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Background: Due to an ageing population, multimorbidity is becoming more common. Treatment burden (the effort required of patients to look after their health and the impact this has on their wellbeing) is prevalent in patients with multimorbidity. The Multimorbidity Treatment Burden Questionnaire (MTBQ) is a patient-reported outcome measure of treatment burden that has been validated amongst patients with multimorbidity in the UK. The aim of this study was to translate and culturally adapt the MTBQ into Chinese and to assess its reliability and validity in elderly patients with multimorbidity in hospital. Methods: The original English version of the MTBQ was translated into Chinese using Brislin's model of crossculture translation. The C-MTBQ was piloted on a sample of 30 elderly patients with multimorbidity prior to being completed by 156 Chinese elderly patients with multimorbidity recruited from a hospital in Zhengzhou, China. We examined the proportion of missing data, the distribution of responses and floor and ceiling effects for each question. Factor analysis, Cronbach's alpha, intraclass coefficient and Spearman's rank correlations assessed dimensional structure, internal consistency reliability, test-retest reliability and criterion validity, respectively. Results: The average age of the respondents was 73.5 years (range 60-99 years). The median C-MTBQ global score was 20.8 (interquartile range 12.5-29.2). Significant floor effects were seen for all items. Factor analysis supported a three-factor structure. The C-MTBQ had high internal consistency (Cronbach's alpha coefficient, 0.76) and test-retest reliability (the intraclass correlation coefficient, 0.944), the correlations between every item and global scores scored > 0.4. The scale content validity index(S-CVI) was 0.89, and the item level content validity index(I-CVI)was 0.83~1.00. The criterion validity was 0.875. Conclusion: The Chinese version of MTBQ showed satisfactory reliability and validity in elderly patients with multimorbidity, and could be used as a tool to measure treatment burden of elderly patients with multimorbidity in hospital.

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It is critical to be able to estimate a ship’s response to waves, since the added resistance and loss of speed may cause delays or course alterations, with consequent financial repercussions. Traditional methods for the study of ship motions are based on potential flow theory without viscous effects. Results of scaling model are used to predict full-scale of response to waves. Scale effect results in differences between the full-scale prediction and reality. The key objective of this study is to perform a fully nonlinear unsteady RANS simulation to predict the ship motions and added resistance of a full-scale KRISO Container Ship. The analyses are performed at design speeds in head waves, using in house computational fluid dynamics (CFD) to solve RANS equation coupled with two degrees of freedom (2DOF) solid body motion equations including heave and pitch. RANS equations are solved by finite difference method and PISO arithmetic. Computations have used structured grid with overset technology. Simulation results show that the total resistance coefficient in calm water at service speed is predicted by 4 .68% error compared to the related towing tank results. The ship motions demonstrated that the current in house CFD model predicts the heave and pitch transfer functions within a reasonable range of the EFD data, respectively.

In this article, we focus on the problem of social event extraction from Twitter, in which event detection, i.e., to identify which messages truly mention events of interest, is an indispensable step due to the fact that most Twitter messages, viz. tweets, are not related to any real-world event. Existing approaches to this problem often use pipelined architectures relying on some hand-crafted features derived using off-the-shelf natural language processing (NLP) tools, which may cause error propagation from the upstream component (event detection) to the downstream one (element extraction) and fail to leverage the interdependencies between them. To overcome these limitations, we propose a deep neural network based framework to Jointly Detect and Extract Events from Twitter (JDEET), which learns, as well as conducts, detection and extraction simultaneously by defining a joint loss function, a bidirectional long short-term memory (LSTM) based common representation layer, and a control gate. A conditional random field (CRF) layer is further employed to capture the strong dependencies among output labels. Experimental results show that the proposed approach outperforms the state-of-the-art ones considerably on a real-world dataset from Twitter.INDEX TERMS Event extraction, social events, Twitter, joint models, a control gate, deep neural networks.

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