Multi-elemental analysis of ready-to-eat “baby leaf” vegetables using microwave digestion and high-resolution continuum source atomic absorption spectrometry

The mineral content (phosphorous (P), potassium (K), sodium (Na), calcium (Ca), magnesium (Mg), iron (Fe), manganese (Mn), zinc (Zn) and copper (Cu)) of eight ready-to-eat baby leaf vegetables was determined. The samples were subjected to microwave-assisted digestion and the minerals were quantified by High-Resolution Continuum Source Atomic Absorption Spectrometry (HR-CS-AAS) with flame and electrothermal atomisation. The methods were optimised and validated producing low LOQs, good repeatability and linearity, and recoveries, ranging from 91% to 110% for the minerals analysed. Phosphorous was determined by a standard colorimetric method. The accuracy of the method was checked by analysing a certified reference material; results were in agreement with the quantified value. The samples had a high content of potassium and calcium, but the principal mineral was iron. The mineral content was stable during storage and baby leaf vegetables could represent a good source of minerals in a balanced diet. A linear discriminant analysis was performed to compare the mineral profile obtained and showed, as expected, that the mineral content was similar between samples from the same family. The Linear Discriminant Analysis was able to discriminate different samples based on their mineral profile.

Distributed controllers modeling through Petri nets with multi-asynchronous-channels

This paper proposes the concept of multi-asynchronous-channel for Petri nets. Petri nets extended with multi-asynchronous-channels and time-domains support the specification of distributed controllers, where each controller has a synchronous execution but the global system is asynchronous (globally-asynchronous locally-synchronous systems). Each multi-asynchronous-channel specify the interaction between two or more distributed controllers. These channels, together with the time-domain concept, ensure the creation of network-independent models to support implementations using heterogeneous communication networks. The created models support not only the systems documentation but also their validation and implementation through simulation tools, verification tools, and automatic code generators. An application example illustrates the use of a Petri net class extended with the proposed channels. © 2015 IEEE.

Robot visual localization through local feature fusion: an evaluation of multiple classifiers combination approaches

In the last decade, local image features have been widely used in robot visual localization. In order to assess image similarity, a strategy exploiting these features compares raw descriptors extracted from the current image with those in the models of places. This paper addresses the ensuing step in this process, where a combining function must be used to aggregate results and assign each place a score. Casting the problem in the multiple classifier systems framework, in this paper we compare several candidate combiners with respect to their performance in the visual localization task. For this evaluation, we selected the most popular methods in the class of non-trained combiners, namely the sum rule and product rule. A deeper insight into the potential of these combiners is provided through a discriminativity analysis involving the algebraic rules and two extensions of these methods: the threshold, as well as the weighted modifications. In addition, a voting method, previously used in robot visual localization, is assessed. Furthermore, we address the process of constructing a model of the environment by describing how the model granularity impacts upon performance. All combiners are tested on a visual localization task, carried out on a public dataset. It is experimentally demonstrated that the sum rule extensions globally achieve the best performance, confirming the general agreement on the robustness of this rule in other classification problems. The voting method, whilst competitive with the product rule in its standard form, is shown to be outperformed by its modified versions.

Multi-objective Parallel Particle Swarm Optimization for Day-ahead Vehicle-To-Grid Scheduling

This paper presents a methodology for multi-objective day-ahead energy resource scheduling for smart grids considering intensive use of distributed generation and Vehicle- To-Grid (V2G). The main focus is the application of weighted Pareto to a multi-objective parallel particle swarm approach aiming to solve the dual-objective V2G scheduling: minimizing total operation costs and maximizing V2G income. A realistic mathematical formulation, considering the network constraints and V2G charging and discharging efficiencies is presented and parallel computing is applied to the Pareto weights. AC power flow calculation is included in the metaheuristics approach to allow taking into account the network constraints. A case study with a 33-bus distribution network and 1800 V2G resources is used to illustrate the performance of the proposed method.

Biomarkers in Solea Senegalensis Kaup, 1858 exposed to contaminated estuarine sediments: a multi-level approach

Thesis submitted to the Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia for the degree of Doctor of Philosophy in Environmental Sciences

Data Mining Approach to support the Generation of Realistic Scenarios for Multi-Agent simulation of Electricity Markets

This paper presents the Realistic Scenarios Generator (RealScen), a tool that processes data from real electricity markets to generate realistic scenarios that enable the modeling of electricity market players’ characteristics and strategic behavior. The proposed tool provides significant advantages to the decision making process in an electricity market environment, especially when coupled with a multi-agent electricity markets simulator. The generation of realistic scenarios is performed using mechanisms for intelligent data analysis, which are based on artificial intelligence and data mining algorithms. These techniques allow the study of realistic scenarios, adapted to the existing markets, and improve the representation of market entities as software agents, enabling a detailed modeling of their profiles and strategies. This work contributes significantly to the understanding of the interactions between the entities acting in electricity markets by increasing the capability and realism of market simulations.

Multi-Objective and Financial Portfolio Optimization of Carrier-Sense Multiple Access Protocols with Cooperative Diversity

8th International Workshop on Multiple Access Communications (MACOM2015), Helsinki, Finland.

Multi-criteria optimisation approach to increase the delivered power in radial distribution networks

This study proposes a new methodology to increase the power delivered to any load point in a radial distribution network, through the identification of new investments in order to improve the repair time. This research work is innovative and consists in proposing a full optimisation model based on mixed-integer non-linear programming considering the Pareto front technique. The goal is to achieve a reduction in repair times of the distribution networks components, while minimising the costs of that reduction as well as non-supplied energy costs. The optimisation model considers the distribution network technical constraints, the substation transformer taps, and it is able to choose the capacitor banks size. A case study based on a 33-bus distribution network is presented in order to illustrate in detail the application of the proposed methodology.

Correlation between specific IgM levels and percentage IgG-class antibody avidity to Toxoplasma gondii

Toxoplasmosis is an usually asymptomatic worldwide disseminated infection. In its congenital presentation it may lead to abortion or fetal malformations. Antenatal evaluation is considered of paramount importance to identify seronegative women and allow for prophylaxis. Recent improvements in sensitivity of IgM tests has made IgM detection an extremely protracted acute phase marker, and IgG avidity evaluation test became necessary. Observation has shown that a correlation can be established between IgM levels and avidity percentages, suggesting that frequently the avidity test may not be necessary. In this study we analyzed Toxoplasma gondii IgM levels of 202 samples and their IgG avidity percentages, in order to define specific levels whose IgM quantification could by itself define serodiagnosis and therefore make the avidity evaluation unnecessary. We showed that for IgM levels bellow 2.0 and above 6.0 serodiagnosis of toxoplasmosis could be established without need of IgG avidity test. IgM levels between these two parameters are associated with varying avidity indexes highlighting the importance of its evaluation as a means to confirm toxoplasmosis. Following this demonstration it was possible to avoid the avidity test for 75% of the cases, to reduce the turnaround time and to reduce costs.

Two competitive enzyme immunoassays for the detection of IgG class antibodies to hepatitis a antigen

Two competitive enzyme immunoassays (EIA) techniques were developed: in the first (COMP-1), test sera were added together with HAV antigen on anti-HAV IgG-coated wells followed by an anti-HA VHRP conjugate; in the second (COMP-2), test sera and anti-HA VHRP conjugate competed for HAV epitopes previously adsorbed to anti-HA V IgG-coated wells. Both procedures used tetramethylbenzidine (TMB) as a substrate. Both competitive tests were shown to be reproducible and suitable for routine diagnosis and research purposes.

Business model analysis of Mobitto: How to solve the chicken and egg problem of a multi-sided platform?

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA – School of Business and Economics

Investigation of class 1 integrons in Klebsiella pneumoniae clinical and microbiota isolates belonging to different phylogenetic groups in Recife, State of Pernambuco

Introduction The high prevalence of Klebsiella pneumoniae infections is related to the ability of K. pneumoniae to acquire and disseminate exogenous genes associated with mobile elements, such as R plasmids, transposons and integrons. This study investigated the presence of class 1 integrons in clinical and microbiota isolates of K. pneumoniae belonging to different phylogenetic groups and correlated these results with the antimicrobial resistance profiles of the studied isolates. Methods Of the 51 isolates of K. pneumoniae selected for this study, 29 were from multidrug-resistant clinical isolates, and 22 were from children's microbiota. The susceptibility profile was determined using the disk diffusion method, and class 1 integrons were detected through polymerase chain reaction (PCR). Results The results showed that none of the 22 microbiota isolates carried class 1 integrons. Among the 29 clinical isolates, 19 (65.5%) contained class 1 integrons, and resistance to sulfamethoxazole/trimethoprim was identified in 18 of these isolates (94.7%). Among the K. pneumoniae isolates with class 1 integrons, 47% belonged to the KpI phylogenetic group, and one isolate (14.3%) carrying these genetic elements belonged to the KpIII group. Conclusions The wide variety of detected class 1 integrons supports the presence of high rates of antimicrobial resistance, genetic variability, and rapid dissemination of beta-lactamase genes among K. pneumoniae clinical isolates in recent years in hospitals in Recife-PE, Brazil. The findings of this study indicate that the surveillance of K. pneumoniae integrons in clinical isolates could be useful for monitoring the spread of antibiotic resistance genes in the hospital environment.

Multi-temporal analysis of radiometric changes in satellite images of forest edges to infer selective-logging areas in the Amazon forest

Radiometric changes observed in multi-temporal optical satellite images have an important role in efforts to characterize selective-logging areas. The aim of this study was to analyze the multi-temporal behavior of spectral-mixture responses in satellite images in simulated selective-logging areas in the Amazon forest, considering red/near-infrared spectral relationships. Forest edges were used to infer the selective-logging infrastructure using differently oriented edges in the transition between forest and deforested areas in satellite images. TM/Landsat-5 images acquired at three dates with different solar-illumination geometries were used in this analysis. The method assumed that the radiometric responses between forest with selective-logging effects and forest edges in contact with recent clear-cuts are related. The spatial frequency attributes of red/near infrared bands for edge areas were analyzed. Analysis of dispersion diagrams showed two groups of pixels that represent selective-logging areas. The attributes for size and radiometric distance representing these two groups were related to solar-elevation angle. The results suggest that detection of timber exploitation areas is limited because of the complexity of the selective-logging radiometric response. Thus, the accuracy of detecting selective logging can be influenced by the solar-elevation angle at the time of image acquisition. We conclude that images with lower solar-elevation angles are less reliable for delineation of selecting logging.