The impact of fire suppression tasks on firefighter hydration: A critical review with consideration of the utility of reported hydration measures

BACKGROUND: Firefighting is a highly stressful occupation with unique physical challenges, apparel and environments that increase the potential for dehydration. Dehydration leaves the firefighter at risk of harm to their health, safety and performance. The purpose of this review was to critically analyse the current literature investigating the impact of fighting ‘live’ fires on firefighter hydration. METHODS: A systematic search was performed of four electronic databases for relevant published studies investigating the impact of live fire suppression on firefighter hydration. Study eligibility was assessed using strict inclusion and exclusion criteria. The included studies were critically appraised using the Downs and Black protocol and graded according to the Kennelly grading system. RESULTS: Ten studies met the eligibility criteria for this review. The average score for methodological quality was 55 %, ranging from 50 % (‘fair’ quality) to 61 % (‘good’ quality) with a ‘substantial agreement’ between raters (k = .772). Wildfire suppression was considered in five studies and structural fire suppression in five studies. Results varied across the studies, reflecting variations in outcome measures, hydration protocols and interventions. Three studies reported significant indicators of dehydration resulting from structural fire suppression, while two studies found mixed results, with some measures indicating dehydration and other measures an unchanged hydration status. Three studies found non-significant changes in hydration resulting from wildfire firefighting and two studies found significant improvements in markers of hydration. Ad libitum fluid intake was a common factor across the studies finding no, or less severe, dehydration. CONCLUSIONS: The evidence confirms that structural and wildfire firefighting can cause dehydration. Ad libitum drinking may be sufficient to maintain hydration in many wildfire environments but possibly not during intense, longer duration, hot structural fire operations. Future high quality research better quantifying the effects of these influences on the degree of dehydration is required to inform policies and procedures that ensure firefighter health and safety

On well-posedness for some thermo-piezoelectric coupling models

There is an increasing reliance on mathematical modelling to assist in the design of piezoelectric ultrasonic transducers since this provides a cost-effective and quick way to arrive at a first prototype. Given a desired operating envelope for the sensor the inverse problem of obtaining the associated design parameters within the model can be considered. It is therefore of practical interest to examine the well-posedness of such models. There is a need to extend the use of such sensors into high temperature environments and so this paper shows, for a broad class of models, the well-posedness of the magneto-electro-thermo-elastic problem. Due to its widespread use in the literature, we also show the well-posedness of the quasi-electrostatic case

Speaking with different voices: the problems with English law and psychiatric injury

Private law courts in the UK have maintained the de minimis threshold as a condition precedent for a successful claim for the infliction of mental harm. This de minimis threshold necessitates the presence of a ‘recognised psychiatric illness’ as opposed to ‘mere emotion’. This standard has also been adopted by the criminal law courts when reading the Offences Against the Person Act 1861 to include non-physical injury. In determining the cut-off point between psychiatric injury and mere emotion, the courts have adopted a generally passive acceptance of expert testimony and the guidelines used by mental health professionals to make diagnoses. Yet these guidelines were developed for use in a clinical setting, not a legal one. This article examines the difficulty inherent in utilising the ‘dimensional’ diagnostic criteria used by mental health professionals to answer ‘categorical’ legal questions. This is of particular concern following publication of the new diagnostic manual, DSM-V in 2013, which will further exacerbate concerns about compatibility. It is argued that a new set of diagnostic guidelines, tailored specifically for use in a legal context, is now a necessity

Towards Narrative Understanding with Deep Neural Networks and Hidden Markov Models

Narratives are central to communication and the human experience. For a computer system to understand a narrative, it must be able to identify the key facts or plot elements that describe what happened or how the world has changed. These element are called events;establishing a document’s events and the relationships between them is central to under-standing a text’s narrative. Events are related to each other temporally and causally by being part of the same story arc. Further, these event sequences typically follow patterns called scripts. In this thesis, I explore three essential stages for narrative understanding. All three stages form an end-to-end system that starts with plain text documents and ultimately produces scripts, a generalization of narrative structure. The first two stages, event detection and event sequence extraction, analyze a document and extract the key information needed to understand a document’s narrative. The final stage, script learning, generalizes the discovered event sequences to find common patterns between them. First, I propose a neural network model based on grammar and syntax. It combines a left-to-right reading of the text along with a reading ordered by the sentence’s syntactic tree. The model is an extension of Gated Recurrent Units and uses an attention mechanism to blend both reading modes. This model achieves state-of-the-art performance on a well-studied task. I present an evaluation that is the first to quantify the substantial variability of neural networks when applied to the nuanced problem of event detection. Two sources of variability are considered: the effect of local optimization of the neural networks’ training procedure and the types of documents used for evaluation and training. I show that the variation involved is often greater than the differences in the state-of-the-art, demonstrating the need for a robust evaluation. Second, the new task of event sequence extraction is addressed with a novel, interpretable neural network framework. The framework represents the problem as a series of graph trans-formations. By doing so, it allows for various neural network architectures to be combined while mirroring the structure of the task. Several models instantiated from the framework are evaluated against a strong baseline showing a substantial improvement on a difficult task.Further, I demonstrate the framework’s flexibility by evaluating it on the entity relation extraction task. Finally, I examine using Hidden Markov Models to learn scripts from event sequences with missing data. This formulation of scripts as Hidden Markov Models is novel and the first to explicitly account for missing observations in the context of natural language processing. The models are learned with a bottom-up induction algorithm based on Structural Expectation Maximization. The scripts are evaluated by inferring omitted events in event sequences and are shown to be more effective than an informed baseline