Sulfuryl fluoride (SO2F2) is a synthetic pesticide and a potent greenhouse gas that is accumulating in the global atmosphere. Rising emissions are a concern since SO2F2has a relatively long atmospheric lifetime and a high global warming potential. The U.S. is thought to contribute substantially to global SO2F2emissions, but there is a paucity of information on how emissions of SO2F2are distributed across the U.S., and there is currently no inventory of SO2F2emissions for the U.S. or individual states. Here we provide an atmospheric measurement-based estimate of U.S. SO2F2emissions using high-precision SO2F2measurements from the NOAA Global Greenhouse Gas Reference Network (GGGRN) and a geostatistical inverse model. We find that California has the largest SO2F2emissions among all U.S. states, with the highest emissions from southern coastal California (Los Angeles, Orange, and San Diego counties). Outside of California, only very small and infrequent SO2F2emissions are detected by our analysis of GGGRN data. We find that California emits 60-85% of U.S. SO2F2emissions, at a rate of 0.26 ( ± 0.10) Gg yr−1. We estimate that emissions of SO2F2from California are equal to 5.5–12% of global SO2F2emissions.
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Abstract In this work, we explore multiplex graph (networks with different types of edges) generation with deep generative models. We discuss some of the challenges associated with multiplex graph generation that make it a more difficult problem than traditional graph generation. We propose T
en GAN, the first neural network for multiplex graph generation, which greatly reduces the number of parameters required for multiplex graph generation. We also propose 3 different criteria for evaluating the quality of generated graphs: a graph-attribute-based, a classifier-based, and a tensor-based method. We evaluate its performance on 4 datasets and show that it generally performs better than other existing statistical multiplex graph generative models. We also adapt HGEN, an existing deep generative model for heterogeneous information networks, to work for multiplex graphs and show that our method generally performs better. -
The Fern Cave System, developed in the western escarpment of the Southern Cumberland Plateau of the Interior Low Plateau karst region in Northeastern Alabama, USA, is a global hotspot of cave-limited biodiversity as well as home to the largest winter hibernaculum for the federally endangered Gray Bat (Myotis grisescens). We combined the existing literature, museum accessions, and database occurrences with new observations from bioinventory efforts conducted in 2018–2022 to generate an updated list of troglobiotic and stygobiotic species for the Fern Cave System. Our list of cave-limited fauna totals twenty-seven species, including nineteen troglobionts and eight stygobionts. Two pseudoscorpions are endemic to the Fern Cave System: Tyrannochthonius torodei and Alabamocreagris mortis. The exceptional diversity at Fern Cave is likely associated with several factors, such as the high dispersal potential of cave fauna associated with expansive karst exposures along the Southern Cumberland Plateau, high surface productivity, organic input from a large bat colony, favorable climate throughout the Pleistocene, and location within a larger regional hotspot of subterranean biodiversity. Nine species are of conservation concern, including the recently discovered Alabama cave shrimp Palaemonias alabamae, because of their small range sizes, few occurrences, and several potential threats.more » « lessFree, publicly-accessible full text available May 1, 2024
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Abstract Background A robotic rehabilitation gym can be defined as multiple patients training with multiple robots or passive sensorized devices in a group setting. Recent work with such gyms has shown positive rehabilitation outcomes; furthermore, such gyms allow a single therapist to supervise more than one patient, increasing cost-effectiveness. To allow more effective multipatient supervision in future robotic rehabilitation gyms, we propose an automated system that could dynamically assign patients to different robots within a session in order to optimize rehabilitation outcome.
Methods As a first step toward implementing a practical patient-robot assignment system, we present a simplified mathematical model of a robotic rehabilitation gym. Mixed-integer nonlinear programming algorithms are used to find effective assignment and training solutions for multiple evaluation scenarios involving different numbers of patients and robots (5 patients and 5 robots, 6 patients and 5 robots, 5 patients and 7 robots), different training durations (7 or 12 time steps) and different complexity levels (whether different patients have different skill acquisition curves, whether robots have exit times associated with them). In all cases, the goal is to maximize total skill gain across all patients and skills within a session.
Results Analyses of variance across different scenarios show that disjunctive and time-indexed optimization models significantly outperform two baseline schedules: staying on one robot throughout a session and switching robots halfway through a session. The disjunctive model results in higher skill gain than the time-indexed model in the given scenarios, and the optimization duration increases as the number of patients, robots and time steps increases. Additionally, we discuss how different model simplifications (e.g., perfectly known and predictable patient skill level) could be addressed in the future and how such software may eventually be used in practice.
Conclusions Though it involves unrealistically simple scenarios, our study shows that intelligently moving patients between different rehabilitation robots can improve overall skill acquisition in a multi-patient multi-robot environment. While robotic rehabilitation gyms are not yet commonplace in clinical practice, prototypes of them already exist, and our study presents a way to use intelligent decision support to potentially enable more efficient delivery of technologically aided rehabilitation.
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Back injuries and other occupational injuries are common in workers who engage in long, arduous physical labor. The risk of these injuries could be reduced using assistive devices that automatically detect an object lifting motion and support the user while they perform the lift; however, such devices must be able to detect the lifting motion as it occurs. We thus developed a system to detect the start and end of a lift (performed as a stoop or squat) in real time based on pelvic angle and the distance between the user's hands and the user's center of mass. The measurements were input to an algorithm that first searches for hand-center distance peaks in a sliding window, then checks the pelvic displacement angle to verify lift occurrence. The approach was tested with 5 participants, who performed a total of 100 lifts of four different types. The times of actual lifts were determined by manual video annotation. The median time error (absolute difference between detected and actual occurrence time) for lifts that were not false negatives was 0.11 s; a lift was considered a false negative if it was not detected within two seconds of it actually occurring. Furthermore, 95% of lifts that were detected occurred within 0.28 s of actual occurrence. This shows that it is possible to reliably detect lifts in real time based on the pelvic displacement angle and the distance between the user's hands and their center of mass.more » « less
