Experimental Study of Dam-Break-Like Tsunami Bore Impact Mechanism on a Container Model

Tsunami disasters have frequently occurred in recent years. More and more researchers are focusing on this topic. To investi-gate the tsunami bore impact mechanism on a container model, a multi-functional slope-changing tsunami flume is built in this study. To simulate a tsunami bore, a dam-break wave was generated by a free-falling gate in a reservoir. A needle water level gauge and a high-speed camera were used to measure the tsunami wave heights and velocities for different storage water levels in the test flume, and the corresponding Froude numbers of tsunami waves were also calculated. The factors af-fecting the movement distance of the tsunami wave impacting the container model are explored in this experiment, and the results show that the movement distance is positively correlated with the storage water level, and negatively correlated with the container density and the coast slope

Experimental Study of Dam-Break-Like Tsunami Bore Impact Mechanism on a Container Model

Tsunami disasters have frequently occurred in recent years. More and more researchers are focusing on this topic. To investi-gate the tsunami bore impact mechanism on a container model, a multi-functional slope-changing tsunami flume is built in this study. To simulate a tsunami bore, a dam-break wave was generated by a free-falling gate in a reservoir. A needle water level gauge and a high-speed camera were used to measure the tsunami wave heights and velocities for different storage water levels in the test flume, and the corresponding Froude numbers of tsunami waves were also calculated. The factors af-fecting the movement distance of the tsunami wave impacting the container model are explored in this experiment, and the results show that the movement distance is positively correlated with the storage water level, and negatively correlated with the container density and the coast slope

Mapping rubber plantations in Xishuangbanna, southwest China based on the re-normalization of two Landsat-based vegetation–moisture indices and meteorological data

Information on where rubber plantations are located and when they were established is essential for understanding changes in the regional carbon cycle, biodiversity, hydrology and ecosystem services. Here, we proposed a simple and modified phenology-based method to map rubber plantations and evaluate the effectiveness of this method in Xishuangbanna in southwest China, the second largest area of natural rubber cultivation. Our phenological algorithm is supported by local meteorological data and involves the re-normalization of two Landsat-8 Operational Land Imager-derived vegetation moisture indices (i.e. the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Moisture Index (NDMI)). We then applied air temperature data (daily records from 1981 to 2015) and periodic in situ observations of rubber plantations (weekly records from 2017 to 2018) to determine the phenological stages of rubber tree growth with the goal of selecting the most effective Landsat images. The re-normalization algorithm was able to highlight the temporal differences in the vegetation canopy and moisture content of rubber plantations, because rubber trees in Xishuangbanna display unique defoliation-foliation signals during the dry season. The resultant map of rubber plantations showed a high overall accuracy of 92.3% and a Kappa coefficient of 0.846. The developed phenological re-normalization method with meteorological data greatly enriches remote sensing-based approaches for mapping rubber plantations

Forest plot with hazard ratio (HR) of OS in subgroup of age and GS.

Forest plot with hazard ratio (HR) of OS in subgroup of age and GS.</p

Flow chart to screen eligible studies.

Flow chart to screen eligible studies.</p

Forest plot with hazard ratio (HR) of OS in subgroup of Asian and not Asian.

Forest plot with hazard ratio (HR) of OS in subgroup of Asian and not Asian.</p

Baseline of race.

Baseline of race.</p

PRISMA 2020 checklist.

(DOCX)</p

Forest plot odds ratio (OR) of Skeletal related events (SREs) in subgroup of HSPC and CRPC.

Forest plot odds ratio (OR) of Skeletal related events (SREs) in subgroup of HSPC and CRPC.</p