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The purpose of this study is contributing to increase the knowledge about Pilgrim’s behaviour during their journey. It focuses mainly on food consumption expenses and habits. The study aims to describe the behavior of pilgrims along the Santiago route. A cross-sectional quantitative study was performed to achieve our objectives. 225 pilgrims fulfilled a survey to characterise their profile and their consumption pattern during the pilgrimage. Our results showed that most of the pilgrims describe themselves as tourist or mystical and transcendental. The pilgrimage lasts 13 days performing 23,6 kilometers for 6,5 hours per day. Regarding differences in the amount of money spent according to the pilgrim profile or the pilgrimage we only observe significant differences for the tourist profile intending to spend more money per day. In the same way, pilgrims performing less than 6 hours of walking per day intend to spend more money. In conclusion, it seems that the Saint James Way seems to attract more people to the local community increasing their availability to spend money locally

A valuable asset for the improvement of aviation maintenance is the correct assessment of the aircraft systems health condition, for a more accurate planning and execution of maintenance routines. As such, the creation of a Prognostic and Health Management (PHM) system, supported by Condition Based Maintenance (CBM) can bring important benefits to the aeronautical field. The ultimate goal of a PHM system is the correct prediction of the Remaining Useful Lifetime (RUL) of a certain aircraft system, using the sensors data extracted during flights. Nevertheless, a relevant stage in the PHM pipeline concerns the estimation of the system condition, expressed by the Health Indicator (HI). The HI value reflects the health condition of a specific aircraft system, which can possibly be affected by degradation, failures or external conditions occurred during flight time. Henceforth, due to the relevancy of the HI assessment for the accuracy of the PHM model, this paper aims to propose a new formulation for the HI computation, derived from raw anonymized data retrieved from different sensors within the aircraft system. The proposed formulation combines information from the different variables (like sensors) that have an impact on the overall system condition, by assigning a positive or negative weight to each variable depending on the influence on the system behaviour. The weights are determined based on the typical and standard data patterns. Thus, the estimated HI aims to reflect the number of hours of flight expected to be flown, based only on raw data extracted from the system. Furthermore, considering that the available sensors data is anonymized, a study of the relevancy of the different sensors features for the degradation assessment is performed, using specific metrics. Considering a scenario where the HI ground truth is unknown, the failure data of each aircraft system is used to evaluate and discuss the formulation suitability. The HI formulation is applied in real datasets, on the environmental systems of two wide body aircraft types.

In this work a web-based tool is presented for the simulation of a Prognostics and Health Management (PHM) system used for exploring and testing different machine learning experimental scenarios with the goal of predicting the Remaining Useful Life (RUL) of aircraft systems. With this tool, the user can select a set of options like the datasets to use, its size, the machine learning method to apply for the RUL prediction and the metrics used for comparing the results. The proposed datasets correspond to public data extracted from a model which aims to simulate a Turbofan Engine dataset of an aircraft. Also, three different State of the Art machine learning techniques are made available to be applied and tested: a Similarity-based, a Neural Network-based and an Extrapolation-based approach. The results obtained by the different approaches can be graphically compared in the web interface. As the methods are executed remotely, the user incurs no computational costs, which constitutes an advantage of using this tool. This web tool aims to be a user-friendly interface used for simulating online experiments regarding the RUL prediction.