Abstract
Aim of study: The study aims to evaluate the maximum potential stocking level in cork oak (Quercus suber L.) woodlands, using the ecologically-based size-density relationship of the self-thinning law. Area of study: The study area refers to cork oak forests in mainland Portugal, distributed along its 18 districts from north to south. Material and Methods: A dataset with a total of 2181 observations regarding pure cork oak stands was collected from the Portuguese Forest Inventory (NFI) databases and from research plots. The dataset was subjected to two filtering procedures, one more restrictive than the other, to select the stands presenting the higher stocking values. The two resulting subsets, with 116 and 36 observations, from 16 and 10 districts of mainland Portugal, respectively, were then used to assess and describe the allometric relationship between tree number and their mean diameter. Main results: The allometric relationship was analysed and modelled using the log transformed variables. A slightly curvilinear trend was identified. Thus, a straight line and a curve were both fitted for comparison purposes. Goodness-of-fit statistics point out for a good performance when the data is set to the uppermost observed stocking values. A self-thinning line for cork oak was projected from the estimated relationship. Research highlights: The self-thinning model can be used as an ecological approach to develop density guidelines for oak woodlands in a scenario of increasing cork demands. The results indicate that the recommendations being applied in Portugal are far below the maximal potential stocking values for the species. It is therefore of the utmost importance to review the traditional silvicultural guidelines and endorse new ones.

Abstract
The use of industrial by-products to produce new types of cement-substitute binders is gaining significant momentum, especially through the alkaline activation technique. However, the exact curing conditions that should be considered with each binder variation have not yet been fully understood. The aim of the present work is thus the statistical analysis of the effects of several factors, namely filler/precursor ratio and curing humidity, on the compressive strength of different mixtures prepared with mine tailings (filler), fly ash (precursor) and an alkali activator based on sodium hydroxide. Five different types of mixture were prepared, with filler/precursor ratios of 80/20, 60/40, 40/60, 20/80 and 0/100. All the specimens were cured at 80 degrees C for 7 days, after which they were submitted to a uniaxial compression strength (UCS) test. Three different values of curing humidity were considered, namely 25%, 50% and 75%. Each UCS value was the average of 9 different specimens tested. The aim of the present research is to establish how much these two factors (inert/precursor ratio and curing humidity) influence the UCS. For that purpose, a two-way Analysis of Variance (ANOVA), with interaction, was performed; followed by a Tuckey's Post hoc test. The results showed statistically significant differences for at least one humidity value F(2,127) = 31.647 (p<0.001) as well as one inert/precursor ratio F(4,127) = 371.64; (p<0.001) and for interaction F(8,127) = 9.33; (p<0.001). To evaluate which level or levels are different a Tuckey's Post hoc test was performed. This test revealed that the humidity value of 50% presented statistically significant differences regarding the remaining two values. In addition, it was concluded that this humidity value (50%) leads to lower binder's resistance. Concerning the inert/precursor ratio, the nonsignificant differences only occur between the 80/20 and 60/40 cases, although the strength values increase, in general, as the ash percentage increases. (C) 2017 The Authors. Published by Elsevier B.V.

Abstract
The expansion of social networks has contributed to the propagation of information relevant to general audiences. However, this is small percentage compared to all the data shared in such online platforms, which also includes private/personal information, simple chat messages and the recent called ‘fake news’. In this paper, we make an exploratory analysis on two social networks to extract features that are indicators of relevant information in social network messages. Our goal is to build accurate machine learning models that are capable of detecting what is journalistically relevant. We conducted two experiments on CrowdFlower to build a solid ground truth for the models, by comparing the number of evaluations per post against the number of posts classified. The results show evidence that increasing the number of samples will result in a better performance on the relevancy classification task, even when relaxing in the number of evaluations per post. In addition, results show that there are significant correlations between the relevance of a post and its interest and whether is meaningfully for the majority of people. Finally, we achieve approximately 80% accuracy in the task of relevance detection using a small set of learning algorithms. © 2017 Copyright is held by the owner/author(s).

Abstract
Annotated data is one of the most important components for supervised learning tasks. To ensure the reliability of the models, this data is usually labeled by several human annotators through volunteering or using Crowdsourcing platforms. However, such approaches are unfeasible (regarding time and cost) in datasets with an enormous number of entries, which in the specific case of journalistic relevance detection in social media posts, is necessary due to the wide scope of topics that can be considered relevant. Therefore, with the goal of building a relevance detection model, we propose an architecture to build a large scale annotated dataset regarding the journalistic relevance of Twitter posts (i.e. tweets). This methodology is based on the predictability of the content in Twitter accounts. Next, we used the retrieved dataset and build relevance detection models, combining text, entities, and sentiment features. Finally, we validated the best model through a smaller manually annotated dataset with posts from Facebook and Twitter. The F1-measure achieved in the validation dataset was 63% which is still far from excellent. However, given the characteristics of the validation data, these results are encouraging since 1) our model is not affected by content from other social networks and 2) our validation dataset was restrained to a specific time interval and specific keywords (which can affect the performance of the model). © 2017 IEEE.

Abstract
Social networks as Facebook and Twitter gained a remarkable attention in the last decade. A huge amount of data is emerging and posted everyday by users that are becoming more interested in and relying on social network for information, news and opinions. Real time posting came to rise and turned easier to report news and events. However, due to its dimensions, in this work we focus on building a system architecture capable of detecting journalistic relevance of posts automatically on this 'haystack' full of data. More specifically, users will have the change to interact with a 'friendly user interface' which will provide several tools to analyze data. © 2017.

Abstract
Purpose - The purpose of this paper is to examine the influence of structural empowerment, psychological empowerment and intrinsic motivation on perceived customer satisfaction in contact centers. Design/methodology/approach - A questionnaire was conducted among 703 employees of a contact center. Data analysis was based on structural equation modeling. Findings - Structural empowerment results in higher levels of perceived customer satisfaction through psychological empowerment and intrinsic motivation. Furthermore, structural empowerment effect on psychological empowerment is mediated by intrinsic motivation. Practical implications - Previous predictions regarding counterproductive impact of empowerment in a low-service heterogeneity sector, such as contact center are challenged and a transformative message is disclosed in what concerns human resource management (HRM) in contact centers. Originality/value - The research provides valuable insights for both scholars and practitioners regarding the process through which employees' psychological empowerment and intrinsic motivation improves customer satisfaction in the context of contact centers.