Multidimensional Scaling Analysis of Stock Market Indexes

This paper applies multidimensional scaling techniques and Fourier transform for visualizing possible time-varying correlations between 25 stock market values. The method is useful for observing clusters of stock markets with similar behavior.

Fractional Dynamics in Forest Fires

Every year forest fires consume large areas, being a major concern in many countries like Australia, United States and Mediterranean Basin European Countries (e.g., Portugal, Spain, Italy and Greece). Understanding patterns of such events, in terms of size and spatiotemporal distributions, may help to take measures beforehand in view of possible hazards and decide strategies of fire prevention, detection and suppression. Traditional statistical tools have been used to study forest fires. Nevertheless, those tools might not be able to capture the main features of fires complex dynamics and to model fire behaviour [1]. Forest fires size-frequency distributions unveil long range correlations and long memory characteristics, which are typical of fractional order systems [2]. Those complex correlations are characterized by self-similarity and absence of characteristic length-scale, meaning that forest fires exhibit power-law (PL) behaviour. Forest fires have also been proved to exhibit time-clustering phenomena, with timescales of the order of few days [3]. In this paper, we study forest fires in the perspective of dynamical systems and fractional calculus (FC). Public domain forest fires catalogues, containing data of events occurred in Portugal, in the period 1980 up to 2011, are considered. The data is analysed in an annual basis, modelling the occurrences as sequences of Dirac impulses. The frequency spectra of such signals are determined using Fourier transforms, and approximated through PL trendlines. The PL parameters are then used to unveil the fractional-order dynamics characteristics of the data. To complement the analysis, correlation indices are used to compare and find possible relationships among the data. It is shown that the used approach can be useful to expose hidden patterns not captured by traditional tools.

Effects of Soil Compaction and Organic Carbon Content on Preferential Flow in Loamy Field Soils

Abstract: Preferential flow and transport through macropores affect plant water use efficiency and enhance leaching of agrochemicals and the transport of colloids, thereby increasing the risk for contamination of groundwater resources. The effects of soil compaction, expressed in terms of bulk density (BD), and organic carbon (OC) content on preferential flow and transport were investigated using 150 undisturbed soil cores sampled from 15 × 15–m grids on two field sites. Both fields had loamy textures, but one site had significantly higher OC content. Leaching experiments were conducted in each core by applying a constant irrigation rate of 10 mm h−1 with a pulse application of tritium tracer. Five percent tritium mass arrival times and apparent dispersivities were derived from each of the tracer breakthrough curves and correlated with texture, OC content, and BD to assess the spatial distribution of preferential flow and transport across the investigated fields. Soils from both fields showed strong positive correlations between BD and preferential flow. Interestingly, the relationships between BD and tracer transport characteristics were markedly different for the two fields, although the relationship between BD and macroporosity was nearly identical. The difference was likely caused by the higher contents of fines and OC at one of the fields leading to stronger aggregation, smaller matrix permeability, and a more pronounced pipe-like pore system with well-aligned macropores.

Brazilian fruit pulps as functional foods and additives: Evaluation of bioactive compounds

Eight tropical fruit pulps from Brazil were simultaneously characterised in terms of their antioxidant and antimicrobial properties. Antioxidant activity was screened by DPPH radical scavenging activity (126–3987 mg TE/100 g DW) and ferric reduction activity power (368–20819 mg AAE/100 g DW), and complemented with total phenolic content (329–12466 mg GAE/100 g DW) and total flavonoid content measurements (46–672 mg EE /100 g DW), whereas antimicrobial activity was tested against the most frequently found food pathogens. Acerola and açaí presented the highest values for the antioxidant-related measurements. Direct correlations between these measurements could be observed for some of the fruits. Tamarind exhibited the broadest antimicrobial potential, having revealed growth inhibition of Pseudomonas aeruginosa. Escherichia coli, Listeria monocytogenes, Salmonella sp. and Staphylococcus aureus. Açaí and tamarind extracts presented an inverse relationship between antibacterial and antioxidant activities, and therefore, the antibacterial activity cannot be attributed (only) to phenolic compounds.

Countries three Wise Men: Sustainability, Innovation, and Competitiveness

Purpose: The studies on links between sustainability, innovation, and competitiveness have been mainly focused at organizational and business level. The purpose of this research is to investigate if there is a correlation between these three variables at country level. Using international well recognized rankings of countries sustainability, innovation, and competitiveness, correlation analysis was performed allowing for the conclusion that there are indeed high correlations (and possible relationships) between the three variables at country level. Design/methodology/approach: Sustainability, innovation, and competitiveness literature were reviewed identifying a lack of studies examining these three variables at country level. Three major well recognized indexes were used to support the quantitative research: The World Economic Forum (2013) Sustainability-adjusted global competitiveness index, the Global Innovation Index (2014) issued by Cornell University, INSEAD, and WIPO and the IMD World Competitiveness Yearbook (2014). After confirming the distributions normality, Pearson correlation analysis was made with results showing high linear correlations between the three indexes. Findings: The results of the correlation analysis using Pearson correlation coefficient (all correlation coefficients are greater than 0.73) give a strong support to the conclusion that there is indeed a high correlation (and a possible relationship) between social sustainability, innovation and competitiveness at country level. Research limitations/implications: Further research is advisable to better understand the factors that contribute to the presented results and to establish a global paradigm linking these three main constructs (social sustainability, innovation, and competitiveness). Some authors consider that these measurements are not fully supported (e.g. due to different countries standards), however, it is assumed these differing underlying methodological approaches, by being used in conjunction, can be considered as a set of reliable and useful performance indicators. Practical implications: The results highlight the simultaneous relationship between social sustainability, innovation and competitiveness superior performance and the need to take that these considerations into business and operating models. Social implications: This research suggests that sustainability and innovation policies, strategies and practices are relevant for countries competitiveness and should be promoted particularly in countries ranked low on sustainability and innovation global scoring indexes. Originality/value: This is one of the few studies addressing the relationships between sustainability, innovation and competitiveness at country level.

Using neural networks and support vector regression to relate marchetti dilatometer test parameters and maximum shear modulus

In the last two decades, small strain shear modulus became one of the most important geotechnical parameters to characterize soil stiffness. Finite element analysis have shown that in-situ stiffness of soils and rocks is much higher than what was previously thought and that stress-strain behaviour of these materials is non-linear in most cases with small strain levels, especially in the ground around retaining walls, foundations and tunnels, typically in the order of 10−2 to 10−4 of strain. Although the best approach to estimate shear modulus seems to be based in measuring seismic wave velocities, deriving the parameter through correlations with in-situ tests is usually considered very useful for design practice.The use of Neural Networks for modeling systems has been widespread, in particular within areas where the great amount of available data and the complexity of the systems keeps the problem very unfriendly to treat following traditional data analysis methodologies. In this work, the use of Neural Networks and Support Vector Regression is proposed to estimate small strain shear modulus for sedimentary soils from the basic or intermediate parameters derived from Marchetti Dilatometer Test. The results are discussed and compared with some of the most common available methodologies for this evaluation.

Using neural networks and support vector regression to relate marchetti dilatometer test parameters and maximum shear modulus

In the last two decades, small strain shear modulus became one of the most important geotechnical parameters to characterize soil stiffness. Finite element analysis have shown that in-situ stiffness of soils and rocks is much higher than what was previously thought and that stress-strain behaviour of these materials is non-linear in most cases with small strain levels, especially in the ground around retaining walls, foundations and tunnels, typically in the order of 10−2 to 10−4 of strain. Although the best approach to estimate shear modulus seems to be based in measuring seismic wave velocities, deriving the parameter through correlations with in-situ tests is usually considered very useful for design practice.The use of Neural Networks for modeling systems has been widespread, in particular within areas where the great amount of available data and the complexity of the systems keeps the problem very unfriendly to treat following traditional data analysis methodologies. In this work, the use of Neural Networks and Support Vector Regression is proposed to estimate small strain shear modulus for sedimentary soils from the basic or intermediate parameters derived from Marchetti Dilatometer Test. The results are discussed and compared with some of the most common available methodologies for this evaluation.

Kalman tracking linear predictor for vowel intelligibility enhancement on european portuguese HMM based speech synthesis

The recent developments on Hidden Markov Models (HMM) based speech synthesis showed that this is a promising technology fully capable of competing with other established techniques. However some issues still lack a solution. Several authors report an over-smoothing phenomenon on both time and frequencies which decreases naturalness and sometimes intelligibility. In this work we present a new vowel intelligibility enhancement algorithm that uses a discrete Kalman filter (DKF) for tracking frame based parameters. The inter-frame correlations are modelled by an autoregressive structure which provides an underlying time frame dependency and can improve time-frequency resolution. The system’s performance has been evaluated using objective and subjective tests and the proposed methodology has led to improved results.

Metodologias no Controlo de Compactação em Aterros

O presente trabalho pretende abordar aspectos relacionados com o controlo de compactação em aterros, com base na avaliação de parâmetros “in situ”, tais como: pesos volúmicos, teores em água, graus de compactação e módulos de deformabilidade. Recorrendo aos métodos correntes no controlo de compactação, como o ensaio de carga em placa, gamadensímetro, garrafa e ao ensaio de deflectómetro de impacto portátil. Visa-se comparar os resultados obtidos, nas diferentes condições, de modo a possibilitar alcançar correlações entre os ensaios, bem como determinar aqueles que apresentam maior grau de confiança técnico e vantagens operacionais e económicas. De modo a garantir os índices de qualidade da obra é necessário fazer cumprir os critérios exigidos pelo caderno de encargos, nomeadamente nos parâmetros de avaliação do controlo de compactação, para que estas satisfaçam o seu estado funcional e estrutural. Neste tipo de obras os cadernos de encargos de referência em Portugal são os das Estradas de Portigal (EP) e da Brisa, Auto-estradas de Portugal (Brisa), os quais se baseiam em recomendações de classificações de solos. No contexto experimental, foram efectuados dois estudos com condições e materiais diferentes, em obras pertencentes à empresa Mota-Engil Engenharia e Construções, S.A. Nas campanhas de ensaios foram realizados ensaios “in situ” na obra da Subconcessão do Douro Interior – Lote 6 – IC5 – Troço Murça/ Nó de Pombal nas camadas de sub-base e base num agregado de granulometria extensa, e na obra de modernização do troço ferroviário Bombel e Vidigal a Évora, em solos.

Perdas por imparidade em intangíveis

Dissertação de Mestrado apresentado ao Instituto de Contabilidade e Administração do Porto para a obtenção do grau de Mestre em Contabilidade e Finanças, sob orientação do Doutor Carlos Quelhas Martins

Os Modos de Entrada em Mercados Internacionais das Empresas da Região do Tâmega e Sousa

Dissertação de Mestrado em Gestão e Internacionalização de Empresas