Abstract

Abstract
Though Motor Imagery (MI) stroke rehabilitation effectively promotes neural reorganization, current therapeutic methods are immeasurable and their repetitiveness can be demotivating. In this work, a real-time electroencephalogram (EEG) based MI-BCI (Brain Computer Interface) system with a virtual reality (VR) game as a motivational feedback has been developed for stroke rehabilitation. If the subject successfully hits one of the targets, it explodes and thus providing feedback on a successfully imagined and virtually executed movement of hands or feet. Novel classification algorithms with deep learning (DL) and convolutional neural network (CNN) architecture with a unique trial onset detection technique was used. Our classifiers performed better than the previous architectures on datasets from PhysioNet offline database. It provided fine classification in the real-time game setting using a 0.5 second 16 channel input for the CNN architectures. Ten participants reported the training to be interesting, fun and immersive. "It is a bit weird, because it feels like it would be my hands", was one of the comments from a test person. The VR system induced a slight discomfort and a moderate effort for MI activations was reported. We conclude that MI-BCI-VR systems with classifiers based on DL for real-time game applications should be considered for motivating MI stroke rehabilitation. © 2019 Association for Computing Machinery.

Abstract
This chapter, based on a single case study, has as its main objective to analyze a real example of creating an inter-organizational network and to perceive what was done for the selection and creation of the strategic partnerships and inter-organizational network and what factors or conditions can inhibit these partnerships from having long-term success and throughout its life cycle. For this, a qualitative study based on action research and semi-structured interviews was conducted. Results show although many companies settle in inter-organizational networks to gain competitive advantage, cases of failure are still quite high. In this case, upstream partnerships have not been based on long-term trust and commitment, which has jeopardized the continuity of the network, although there is an express desire to re-establish contacts. The partnership established downstream did not show the same commitment to continue the partnership with a total termination of the relationship.

Abstract
In this paper, we propose a branch-and-price approach for solving the problem of maximizing the expected number of transplants in Kidney Exchange Programs (KEPs). In these programs, the decision on which transplants will be conducted is usually made with the support of optimization models with the assumption that all operations will take place. However, after a plan of transplants is defined, a pair may leave the KEP or a more accurate compatibility evaluation exam may invalidate a transplant. To model these possible events we consider probabilities of failure of vertices and of arcs and the objective of maximizing the expected number of transplants. The proposed approach is based on the so-called cycle formulation, where decision variables are associated with cycles. Built on the concept of type of cycle a branch-and-price algorithm is conceived. One subproblem is defined for each type of cycle. We present computational results of the proposed branch-and-price algorithm and compare them with solving directly the cycle formulation (with a general purpose mixed integer programming solverCPLEX) showing that the proposed approach is the only one suitable for larger instances.

Abstract
As higher education evolves into a multifaceted and complex activity, the incorporation of education management information systems (EMIS) that allows for the production of relevant, organized and structured information, becomes a necessity for both institutions and students. Despite the recognition of this requirement, existing literature does not focus on how EMIS might trigger students' success. With this in mind, an initial proposal of a multi-perspective EMIS success model is presented and a validation on the possible existence of linear correlations between the model contexts is described. Moderate correlations have been detected between the majority of the model contexts and a very strong correlation has been detected between students' satisfaction and the arise of net benefits associated with the use of EMIS.

Abstract
Vine culture is influenced by many factors, such as the weather, soil or topography, which are triggers to phytosanitary issues. Among them are some diseases, that are responsible for major economic losses that can, however, be managed with timely interventions in the field, viable of leading to effective results by preventing damage propagation. While not all symptoms might present a visible evidence, hyperspectral sensors can tackle this aspect with their ability for measuring hundreds of continuously sparse bands that range beyond the eye-perceptible spectrum. Having such research line in mind in this work, a hyperspectral sensor was applied to analyse the spectral status of vine leaves samples, collected in three chronologically distinct campaigns, while costly and destructive laboratory methods were used to track Flavescence Dorée (FD) in the same samples, for a ground truth information. Regarding data processing, machine learning approaches were used, in which several classifiers were selected to detect FD in vine leaves hyperspectral images. The goal was to evaluate and find most suitable classifier for this task. © 2019 International Society for Photogrammetry and Remote Sensing.