Background: Prescriptions contain a lot of clinical information and play a pivotal role in the clinical diagnosis of Traditional Chinese Medicine (TCM), which is a combination of herb to treat the symptoms of a patient from decision-making of doctors. In the process of clinical decision-making, a large number of prescriptions have been invented and accumulated based on TCM theories. Mining complex and the regular relationships between symptoms and herbs in the prescriptions are important for both clinical practice and novel prescription development. Previous work used several machine learning methods to discover regularities and generate prescriptions but rarely used TCM knowledge to guide prescription generation and described why each herb is predicted for treating a symptom. Methods: In this work, we employ a machine translation mechanism and propose a novel sequence-to-sequence (seq2seq) architecture termed TPGen to generate prescriptions. TPGen consisting of an encoder and a decoder is a well-known framework for resolving the machine translation problem in the natural language processing (NLP) domain. We use the lite transformer and Bi-directional Gate Recurrent Units(Bi-GRUS) as a fundamental model in TPGen, and integrate TCM clinical knowledge to guide the model improvement termed TPGen+. Results: We conduct extensive experiments on a public TCM dataset and clinical data. The experimental results demonstrate that our proposed model is effective and outperforms other state-of-the-art methods in TCM expert evaluation. The approach will be beneficial for clinical prescription discovery and diagnosis

The (1 − x) La0.67Ca0.33MnO3(LCMO)/xCaCu3Ti4O12 (CCTO) (x = 0, 0.01, 0.03, 0.05, and 0.10) composites were fabricated by the sol–gel method and investigated in detail for their electrical transport and magnetoresistance (MR) properties. The X‐ray diffraction and scanning electronic microscopy analysis reveal that both LCMO and CCTO phases are distributed in the composites. Compared with pure LCMO, an obvious enhancement of MR is observed over a wide temperature range for the composites at high external magnetic field. Under 3 T field, the MR increases to a maximum value of 73.4% for x = 0.1 composite. Simultaneously, the thermal hysteresis behavior in resistivity is observed in x = 0.03, 0.05, and 0.1 samples, respectively. The MR enhancement and the abnormal experimental observations are attributed to the enhanced spin disorder at grain boundaries induced by the interaction product layer between LCMO and CCTO.

Aiming at better understanding the functioning mechanism of factors affecting consumers to adopt e-commerce, the paper adopted empirical methods such as questionnaire investigation, regression analysis, and cluster analysis, provided a quantitative analysis. Based on an analysis of the overall data, the degree of affecting on adopting motives was obtained. Consequently, firms can make effective decisions and formulate long-term strategies on e-commerce.

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