2021
Authors
S. Resende, J; Almeida, M; Martins, R; Antunes, L;
Publication
Proceedings of Entropy 2021: The Scientific Tool of the 21st Century
Abstract
2021
Authors
Silva, DM; Bernardin, T; Fanton, K; Nepaul, R; Pádua, L; Sousa, JJ; Cunha, A;
Publication
Procedia Computer Science
Abstract
The technological revolution that we have been witnessing recently has allowed components miniaturization and made electronic components accessible. Hyperspectral sensors benefited from these advances and could be mounted on unmanned aerial vehicles, which was unthinkable until recently. This fact significantly increased the applications of hyperspectral data, namely in agriculture, especially in the detection of diseases at an early stage. The vineyard is one of the agricultural sectors that has the most to gain from the use of this type of data, both by the economic value and by the number of diseases the plants are exposed to. The Flavescense dorée is a disease that attacks vineyards and may conduct to a significant loss. Nowadays, the detection of this disease is based on the visual identification of symptoms performed by experts who cover the entire area. However, this work remains tedious and relies only on the human eye, which is a problem since sometimes healthy plants are torn out, while diseased ones are left. If the experts think they have found symptoms, they take samples to send to the laboratory for further analysis. If the test is positive, then the whole vine is uprooted, to limit the spread of the disease. In this context, the use of hyperspectral data will allow the development of new disease detection methods. However, it will be necessary to reduce the volume of data used to make them usable by conventional resources. Fortunately, the advent of machine learning techniques empowered the development of systems that allow better decisions to be made, and consequently save time and money. In this article, a machine learning approach, which is based on an Autoencoder to automatically detect wine disease, is proposed.
2021
Authors
Matos, M; Fernandes, T;
Publication
INTERNATIONAL REVIEW ON PUBLIC AND NONPROFIT MARKETING
Abstract
Engagement plays a key role for most organizations. Establishing close relationships with consumers and other stakeholders - thus promoting their loyalty and participation in the value creation process - has become an element of competitive advantage. Extant literature has focused consumer-brands relationships in commercial contexts; yet, when it comes to non-commercial or non-profit contexts - where communities of decidedly engaged individuals voluntarily invest their time and energy to a cause - research is still in its infancy. This study sets out to understand how non-profit organizations (NPO) can generate a sense of engagement among volunteers and which volunteers' behaviours - associated with that psychological state - entice value co-creation with NPO. Group interviews were carried out with volunteers to gain insights on drivers and outcomes of Volunteer Engagement (VE). Value congruence between volunteers and NPO, a sense of community, as well as perceptions of competence and autonomy, were identified as drivers of VE. The study further validated the impact of VE not only on somewhat predictable outcomes, such as NPO loyalty and advocacy, but also on the co-creation of value with NPO through the recruitment of new volunteers and the development of new ideas for service innovation.
2021
Authors
Chen, X; Barbosa, S; Paatero, J; Kulmala, M; Junninen, H;
Publication
Abstract
2021
Authors
Faria, H; Valença, JM;
Publication
IACR Cryptol. ePrint Arch.
Abstract
2021
Authors
Neves Moreira, F; Veldman, J; Teunter, RH;
Publication
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
Abstract
Service operation vessels are becoming the dominant mode for the maintenance of most offshore wind farms. To minimize turbine downtime, it is essential to bring the right components to the wind farm, while budget and volume constraints prohibit having excess inventories on board. This setting can be interpreted as a repair kit problem, which seeks to define a set of components that may be necessary for on-site maintenance operations in a given time period during which emergency resupply is costly. Current repair kit problem approaches however, do not cater sufficiently for some of the characteristics of offshore wind farm maintenance, including weather-dependent deterioration and the possibility to perform emergency resupplies. We propose mixed-integer programming models both to determine (tactical model) and validate (operational model) repair kits when maintenance operations are performed under different weather conditions. The models are flexible enough to be used with real world data considering multiple turbines composed of different deteriorating components, service operation vessels characteristics (speed and volumetric capacity), different weather conditions, and emergency resupplies. An important feature of this approach is its ability to consider detailed maintenance and vessel routing operations to test and validate repair kits in realistic wind farm environments. We provide valuable insights on the composition of repair kits and on relevant business indicators for a set of different scenarios. The practical implications are that repair kits should be adapted depending on weather forecasts and that considerable downtime reductions can be achieved by allowing emergency resupplies. © 2021 The Author(s)
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