Human factors analysis and classification system for the oil and gas industry (HFACS-OGI)

The oil and gas industry has been beset with several catastrophic accidents, most of which have been attributed to organisational and operational human factor errors. The current HFACS developed for the aviation industry, cannot be used to simultaneously analyse regulatory deficiencies and emerging violation issues, such as sabotage in the oil and gas industry. This paper presents an attempt to improve the existing HFACS investigation tool and proposes a novel HFACS named the Human Factors Analysis and Classification System for the Oil and Gas Industry (HFACS-OGI). Results found the HFACS-OGI system to be suitable for categorising accidents, following the analysis of 11 accident reports from the US Chemical Safety Board (US CSB). The HFACS-OGI system moreover revealed some significant relationships between the different categories. Furthermore, the results indicated that failures in national and international industry regulatory standards would automatically create the preconditions for accidents to occur

Carbon contamination topography analysis of EUV masks

The impact of carbon contamination on extreme ultraviolet (EUV) masks is significant due to throughput loss and potential effects on imaging performance. Current carbon contamination research primarily focuses on the lifetime of the multilayer surfaces, determined by reflectivity loss and reduced throughput in EUV exposure tools. However, contamination on patterned EUV masks can cause additional effects on absorbing features and the printed images, as well as impacting the efficiency of cleaning process. In this work, several different techniques were used to determine possible contamination topography. Lithographic simulations were also performed and the results compared with the experimental data

A Note on Information Flows and Identification of News Shocks Models

This note points out a hitherto unrecognized identification issue in a class of rational expectations (RE) models with news shocks. We show that different degrees of anticipation (information flows) have strikingly different implications for the identifiability of the underlying structural model, irrespective of its non-fundamental time-series representation. In particular, under full shock anticipation equilibrium reduced forms behave as noisy perfect foresight state motions, which are non-identifiable. As a consequence, the underlying news shocks model fails to be (first-order) identified. The identification failure is illustrated with a New Keynesian model that can be solved analytically