The billions of specimens housed in natural science collections provide a tremendous source of under–utilized data that are useful for scientific research, conservation, commerce, and education. Digitization and mobilization of specimen data and images promises to greatly accelerate their utilization. While digitization of natural science collection specimens has been occurring for decades, the vast majority of specimens remain un–digitized. If the digitization task is to be completed in the near future, innovative, high–throughput approaches are needed. To create a dataset for the study of global change in New England, we designed and implemented an industrial–scale, conveyor–based digitization workflow for herbarium specimen sheets. The workflow is a variation of an object–to–image–to–data workflow that prioritizes imaging and the capture of storage container–level data. The workflow utilizes a novel conveyor system developed specifically for the task of imaging flattened herbarium specimens. Using our workflow, we imaged and transcribed specimen–level data for almost 350,000 specimens over a 131–week period; an additional 56 weeks was required for storage container–level data capture. Our project has demonstrated that it is possible to capture both an image of a specimen and a core database record in 35 seconds per herbarium sheet (for intervals between images of 30 minutes or less) plus some additional overhead for container–level data capture. This rate was in line with the pre–project expectations for our approach. Our throughput rates are comparable with some other similar, high–throughput approaches focused on digitizing herbarium sheets and is as much as three times faster than rates achieved with more conventional non–automated approaches used during the project. We report on challenges encountered during development and use of our system and discuss ways in which our workflow could be improved. The conveyor apparatus software, database schema, configuration files, hardware list, and conveyor schematics are available for download on GitHub.
Existing move-restricted node self-deployment algorithms are based on a fixed node communication radius, evaluate the performance based on network coverage or the connectivity rate and do not consider the number of nodes near the sink node and the energy consumption distribution of the network topology, thereby degrading network reliability and the energy consumption balance. Therefore, we propose a distributed underwater node self-deployment algorithm. First, each node begins the uneven clustering based on the distance on the water surface. Each cluster head node selects its next-hop node to synchronously construct a connected path to the sink node. Second, the cluster head node adjusts its depth while maintaining the layout formed by the uneven clustering and then adjusts the positions of in-cluster nodes. The algorithm originally considers the network reliability and energy consumption balance during node deployment and considers the coverage redundancy rate of all positions that a node may reach during the node position adjustment. Simulation results show, compared to the connected dominating set (CDS) based depth computation algorithm, that the proposed algorithm can increase the number of the nodes near the sink node and improve network reliability while guaranteeing the network connectivity rate. Moreover, it can balance energy consumption during network operation, further improve network coverage rate and reduce energy consumption.
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