Evaluation of force-torque displays for use with space station telerobotic activities

Recent experiments which addressed Space Station remote manipulation tasks found that tactile force feedback (reflecting forces and torques encountered at the end-effector through the manipulator hand controller) does not improve performance significantly. Subjective response from astronaut and non-astronaut test subjects indicated that force information, provided visually, could be useful. No research exists which specifically investigates methods of presenting force-torque information visually. This experiment was designed to evaluate seven different visual force-torque displays which were found in an informal telephone survey. The displays were prototyped in the HyperCard programming environment. In a within-subjects experiment, 14 subjects nullified forces and torques presented statically, using response buttons located at the bottom of the screen. Dependent measures included questionnaire data, errors, and response time. Subjective data generally demonstrate that subjects rated variations of pseudo-perspective displays consistently better than bar graph and digital displays. Subjects commented that the bar graph and digital displays could be used, but were not compatible with using hand controllers. Quantitative data show similar trends to the subjective data, except that the bar graph and digital displays both provided good performance, perhaps do to the mapping of response buttons to display elements. Results indicate that for this set of displays, the pseudo-perspective displays generally represent a more intuitive format for presenting force-torque information

A human factors evaluation of the robotic interface for Space Station Freedom orbital replaceable units

An orbital replaceable unit (ORU) is often defined as any orbital unit aboard Space Station with a wearout life of less than 30 years. The capability of successful changeout of these units by remote manipulation is critical to the ORU to telerobot interface design. A human factors evaluation of the selected interface showed certain inadequacies of the alignment target concept that was part of the interface package. Alternative target concepts which addressed these inadequacies were developed and are presented. Recommendations will be incorporated into NASA requirements documents which ORU suppliers and manufacturers must then build to

High Levels of DEK Autoantibodies in Sera of Patients With Polyarticular Juvenile Idiopathic Arthritis and With Early Disease Flares Following Cessation of Antiâ Tumor Necrosis Factor Therapy

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/142962/1/art40404_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/142962/2/art40404-sup-0001-Supinfo.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/142962/3/art40404.pd

Explaining Andean Potato Weevils in Relation to Local and Landscape Features: A Facilitated Ecoinformatics Approach

BACKGROUND: Pest impact on an agricultural field is jointly influenced by local and landscape features. Rarely, however, are these features studied together. The present study applies a "facilitated ecoinformatics" approach to jointly screen many local and landscape features of suspected importance to Andean potato weevils (Premnotrypes spp.), the most serious pests of potatoes in the high Andes. METHODOLOGY/PRINCIPAL FINDINGS: We generated a comprehensive list of predictors of weevil damage, including both local and landscape features deemed important by farmers and researchers. To test their importance, we assembled an observational dataset measuring these features across 138 randomly-selected potato fields in Huancavelica, Peru. Data for local features were generated primarily by participating farmers who were trained to maintain records of their management operations. An information theoretic approach to modeling the data resulted in 131,071 models, the best of which explained 40.2-46.4% of the observed variance in infestations. The best model considering both local and landscape features strongly outperformed the best models considering them in isolation. Multi-model inferences confirmed many, but not all of the expected patterns, and suggested gaps in local knowledge for Andean potato weevils. The most important predictors were the field's perimeter-to-area ratio, the number of nearby potato storage units, the amount of potatoes planted in close proximity to the field, and the number of insecticide treatments made early in the season. CONCLUSIONS/SIGNIFICANCE: Results underscored the need to refine the timing of insecticide applications and to explore adjustments in potato hilling as potential control tactics for Andean weevils. We believe our study illustrates the potential of ecoinformatics research to help streamline IPM learning in agricultural learning collaboratives

Identifying genetic signatures of selection in a non-model species, alpine gentian (Gentiana nivalis L.), using a landscape genetic approach

It is generally accepted that most plant populations are locally adapted. Yet, understanding how environmental forces give rise to adaptive genetic variation is a challenge in conservation genetics and crucial to the preservation of species under rapidly changing climatic conditions. Environmental variation, phylogeographic history, and population demographic processes all contribute to spatially structured genetic variation, however few current models attempt to separate these confounding effects. To illustrate the benefits of using a spatially-explicit model for identifying potentially adaptive loci, we compared outlier locus detection methods with a recently-developed landscape genetic approach. We analyzed 157 loci from samples of the alpine herb Gentiana nivalis collected across the European Alps. Principle coordinates of neighbor matrices (PCNM), eigenvectors that quantify multi-scale spatial variation present in a data set, were incorporated into a landscape genetic approach relating AFLP frequencies with 23 environmental variables. Four major findings emerged. 1) Fifteen loci were significantly correlated with at least one predictor variable (R (adj) (2) > 0.5). 2) Models including PCNM variables identified eight more potentially adaptive loci than models run without spatial variables. 3) When compared to outlier detection methods, the landscape genetic approach detected four of the same loci plus 11 additional loci. 4) Temperature, precipitation, and solar radiation were the three major environmental factors driving potentially adaptive genetic variation in G. nivalis. Techniques presented in this paper offer an efficient method for identifying potentially adaptive genetic variation and associated environmental forces of selection, providing an important step forward for the conservation of non-model species under global change