A Statistical Learning Regression Model Utilized To Determine Predictive Factors of Social Distancing During COVID-19 Pandemic

In an application of the mathematical theory of statistics, predictive regression modelling can be used to determine if there is a trend to predict the response variable of social distancing in terms of multiple predictor input “predictor” variables. In this study the social distancing is measured as the percentage reduction in average mobility by GPS records, and the mathematical results obtained are interpreted to determine what factors drive that response. This study was done on county level data from the state of Florida during the COVID-19 pandemic, and it is found that the most deterministic predictors are county population density along with median income

A Statistical Learning Regression Model utilized to determine predictive factors of social distancing during COVID-19 pandemic

In an application of the mathematical theory of statistics, predictive regression modeling can be used to determine if there is a trend to predict the response variable of social distancing in terms of multiple predictor input variables. In this study, the social distancing was measured as the percentage reduction in average mobility by GPS records, and the mathematical results obtained are interpreted to determine what factors drive that response. This study was done with county level data obtained from the State of Florida during the COVID-19 pandemic. The predicting factors found that were most deterministic was the county population density along with median income

Flow Disruptions as a Result of Personal Electronic Devices in Orthopedic Surgery

The operating room (OR) is a complex environment in which highly trained individuals perform cognitively demanding tasks. Distractions in this environment may lead to deleterious effects, as a loss of situational awareness can interfere with surgical procedures. The present study aims to quantify the frequency and nature of distracting events associated with personal electronic devices (PEDs) during twenty elective orthopedic surgery cases. PED use was coded using a real-time, custom data collection tool beginning in the pre-operative area and terminating at the time of handoff with the post-anesthesia care team. PED use accounted for 242 flow disruptions in the OR. The vendor showed the highest frequency of flow disruptions (73), followed by the circulating nurse (52) and the certified registered nurse anesthetist (CRNA) (52). Thus, taking a proactive safety approach to account for intraoperative distractions associated with PEDs among OR team members will be critical to ensure high-quality patient care

Human Error and Commercial Aviation Accidents: A Comprehensive, Fine-Grained Analysis Using HFACS

The Human Factors Analysis and Classification System (HFACS) is a theoretically based tool for investigating and analyzing human error associated with accidents and incidents. Previous research has shown that HFACS can be reliably used to identify general trends in the human factors associated with military and general aviation accidents. The aim of this study was to extend previous examinations of aviation accidents to include specific aircrew, environmental, supervisory, and organizational factors associated with 14 CFR Part 121 (Air Carrier) and 14 CFR Part 135 (Commuter) accidents using HFACS. The majority of causal factors were attributed to the aircrew and the environment, with decidedly fewer associated with supervisory and organizational causes. Comparisons were made between HFACS categories and traditional situational variables such as weather, lighting, and geographic region. Recommendations were made based on the HFACS findings presented

Human Error and General Aviation Accidents: A Comprehensive, Fine-Grained Analysis Using HFACS

The Human Factors Analysis and Classification System (HFACS) is a theoretically based tool for investigating and analyzing human error associated with accidents and incidents. Previous research performed at both the University of Illinois and the Civil Aerospace Medical Institute has successfully shown that HFACS can be reliably used to analyze the underlying human causes of both commercial and general aviation (GA) accidents. These analyses have helped identify general trends in the types of human factors issues and aircrew errors that have contributed to civil aviation accidents. The next step was to identify the exact nature of the human errors identified. The purpose of this research effort therefore, was to address these questions by performing a fine-grained HFACS analysis of the individual human causal factors associated with GA accidents and to assist in the generation of intervention programs. This report details those findings and offers an approach for developing interventions to address them

Beneath the Tip of the Iceberg: A Human Factors Analysis of General Aviation Accidents in Alaska Versus the Rest of the United States

Historically, general aviation (GA) accidents have been overlooked and their impact under-appreciated when compared with those in the commercial or military sector. Recently however, the Federal Aviation Administration and other governmental and civilian organizations have focused their attention on one piece of this proverbial “iceberg,” that being GA accidents occurring in Alaska. This study examines more than 17,000 GA accidents using the Human Factors Analysis and Classification System. Comparisons of Alaska to the rest of the U.S. (RoUS) included traditional demographic and environmental variables, as well as the human errors committed by aircrews. Overall, categorical differences among unsafe acts (decision errors, skill-based errors, perceptual errors, and violations) committed by pilots involved in accidents in Alaska and those in the RoUS were minimal. However, a closer inspection of the data revealed notable variations in the specific forms these unsafe acts took within the accident record. Specifically, skill-based errors associated with loss of directional control were more likely to occur in Alaska than the rest of the U.S. Likewise, the decision to utilize unsuitable terrain was more likely to occur in Alaska. Additionally, accidents in Alaska were associated with violations concerning Visual Flight Rules into Instrument Meteorological Conditions. These data provide valuable information for those government and civilian programs tasked with improving GA safety in Alaska and the RoUS

A Laboratory Comparison of Clockwise and Counter-Clockwise Rapidly Rotating Shift Schedules, Part I. Sleep

This document is available to the public through the National Technical Informatio

Effect of HFACS and non-HFACS-related factors on fatalities in general aviation accidents using neural networks.

This study applied a backpropagation artificial neural network approach to investigate both the Human Factors Analysis and Classification System (HFACS)-related unsafe act tiers of factors and other non-HFACS factors in an attempt to recognize patterns for general aviation accident fatalities. Data were obtained from the HFACS database and extracted from the National Transportation Safety Board database from 1990 to 2002. Multiple neural network models were created and the best fit model was selected based on a sequence of criteria. A sensitivity analysis was performed on the validated model to rank the factors that lead to general aviation fatalities. Results are discussed and practical implications are given

Investigation of chaotic behavior in flight airspeed error performance data.

Human pilot flight performance data was investigated using nonlinear time series analysis methods to determine deterministic chaotic behavior possessed in the data. Using a sequence of steps of nonlinear methods, flight performance data set was chosen for this study. Results revealed that flight performance data exhibit chaotic behavior with low determinism value. It was also found that data was originated from non-stationary process. The Maximal Lyapunov Exponent (MLE) value further revealed that most of the data examined possessed traces of deterministic chaotic behavior. These findings were discussed and the implication of the findings were given for the future analysis of these kind of data