An Analysis of the Role of Safety Nets in the National Airspace System

Safe operations of aircraft in the National Airspace System (NAS) may be attributed to many factors, including the application of a variety of safety nets (SNs) as a last line of defense. In preparation for the Next Generation Air Transportation System (NextGen), a review of Aviation Safety Reporting System (ASRS) reports for incidents with positive outcomes was conducted to investigate the importance of current safety nets. The examination of positive outcomes not only shows what went wrong, but also what went right to prevent accidents and save the day. More than 400 incident reports for 2015 from the voluntary ASRS reporting database were studied in detail to create event sequence diagrams (ESDs), illustrating the effectiveness of SNs. The developed ESDs are considered top-level, representative models and are limited with respect to being reliably quantitative because they are based on only reports from a single year. The ESDs could offer insights into human systems integration research, such as strategically using technologies as SNs without human interface or alleviating human workload with new technologies to provide resilient recovery from off-nominal conditions ensuring flight safety

AAHES: A hybrid expert system realization of Adaptive Autonomy for smart grid

Abstract--Smart grid expectations objectify the need for optimizing power distribution systems greater than ever. Distribution Automation (DA) is an integral part of the SG solution; however, disregarding human factors in the DA systems can make it more problematic than beneficial. As a consequence, Human-Automation Interaction (HAI) theories can be employed to optimize the DA systems in a human-centered manner. Earlier we introduced a novel framework for the realization of Adaptive Autonomy (AA) concept in the power distribution network using expert systems. This research presents a hybrid expert system for the realization of AA, using both Artificial Neural Networks (ANN) and Logistic Regression (LR) models, referred to as AAHES, respectively. AAHES uses neural networks and logistic regression as an expert system inference engine. This system fuses LR and ANN models' outputs which will results in a progress, comparing to both individual models. The practical list of environmental conditions and superior experts' judgments are used as the expert systems database. Since training samples will affect the expert systems performance, the AAHES is implemented using six different training sets. Finally, the results are interpreted in order to find the best training set. As revealed by the results, the presented AAHES can effectively determine the proper level of automation for changing the performance shaping factors of the HAI systems in the smart grid environment

Human Factors Considerations for Area Navigation Departure and Arrival Procedures

Area navigation (RNAV) procedures are being implemented in the United States and around the world as part of a transition to a performance-based navigation system. These procedures are providing significant benefits and have also caused some human factors issues to emerge. Under sponsorship from the Federal Aviation Administration (FAA), the National Aeronautics and Space Administration (NASA) has undertaken a project to document RNAV-related human factors issues and propose areas for further consideration. The component focusing on RNAV Departure and Arrival Procedures involved discussions with expert users, a literature review, and a focused review of the NASA Aviation Safety Reporting System (ASRS) database. Issues were found to include aspects of air traffic control and airline procedures, aircraft systems, and procedure design. Major findings suggest the need for specific instrument procedure design guidelines that consider the effects of human performance. Ongoing industry and government activities to address air-ground communication terminology, design improvements, and chart-database commonality are strongly encouraged. A review of factors contributing to RNAV in-service errors would likely lead to improved system design and operational performance

The effects of data models and conceptual models of the structured query language on the task of query writing by end users

This research is an empirical investigation of human factors on the use of database systems. The problem motivating the study is the difficulty encountered by end-users in retrieving data from a database

KEGG for representation and analysis of molecular networks involving diseases and drugs

Most human diseases are complex multi-factorial diseases resulting from the combination of various genetic and environmental factors. In the KEGG database resource (http://www.genome.jp/kegg/), diseases are viewed as perturbed states of the molecular system, and drugs as perturbants to the molecular system. Disease information is computerized in two forms: pathway maps and gene/molecule lists. The KEGG PATHWAY database contains pathway maps for the molecular systems in both normal and perturbed states. In the KEGG DISEASE database, each disease is represented by a list of known disease genes, any known environmental factors at the molecular level, diagnostic markers and therapeutic drugs, which may reflect the underlying molecular system. The KEGG DRUG database contains chemical structures and/or chemical components of all drugs in Japan, including crude drugs and TCM (Traditional Chinese Medicine) formulas, and drugs in the USA and Europe. This database also captures knowledge about two types of molecular networks: the interaction network with target molecules, metabolizing enzymes, other drugs, etc. and the chemical structure transformation network in the history of drug development. The new disease/drug information resource named KEGG MEDICUS can be used as a reference knowledge base for computational analysis of molecular networks, especially, by integrating large-scale experimental datasets

Investigating metabolic dysfunction and arrhythmogenesis in an early-onset atrial fibrillation patient cohort

Despite the prevalence of atrial fibrillation (AF) and the burden it places on health care systems, there remains much that is unknown regarding heritable factors influencing its development and progression. In this study, I investigated whole-exome sequencing (WES) data from a cohort of patients presenting with early-onset AF to explore the role that metabolic dysfunction might play in contributing to disease onset. I curated a metabolism-related gene panel and, following in silico prediction of variant pathogenicity, performed gene-level burden testing using reference data from the Genome Aggregation Database (gnomAD) and the human mitochondrial genome database MITOMAP. I further explored genes associating with AF in the UK Biobank data set, and discovered associations with several AF comorbidities including diabetes, hypertension, and stroke