Performance Energética no contexto do sistema LEED

Com o aumento constante de procura de recursos naturais por parte dos vários setores da sociedade é urgente encontrar soluções para reduzir o seu consumo sem se travar a expansão demográfica que se tem vindo a sentir nos grandes centros urbanos. É através da implementação de medidas de sustentabilidade e pelo aumento da eficiência de utilização desses recursos que se tem vindo a combater esta tendência cada vez maior de consumismo global, sendo isto apenas possível com a implementação de ferramentas tecnológicas avançadas que permitem estabelecer limites ao considerado eficiente e premiando, em termos financeiros e de imagem de marketing, as entidades que o alcancem. O LEED é um sistema de certificação de sustentabilidade voluntário de edifícios residenciais e comerciais que estabelece métricas de comparação de parâmetros indicadores de consumos energéticos, hídricos e de materiais em todo o ciclo de vida do edifício e que tem vindo a ganhar destaque em crescendo a nível mundial. Esta dissertação teve como objetivo comparar a performance de consumo energético no âmbito do sistema LEED com a do sistema de certificação energética de edifícios nacional (SCE) de um grande edifício de serviços, estabelecendo um paralelismo de semelhanças e diferenças entre os dois e de avaliar os efeitos de potenciais medidas de eficiência energética e seus efeitos nas classificações de mérito obtidas em cada sistema. Os resultados obtidos na simulação que permitiu avaliar a performance foi muito satisfatório, tendo sido aproveitado pela empresa para efeitos de certificação LEED do edifício em estudo.

Estudo de um método de avaliação de desempenho aplicado aos técnicos de radiologia do Hospital Curry Cabral

A avaliação do desempenho e a sua aplicação são, no actual enquadramento socioeconómico, cada vez mais necessárias, como forma de melhorar a eficácia e a eficiência entre as organizações. Para a prática profissional verifica-se que a implementação da avaliação de desempenho, apresenta lacunas que podem comprometê-la. Sabendo da importância do profissional para o sucesso da implementação da avaliação de desempenho, toma-se fundamental, analisar a sua percepção face ao modelo e sua implementação. O problema da instrução do presente estudo pretende saber quais as percepções dos técnicos de radiologia avaliados sujeitos ao modelo de avaliação de desempenho implantado no Hospital Curry Cabral. Tendo como objectivo ao nível da gestão, minimizar os obstáculos de implementação e maximizar os pontos fortes. Após pesquisa bibliográfica sobre os principais conceitos, foi definido domo objectivo da investigação empírica: - Comparar as percepções dos técnicos de radiologia submetidos ao modelo de avaliação desempenho ”Pró-Activo”. Recorrendo a uma metodologia exploratória e descritiva, estudámos o impacto dessas ferramentas utilizadas dentro da Unidade Hospitalar do estudo. A metodologia de recolha de informação aos profissionais expostos do estudo assentou em questionários de perguntas fechadas e abertas ambas com uma abordagem de carácter quantitativo. Para a análise e tratamento dos dados utilizou-se programas informáticos. As principais conclusões: verifica-se a falta de formação para todos os envolvidos, um processo desprovido de imparcialidade, neutralidade e rigor, bem como uma motivação geral dos profissionais em matéria de avaliação de desempenho. ABSTRACT - The evaluation of performance and its implementation are, in the current socioeconomic framing, each time more necessary as form to improve the effectiveness and the efficiency amidst the organizations. For the practical professional it is verified that the implementation of the performance evaluation, presents gaps that can compromise it. Knowing the importance of the individuals in the success of the implementation of the performance evaluation, it becomes basic to analyze its perceptions face to the model and its implementation. The problem of inquiry of this study pretends to know which the perceptions of the technicians of radiology evaluated and appraisers face to the models of evaluation of performance implanted in the Portuguese Hospitals (Hospital Curry Cabral). The purpose to the level of the management is to minimize the obstacles of implementation and to maximize the strong points. Bibliographical research on the main concepts was effectuated, after what we define the objectives of empirica inquiry: - To compare the perceptions of the radiology technicians subjected to models of performance evaluation “Pro-Activa”. Using an exploratory and descriptive approach, studied the impact of these tools used in the hospitals of the study. The methodology for collecting information to professionals out of the study based on questionnaires of both open and closed questions with a quantitative approach to nature. For analysis and data processing software was used. The main conclusions: there is the lack of training for all involved, a process devoid of impartiality, neutrality and accuracy, as well as a general motivation of the professionals regarding the evaluation of performance.

Avaliação da performance do árbitro de futebol 11 : estudo de caso

O aumento da carga física do jogo de futebol provocou uma maior exigência e desenvolvimento na condição física dos jogadores e por inerência, nos árbitros. Assim o presente estudo procurou identificar e desenvolver um teste para a avaliação dos árbitros de futebol. Foi realizada uma análise sistemática para identificação e descrição da produção científica na área da arbitragem no sentido de sustentar o argumento de insuficiência dos testes vigentes e propor o novo teste que denominámos ETSOR. Após esta, foi realizada uma aplicação piloto com recurso ao método de estudo de caso para testagem do ETSOR. Os resultados revelaram que existe uma dispersão nas formas e conteúdos abordados face à caracterização do árbitro de futebol de 11. A partir do método de meta-análise, é apresentada uma proposta de categorização dos conteúdos. Os resultados revelaram também que o teste FIFA não identifica as intensidades irregulares que decorrem das situações do jogo, nem representa a uma distribuição das intensidades dos esforços dos árbitros nas situações de jogo. O Teste ETSOR, como teste ecológico, capta em termos de densidade, de distribuição, de variação da potência e de resistência, os esforços dos árbitros nas situações de jogo, como testa a características das intensidades máximas da atividade do árbitro. Por último, os resultados reforçaram que este processo que se deve estender de forma periodizada ao longo de cada época tornando-se útil, na medida em que permite a otimização e monitorização da prestação do árbitro.

A survey on multi-output regression

In recent years, a plethora of approaches have been proposed to deal with the increasingly challenging task of multi-output regression. This paper provides a survey on state-of-the-art multi-output regression methods, that are categorized as problem transformation and algorithm adaptation methods. In addition, we present the mostly used performance evaluation measures, publicly available data sets for multi-output regression real-world problems, as well as open-source software frameworks.

A mobile cloud computing framework integrating multilevel encoding for performance monitoring in telerehabilitation

Recent years have witnessed a surge in telerehabilitation and remote healthcare systems blessed by the emerging low-cost wearable devices to monitor biological and biokinematic aspects of human beings. Although such telerehabilitation systems utilise cloud computing features and provide automatic biofeedback and performance evaluation, there are demands for overall optimisation to enable these systems to operate with low battery consumption and low computational power and even with weak or no network connections. This paper proposes a novel multilevel data encoding scheme satisfying these requirements in mobile cloud computing applications, particularly in the field of telerehabilitation. We introduce architecture for telerehabilitation platform utilising the proposed encoding scheme integrated with various types of sensors. The platform is usable not only for patients to experience telerehabilitation services but also for therapists to acquire essential support from analysis oriented decision support system (AODSS) for more thorough analysis and making further decisions on treatment.

The conditional performance of euro bond funds: evidence from Portugal during the debt crisis

This paper evaluates the performance of a survivorship bias-free data set of Portuguese funds investing in Euro-denominated bonds by using conditional models that consider the public information available to investors when the returns are generated. We find that bond funds underperform the market significantly and by an economically relevant magnitude. This underperformance cannot be explained by the expenses they charge. Our findings support the use of conditional performance evaluation models, since we find strong evidence of both time-varying risk and performance, dependent on the slope of the term structure and the inverse relative wealth variables. We also show that survivorship bias has a significant impact on performance estimates. Furthermore, during the European debt crisis, bond fund managers performed significantly better than in non-crisis periods and were able to achieve neutral performance. This improved performance throughout the crisis seems to be related to changes in funds’ investment styles.

Sleeper [for laptop computer and live performer]

Sleeper is an 18'00" musical work for live performer and laptop computer which exists as both a live performance work and a recorded work for audio CD. The work has been presented at a range of international performance events and survey exhibitions. These include the 2003 International Computer Music Conference (Singapore) where it was selected for CD publication, Variable Resistance (San Francisco Museum of Modern Art, USA), and i.audio, a survey of experimental sound at the Performance Space, Sydney. The source sound materials are drawn from field recordings made in acoustically resonant spaces in the Australian urban environment, amplified and acoustic instruments, radio signals, and sound synthesis procedures. The processing techniques blur the boundaries between, and exploit, the perceptual ambiguities of de-contextualised and processed sound. The work thus challenges the arbitrary distinctions between sound, noise and music and attempts to reveal the inherent musicality in so-called non-musical materials via digitally re-processed location audio. Thematically the work investigates Paul Virilio’s theory that technology ‘collapses space’ via the relationship of technology to speed. Technically this is explored through the design of a music composition process that draws upon spatially and temporally dispersed sound materials treated using digital audio processing technologies. One of the contributions to knowledge in this work is a demonstration of how disparate materials may be employed within a compositional process to produce music through the establishment of musically meaningful morphological, spectral and pitch relationships. This is achieved through the design of novel digital audio processing networks and a software performance interface. The work explores, tests and extends the music perception theories of ‘reduced listening’ (Schaeffer, 1967) and ‘surrogacy’ (Smalley, 1997), by demonstrating how, through specific audio processing techniques, sounds may shifted away from ‘causal’ listening contexts towards abstract aesthetic listening contexts. In doing so, it demonstrates how various time and frequency domain processing techniques may be used to achieve this shift.

A New robot for environmental monitoring on the Great Barrier Reef

A vast amount of research into autonomous underwater navigation has, and is, being conducted around the world. However, typical research and commercial platforms have limited autonomy and are generally unable to navigate efficiently within coral reef environments without tethers and significant external infrastructure. This paper outlines the development and presents experimental results into the performance evaluation of a new robotic vehicle for underwater monitoring and surveying in highly unstructured environments. The hybrid AUV design developed by the CSIRO robotic reef monitoring team realises a compromise between endurance, manoeuvrability and functionality. The vehicle represents a new era in AUV design specifically focused at providing a truly lowcost research capability that will progress environmental monitoring through unaided navigation, cooperative robotics, sensor network distribution and data harvesting.

Application of data mining techniques in the prediction of coronary artery disease : use of anaesthesia time-series and patient risk factor data

The high morbidity and mortality associated with atherosclerotic coronary vascular disease (CVD) and its complications are being lessened by the increased knowledge of risk factors, effective preventative measures and proven therapeutic interventions. However, significant CVD morbidity remains and sudden cardiac death continues to be a presenting feature for some subsequently diagnosed with CVD. Coronary vascular disease is also the leading cause of anaesthesia related complications. Stress electrocardiography/exercise testing is predictive of 10 year risk of CVD events and the cardiovascular variables used to score this test are monitored peri-operatively. Similar physiological time-series datasets are being subjected to data mining methods for the prediction of medical diagnoses and outcomes. This study aims to find predictors of CVD using anaesthesia time-series data and patient risk factor data. Several pre-processing and predictive data mining methods are applied to this data. Physiological time-series data related to anaesthetic procedures are subjected to pre-processing methods for removal of outliers, calculation of moving averages as well as data summarisation and data abstraction methods. Feature selection methods of both wrapper and filter types are applied to derived physiological time-series variable sets alone and to the same variables combined with risk factor variables. The ability of these methods to identify subsets of highly correlated but non-redundant variables is assessed. The major dataset is derived from the entire anaesthesia population and subsets of this population are considered to be at increased anaesthesia risk based on their need for more intensive monitoring (invasive haemodynamic monitoring and additional ECG leads). Because of the unbalanced class distribution in the data, majority class under-sampling and Kappa statistic together with misclassification rate and area under the ROC curve (AUC) are used for evaluation of models generated using different prediction algorithms. The performance based on models derived from feature reduced datasets reveal the filter method, Cfs subset evaluation, to be most consistently effective although Consistency derived subsets tended to slightly increased accuracy but markedly increased complexity. The use of misclassification rate (MR) for model performance evaluation is influenced by class distribution. This could be eliminated by consideration of the AUC or Kappa statistic as well by evaluation of subsets with under-sampled majority class. The noise and outlier removal pre-processing methods produced models with MR ranging from 10.69 to 12.62 with the lowest value being for data from which both outliers and noise were removed (MR 10.69). For the raw time-series dataset, MR is 12.34. Feature selection results in reduction in MR to 9.8 to 10.16 with time segmented summary data (dataset F) MR being 9.8 and raw time-series summary data (dataset A) being 9.92. However, for all time-series only based datasets, the complexity is high. For most pre-processing methods, Cfs could identify a subset of correlated and non-redundant variables from the time-series alone datasets but models derived from these subsets are of one leaf only. MR values are consistent with class distribution in the subset folds evaluated in the n-cross validation method. For models based on Cfs selected time-series derived and risk factor (RF) variables, the MR ranges from 8.83 to 10.36 with dataset RF_A (raw time-series data and RF) being 8.85 and dataset RF_F (time segmented time-series variables and RF) being 9.09. The models based on counts of outliers and counts of data points outside normal range (Dataset RF_E) and derived variables based on time series transformed using Symbolic Aggregate Approximation (SAX) with associated time-series pattern cluster membership (Dataset RF_ G) perform the least well with MR of 10.25 and 10.36 respectively. For coronary vascular disease prediction, nearest neighbour (NNge) and the support vector machine based method, SMO, have the highest MR of 10.1 and 10.28 while logistic regression (LR) and the decision tree (DT) method, J48, have MR of 8.85 and 9.0 respectively. DT rules are most comprehensible and clinically relevant. The predictive accuracy increase achieved by addition of risk factor variables to time-series variable based models is significant. The addition of time-series derived variables to models based on risk factor variables alone is associated with a trend to improved performance. Data mining of feature reduced, anaesthesia time-series variables together with risk factor variables can produce compact and moderately accurate models able to predict coronary vascular disease. Decision tree analysis of time-series data combined with risk factor variables yields rules which are more accurate than models based on time-series data alone. The limited additional value provided by electrocardiographic variables when compared to use of risk factors alone is similar to recent suggestions that exercise electrocardiography (exECG) under standardised conditions has limited additional diagnostic value over risk factor analysis and symptom pattern. The effect of the pre-processing used in this study had limited effect when time-series variables and risk factor variables are used as model input. In the absence of risk factor input, the use of time-series variables after outlier removal and time series variables based on physiological variable values’ being outside the accepted normal range is associated with some improvement in model performance.

Markov modelling of the IEEE 802.11 DCF for real-time applications with periodic traffic

Popular wireless network standards, such as IEEE 802.11/15/16, are increasingly adopted in real-time control systems. However, they are not designed for real-time applications. Therefore, the performance of such wireless networks needs to be carefully evaluated before the systems are implemented and deployed. While efforts have been made to model general wireless networks with completely random traffic generation, there is a lack of theoretical investigations into the modelling of wireless networks with periodic real-time traffic. Considering the widely used IEEE 802.11 standard, with the focus on its distributed coordination function (DCF), for soft-real-time control applications, this paper develops an analytical Markov model to quantitatively evaluate the network quality-of-service (QoS) performance in periodic real-time traffic environments. Performance indices to be evaluated include throughput capacity, transmission delay and packet loss ratio, which are crucial for real-time QoS guarantee in real-time control applications. They are derived under the critical real-time traffic condition, which is formally defined in this paper to characterize the marginal satisfaction of real-time performance constraints.

Analysis of cardiac and epileptic signals using higher order spectra

The theory of nonlinear dyamic systems provides some new methods to handle complex systems. Chaos theory offers new concepts, algorithms and methods for processing, enhancing and analyzing the measured signals. In recent years, researchers are applying the concepts from this theory to bio-signal analysis. In this work, the complex dynamics of the bio-signals such as electrocardiogram (ECG) and electroencephalogram (EEG) are analyzed using the tools of nonlinear systems theory. In the modern industrialized countries every year several hundred thousands of people die due to sudden cardiac death. The Electrocardiogram (ECG) is an important biosignal representing the sum total of millions of cardiac cell depolarization potentials. It contains important insight into the state of health and nature of the disease afflicting the heart. Heart rate variability (HRV) refers to the regulation of the sinoatrial node, the natural pacemaker of the heart by the sympathetic and parasympathetic branches of the autonomic nervous system. Heart rate variability analysis is an important tool to observe the heart's ability to respond to normal regulatory impulses that affect its rhythm. A computerbased intelligent system for analysis of cardiac states is very useful in diagnostics and disease management. Like many bio-signals, HRV signals are non-linear in nature. Higher order spectral analysis (HOS) is known to be a good tool for the analysis of non-linear systems and provides good noise immunity. In this work, we studied the HOS of the HRV signals of normal heartbeat and four classes of arrhythmia. This thesis presents some general characteristics for each of these classes of HRV signals in the bispectrum and bicoherence plots. Several features were extracted from the HOS and subjected an Analysis of Variance (ANOVA) test. The results are very promising for cardiac arrhythmia classification with a number of features yielding a p-value < 0.02 in the ANOVA test. An automated intelligent system for the identification of cardiac health is very useful in healthcare technology. In this work, seven features were extracted from the heart rate signals using HOS and fed to a support vector machine (SVM) for classification. The performance evaluation protocol in this thesis uses 330 subjects consisting of five different kinds of cardiac disease conditions. The classifier achieved a sensitivity of 90% and a specificity of 89%. This system is ready to run on larger data sets. In EEG analysis, the search for hidden information for identification of seizures has a long history. Epilepsy is a pathological condition characterized by spontaneous and unforeseeable occurrence of seizures, during which the perception or behavior of patients is disturbed. An automatic early detection of the seizure onsets would help the patients and observers to take appropriate precautions. Various methods have been proposed to predict the onset of seizures based on EEG recordings. The use of nonlinear features motivated by the higher order spectra (HOS) has been reported to be a promising approach to differentiate between normal, background (pre-ictal) and epileptic EEG signals. In this work, these features are used to train both a Gaussian mixture model (GMM) classifier and a Support Vector Machine (SVM) classifier. Results show that the classifiers were able to achieve 93.11% and 92.67% classification accuracy, respectively, with selected HOS based features. About 2 hours of EEG recordings from 10 patients were used in this study. This thesis introduces unique bispectrum and bicoherence plots for various cardiac conditions and for normal, background and epileptic EEG signals. These plots reveal distinct patterns. The patterns are useful for visual interpretation by those without a deep understanding of spectral analysis such as medical practitioners. It includes original contributions in extracting features from HRV and EEG signals using HOS and entropy, in analyzing the statistical properties of such features on real data and in automated classification using these features with GMM and SVM classifiers.