Safe and effective navigation of the world's oceans and waterways relies on maritime education and training. This involves the learning of motor, procedural and verbal components of complex skills. Motor learning theory evaluates training variables, such as instructions, feedback and scheduling, to determine best practices for long-term retention of such skills. Motor learning theory has come a long way from focusing primarily on underlying cognitive processes to now including individual and contextual characteristics in making predictions about instructional strategies and their role in performance and learning. A remaining challenge in applying recent motor learning theory to maritime education and training is a lack of empirical testing of complex vocational skills, such as simulation scenarios, with delayed retention and transfer tests. Incorporating theory-based understanding of beneficial instructional practices, through both cognitive approaches and those considering context and environment, task complexity and learner characteristics is a fruitful way forward in advancing maritime education and training.

As recent immigration flows are reshaping the host countries workforce composition, this paper aims to assess whether age composition and skill adjustment dynamics play a relevant role in the welfare impact of immigration. To this end, we build and simulate a search and matching model that allows for endogenous natives’ skill acquisition and intergenerational transfers to analyze the welfare effects of immigration on a selected group of 19 OECD countries. The obtained results are then compared with those obtained under different assumptions on age composition and skill adjustment dynamics. Our comparative statics analysis shows that stronger job creation effects take place when natives adjust their skill in response to immigration. Moreover, taking into account age composition plays a key role in assessing the fiscal impact of immigration, which turns out to be positive when we include intergenerational transfers to retirees and immigration is high-skilled. Finally, we find that our model yields more optimistic welfare effects than standard search models that abstract from skill decision and intergenerational redistribution.

Industrial robots in poorly structured environments have to interact compliantly with this environment for successful operations. In this paper, we present a behaviour-based approach to learn peg-in-hole operations from scratch. The robot learns autonomously the initial mapping between contact states to motion commands employing fuzzy rules and creating an Acquired-Primitive Knowledge Base (ACQ-PKB), which is later used and refined on-line by a Fuzzy ARTMAP neural network-based controller. The effectiveness of the approach is tested comparing the compliant motion behaviour using the ACQ-PKB and a priori Given-Primitive Knowledge Base (GVN-PKB). Results using a KUKA KR15 industrial robot validate the approach.