A structured framework improves clinical patient assessment and nontechnical skills of early career emergency nurses: a pre-post study using full immersion simulation

AIMS AND OBJECTIVES: The aim of this study was to evaluate the effect of the new evidence-informed nursing assessment framework HIRAID (History, Identify Red flags, Assessment, Interventions, Diagnostics, reassessment and communication) on the quality of patient assessment and fundamental nontechnical skills including communication, decision making, task management and situational awareness. BACKGROUND: Assessment is a core component of nursing practice and underpins clinical decisions and the safe delivery of patient care. Yet there is no universal or validated system used to teach emergency nurses how to comprehensively assess and care for patients. DESIGN: A pre-post design was used. METHODS: The performance of thirty eight emergency nurses from five Australian hospitals was evaluated before and after undertaking education in the application of the HIRAID assessment framework. Video recordings of participant performance in immersive simulations of common presentations to the emergency department were evaluated, as well as participant documentation during the simulations. Paired parametric and nonparametric tests were used to compare changes from pre to postintervention. RESULTS: From pre to postintervention, participant performance increases were observed in the percentage of patient history elements collected, critical indicators of urgency collected and reported to medical officers, and patient reassessments performed. Participants also demonstrated improvement in each of the four nontechnical skills categories: communication, decision making, task management and situational awareness. CONCLUSION: The HIRAID assessment framework improves clinical patient assessments performed by emergency nurses and has the potential to enhance patient care. RELEVANCE TO CLINICAL PRACTICE: HIRAID should be considered for integration into clinical practice to provide nurses with a systematic approach to patient assessment and potentially improve the delivery of safe patient care.

Trajectories of intensity. Looking at structured improvisation in 'the weight of the thing left its mark'

This article examines the intentions and implications of ‘structured improvisation’ in relation to my work 'The weight of the thing left its mark' performed in Melbourne in April 2009. The performance piece is premised on the dancers maintaining a performance improvisation practice but to frame these and add predetermined context as a choreographic overlay. In so doing the aim was to create spaces within the work where improvisation could authentically exist while striving for a definable and repeatable ‘work’. The presentation will examine some of the complexities and difficulties inherent in this aim and grapples with the different expectations and conflicts that exist between performance improvisation and choreography.

Reaching for the heavenly bead of teaching through structured problem solving

Japanese Lesson Study has provoked intense interest since it became known in the mid-1990s due to descriptions of the "typical" Japanese abacus - the soroban - with its Earth and Heaven beads as its metaphor, this paper examines the Earth beads necessary to reach the goal of a Heavenly bead - that is, an effective structured problem-solving mathematics lesson.

Development of an innovative non rule-based energy management strategy for a Hybrid Electric Vehicle, structured for predictive driving controls based on external information knowledge

Recently, the interest of the automotive market for hybrid vehicles has increased due to the more restrictive pollutants emissions legislation and to the necessity of decreasing the fossil fuel consumption, since such solution allows a consistent improvement of the vehicle global efficiency. The term hybridization regards the energy flow in the powertrain of a vehicle: a standard vehicle has, usually, only one energy source and one energy tank; instead, a hybrid vehicle has at least two energy sources. In most cases, the prime mover is an internal combustion engine (ICE) while the auxiliary energy source can be mechanical, electrical, pneumatic or hydraulic. It is expected from the control unit of a hybrid vehicle the use of the ICE in high efficiency working zones and to shut it down when it is more convenient, while using the EMG at partial loads and as a fast torque response during transients. However, the battery state of charge may represent a limitation for such a strategy. That’s the reason why, in most cases, energy management strategies are based on the State Of Charge, or SOC, control. Several studies have been conducted on this topic and many different approaches have been illustrated. The purpose of this dissertation is to develop an online (usable on-board) control strategy in which the operating modes are defined using an instantaneous optimization method that minimizes the equivalent fuel consumption of a hybrid electric vehicle. The equivalent fuel consumption is calculated by taking into account the total energy used by the hybrid powertrain during the propulsion phases. The first section presents the hybrid vehicles characteristics. The second chapter describes the global model, with a particular focus on the energy management strategies usable for the supervisory control of such a powertrain. The third chapter shows the performance of the implemented controller on a NEDC cycle compared with the one obtained with the original control strategy.

Leakage Temperature Dependency Aware Real-Time Scheduling for Power and Thermal Optimization

Catering to society’s demand for high performance computing, billions of transistors are now integrated on IC chips to deliver unprecedented performances. With increasing transistor density, the power consumption/density is growing exponentially. The increasing power consumption directly translates to the high chip temperature, which not only raises the packaging/cooling costs, but also degrades the performance/reliability and life span of the computing systems. Moreover, high chip temperature also greatly increases the leakage power consumption, which is becoming more and more significant with the continuous scaling of the transistor size. As the semiconductor industry continues to evolve, power and thermal challenges have become the most critical challenges in the design of new generations of computing systems. In this dissertation, we addressed the power/thermal issues from the system-level perspective. Specifically, we sought to employ real-time scheduling methods to optimize the power/thermal efficiency of the real-time computing systems, with leakage/ temperature dependency taken into consideration. In our research, we first explored the fundamental principles on how to employ dynamic voltage scaling (DVS) techniques to reduce the peak operating temperature when running a real-time application on a single core platform. We further proposed a novel real-time scheduling method, “M-Oscillations” to reduce the peak temperature when scheduling a hard real-time periodic task set. We also developed three checking methods to guarantee the feasibility of a periodic real-time schedule under peak temperature constraint. We further extended our research from single core platform to multi-core platform. We investigated the energy estimation problem on the multi-core platforms and developed a light weight and accurate method to calculate the energy consumption for a given voltage schedule on a multi-core platform. Finally, we concluded the dissertation with elaborated discussions of future extensions of our research.

Towards flexible parsing of structured textual model representations

Existing parsers for textual model representation formats such as XMI and HUTN are unforgiving and fail upon even the smallest inconsistency between the structure and naming of metamodel elements and the contents of serialised models. In this paper, we demonstrate how a fuzzy parsing approach can transparently and automatically resolve a number of these inconsistencies, and how it can eventually turn XML into a human-readable and editable textual model representation format for particular classes of models.

Investigating the mesh dependency and upscaling of 3D grain-based models for the simulation of brittle fracture processes in low-porosity crystalline rock

The application of 3D grain-based modelling techniques is investigated in both small and large scale 3DEC models, in order to simulate brittle fracture processes in low-porosity crystalline rock. Mesh dependency in 3D grain-based models (GBMs) is examined through a number of cases to compare Voronoi and tetrahedral grain assemblages. Various methods are used in the generation of tessellations, each with a number of issues and advantages. A number of comparative UCS test simulations capture the distinct failure mechanisms, strength profiles, and progressive damage development using various Voronoi and tetrahedral GBMs. Relative calibration requirements are outlined to generate similar macro-strength and damage profiles for all the models. The results confirmed a number of inherent model behaviors that arise due to mesh dependency. In Voronoi models, inherent tensile failure mechanisms are produced by internal wedging and rotation of Voronoi grains. This results in a combined dependence on frictional and cohesive strength. In tetrahedral models, increased kinematic freedom of grains and an abundance of straight, connected failure pathways causes a preference for shear failure. This results in an inability to develop significant normal stresses causing cohesional strength dependence. In general, Voronoi models require high relative contact tensile strength values, with lower contact stiffness and contact cohesional strength compared to tetrahedral tessellations. Upscaling of 3D GBMs is investigated for both Voronoi and tetrahedral tessellations using a case study from the AECL’s Mine-by-Experiment at the Underground Research Laboratory. An upscaled tetrahedral model was able to reasonably simulate damage development in the roof forming a notch geometry by adjusting the cohesive strength. An upscaled Voronoi model underestimated the damage development in the roof and floor, and overestimated the damage in the side-walls. This was attributed to the discretization resolution limitations.

Structured professional judgement approach to risk assessment: generalisability across patient groups for the prediction of adverse outcomes in secure mental health care

This is a redacted version of the the final thesis. Copyright material has been removed to comply with UK Copyright Law.

An empirical investigation of the impact of global energy transition on Nigerian oil and gas exports

18 months embargo on the thesis and check appendix for copy right materials

Layer-by-layer structured membranes of silk fibroin and polyethylenimine on electrospun silk fibroin nanofibers

Self-assembled silk fibroin (SF)-polyethylenimine (PEI) multilayered films were fabricated on ethanol treated electrospun SF nanofibrous substrates via the electrostatic layer-by-layer (LBL) adsorption. The film coated membranes were characterized using scanning electron microscopy (SEM), transmission electron microscope (TEM) and X-ray photoelectron spectrophotometer (XPS). The SEM images showed that the multilayers of SF-PEI were formed on the surface of the ethanol treated SF nanofibres. The characteristics such as the fiber shape and porous structure were well maintained as the number of the coated SF-PEI bilayers was less than five. However, obvious adhesive substances and blocked pores were observed on the surface of the fibers as the number of bilayers of SF-PEI increased to six. Furthermore, the obvious core-shell structures were observed by TEM. The thickness of five SF-PEI bilayers was approximately 80nm. Additionally, the XPS results also revealed that the SF-PEI multilayer composite membranes formed. The adsorption mainly depended on a simple electrostatic interaction between the layers of SF and PEI. These SF-PEI multilayer assembled nanofibrous membranes could be a promising material for use as a sensor, gene delivery agent and scaffolds.

Fabrication of core-shell structured natural rubber based microfibres for artificial blood vessel tissue engineering scaffolds

A successful trial on preparing natural rubber based core-shell structured fibres by co-axial electrospinning and fabrication of artificial blood vessel scaffolds from crosslinked fibres.

An embedding scheme for detecting anomalous block structured graphs

Graph-based anomaly detection plays a vital role in various application domains such as network intrusion detection, social network analysis and road traffic monitoring. Although these evolving networks impose a curse of dimensionality on the learning models, they usually contain structural properties that anomaly detection schemes can exploit. The major challenge is finding a feature extraction technique that preserves graph structure while balancing the accuracy of the model against its scalability. We propose the use of a scalable technique known as random projection as a method for structure aware embedding, which extracts relational properties of the network, and present an analytical proof of this claim. We also analyze the effect of embedding on the accuracy of one-class support vector machines for anomaly detection on real and synthetic datasets. We demonstrate that the embedding can be effective in terms of scalability without detrimental influence on the accuracy of the learned model.

A data dependency based strategy for intermediate data storage in scientific cloud workflow systems

Many scientific workflows are data intensive where large volumes of intermediate data are generated during their execution. Some valuable intermediate data need to be stored for sharing or reuse. Traditionally, they are selectively stored according to the system storage capacity, determined manually. As doing science in the cloud has become popular nowadays, more intermediate data can be stored in scientific cloud workflows based on a pay-for-use model. In this paper, we build an intermediate data dependency graph (IDG) from the data provenance in scientific workflows. With the IDG, deleted intermediate data can be regenerated, and as such we develop a novel intermediate data storage strategy that can reduce the cost of scientific cloud workflow systems by automatically storing appropriate intermediate data sets with one cloud service provider. The strategy has significant research merits, i.e. it achieves a cost-effective trade-off of computation cost and storage cost and is not strongly impacted by the forecasting inaccuracy of data sets' usages. Meanwhile, the strategy also takes the users' tolerance of data accessing delay into consideration. We utilize Amazon's cost model and apply the strategy to general random as well as specific astrophysics pulsar searching scientific workflows for evaluation. The results show that our strategy can reduce the overall cost of scientific cloud workflow execution significantly.

Query routing in cooperative semi-structured peer-to-peer information retrieval networks

Conventional web search engines are centralised in that a single entity crawls and indexes the documents selected for future retrieval, and the relevance models used to determine which documents are relevant to a given user query. As a result, these search engines suffer from several technical drawbacks such as handling scale, timeliness and reliability, in addition to ethical concerns such as commercial manipulation and information censorship. Alleviating the need to rely entirely on a single entity, Peer-to-Peer (P2P) Information Retrieval (IR) has been proposed as a solution, as it distributes the functional components of a web search engine – from crawling and indexing documents, to query processing – across the network of users (or, peers) who use the search engine. This strategy for constructing an IR system poses several efficiency and effectiveness challenges which have been identified in past work. Accordingly, this thesis makes several contributions towards advancing the state of the art in P2P-IR effectiveness by improving the query processing and relevance scoring aspects of a P2P web search. Federated search systems are a form of distributed information retrieval model that route the user’s information need, formulated as a query, to distributed resources and merge the retrieved result lists into a final list. P2P-IR networks are one form of federated search in routing queries and merging result among participating peers. The query is propagated through disseminated nodes to hit the peers that are most likely to contain relevant documents, then the retrieved result lists are merged at different points along the path from the relevant peers to the query initializer (or namely, customer). However, query routing in P2P-IR networks is considered as one of the major challenges and critical part in P2P-IR networks; as the relevant peers might be lost in low-quality peer selection while executing the query routing, and inevitably lead to less effective retrieval results. This motivates this thesis to study and propose query routing techniques to improve retrieval quality in such networks. Cluster-based semi-structured P2P-IR networks exploit the cluster hypothesis to organise the peers into similar semantic clusters where each such semantic cluster is managed by super-peers. In this thesis, I construct three semi-structured P2P-IR models and examine their retrieval effectiveness. I also leverage the cluster centroids at the super-peer level as content representations gathered from cooperative peers to propose a query routing approach called Inverted PeerCluster Index (IPI) that simulates the conventional inverted index of the centralised corpus to organise the statistics of peers’ terms. The results show a competitive retrieval quality in comparison to baseline approaches. Furthermore, I study the applicability of using the conventional Information Retrieval models as peer selection approaches where each peer can be considered as a big document of documents. The experimental evaluation shows comparative and significant results and explains that document retrieval methods are very effective for peer selection that brings back the analogy between documents and peers. Additionally, Learning to Rank (LtR) algorithms are exploited to build a learned classifier for peer ranking at the super-peer level. The experiments show significant results with state-of-the-art resource selection methods and competitive results to corresponding classification-based approaches. Finally, I propose reputation-based query routing approaches that exploit the idea of providing feedback on a specific item in the social community networks and manage it for future decision-making. The system monitors users’ behaviours when they click or download documents from the final ranked list as implicit feedback and mines the given information to build a reputation-based data structure. The data structure is used to score peers and then rank them for query routing. I conduct a set of experiments to cover various scenarios including noisy feedback information (i.e, providing positive feedback on non-relevant documents) to examine the robustness of reputation-based approaches. The empirical evaluation shows significant results in almost all measurement metrics with approximate improvement more than 56% compared to baseline approaches. Thus, based on the results, if one were to choose one technique, reputation-based approaches are clearly the natural choices which also can be deployed on any P2P network.

Sobreuso de analgésicos en pacientes con cefaleas primarias de un centro de referencia de Medellín

Introducción: Los pacientes con cefaleas primarias están predispuestos a desarrollar una cefalea secundaria al consumo excesivo de analgésicos. En nuestro medio, la adquisición fácil de medicamentos sin fórmula médica incrementa su frecuencia de presentación. Objetivo: Describir el perfil epidemiológico, clínico y de tratamiento de los pacientes con cefaleas primarias y cefalea secundaria por sobreuso de analgésicos atendidos en el programa especial de cefaleas del Instituto Neurológico de Colombia en el periodo 2014-2015. Pacientes y métodos: estudio descriptivo, retrospectivo. Se evaluaron características de la cefalea, comorbilidades, discapacidad y patrón de consumo de medicamentos. Resultados: Se incluyeron 834 pacientes con diagnóstico de cefalea por sobreuso de analgésicos, 85.1% con migraña crónica. El 87.6% de los pacientes eran mujeres y tenían 44 años en promedio. La mitad de los pacientes tomaba analgésicos todos los días de la semana (P25-P75: 4 – 7 días); consumiendo, en promedio, tres analgésicos/día (DE: 1.9). La mayoría de pacientes presentaba un consumo elevado de analgésicos simples (95.2%), AINES (92.2%) y analgésicos combinados (89.2%); 51% consumían opiodes y sólo 14.6% consumían triptanes. Conclusión: La identificación de esta cefalea secundaria es de vital importancia para un tratamiento adecuado. El uso medicamentos de libre venta afecta el manejo óptimo de esta entidad asociada a las cefaleas primarias, principalmente la migraña.