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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.

Abstract
A traffic incident is defined by an event which provokes a disruption on the normal (free) flow condition of any highway. Such incidents must be caused by a recurrent excessive demand or, in alternative, by a series of possible stochastic occurrences which may suddenly reduce the road capacity (e.g. car accidents, extreme weather changes). This paper proposes a novel binary supervised learning method to classify congestion predictions regarding their causes - CJAMmer. It leverages on heterogeneous and ubiquitous data sources - such as weather, flow counts and traffic incident event logs -To generalize decision models able to understand the road congestion nature. CJAMmer settles on boosted decision trees using the well-known C4.5, as well as a straightforward feature generation process. A real world experiment was used to compare this method against other state-of-The-Art classifiers. The results uncovered the high potential impact of this methodology on industrial scale traffic control systems. © 2016 IEEE.

Abstract
Background/Aims: Intradialytic hypotension (IDH) is a serious and frequent complication of hemodialysis (HD). Thus far, data are scarcely available to assess the impact of first versus subsequent HD sessions of the week in IDH. Therefore, the purpose of this work was to evaluate IDH risk in patients on thrice-weekly HD. Methods: We conducted an analysis of all blood pressure (BP) measurements obtained during 492 HD treatments given to 41 prevalent adult patients over a one month period. A logistic regression model for repeated binary observations was used to determine the association between hypotension and patient and dialysis factors. Results: The incidence of IDH was 32.5%. First dialysis session of the week was associated with a 9% higher risk of hypotension relatively to the second one. The risk was even higher from the first to the third session of the week (60%) and from the second to the third (50%). A higher hypotension odds ratio was also associated with age (1.03, 90% CI: 1.01-1.06), higher predialysis BP (1.04, 90% CI: 1.03-1.05) and higher phosphorus level (1.38, 90% CI: 1.07-1.76). The risk decreased 24.4% for each additional antihypertensive drug taken by the patient. Conclusions: The odds of hypotension occurrence decrease throughout dialysis sessions of the week. Minimizing modifiable risk factors may decrease IDH episodes. (C) 2016 The Author(s) Published by S. Karger AG, Basel