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 re展开更多
The modelling is widely used in determining the best strategies for the mitigation of the impact of infectious diseases.Currently,the modelling of a complex system such as the spread of COVID-19 infection is among the...The modelling is widely used in determining the best strategies for the mitigation of the impact of infectious diseases.Currently,the modelling of a complex system such as the spread of COVID-19 infection is among the topical issues.The aim of this article is graphbased modelling of the COVID-19 infection spread.The article investigates the studies related to the modelling of COVID-19 pandemic and analyses the factors affecting the spread of the disease and its main characteristics.We propose a conceptual model of COVID-19 epidemic by considering the social distance,the duration of contact with an infected person and their location-based demographic characteristics.Based on the hypothetical scenario of the spread of the virus,a graph model of the process are developed starting from the first confirmed infection case to human-to-human transmission of the virus and visualized by considering the epidemiological characteristics of COVID-19.The application of graph for the pandemic modelling allows for considering multiple factors affecting the epidemiological process and conducting numerical experiments.The advantage of this approach is justified with the fact that it enables the reverse analysis the spread as a result of the dynamic record of detected cases of the infection in the model.This approach allows for to determining undetected cases of infection based on the social distance and duration of contact and eliminating the uncertainty significantly.Note that social,economic,demographic factors,the population density,mental values and etc.affect the increase in number of cases of infection and hence,the research was not able to consider all factors.In future research will analyze multiple factors impacting the number of infections and their use in the models will be considered.展开更多
超高层建筑结构复杂,施工周期长,在进行超高层建筑结构监测的过程中,如何准确、及时地完成监测内容是监测的重点和难点.针对以上问题,结合某超高层结构,研究了多源信息融合方法、建筑信息模型(building information modelling,BIM)技术...超高层建筑结构复杂,施工周期长,在进行超高层建筑结构监测的过程中,如何准确、及时地完成监测内容是监测的重点和难点.针对以上问题,结合某超高层结构,研究了多源信息融合方法、建筑信息模型(building information modelling,BIM)技术在结构监测方案制定、三维可视化施工模拟指导传感器安装及可视化智能监测系统搭建等方面的具体应用.解决了超高层建筑结构监测过程中,由于施工过程复杂并且传感器种类、数量过多导致的未能及时完成监测内容的问题,搭建基于BIM技术的智能监测平台,可以更直观地读取监测数据,为类似的超高层建筑施工过程监测项目提供参考.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 re
文摘The modelling is widely used in determining the best strategies for the mitigation of the impact of infectious diseases.Currently,the modelling of a complex system such as the spread of COVID-19 infection is among the topical issues.The aim of this article is graphbased modelling of the COVID-19 infection spread.The article investigates the studies related to the modelling of COVID-19 pandemic and analyses the factors affecting the spread of the disease and its main characteristics.We propose a conceptual model of COVID-19 epidemic by considering the social distance,the duration of contact with an infected person and their location-based demographic characteristics.Based on the hypothetical scenario of the spread of the virus,a graph model of the process are developed starting from the first confirmed infection case to human-to-human transmission of the virus and visualized by considering the epidemiological characteristics of COVID-19.The application of graph for the pandemic modelling allows for considering multiple factors affecting the epidemiological process and conducting numerical experiments.The advantage of this approach is justified with the fact that it enables the reverse analysis the spread as a result of the dynamic record of detected cases of the infection in the model.This approach allows for to determining undetected cases of infection based on the social distance and duration of contact and eliminating the uncertainty significantly.Note that social,economic,demographic factors,the population density,mental values and etc.affect the increase in number of cases of infection and hence,the research was not able to consider all factors.In future research will analyze multiple factors impacting the number of infections and their use in the models will be considered.
文摘超高层建筑结构复杂,施工周期长,在进行超高层建筑结构监测的过程中,如何准确、及时地完成监测内容是监测的重点和难点.针对以上问题,结合某超高层结构,研究了多源信息融合方法、建筑信息模型(building information modelling,BIM)技术在结构监测方案制定、三维可视化施工模拟指导传感器安装及可视化智能监测系统搭建等方面的具体应用.解决了超高层建筑结构监测过程中,由于施工过程复杂并且传感器种类、数量过多导致的未能及时完成监测内容的问题,搭建基于BIM技术的智能监测平台,可以更直观地读取监测数据,为类似的超高层建筑施工过程监测项目提供参考.
