A series of epoxy composite coatings filled with liquid metal (LM) and graphene oxide (GO) are successfully prepared as the self-lubricating material in this work. The dispersion of LM and GO (LM@GO) in epoxy coating and its effect on the thermal, tribological, and mechanical properties of epoxy coating are systematically studied by using thermogravimetric analysis, differential scanning calorimetry, mechanical and friction, and wear tester. The LM@GO filler has been proved to be an effective composite filler, which can be uniformly dispersed in epoxy coating, reduce the wear volume, and improve its mechanical properties of epoxy coating. When the filler content is 0.5 wt% LM and 0.03 wt% GO, the wear volume of epoxy composite coating is dramatically reduced by 91.7% and the elastic modulus is increased by 16.3% compared with pure epoxy. The synergistic effect of the friction reduction of LM and the mechanical support of GO makes the composite coating form a uniform and stable transfer film, which improves its wear resistance. And through chemical stability experiments, the composite coating has a strong resistance to acid and alkali. This work will render the epoxy-LM@GO have the potential of application on the mechanical molds, electronic components.
The effects of recombinant human growth hormone (rhGH) in the treatment of dwarfism and the relationship between insulin-like growth factor (IGF)-1, IGF-binding protein (IGFBP)-3 and thyroid hormone were examined in the present study. For this purpose, 66 patients diagnosed with dwarfism were selected retrospectively, with 36 cases of growth hormone deficiency (GHD) and 30 cases of idiopathic short stature (ISS). The therapeutic dose of GHD 0.10 IU/kg·day and ISS 0.15 IU/kg·day were injected subcutaneously every night before sleep until adulthood. The average follow-up was 5 years, and the results were evaluated and measured every 3 months, including height, BA, secondary test of growth hormone (GH peak), IGF-1, IGFBP-3 and thyroid hormone (FT3, FT4 and TSH). After treatment, the height, BA, GH peak, IGF-A and IGFBP-3 of the GHD group were all increased, and the differences were statistically significant (P<0.05), while FT3, FT4 and TSH had no significant change (P<0.05). The height and BA increased and the differences were statistically significant (P<0.05). The indexes of the ISS group were not statistically significant (P>0.05). The results of the Pearson-related analysis suggested that GH peak of the GHD group, IGF-1 and IGFBP-3 were positively associated with height (P<0.05), and had no relationship with BA (P<0.05). None of the above indexes of the ISS group had an obvious correlation with height and BA (P>0.05). rhGH was effective for GHD and ISS, with the GHD effect being positively associated with the GH peak, IGF-1 and IGFBP-3. ISS had no obvious relationship with GH peak, IGF-1 and IGFBP-3 although other influencing factors may be involved.
Second language acquisition (SLA) modeling is to predict whether second language learners could correctly answer the questions according to what they have learned. It is a fundamental building block of the personalized learning system and has attracted more and more attention recently. However, as far as we know, almost all existing methods cannot work well in lowresource scenarios because lacking of training data. Fortunately, there are some latent common patterns among different languagelearning tasks, which gives us an opportunity to solve the lowresource SLA modeling problem. Inspired by this idea, in this paper, we propose a novel SLA modeling method, which learns the latent common patterns among different language-learning datasets by multi-task learning and are further applied to improving the prediction performance in low-resource scenarios. Extensive experiments show that the proposed method performs much better than the state-of-the-art baselines in the low-resource scenario. Meanwhile, it also obtains improvement slightly in the non-low-resource scenario.
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