Abstract
It is estimated that Europe alone will need to add over 250,000 km of transmission capacity by 2050, if it is to meet renewable energy production goals while maintaining security of supply. Estimating the cost of new transmission infrastructure is difficult, but it is crucial to predict these costs as accurately as possible, given their importance to the energy transition. Transmission capacity expansion plans are often founded on optimistic projections of expansion costs. We present probabilistic predictive models of the cost of submarine power cables, which can be used by policymakers, industry, and academia to better approximate the true cost of transmission expansion plans. The models are both generalizable and well specified for a variety of submarine applications, across a variety of regions. The best performing statistical learning model has slightly more predictive power than a simpler, linear econometric model. The specific decision context will determine whether the extra data gathering effort for the statistical learning model is worth the additional precision. A case study illustrates that incorporating the uncertainty associated with the cost prediction to calculate risk metrics - value-at-risk and conditional-value-at-risk provides useful information to the decision-maker about cost variability and extremes.

Abstract
The provisioning of a growing number of services depends on the proper interoperation of multiple products, forming a new distributed system, often subject to timing requirements. To ensure the interoperability and timely behavior of this new distributed system, it is important to conduct integration tests that verify the interactions with the environment and between the system components. Integration test scenarios for that purpose may be conveniently specified by means of UML sequence diagrams (SDs) enriched with time constraints. The automation of such integration tests requires that test components are also distributed, with a local tester deployed close to each system component, coordinated by a central tester. The distributed observation of execution events, combined with the impossibility to ensure clock synchronization in a distributed system, poses special challenges for checking the conformance of the observed execution traces against the specification, possibly yielding inconclusive verdicts. Hence, in this paper we investigate decision procedures and criteria to check the conformance of observed execution traces against a specification set by a UML SD enriched with time constraints. The procedures and criteria are specified in a formal language that allows executing and validating the specification. Examples are presented to illustrate the approach. Copyright

Abstract
Many studies regarding environmental concerns in forest harvest scheduling problems deal with constraints on the maximum clearcut size. However, these constraints tend to disperse harvests across the forest and thus to generate a more fragmented landscape. When a forest is fragmented, the amount of edge increases at the expense of the core area. Highly fragmented forests can neither provide the food, cover, nor the reproduction needs of core-dependent species. This study presents a branch-and-bound procedure designed to find good feasible solutions, in a reasonable time, for forest harvest scheduling problems with constraints on maximum clearcut size and minimum core habitat area. The core area is measured by applying the concept of subregions. In each branch of the branch-and-bound tree, a partial solution leads to two children nodes, corresponding to the cases of harvesting or not a given stand in a given period. Pruning is based on constraint violations or unreachable objective values. The approach was tested with forests ranging from some dozens to more than a thousand stands. In general, branch-and-bound was able to quickly find optimal or good solutions, even for medium/large instances.

Abstract

Abstract
Retinal vessel segmentation is a fundamental and well-studied problem in the retinal image analysis field. The standard images in this context are color photographs acquired with standard fundus cameras. Several vessel segmentation techniques have been proposed in the literature that perform successfully on this class of images. However, for other retinal imaging modalities, blood vessel extraction has not been thoroughly explored. In this paper, we propose a vessel segmentation technique for Scanning Laser Opthalmoscopy (SLO) retinal images. Our method adapts a Deep Neural Network (DNN) architecture initially devised for segmentation of biological images (U-Net), to perform the task of vessel segmentation. The model was trained on a recent public dataset of SLO images. Results show that our approach efficiently segments the vessel network, achieving a performance that outperforms the current state-of-the-art on this particular class of images. © Springer International Publishing AG 2017.

Abstract
Policymakers have an important role in enabling eco-innovation. To assess the effectivity of these interventions, it is necessary to characterize policies, namely the level of policy stringency. The present study contributes to extant empirical literature by performing a cross-country assessment of the impact of policy stringency on the outcomes (rather than the inputs) of the eco-innovation process. Contrasting with extant evidence, results fail to evidence the relevance of policy stringency for eco-innovation performance. Notwithstanding, policy stringency emerged indirectly as a potential critical determinant. Indeed, the possibility to save costs is often driven by policy instruments that punish pollution intensive firms.