Abstract
The aim of this study was to evaluate the effect of essential oils (EOs) of plants naturally occurring in northern Portugal on the spoilage of fresh Maronesa beef burgers stored at 2 and 8 degrees C under different packaging conditions. EOs were obtained from dried leaves of laurel (Laurus Nobilis L.) and rosemary (Rosmarinus officinallis L.) by hydro-distillation using a Clevenger-type apparatus. Analysis of volatile composition of essential oils of rosemary and laurel was achieved by Gas Chromatography-Mass Spectrometry (GC-MS) and Gas Chromatography-Thermal Conductivity Detection (GC-TCD) resulting in the detection of 95.8% and 89.4% of its compounds, respectively. Fresh beef (semitendinosus and semimembranosus) of DOP-Maronesa breed (males; n = 4) were obtained from local market and transported to the laboratory. Samples were stored at 2 and 8 degrees C in two different conditions: aerobiosis (A) and vacuum (V) and analyzed at 0,1, 2, 3, 5, 7,10,14, 21 and 28 days for Lactic acid bacteria (LAB), Enterobacteriaceae, Pseudomonas spp., Fungi, Total mesophilic (TM) and psychrotrophic (TP), color (L*a*b*) and pH. Laurel was the most effective EO keeping pH from increasing. Coordinates L* and a* were higher on samples containing laurel EO for both A and V packaging. Laurel also showed better effect in reducing microbiologic counts in samples packed in A at both 2 and 8 degrees C and packed in V at 8 degrees C. Rosemary was effective in reducing microbial counts on all V samples stored at 2 degrees C. This study allows to conclude that Laurel EO has significant effect in shelf-life, maintaining fresh beef color.

Abstract
There are a vast number of studies on the relationship between R&D and exports. However, the results are not always clear-cut. This study evaluates whether, in the case of a small, open and peripheral country in which exports are the engine of economic growth despite a noticeable laggardness in terms of R&D, the firms' R&D impacts on and/or is influenced by their exports, as well as whether the interrelation between R&D and exports impacts on the performance of firms. Using an unique dataset comprising all (more than 340 thousands) non-financial companies based in Portugal, over the period 2006-2012, estimations based on bivariate probit models, which provide the simultaneous estimation of the two decisions (R&D and exports), taking into account the correlation between the estimation errors of the equations for R&D and exports, confirm there is complementarity between R&D and exports, which means that engaging in R&D activities will increase the firm's probability of engaging in export activities. Additionally, engaging in export activities will also increase the probability of engaging in R&D. The results also provide support for the hypothesis that more productive firms self-select into exporting activities and also provide support for the learning-by-exporting hypothesis. Finally, based on a panel model we further found that R&D and exports have a positive effect on sales growth, which is enhanced when both activities occur simultaneously.

Abstract
Nowadays, clusters of multicores are becoming the norm and, although, many or-parallel Prolog systems have been developed in the past, to the best of our knowledge, none of them was specially designed to explore the combination of shared and distributed memory architectures. In recent work, we have proposed a novel computational model specially designed for such combination which introduces a layered model with two scheduling levels, one for workers sharing memory resources, which we named a team of workers, and another for teams of workers (not sharing memory resources). In this work, we present a first implementation of such model and for that we revive and extend the YapOr system to exploit or-parallelism between teams of workers. We also propose a new set of built-in predicates that constitute the syntax to interact with an or-parallel engine in our platform. Experimental results show that our implementation is able to increase speedups as we increase the number of workers per team, thus taking advantage of the maximum number of cores in a machine, and to increase speedups as we increase the number of teams, thus taking advantage of adding more computer nodes to a cluster.

Abstract
BACKGROUND/AIM: In this study, we evaluated the dimensions and volume of rat mammary tumors and the association of these variables with tumor invasiveness.MATERIALS AND METHODS: Tumors were measured by caliper and ultrasonography. Volume was determined by water displacement and by application of four formulas using tumor length (L), width (W) and depth (D) or tumor weight.RESULTS: Results confirmed the data obtained in our previous work, where we verified that mammary tumors grow as oblate spheroids.CONCLUSION: The determination of mammary tumor volume by applying the formula V=(4/3)×p×(L/2)×(L/2)×(D/2) is the best way to evaluate tumor volume in vivo. Beyond volume evaluation by water displacement, the determination on the basis of tumor weight is the most accurate way to evaluate tumor volume after animal sacrifice or tumor excision. According to our results, it is not possible to predict if a tumor is invasive or non-invasive by its dimensions, volume or weight. Future work in chemically-induced mammary cancer should use ultrasonography and water displacement or tumor weight to determine tumor volume in vivo and after animal sacrifice or tumor excision, respectively. Copyright

Abstract
The interest on online classification has been increasing due to data streams systems growth and the need for Multi-label Classification applications have followed the same trend. However, most of classification methods are not performed on-line. Moreover, data streams produce huge amounts of data and the available processing resources may not be sufficient. This work-in-progress paper proposes an algorithm for Multi-label Classification applications in data streams scenarios. The proposed method is derived from multi-target structured regressor AMRules that produces models using subsets of output attributes (output specialization strategy). Performance tests were conducted where the operation modes global, local and subset approaches of the proposed method were compared to each other and to others online multi-label classifiers described in the literature. Three datasets of real scenarios were used for evaluation. The results indicate that the subset specialization mode is competitive in comparison to local and global approaches and to other online multi-label classifiers.

Abstract
Open development has emerged as a method for creating versatile and complex products through free collaboration of individuals. This free collaboration gathers globally distributed teams. Similarly, it is common today to view businesses and other human organisations as ecosystems, where several participating companies and organisations cooperate and compete together. As an example, Free/Libre Open Source Software ( FLOSS) development is one area where community driven development provides a plausible platform for both development of products and establishing a software ecosystem where a set of businesses contribute their own innovations. Equally, open and informal learning environments and open innovation platforms are also gaining ground. While such initiatives are not limited to any specific area, they typically offer a technological, legal, social, and economic framework for development, relying always on people as open development would not exist without the active participation of them. This paper explores the participation of master students in FLOSS projects, while merging two different settings of learning: formal and open/informal education.