivo assay should be the same to the optimal growth temperature

    Mechanics of design and model development of CVC-plus roll curve

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    The mathematic model of CVC-Plus work roll curve is built. The ratio of the initial shifting value to the target crown is determined, and the mathematical model considering the relationship between the coefficients A2, A3, A4, A5 and is established. According to the theoretical analysis, the distance between the maximum or minimum point of the high order equivalent crown for work roll with CVC-plus roll curve and the rolling central point is the times of the roll barrel length. In general, the initial shifting value of the CVC-plus roll curve is not equal to the initial shifting value of the 3-order CVC roll curve . The coefficient A1 can also be obtained by optimizing the target function with minimizing the axial force

    V3D: Video Diffusion Models are Effective 3D Generators

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    Automatic 3D generation has recently attracted widespread attention. Recent methods have greatly accelerated the generation speed, but usually produce less-detailed objects due to limited model capacity or 3D data. Motivated by recent advancements in video diffusion models, we introduce V3D, which leverages the world simulation capacity of pre-trained video diffusion models to facilitate 3D generation. To fully unleash the potential of video diffusion to perceive the 3D world, we further introduce geometrical consistency prior and extend the video diffusion model to a multi-view consistent 3D generator. Benefiting from this, the state-of-the-art video diffusion model could be fine-tuned to generate 360degree orbit frames surrounding an object given a single image. With our tailored reconstruction pipelines, we can generate high-quality meshes or 3D Gaussians within 3 minutes. Furthermore, our method can be extended to scene-level novel view synthesis, achieving precise control over the camera path with sparse input views. Extensive experiments demonstrate the superior performance of the proposed approach, especially in terms of generation quality and multi-view consistency. Our code is available at https://github.com/heheyas/V3DComment: Code available at https://github.com/heheyas/V3D Project page: https://heheyas.github.io/V3D
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