Abstract
Achilles tendinopathy remains a prevalent condition among recreational and high-level athletes. Mechanical loading has become the gold standard in managing these injuries, but exercises are often generic and prescribed in a "one-size-fits-all" principle. The aim of this study was to evaluate the impact of knee angle changes and different levels of force production on the non-uniform behavior in the Achilles tendon during isometric contractions. It was hypothesized that a flexed knee position would lead to a more distinct non-uniform behavior, due to greater differential loading of soleus vs gastrocnemius, and that this effect would be attenuated by higher levels of force production. Contrary to the hypotheses, it was found that the non-uniform deformation, that is, superficial-to-deep variation in displacement with highest displacement in the deep layer, is consistently present, irrespective of the level of force production and knee angle (n = 19; mean normalized displacement ratio 6.32%, 4.88%, and 4.09% with extended knee vs 5.47%, 2.56%, and 6.01% with flexed knee, at 25%, 50%, and 75% MVC, respectively; P > .05). From tendon perspective, aside from the influence on muscle behavior, this might question the mechanical rationale for a change in knee angle during eccentric heel drops. Additionally, despite reaching high levels of plantar flexion force, the relative contribution of the AT sometimes appears to be decreased, potentially due to compensatory actions by agonist muscle groups. These results are relevant for optimizing AT rehabilitation as the goal is to reach specific local tendon loading.

Abstract
In recent years, the artificial intelligence community has taken big strides in the application of reinforcement learning to games or similar environments using deep learning. From Atari to board games, including motor control or riddle solving, fairly generic deep learning algorithms can now achieve great policies by simply learning to play from experience, and minimal knowledge of the specific domain. However, these algorithms are very demanding in terms of time and hardware in order to achieve the results reported in the literature. So much so, that some algorithms would take years to achieve state-of-the-art performance in commodity hardware. Not only that, but even the learning environments can hinder the speed of the learning process, if they have not been performance optimized. In this paper, we evaluate a complex existing environment, and propose a performance-oriented version, which we call GeoFriends2. We describe the motivation behind the creation of our version, and how it is suitable for both single- and multi-agent reinforcement learning. We then use Asynchronous Deep Learning to create complex policies that can act as baselines for future research on this environment. We also describe a set of techniques that speed up the learning process such that tests can be run with commodity hardware in hours, and not weeks, and using much simpler network architectures. © 2018 IEEE.

Abstract
Most generation expansion planning (GEP) methodologies developed under the monopolistic power industry framework were based on optimization models considering static and detailed descriptions of the power system's equipment and operating conditions. Many of these techniques are still in use in the current market framework, although planning in a liberalized environment is affected by a set of uncertainties and dynamics that traditional models are not designed to capture. In this setting, this research describes a GEP approach that uses System Dynamics (SD) to construct a simulation tool to provide planners, regulators, policy and decision makers with strategic and broader insights regarding policies to apply to power systems. The developed tool models the four Brazilian electricity submarkets, providing electricity prices and expansion scenarios in each region, given that policies recently applied at a country level are leading to different outcomes in each of these submarkets.

Abstract
This work combines a detailed model of the electricity sector with a general equilibrium model for Spain, to analyze the effects of new investments and technological evolution in the electricity sector, as well as their impact in global aspects of the economy. A reference scenario with high prices for CO2 emissions together with insufficient investments in renewable energy was simulated, showing an expected negative economic impact. This scenario was then combined with five potential policies of economic reactivation. The most positive one was related to the reduction of the cost of access to capital, leading to improvements in capital income and GDP, thus mitigating the impact of the electricity price increase. This policy also leads to a migration of the labour from the production to the service sectors and suggests that a transition towards a cleaner electricity sector with minor economic impacts is possible, when energy policies are combined with adequate fiscal policies.

Abstract
Solving nesting problems or irregular strip packing problems is to position polygons on a fixed width and unlimited length strip, obeying polygon integrity containment constraints and non-overlapping constraints, in order to minimize the used length of the strip. To ensure non-overlapping, we use separation lines, i.e., straight lines that separate polygons. We present a nonlinear programming model that considers free rotations of the polygons and of the separation lines. This model uses a considerable smaller number of variables than the few other approaches proposed in the literature. We use the nonlinear programming solver IPOPT (an algorithm of interior points type), which is part of COIN-OR. Computational tests were run using established benchmark instances and the results were compared with the ones obtained with other methodologies in the literature that use free rotations. © 2018 Brazilian Operations Research Society.

Abstract
The Portuguese business structure is essentially composed of small and medium-sized enterprises (SMEs), which, despite their size, generate employment and contribute to the increase of national income. In many of these companies, some of them having less than ten employees, management and operational functions are often carried out by the entrepreneur. In addition, the entrepreneur does not always have technical knowledge in management, nor do they have the available time (since they have various responsibilities within the company) to develop and implement a management strategy that allows the enterprise to remain in the market while adopting a more conscious, consistent and sustained orientation. Within the scope of the Operational Program for Competitiveness and Internationalization - Portugal 2020, the Balanced ScoreCard (BSC) strategic management instrument was developed and implemented in several companies and sectors of the Portuguese economy. This instrument was crucial in initiating a process of strategic thinking which, quite possibly, would not have happened in such a short time horizon. This research work analyses through an essentially qualitative research the way the BSC instrument was developed and implemented. Another objective is to understand the main advantages of its use and its main application constraints. The data was gathered through a semi-structured interview developed for the owners of four Portuguese SMEs in the northern region Portugal, targeted under the abovementioned programme framework. The results demonstrate the instrument was essential for the development of a structured strategic thinking, as well as for a better performance and, consequently, to the improvement of the competitiveness of the targeted companies. The results also show the instrument had to be adapted and simplified and its implementation has to involve all the employees of the enterprises.