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
全球离散格网系统(Discrete Global Grid System,DGGS)是数字化的地球参考框架,在多源、多尺度地球空间数据集成分析方面优势明显。本文选择菱形三十面体六边形全球离散格网系统,提高格网与地球的整体拟合精度和空间采样率;建立遥感图...全球离散格网系统(Discrete Global Grid System,DGGS)是数字化的地球参考框架,在多源、多尺度地球空间数据集成分析方面优势明显。本文选择菱形三十面体六边形全球离散格网系统,提高格网与地球的整体拟合精度和空间采样率;建立遥感图像六边形像素数学模型,提出兼容开放标准格式的数据存储方案。①根据地理位置将遥感图像格网化,完成遥感图像六边形DGGS建模;其次,建立六边形单元与矩形像素严密对应关系,等效保留六边形单元的邻域信息;②采用GeoTIFF开放标准格式精确存储六边形属性值以及投影、变换参数;③设计依托六边形DGGS格网标准数据集为基础的多尺度六边形DGGS生成算法。实验结果表明:本文方案不仅能保证六边形像素遥感图像数据与标准文件格式兼容,而且能保证矩形像素与六边形单元逐一对应,较好地保留了六边形单元数据的图像信息和空间分布特征,相较于欧空局SMOS数据组织方案更具优势。本文方案打破了六边形单元与矩形像素遥感图像的数据组织壁垒,使用常见GIS/RS软件即可读取六边形像素的遥感图像,并可通过对矩形像素的操作等效实现对六边形单元的处理,有望推动六边形DGGS在遥感数据组织、处理、共享等方面的应用。展开更多
The objective of modelling from data is not that the model simply fits the training data well. Rather, the goodness of a model is characterized by its generalization capability, interpretability and ease for knowledge...The objective of modelling from data is not that the model simply fits the training data well. Rather, the goodness of a model is characterized by its generalization capability, interpretability and ease for knowledge extraction. All these desired properties depend crucially on the ability to construct appropriate parsimonious models by the modelling process, and a basic principle in practical nonlinear data modelling is the parsimonious principle of ensuring the smallest possible model that explains the training data. There exists a vast amount of works in the area of sparse modelling, and a widely adopted approach is based on the linear-in-the-parameters data modelling that include the radial basis function network, the neurofuzzy network and all the sparse kernel modelling techniques. A well tested strategy for parsimonious modelling from data is the orthogonal least squares (OLS) algorithm for forward selection modelling, which is capable of constructing sparse models that generalise well. This contribution continues this theme and provides a unified framework for sparse modelling from data that includes regression and classification, which belong to supervised learning, and probability density function estimation, which is an unsupervised learning problem. The OLS forward selection method based on the leave-one-out test criteria is presented within this unified data-modelling framework. Examples from regression, classification and density estimation applications are used to illustrate the effectiveness of this generic parsimonious modelling approach from data.展开更多
The direction of oil charges within a field in the Niger Delta, Nigeria was determined by the sum of differences ranking method of carbazole concentrations after ascertaining other possible geological constraints on t...The direction of oil charges within a field in the Niger Delta, Nigeria was determined by the sum of differences ranking method of carbazole concentrations after ascertaining other possible geological constraints on their compositional variations. The principle is that the smaller the sum, the closer the well to the source kitchen. The approach makes use of carbazoles' interaction with the matrix, which leads to a reduction in their concentration with increasing distance from the source kitchen, allowing prediction of the charging direction. A wide range of compositional variations was observed for C1(806.72–2152.90 lg/g) and C2(767–2469.72 lg/g) carbazoles within the field. Based on these results, we inferred a filling pathway orientation from west to east. This suggests that the source kitchen—the most promising region for oil exploration—is located in the western part of the oil field.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
文摘全球离散格网系统(Discrete Global Grid System,DGGS)是数字化的地球参考框架,在多源、多尺度地球空间数据集成分析方面优势明显。本文选择菱形三十面体六边形全球离散格网系统,提高格网与地球的整体拟合精度和空间采样率;建立遥感图像六边形像素数学模型,提出兼容开放标准格式的数据存储方案。①根据地理位置将遥感图像格网化,完成遥感图像六边形DGGS建模;其次,建立六边形单元与矩形像素严密对应关系,等效保留六边形单元的邻域信息;②采用GeoTIFF开放标准格式精确存储六边形属性值以及投影、变换参数;③设计依托六边形DGGS格网标准数据集为基础的多尺度六边形DGGS生成算法。实验结果表明:本文方案不仅能保证六边形像素遥感图像数据与标准文件格式兼容,而且能保证矩形像素与六边形单元逐一对应,较好地保留了六边形单元数据的图像信息和空间分布特征,相较于欧空局SMOS数据组织方案更具优势。本文方案打破了六边形单元与矩形像素遥感图像的数据组织壁垒,使用常见GIS/RS软件即可读取六边形像素的遥感图像,并可通过对矩形像素的操作等效实现对六边形单元的处理,有望推动六边形DGGS在遥感数据组织、处理、共享等方面的应用。
文摘The objective of modelling from data is not that the model simply fits the training data well. Rather, the goodness of a model is characterized by its generalization capability, interpretability and ease for knowledge extraction. All these desired properties depend crucially on the ability to construct appropriate parsimonious models by the modelling process, and a basic principle in practical nonlinear data modelling is the parsimonious principle of ensuring the smallest possible model that explains the training data. There exists a vast amount of works in the area of sparse modelling, and a widely adopted approach is based on the linear-in-the-parameters data modelling that include the radial basis function network, the neurofuzzy network and all the sparse kernel modelling techniques. A well tested strategy for parsimonious modelling from data is the orthogonal least squares (OLS) algorithm for forward selection modelling, which is capable of constructing sparse models that generalise well. This contribution continues this theme and provides a unified framework for sparse modelling from data that includes regression and classification, which belong to supervised learning, and probability density function estimation, which is an unsupervised learning problem. The OLS forward selection method based on the leave-one-out test criteria is presented within this unified data-modelling framework. Examples from regression, classification and density estimation applications are used to illustrate the effectiveness of this generic parsimonious modelling approach from data.
文摘The direction of oil charges within a field in the Niger Delta, Nigeria was determined by the sum of differences ranking method of carbazole concentrations after ascertaining other possible geological constraints on their compositional variations. The principle is that the smaller the sum, the closer the well to the source kitchen. The approach makes use of carbazoles' interaction with the matrix, which leads to a reduction in their concentration with increasing distance from the source kitchen, allowing prediction of the charging direction. A wide range of compositional variations was observed for C1(806.72–2152.90 lg/g) and C2(767–2469.72 lg/g) carbazoles within the field. Based on these results, we inferred a filling pathway orientation from west to east. This suggests that the source kitchen—the most promising region for oil exploration—is located in the western part of the oil field.
