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Abstract. Slump blocks are widely distributed features along marsh shorelines that can disturb marsh edge habitats and affect marsh geomorphology and sediment dynamics. However, little is known about their spatial distribution patterns or their longevity and movement. We employed an unoccupied aerial vehicle (UAV) to track slump blocks in 11 monthly images (March 2020–March 2021) of Dean Creek, a tidal creek surrounded by salt marsh located on Sapelo Island (GA, USA). Slump blocks were observed along both convex and concave banks of the creek in all images, with sizes between 0.03 and 72.51 m2. Although the majority of blocks were categorized as persistent, there were also new blocks in each image. Most blocks were lost through submergence, and both decreased in area and moved towards the center of the channel over time. However, some blocks reconnected to the marsh platform, which has not been previously observed. These blocks were initially larger and located closer to the marsh edge than those that submerged, and they increased in area over time. Only 13 out of a cohort of 61 newly created blocks observed in May 2020 remained after 5 months, suggesting that most blocks persist for only a short time. When taken together, the total area of new slump blocks was 886 m2, and that of reconnected blocks was 652 m2. This resulted in a net expansion of the channel by 234 m2 over the study period, accounting for about 66 % of the overall increase in the channel area of Dean Creek, and this suggests that slump block processes play an important role in tidal creek channel widening. This study illustrates the power of repeated UAV surveys to monitor short-term geomorphological processes, such as slump block formation and loss, to provide new insights into marsh eco-geomorphological processes. 

Mobile devices supporting the "Internet of Things" (IoT), often have limited capabilities in computation, battery energy, and storage space, especially to support resource-intensive applications involving virtual reality (VR), augmented reality (AR), multimedia delivery and artificial intelligence (AI), which could require broad bandwidth, low response latency and large computational power. Edge cloud or edge computing is an emerging topic and technology that can tackle the deficiency of the currently centralized-only cloud computing model and move the computation and storage resource closer to the devices in support of the above-mentioned applications. To make this happen, efficient coordination mechanisms and “offloading” algorithms are needed to allow the mobile devices and the edge cloud to work together smoothly. In this survey paper, we investigate the key issues, methods, and various state-of-the-art efforts related to the offloading problem. We adopt a new characterizing model to study the whole process of offloading from mobile devices to the edge cloud. Through comprehensive discussions, we aim to draw an overall “big picture” on the existing efforts and research directions. Our study also indicates that the offloading algorithms in edge cloud have demonstrated profound potentials for future technology and application development.