Objective: Pulmonary complications could badly affect the recovery of neurological function and neurological prognosis of neurological critically ill patients. This study evaluated the effect of high-flow nasal cannula therapy on decreasing pulmonary complications in neurologically critically ill patients.Patients and Methods: The patients admitted to the intensive care unit with serious neurological disease and receiving oxygen therapy were retrospectively reviewed. Patients were divided into the HFNC group and the conventional oxygen therapy group. We analyzed the data within these two groups, including (...) patients’ baseline data, short-term outcomes of respiratory complications, general outcomes including hospital stay, ICU stay and mortality, and neurological functions. To analyze the relevant factors, we performed multivariable logistic regression analysis.Results: A total of 283 patients met the criteria, including 164 cases in the HFNC group and 119 cases in the COT group. The HFNC group had remarkably less mechanical ventilation requirement with lower phlegm viscosity. Even more, ICU stay and total hospital stay were significantly shortened in the HNFC group.Conclusion: HFNC decreased pulmonary complications in neurologically critically ill patients and improved recovery of neurological function and neurological prognosis. (shrink)

Color harmony is the focus of many researchers in the field of art and design, and its research results have been widely used in artistic creation and design activities. With the development of signal processing and artificial intelligence technology, new ideas and methods are provided for color harmony theory and color harmony calculation. In this article, psychological experimental methods and information technology are combined to design and quantify the 16-dimensional physical features of multiple colors, including multi-color statistical features and multi-color (...) contrast features. Eighty-four subjects are invited to give a 5-level score on the degree of color harmony for 164 multi-color materials selected from the screenshots of film and television scenes. Based on the multi-color physical features and the subjective evaluation experiment, the correlation analysis is firstly carried out, which shows that the overall lightness, difference of the color tones, number of multiple colors, lightness contrast, color tone contrast, and cool/warm contrast are significantly correlated with color harmony. On the other hand, the regression prediction model and classification prediction model of color harmony are constructed based on machine learning algorithms. In terms of regression prediction model, the prediction accuracy of linear models is higher than that of nonlinear models, with 63.9% as the highest, indicating that the multi-color physical features can explain color harmony well. In terms of classification prediction model, the Random Forest has the best prediction performance, with an accuracy of 80.2%. (shrink)

With the popularity of location-based social networks, location prediction has become an important task and has gained significant attention in recent years. However, how to use massive trajectory data and spatial-temporal context information effectively to mine the user’s mobility pattern and predict the users’ next location is still unresolved. In this paper, we propose a novel network named STSAN, which can integrate spatial-temporal information with the self-attention for location prediction. In STSAN, we design a trajectory attention module to learn users’ (...) dynamic trajectory representation, which includes three modules: location attention, which captures the location sequential transitions with self-attention; spatial attention, which captures user’s preference for geographic location; and temporal attention, which captures the user temporal activity preference. Finally, extensive experiments on four real-world check-ins datasets are designed to verify the effectiveness of our proposed method. Experimental results show that spatial-temporal information can effectively improve the performance of the model. Our method STSAN gains about 39.8% Acc@1 and 4.4% APR improvements against the strongest baseline on New York City dataset. (shrink)

本书是文艺争鸣杂志上发表的有关"强制阐释"的讨论的论文,包括文学阐释与对话精神"强制阐释论"的方法论元素场外征用的必要性与有效度古代文论研究中阐释的有效性问题等.