A framework to support human factors of automation in railway intelligent infrastructure

Technological and organisational advances have increased the potential for remote access and proactive monitoring of the infrastructure in various domains and sectors – water and sewage, oil and gas and transport. Intelligent Infrastructure (II) is an architecture that potentially enables the generation of timely and relevant information about the state of any type of infrastructure asset, providing a basis for reliable decision-making. This paper reports an exploratory study to understand the concepts and human factors associated with II in the railway, largely drawing from structured interviews with key industry decision-makers and attachment to pilot projects. Outputs from the study include a data-processing framework defining the key human factors at different levels of the data structure within a railway II system and a system-level representation. The framework and other study findings will form a basis for human factors contributions to systems design elements such as information interfaces and role specifications

New measurements of total ionizing dose in the lunar environment

[1] We report new measurements of solar minimum ionizing radiation dose at the Moon onboard the Lunar Reconnaissance Orbiter (LRO) from June 2009 through May 2010. The Cosmic Ray Telescope for the Effects of Radiation (CRaTER) instrument on LRO houses a compact and highly precise microdosimeter whose design allows measurements of dose rates below 1 micro-Rad per second in silicon achieved with minimal resources (20 g, ∼250 milliwatts, and ∼3 bits/second). We envision the use of such a small yet accurate dosimeter in many future spaceflight applications where volume, mass, and power are highly constrained. As this was the first operation of the microdosimeter in a space environment, the goal of this study is to verify its response by using simultaneous measurements of the galactic cosmic ray ionizing environment at LRO, at L1, and with other concurrent dosimeter measurements and model predictions. The microdosimeter measured the same short timescale modulations in the galactic cosmic rays as the other independent measurements, thus verifying its response to a known source of minimum-ionizing particles. The total dose for the LRO mission over the first 333 days was only 12.2 Rads behind ∼130 mils of aluminum because of the delayed rise of solar activity in solar cycle 24 and the corresponding lack of intense solar energetic particle events. The dose rate in a 50 km lunar orbit was about 30 percent lower than the interplanetary rate, as one would expect from lunar obstruction of the visible sky

Earth‐Moon‐Mars Radiation Environment Module framework

[1] We are preparing to return humans to the Moon and setting the stage for exploration to Mars and beyond. However, it is unclear if long missions outside of low-Earth orbit can be accomplished with acceptable risk. The central objective of a new modeling project, the Earth-Moon-Mars Radiation Exposure Module (EMMREM), is to develop and validate a numerical module for characterizing time-dependent radiation exposure in the Earth-Moon-Mars and interplanetary space environments. EMMREM is being designed for broad use by researchers to predict radiation exposure by integrating over almost any incident particle distribution from interplanetary space. We detail here the overall structure of the EMMREM module and study the dose histories of the 2003 Halloween storm event and a June 2004 event. We show both the event histories measured at 1 AU and the evolution of these events at observer locations beyond 1 AU. The results are compared to observations at Ulysses. The model allows us to predict how the radiation environment evolves with radial distance from the Sun. The model comparison also suggests areas in which our understanding of the physics of particle propagation and energization needs to be improved to better forecast the radiation environment. Thus, we introduce the suite of EMMREM tools, which will be used to improve risk assessment models so that future human exploration missions can be adequately planned for

FOOD HABITS AND MANAGEMENT OF INTRODUCED RED FOX IN SOUTHERN CALIFORNIA

Introduced red fox in urban Orange County, California ate a wide variety of foods. Mammals and birds were consumed at all times of the year and both taxa appeared in approximately half or more of the fecal samples at all times of the year. Human supplied food remains were also common and supplemental feeding occurred at all study sites. Supplemental feeding has the potential to exacerbate problems for management of introduced red fox and several endangered species

Inference for SDE models via Approximate Bayesian Computation

Models defined by stochastic differential equations (SDEs) allow for the representation of random variability in dynamical systems. The relevance of this class of models is growing in many applied research areas and is already a standard tool to model e.g. financial, neuronal and population growth dynamics. However inference for multidimensional SDE models is still very challenging, both computationally and theoretically. Approximate Bayesian computation (ABC) allow to perform Bayesian inference for models which are sufficiently complex that the likelihood function is either analytically unavailable or computationally prohibitive to evaluate. A computationally efficient ABC-MCMC algorithm is proposed, halving the running time in our simulations. Focus is on the case where the SDE describes latent dynamics in state-space models; however the methodology is not limited to the state-space framework. Simulation studies for a pharmacokinetics/pharmacodynamics model and for stochastic chemical reactions are considered and a MATLAB package implementing our ABC-MCMC algorithm is provided.Comment: Version accepted for publication in Journal of Computational & Graphical Statistic

Physical exercise as non-pharmacological treatment of chronic pain: Why and when

Chronic pain broadly encompasses both objectively defined conditions and idiopathic conditions that lack physical findings. Despite variance in origin or pathogenesis, these conditions are similarly characterized by chronic pain, poor physical function, mobility limitations, depression, anxiety and sleep disturbance and are treated alone or in combination by pharmacologic and nonpharmacologic approaches, such as physical activity (aerobic conditioning, muscle strengthening, flexibility training and movement therapies). Physical activity improves general health, disease risk and progression of chronic illnesses such as cardiovascular disease, type-2 diabetes and obesity. When applied to chronic pain conditions within appropriate parameters (frequency, duration, intensity), physical activity significantly improves pain and related symptoms. For chronic pain, strict guidelines for physical activity are lacking, but frequent movement is preferable to sedentary behavior. This gives considerable freedom in prescribing physical activity treatments, which are most successful when tailored individually, progressed slowly and account for physical limitations, psychosocial needs and available resources

Train driving simulator studies: can novice drivers deliver the goods?

Early research suggests that, in a simulated train-driving environment, unskilled, novice drivers may exhibit comparable behaviour and performance to experienced, professional train drivers after receiving only minimal, task-specific training. However, this conclusion is based on exiguous performance indicators, such as speed limit exceedances, SPAD violations etc., and considers only limited data. This paper presents further, detailed analysis of driving performance data obtained from 20 drivers (13 novices and 7 experienced train drivers), who took part in a previous simulator-based research study, utilising more sensitive and perspicuous measures, namely acceleration noise and control actuation. Results indicate that, although both cohorts exhibited similar performance using the original metrics, and would thus support the same conclusions, the manner in which this performance was effected is fundamentally different between groups. Trained novice drivers (mainly comprising students and staff at the University of Nottingham) adopted far more erratic speed control profiles, characterised by longer control actions and frequent switching between power and brake actuation. In contrast, experienced drivers delivered smoother acceleration/braking profiles with more subtle (and shorter) control actions and less variance in speed. We conclude that although utilising trained non-drivers may offer an appealing solution in the absence of professional train drivers during simulator-based research, and their input remains of value, researchers should remain mindful when interpreting results and drawing conclusions from a contingent comprising non-drivers. The work also demonstrates the value of dependent variables such as acceleration noise, and quantitative measures of control actuation, which may offer an insightful portfolio of measures in train driving research studies

Train driving simulator studies: can novice drivers deliver the goods?

Early research suggests that, in a simulated train-driving environment, unskilled, novice drivers may exhibit comparable behaviour and performance to experienced, professional train drivers after receiving only minimal, task-specific training. However, this conclusion is based on exiguous performance indicators, such as speed limit exceedances, SPAD violations etc., and considers only limited data. This paper presents further, detailed analysis of driving performance data obtained from 20 drivers (13 novices and 7 experienced train drivers), who took part in a previous simulator-based research study, utilising more sensitive and perspicuous measures, namely acceleration noise and control actuation. Results indicate that, although both cohorts exhibited similar performance using the original metrics, and would thus support the same conclusions, the manner in which this performance was effected is fundamentally different between groups. Trained novice drivers (mainly comprising students and staff at the University of Nottingham) adopted far more erratic speed control profiles, characterised by longer control actions and frequent switching between power and brake actuation. In contrast, experienced drivers delivered smoother acceleration/braking profiles with more subtle (and shorter) control actions and less variance in speed. We conclude that although utilising trained non-drivers may offer an appealing solution in the absence of professional train drivers during simulator-based research, and their input remains of value, researchers should remain mindful when interpreting results and drawing conclusions from a contingent comprising non-drivers. The work also demonstrates the value of dependent variables such as acceleration noise, and quantitative measures of control actuation, which may offer an insightful portfolio of measures in train driving research studies

Alarm handling for health monitoring: operator strategies used in an electrical control room of a rail network

Alarm management is a key component of the successful operation of a prognostic or health-monitoring technology. Although alarms can alert the operator to critical information, false alarms and alarm flooding can cause major difficulties for successfully diagnosing and acting upon infrastructure faults. Human factors approaches seek to design more-effective alarm systems through a deep understanding of the contextual factors that influence alarm response, including strategies and heuristics used by operators. This paper presents an extensive analysis of alarm-handling activity in the setting of an Electrical Control Room on the rail network. The analysis is based on contextual observation, and the application of a time-stamped observation checklist. Functions, performance requirements, and general operating conditions that influence alarm handling are presented, delineating the typical operational constraints that need to be considered in the design and deployment of asset-based alarm systems. The analysis of specific alarm-handling incidents reveals the use of specific strategies that may bias operator performance. Implications for the design of health-monitoring systems are discussed