Abstract
Background: This article seeks to complement the previous literature and clarify the particularities of the capital structure policy of firms with foreign direct investment in Angola. Aim: This article seeks to identify the determinants of the capital structure of Portuguese firms with direct investment in Angola and to understand whether the determinants normally considered by standard finance theory are in line with those used by firms when structuring their capital structure policy to participate in the specific market of Angola. Setting: This article examines 26 large Portuguese firms with investments in Angola using econometric panel data for the period 2006-2010. Methods: The study applied fixed and random effects methods and panel-corrected standard errors that maintain efficiency and unbiased behaviour even in the presence of panel-level heteroscedasticity and contemporaneous correlation of observations among panels. Results: The results provide evidence that the determinants normally considered by standard finance theory are in fact - in terms of sign and coefficient dimension - those used by firms for structuring their capital structure policy when involved in the internationalisation process of entering Angola. Specifically, age, asset structure, return on assets and tangibility have a positive influence on the capital structure of Portuguese firms that have invested in Angola, while non-debt tax shields and liquidity have a negative influence on these companies' leverage ratios. When comparing our results with studies that have analysed the capital structure determinants of listed Portuguese firms - firms belonging to the PSI 20 Index and large firms in the Portuguese corporate sector - we found similarities in the sign and coefficient dimension of the determinants of capital structure. However, the profitability coefficient sign is in line with the trade-off framework (i.e. profitability is positively related to debt) but not with pecking order theory (i.e. profitability is negatively related to debt). Conclusion: Our results suggest that the high-growth Angolan market is seen by larger Portuguese firms as a low-risk diversification process because of the economic hardship Portugal has gone through, as well as cultural and linguistic similarities to Portugal. As such, the Angolan market is seen as an extension of the Portuguese domestic market that has increased potential. This scenario potentially reduces the firm default probability and the cost of debt. Maintaining the tax shield benefits of debt and decreasing the cost of debt - through a reduction in the default probability - have induced profitable firms to use more debt.

Abstract
As technology innovation rapidly changes service experiences, service designers need to leverage technology and orchestrate complex service systems to create innovative services while enabling seamless customer experiences. Service design builds upon contributions from multiple fields, including management, information technology, and interaction design. Still, more integration to leverage the role of technology for service innovation is needed. This article integrates these two service design perspectives, management and interaction design, into an interdisciplinary methodthe Management and INteraction Design for Service (MINDS). Using a design science research approach, MINDS synthesizes management perspective models, which focus on creating new value propositions and orchestrating multiple service interfaces, with interaction design perspective models, which focus on technology usage and its surrounding context. This article presents applications of the MINDS method in two different service industries (media and health care) to demonstrate how MINDS enables creating innovative technology-enabled services and advances interdisciplinary service research.

Abstract
This works intends to be a reduced, but complete literature review about the role of the implementation process in the new technological paradigm for manufacturing, called "Industry 4.0". This is a very recent subject that already generated a wide range of literature and discussion, although it had not yet been studied in-depth, making the term "industry 4.0" and its related concepts blurrier than concrete. The expression "implementation of industry 4.0" is too wide, since it is the result of the implementation of "industry 4.0" technologies and not the paradigm per se. The main objective of this work is to study what is known until now about the implementation of "industry 4.0". With that objective, this paper starts by presenting a definition of what is "industry 4.0" and contextualizing it in today's manufacturing environment. Then some preconditions that are required for the implementation of "industry 4.0" are presented, followed by the specificities that some particular technologies have.

Abstract
An unsupervised method for convolutional neural network (CNN) architecture design is proposed. The method relies on a variable neighborhood search-based approach for finding CNN architectures and hyperparameter values that improve classification performance. For this purpose, t-Distributed Stochastic Neighbor Embedding (t-SNE) is applied to effectively represent the solution space in 2D. Then, k-Means clustering divides this representation space having in account the relative distance between neighbors. The algorithm is tested in the CIFAR-10 image dataset. The obtained solution improves the CNN validation loss by over 15% and the respective accuracy by 5%. Moreover, the network shows higher predictive power and robustness, validating our method for the optimization of CNN design.

Abstract
Allocation of jobs to machines and subsequent sequencing each machine is known as job scheduling problem. Classically, both operations are done in a centralized and static/offline structure, considering some assumptions about the jobs and machining environment. Today, with the advent of Industry 4.0, the need to incorporate real-time data in the scheduling decision process is clear and facilitated. Recently, several studies have been conducted on the collection and application of distributed data in real-time of operations, e.g., job scheduling and control. In practice, pure distribution and decentralization is not yet fully realizable because of e.g., transformation complexity and classical resistance to change. This paper studies a combination of decentralized sequencing and central optimum allocation in a lithography job-shop problem. It compares the level of applicability of two decentralized algorithms against the central scheduling. The results show better relative performance of sequencing in stochastic cases. © IFIP International Federation for Information Processing 2017.

Abstract
The pharmaceutical industry operates in a very competitive and regulated market The increased pressure of pharmacies to order fewer products and to receive them more frequently is overcharging the pharmaceutical's distribution network Furthermore, the tight margins and the continuous growth of generic drugs consumption are pressing wholesalers to optimize their supply chains. In order to survive, wholesalers are rethinking their strategies to increase competitiveness. This paper proposes an optimization-simulation approach to address the wholesalers network redesign problem, trading off the operational costs and customer service level. Firstly, at a strategic-tactical level, the supply chain network redesign decisions are optimized via a mixed integer programming model. Here, the number, location, function and capacity of the warehouses, the allocation of customers to the warehouses and the capacity and function of the distribution channels are defined. Secondly, at an operation level, the solution found is evaluated by means of a discrete event simulation model to assess the impact of the redesign in the wholesaler's daily activities. Computational results on a pharmaceutical wholesaler case-study are discussed and the benefits of this solution approach exposed.