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Although there have been oscillations in airship interest since their use in the early 1900s, technological advancements and the need for more flexible and environmentally friendly transportation modes have caused a stream of study and surge in airship development in recent years. For companies and governments to understand how airships can be incorporated into their fleets to fulfill new or existing mission types, system design space exploration is an important step in understanding airships, their uses, and their design parameters. A decision support system (DSS), Design Exploration of Lighter-Than-Air Systems (DELTAS), was developed to help stakeholders with this task. DELTAS allows users to design airships and missions to determine how a design will perform in the scenario. As airships are sensitive to elevation and altitude, wind and terrain can also be studied to see how they may affect a mission scenario. Simulations can also be run for a given mission to find the Pareto-optimal designs for user-defined ranges of high-level airship design parameters. A case study is provided that demonstrates how DELTAS can be used to explore the airship design space for a specified mission. This case study shows how a design of experiments is important to more thoroughly cover the design space and to find and understand the relationships between airship design variables that lead to optimal mission times and costs. [ABSTRACT FROM AUTHOR]

Despite the existence of robots that can physically lift heavy loads, robots that can collaborate with people to move heavy objects are not readily available. This article makes progress toward effective human-robot co-manipulation by studying 30 human-human dyads that collaboratively manipulated an object weighing \(27 \mathrm{kg}\) without being co-located (i.e., participants were at either end of the extended object). Participants maneuvered around different obstacles with the object while exhibiting one of four modi–the manner or objective with which a team moves an object together–at any given time. Using force and motion signals to classify modus or behavior was the primary objective of this work. Our results showed that two of the originally proposed modi were very similar, such that one could effectively be removed while still spanning the space of common behaviors during our co-manipulation tasks. The three modi used in classification were quickly, smoothly and avoiding obstacles. Using a deep convolutional neural network (CNN), we classified three modi with up to 89% accuracy from a validation set. The capability to detect or classify modus during co-manipulation has the potential to greatly improve human-robot performance by helping to define appropriate robot behavior or controller parameters depending on the objective or modus of the team. [ABSTRACT FROM AUTHOR]

Primary angle closure glaucoma (PACG) is a major cause of blindness worldwide. We conducted a genome-wide association study (GWAS) followed by replication in a combined total of 10,503 PACG cases and 29,567 controls drawn from 24 countries across Asia, Australia, Europe, North America, and South America. We observed significant evidence of disease association at five new genetic loci upon meta-analysis of all patient collections. These loci are at EPDR1 rs3816415 (odds ratio (OR) = 1.24, P = 5.94 × 10(-15)), CHAT rs1258267 (OR = 1.22, P = 2.85 × 10(-16)), GLIS3 rs736893 (OR = 1.18, P = 1.43 × 10(-14)), FERMT2 rs7494379 (OR = 1.14, P = 3.43 × 10(-11)), and DPM2-FAM102A rs3739821 (OR = 1.15, P = 8.32 × 10(-12)). We also confirmed significant association at three previously described loci (P < 5 × 10(-8) for each sentinel SNP at PLEKHA7, COL11A1, and PCMTD1-ST18), providing new insights into the biology of PACG.