Abstract
Onshore wind energy (WE) has achieved a significant diffusion worldwide, in spite of the existence of multiple barriers to the large-scale implementation of wind farms. These barriers have been reported in a large number of studies, but the literature is lacking a systematized overview of their categories and locations. Based on a framework for the analysis of barriers to the penetration of renewable energy sources proposed by Painuly [363], this systematic literature review contributes to addressing this gap, identifying barriers to the large-scale implementation of onshore wind farms by category (market failures, market distortions, economic and financial, institutional, technical, social and other barriers) and location (countries around the world), and characterizing them by the level of economic development (least developed, developing, in transition, and developed) and stage of diffusion (recent or advanced) in their locations. The framework showed a high level of fit with the case of WE and allowed the identification of 31 barriers in 159 countries. The barriers were found to be mostly present in developing economies with recent diffusion, although some barriers were found to occur broadly across developed economies, regardless of the stage of diffusion. The three most frequently observed barriers were the inadequate consideration of externalities, uncertain and unsupportive governmental policies, and insufficient transmission grids.

Abstract
Industry 4.0 is a result of technological evolution and is intended to promote technological transformations in industry at different levels. The impact in human employment has been perceived as a major threat and is a matter of concern. Some authors argue that automation will bring unimaginable changes as soon as computers get more intelligence and as machines become able to perform complex tasks more efficiently than humans. However, technological progress is also pointed out as a stimulus for human-beings to develop the competencies that differentiate them from the machines. In this context, this study aims to explore the impacts of adopting Industry 4.0 technologies on work. The results of a comprehensive literature review provide an integrated perspective to identify and understand such impacts, analysing them in four categories: evolution of employment and creation of new jobs, human-machine interaction, new competencies creation/ development, and, organizational and professional changes. © 2020 IEEE.

Abstract
Similar to the case of biotechnology industry, companies providing devices in the biomedicine industry face several challenges, and to stand out from competitors need to know how to get to the right customer. Potential customers (i.e., individuals and organizations) may choose to adopt or reject an innovative product and will later confirm that decision or not. Such decision is of utmost importance to the success of innovative products and, therefore, of the company that provides them. The aim of this study is to understand how perceptions formed about a biomedical product can influence its adoption intention and behavior and, hereafter, influence the decision of other potential adopters. Findings from a multiple case study provide a clear definition of the adoption process of a specific biomedical product, combining two existing theories - the Diffusion of Innovations Theory and the Technology Acceptance Model - and including the feedback created by interactions between current users of the product and potential users, to understand what influences potential adopters' decisions. © 2020 IEEE.

Abstract
There are extensive studies in the literature about the reorder point/order quantity policies for inventory management, also known as policies. Over time different algorithms have been proposed to calculate the optimal parameters given the demand characteristics and a fixed cost structure, as well as several heuristics and meta-heuristics that calculate approximations with varying accuracy. This work proposes a new meta-heuristic that evolves closed-form expressions for both policy parameters simultaneously - Cooperative Coevolutionary Genetic Programming. The implementation used for the experimental work is verified with published results from the optimal algorithm, and a well-known hybrid heuristic. The evolved expressions are compared to those algorithms, and to the expressions of previous Genetic Programming approaches available in the literature. The results outperform the previous closed-form expressions and demonstrate competitiveness against numerical methods, reaching an optimality gap of less than , while being two orders of magnitude faster. Moreover, the evolved expressions are compact, have good generalisation capabilities, and present an interesting structure resembling previous heuristics.

Abstract
This paper describes a customized risk assessment framework to be applied in military shipbuilding projects. The framework incorporates the Delphi method with visual diagrams, Bayesian Networks (BN) and the expression of expert opinions through linguistic variables. Noisy-OR and Leak Canonical models are used to determine the conditional probabilities of the BN model. The approach can easily be adapted for other shipbuilding construction projects. The visual diagrams that support the Delphi questionnaire favor the comprehensive visualization of the interdependencies between risks, causes, risks and causes, and risks and effects. The applicability of the framework is illustrated through the assessment of risk of two real military shipbuilding projects. This assessment includes a sensitivity analysis that is useful to prioritize mitigation actions. In the two cases studies, the risks with higher probability of occurrence were failures or errors in production, of the contracted, in the requirements, and in planning. The results of the sensitivity analysis showed that a set of mitigation actions directed at relatively easily controllable causes would have achieved important reductions in risk probabilities.

Abstract
Governance has a significant impact on the success of programs and projects. However, governance of collaborative university-industry projects and programs in literature, is a rather scarce topic. Based on an ethnographic study of a large university-industry collaboration, this paper proposes a conceptual framework of Organizational Enablers (OEs) to improve the governance of collaborative university-industry RD programs. An exploratory research was carried out, aiming to learn from the experience of program and project managers and other program stakeholders of the case under study. Qualitative data was collected using participant observation and document analysis. The framework highlights nine OEs: 'Established governance policies and values', 'Formal Governance support structures', 'Flexible organization structures', 'Standardization of program and project management practices', 'Different management approaches to fit the project needs', 'Clearly defined roles and responsibilities', 'Different means of communication and interaction', 'Top management Support' and 'Projects strategic alignment within the industry and university roadmaps'. © 2020 IEEE.