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Large-scale soft robots have the capability and potential to perform highly dynamic tasks such as hammering a nail into a board, throwing items long distances, or manipulating objects in cluttered environments. This is due to their joints being underdamped and their ability to store potential energy. The soft robots presented in this article are pneumatically actuated and thus have the ability to perform these tasks without the need for large motors or gear trains. However, getting soft robots to perform highly dynamic tasks requires controllers that can track highly dynamic trajectories to complete those tasks. For soft robots, this is a difficult problem to solve due to the uncertainty in their shape and their complicated dynamics and kinematics. This article presents a formulation of a model reference adaptive controller (MRAC) that causes a three-link soft robot arm to behave like a highly dynamic 2nd-order critically damped system. Using the dynamics of a 2nd-order system, we also present a method to generate joint trajectories for throwing a ball to a desired point in Cartesian space. We demonstrate the viability of our joint-level controller in simulation and on hardware with a reported maximum root mean square error of 0.0872 radians between a reference and executed trajectory. We also demonstrate that our combined MRAC controller and trajectory generator can, on average, throw a ball to within 25–28% of a desired landing location for a throwing distance of between 1.5 and 2 m on real hardware. 

{"Abstract":["This data product contains physical, chemical, and biological data ranging from the minute to daily to weekly scale in six artificial ponds (400 square meter surface area, 2m depth) in central Iowa (USA) 2020. Ponds were paired into three sets of treatment and reference with treatment ponds receiving two nutrient pulses designed to increase ambient phosphorus concentrations ~ 3 - 5%. Nitrogen and phosphorus were added as NH4NO3 and H3PO4, respectively, at a 24:1 molar ratio. The first nutrient pulse occurred on Julian day of year (DOY) 176 corresponding to a 3% increase and the second nutrient pulse occurred on DOY 211 to a 5% increase. Each treatment-reference set had a different food web structure established ranging between low, intermediate, and high complexity based on trophic connectivity and food chain length. \n \n Added to this data package is a document titled "2020 Iowa State University Horticultural Farm Experimental Ponds Nutrient Addition Experiment". For experimental set up, context, and a summary table of the data tables archived herein with available variables please review this document. It is added to aid in successful interpretation and to increase ease-of-use. Please email Tyler Butts (tyler.james.butts@gmail.com) for any and all questions regarding context or use of this dataset!"]} 

ABSTRACT Direct RNA nanopore sequencing allows for the identification of full-length RNAs with a ∼10% error rate consisting of mismatches and small deletions. These errors are thought to be randomly distributed and structure-independent since RNA/cDNA duplexes are generated to prevent RNA structure formation prior to sequencing. When analyzing citrus yellow vein associated virus (CY1) reads during infection ofNicotiana benthamiana,viral (+/-)foldback RNAs (i.e., viral plus [+]-strands joined to [-]-strands) showed significantly higher error rates (mismatches and deletions) in the 5ʹ (+)RNA portion with errors that were relatively evenly distributed, while errors in the attached (-)RNA portion were less frequent and unevenly distributed. Non-foldback CY1 (+)RNAs from infected plants also showed an uneven distribution of errors, which correlated with errors inin vitrotranscribed CY1 (+)RNA reads in both position and frequency. Hotspot errors in non-foldback CY1 (+)RNA and (-)RNA reads only weakly correlated, and hotspots were frequently located 5ʹ of known structural elements. Since nanopore sequencing is also used to identify RNA modifications, which depend on base-specific sequencing errors, algorithms for RNA modification detection were also examined for bias. We found that multiple programs predicted RNA modifications inin vitrotranscribed CY1 RNA at the same positions and with similar confidence levels as within plantaCY1 RNA. These data suggest that direct RNA sequencing contains inherent error biases that may be associated with post-translocation RNA folding and low sequence complexity, and therefore extrapolations based on sequencing error require special consideration.