Gestational Diabetes Mellitus (GDM) is a significant health concern affecting pregnant women worldwide. It is characterized by elevated blood sugar levels during pregnancy and poses risks to both maternal and fetal he...Gestational Diabetes Mellitus (GDM) is a significant health concern affecting pregnant women worldwide. It is characterized by elevated blood sugar levels during pregnancy and poses risks to both maternal and fetal health. Maternal complications of GDM include an increased risk of developing type 2 diabetes later in life, as well as hypertension and preeclampsia during pregnancy. Fetal complications may include macrosomia (large birth weight), birth injuries, and an increased risk of developing metabolic disorders later in life. Understanding the demographics, risk factors, and biomarkers associated with GDM is crucial for effective management and prevention strategies. This research aims to address these aspects comprehensively through the analysis of a dataset comprising 600 pregnant women. By exploring the demographics of the dataset and employing data modeling techniques, the study seeks to identify key risk factors associated with GDM. Moreover, by analyzing various biomarkers, the research aims to gain insights into the physiological mechanisms underlying GDM and its implications for maternal and fetal health. The significance of this research lies in its potential to inform clinical practice and public health policies related to GDM. By identifying demographic patterns and risk factors, healthcare providers can better tailor screening and intervention strategies for pregnant women at risk of GDM. Additionally, insights into biomarkers associated with GDM may contribute to the development of novel diagnostic tools and therapeutic approaches. Ultimately, by enhancing our understanding of GDM, this research aims to improve maternal and fetal outcomes and reduce the burden of this condition on healthcare systems and society. However, it’s important to acknowledge the limitations of the dataset used in this study. Further research utilizing larger and more diverse datasets, perhaps employing advanced data analysis techniques such as Power BI, is warranted to corroborate and expand upon the findings of this research. This underscores the ongoing need for continued investigation into GDM to refine our understanding and improve clinical management strategies.展开更多
目的利用Power BI Desktop对医院DIP分组明细进行多维度对比分析及数据可视化,寻找医院DIP亏损原因。方法提取某三甲肿瘤专科医院2022年度部分出院结算DIP入组明细数据,通过数据导入、数据类型转换、建立数据关系、数据建模、数据可视...目的利用Power BI Desktop对医院DIP分组明细进行多维度对比分析及数据可视化,寻找医院DIP亏损原因。方法提取某三甲肿瘤专科医院2022年度部分出院结算DIP入组明细数据,通过数据导入、数据类型转换、建立数据关系、数据建模、数据可视化等方法,建立全院、科室、病种等多个维度的可视化动态分析报表。结果Power BI Desktop能有效提升DIP精细化管理水平,提升工作效率,精准分析亏损原因,指导科室进行改进,有效减少医院DIP医保资金亏损。结论Power BI Desktop具有成本低廉,定制化、可视化、自动化程度高,用户界面友好等特点,值得在各医院尤其是信息化程度低、专科运营人员不足、资金预算不足的医院DIP精细化管理中进行推广。展开更多
In order to make an intensive study of the development of smart power distribution and utilization technology in China, their research hotspots and frontier technology are selected out through combining the informatic...In order to make an intensive study of the development of smart power distribution and utilization technology in China, their research hotspots and frontier technology are selected out through combining the informatics method, and using the CiteSpace which can take keyword cooccurrence analysis and draw the visualization graph. According to this result, we can infer the development trend of smart power distribution and utilization in the future, and providing reference for the researcher whose engage in this domain. The electric related literature was collected from the CNKI database in China. Under the smart power distribution and utilization domain, we also analyze the development of the power quality and the energy internet in detail.展开更多
The visualization techniques were explored for power quality monitoring.And remote visualization solutions were proposed for highspeed rail power quality monitoring.Taking the Beijing-Shanghai highspeed rail power mon...The visualization techniques were explored for power quality monitoring.And remote visualization solutions were proposed for highspeed rail power quality monitoring.Taking the Beijing-Shanghai highspeed rail power monitoring as a study case,a remote visualization client,based on our proposed solutions,was developed for high-speed rail power quality monitoring to efficiently support power quality data analysis of the electricity business.The solutions collected data from monitoring stations deployed along the high-speed rail route and visualized the data set with a variety of visualization technologies to alert the specific stations of catastrophic events.The proposed solutions have been proved to be effective in supporting decision-making for the railway power scheduling and providing diagnosis information for quickly spotting any possible runtime failure in operation.展开更多
基于中国知网(CNKI)数据库和Web of Science核心数据库,运用文献计量软件CiteSpace对2000—2023年退役动力电池回收研究的发文趋势、高产作者、高产机构、关键词共现和聚类等进行可视化分析。结果发现:自2000年以来,动力电池回收研究呈...基于中国知网(CNKI)数据库和Web of Science核心数据库,运用文献计量软件CiteSpace对2000—2023年退役动力电池回收研究的发文趋势、高产作者、高产机构、关键词共现和聚类等进行可视化分析。结果发现:自2000年以来,动力电池回收研究呈持续上升趋势,国外高产作者聚集且合作关系紧,国内高产作者分散且合作关系疏;国外高产机构集中于发达国家,国内高产机构多来自经济发展水平较高的城市;关键词共现和聚类分析表明,退役动力电池回收工艺、梯次利用和回收体系是研究焦点。未来应积极开展跨区域、跨学科和跨产业合作,加强高效、智能、安全的退役动力电池回收体系及其梯次利用产业链构建研究,以促进新能源汽车产业链的健康发展。展开更多
文摘Gestational Diabetes Mellitus (GDM) is a significant health concern affecting pregnant women worldwide. It is characterized by elevated blood sugar levels during pregnancy and poses risks to both maternal and fetal health. Maternal complications of GDM include an increased risk of developing type 2 diabetes later in life, as well as hypertension and preeclampsia during pregnancy. Fetal complications may include macrosomia (large birth weight), birth injuries, and an increased risk of developing metabolic disorders later in life. Understanding the demographics, risk factors, and biomarkers associated with GDM is crucial for effective management and prevention strategies. This research aims to address these aspects comprehensively through the analysis of a dataset comprising 600 pregnant women. By exploring the demographics of the dataset and employing data modeling techniques, the study seeks to identify key risk factors associated with GDM. Moreover, by analyzing various biomarkers, the research aims to gain insights into the physiological mechanisms underlying GDM and its implications for maternal and fetal health. The significance of this research lies in its potential to inform clinical practice and public health policies related to GDM. By identifying demographic patterns and risk factors, healthcare providers can better tailor screening and intervention strategies for pregnant women at risk of GDM. Additionally, insights into biomarkers associated with GDM may contribute to the development of novel diagnostic tools and therapeutic approaches. Ultimately, by enhancing our understanding of GDM, this research aims to improve maternal and fetal outcomes and reduce the burden of this condition on healthcare systems and society. However, it’s important to acknowledge the limitations of the dataset used in this study. Further research utilizing larger and more diverse datasets, perhaps employing advanced data analysis techniques such as Power BI, is warranted to corroborate and expand upon the findings of this research. This underscores the ongoing need for continued investigation into GDM to refine our understanding and improve clinical management strategies.
文摘目的利用Power BI Desktop对医院DIP分组明细进行多维度对比分析及数据可视化,寻找医院DIP亏损原因。方法提取某三甲肿瘤专科医院2022年度部分出院结算DIP入组明细数据,通过数据导入、数据类型转换、建立数据关系、数据建模、数据可视化等方法,建立全院、科室、病种等多个维度的可视化动态分析报表。结果Power BI Desktop能有效提升DIP精细化管理水平,提升工作效率,精准分析亏损原因,指导科室进行改进,有效减少医院DIP医保资金亏损。结论Power BI Desktop具有成本低廉,定制化、可视化、自动化程度高,用户界面友好等特点,值得在各医院尤其是信息化程度低、专科运营人员不足、资金预算不足的医院DIP精细化管理中进行推广。
文摘In order to make an intensive study of the development of smart power distribution and utilization technology in China, their research hotspots and frontier technology are selected out through combining the informatics method, and using the CiteSpace which can take keyword cooccurrence analysis and draw the visualization graph. According to this result, we can infer the development trend of smart power distribution and utilization in the future, and providing reference for the researcher whose engage in this domain. The electric related literature was collected from the CNKI database in China. Under the smart power distribution and utilization domain, we also analyze the development of the power quality and the energy internet in detail.
基金the State Grid Corporation and Computer Science Experimental Center of Beihang University,China
文摘The visualization techniques were explored for power quality monitoring.And remote visualization solutions were proposed for highspeed rail power quality monitoring.Taking the Beijing-Shanghai highspeed rail power monitoring as a study case,a remote visualization client,based on our proposed solutions,was developed for high-speed rail power quality monitoring to efficiently support power quality data analysis of the electricity business.The solutions collected data from monitoring stations deployed along the high-speed rail route and visualized the data set with a variety of visualization technologies to alert the specific stations of catastrophic events.The proposed solutions have been proved to be effective in supporting decision-making for the railway power scheduling and providing diagnosis information for quickly spotting any possible runtime failure in operation.
文摘基于中国知网(CNKI)数据库和Web of Science核心数据库,运用文献计量软件CiteSpace对2000—2023年退役动力电池回收研究的发文趋势、高产作者、高产机构、关键词共现和聚类等进行可视化分析。结果发现:自2000年以来,动力电池回收研究呈持续上升趋势,国外高产作者聚集且合作关系紧,国内高产作者分散且合作关系疏;国外高产机构集中于发达国家,国内高产机构多来自经济发展水平较高的城市;关键词共现和聚类分析表明,退役动力电池回收工艺、梯次利用和回收体系是研究焦点。未来应积极开展跨区域、跨学科和跨产业合作,加强高效、智能、安全的退役动力电池回收体系及其梯次利用产业链构建研究,以促进新能源汽车产业链的健康发展。
