The Commercial Aviation Safety Team found the majority of recent international commercial aviation accidents attributable to loss of control inflight involved flight crew loss of airplane state awareness (ASA), and distraction was involved in all of them. Research on attention-related human performance limiting states (AHPLS) such as channelized attention, diverted attention, startle/surprise, and confirmation bias, has been recommended in a Safety Enhancement (SE) entitled "Training for Attention Management." To accomplish the detection of such cognitive and psychophysiological states, a broad suite of sensors was implemented to simultaneously measure their physiological markers during a high fidelity flight simulation human subject study. Twenty-four pilot participants were asked to wear the sensors while they performed benchmark tasks and motion-based flight scenarios designed to induce AHPLS. Pattern classification was employed to predict the occurrence of AHPLS during flight simulation also designed to induce those states. Classifier training data were collected during performance of the benchmark tasks. Multimodal classification was performed, using pre-processed electroencephalography, galvanic skin response, electrocardiogram, and respiration signals as input features. A combination of one, some or all modalities were used. Extreme gradient boosting, random forest and two support vector machine classifiers were implemented. The best accuracy for each modality-classifier combination is reported. Results using a select set of features and using the full set of available features are presented. Further, results are presented for training one classifier with the combined features and for training multiple classifiers with features from each modality separately. Using the select set of features and combined training, multistate prediction accuracy averaged 0.64 +/-0.14 across thirteen participants and was significantly higher than that for the separate training case. These results support the goal of demonstrating simultaneous real-time classification of multiple states using multiple sensing modalities in high fidelity flight simulators. This detection is intended to support and inform training methods under development to mitigate the loss of ASA and thus reduce accidents and incidents.

Background:Robotic-assisted (RA) technology is becoming increasingly popular in total knee arthroplasty (TKA) due to its improved alignment, accuracy, and precision compared with the conventional TKA. Despite reported benefits, disagreements exist regarding patientreported outcomes and complication rates comparing RA TKA and conventional TKA. Thus, the purpose of the study is to report differences in patient outcomes and complication rates between patients who underwent RA versus conventional TKA. Methods:The authors retrospectively reviewed 239 primary knee arthroplasty cases (n = 137 robot-assisted and n = 102 conventional TKA) performed by a single fellowship-trained orthopaedic surgeon from January 1, 2016 to February 26, 2019. The electronic medical record and patient outcomes database were reviewed for demographic characteristics (age, sex, body mass index, and comorbidities), patient-reported outcomes (Short Form Health Survey and Oxford Knee Score), 90-day complications, and revision rates. Results:There was no statistically significant difference in patient-reported outcomes between conventional versus RA groups at two time points: preoperative and 2-year. Differences remained insignificant after controlling for age, sex, body mass index, and comorbidities. There was no statistically significant difference between the conventional and RA groups in revision rates (0.7% and 1%, respectively; P = 1.00) or complication rates (1.5% and 3.9%, respectively; P = 0.406). Conclusions:There were no differences in 90-day complications, revisions, and patient-reported outcome scores between RA TKA and conventional TKA groups at short-term follow-up. Surgeons can expect similar clinical outcomes without an increase in complications while taking advantage of increased accuracy in alignment and component placement. Further long-term study of RA TKA outcomes is warranted.

A modal test was performed on the six-meter Hypersonic Inflatable Aerodynamic Decelerator (HIAD) test article to gain a firm understanding of the dynamic characteristics of the unloaded structure within the low frequency range. The tests involved various configurations of the HIAD to understand the influence of the tri-torus, the varying pressure within the toroids and the influence of straps. The primary test was conducted utilizing an eletrodynamic shaker and the results were verified using a step relaxation technique. The analysis results show an increase in the structure's stiffness with respect to increasing pressure. The results also show the rise of coupled modes with the tri-torus configurations. During the testing activity, the attached straps exhibited a behavior that is similar to that described as fuzzy structures in the literature. Therefore extensive tests were also performed by utilizing foam to mitigate these effects as well as understand the modal parameters of these fuzzy sub structures. Results are being utilized to update the finite element model of the six-meter HIAD and to gain a better understanding of the modeling of complex inflatable structures.

Background:With projected increases in total knee arthroplasties (TKA), patient outcomes without complications are essential. Arthrofibrosis, a potential complication after TKA that may impact longterm patient outcome, may be remedied by manipulation under anesthesia (MUA); however, it is not risk-free. This study investigated the association between manipulation and newer implants and sophisticated techniques, which hold promise for preventing arthrofibrosis and improving patient outcomes. Methods:The authors retrospectively reviewed 1260 primary knee arthroplasty cases (717 conventional, 217 customized, and 326 robot-assisted) performed by an orthopaedic surgeon from January 1, 2016 to May 31, 2020. Patient records were reviewed for manipulation and demographics (type of implant, sex, body mass index [BMI], smoking status, and prior surgery). Results:Overall manipulation rate was 1.3% (n = 17). Manipulation rates for conventional customized and robot-assisted TKAs did not vary significantly (1.84%, n = 6; 0.46%, n = 1; 1.39%, n = 10, respectively; P = 0.466). Multivariable logistic regression showed no statistically significant difference in the odds of manipulation depending on the type of implant. However, those who smoked were 4 times more likely to have a manipulation (OR: 4.187, 95% CI: 1.119 to 15.673) when controlling for covariates (type of implant, sex, BMI, and prior surgery). Additionally, those with prior surgery were 2.8 times as likely to have a manipulation (OR: 2.808, 95% CI: 1.039 to 7.589) when controlling for covariates. Conclusions:There were no statistically significant differences in manipulation rates among conventional, customized, and robot-assisted TKAs. However, current smoking status and prior surgery were associated with higher risk of manipulation.

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