Acqusition and data analysis of the eco-efficiency for the establishment of indicators parameters for industrial environmental sustainability

The industrial activity is inevitably associated with a certain degradation of the environmental quality, because is not possible to guarantee that a manufacturing process can be totally innocuous. The eco-efficiency concept is globally accepted as a philosophy of entreprise management, that encourages the companies to become more competitive, innovative and environmentally responsible by promoting the link between its companies objectives for excellence and its objectives of environmental excellence issues. This link imposes the creation of an organizational methodology where the performance of the company is concordant with the sustainable development. The main propose of this project is to apply the concept of eco-efficiency to the particular case of the metallurgical and metal workshop industries through the development of the particular indicators needed and to produce a manual of procedures for implementation of the accurate solution.

Organic versus conventional tomatoes: Influence on physicochemical parameters, bioactive compounds and sensorial attributes

The effect of organic and conventional agricultural systems on the physicochemical parameters, bioactive compounds content, and sensorial attributes of tomatoes (‘‘Redondo’’ cultivar) was studied. The influence on phytochemicals distribution among peel, pulp and seeds was also accessed. Organic tomatoes were richer in lycopene (+20%), vitamin C (+30%), total phenolics (+24%) and flavonoids (+21%) and had higher (+6%) in vitro antioxidant activity. In the conventional fruits, lycopene was mainly concentrated in the pulp, whereas in the organic ones, the peel and seeds contained high levels of bioactive compounds. Only the phenolic compounds had a similar distribution among the different fractions of both types of tomatoes. Furthermore, a sensorial analysis indicated that organic farming improved the gustative properties of this tomato cultivar.

Kinematic parameters of throwing performance in patients with schizophrenia using a markerless motion capture system

Motor dysfunction is consistently reported but understudied in schizophrenia. It has been hypothesized that this abnormality may reflect a neuro-developmental disorder underlying this illness. The main goal of this study was to analyze movement patterns used by participants with schizophrenia and healthy controls during overarm throwing performance, using a markerless motion capture system. Thirteen schizophrenia patients and 16 healthy control patients performed the overarm throwing task in a markerless motion capture system. Participants were also examined for the presence of motor neurological soft signs (mNSS) using the Brief Motor Scale. Schizophrenia patients demonstrated a less developed movement pattern with low individualization of components compared to healthy controls. The schizophrenia group also displayed a higher incidence of mNSS. The presence of a less mature movement pattern can be an indicator of neuro-immaturity and a marker for atypical neurological development in schizophrenia. Our findings support the understanding of motor dysfunction as an intrinsic part of the disorder of schizophrenia.

Determining the Adjustment Baseline Parameters to Define an Accurate Customer Baseline Load

Demand response is an energy resource that has gained increasing importance in the context of competitive electricity markets and of smart grids. New business models and methods designed to integrate demand response in electricity markets and of smart grids have been published, reporting the need of additional work in this field. In order to adequately remunerate the participation of the consumers in demand response programs, improved consumers’ performance evaluation methods are needed. The methodology proposed in the present paper determines the characterization of the baseline approach that better fits the consumer historic consumption, in order to determine the expected consumption in absent of participation in a demand response event and then determine the actual consumption reduction. The defined baseline can then be used to better determine the remuneration of the consumer. The paper includes a case study with real data to illustrate the application of the proposed methodology.

Virulence parameters in the characterization of strains of Entamoeba histolytica

Differences in virulence of strains of Entamoeba histolytica have long been detected by various experimental assays, both in vivo and in vitro. Discrepancies in the strains characterization have been arisen when different biological assays are compared. In order to evaluate different parameters of virulence in the strains characterization, five strains of E. histolytica, kept under axenic culture, were characterized in respect to their, capability to induce hamster liver abscess, erythrophagocytosis rate and cytopathic effect upon VERO cells. It was found significant correlation between in vitro biological assays, but not between in vivo and in vitro assays. Good correlation was found between cytopathic effect and the mean number of uptaken erythrocytes, but not with percentage of phagocytic amoebae, showing that great variability can be observed in the same assay, according to the variable chosen. It was not possible to correlate isoenzyme and restriction fragment pattern with virulence indexes since all studied strains presented pathogenic patterns. The discordant results observed in different virulence assays suggests that virulence itself may not the directly assessed. What is in fact assessed are different biological characteristics or functions of the parasite more than virulence itself. These characteristics or functions may be related or not with pathogenic mechanisms occurring in the development of invasive amoebic disease

Chronic hepatitis C virus infections in Brazilian patients: association with genotypes, clinical parameters and response to long term alpha interferon therapy

The present study assessed the clinical significance of hepatitis C virus (HCV) genotypes and their influence on response to long term recombinant-interferon-alpha (r-IFN-a) therapy in Brazilian patients. One hundred and thirty samples from patients previously genotyped for the HCV and with histologically confirmed chronic hepatitis C (CH-C) were evaluated for clinical and epidemiological parameters (sex, age, time of HCV infection and transmission routes). No difference in disease activity, sex, age or mode and time of transmission were seen among patients infected with HCV types 1, 2 or 3. One hundred and thirteen of them were treated with 3 million units of r-IFN-a, 3 times a week for 12 months. Initial response (IR) was significantly better in patients with genotype 2 (100%) and 3 (46%) infections than in patients with genotype 1 (29%) (p < 0.005). Among subtypes, difference in IR was observed between 1b and 2 (p < 0.005), and between 1b and 3a (p < 0.05). Sustained response (SR) was observed in 12% for (sub)type 1a, 13% for 1b, 19% for 3a, and 40% for type 2; significant differences were found between 1b and 2 (p < 0.001), and between 1b and 3a (p < 0.05). Moreover, presence of cirrhosis was significantly associated with non response and response with relapse (p < 0.05). In conclusion, non-1 HCV genotype and lack of histological diagnosis of cirrhosis were the only baseline features associated with sustained response to treatment. These data indicate that HCV genotyping may have prognostic relevance in the responsiveness to r-IFN-a therapy in Brazilian patients with chronic HCV infection, as seen in other reports worldwide.

Generalized Extraction of Real-Time Parameters for Homogeneous Synchronous Dataflow Graphs

23rd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP 2015). 4 to 6, Mar, 2015. Turku, Finland.

Response Time Analysis for Fixed-Priority Tasks with Multiple Probabilistic Parameters

Demo presented in 12th Workshop on Models and Algorithms for Planning and Scheduling Problems (MAPSP 2015). 8 to 12, Jun, 2015. La Roche-en-Ardenne, Belgium. Extended abstract.

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.

A comparison of the immune parameters of dogs infected with visceral leishmaniasis using Western blot and neutralization techniques

The Western blot technique was used to demonstrate the presence of antibodies in the blood of dogs that presented canine visceral leishmaniasis. This technique was used against some specific molecules present in the lysate of the promastigote form of Leshmania chagasi.Through the association of the results of the Western blot technique with the morphological alterations seen as a result of the serum neutralization technique performed in McCoy cells (which mimetizes the macrophage) it was possible to observe the role of some molecules of great relevance in determining the disease in symptomatic dogs as well as that of some other molecules associated with asymptomatic infected dogs that may become transmitters as well as differentiating them as asymptomatic resistant dogs. In the sera analyses carried out during the immunobloting a variation of 9 to 27 immunoreacting bands was observed, which were then compared using Dice's similarity coefficient. In the dendrogram constructed on the basis of the coefficient, 50% similarity was observed among the total number of reagent bands with the promastigote lysate, thus creating five groups. The main difference observed related to the clinical condition of the dogs: symptomatic and asymptomatic dogs were found in separate groups. The asymptomatic group of dogs was distributed in two different places in the dendrogram because they presented two different behavior patterns regarding the cellular morphology in the serum neutralization reaction: the presence or absence of cellular lysis. According to this analysis it is possible to evaluate the immune status and associate it with specific markers observed in the reaction found in the Western blot strips.

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.