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.

Abstract

Abstract
The synchronization of Public Transportation (PT) systems usually considers a simplified network to optimize the flows of passengers at the principal axes of the network. This work aims to identify the most relevant transfer-connections in a PT network. This goal is pursued with the development of a methodology to identify relevant transfer-connections from entry-only Automatic Fare Collection (AFC) data. The methodology has three main steps: the implementation of the Trip-Chaining-Method (TCM) to estimate the alighting stops of each AFC record, the identification of transfers, and finally, the selection of relevant transfer-connections. The adequacy of the methodology was demonstrated with its implementation to the case study of Porto. This methodology can also be applied to PT systems using entry-exit AFC data, and in that case, the TCM would not be required.