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
This paper investigates the implications of the unequal division of the domestic labor in men and women’s participation and effort incentives in competitive relations, in which the labor market is the main example. We found that moderate levels of affirmative action (i.e., bias in favor of women) incentivize men and women to exert more effort and women’s participation. However, it cannot guarantee full participation and equal effort among men and women without inducing economic inefficiency or even distorting the labor market. Given these limitations, we consider the effects of an alternative policy that supports the men’s involvement in the domestic tasks. The main conclusion is that if we want men and women to have the same opportunities in the labor market, we must solve the household problem first. While women hold a larger share of the domestic labor, they are in a weaker position to compete with men. We expect that our findings will guide researchers and decision-makers implementing effective policies that can allow men and women to have the same labor market opportunities.

Abstract
Preferential sampling in time occurs when there is stochastic dependence between the process being modeled and the times of the observations. Examples occur in fisheries if the data are observed when the resource is available, in sensoring when sensors keep only some records in order to save memory and in clinical studies, when a worse clinical condition leads to more frequent observations of the patient. In all such situations the observation times are informative on the underlying process. To make inference in time series observed under Preferential Sampling we propose, in this work, a numerical method based on a Laplace approach to optimize the likelihood and to approximate the underlying process we adopt a technique based on stochastic partial differential equation. Numerical studies with simulated and real data sets are performed to illustrate the benefits of the proposed approach. © 2019 SEIO