Abstract
This paper presents a methodology based on random forest algorithm to predict the undergraduate academic performance of students from a polytechnic institution. The approach followed enabled to select 11 explanatory variables, starting from an initial set of around fifty, which allow to obtain a good predictive performance (R-2=0.79). These variables reveal crucial aspects for the definition of management strategies focused on promoting academic success.

Abstract
The applications of the blockchain technology are still being discovered. When a new potential disruptive technology emerges, there is a tendency to try to solve every problem with that technology. However, it is still necessary to determine what approach is the best for each type of application. To find how distributed ledgers solve existing problems, this study looks for blockchain frameworks in the academic world. Identifying the existing frameworks can demonstrate where the interest in the technology exists and where it can be missing. This study encountered several blockchain frameworks in development. However, there are few references to operational needs, testing, and deploy of the technology. With the widespread use of the technology, either integrating with pre-existing solutions, replacing legacy systems, or new implementations, the need for testing, deploying, exploration, and maintenance is expected to intensify.

Abstract
Tomographic wavefront reconstruction is the main computational bottleneck to realize real-time correction for turbulence-induced wavefront aberrations in future laser-assisted tomographic adaptive-optics (AO) systems for ground-based giant segmented mirror telescopes because of its unprecedented number of degrees of freedom, N, i.e., the number of measurements from wavefront sensors. In this paper, we provide an efficient implementation of the minimum-mean-square error (MMSE) tomographic wavefront reconstruction, which is mainly useful for some classes of AO systems not requiring multi-conjugation, such as laser-tomographic AO, multi-object AO, and ground-layer AO systems, but is also applicable to multi-conjugate AO systems. This work expands that by Conan [Proc. SPIE 9148, 91480R (2014)] to the multi-wavefront tomographic case using natural and laser guide stars. The new implementation exploits the Toeplitz structure of covariance matrices used in an MMSE reconstructor, which leads to an overall ON log N real-time complexity compared with ON2 of the original implementation using straight vector-matrix multiplication. We show that the Toeplitz-based algorithm leads to 60 nm rms wavefront error improvement for the European Extremely Large Telescope laser-tomography AO system over a well-known sparse-based tomographic reconstruction; however, the number of iterations required for suitable performance is still beyond what a real-time system can accommodate to keep up with the time-varying turbulence.

Abstract
More and more technological advances offer new paradigms for training, allowing novel forms of teaching and learning to be devised. A widely accepted prediction is that computing will move to the background, weaving itself into the fabric of our everyday living spaces and projecting the human user into the foreground. This forecast turns out to be an opportunity for human-computer interaction as a way to monitor and assess the user’s stress levels during high-risk tasks. The main effects of stress are increased physiological arousal, somatic complaints, mood disturbances (anxiety, fear and anger) and diminished quality of working life (e.g. reduced job satisfaction). To mitigate these problems, it is necessary to detect stressful users and apply coping measures to manage stress. Human-computer interaction could be improved by having machines naturally monitor their users’ stress, in a non-invasive and non-intrusive way. This article discusses the development of a random forest classifier with the goal of enabling the assessment of high school students’ stress during academic exams, through the analysis of mouse behaviour and decision-making patterns.

Abstract
The top management view of organizations tends not to reach consensus on the prioritization of investments in Information Systems, particularly when they must prioritize their impact on overall performance and budget constraints. This paper presents the results of applying CRUDi Framework to a bank. This allows to obtain new indicators to support the decision and alignment of investment priorities in the processes that support the business strategy. The Framework introduces a new method and tools that allow us to gauge the relative importance of Information Systems to the organizations’ businesses.

Abstract
The uncertainty and variability of renewable energy sources, wind energy in particular, poses serious challenges for the optimal operation and planning of power systems. In this paper, in order to obtain flexible market conditions while power generated by renewable units is short and supply and demand are imbalanced, a Demand Response (DR) strategy is studied to provide network requirements, because Demand Response Programs (DRPs) improve demand potential and increase security, stability and economic performance. The proposed hybrid model created by the integration of wind energy and DR using Time of Use (ToU) or Emergency DRP (EDRP) improves supply and demand. The problem is solved considering the Independent System Operator (ISO) and using a stochastic multiple-objective (MO) method. The objective is to simultaneously minimize the operation costs and the environmental pollution while assuring compliance of network security constraints and considering multiple economical and technical indexes.