"Two separate logistic regression analyses were conducted for low- and high-altitude sectors to determine whether a set of dynamic sector characteristics variables could reliably discriminate between operational error (OE) and routine operation (RO) traffic samples. OE data were derived from SATORI re-creations of OEs occurring at the Indianapolis Air Route Traffic Control Center between 9/17/2001 and 12/10/2003. RO data were extracted from System Analysis Recordings (SARs) taped between 5/8/2003 and 5/10/2003"--Report documentation page.

This study examined prediction and classification of operational errors (OEs) and routine operations (ROs) using sector characteristics variables. Average Control Duration, Aircraft Mix Index, Average Lateral Distance, Average Vertical Distance, Number of Handoffs, Number of Point Outs, Number of Transitioning Aircraft, and Number of Heading Changes were used as predictors in two stepwise logistic regression analyses conducted for the high-altitude and low-altitude sectors. In the high-altitude sample, variables included in the final model (Number of Heading Changes, Number of Transitioning Aircraft, and Average Control Duration) accurately classified OE and RO samples for 80% of the cases. In the low-altitude sample, variables included in the final model (Number of Point Outs, the Number of Handoffs, and the Number of Heading Changes) accurately classified OE and RO samples for 79% of the cases. Although logistic regression cannot be used to determine causation, it effectively identified variables that predicted the occurrence of OEs.

This open access Pivot demonstrates how a variety of technologies act as innovation catalysts within the banking and financial services sector. Traditional banks and financial services are under increasing competition from global IT companies such as Google, Apple, Amazon and PayPal whilst facing pressure from investors to reduce costs, increase agility and improve customer retention. Technologies such as blockchain, cloud computing, mobile technologies, big data analytics and social media therefore have perhaps more potential in this industry and area of business than any other. This book defines a fintech ecosystem for the 21st century, providing a state-of-the art review of current literature, suggesting avenues for new research and offering perspectives from business, technology and industry.

This paper discusses the impact of the rapid adoption of artificial intelligence (AI) and machine learning (ML) in the financial sector. It highlights the benefits these technologies bring in terms of financial deepening and efficiency, while raising concerns about its potential in widening the digital divide between advanced and developing economies. The paper advances the discussion on the impact of this technology by distilling and categorizing the unique risks that it could pose to the integrity and stability of the financial system, policy challenges, and potential regulatory approaches. The evolving nature of this technology and its application in finance means that the full extent of its strengths and weaknesses is yet to be fully understood. Given the risk of unexpected pitfalls, countries will need to strengthen prudential oversight.

Human reliability is an issue that is increasingly discussed in the process and manufacturing industries to check factors that influence operator performance and trigger errors. Human Factor and Reliability Analysis to Prevent Losses in Industrial Processes: An Operational Culture Perspective provides a multidisciplinary analysis of work concepts and environments to reduce human error and prevent material, energy, image, and time losses. The book presents a methodology for the quantification and investigation of human reliability, and verification of the influence of human factors in the generation of process losses, consisting of the following steps: contextualization, data collection, and results; performing task and loss observation; socio-technical variable analyses; and data processing. Investigating human reliability, concepts, and models in situations of human error in practice, the book identifies where low reliability occurs and then visualizes where and how to perform an intervention. This guide is an excellent resource for professionals in chemical, petrochemical, oil, and nuclear industries for managing and analyzing safety and loss risks and for students in chemical and process engineering. - Relates human reliability to the environment, leadership, decision models, possible mistakes and successes, mental map constructions, and organizational cultures - Provides techniques for the diagnosis of human and operational reliability - Gives examples of the application of methodologies in the stage of diagnosis and program construction - Discusses competences for the analysis of process losses in industry - Investigates real-life situations where human errors cause losses - Includes practical examples and case studies

The Bulletin of the Atomic Scientists is the premier public resource on scientific and technological developments that impact global security. Founded by Manhattan Project Scientists, the Bulletin's iconic "Doomsday Clock" stimulates solutions for a safer world.