A checklist for patient safety rounds at the care pathway level

Objective - To define a checklist that can be used to assess the performance of a department and evaluate the implementation of quality management (QM) activities across departments or pathways in acute care hospitals. Design - We developed and tested a checklist for the assessment of QM activities at department level in a cross-sectional study using on-site visits by trained external auditors. Setting and Participants - A sample of 292 hospital departments of 74 acute care hospitals across seven European countries. In every hospital, four departments for the conditions: acute myocardial infarction (AMI), stroke, hip fracture and deliveries participated. Main outcome measures - Four measures of QM activities were evaluated at care pathway level focusing on specialized expertise and responsibility (SER), evidence-based organization of pathways (EBOP), patient safety strategies and clinical review (CR). Results - Participating departments attained mean values on the various scales between 1.2 and 3.7. The theoretical range was 0-4. Three of the four QM measures are identical for the four conditions, whereas one scale (EBOP) has condition-specific items. Correlations showed that every factor was related, but also distinct, and added to the overall picture of QM at pathway level. Conclusion - The newly developed checklist can be used across various types of departments and pathways in acute care hospitals like AMI, deliveries, stroke and hip fracture. The anticipated users of the checklist are internal (e.g. peers within the hospital and hospital executive board) and external auditors (e.g. healthcare inspectorate, professional or patient organizations).

Simulation of offshore wind system with two-level converters: HVDC power transmission

A new integrated mathematical model for the simulation of offshore wind energy conversion system performance is presented in this paper. The mathematical model considers an offshore variable-speed turbine in deep water equipped with a permanent magnet synchronous generator using full-power two-level converter, converting the energy of a variable frequency source in injected energy into the electric network with constant frequency, through a high voltage DC transmission submarine cable. The mathematical model for the drive train is a concentrate two mass model which incorporates the dynamic for the structure and tower due to the need to emulate the effects of the moving surface. Controller strategy considered is a proportional integral one. Also, pulse width modulation using space vector modulation supplemented with sliding mode is used for trigger the transistor of the converter. Finally, a case study is presented to access the system performance. © 2014 IEEE.

Climatology of the Iberia coastal low-level wind jet: weather research forecasting model high-resolution results

Coastal low-level jets (CLLJ) are a low-tropospheric wind feature driven by the pressure gradient produced by a sharp contrast between high temperatures over land and lower temperatures over the sea. This contrast between the cold ocean and the warm land in the summer is intensified by the impact of the coastal parallel winds on the ocean generating upwelling currents, sharpening the temperature gradient close to the coast and giving rise to strong baroclinic structures at the coast. During summertime, the Iberian Peninsula is often under the effect of the Azores High and of a thermal low pressure system inland, leading to a seasonal wind, in the west coast, called the Nortada (northerly wind). This study presents a regional climatology of the CLLJ off the west coast of the Iberian Peninsula, based on a 9km resolution downscaling dataset, produced using the Weather Research and Forecasting (WRF) mesoscale model, forced by 19 years of ERA-Interim reanalysis (1989-2007). The simulation results show that the jet hourly frequency of occurrence in the summer is above 30% and decreases to about 10% during spring and autumn. The monthly frequencies of occurrence can reach higher values, around 40% in summer months, and reveal large inter-annual variability in all three seasons. In the summer, at a daily base, the CLLJ is present in almost 70% of the days. The CLLJ wind direction is mostly from north-northeasterly and occurs more persistently in three areas where the interaction of the jet flow with local capes and headlands is more pronounced. The coastal jets in this area occur at heights between 300 and 400 m, and its speed has a mean around 15 m/s, reaching maximum speeds of 25 m/s.

Enhancing Multimodal Silent Speech Interfaces With Feature Selection

In research on Silent Speech Interfaces (SSI), different sources of information (modalities) have been combined, aiming at obtaining better performance than the individual modalities. However, when combining these modalities, the dimensionality of the feature space rapidly increases, yielding the well-known "curse of dimensionality". As a consequence, in order to extract useful information from this data, one has to resort to feature selection (FS) techniques to lower the dimensionality of the learning space. In this paper, we assess the impact of FS techniques for silent speech data, in a dataset with 4 non-invasive and promising modalities, namely: video, depth, ultrasonic Doppler sensing, and surface electromyography. We consider two supervised (mutual information and Fisher's ratio) and two unsupervised (meanmedian and arithmetic mean geometric mean) FS filters. The evaluation was made by assessing the classification accuracy (word recognition error) of three well-known classifiers (knearest neighbors, support vector machines, and dynamic time warping). The key results of this study show that both unsupervised and supervised FS techniques improve on the classification accuracy on both individual and combined modalities. For instance, on the video component, we attain relative performance gains of 36.2% in error rates. FS is also useful as pre-processing for feature fusion. Copyright © 2014 ISCA.

Fifth harmonic and sag impact on PMSG wind turbines with a balancing new strategy for capacitor voltages

This paper deals with the computing simulation of the impact on permanent magnet synchronous generator wind turbines due to fifth harmonic content and grid voltage decrease. Power converter topologies considered in the simulations are the two-level and the three-level ones. The three-level converters are limited by unbalance voltages in the DC-link capacitors. In order to lessen this limitation, a new control strategy for the selection of the output voltage vectors is proposed. Controller strategies considered in the simulation are respectively based on proportional integral and fractional-order controllers. Finally, a comparison between the results of the simulations with the two controller strategies is presented in order to show the main advantage of the proposed strategy. (C) 2014 Elsevier Ltd. All rights reserved.

Enhancing the selection of a model-based clustering with external categorical variables

In cluster analysis, it can be useful to interpret the partition built from the data in the light of external categorical variables which are not directly involved to cluster the data. An approach is proposed in the model-based clustering context to select a number of clusters which both fits the data well and takes advantage of the potential illustrative ability of the external variables. This approach makes use of the integrated joint likelihood of the data and the partitions at hand, namely the model-based partition and the partitions associated to the external variables. It is noteworthy that each mixture model is fitted by the maximum likelihood methodology to the data, excluding the external variables which are used to select a relevant mixture model only. Numerical experiments illustrate the promising behaviour of the derived criterion. © 2014 Springer-Verlag Berlin Heidelberg.

An unsupervised approach to feature discretization and selection

Many learning problems require handling high dimensional datasets with a relatively small number of instances. Learning algorithms are thus confronted with the curse of dimensionality, and need to address it in order to be effective. Examples of these types of data include the bag-of-words representation in text classification problems and gene expression data for tumor detection/classification. Usually, among the high number of features characterizing the instances, many may be irrelevant (or even detrimental) for the learning tasks. It is thus clear that there is a need for adequate techniques for feature representation, reduction, and selection, to improve both the classification accuracy and the memory requirements. In this paper, we propose combined unsupervised feature discretization and feature selection techniques, suitable for medium and high-dimensional datasets. The experimental results on several standard datasets, with both sparse and dense features, show the efficiency of the proposed techniques as well as improvements over previous related techniques.

Efficient feature selection filters for high-dimensional data

Feature selection is a central problem in machine learning and pattern recognition. On large datasets (in terms of dimension and/or number of instances), using search-based or wrapper techniques can be cornputationally prohibitive. Moreover, many filter methods based on relevance/redundancy assessment also take a prohibitively long time on high-dimensional. datasets. In this paper, we propose efficient unsupervised and supervised feature selection/ranking filters for high-dimensional datasets. These methods use low-complexity relevance and redundancy criteria, applicable to supervised, semi-supervised, and unsupervised learning, being able to act as pre-processors for computationally intensive methods to focus their attention on smaller subsets of promising features. The experimental results, with up to 10(5) features, show the time efficiency of our methods, with lower generalization error than state-of-the-art techniques, while being dramatically simpler and faster.

Commercial agentes portfolio optimization in electricity markets

As it is well known, competitive electricity markets require new computing tools for power companies that operate in retail markets in order to enhance the management of its energy resources. During the last years there has been an increase of the renewable penetration into the micro-generation which begins to co-exist with the other existing power generation, giving rise to a new type of consumers. This paper develops a methodology to be applied to the management of the all the aggregators. The aggregator establishes bilateral contracts with its clients where the energy purchased and selling conditions are negotiated not only in terms of prices but also for other conditions that allow more flexibility in the way generation and consumption is addressed. The aggregator agent needs a tool to support the decision making in order to compose and select its customers' portfolio in an optimal way, for a given level of profitability and risk.

Bisphenol A at the reference level counteracts doxorubicin transcriptional effects on cancer related genes in HT29 cells

Human exposure to Bisphenol A (BPA) results mainly from ingestion of food and beverages. Information regarding BPA effects on colon cancer, one of the major causes of death in developed countries, is still scarce. Likewise, little is known about BPA drug interactions although its potential role in doxorubicin (DOX) chemoresistance has been suggested. This study aims to assess potential interactions between BPA and DOX on HT29 colon cancer cells. HT29 cell response was evaluated after exposure to BPA, DOX, or co-exposure to both chemicals. Transcriptional analysis of several cancer-associated genes (c-fos, AURKA, p21, bcl-xl and CLU) shows that BPA exposure induces slight up-regulation exclusively of bcl-xl without affecting cell viability. On the other hand, a sub-therapeutic DOX concentration (40nM) results in highly altered c-fos, bcl-xl, and CLU transcript levels, and this is not affected by co-exposure with BPA. Conversely, DOX at a therapeutic concentration (4μM) results in distinct and very severe transcriptional alterations of c-fos, AURKA, p21 and CLU that are counteracted by co-exposure with BPA resulting in transcript levels similar to those of control. Co-exposure with BPA slightly decreases apoptosis in relation to DOX 4μM alone without affecting DOX-induced loss of cell viability. These results suggest that BPA exposure can influence chemotherapy outcomes and therefore emphasize the necessity of a better understanding of BPA interactions with chemotherapeutic agents in the context of risk assessment.

Application of a control banding tool for risk level assessment and control of nanoparticles exposure in welding operations

Scope of study: welding operations result in harmful emissions of nanoparticles; the aim of emissions monitorisation is to evaluate exposure levels and to derive protection measures in order to protect exposed workers; however, the traditional approach of comparing measured concentrations with exposure limits cannot be used; but risk levels can be quantified by using Control Banding Strategies.

Metodologia para otimização da manutenção

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia da Manutenção

Simulation of rectifier voltage malfunction on OWECS, four-level converter, HVDC light link: Smart grid context tool

This paper presents a model for the simulation of an offshore wind system having a rectifier input voltage malfunction at one phase. The offshore wind system model comprises a variable-speed wind turbine supported on a floating platform, equipped with a permanent magnet synchronous generator using full-power four-level neutral point clamped converter. The link from the offshore floating platform to the onshore electrical grid is done through a light high voltage direct current submarine cable. The drive train is modeled by a three-mass model. Considerations about the smart grid context are offered for the use of the model in such a context. The rectifier voltage malfunction domino effect is presented as a case study to show capabilities of the model. (C) 2015 Elsevier Ltd. All rights reserved.

Enhancing the selection of a model-based clustering with external categorical variables

In cluster analysis, it can be useful to interpret the partition built from the data in the light of external categorical variables which are not directly involved to cluster the data. An approach is proposed in the model-based clustering context to select a number of clusters which both fits the data well and takes advantage of the potential illustrative ability of the external variables. This approach makes use of the integrated joint likelihood of the data and the partitions at hand, namely the model-based partition and the partitions associated to the external variables. It is noteworthy that each mixture model is fitted by the maximum likelihood methodology to the data, excluding the external variables which are used to select a relevant mixture model only. Numerical experiments illustrate the promising behaviour of the derived criterion.

High-level Petri nets modules for embedded controllers design

Modular design is crucial to manage large-scale systems and to support the divide-and-conquer development approach. It allows hierarchical representations and, therefore, one can have a system overview, as well as observe component details. Petri nets are suitable to model concurrent systems, but lack on structuring mechanisms to support abstractions and the composition of sub-models, in particular when considering applications to embedded controllers design. In this paper we present a module construct, and an underlying high-level Petri net type, to model embedded controllers. Multiple interfaces can be declared in a module, thus, different instances of the same module can be used in different situations. The interface is a subset of the module nodes, through which the communication with the environment is made. Module places can be annotated with a generic type, overridden with a concrete type at instance level, and constants declared in a module may have a new value in each instance.