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Multimedia Tools and Applications - Mammograms are the images used by radiologists to diagnose breast cancer. Breast cancer is one of the most common cancers in women. The early detection of breast...  相似文献   
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Combustion, Explosion, and Shock Waves - Results of a numerical study of mixing, ignition, and combustion of a cold hydrogen jet propagating along the lower wall of a channel parallel to a...  相似文献   
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Journal of Communications Technology and Electronics - In recent years, there has been a rapid improvement in photonics products due to the use of multilayer heterostructures grown on the basis of...  相似文献   
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Theoretical Foundations of Chemical Engineering - Calcium formate is widely used in construction, tanning, and textile manufacture and as an E238 biological additive in cosmetology and the food...  相似文献   
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Glass and Ceramics - An analysis of the specific mechanisms of the influence of the state of the grain structure on the thermoelectric properties (electrical resistivity and total thermal...  相似文献   
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This work presents the dielectric properties of YNbO4 (YNO)–TiO2 composites in the microwave range. X-ray diffraction analysis demonstrates that the addition of TiO2 to YNO results in the formation of a Y(Nb0.5Ti0.5)2O6 phase. In the microwave range, the values of permittivity and dielectric loss did not present major changes with the increment of TiO2. Moreover, the addition of TiO2 results in an improvement in the thermal stability of YNO, with YNO63 demonstrating a resonant frequency of ?8.96 ppm.°C?1. We utilised numerical simulations to evaluate the behaviour of these materials as dielectric resonator antennae and it is found that they exhibit a reflection coefficient below ?10 dB at the resonant frequency, with a realised gain of 4.94 – 5.76 dBi, a bandwidth of 665–1050 MHz and a radiation efficiency above 84%. Our results indicate that YNO–TiO2 composites are interesting candidates for microwave operating devices.  相似文献   
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Probabilistic topic modeling algorithms like Latent Dirichlet Allocation (LDA) have become powerful tools for the analysis of large collections of documents (such as papers, projects, or funding applications) in science, technology an innovation (STI) policy design and monitoring. However, selecting an appropriate and stable topic model for a specific application (by adjusting the hyperparameters of the algorithm) is not a trivial problem. Common validation metrics like coherence or perplexity, which are focused on the quality of topics, are not a good fit in applications where the quality of the document similarity relations inferred from the topic model is especially relevant. Relying on graph analysis techniques, the aim of our work is to state a new methodology for the selection of hyperparameters which is specifically oriented to optimize the similarity metrics emanating from the topic model. In order to do this, we propose two graph metrics: the first measures the variability of the similarity graphs that result from different runs of the algorithm for a fixed value of the hyperparameters, while the second metric measures the alignment between the graph derived from the LDA model and another obtained using metadata available for the corresponding corpus. Through experiments on various corpora related to STI, it is shown that the proposed metrics provide relevant indicators to select the number of topics and build persistent topic models that are consistent with the metadata. Their use, which can be extended to other topic models beyond LDA, could facilitate the systematic adoption of this kind of techniques in STI policy analysis and design.

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