Abstract
This paper describes a control technique for enhancing the stable operation of distributed generation (DG) units based on renewable energy sources, during islanding and grid-connected modes. The Passivity-based control technique is considered to analyze the dynamic and steady-state behaviors of DG units during integration and power sharing with loads and/or power grid, which is an appropriate tool to analyze and define a stable operating condition for DG units in microgrid technology. The compensation of instantaneous variations in the reference current components of DG units in ac-side, and dc-link voltage variations in dc-side of interfaced converters, are considered properly in the control loop of DG units, which is the main contribution and novelty of this control technique over other control strategies. By using the proposed control technique, DG units can provide the continuous injection of active power from DG sources to the local loads and/or utility grid. Moreover, by setting appropriate reference current components in the control loop of DG units, reactive power and harmonic current components of loads can be supplied during the islanding and grid-connected modes with a fast dynamic response. Simulation results confirm the performance of the control scheme within the microgrid during dynamic and steady-state operating conditions.

Abstract
Optical music recognition (OMR) is an important tool to recognize a scanned page of music sheet automatically, which has been applied to preserving music scores. In this paper, we propose a new OMR system to recognize the music symbols without segmentation. We present a new classifier named combined neural network (CNN) that offers superior classification capability. We conduct tests on fifteen pages of music sheets, which are real and scanned images. The tests show that the proposed method constitutes an interesting contribution to OMR.

Abstract
The performance of multi-objective evolutionary algorithms can severely deteriorate when applied to problems with 4 or more objectives, called many-objective problems. For Pareto dominance based techniques, available information about some optimal solutions can be used to improve their performance. This is the case of corner solutions. This work considers the behaviour of three multi-objective algorithms [Non-dominated sorting genetic algorithm (NSGA-II), Speed-constrained multi-objective particle swarm optimization (SMPSO) and generalized differential evolution (GDE3)] when corner solutions are inserted into the population at different evolutionary stages. The problem of finding corner solutions is addressed by proposing a new algorithm based in multi-objective particle swarm optimization (MOPSO). Results concerning the behaviour of the aforementioned algorithms in five benchmark problems (DTLZ1-5) and respective analysis are presented.

Abstract
This study reviews performance criteria adequate to validate simulation models through the comparison of two quantitative data sets, concerning historical and simulated data. The criteria reviewed were organized according to its characteristics into the groups: error-based measures, information theory measures, information criteria, parametric tests, non-parametric tests, distance-based measures and combined measures. Each criterion is reviewed through its mathematic definition, its applications in literature and the identification of its advantages and drawbacks. The features assessed by each criterion are identified and discussed. This study provides a concise outline over the criteria reviewed, which can be used as a guide to help developers of simulation models into the decision on the most appropriate criteria to validate their models.

Abstract
Ralstonia solanacearum (Rs) is a quarantine phytopathogenic bacterium accountable for heavy economic losses worldwide. Monitoring and eradication programmes required for this pathogen are dependent on the availability of time-and cost-efficient detection and typing methods. However, members of the Rs species complex are characterized by a high phenotypic and genetic diversity, which requires improved diagnostics methods. The currently available full genome sequences of several Rs strains allow for the selection of novel specific DNA markers using comparative genomics tools. In this work, 17 novel markers were selected based on Rs-specific protein domains and thoroughly validated for specificity and stability, both in silico and using 'wet lab' assays. Polymerase chain reaction-and hybridization-based validation assays revealed that the DNA regions selected as markers were unevenly distributed amongst the tested strains, with nine markers present throughout the species complex. The distribution of the remaining eight markers was highly variable between the different analysed strains and enabled the attainment of strain-specific dot blot hybridization patterns, particularly informative for typing. The average probability value of each strain being positive for each of the 17 markers was calculated by an algorithm and used to obtain a dendrogram representing hierarchical clustering analysis of Rs, according to the similarity of their hybridization patterns. This method should prove to be a robust and straightforward procedure for genotyping members of the Rs species complex. Furthermore, this quantitative hybridization approach will allow the construction of informative databases to determine new Rs genotypes and infer epidemiological patterns.

Abstract
The first step in any non linear time series analysis, is to characterize signals in terms of periodicity, stationarity, linearity and predictability. In this work we aim to find if PCG (phonocardiogram) and ECG (electrocardiogram) time series are generated by a deterministic system and not from a random stochastic process. If PCG and ECG are non-linear deterministic systems and they are not very contaminated with noise, data should be confined to a finite dimensional manifold, which means there are structures hidden under the signal that could be used to increase our knowledge in forecasting future values of the time series. A non-linear process can give rise to very complex dynamic behaviours, even though the underlying process is purely deterministic and probably low-dimensional. To test this hypothesis, we have generated 99 surrogates and then we compared the fitting capability of AR (auto-regressive) models on the original and surrogate data. The results show with a 99\% of confidence level that PCG and ECG were generated by a deterministic process. We compared the fitting capability of an ECG and PCG to AR linear models, using a multi-channel approach. We make an assumption that if a signal is more linearly predictable than another one, it may adjust better to these AR linear models. The results showed that ECG is more linearly predictable (for both channels) than PCG, although a filtering step is needed for the first channel. Finally we show that the false nearest neighbour method is insufficient to identify the correct dimension of the attractor in the reconstructed state space for both PCG and ECG signals.