Abstract
In the last years, the paradigm of the Portuguese footwear industry has improved drastically to become one of the main world players. In fact, a lot has changed, from low-cost mass production to serving clients consisting of small retail chains, where orders are small and models are varied. In order to deal with such modifications, the footwear industry started investing in technological solutions. The industrial case presented in this paper fits that purpose. The goal is to contribute to the solution of complex scheduling problems arising in the new mixed-model flexible automatic stitching systems of an important footwear factory. The project starts by building an optimization model. Although the model has its own usefulness, the CPLEX program is only capable of reaching optimal solutions for small problem instances. Therefore, a recent metaheuristic, the Imperialist Competitive Algorithm (ICA), has been chosen to tackle larger problems. The ICA is capable of finding optimal results for smaller instances and achieving adequate solutions for real problems in short periods of time. Moreover, ICA improves the results obtained so far by the method currently used in the factory.

Abstract
At the moment, the vast majority of Portuguese archives with an online presence use a software solution to manage their finding aids: e.g. Digitarq or Archeevo. Most of these finding aids are written in natural language without any annotation that would enable a machine to identify named entities, geographical locations or even some dates. That would allow the machine to create smart browsing tools on top of those record contents like entity linking and record linking. In this work we have created a set of datasets to train Machine Learning algorithms to find those named entities and geographical locations. After training several algorithms we tested them in several datasets and registered their precision and accuracy. These results enabled us to achieve some conclusions about what kind of precision we can achieve with this approach in this context and what to do with the results: do we have enough precision and accuracy to create toponymic and anthroponomic indexes for archival finding aids? Is this approach suitable in this context? These are some of the questions we intend to answer along this paper.

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Microservices emerged as one of the most popular architectural patterns in the recent years given the increased need to scale, grow and flexibilize software projects accompanied by the growth in cloud computing and DevOps. Many software applications are being submitted to a process of migration from its monolithic architecture to a more modular, scalable and flexible architecture of microservices. This process is slow and, depending on the project's complexity, it may take months or even years to complete. This paper proposes a new approach on microservice identification by resorting to topic modelling in order to identify services according to domain terms. This approach in combination with clustering techniques produces a set of services based on the original software. The proposed methodology is implemented as an open-source tool for exploration of monolithic architectures and identification of microservices. A quantitative analysis using the state of the art metrics on independence of functionality and modularity of services was conducted on 200 open-source projects collected from GitHub. Cohesion at message and domain level metrics' showed medians of roughly 0.6. Interfaces per service exhibited a median of 1.5 with a compact interquartile range. Structural and conceptual modularity revealed medians of 0.2 and 0.4 respectively. Our first results are positive demonstrating beneficial identification of services due to overall metrics' results.

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