COMPARING SAFE VS. AT-RISK BEHAVIORAL DATA TO PREDICT ACCIDENTS

The Safety Observations Achieve Results (SOAR) program at the Idaho National Laboratory (INL) encourages employees to perform in-field observations of each other’s behaviors. One purpose for performing these observations is that it gives the observers the opportunity to correct, if needed, their co-worker’s at-risk work practices and habits (i.e., behaviors). The underlying premise of doing this is that major injuries (e.g., OSHA-recordable events) are prevented from occurring because the lower level at-risk behaviors are identified and corrected before they can propagate into culturally accepted unsafe behaviors that result in injuries or fatalities. However, unlike other observation programs, SOAR also emphasizes positive reinforcement for safe behaviors observed. The underlying premise of doing this is that positive reinforcement of safe behaviors helps establish a strong positive safety culture. Since the SOAR program collects both safe and at-risk leading indicator data, this provides a unique opportunity to assess and compare the two kinds of data in terms of their ability to predict future adverse safety events. This paper describes the results of analyses performed on SOAR data to assess their relative predictive ability. Implications are discussed

Function Allocation for Humans and Automation in the Context of Team Dynamics

AbstractWithin Human Factors Engineering, a decision-making process called function allocation (FA) is used during the design life cycle of complex systems to distribute the system functions, typically identified through a functional requirements analysis, to all human and automated machine agents (or teammates) involved in controlling the system. Most FA methods make allocation decisions primarily by comparing the capabilities of humans and automation, and then by considering secondary factors such as cost, regulations, and the health and safety of workers. The primary analysis of the strengths and weaknesses of humans and machines, however, is almost always considered in terms of individual human or machine capabilities. Yet, FA is fundamentally about teamwork in that the goal of the FA decision-making process is to determine the optimal allocations of functions among agents. Given this framing of FA, and the increasing use of and sophistication of automation, there are two related social psychological issues that current FA methods need to address more thoroughly. First, many principles for effective human teamwork are not considered as central decision points or in the iterative hypothesis and testing phase in most FA methods, despite the fact that social factors have numerous positive and negative effects on individual and team capabilities. Second, social psychological factors affecting team performance can be difficult to translate to automated agents, and most FA methods currently do not account for this effect. The implications for these issues are discussed

Lessons Learned at the Idaho National Laboratory for the Entry into Force of the U.S. Additional Protocol

For a number of years, the Idaho National Laboratory (INL) has been preparing for the entry into force of the U.S. Additional Protocol (AP). These preparations included attending training, participating in tabletop exercises, preparing draft declarations, developing INL-specific guidance documents, preparing for and hosting a mock complementary access visit, and preparing declarations for official submittal. All of these activities, the training materials, and software developed by other U.S. DOE national laboratories (PNNL, ORNL, LANL, and BNL) were very helpful in preparing for the entry into force of the AP. As with any endeavor of this size and complexity, however, there are always instances where even the best preparations and advanced planning do not anticipate every challenge. As the DOE's lead nuclear energy research and development facility, the INL faced many unique challenges. The majority of research conducted at the INL is nuclear fuel cycle related, most of which is not protected by the National Security Exclusion. This paper describes the lessons learned from the INL’s experience of preparing for the entry into force of the AP, specifically how translating and implementing general principles into actual activities proved to be one of many challenges, and provides general suggestions on how to respond effectively and efficiently to routine annual data calls and other AP requests

Framework For Human-Automation Collaboration: Proj

The Human Automation Collaboration (HAC) researc

The Measure of Human Error: Direct and Indirect Performance Shaping Factors

The goal of performance shaping factors (PSFs) is to provide measures to account for human performance. PSFs fall into two categories—direct and indirect measures of human performance. While some PSFs such as “time to complete a task” are directly measurable, other PSFs, such as “fitness for duty,” can only be measured indirectly through other measures and PSFs, such as through fatigue measures. This paper explores the role of direct and indirect measures in human reliability analysis (HRA) and the implications that measurement theory has on analyses and applications using PSFs. The paper concludes with suggestions for maximizing the reliability and validity of PSFs

COMMERCIAL UTILITY PERSPECTIVES ON NUCLEAR POWER PLANT CONTROL ROOM MODERNIZATION

Commercial nuclear power plants (NPPs) in the United States need to modernize their main control rooms (MCR). Many NPPs have done partial upgrades with some success and with some challenges. The Department of Energy’s (DOE) Light Water Reactor Sustainability (LWRS) Program, and in particular the Advanced Instrumentation and Controls (I&C) and Information Systems Technologies Research and Development (R&D) Pathway within LWRS, is designed to assist commercial nuclear power industry with their MCR modernization efforts. As part of this framework, a survey was issued to utility representatives of the LWRS Program Advanced Instrumentation, Information, and Control Systems/Technologies (II&C) Utility Working Group to obtain their views on a range of issues related to MCR modernization, including: drivers, barriers, and technology options, and the effects these aspects will have on concepts of operations, modernization strategies, and staffing. This paper summarizes the key survey results and discusses their implications

Elemental concentrations in the seed of mutants and natural variants of Arabidopsis thaliana grown under varying soil conditions

Peer reviewedPublisher PD

Arctic system on trajectory to new state

The Arctic system is moving toward a new state that falls outside the envelope of glacial-interglacial fluctuations that prevailed during recent Earth history. This future Arctic is likely to have dramatically less permanent ice than exists at present. At the present rate of change, a summer ice-free Arctic Ocean within a century is a real possibility, a state not witnessed for at least a million years. The change appears to be driven largely by feedback-enhanced global climate warming, and there seem to be few, if any processes or feedbacks within the Arctic system that are capable of altering the trajectory toward this “super interglacial” state

Estimating Effectiveness of Identifying Human Trafficking via Data Envelopment Analysis

Transit monitoring is a preventative approach used to identify possible cases of human trafficking while an individual is in transit or before one crosses a border. Transit monitoring is often conducted by non-governmental organizations (NGOs) who train staff to identify and intercept suspicious activity. Love Justice International (LJI) is one such NGO that has been conducting transit monitoring for 14 years along the Nepal-India border at approximately 25-30 monitoring stations. In partnership with LJI, we developed a system that uses data envelopment analysis (DEA) to help LJI decision-makers evaluate the performance of these stations and make specific operational improvement recommendations. We identified efficient stations, compared rankings of station performance, and recommended strategies to improve efficiency. To the best of our knowledge, this is the first application of DEA in the anti-human trafficking domain