Order-detection, representation-detection, and applications to cable knots

Given a $3$-manifold $M$ with multiple incompressible torus boundary components, we develop a general definition of order-detection of tuples of slopes on the boundary components of $M$. In parallel, we arrive at a general definition of representation-detection of tuples of slopes, and show that these two kinds of slope detection are equivalent -- in the sense that a tuple of slopes on the boundary of $M$ is order-detected if and only if it is representation-detected. We use these results, together with new "relative gluing theorems," to show how the work of Eisenbud-Hirsch-Neumann, Jankins-Neumann and Naimi can be used to determine tuples of representation-detected slopes and, in turn, the behaviour of order-detected slopes on the boundary of a knot manifold with respect to cabling. Our cabling results improve upon work of the first author and Watson, and in particular, this new approach shows how one can use the equivalence between representation-detection and order-detection to derive orderability results that parallel known behaviour of L-spaces with respect to Dehn filling.Comment: 46 pages, 2 figure

Can virtual tourism aid in the recovery of tourism industry in the COVID-19 pandemic?

The COVID-19 pandemic has imposed tremendous impacts on the tourism industry worldwide. The tourism sector can take advantage of the new technology (e.g., virtual tourism), to respond to the challenges. This study aims to explore how virtual tourism can aid the recovery of tourism industry. We explore this through a mixed-method approach. Our results show that the use of virtual tourism can be partially explained by the theory of planned behavior. Virtual tourism has a strong influence on people’s onsite destination choices and can be used as an effective marketing tool. Virtual tourism can be an entertainment activity to bring immersed experience without being actually in the destinations, and thus reinforce stay-at-home order. Even after the pandemic is over, people still show willingness to use virtual tourism for diverse purposes. Virtual tourism can also help promote sustainable tourism by reducing unnecessary greenhouse gas emissions and enhance “virtual accessibility” especially for the elderly and disabled with limited mobility

Understanding the Spatiotemporal Development of Human Settlement in Hurricane-Prone Areas on the US Atlantic and Gulf Coasts Using Nighttime Remote Sensing

Hurricanes, as one of the most devastating natural hazards, have posed a great threat to people in coastal areas. A better understanding of the spatiotemporal dynamics of human settlement in hurricane-prone areas largely benefits sustainable development. This study uses the nighttime light (NTL) data from the Defense Meteorological Satellite Program\u27s Operational Linescan System (DMSP/OLS) to examine human settlement development in areas with different levels of hurricane proneness from 1992 to 2013. The DMSP/OLS NTL data from six satellites were intercalibrated and desaturated with the Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) optical imagery to derive the Vegetation Adjusted NTL Urban Index (VANUI), a popular index that quantifies human settlement intensity. The derived VANUI time series was examined with the Mann–Kendall test and Theil–Sen test to identify significant spatiotemporal trends. To link the VANUI product to hurricane impacts, four hurricane-prone zones were extracted to represent different levels of hurricane proneness. Aside from geographic division, a wind-speed-weighted track density function was developed and applied to historical storm tracks which originated in the North Atlantic Basin to better categorize the four levels of hurricane proneness. Spatiotemporal patterns of human settlement in the four zones were finally analyzed. The results clearly exhibit a north–south and inland–coastal discrepancy of human settlement dynamics. This study also reveals that both the zonal extent and zonal increase rate of human settlement positively correlate with hurricane proneness levels. The intensified human settlement in high hurricane-exposure zones deserves further attention for coastal resilience

Understanding the Spatiotemporal Development of Human Settlement in Hurricane-Prone Areas on the Us Atlantic and Gulf Coasts Using Nighttime Remote Sensing

Hurricanes, as one of the most devastating natural hazards, have posed a great threat to people in coastal areas. A better understanding of the spatiotemporal dynamics of human settlement in hurricane-prone areas largely benefits sustainable development. This study uses the nighttime light (NTL) data from the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) to examine human settlement development in areas with different levels of hurricane proneness from 1992 to 2013. The DMSP/OLS NTL data from six satellites were intercalibrated and desaturated with the Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) optical imagery to derive the Vegetation Adjusted NTL Urban Index (VANUI), a popular index that quantifies human settlement intensity. The derived VANUI time series was examined with the Mann– Kendall test and Theil–Sen test to identify significant spatiotemporal trends. To link the VANUI product to hurricane impacts, four hurricane-prone zones were extracted to represent different levels of hurricane proneness. Aside from geographic division, a wind-speed-weighted track density function was developed and applied to historical storm tracks which originated in the North Atlantic Basin to better categorize the four levels of hurricane proneness. Spatiotemporal patterns of human settlement in the four zones were finally analyzed. The results clearly exhibit a north–south and inland–coastal discrepancy of human settlement dynamics. This study also reveals that both the zonal extent and zonal increase rate of human settlement positively correlate with hurricane proneness levels. The intensified human settlement in high hurricane-exposure zones deserves further attention for coastal resilience

Measuring Agricultural Drought And Uncertainty In Future Drought Projections

Drought is a devastating, recurring, and widespread natural hazard that affects natural habitats, ecosystems, and economic and social sectors. Within the agricultural sector, droughts can reduce soil-water availability, affect water and soil quality, contribute to crop failures and pasture losses, and severely reduce crop yield. Effective drought quantification and early warning are critical for drought risk adaptation. Moreover, future drought risks could be exacerbated due to climate change. Modeling how climate change might influence future drought risks is of great importance in natural resources and water resources planning management. This dissertation has three parts. 1) The first part compares and evaluates six trend simulation models to simulate the nonlinear trend and two decomposition models to remove the nonlinear trend from the yield time series. Study resultsfindthat a locally weighted regression model, coupled with a multiplicative decomposition model, is the most appropriate data self-adaptive detrending method, which allows spatial visualization of drought impact on corn yield inUSby highlighting six historical major drought events. 2) The second part develops a new agriculturally-based drought index, called the Integrated Scaled Drought Index (ISDI). This index incorporates important components controlling agriculturaldrought, such as vegetation, temperature, precipitation, and soil moisture. The robustness and usefulness of this indexisvalidated by multiple data sources. This index integrates the benefits of numerical model simulation and remote sensing technology to account for interannual variability of drought for the longest possible time-frame in the satellite era. 3) The third part focuses on identifying hotspots and uncertainty in agricultural drought projections by analyzing surface soil moisture outputs from CMIP5 multi-model ensembles (MMEs) under RCP2.6, RCP4.5, RCP6.0, and RCP8.5 scenarios. This part investigates the MME annual and seasonal percentage change of surface soil moisture and examines the change in duration, frequency, severity, and spatial extent of severe agricultural drought. This part also quantifies and partitions three sources of uncertainty associated with these drought projections: internal variability, model uncertainty, and scenario uncertainty, and examines the spatiotemporal variability of annual and seasonal signal to noise (S/N) change in soil moisture anomalies across the globe and for different lead times