目的设计一款移动护理技能考核软件,提高医院内临床护理技能考核管理效率。方法在充分学习卫生部规定的常用护理技能操作考核的质量标准下,借助护理管理信息系统平台(nursing information system,NIS),以移动4 G网络为核心技术设计了一...目的设计一款移动护理技能考核软件,提高医院内临床护理技能考核管理效率。方法在充分学习卫生部规定的常用护理技能操作考核的质量标准下,借助护理管理信息系统平台(nursing information system,NIS),以移动4 G网络为核心技术设计了一款移动护理技能考核软件,主要包括护理人员信息档案模块、技能考核标准管理模块、技能考核模块、个人成绩查询模块和考试成绩管理-统计分析模块。结果我院自使用该软件以来,与往年同期相比,各项技能考核周期缩短34.0%,极大地提高了护理技能考核工作的管理效率,成绩录入准确性达100%。结论移动护理技能考核软件的应用提高了护理技能考核工作的管理效率、分数录入的准确性与及时性,同时减少了考核前、考核中、考核后大量的人力、物力的调配,是护理管理人员值得推广和使用的一款护理技能考核软件。展开更多
As a food consumed worldwide,ginger is often sulfur-fumigated.Sulfur-fumigated ginger is harmful to health.However,traditional methods to detect sulfur-fumigated ginger are expensive and unpractical for the general pu...As a food consumed worldwide,ginger is often sulfur-fumigated.Sulfur-fumigated ginger is harmful to health.However,traditional methods to detect sulfur-fumigated ginger are expensive and unpractical for the general public.In this paper,we present an efficient and convenient identification method based on image processing.First,rapid detection kits were employed to mark three levels of sulfur-fumigated gingers,and the RGB images of the gingers of each sulfur-fumigated level are collected.Second,the brightness and texture features were extracted from the images.Three machine learning methods,Support Vector Machine,Back Propagation Neural Network and Random Forest,were applied to establish prediction models.Third,the accuracy of each model was calculated and different weights were assigned for different models.Finally,models with different weights determined whether the ginger was sulfur-fumigated or non-sulfur-fumigated,and then the results were summarized to establish the final identification model.The experimental results show that the proposed method is robust.When the training set occupies 90%,the prediction accuracy is up to 100%.When the training set only occupies 10%,the accuracy remains high at 80%.Meanwhile,the proposed method is more competitive than other methods in terms of accuracy.展开更多
文摘目的设计一款移动护理技能考核软件,提高医院内临床护理技能考核管理效率。方法在充分学习卫生部规定的常用护理技能操作考核的质量标准下,借助护理管理信息系统平台(nursing information system,NIS),以移动4 G网络为核心技术设计了一款移动护理技能考核软件,主要包括护理人员信息档案模块、技能考核标准管理模块、技能考核模块、个人成绩查询模块和考试成绩管理-统计分析模块。结果我院自使用该软件以来,与往年同期相比,各项技能考核周期缩短34.0%,极大地提高了护理技能考核工作的管理效率,成绩录入准确性达100%。结论移动护理技能考核软件的应用提高了护理技能考核工作的管理效率、分数录入的准确性与及时性,同时减少了考核前、考核中、考核后大量的人力、物力的调配,是护理管理人员值得推广和使用的一款护理技能考核软件。
基金the National Key Research and Development Project of China(No.2020YFD1104100)the National Natural Science Foundation of China(Nos.82204770,62101268,82074580)+4 种基金the Youth Science Foundation of Jiangsu Province(No.BK20210696)the Future Network Scientific Research Fund Project(No.FNSRFP-2021-ZD-24)the Natural Science Foundation of the Jiangsu Higher Education Institutions of China(No.20KJB510021)China Agriculture Research System of MOF and MARA(No.CARS-21)Jiangsu Province 333 High-level Talents Training Project,and the‘Qing Lan Project'in colleges and universities in Jiangsu,China.
文摘As a food consumed worldwide,ginger is often sulfur-fumigated.Sulfur-fumigated ginger is harmful to health.However,traditional methods to detect sulfur-fumigated ginger are expensive and unpractical for the general public.In this paper,we present an efficient and convenient identification method based on image processing.First,rapid detection kits were employed to mark three levels of sulfur-fumigated gingers,and the RGB images of the gingers of each sulfur-fumigated level are collected.Second,the brightness and texture features were extracted from the images.Three machine learning methods,Support Vector Machine,Back Propagation Neural Network and Random Forest,were applied to establish prediction models.Third,the accuracy of each model was calculated and different weights were assigned for different models.Finally,models with different weights determined whether the ginger was sulfur-fumigated or non-sulfur-fumigated,and then the results were summarized to establish the final identification model.The experimental results show that the proposed method is robust.When the training set occupies 90%,the prediction accuracy is up to 100%.When the training set only occupies 10%,the accuracy remains high at 80%.Meanwhile,the proposed method is more competitive than other methods in terms of accuracy.
