Team Vigilance: The Effects of Co-Action on Workload and Stress

Operator vigilance is a vital concern to the Air Force in regard to cockpit monitoring, air-traffic control, and the supervisory control of unmanned aerial vehicles. A key interest is the performance of teams of observers because of the reliance of military operations on good teamwork. Previous literature has examined the efficacy of team vigilance performance by comparing the frequency of target detections by teams in comparison to those obtained by operators working alone. Team performance has consistently exceeded single-operator performance. The present study replicates this effect and provides the initial experimental investigation of the cost of being a team member. Results indicated that team members worked harder but reported less distress than single operators in the performance of a simulated UAV monitoring task

TED: a tolerant edit distance for segmentation evaluation

© . This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/In this paper, we present a novel error measure to compare a computer-generated segmentation of images or volumes against ground truth. This measure, which we call Tolerant Edit Distance (TED), is motivated by two observations that we usually encounter in biomedical image processing: (1) Some errors, like small boundary shifts, are tolerable in practice. Which errors are tolerable is application dependent and should be explicitly expressible in the measure. (2) Non-tolerable errors have to be corrected manually. The effort needed to do so should be reflected by the error measure. Our measure is the minimal weighted sum of split and merge operations to apply to one segmentation such that it resembles another segmentation within specified tolerance bounds. This is in contrast to other commonly used measures like Rand index or variation of information, which integrate small, but tolerable, differences. Additionally, the TED provides intuitive numbers and allows the localization and classification of errors in images or volumes. We demonstrate the applicability of the TED on 3D segmentations of neurons in electron microscopy images where topological correctness is arguable more important than exact boundary locations. Furthermore, we show that the TED is not just limited to evaluation tasks. We use it as the loss function in a max-margin learning framework to find parameters of an automatic neuron segmentation algorithm. We show that training to minimize the TED, i.e., to minimize crucial errors, leads to higher segmentation accuracy compared to other learning methods.Peer ReviewedPostprint (author's final draft

Aureobacterium resistens sp. nov., exhibiting vancomycin resistance and teicoplanin susceptibility

Two similar strains of a coryneform bacterium were isolated from human clinical material. Both strains were resistant to vancomycin but susceptible to teicoplanin. Detailed biochemical, chemotaxonomical, and molecular genetic investigations revealed that both isolates were members of a hitherto undescribed species of the genus Aureobacterium. The name Aureobacterium resistens sp. nov. is proposed for the new bacterium and the type strain is CCUG 3831

Corynebacterium lipophiloflavum sp. nov. isolated from a patient with bacterial vaginosis

A unique coryneform bacterium was isolated from a patient with bacterial vaginosis. Chemotaxonomical investigations demonstrated that the unknown bacterium belonged to the genus Corynebacterium. The yellow-pigmented, slightly lipophilic, oxidative, urea-hydrolyzing bacterium could be phenotypically readily differentiated from the other members of the genus Corynebacterium. Comparative 16S rRNA gene analysis revealed that the bacterium represented a new subline within the genus Corynebacterium for which the name Corynebacterium lipophiloflavum sp. nov. is proposed. The type strain is CCUG 37336 (DSM 44291

Application of the adjoint approach to optimise the initial conditions of a turbidity current with the AdjointTurbidity 1.0 model

Turbidity currents are one of the main drivers of sediment transport from the continental shelf to the deep ocean. The resulting sediment deposits can reach hundreds of kilometres into the ocean. Computer models that simulate turbidity currents and the resulting sediment deposit can help us to understand their general behaviour. However, in order to recreate real-world scenarios, the challenge is to find the turbidity current parameters that reproduce the observations of sediment deposits. This paper demonstrates a solution to the inverse sediment transportation problem: for a known sedimentary deposit, the developed model reconstructs details about the turbidity current that produced the deposit. The reconstruction is constrained here by a shallow water sediment-laden density current model, which is discretised by the finite-element method and an adaptive time-stepping scheme. The model is differentiated using the adjoint approach, and an efficient gradient-based optimisation method is applied to identify the turbidity parameters which minimise the misfit between the modelled and the observed field sediment deposits. The capabilities of this approach are demonstrated using measurements taken in the Miocene Marnoso-arenacea Formation (Italy). We find that whilst the model cannot match the deposit exactly due to limitations in the physical processes simulated, it provides valuable insights into the depositional processes and represents a significant advance in our toolset for interpreting turbidity current deposits

The independence and interdependence of coacting observers in regard to performance efficiency, workload, and stress in a vigilance task

Objective We investigated performance, workload, and stress in groups of paired observers who performed a vigilance task in a coactive (independent) manner. Background Previous studies have demonstrated that groups of coactive observers detect more signals in a vigilance task than observers working alone. Therefore, the use of such groups might be effective in enhancing signal detection in operational situations. However, concern over appearing less competent than one's cohort might induce elevated levels of workload and stress in coactive group members and thereby undermine group performance benefits. Accordingly, we performed the initial experiment comparing workload and stress in observers who performed a vigilance task coactively with those of observers who performed the vigilance task alone. Method Observers monitored a video display for collision flight paths in a simulated unmanned aerial vehicle control task. Self-reports of workload and stress were secured via the NASA-Task Load Index and the Dundee Stress State Questionnaire, respectively. Results Groups of coactive observers detected significantly more signals than did single observers. Coacting observers did not differ significantly from those operating by themselves in terms of workload but did in regard to stress; posttask distress was significantly lower for coacting than for single observers. Conclusion Performing a visual vigilance task in a coactive manner with another observer does not elevate workload above that of observers working alone and serves to attenuate the stress associated with vigilance task performance. Application The use of coacting observers could be an effective vehicle for enhancing performance efficiency in operational vigilance