Abstract
Research Highlights: This study bridges a gap of knowledge about the maximum size-density trajectory for juvenile stands of maritime pine. The continuity of the trajectory along the development stages to maturity is assured with a straightforward approach providing support to determine optimum density along all the revolution periods for the species. Background and Objectives: Forest fire is a significant threat to forests in the Mediterranean regions, but also a natural disturbance that plays a vital role in the perpetuation of forest stands. In recent decades, there has been an increase of burnt area in maritime forests in Portugal, followed by an increased interest in managing the natural and usually abundant regeneration occurring after the fires. The gap in the knowledge of growth dynamics for juvenile stages, for these forest systems, currently constrains their correct management, for forest planning, particularly in determining the optimal densities. The study aims to identify the maximum attainable density trajectory at the early stages of development of the species that could support a non-empirical definition of silvicultural prescriptions and thinning decisions, along the revolution. Materials and Methods: A representative data set collected in stands regenerated after fire supports the analysis of the maximum size-density trajectory for the species. Results: The maximum size-density trajectory for the juvenile stands deviates from the expected trajectory defined in the self-thinning line published for the species. Significant deviation occurs at the lower end of the line, indicating the need for a reevaluation of the existing self-thinning line. We propose a new self-thinning model for the species that explicitly considers the behavior of size-density for juvenile stands. The new model assures a logical continuity for the trajectory from the young stages of development to maturity. Conclusions: The proposed model based on the maximum attainable size-density trajectory provides ecological-based support to define silvicultural guidelines for management of the species.

Abstract
A multi-item inventory distribution problem motivated by a practical case study occurring in the logistic operations of a hospital is considered. There, a single warehouse supplies several nursing wards. The distribution of medical products is done by two different teams of workers using a heterogeneous fleet, that is, the available vehicles have different capacities and different structures required to be used in specific nursing wards. The goal is to define a weekly distribution plan of medical products ensuring a balanced workload of both working teams and satisfying all the required constraints (inventory capacities, safety stock levels, vehicle capacities, etc.) that minimizes the total number of visits to locations. A mixed integer formulation is presented and several improvements are discussed. This is a NP-hard problem hardly solved to optimality within a reasonable amount of time, and more so for real size instances, with hundreds to few thousand of products. To circumvent this issue, a matheuristic is proposed to solve the problem. Finally, computational tests are reported and discussed. © 2019, Springer Nature Switzerland AG.

Abstract
The proliferation of false information on social networks is one of the hardest challenges in today's society, with implications capable of changing users perception on what is a fact or rumor. Due to its complexity, there has been an overwhelming number of contributions from the research community like the analysis of specific events where rumors are spread, analysis of the propagation of false content on the network, or machine learning algorithms to distinguish what is a fact and what is "fake news". In this paper, we identify and summarize some of the most prevalent works on the different categories studied. Finally, we also discuss the methods applied to deceive users and what are the next main challenges of this area.

Abstract
The rise of online social networks has reshaped the way information is published and spread. Users can now post in an effortless way and in any location, making this medium ideal for searching breaking news and journalistic relevant content. However, due to the overwhelming number of posts published every second, such content is hard to trace. Thus, it is important to develop methods able to detect and analyze whether a certain text contains journalistic relevant information. Furthermore, it is also important that this detection system can provide additional information towards a better comprehension of the prediction made. In this work, we overview our system, based on an ensemble classifier that is able to predict if a certain post is relevant from a journalistic perspective which outperforms the previous relevant systems in their original datasets. In addition, we describe REMINDS: a web platform built on top of our relevance system that is able to provide users with the visualization of the system's features as well as additional information on the text, ultimately leading to a better comprehension of the system's prediction capabilities. (C) 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the CENTERIS -International Conference on ENTERprise Information Systems / ProjMAN - International Conference on Project MANagement / HCist - International Conference on Health and Social Care Information Systems and Technologies.

Abstract
For Programming teachers it is of utter most importance to understand the factors that impact on students' motivation to improve their ability to become good computer programmers. To understand a problem, to develop an algorithm for its solution, and to write the corresponding program is a challenging and arduous task, demanding time and self-confidence. In previous work we studied computer based technics to engage students in the learning activity; visualization, animation, automatic program assessment were some approaches that we combined. To support that work we studied carefully students' motivation and complemented that study with an inquiry to a group of students of Algorithm and Programming course of the first year of an Engineering degree. In this paper we show how Association Rules can be used to mine the data gathered in the inquiry to discover relationships among factors influencing extrinsic motivation.

Abstract