Abstract
State-based CRDTs allow updates on local replicas without remote synchronization. Once these updates are propagated, possible conflicts are resolved deterministically across all replicas. d-CRDTs bring significant advantages in terms of the size of messages exchanged between replicas during normal operation. However, when a replica joins the system after a network partition, it needs to receive the updates it missed and propagate the ones performed locally. Current systems solve this by exchanging the full state bidirectionally or by storing additional metadata along the CRDT. We introduce the concept of join-decomposition for state-based CRDTs, a technique orthogonal and complementary to delta-mutation, and propose two synchronization methods that reduce the amount of information exchanged, with no need to modify current CRDT definitions.

Abstract
Forecasting future sales is one of the most important issues that is beyond all strategic and planning decisions in effective operations of retail supply chains. For profitable retail businesses, accurate sales forecasting is crucial in organizing and planning purchasing, production, transportation and labor force. Retail sales series belong to a special type of time series that typically contain strong trend and seasonal patterns, presenting challenges in developing effective forecasting models. This paper compares the forecasting performance of state space models and ARIMA models. The forecasting performance is demonstrated through a case study of retail sales of five different categories of women footwear: Boots, Booties, Flats, Sandals and Shoes. An approach based on cross-validation is used to identify automatically appropriate state space and ARIMA models. The forecasting performance of these models is also compared by examining the out-of-sample forecasts. The results indicate that the overall out-of-sample forecasting performance of ARIMA models evaluated via RMSE, MAE and MAPE is better than state space models. The performance of both forecasting methodologies in producing forecast intervals was also evaluated and the results indicate that ARIMA produces slightly better coverage probabilities than state space models for the nominal 95% forecast intervals. For the nominal 80% forecast intervals the performance of state space models is slightly better.

Abstract
The empirical literature focusing mainly on the USA suggests that class absenteeism undermines students' academic performance and that an enforced mandatory attendance policy may be beneficial. Based on a different cultural and economic context, and using data on 146 second-year management students enrolled in a macroeconomics course at an elite economics school in Portugal, it is shown that even when controlling for potential endogenous factors associated with attendance and academic performance, absenteeism considerably lowers the students' final grade (about 2 points in a 0-20-point grading scheme). In addition, it is established that a compulsory, though flexible, attendance policy contributes to improving students' academic performance.

Abstract

Abstract
There is no doubt that nowadays internationalization is increasingly attractive for companies that want to become more competitive. However, many small and medium enterprises (SMEs) still are resourceconstrained, lacking adequate information and facing problems to establish and manage relationships to operate in a sustainable way in international markets. Despite receiving some support from governments and from other institutional entities, these difficulties faced by SMEs call for a stronger support, which can be played by industrial enterprises associations (IEAs). This chapter brings the perspective of five different companies that are associated with IEAs. The main objective is to have their opinion about the role of IEAs as information managers and promoters of collaborations, mainly for internationalization processes. Another objective is to understand the acceptance by these companies regarding the use of collaborative platforms, managed by IEAs, for supporting their information and collaboration management activities. © 2017, IGI Global.

Abstract
The role of referees and the decisions they take have, in many cases, a decisive and direct influence on the course of a game. Unfortunately these decisions are not always correct. In a large amount of times, the mistakes are due to referee's bad positioning on the field. So it is fundamental to develop a model that may determine the correct position for a soccer referee. For this research work, data with all the game participants' positions in the field during the match has been used as its basis. The data included, players, ball and referees positions gathered by an automatic system. Referees with large experience in international competitions were used to obtain correct position knowledge. The achieved results, using a data mining approach, are very positive, both for predicting the assistant referee position, regardless of the team he is accompanying or the match with 77% of success, as to predict the referee position with 82% of success, in controlled game situations. In short, despite the limitations, the models are conclusive and can efficiently estimate the correct positioning of the referee in a soccer match.