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Abstract
This descriptive study explores the state of CSR education and research in Portugal. It aims to depict the state of CSR in Portugal, in particular in what concerns the nature and the extent of education and research on CSR being undertaken in Portugal. The methodology used includes analysis of relevant literature and of business schools’ websites, and a survey by questionnaire among students at the business school at which the authors of this chapter teach and research. In terms of CSR practices, there is still some focus on social issues, given the state of development of the Portuguese economy. There are signs of CSR having reached a reasonable level of maturity. Although CSR education and research seem to be reasonably well developed in the leading business schools, the same is not the case with other schools. Research on CSR is concentrated in the schools in which CSR education is more developed.

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Abstract

Abstract
Sugarcane is an important product to the Brazilian economy because it is the primary ingredient of ethanol which is used as a gasoline substitute. Sugarcane is aflected by many factors which can be modelled in a Bayesian Graph. This paper describes a technique to build a Causal Bayesian Network from information in news stories. The technique: extracts causal relations from news stories, converts them into an event graph, removes irrelevant information, solves structure problems, and clusters the event graph by topic distribution. Finally, the paper describes a method for generating inferences from the graph based upon evidence in agricultural news stories. The graph is evaluated through a manual inspection and with a comparison with the EMBRAPA sugarcane taxonomy. © ACM 2016.

Abstract
The efficiency of Public Transportation (PT) Networks is a major goal of any urban area authority. Advances on both location and communication devices drastically increased the availability of the data generated by their operations. Adequate Machine Learning methods can thus be applied to identify patterns useful to improve the Schedule Plan. In this paper, the authors propose a fully automated learning framework to determine the best Schedule Coverage to be assigned to a given PT network based on Automatic Vehicle location (AVL) and Automatic Passenger Counting (APC) data. We formulate this problem as a clustering one, where the best number of clusters is selected through an ad-hoc metric. This metric takes into account multiple domain constraints, computed using Sequence Mining and Probabilistic Reasoning. A case study from a large operator in Sweden was selected to validate our methodology. Experimental results suggest necessary changes on the Schedule coverage. Moreover, an impact study was conducted through a large-scale simulation over the affected time period. Its results uncovered potential improvements of the schedule reliability on a large scale. © Springer International Publishing Switzerland 2016.