Abstract:The main purpose of this study is to identify the major factors affecting groundwater quality by means of multivariate statistical analysis of the physico-chemical compositions. Cluster analysis results show that the groundwater in the study area is classified into four groups (A, B, C and D), and factor analysis indicates that groundwater composition, 81Ð9% of the total variance of 17 variables, is mainly affected by three factors: seawater intrusion, microbial activity and chemical fertilizers. These results might be related to the geographical characteristics of the study area. The main influence on groundwater in groups B, C and D, which are close to the Yellow Sea and contain reclaimed areas, is the seawater intrusion by the present seawater, the trapped seawater, and microbial activity. Group A, however, has been used for agriculture for a long time, and thus groundwater in this group has been largely affected by chemical fertilizers. As groundwater flows from group A to group D according to its path, the governing factor of the groundwater quality gradually changes from chemical fertilizers to microbial activity and seawater intrusion.

Bucky gels are gelatinous composite materials consisting of carbon nanotubes and ionic liquids. This article gives an overview of some promising applications of bucky gels reported mostly in the last few years and a possible extension to the dispersion of graphene sheets.

Highly accurate detection of the intracranial hemorrhage without delay is a critical clinical issue for the diagnostic decision and treatment in an emergency room. In the context of a study on diagnostic accuracy, there is a tradeoff between sensitivity and specificity. In order to improve sensitivity while preserving specificity, we propose a cascade deep learning model constructed using two convolutional neural networks (CNNs) and dual fully convolutional networks (FCNs). The cascade CNN model is built for identifying bleeding; hereafter the dual FCN is to detect five different subtypes of intracranial hemorrhage and to delineate their lesions. Using a total of 135,974 CT images including 33,391 images labeled as bleeding, each of CNN/FCN models was trained separately on image data preprocessed by two different settings of window level/width. One is a default window (50/100[level/width]) and the other is a stroke window setting (40/40). By combining them, we obtained a better outcome on both binary classification and segmentation of hemorrhagic lesions compared to a single CNN and FCN model. In determining whether it is bleeding or not, there was around 1% improvement in sensitivity (97.91% [± 0.47]) while retaining specificity (98.76% [± 0.10]). For delineation of bleeding lesions, we obtained overall segmentation performance at 80.19% in precision and 82.15% in recall which is 3.44% improvement compared to using a single FCN model.

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