Objective To develop a robotic surgery training regimen integrating objective skill assessment for otolaryngology and head and neck surgery trainees consisting of training modules of increasing complexity and leading up to procedure specific training. In particular, we investigate applications of such a training approach for surgical extirpation of oropharyngeal tumors via a transoral approach using the da Vinci Robotic system. Study Design Prospective blinded data collection and objective evaluation (OSATS) of three distinct phases using the da Vinci Robotic surgical system. Setting Academic University Medical Engineering/Computer Science laboratory Methods Between September 2010 and July 2011, 8 Otolaryngology Head and Neck Surgery residents and 4 staff “experts” from an academic hospital participated in three distinct phases of robotic surgery training involving 1) robotic platform operational skills, 2) set-up of the patient side system, and 3) a complete ex-vivo surgical extirpation of an oropharyngeal “tumor” located in the base of tongue. Trainees performed multiple (4) approximately equally spaced training sessions in each stage of the training. In addition to trainees, baseline performance data was obtained for the experts. Each surgical stage was documented with motion and event data captured from the application programming interfaces (API) of the da Vinci system, as well as separate video cameras as appropriate. All data was assessed using automated skill measures of task efficiency, and correlated with structured assessment (OSATS, and similar Likert scale) from three experts to assess expert and trainee differences, and compute automated and expert assessed learning curves. Results Our data shows that such training results in an improved didactic robotic knowledge base and improved clinical efficiency with respect to the set-up and console manipulation. Experts (e.g. average OSATS 25, Stdev. 3.1, module 1 – suturing) and trainees (average OSATS 15.9, Stdev. 3.9, week 1) are well separated at the beginning of the training, and the separation reduces significantly (expert average OSATS 27.6, Std. 2.7, trainee average OSATS 24.2, Std. 6.8, module 3) at the conclusion of the training. Learning curves in each of the three stages show diminishing differences between the experts and trainees, also consistent with expert assessment. Subjective assessment by experts verified the clinical utility of the module 3 surgical environment and a survey of trainees consistently rated the curriculum as very useful in progression to human operating room assistance. Conclusions Structured curricular robotic surgery training with objective assessment promises to reduce the overhead for mentors, allow detailed assessment of human-machine interface skills and create customized training models for individualized training. This preliminary study verifies the utility of such training in improving human-machine operations skills (module 1), and operating room and surgical skills (module 2 and 3). In contrast to cur...
Abstract-Networks with homogeneous routing nodes are constantly at risk as any vulnerability found against a node could be used to compromise all nodes. Introducing diversity among nodes can be used to address this problem. With few variants, the choice of assignment of variants to nodes is critical to the overall network resiliency.We present the Diversity Assignment Problem (DAP), the assignment of variants to nodes in a network, and we show how to compute the optimal solution in medium-size networks. We also present a greedy approximation to DAP that scales well to large networks. Our solution shows that a high level of overall network resiliency can be obtained even from variants that are weak on their own.For real-world systems that grow incrementally over time, we provide an online version of our solution. Lastly, we provide a variation of our solution that is tunable for specific applications (e.g., BFT).
Abstract-Networks with homogeneous routing nodes are constantly at risk as any vulnerability found against a node could be used to compromise all nodes. Introducing diversity among nodes can be used to address this problem. With few variants, the choice of assignment of variants to nodes is critical to the overall network resiliency.We present the Diversity Assignment Problem (DAP), the assignment of variants to nodes in a network, and we show how to compute the optimal solution in medium-size networks. We also present a greedy approximation to DAP that scales well to large networks. Our solution shows that a high level of overall network resiliency can be obtained even from variants that are weak on their own.We provide two variations of our problem to meet real-world system needs. First, for networks with knowledge of higherlevel protocols we offer a technique to create assignments that maximize the needs of a specific application (e.g., Paxos and BFT). Second, for networks with knowledge of the value of traffic between each communicating pair of nodes, we offer a weighted version that can increase resiliency between important communicating pairs while sacrificing resiliency for the less important pairs.Our assignments are based on assumed compromise probabilities and independence of compromises between different diverse variants. We provide analysis when these assumed probabilities or independence are inaccurate.
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