Multi-functional carbon fiber (CF) based composites have great potential as new-type microwave absorption materials (MAMs). However, it was still a huge challenge to integrate antioxidation and MA properties into CF based composites. Herein, the SiOC ceramics coating modified carbon fibers (SiOC/ CFs) were prepared by a polymer precursor pyrolysis method. The X-ray photoelectron spectroscopy (XPS) revealed that the SiOC coating was composed of SiOC, SiO 2 , and amorphous carbon phases. The SiOC ceramics as dual-functional coating not only heightened the oxidation temperature from 415 C to 890 C, but also highly improved the microwave absorbing ability from À12.60 dB to À47.50 dB. The enhanced MA performance could be attributed to multiple reflections in the cross-linked structure, various polarization relaxation processes, and the favorable impedance matching effect. The SiOC ceramics coating as a semiconductor could suppress the skin effect originating from the cross-linked CF network, thus leading to a favorable impedance matching behavior. Fig. 1 (a) The schematic representation of the preparation route of SiOC/CFs. The SEM images of (b) PAN fibers, (c) PAN-derived CFs, and SiOC/ CFs at different magnification of (d) Â1.5k and (e) Â5.0k.30686 | RSC Adv., 2019,9,[30685][30686][30687][30688][30689][30690][30691][30692] This journal is Fig. 4 The three-dimensional microwave RL curves of (a) SiOC/CFs composites and (b) CFs in the frequency range of 2-18 GHz, (c) the optimal RL values at different layer thicknesses, and (d) the comparison of MA performance of Si-based CFs composites.This journal is
Background Dental visits can provide education, prevention and treatment measures for teenagers, and help to form correct oral health knowledge and attitude. The purpose of this study was to evaluate the effects of socio-demographic factors, dental status, oral health literacy, and health-related behaviors on dental visits in early 12-year-old adolescents. Methods 953 subjects aged 12 in Longhua district of Shenzhen were investigated. The questionnaire and clinical examination were applied in schools, and two-level logistic regression models were constructed to interpret the effect of individual and contextual factors on Shenzhen adolescents' dental visits. Results A total of 27.6% of the participants had not been to a dentist. After the multiple factors binary logistic regression analysis, it confirmed that the following variables: Shenzhen Hukou (OR 2.133, 95% CI 1.429–3.185), moderate caries (OR 1.404, 95% CI 1.022–1.928) and severe caries (OR 2.546, 95% CI 1.461–4.437), Angle Class II malocclusion (OR 1.703, 95% CI 1.134–2.556), sometimes or never toothbrushing (OR 2.985, 95% CI 1.491–5.975), dental floss usage (OR 1.829, 95% CI 1.250–2.677), having had a toothache within the last 12 months (OR 1.469, 95% CI 1.086–1.986), high knowledge attitude level (OR 1.570, 95% CI 1.106–2.229), moderate knowledge attitude level (OR 1.534, 95% CI 1.073–2.193), were associated factors for dental visit experience. Conclusions The dental visits of 12-year-old children in Longhua district of Shenzhen is affected by multi-dimensional factors. It is suggested that oral health education should be strengthened, good oral hygiene habits should be cultivated, and the needs and utilization of oral health services for non-Shenzhen Hukou adolescents should be paid attention to, so as to effectively improve the overall oral health level of adolescents.
As psychological diseases become more prevalent and are identified as the leading cause of acquired disability, it is essential to assist people in improving their mental health. Digital therapeutics (DTx) has been widely studied to treat psychological diseases with the advantage of cost savings. Among the techniques of DTx, a conversational agent can interact with patients through natural language dialog and has become the most promising one. However, conversational agents' ability to accurately show emotional support (ES) limits their role in DTx solutions, especially in mental health support. One of the main reasons is that the prediction of emotional support systems does not extract effective information from historical dialog data and only depends on the data derived from one single-turn interaction with users. To address this issue, we propose a novel emotional support conversation agent called the STEF agent that generates more supportive responses based on a thorough view of past emotions. The proposed STEF agent consists of the emotional fusion mechanism and strategy tendency encoder. The emotional fusion mechanism focuses on capturing the subtle emotional changes throughout a conversation. The strategy tendency encoder aims at foreseeing strategy evolution through multi-source interactions and extracting latent strategy semantic embedding. Experimental results on the benchmark dataset ESConv demonstrate the effectiveness of the STEF agent compared with competitive baselines.
While there is increasing concern about the interpretability of neural models, the evaluation of interpretability remains an open problem, due to the lack of proper evaluation datasets and metrics. In this paper, we present a novel benchmark to evaluate the interpretability of both neural models and saliency methods. This benchmark covers three representative NLP tasks: sentiment analysis, textual similarity and reading comprehension, each provided with both English and Chinese annotated data. In order to precisely evaluate the interpretability, we provide token-level rationales that are carefully annotated to be sufficient, compact and comprehensive. We also design a new metric, i.e., the consistency between the rationales before and after perturbations, to uniformly evaluate the interpretability of models and saliency methods on different tasks. Based on this benchmark, we conduct experiments on three typical models with three saliency methods, and unveil their strengths and weakness in terms of interpretability. We will release this benchmark at https://xyz and hope it can facilitate the research in building trustworthy systems.
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