Abstract
Marine polymers such as collagen, chitosan, and fucoidan can be combined to form ionic-linked hydrogel networks towards applications in tissue engineering (TE). The use of greener approaches (as determined by green metrics - E-factor), including the absence of external chemical cross-linking agents, has advantages regarding the potential cytotoxicity. By tailoring the formulation of such an ionic-linked hydrogel, it is possible to fine-tune scaffold biofunctionality. In this study, a comparative study of composite hydrogels was accomplished, seeking to understand the correlation between polymer characteristics and physical behaviour to develop the applicability of this technology in soft-to-hard TE. Parameters such as polymer concentration, molecular weight, polymer-biomaterials bonds, biomaterial structural architecture, pore size, and mechanical rheological properties were directly correlated to the hydrogel's formulation. The results highlight that the formulation with greatest potential was the 3-component hydrogel (H-12, followed by H-10, H-11), due to its superior mechanical properties, making it suitable for cartilage TE. This research offers a valuable perspective on hydrogel formulation and a new processing methodology, as well as how tailoring the hydrogel composition influences mechanical behaviour to support selecting the best composition for tissue engineering applications.

Abstract

Abstract
The main goal of this article is to identify the main dimensions of a model proposal for increasing the potential of big data research in Healthcare for medical doctors' (MDs') learning, which appears as a major issue in continuous medical education and learning. The paper employs a systematic literature review of main scientific databases (PubMed and Google Scholar), using the VOSviewer software tool, which enables the visualization of scientific landscapes. The analysis includes a co-authorship data analysis as well as the co-occurrence of terms and keywords. The results lead to the construction of the learning model proposed, which includes four health big data key areas for MDs' learning: 1) data transformation is related to the learning that occurs through medical systems; 2) health intelligence includes the learning regarding health innovation based on predictions and forecasting processes; 3) data leveraging regards the learning about patient information; and 4) the learning process is related to clinical decision-making, focused on disease diagnosis and methods to improve treatments. Practical models gathered from the scientific databases can boost the learning process and revolutionise the medical industry, as they store the most recent knowledge and innovative research.

Abstract
More and more organizations are seeking to adopt organization and management models oriented to their key processes rather than the traditional functional orientation. However, as organizations are seeking to become more process-oriented, numerous gaps and difficulties are recognized at the level of analysis, modelling, management and improvement of processes. The issues surrounding processes are not properly understood and internalised, leading to increased difficulties in implementation and management by processes. There is thus a clear need for expertise in this area of knowledge. In response to this growing demand, in last year's we identify several universities and engineering schools incorporating specific curricular units in their teaching offer. This paper presents some education courses and specialized programs of the Faculty of Engineering of the University of Porto, specifically oriented to analysis, modelling, management and improvement of processes (engineering and business processes). Firstly, the concept of process and process thinking is presented. It will then present the approach followed in some curricular units incorporated in three Master of Science programs and also provides the design of a specialized program oriented to more experienced participants.

Abstract
When making long-term plans for their asset portfolios, decision-makers have to define a priori a maintenance budget that is to be shared among the several assets and managed throughout the planning period. During the planning period, the a priori budget is then allocated by managers to different operation and maintenance interventions ensuring the overall performance of the system. Because asset degradation is stochastic, a considerable amount of uncertainty is associated with this problem. Hence, to define a robust budget, it is essential to account for several degradation scenarios pertaining to the individual condition of each asset. This paper presents a novel mathematical formulation to tackle this problem in a heterogeneous multiasset portfolio. The proposed mathematical model was formulated as a mixed-integer programming two-stage stochastic optimization model with mean-variance constraints to minimize the number of scenarios with an insufficient budget. A Gamma process was used to model the condition of each individual asset while taking into consideration different technological features and operating conditions. We compared the solutions obtained with our model to alternative practices in a set of generated instances covering different types of multi-asset portfolios. This comparison allowed us to explore the value of modeling uncertainty and how it affects the generated solutions. The proposed approach led to gains in performance of up to 50% depending on the level of uncertainty. Furthermore, the model was validated using real-world data from a utility company working with portfolios of power transformers. The results obtained showed that the company could reduce costs by as much as 40%. Further conclusions showed that the cost-saving potential was higher in asset portfolios in worse condition and that defining a priori operation and maintenance interventions led to worse results. Finally, the results showcased how different decision-maker risk-levels affect the value of taking uncertainty into account.

Abstract
In spite of their growing maturity, telecommunication operators lack complete client characterisation, essential to improve quality of service. Additionally, studies show that the cost to retain a client is lower than the cost associated to acquire new ones. Hence, understanding and predicting future client actions is a trend on the rise, crucial to improve the relationship between operator and client. In this paper, we focus in pay-as-you-go clients with uneven top-ups. We aim to determine to what extent we are able to predict the individual frequency and average value of monthly top-ups. To answer this question, we resort to a Portuguese mobile network operator data set with around 200 000 clients, and nine-month of client top-up events, to build client profiles. The proposed method adopts sliding window multiple linear regression and accuracy metrics to determine the best set of features and window size for the prediction of the individual top-up monthly frequency and monthly value. Results are very promising, showing that it is possible to estimate the upcoming individual target values with high accuracy.