舌诊是中医望诊的重要手段,同时,温度与人体的健康息息相关。为了研究舌面的脏腑功能定位及舌象温度关系的反映,论文提出了一种红外技术的感兴趣区域(region of interest, ROI)模型研究方法。首先,利用葛立恒扫描法和Bezier曲线对多边形...舌诊是中医望诊的重要手段,同时,温度与人体的健康息息相关。为了研究舌面的脏腑功能定位及舌象温度关系的反映,论文提出了一种红外技术的感兴趣区域(region of interest, ROI)模型研究方法。首先,利用葛立恒扫描法和Bezier曲线对多边形ROI模型进行改进;然后,借助U-Net分割网络将提取出的温度信息进行训练与学习,从而做到批量处理舌体温度信息;最后,利用HSV色彩模型进行3D可视化,达成舌象温度分区的可视化研究。此外,为了验证该方法的准确性,实验还对模型截取出的舌体进行了评价指标验证,准确度可以达到0.991 1,分割效果极佳。研究表明:改进后的红外信息提取技术既能直观地观察到舌体的分区状况,也可以完整保留舌体的信息变化,为中医的数据化提供了完整可行性方案。实现了舌体红外信息数据的提取与中医诊断技术的有机结合。解决了中医一体化望诊的舌体信息完整性及准确性问题。展开更多
In this paper, we build the Linear Programming (LP) model, factor analysis model and return on investment model to measure the investment amount and which year to invest of each selected schools. We firstly analyze th...In this paper, we build the Linear Programming (LP) model, factor analysis model and return on investment model to measure the investment amount and which year to invest of each selected schools. We firstly analyze the indicators from attached files, and select effective indexes to choose schools donated. Then we select 17 indexes out after preprocessing all the indices. Secondly, we extract 1064 schools by MATLAB which is the Potential Candidate Schools from the table of attached files;we extract 10 common factors of these schools by factor analysis. After calculation, we rank the universities and select the top 100. We calculate the Return on Investment (ROI) based on these 17 indexes. Thirdly, we figure out the investment amount by conducting LP model through MATLAB. According to the property of schools, we calculate the annual limit investment and the mount of investment of each school. Fourthly, we determine which year to invest by ROI model which is operated by LINGO. In order to achieve optimal investment strategy and not duplication of investment, for five years, starting July 2016, we assume that the time duration that the organization’s money should be provided is one year, and the school return to the Good grant Foundation only one year. Then we can get the investment amount per school, the return on that investment, and which years to invest. Fifthly, by changing parameter, the sensitivity analysis is conducted for our models. The result indicates that our models are feasible and robust. Finally, we evaluate our models, and point out the strengths and weakness. Through previous analysis, we can find that our models can be applied to many fields, which have a relatively high generalization.展开更多
Background: To evaluate the care given using Roy’s Adaptation Model. Materials and Methods: A pretest-posttest experimental model with a control group. Study population comprised postpartum women (N = 134;65 in the e...Background: To evaluate the care given using Roy’s Adaptation Model. Materials and Methods: A pretest-posttest experimental model with a control group. Study population comprised postpartum women (N = 134;65 in the experimental group, 69 in the control group) who had caesarean full-term delivery in a Turkish maternity hospital between September 2009 and February 2011. Data were collected from the experimental group during seven home visits and from the control group at the end of the 6th week postpartum. Results: Percentage, chi-square, arithmetic mean, standard deviation, and the McNamer test were used to evaluate data establishing 36 nursing diagnoses: Physiological requirements (22), Self requirements (7), Role Function requirements (4), and Interdependence Mode requirements (3). It was determined that the care given during the postpartum period using Roy’s Adaptation Model resolved or prevented the majority of postpartum problems. The difference between most diagnoses was found to be statistically significant (p p < 0.001) during the last week of data collection. Conclusion: The care given in the postpartum period using Roy’s Adaptation Model resolved or prevented postpartum problems.展开更多
文摘舌诊是中医望诊的重要手段,同时,温度与人体的健康息息相关。为了研究舌面的脏腑功能定位及舌象温度关系的反映,论文提出了一种红外技术的感兴趣区域(region of interest, ROI)模型研究方法。首先,利用葛立恒扫描法和Bezier曲线对多边形ROI模型进行改进;然后,借助U-Net分割网络将提取出的温度信息进行训练与学习,从而做到批量处理舌体温度信息;最后,利用HSV色彩模型进行3D可视化,达成舌象温度分区的可视化研究。此外,为了验证该方法的准确性,实验还对模型截取出的舌体进行了评价指标验证,准确度可以达到0.991 1,分割效果极佳。研究表明:改进后的红外信息提取技术既能直观地观察到舌体的分区状况,也可以完整保留舌体的信息变化,为中医的数据化提供了完整可行性方案。实现了舌体红外信息数据的提取与中医诊断技术的有机结合。解决了中医一体化望诊的舌体信息完整性及准确性问题。
文摘In this paper, we build the Linear Programming (LP) model, factor analysis model and return on investment model to measure the investment amount and which year to invest of each selected schools. We firstly analyze the indicators from attached files, and select effective indexes to choose schools donated. Then we select 17 indexes out after preprocessing all the indices. Secondly, we extract 1064 schools by MATLAB which is the Potential Candidate Schools from the table of attached files;we extract 10 common factors of these schools by factor analysis. After calculation, we rank the universities and select the top 100. We calculate the Return on Investment (ROI) based on these 17 indexes. Thirdly, we figure out the investment amount by conducting LP model through MATLAB. According to the property of schools, we calculate the annual limit investment and the mount of investment of each school. Fourthly, we determine which year to invest by ROI model which is operated by LINGO. In order to achieve optimal investment strategy and not duplication of investment, for five years, starting July 2016, we assume that the time duration that the organization’s money should be provided is one year, and the school return to the Good grant Foundation only one year. Then we can get the investment amount per school, the return on that investment, and which years to invest. Fifthly, by changing parameter, the sensitivity analysis is conducted for our models. The result indicates that our models are feasible and robust. Finally, we evaluate our models, and point out the strengths and weakness. Through previous analysis, we can find that our models can be applied to many fields, which have a relatively high generalization.
文摘Background: To evaluate the care given using Roy’s Adaptation Model. Materials and Methods: A pretest-posttest experimental model with a control group. Study population comprised postpartum women (N = 134;65 in the experimental group, 69 in the control group) who had caesarean full-term delivery in a Turkish maternity hospital between September 2009 and February 2011. Data were collected from the experimental group during seven home visits and from the control group at the end of the 6th week postpartum. Results: Percentage, chi-square, arithmetic mean, standard deviation, and the McNamer test were used to evaluate data establishing 36 nursing diagnoses: Physiological requirements (22), Self requirements (7), Role Function requirements (4), and Interdependence Mode requirements (3). It was determined that the care given during the postpartum period using Roy’s Adaptation Model resolved or prevented the majority of postpartum problems. The difference between most diagnoses was found to be statistically significant (p p < 0.001) during the last week of data collection. Conclusion: The care given in the postpartum period using Roy’s Adaptation Model resolved or prevented postpartum problems.
