The role of data visualization in Railway Big Data Risk Analysis

Big Data Risk Analysis (BDRA) is one of the possible alleys for the further development of risk models in the railway transport. Big Data techniques allow a great quantity of information to be handled from different types of sources (e.g. unstructured text, signaling and train data). The benefits of this approach may lie in improving the understanding of the risk factors involved in railways, detecting possible new threats or assessing the risk levels for rolling stock, rail infrastructure or railway operations. For the efficient use of BDRA, the conversion of huge amounts of data into a simple and effective display is particularly challenging. Especially because it is presented to various specific target audiences. This work reports a literature review of risk communication and visualization in order to find out its applicability to BDRA, and beyond the visual techniques, what human factors have to be considered in the understanding and risk perception of the infor-mation when safety analysts and decision-makers start basing their decisions on BDRA analyses. It was found that BDRA requires different visualization strategies than those that have normally been carried out in risk analysis up to now

Supervision and feedback for junior medical staff in Australian emergency departments: findings from the emergency medicine capacity assessment study

<p>Abstract</p> <p>Background</p> <p>Clinical supervision and feedback are important for the development of competency in junior doctors. This study aimed to determine the adequacy of supervision of junior medical staff in Australian emergency departments (EDs) and perceived feedback provided.</p> <p>Methods</p> <p>Semi-structured telephone surveys sought quantitative and qualitative data from ED Directors, Directors of Emergency Medicine Training, registrars and interns in 37 representative Australian hospitals; quantitative data were analysed with SPSS 15.0 and qualitative data subjected to content analysis identifying themes.</p> <p>Results</p> <p>Thirty six of 37 hospitals took part. Of 233 potential interviewees, 95 (40.1%) granted interviews including 100% (36/36) of ED Directors, and 96.2% (25/26) of eligible DEMTs, 24% (19/81) of advanced trainee/registrars, and 17% (15/90) of interns. Most participants (61%) felt the ED was adequately supervised in general and (64.2%) that medical staff were adequately supervised. Consultants and registrars were felt to provide most intern supervision, but this varied depending on shift times, with registrars more likely to provide supervision on night shift and at weekends. Senior ED medical staff (64%) and junior staff (79%) agreed that interns received adequate clinical supervision. Qualitative analysis revealed that good processes were in place to ensure adequate supervision, but that service demands, particularly related to access block and overcrowding, had detrimental effects on both supervision and feedback.</p> <p>Conclusions</p> <p>Consultants appear to provide the majority of supervision of junior medical staff in Australian EDs. Supervision and feedback are generally felt to be adequate, but are threatened by service demands, particularly related to access block and ED overcrowding.</p

'Everyday memory' impairments in autism spectrum disorders

‘Everyday memory’ is conceptualised as memory within the context of day-to-day life and, despite its functional relevance, has been little studied in individuals with autism spectrum disorders (ASDs). In the first study of its kind, 94 adolescents with an ASD and 55 without an ASD completed measures of everyday memory from the Rivermead Behavioural Memory Test (RBMT) and a standard word recall task (Children’s Auditory Verbal Learning Test-2: CAVLT-2). The ASD group showed significant impairments on the RBMT, including in prospective memory, alongside impaired performance on the CAVLT-2. Social and communication ability was significantly associated with prospective remembering in an everyday memory context but not with the CAVLT-2. The complex nature of everyday memory and its relevance to ASD is discussed

Detecting modification of biomedical events using a deep parsing approach

<p>Abstract</p> <p>Background</p> <p>This work describes a system for identifying event mentions in bio-molecular research abstracts that are either speculative (e.g. <it>analysis of IkappaBalpha phosphorylation</it>, where it is not specified whether phosphorylation did or did not occur) or negated (e.g. <it>inhibition of IkappaBalpha phosphorylation</it>, where phosphorylation did <it>not </it>occur). The data comes from a standard dataset created for the BioNLP 2009 Shared Task. The system uses a machine-learning approach, where the features used for classification are a combination of shallow features derived from the words of the sentences and more complex features based on the semantic outputs produced by a deep parser.</p> <p>Method</p> <p>To detect event modification, we use a Maximum Entropy learner with features extracted from the data relative to the trigger words of the events. The shallow features are bag-of-words features based on a small sliding context window of 3-4 tokens on either side of the trigger word. The deep parser features are derived from parses produced by the English Resource Grammar and the <it>RASP </it>parser. The outputs of these parsers are converted into the Minimal Recursion Semantics formalism, and from this, we extract features motivated by linguistics and the data itself. All of these features are combined to create training or test data for the machine learning algorithm.</p> <p>Results</p> <p>Over the test data, our methods produce approximately a 4% absolute increase in F-score for detection of event modification compared to a baseline based only on the shallow bag-of-words features.</p> <p>Conclusions</p> <p>Our results indicate that grammar-based techniques can enhance the accuracy of methods for detecting event modification.</p

Measures in Visualization Space

Postponed access: the file will be available after 2021-08-12Measurement is an integral part of modern science, providing the fundamental means for evaluation, comparison, and prediction. In the context of visualization, several different types of measures have been proposed, ranging from approaches that evaluate particular aspects of visualization techniques, their perceptual characteristics, and even economic factors. Furthermore, there are approaches that attempt to provide means for measuring general properties of the visualization process as a whole. Measures can be quantitative or qualitative, and one of the primary goals is to provide objective means for reasoning about visualizations and their effectiveness. As such, they play a central role in the development of scientific theories for visualization. In this chapter, we provide an overview of the current state of the art, survey and classify different types of visualization measures, characterize their strengths and drawbacks, and provide an outline of open challenges for future research.acceptedVersio