A Case Study of Mass Transport during the East-West Oscillation of the Asian Summer Monsoon Anticyclone

We use ERA-Interim reanalysis, MLS observations, and a trajectory model to examine the chemical transport and tracers distribution in the Upper Troposphere and Lower Stratosphere (UTLS) associated with an east-west oscillation case of the anticyclone in 2016. The results show that the spatial distribution of water vapor (H2O) was more consistent with the location of the anticyclone than carbon monoxide (CO) at 100 hPa, and an independent relative high concentration center was only found in H2O field. At 215 hPa, although the anticyclone center also migrated from the Tibetan Mode (TM) to the Iranian Mode (IM), the relative high concentration centers of both tracers were always colocated with regions where upward motion was strong in the UTLS. When the anticyclone migrated from the TM, air within the anticyclone over Tibetan Plateau may transport both westward and eastward but was always within the UTLS. The relative high concentration of tropospheric tracers within the anticyclone in the IM was from the east and transported by the westward propagation of the anticyclone rather than being lifted from surface directly. Air within the relative high geopotential height centers over Western Pacific was partly from the main anticyclone and partly from lower levels

Neural Simile Recognition with Cyclic Multitask Learning and Local Attention

Simile recognition is to detect simile sentences and to extract simile components, i.e., tenors and vehicles. It involves two subtasks: {\it simile sentence classification} and {\it simile component extraction}. Recent work has shown that standard multitask learning is effective for Chinese simile recognition, but it is still uncertain whether the mutual effects between the subtasks have been well captured by simple parameter sharing. We propose a novel cyclic multitask learning framework for neural simile recognition, which stacks the subtasks and makes them into a loop by connecting the last to the first. It iteratively performs each subtask, taking the outputs of the previous subtask as additional inputs to the current one, so that the interdependence between the subtasks can be better explored. Extensive experiments show that our framework significantly outperforms the current state-of-the-art model and our carefully designed baselines, and the gains are still remarkable using BERT.Comment: AAAI 202

Study of imbibition in various geometries using phase field method

Phase field method has been widely utilized to study multiphase flow problems, but has seldom been applied to the study of imbibition. Previous methods used to simulate imbibition, such as moving mesh method, need to specify capillary pressure as a boundary condition a priori, whereas phase field method can calculate capillary pressure automatically for various geometries. Therefore, phase field method would be a versatile tool for the study of imbibition in various geometries. In this paper, phase field method is employed to solve dynamical imbibition problem in various geometries, including straight tube, conical tube and structures in which the topology changes. The variation of the imbibition height with respect to time from phase field simulation is verified with theoretical predictions from Lucas-Washburn law in a straight capillary tube with three gravitational scenarios. In addition, the capillary pressure and velocity field are found to be consistent with Laplace-Young equation and Hagen-Poiseuille equation in various geometries. The applicability and accuracy of the phase field method for the study of imbibition in structures with changing topology are also discussed.Cited as: Xiao, J., Luo, Y., Niu, M., Wang, Q., Wu, J., Liu, X., Xu, J. Study of imbibition in various geometries using phase field method. Capillarity, 2019, 2(4): 57-65, doi: 10.26804/capi.2019.04.0

Intelligent grading method for walnut kernels based on deep learning and physiological indicators

Walnut grading is an important step before the product enters the market. However, traditional walnut grading primarily relies on manual assessment of physiological features, which is difficult to implement efficiently. Furthermore, walnut kernel grading is, at present, relatively unsophisticated. Therefore, this study proposes a novel deep-learning model based on a spatial attention mechanism and SE-network structure to grade walnut kernels using machine vision to ensure accuracy and improve assessment efficiency. In this experiment, we found through the literature that both the lightness (L* value) and malondialdehyde (MDA) contens of walnut kernels were correlated with the oxidation phenomenon in walnuts. Subsequently, we clustered four partitionings using the L* values. We then used the MDA values to verify the rationality of these partitionings. Finally, four network models were used for comparison and training: VGG19, EfficientNetB7, ResNet152V2, and spatial attention and spatial enhancement network combined with ResNet152V2 (ResNet152V2-SA-SE). We found that the ResNet152V2-SA-SE model exhibited the best performance, with a maximum test set accuracy of 92.2%. The test set accuracy was improved by 6.2, 63.2, and 74.1% compared with that of ResNet152V2, EfficientNetB7, and VGG19, respectively. Our testing demonstrated that combining spatial attention and spatial enhancement methods improved the recognition of target locations and intrinsic information, while decreasing the attention given to non-target regions. Experiments have demonstrated that combining spatial attention mechanisms with SE networks increases focus on recognizing target locations and intrinsic information, while decreasing focus on non-target regions. Finally, by comparing different learning rates, regularization methods, and batch sizes of the model, we found that the training performance of the model was optimal with a learning rate of 0.001, a batch size of 128, and no regularization methods. In conclusion, this study demonstrated that the ResNet152V2-SA-SE network model was effective in the detection and evaluation of the walnut kernels

LLM-Mini-CEX: Automatic Evaluation of Large Language Model for Diagnostic Conversation

There is an increasing interest in developing LLMs for medical diagnosis to improve diagnosis efficiency. Despite their alluring technological potential, there is no unified and comprehensive evaluation criterion, leading to the inability to evaluate the quality and potential risks of medical LLMs, further hindering the application of LLMs in medical treatment scenarios. Besides, current evaluations heavily rely on labor-intensive interactions with LLMs to obtain diagnostic dialogues and human evaluation on the quality of diagnosis dialogue. To tackle the lack of unified and comprehensive evaluation criterion, we first initially establish an evaluation criterion, termed LLM-specific Mini-CEX to assess the diagnostic capabilities of LLMs effectively, based on original Mini-CEX. To address the labor-intensive interaction problem, we develop a patient simulator to engage in automatic conversations with LLMs, and utilize ChatGPT for evaluating diagnosis dialogues automatically. Experimental results show that the LLM-specific Mini-CEX is adequate and necessary to evaluate medical diagnosis dialogue. Besides, ChatGPT can replace manual evaluation on the metrics of humanistic qualities and provides reproducible and automated comparisons between different LLMs

“We are in the forgotten corner!” a qualitative study of experiences and challenges among Chinese older women at the onset of acute myocardial infarction

BackgroundAcute myocardial infarction (AMI) is a common and serious cardiovascular disease (CVD) that is one of the leading causes of death among women globally and in China. However, there are sex-associated differences and inequalities in the detection and management of AMI, especially in older people. There is little research demonstrating how challenges and barriers affect older women’s help-seeking behavior and health-related procedures in China.PurposeThe objective of this study was to explore the experiences of older women with AMI, focusing on their perception, challenges, and coping strategies at the onset of AMI in Wuhan, China.MethodsThis study utilized a qualitative research design approach and conducted semi-structured, in-depth, and audio-recorded interviews with 18 women aged 65–84 years, purposively selected from two tertiary hospitals in Wuhan City from November 2021 to April 2022.ResultsInterpretative Phenomenological Analysis (IPA) was used in this study to analyze the data on 18 participants and three major themes were generated: disease perception disorder, negative coping strategies, and barriers due to social-environmental contexts.ConclusionTo reduce older women’s delay in seeking help, healthcare professionals should provide public health education that emphasizes sex-related disparities, and age-specific knowledge-attitude aspects to high-risk groups. Policy-based and health administration recommendations, including e-health information support, access to care, and social-environmental factors, should be highlighted to promote women’s health behavior