On the mechanism of the anticlotting action of vitamin E quinone.

Vitamin E in the reduced, alpha-tocopherol form shows very modest anticlotting activity. By contrast, vitamin E quinone is a potent anticoagulant. This observation may have significance for field trials in which vitamin E is observed to exhibit beneficial effects on ischemic heart disease and stroke. Vitamin E quinone is a potent inhibitor of the vitamin K-dependent carboxylase that controls blood clotting. A newly discovered mechanism for the inhibition requires attachment of the active site thiol groups of the carboxylase to one or more methyl groups on vitamin E quinone. The results from a series of model reactions support this interpretation of the anticlotting activity associated with vitamin E.

An Analytical Solution for the Elastic Lateral-Distortional Buckling of I-section Beams

Lateral-distortional buckling may occur in I-section beams with slender webs and stocky flanges. A computationally efficient method is presented in this paper to study this phenomenon. Previous studies on distortional buckling have been on the use of 3(rd) and 5(th) order polynomials to model the displacements. The present study provides an alternative way, using Fourier Series, to model the behaviour. Beams of different cross-sectional dimensions, load cases and restraint conditions are examined and compared. The accuracy and versatility of the method are verified by calibrating against the results of other published studies. The present method is believed to be a simple and efficient way of determining the buckling load and mode shapes of I-section beams that are susceptible to lateral-distortional buckling modes.

Anomalous Excitation Spectra of Frustrated Quantum Antiferromagnets

We use series expansions to study the excitation spectra of spin-1/2 antiferromagnets on anisotropic triangular lattices. For the isotropic triangular lattice model (TLM), the high-energy spectra show several anomalous features that differ strongly from linear spin-wave theory (LSWT). Even in the Neel phase, the deviations from LSWT increase sharply with frustration, leading to rotonlike minima at special wave vectors. We argue that these results can be interpreted naturally in a spinon language and provide an explanation for the previously observed anomalous finite-temperature properties of the TLM. In the coupled-chains limit, quantum renormalizations strongly enhance the one-dimensionality of the spectra, in agreement with experiments on Cs2CuCl4.

A study in the application of domestic solar assisted heat pumps for heating and cooling

In the present work, the more important parameters of the heat pump system and of solar assisted heat pump systems were analysed in a quantitative way. Ideal and real Rankine cycles applied to the heat pump, with and without subcooling and superheating were studied using practical recommended values for their thermodynamics parameters. Comparative characteristics of refrigerants here analysed looking for their applicability in heat pumps for domestic heating and their effect in the performance of the system. Curves for the variation of the coefficient of performance as a function of condensing and evaporating temperatures were prepared for R12. Air, water and earth as low-grade heat sources and basic heat pump design factors for integrated heat pumps and thermal stores and for solar assisted heat pump-series, parallel and dual-systems were studied. The analysis of the relative performance of these systems demonstrated that the dual system presents advantages in domestic applications. An account of energy requirements for space and hater heating in the domestic sector in the O.K. is presented. The expected primary energy savings by using heat pumps to provide for the heating demand of the domestic sector was found to be of the order of 7%. The availability of solar energy in the U.K. climatic conditions and the characteristics of the solar radiation here studied. Tables and graphical representations in order to calculate the incident solar radiation over a tilted roof were prepared and are given in this study in section IV. In order to analyse and calculate the heating load for the system, new mathematical and graphical relations were developed in section V. A domestic space and water heating system is described and studied. It comprises three main components: a solar radiation absorber, the normal roof of a house, a split heat pump and a thermal store. A mathematical study of the heat exchange characteristics in the roof structure was done. This permits to evaluate the energy collected by the roof acting as a radiation absorber and its efficiency. An indication of the relative contributions from the three low-grade sources: ambient air, solar boost and heat loss from the house to the roof space during operation is given in section VI, together with the average seasonal performance and the energy saving for a prototype system tested at the University of Aston. The seasonal performance as found to be 2.6 and the energy savings by using the system studied 61%. A new store configuration to reduce wasted heat losses is also discussed in section VI.

A Framework for Fast Classification Algorithms

Today, due to globalization of the world the size of data set is increasing, it is necessary to discover the knowledge. The discovery of knowledge can be typically in the form of association rules, classification rules, clustering, discovery of frequent episodes and deviation detection. Fast and accurate classifiers for large databases are an important task in data mining. There is growing evidence that integrating classification and association rules mining, classification approaches based on heuristic, greedy search like decision tree induction. Emerging associative classification algorithms have shown good promises on producing accurate classifiers. In this paper we focus on performance of associative classification and present a parallel model for classifier building. For classifier building some parallel-distributed algorithms have been proposed for decision tree induction but so far no such work has been reported for associative classification.

Ferroelectric-like behaviour of melanin:humidity effect on current-voltage characteristics

The influence of low vacuum on quasistatic current-voltage (I–V) dependences and the impact of wet air pulse on dynamic bipolar I-V-loops and unipolar I-V-curves of fungal melanin thin layers have been studied for the first time. The threshold hysteresis voltages of I–V dependences are near to the standard electrode potentials of anodic water decomposition. Short wet air pulse impact leads to sharp increase of the current and appearance of “hump”-like and “knee”-like features of I-V-loops and I-V-curves, respectively. By treatment of I-V-loop allowing for I-V-curve shape the maxima of displacement current are revealed. The peculiarities of I-V-characteristics were modelled by series-parallel RC-circuit with Zener diodes as nonlinear elements. As a reason of appearance of temporal polar media with reversible ferroelectric-like polarization and ionic space charge transfer is considered the water-assisted dissociation of some ionic groups of melanin monomers that significantly influences electrophysical parameters of melanin nanostructures.

Beszerzés az innováció támogatásában = Involving purchasing in innovation management

Számos hazai kutatás foglalkozik az innováció alacsony szintjével a hazai vállalatoknál. Az innováció nemzetközi szakirodalma a fejlődés egyik fő tendenciájaként emeli ki a nyitott innováció (open innovation), a korai bevonás (early involvement) és a párhuzamos fejlesztés gyakorlatát. Ezek a megoldások (kutatásunk szempontjából közelítve a beszerzés és a beszállító bevonása az innováció korai szakaszába) hatékonyabbá teheti az innovációs folyamatot, mivel erőforrásokat vonhat be feloldva az innováció előtti akadályokat. Tanulmányunk a beszerzés szerepét elemzi az innovációban, igyekszik feltárni azokat a motivációs illetve gátló tényezőket, amelyek ezt a szerepet erősíthetik vagy gátolhatják. Vizsgálatunk kiterjed a vállalaton belüli és a beszállítói környezetre is. Vizsgáljuk azt az eszközrendszert is, amellyel a beszerzés az innováció támogatni, elősegíteni tudja. ____ Literature on innovation management highlight the role of early supplier involvement, open innovation and parallel model of innovation. Collaboration within the company and with suppliers is a common part of these concepts which makes innovation process more effective. This paper aims to investigate those factors that promotes and hinders the involvement of purchasing and suppliers into the innovations process. Based on literature review and interviews this paper aims to structure the supporting factors and applicable tools.

Scaling geospatial searches in large spatial databases

Modern geographical databases, which are at the core of geographic information systems (GIS), store a rich set of aspatial attributes in addition to geographic data. Typically, aspatial information comes in textual and numeric format. Retrieving information constrained on spatial and aspatial data from geodatabases provides GIS users the ability to perform more interesting spatial analyses, and for applications to support composite location-aware searches; for example, in a real estate database: “Find the nearest homes for sale to my current location that have backyard and whose prices are between $50,000 and $80,000”. Efficient processing of such queries require combined indexing strategies of multiple types of data. Existing spatial query engines commonly apply a two-filter approach (spatial filter followed by nonspatial filter, or viceversa), which can incur large performance overheads. On the other hand, more recently, the amount of geolocation data has grown rapidly in databases due in part to advances in geolocation technologies (e.g., GPS-enabled smartphones) that allow users to associate location data to objects or events. The latter poses potential data ingestion challenges of large data volumes for practical GIS databases. In this dissertation, we first show how indexing spatial data with R-trees (a typical data pre-processing task) can be scaled in MapReduce—a widely-adopted parallel programming model for data intensive problems. The evaluation of our algorithms in a Hadoop cluster showed close to linear scalability in building R-tree indexes. Subsequently, we develop efficient algorithms for processing spatial queries with aspatial conditions. Novel techniques for simultaneously indexing spatial with textual and numeric data are developed to that end. Experimental evaluations with real-world, large spatial datasets measured query response times within the sub-second range for most cases, and up to a few seconds for a small number of cases, which is reasonable for interactive applications. Overall, the previous results show that the MapReduce parallel model is suitable for indexing tasks in spatial databases, and the adequate combination of spatial and aspatial attribute indexes can attain acceptable response times for interactive spatial queries with constraints on aspatial data.

Mangrove tannins in aquatic ecosystems: Their fate and possible influence on dissolved organic carbon and nitrogen cycling

We describe the fate of mangrove leaf tannins in aquatic ecosystems and their possible influence on dissolved organic nitrogen (DON) cycling. Tannins were extracted and purified from senescent yellow leaves of the red mangrove (Rhizophora mangle) and used for a series of model experiments to investigate their physical and chemical reactivity in natural environments. Physical processes investigated included aggregation, adsorption to organic matter-rich sediments, and co-aggregation with DON in natural waters. Chemical reactions included structural change, which was determined by excitation–emission matrix fluorescence spectra, and the release of proteins from tannin–protein complexes under solar-simulated light exposure. A large portion of tannins can be physically eliminated from aquatic environments by precipitation in saline water and also by binding to sediments. A portion of DON in natural water can coprecipitate with tannins, indicating that mangrove swamps can influence DON cycling in estuarine environments. The chemical reactivity of tannins in natural waters was also very high, with a half-life of less than 1 d. Proteins were released gradually from tannin–protein complexes incubated under light conditions but not under dark conditions, indicating a potentially buffering role of tannin– protein complexes on DON recycling in mangrove estuaries. Although tannins are not detected at a significant level in natural waters, they play an important ecological role by preserving nitrogen and buffering its cycling in estuarine ecosystems through the prevention of rapid DON export/loss from mangrove fringe areas and/or from rapid microbial mineralization.

Modelos autorregressivos com valores inteiros não negativos : aplicação em séries económicas de Cabo Verde

No estudo de séries temporais, os processos estocásticos usuais assumem que as distribuições marginais são contínuas e, em geral, não são adequados para modelar séries de contagem, pois as suas características não lineares colocam alguns problemas estatísticos, principalmente na estimação dos parâmetros. Assim, investigou-se metodologias apropriadas de análise e modelação de séries com distribuições marginais discretas. Neste contexto, Al-Osh and Alzaid (1987) e McKenzie (1988) introduziram na literatura a classe dos modelos autorregressivos com valores inteiros não negativos, os processos INAR. Estes modelos têm sido frequentemente tratados em artigos científicos ao longo das últimas décadas, pois a sua importância nas aplicações em diversas áreas do conhecimento tem despertado um grande interesse no seu estudo. Neste trabalho, após uma breve revisão sobre séries temporais e os métodos clássicos para a sua análise, apresentamos os modelos autorregressivos de valores inteiros não negativos de primeira ordem INAR (1) e a sua extensão para uma ordem p, as suas propriedades e alguns métodos de estimação dos parâmetros nomeadamente, o método de Yule-Walker, o método de Mínimos Quadrados Condicionais (MQC), o método de Máxima Verosimilhança Condicional (MVC) e o método de Quase Máxima Verosimilhança (QMV). Apresentamos também um critério automático de seleção de ordem para modelos INAR, baseado no Critério de Informação de Akaike Corrigido, AICC, um dos critérios usados para determinar a ordem em modelos autorregressivos, AR. Finalmente, apresenta-se uma aplicação da metodologia dos modelos INAR em dados reais de contagem relativos aos setores dos transportes marítimos e atividades de seguros de Cabo Verde.

Previsão do índice bovespa por meio de redes neurais artificiais: uma análise comparada aos métodos tradicionais de séries de tempo

Forecast is the basis for making strategic, tactical and operational business decisions. In financial economics, several techniques have been used to predict the behavior of assets over the past decades.Thus, there are several methods to assist in the task of time series forecasting, however, conventional modeling techniques such as statistical models and those based on theoretical mathematical models have produced unsatisfactory predictions, increasing the number of studies in more advanced methods of prediction. Among these, the Artificial Neural Networks (ANN) are a relatively new and promising method for predicting business that shows a technique that has caused much interest in the financial environment and has been used successfully in a wide variety of financial modeling systems applications, in many cases proving its superiority over the statistical models ARIMA-GARCH. In this context, this study aimed to examine whether the ANNs are a more appropriate method for predicting the behavior of Indices in Capital Markets than the traditional methods of time series analysis. For this purpose we developed an quantitative study, from financial economic indices, and developed two models of RNA-type feedfoward supervised learning, whose structures consisted of 20 data in the input layer, 90 neurons in one hidden layer and one given as the output layer (Ibovespa). These models used backpropagation, an input activation function based on the tangent sigmoid and a linear output function. Since the aim of analyzing the adherence of the Method of Artificial Neural Networks to carry out predictions of the Ibovespa, we chose to perform this analysis by comparing results between this and Time Series Predictive Model GARCH, developing a GARCH model (1.1).Once applied both methods (ANN and GARCH) we conducted the results' analysis by comparing the results of the forecast with the historical data and by studying the forecast errors by the MSE, RMSE, MAE, Standard Deviation, the Theil's U and forecasting encompassing tests. It was found that the models developed by means of ANNs had lower MSE, RMSE and MAE than the GARCH (1,1) model and Theil U test indicated that the three models have smaller errors than those of a naïve forecast. Although the ANN based on returns have lower precision indicator values than those of ANN based on prices, the forecast encompassing test rejected the hypothesis that this model is better than that, indicating that the ANN models have a similar level of accuracy . It was concluded that for the data series studied the ANN models show a more appropriate Ibovespa forecasting than the traditional models of time series, represented by the GARCH model

Explaining dinophysis cf. acuminata abundance in Antifer (Normandy, France) using dynamic linear regression

Classical regression analysis can be used to model time series. However, the assumption that model parameters are constant over time is not necessarily adapted to the data. In phytoplankton ecology, the relevance of time-varying parameter values has been shown using a dynamic linear regression model (DLRM). DLRMs, belonging to the class of Bayesian dynamic models, assume the existence of a non-observable time series of model parameters, which are estimated on-line, i.e. after each observation. The aim of this paper was to show how DLRM results could be used to explain variation of a time series of phytoplankton abundance. We applied DLRM to daily concentrations of Dinophysis cf. acuminata, determined in Antifer harbour (French coast of the English Channel), along with physical and chemical covariates (e.g. wind velocity, nutrient concentrations). A single model was built using 1989 and 1990 data, and then applied separately to each year. Equivalent static regression models were investigated for the purpose of comparison. Results showed that most of the Dinophysis cf. acuminata concentration variability was explained by the configuration of the sampling site, the wind regime and tide residual flow. Moreover, the relationships of these factors with the concentration of the microalga varied with time, a fact that could not be detected with static regression. Application of dynamic models to phytoplankton time series, especially in a monitoring context, is discussed.

Previsão do índice bovespa por meio de redes neurais artificiais: uma análise comparada aos métodos tradicionais de séries de tempo

Forecast is the basis for making strategic, tactical and operational business decisions. In financial economics, several techniques have been used to predict the behavior of assets over the past decades.Thus, there are several methods to assist in the task of time series forecasting, however, conventional modeling techniques such as statistical models and those based on theoretical mathematical models have produced unsatisfactory predictions, increasing the number of studies in more advanced methods of prediction. Among these, the Artificial Neural Networks (ANN) are a relatively new and promising method for predicting business that shows a technique that has caused much interest in the financial environment and has been used successfully in a wide variety of financial modeling systems applications, in many cases proving its superiority over the statistical models ARIMA-GARCH. In this context, this study aimed to examine whether the ANNs are a more appropriate method for predicting the behavior of Indices in Capital Markets than the traditional methods of time series analysis. For this purpose we developed an quantitative study, from financial economic indices, and developed two models of RNA-type feedfoward supervised learning, whose structures consisted of 20 data in the input layer, 90 neurons in one hidden layer and one given as the output layer (Ibovespa). These models used backpropagation, an input activation function based on the tangent sigmoid and a linear output function. Since the aim of analyzing the adherence of the Method of Artificial Neural Networks to carry out predictions of the Ibovespa, we chose to perform this analysis by comparing results between this and Time Series Predictive Model GARCH, developing a GARCH model (1.1).Once applied both methods (ANN and GARCH) we conducted the results' analysis by comparing the results of the forecast with the historical data and by studying the forecast errors by the MSE, RMSE, MAE, Standard Deviation, the Theil's U and forecasting encompassing tests. It was found that the models developed by means of ANNs had lower MSE, RMSE and MAE than the GARCH (1,1) model and Theil U test indicated that the three models have smaller errors than those of a naïve forecast. Although the ANN based on returns have lower precision indicator values than those of ANN based on prices, the forecast encompassing test rejected the hypothesis that this model is better than that, indicating that the ANN models have a similar level of accuracy . It was concluded that for the data series studied the ANN models show a more appropriate Ibovespa forecasting than the traditional models of time series, represented by the GARCH model

Modelos autorregressivos com valores inteiros não negativos : aplicação em séries económicas de Cabo Verde

No estudo de séries temporais, os processos estocásticos usuais assumem que as distribuições marginais são contínuas e, em geral, não são adequados para modelar séries de contagem, pois as suas características não lineares colocam alguns problemas estatísticos, principalmente na estimação dos parâmetros. Assim, investigou-se metodologias apropriadas de análise e modelação de séries com distribuições marginais discretas. Neste contexto, Al-Osh and Alzaid (1987) e McKenzie (1988) introduziram na literatura a classe dos modelos autorregressivos com valores inteiros não negativos, os processos INAR. Estes modelos têm sido frequentemente tratados em artigos científicos ao longo das últimas décadas, pois a sua importância nas aplicações em diversas áreas do conhecimento tem despertado um grande interesse no seu estudo. Neste trabalho, após uma breve revisão sobre séries temporais e os métodos clássicos para a sua análise, apresentamos os modelos autorregressivos de valores inteiros não negativos de primeira ordem INAR (1) e a sua extensão para uma ordem p, as suas propriedades e alguns métodos de estimação dos parâmetros nomeadamente, o método de Yule-Walker, o método de Mínimos Quadrados Condicionais (MQC), o método de Máxima Verosimilhança Condicional (MVC) e o método de Quase Máxima Verosimilhança (QMV). Apresentamos também um critério automático de seleção de ordem para modelos INAR, baseado no Critério de Informação de Akaike Corrigido, AICC, um dos critérios usados para determinar a ordem em modelos autorregressivos, AR. Finalmente, apresenta-se uma aplicação da metodologia dos modelos INAR em dados reais de contagem relativos aos setores dos transportes marítimos e atividades de seguros de Cabo Verde.

High performance battery chargers for full electric and hybrid cars

This Doctoral Thesis aims to study and develop advanced and high-efficient battery chargers for full electric and plug-in electric cars. The document is strictly industry-oriented and relies on automotive standards and regulations. In the first part a general overview about wireless power transfer battery chargers (WPTBCs) and a deep investigation about international standards are carried out. Then, due to the highly increasing attention given to WPTBCs by the automotive industry and considering the need of minimizing weight, size and number of components this work focuses on those architectures that realize a single stage for on-board power conversion avoiding the implementation of the DC/DC converter upstream the battery. Based on the results of the state-of-the-art, the following sections focus on two stages of the architecture: the resonant tank and the primary DC/AC inverter. To reach the maximum transfer efficiency while minimizing weight and size of the vehicle assembly a coordinated system level design procedure for resonant tank along with an innovative control algorithm for the DC/AC primary inverter is proposed. The presented solutions are generalized and adapted for the best trade-off topologies of compensation networks: Series-Series and Series-Parallel. To assess the effectiveness of the above-mentioned objectives, validation and testing are performed through a simulation environment, while experimental test benches are carried out by the collaboration of Delft University of Technology (TU Delft).