Operator State Estimation for Adaptive Aiding in Uninhabited Combat Air Vehicles

This research demonstrated the first closed-loop implementation of adaptive automation using operator functional state in an operationally relevant environment. In the Uninhabited Combat Air Vehicle (UCAV) environment, operators can become cognitively overloaded and their performance may decrease during mission critical events. This research demonstrates an unprecedented closed-loop system, one that adaptively aids UCAV operators based on their cognitive functional state A series of experiments were conducted to 1) determine the best classifiers for estimating operator functional state, 2) determine if physiological measures can be used to develop multiple cognitive models based on information processing demands and task type, 3) determine the salient psychophysiological measures in operator functional state, and 4) demonstrate the benefits of intelligent adaptive aiding using operator functional state. Aiding the operator actually improved performance and increased mission effectiveness by 67%

Bayesian Spatial Binary Regression for Label Fusion in Structural Neuroimaging

Many analyses of neuroimaging data involve studying one or more regions of interest (ROIs) in a brain image. In order to do so, each ROI must first be identified. Since every brain is unique, the location, size, and shape of each ROI varies across subjects. Thus, each ROI in a brain image must either be manually identified or (semi-) automatically delineated, a task referred to as segmentation. Automatic segmentation often involves mapping a previously manually segmented image to a new brain image and propagating the labels to obtain an estimate of where each ROI is located in the new image. A more recent approach to this problem is to propagate labels from multiple manually segmented atlases and combine the results using a process known as label fusion. To date, most label fusion algorithms either employ voting procedures or impose prior structure and subsequently find the maximum a posteriori estimator (i.e., the posterior mode) through optimization. We propose using a fully Bayesian spatial regression model for label fusion that facilitates direct incorporation of covariate information while making accessible the entire posterior distribution. We discuss the implementation of our model via Markov chain Monte Carlo and illustrate the procedure through both simulation and application to segmentation of the hippocampus, an anatomical structure known to be associated with Alzheimer's disease.Comment: 24 pages, 10 figure

Self-immolative linkers in polymeric delivery systems

There has been significant interest in the methodologies of controlled release for a diverse range of applications spanning drug delivery, biological and chemical sensors, and diagnostics. The advancement in novel substrate-polymer coupling moieties has led to the discovery of self-immolative linkers. This new class of linker has gained popularity in recent years in polymeric release technology as a result of stable bond formation between protecting and leaving groups, which becomes labile upon activation, leading to the rapid disassembly of the parent polymer. This ability has prompted numerous studies into the design and development of self-immolative linkers and the kinetics surrounding their disassembly. This review details the main concepts that underpin self-immolative linker technologies that feature in polymeric or dendritic conjugate systems and outlines the chemistries of amplified self-immolative elimination

A cross-sectional study of predatory publishing emails received by career development grant awardees

OBJECTIVE: To investigate the scope of academic spam emails (ASEs) among career development grant awardees and the factors associated with the amount of time spent addressing them. DESIGN: A cross-sectional survey of career development grant investigators via an anonymous online survey was conducted. In addition to demographic and professional information, we asked investigators to report the number of ASEs received each day, how they determined whether these emails were spam and time they spent per day addressing them. We used bivariate analysis to assess factors associated with the amount of time spent on ASEs. SETTING: An online survey sent via email on three separate occasions between November and December 2016. PARTICIPANTS: All National Institutes of Health career development awardees funded in the 2015 fiscal year. MAIN OUTCOME MEASURES: Factors associated with the amount of time spent addressing ASEs. RESULTS: A total of 3492 surveys were emailed, of which 206 (5.9%) were returned as undeliverable and 96 (2.7%) reported an out-of-office message; our overall response rate was 22.3% (n=733). All respondents reported receiving ASEs, with the majority (54.4%) receiving between 1 and 10 per day and spending between 1 and 10 min each day evaluating them. The amount of time respondents reported spending on ASEs was associated with the number of peer-reviewed journal articles authored (p<0.001), a history of publishing in open access format (p<0.01), the total number of ASEs received (p<0.001) and a feeling of having missed opportunities due to ignoring these emails (p=0.04). CONCLUSIONS: ASEs are a common distraction for career development grantees that may impact faculty productivity. There is an urgent need to mitigate this growing problem

FIELD TESTING OF REMOTE SENSOR GAS LEAK DETECTION SYSTEMS FINAL REPORT

The natural gas pipeline industry routinely checks their pipeline right-of-ways to ensure that leaks are detected. Pipeline companies use various processes to detect signs of leaking pipes, including using vehicles or low-flying aircraft. The leak detection methods range from directly sensing the gas to looking for indirect signs of leakage. The U.S. Department of Energy (DOE) and the U.S. Department of Transportation’s Office of Pipeline Safety (OPS) have provided funding to several commercial companies and research laboratories to develop advanced remote sensor systems to provide high quality, cost-effective leak detection information. To aid in the development and availability of these remote detection systems, the DOE funded a project to conduct field testing of five remote sensor leak detection systems. OPS provided co-funding for this project. The five systems chosen to be included in the field test were being developed by En’Urga Inc., ITT Industries, Inc., LaSen, Inc., Lawrence Livermore National Laboratories, and Physical Sciences Inc. The technologies included passive infrared multi-spectral scanning, laser-based differential absorption LIDAR (Light Detection and Ranging), hyperspectral imaging, and tunable diode laser absorption spectroscopy. The sensor systems were mounted in an unmodified automobile, a helicopter, or a fixed-wing aircraft. A “virtual pipeline,” that simulated conditions of an actual pipeline was created at the Rocky Mountain Oilfield Testing Center field site at NPR-3, north of Casper, Wyoming. The pipeline route was approximately 7.5 miles long and was marked by 14 direction change markers and 22 sets of road crossing markers. Fifteen leak sites, which included three types of gas releases, were established along the route, with natural gas leak rates ranging from 1 scfh to 5,000 scfh. One leak site was designated as a “calibration” site, and the location and leak rate for this site were provided to the equipment providers. Leak sites that were designed to cause plant stress were on continuously from August 30, 2004 through September 17, 2004. The remaining leak sites were set daily during the test week of September 13 to 17, 2004. Four equipment providers were scheduled to collect data along the pipeline path twice each test day. One equipment provider, at their request, was scheduled to collect data once each day for one of their platforms and twice during the entire week for their other platform. Reports of the findings for the individual equipment providers were due to Southwest Research Institute® (SwRI®) within two weeks after the testing period and are included in this report as Appendix I. Based on the data provided, leaks at many of the leak sites were successfully detected. Leak rates of 500 scfh or higher were detected at least 50% of the time. Leak rates of 100 scfh were only detected 15% of the time. Leak rates of 15 scfh and 10 scfh were only detected about 5% of the time. The 1-scfh leak was never detected. There were also a large number of “false positive” leak sites identified by the equipment providers. Some of the equipment providers made system improvements during the week including repairing malfunctioning equipment, mechanical modifications to improve performance in field applications, and developing improved data handling schemes. Other modifications have been defined for future work by some of the equipment providers. Improvements for potential future testing efforts have been identified and include improving the pipeline route and adding more leak sites