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Objective To explore the construction and management of Traditional Chinese Medicine nursing team and talent training, improve the supervision system, establish a reasonable structure, professional, high quality of integrated traditional Chinese and western medicine nursing talent echelon, and then lead and promote the high-quality development of nursing work in the hospital. Methods After personal recommendation, department recommendation and selection of the nursing department, 219 nursing staff were included in the training plan of integrated traditional Chinese and Western medicine nursing talents, and corresponding selection conditions, training objectives, training methods and contents were formulated for nursing staff at different levels. Compare the effectiveness of TCM nursing scientific research and the development of characteristic nursing technology before and after the training. Results After the training, The scientific research ability of nurses had been significantly improved. There was a remarkable increase in the number of inpatients with appropriate Traditional Chinese Medicine acupoint application, Traditional Chinese Medicine collapse stains, auricular acupressure therapy, Traditional Chinese Medicine hot compress and Traditional Chinese Medicine ointment rubbing therapy. Nurses had achieved higher scores in assessment of Traditional Chinese Medicine nursing knowledge and practice skills. Conclusion The echelon talent of Integrated Chinese and Western Medicine nursing enhanced the nurse nursing knowledge and skills of Traditional Chinese Medicine, promoted the clinical practice of Traditional Chinese Medicine nursing technology, met the needs of patients, gave full play to the nursing staff in nursing management, specialized nursing, clinical teaching, nursing research demonstration leading role. It is the need of the development of nursing discipline. (目的 探索中医护理队伍建设与人才培养, 完善监管体系, 建立一支结构合理、专业性强、素质高的中西医结合护理人才梯队, 进而引领、推动我院护理工作高质量发展。方法 经个人自荐、科室推荐和护理部遴选, 将219名护理人员列入中西医结合护理人才梯队培养计划, 对不同层次护理人员制订相应的遴选条件、培养目标、培养方式和内容。比较培训前后护理人员中医护理科研成效和特色护理技术开展情况。结果 培训后, 护理人员科研水平得到明显提升; 住院患者开展中医护理适宜技术穴位贴敷、中药塌渍、耳穴压丸、中药热奄包应用、中药膏摩应用人次增加; 护理人员中医护理知识和中医护理技能评分得到提高。结论 综合性医院的中医护理人员人才梯队建设提升了护士中医护理的知识和技能, 使中医护理技术操作广泛应用于临床, 满足患者不同的需求, 充分发挥护理人员在护理管理、专科护理、临床教学、护理科研等方面的示范带头作用, 是护理学科发展的需要。)

Multiplex network community detection methods based on graph neural network face two main challenges. Firstly, how to effectively utilize the node content information of multiplex network; and secondly, how to effectively utilize the interlayer relationships in multiplex networks. Therefore, this paper proposed the multiplex network community detection model AGCFN. Firstly, the autoencoder independently extracted the node content information of each network layer and passed the extracted node content information to the graph autoencoder for fusing the node content information of the current network layer with the topology information through the transfer operator to obtain the representation of each node of the current network layer, which made full use of the node content information of the network and the topology information of the network. The modularity maximization module and graph decoder optimized the obtained node representation. Secondly, the multilayer information fusion module fused the node representations extracted from each network layer to obtain a comprehensive representation of each node. Finally, the model under went training, and it achieved community detection results through a self-training mechanism. Comparison with six models on three datasets demonstrate improvements in both ACC and NMI evaluation metrics, thereby validating the effectiveness of AGCFN. [ABSTRACT FROM AUTHOR]

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It is difficult to collect complete structural texture information quickly and efficiently with a single modeling method considering the unique architectural forms of iconic buildings in urban areas. In order to solve this problem, manual removal if fully used in multiple data sources in large areas of deformation, a combination of coarse and ICP fine registration methods were used to stitch data and unify fused data in the same coordinate system together. This method not only guarantees the integrity of textural and structural information of the unique buildings collected by ground-based LiDAR, but also resolves issues such as distortion and deformation which is caused by self-occlusion during the collection of building information by UAV. Results show that the fused geometric and textural information of the unique buildings is more refined and complete than the unfused. It optimizes the process of modeling unique buildings. [ABSTRACT FROM AUTHOR]

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