Abstract
The economic sector of tourism has gained significant weight in the economy of many countries, highlighting the weight of this sector in Portugal. However, the inconsistency and seasonality of demand causes companies linked to the sector to encounter difficulties regarding the planning and management of resources allocated to the activity. It is often the case that there are periods of economic loss caused by a small volume of demand that is insufficient to support the costs of the activity. In this context, this article proposes a system that, based on intelligent data analysis, allows a hotel chain to segment customers and enhance exclusive offers to minimize fluctuation and demand gaps in hotel units installed in thermal instances. © 2021 AISTI.

Abstract
Kidney exchange programs (KEP) allow an incompatible patient-donor pair, whose donor cannot provide a kidney to the respective patient, to have a transplant exchange with another in a similar situation if there is compatibility. Exchanges can be performed via cycles or chains initiated by non-directed donors (NDD), i.e., donors that do not have an associated patient. The objective for optimization in KEP is generally to maximize the number of possible transplants. Following the course of recent approaches that consider a dynamic matching (exchanges are decided every time a pair or a NDD joins the pool), in this paper we explore two matching policies to find feasible exchanges: periodic, where the algorithm runs within some period (e.g each 3 month); and greedy, in which a matching run is done as soon as the pool is updated with a new pair or NDD. For each policy, we propose a matching algorithm that addresses the waiting times of pairs in a pool. We conduct computational experiments with the proposed algorithms and compare the results with those obtained when periodic and greedy matching aim at maximizing the number of transplants.

Abstract

Abstract
The automotive industry has an extremely high-quality product standard, not just for the security risks each faulty component can present, but the very brand image it must uphold at all times to stay competitive. In this paper, a prototype model is proposed for smart quality inspection using machine vision. The engine labels are detected using Faster-RCNN and YOLOv3 object detection algorithms. All the experiments were carried out using a custom dataset collected at an automotive assembly plant. Eight engine labels of two brands (Citroën and Peugeot) and more than ten models were detected. The results were evaluated using the metrics Intersection of Union (IoU), mean of Average Precision (mAP), Confusion Matrix, Precision and Recall. The results were validated in three folds. The models were trained using a custom dataset containing images and annotation files collected and prepared manually. Data Augmentation techniques were applied to increase the image diversity. The result without data augmentation was 92.5%, and with it the value was up-to 100%. Faster-RCNN has more accurate results compared to YOLOv3. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

Abstract
Growing demand for energy carriers has led to an increased interest in developing and managing multiple energy carrier microgrids. Furthermore, the volatile nature of renewable resources as well as the uncertain electrical and thermal demands imposes significant challenges for the operation of microgrids. Motivated by this, the paper leverages a min max min robust framework for short-term operation of microgrids with natural gas network to capture the uncertainty of wind generation and electrical/thermal loads. The proposed model is linearized and solved using the column-and-constraint generation (C&CG) procedure that decomposes the framework into a master problem and a subproblem. The master problem minimizes the unit commitment cost, while the sub-problem determines the dispatch cost associated with the worst realization of uncertainties via a max min objective function. Also, polyhedral uncertainty sets are defined with budget of uncertainty parameter that adjusts the trade-off between the operation cost and the degree of robustness. The effectiveness of the framework is assessed and discussed via a 21-node energy hub-based microgrid. It can be seen that the solution immunizes against all realizations of uncertainties, whereby increasing the budget of uncertainty and the forecast error, the system robustness is improved. Moreover, the dual variables of the subproblem are converted to the primary variables in order to evaluate the unit commitment and energy dispatch results.

Abstract
Energy demand in the world is mostly met by conventional sources that cause carbon emissions. Considering environmental problems and the depletion of these sources in the near future, there is a trend towards renewable energy sources (RESs). Also, countries are implementing policies such as investment support, production support, quantity target, and limiting carbon emissions to increase the number of RESs. When these policies are compared, one of them can be superior to another in different countries. Also, superficial supports can cause an excessive financial burden on the governments. RESs have inherently intermittent power generation and in this respect, it is important to correctly estimate the RESs whose production changes with environmental conditions and to offer to the electricity markets optimally. For this reason, it is also important to know the structures of the electricity markets in bidding. Besides, RESs can come together to take an effective position in the market in terms of price and manage their imbalances. These structures can take names such as aggregator, virtual power plant (VPP), and portfolio. Considering the above-mentioned issues, this study aims to investigate in detail the methods applied to increase the number of RESs and the ways these resources participate in the electricity markets. In this context, subjects of policies promoting RESs, electricity market structures, development of the electricity market, optimum bidding strategy and ways of collective participation of RESs in the electricity markets are comprehensively examined under different sections.