Initial flight qualification and operational maintenance of X-29A flight software

A discussion is presented of some significant aspects of the initial flight qualification and operational maintenance of the flight control system softward for the X-29A technology demonstrator. Flight qualification and maintenance of complex, embedded flight control system software poses unique problems. The X-29A technology demonstrator aircraft has a digital flight control system which incorporates functions generally considered too complex for analog systems. Organizational responsibilities, software assurance issues, tools, and facilities are discussed

Flight control systems development and flight test experience with the HiMAT research vehicles

Two highly maneuverable aircraft technology (HiMAT) remotely piloted vehicles were flown a total of 26 flights. These subscale vehicles were of advanced aerodynamic configuration with advanced technology concepts such as composite and metallic structures, digital integrated propulsion control, and ground (primary) and airborne (backup) relaxed static stability, digital fly-by-wire control systems. Extensive systems development, checkout, and flight qualification were required to conduct the flight test program. The design maneuver goal was to achieve a sustained 8-g turn at Mach 0.9 at an altitude of 25,000 feet. This goal was achieved, along with the acquisition of high-quality flight data at subsonic and supersonic Mach numbers. Control systems were modified in a variety of ways using the flight-determined aerodynamic characteristics. The HiMAT program was successfully completed with approximately 11 hours of total flight time

Design and Integration of an Actuated Nose Strake Control System

Aircraft flight characteristics at high angles of attack can be improved by controlling vortices shed from the nose. These characteristics have been investigated with the integration of the actuated nose strakes for enhanced rolling (ANSER) control system into the NASA F-18 High Alpha Research Vehicle. Several hardware and software systems were developed to enable performance of the research goals. A strake interface box was developed to perform actuator control and failure detection outside the flight control computer. A three-mode ANSER control law was developed and installed in the Research Flight Control System. The thrust-vectoring mode does not command the strakes. The strakes and thrust-vectoring mode uses a combination of thrust vectoring and strakes for lateral- directional control, and strake mode uses strakes only for lateral-directional control. The system was integrated and tested in the Dryden Flight Research Center (DFRC) simulation for testing before installation in the aircraft. Performance of the ANSER system was monitored in real time during the 89-flight ANSER flight test program in the DFRC Mission Control Center. One discrepancy resulted in a set of research data not being obtained. The experiment was otherwise considered a success with the majority of the research objectives being met

The effect of scan and patient parameters on the diagnostic performance of AI for detecting coronary stenosis on coronary CT angiography

Publisher Copyright: © 2022 The AuthorsObjectives: To determine whether coronary computed tomography angiography (CCTA) scanning, scan preparation, contrast, and patient based parameters influence the diagnostic performance of an artificial intelligence (AI) based analysis software for identifying coronary lesions with ≥50% stenosis. Background: CCTA is a noninvasive imaging modality that provides diagnostic and prognostic benefit to patients with coronary artery disease (CAD). The use of AI enabled quantitative CCTA (AI-QCT) analysis software enhances our diagnostic and prognostic ability, however, it is currently unclear whether software performance is influenced by CCTA scanning parameters. Methods: CCTA and quantitative coronary CT (QCT) data from 303 stable patients (64 ± 10 years, 71% male) from the derivation arm of the CREDENCE Trial were retrospectively analyzed using an FDA-cleared cloud-based software that performs AI-enabled coronary segmentation, lumen and vessel wall determination, plaque quantification and characterization, and stenosis determination. The algorithm's diagnostic performance measures (sensitivity, specificity, and accuracy) for detecting coronary lesions of ≥50% stenosis were determined based on concordance with QCA measurements and subsequently compared across scanning parameters (including scanner vendor, model, single vs dual source, tube voltage, dose length product, gating technique, timing method), scan preparation technique (use of beta blocker, use and dose of nitroglycerin), contrast administration parameters (contrast type, infusion rate, iodine concentration, contrast volume) and patient parameters (heart rate and BMI). Results: Within the patient cohort, 13% demonstrated ≥50% stenosis in 3 vessel territories, 21% in 2 vessel territories, 35% in 1 vessel territory while 32% had 400 mg/ml 95.2%; p = 0.0287) in the context of low injection flow rates. On a per patient basis there were no significant differences in AI diagnostic performance measures across all measured scanner, scan technique, patient preparation, contrast, and individual patient parameters. Conclusion: The diagnostic performance of AI-QCT analysis software for detecting moderate to high grade stenosis are unaffected by commonly used CCTA scanning parameters and across a range of common scanning, scanner, contrast and patient variables. Condensed abstract: An AI-enabled quantitative CCTA (AI-QCT) analysis software has been validated as an effective tool for the identification, quantification and characterization of coronary plaque and stenosis through comparison to blinded expert readers and quantitative coronary angiography. However, it is unclear whether CCTA screening parameters related to scanner parameters, scan technique, contrast volume and rate, radiation dose, or a patient's BMI or heart rate at time of scan affect the software's diagnostic measures for detection of moderate to high grade stenosis. AI performance measures were unaffected across a broad range of commonly encountered scanner, patient preparation, scan technique, intravenous contrast and patient parameters.publishersversionpublishe

Relationship of age, atherosclerosis and angiographic stenosis using artificial intelligence

Objective: The study evaluates the relationship of coronary stenosis, atherosclerotic plaque characteristics (APCs) and age using artificial intelligence enabled quantitative coronary computed tomographic angiography (AI-QCT). Methods: This is a post-hoc analysis of data from 303 subjects enrolled in the CREDENCE (Computed TomogRaphic Evaluation of Atherosclerotic Determinants of Myocardial IsChEmia) trial who were referred for invasive coronary angiography and subsequently underwent coronary computed tomographic angiography (CCTA). In this study, a blinded core laboratory analysing quantitative coronary angiography images classified lesions as obstructive (≥50%) or non-obstructive (\u3c50%) while AI software quantified APCs including plaque volume (PV), low-density non-calcified plaque (LD-NCP), non-calcified plaque (NCP), calcified plaque (CP), lesion length on a per-patient and per-lesion basis based on CCTA imaging. Plaque measurements were normalised for vessel volume and reported as % percent atheroma volume (%PAV) for all relevant plaque components. Data were subsequently stratified by age \u3c65 and ≥65 years. Results: The cohort was 64.4±10.2 years and 29% women. Overall, patients \u3e65 had more PV and CP than patients \u3c65. On a lesion level, patients \u3e65 had more CP than younger patients in both obstructive (29.2 mm3 vs 48.2 mm3; p\u3c0.04) and non-obstructive lesions (22.1 mm3 vs 49.4 mm3; p\u3c0.004) while younger patients had more %PAV (LD-NCP) (1.5% vs 0.7%; p\u3c0.038). Younger patients had more PV, LD-NCP, NCP and lesion lengths in obstructive compared with non-obstructive lesions. There were no differences observed between lesion types in older patients. Conclusion: AI-QCT identifies a unique APC signature that differs by age and degree of stenosis and provides a foundation for AI-guided age-based approaches to atherosclerosis identification, prevention and treatment

The effect of scan and patient parameters on the diagnostic performance of AI for detecting coronary stenosis on coronary CT angiography

Objectives: To determine whether coronary computed tomography angiography (CCTA) scanning, scan preparation, contrast, and patient based parameters influence the diagnostic performance of an artificial intelligence (AI) based analysis software for identifying coronary lesions with ≥50% stenosis. Background: CCTA is a noninvasive imaging modality that provides diagnostic and prognostic benefit to patients with coronary artery disease (CAD). The use of AI enabled quantitative CCTA (AI-QCT) analysis software enhances our diagnostic and prognostic ability, however, it is currently unclear whether software performance is influenced by CCTA scanning parameters. Methods: CCTA and quantitative coronary CT (QCT) data from 303 stable patients (64 ± 10 years, 71% male) from the derivation arm of the CREDENCE Trial were retrospectively analyzed using an FDA-cleared cloud-based software that performs AI-enabled coronary segmentation, lumen and vessel wall determination, plaque quantification and characterization, and stenosis determination. The algorithm\u27s diagnostic performance measures (sensitivity, specificity, and accuracy) for detecting coronary lesions of ≥50% stenosis were determined based on concordance with QCA measurements and subsequently compared across scanning parameters (including scanner vendor, model, single vs dual source, tube voltage, dose length product, gating technique, timing method), scan preparation technique (use of beta blocker, use and dose of nitroglycerin), contrast administration parameters (contrast type, infusion rate, iodine concentration, contrast volume) and patient parameters (heart rate and BMI). Results: Within the patient cohort, 13% demonstrated ≥50% stenosis in 3 vessel territories, 21% in 2 vessel territories, 35% in 1 vessel territory while 32% had \u3c50% stenosis in all vessel territories evaluated by QCA. Average AI analysis time was 10.3 ± 2.7 min. On a per vessel basis, there were significant differences only in sensitivity for ≥50% stenosis based on contrast type (iso-osmolar 70.0% vs non isoosmolar 92.1% p = 0.0345) and iodine concentration (\u3c350 mg/ml 70.0%, 350-369 mg/ml 90.0%, 370-400 mg/ml 90.0%, \u3e400 mg/ml 95.2%; p = 0.0287) in the context of low injection flow rates. On a per patient basis there were no significant differences in AI diagnostic performance measures across all measured scanner, scan technique, patient preparation, contrast, and individual patient parameters. Conclusion: The diagnostic performance of AI-QCT analysis software for detecting moderate to high grade stenosis are unaffected by commonly used CCTA scanning parameters and across a range of common scanning, scanner, contrast and patient variables. Condensed abstract: An AI-enabled quantitative CCTA (AI-QCT) analysis software has been validated as an effective tool for the identification, quantification and characterization of coronary plaque and stenosis through comparison to blinded expert readers and quantitative coronary angiography. However, it is unclear whether CCTA screening parameters related to scanner parameters, scan technique, contrast volume and rate, radiation dose, or a patient\u27s BMI or heart rate at time of scan affect the software\u27s diagnostic measures for detection of moderate to high grade stenosis. AI performance measures were unaffected across a broad range of commonly encountered scanner, patient preparation, scan technique, intravenous contrast and patient parameters

Unpacking the Racial Disparity in Crime from a Racialized General Strain Theory Perspective

Criminologists have long theorized the relationship between race and crime using traditional criminological theories, suggesting that Blacks simply experience more factors conducive to crime than Whites. Race scholars have criticized this “add and stir” approach and, instead, argue for race-based explanations. Racialized General Strain Theory (RGST) is the first traditional approach to address this call to action. To date, however, little research has fully assessed RGST’s propositions. Using the Project on Human Development in Chicago Neighborhoods, we find mixed support for RGST. Findings also suggest racial differences in experiences and responses to strain are much more complicated than originally theorized