Abstract
iMOOC is a new pedagogical model for massive open online courses (Teixeira & Mota, 2013), that evolved from UAb’s online model (Pereira et al., 2008), based on its four pillars of student-centered learning, interaction, flexibility and digital inclusion. It is also a software platform, that supports this model, and that was developed at UAb in close articulation with the pedagogical model. In this paper we describe the guidelines that oriented such development, and argue in favor of the use (or re-use) of well-established and robust software components for this purpose, as opposed to building platforms from scratch. The emergence of MOOCs as open courses, where participants have free access to the course, created new challenges in a closed, formatted LMS landscape. This led to the development of whole new environments that addressed those requirements (edX, Coursera). The iMOOC approach, however, was to build a platform from existing open source software components using an integration of Moodle (https://moodle.org/), which was previously adapted to UAb’s pedagogical model (Rocio & Coelho, 2009), and Elgg (http://elgg.org/), combining the advantages of both formal and informal learning modes, and addressing the pedagogical requirements in a cost-effective way. The integration was achieved using the IMS specification for LTI (learning tools interoperability) (Severance, 2010). As a result, the iMOOC platform has been successfully used both in stand-alone projects, and also in the european ECO project, where the effort to turn it project-compliant was relatively simple, due to the adoption of well-established protocols.

Abstract
This chapter reports on a new solution approach for the multi-mode resource-constrained project scheduling problem (MRCPSP, MPS|jprec|Cmax). This problem type aims at the selection of a single activity mode from a set of available modes in order to construct a precedence and a (renewable and nonrenewable) resource-feasible project schedule with a minimal makespan. The problem type is known to be N P-hard and has been solved using various exact as well as (meta-)heuristic procedures. The new algorithm splits the problem type into a mode assignment and a single mode project scheduling step. The mode assignment step is solved by a satisfiability (SAT) problem solver and returns a feasible mode selection to the project scheduling step. The project scheduling step is solved using an efficient meta-heuristic procedure from literature to solve the resourceconstrained project scheduling problem (RCPSP). However, unlike many traditional meta-heuristic methods in literature to solve the MRCPSP, the new approach executes these two steps in one run, relying on a single priority list. Straightforward adaptations to the pure SAT solver by using pseudo boolean nonrenewable resourceconstraints has led to a high quality solution approach in a reasonable computational time. Computational results show that the procedure can report similar or sometimes even better solutions than found by other procedures in literature, although it often requires a higher CPU time. © Springer International Publishing Switzerland 2015.

Abstract
We consider the problem of scheduling a number of jobs on a number of unrelated parallel machines in order to minimize the makespan. We develop three heuristic approaches, i.e., a genetic algorithm, a tabu search algorithm and a hybridization of these heuristics with a truncated branch-and-bound procedure. This hybridization is made in order to accelerate the search process to near-optimal solutions. The branch-and-bound procedure will check whether the solutions obtained by the meta-heuristics can be scheduled within a tight upper bound. We compare the performances of these heuristics on a standard dataset available in the literature. Moreover, the influence of the different heuristic parameters is examined as well. The computational experiments reveal that the hybrid heuristics are able to compete with the best known results from the literature.

Abstract

Abstract
Intelligence involves knowing information about some competitive factors like competitors' profitability and turnover rate. Information technology can help organizations to seize the information available. In this chapter we will present the solution architected and developed for a Portuguese company with a starting scenario that showed characteristics like dispersed information, concentration of knowledge in a single individual, no defined architecture for data or information, simple changes involving huge efforts and lack of agility. We found that business decision makers had problems of relying on the results, as the system was like a black box and often provided not accurate data. We will describe how we solved the problem, designing and implementing a business intelligence solution and the impact of having an information architecture.

Abstract
The atmospheric electric field near the Earth's surface is dominated by atmospheric pollutants and natural radioactivity, with the latter directly linked to radon (Rn-222) gas. For a better comprehension on the temporal variability of both the atmospheric electric field and the radon concentration and its relation with local atmospheric variables, simultaneous measurements of soil-emitted gamma radiation and potential gradient (defined from the vertical component of the atmospheric electric field) were taken every minute, along with local meteorological parameters (e.g., temperature, atmospheric pressure, relative humidity and daily solar radiation). The study region is Amieira, part of the Alqueva lake in Alentejo Portugal, where an interdisciplinary meteorological campaign, ALEX2014, took place from June to August 2014. Soil gamma radiation is more sensitive to small concentrations of radon as compared with alpha particles measurements, for that reason it is more suited for sites with low radon levels, as expected in this case. Preliminary results are presented here: statistical and spectral analysis show that i)the potential gradient has a stronger daily cycle as compared with the gamma radiation, ii) most of the energy of the gamma signal is concentrated in the low frequencies (close to 0), contrary to the potential gradient that has most of the energy in frequency 1 (daily cycle) and iii) a short-term relation between gamma radiation and the potential gradient has not been found. Future work and plans are also discussed.