Abstract
The integration of distributed energy resources contributes to accomplishing a balance between the supply and demand inside a local market. The operation of these markets is based on the peer-to-peer negotiations between users, whose cooperation leads to an increase in the social welfare of the community, thus creating a more user-centric market. This work fits in the context of the energy community, where members of a community can exchange energy in peer-to-peer transactions and use the public electricity grid as a backup. The market aims at maximizing the social welfare of the community considering the operational costs of all community members. A particle swarm optimization algorithm implemented in Python is used to solve the problem. © 2022 IEEE.

Abstract
Vineyard classification is an important process within viticulture-related decision-support systems. Indeed, it improves grapevine vegetation detection, enabling both the assessment of vineyard vegetative properties and the optimization of in-field management tasks. Aerial data acquired by sensors coupled to unmanned aerial vehicles (UAVs) may be used to achieve it. Flight campaigns were conducted to acquire both RGB and multispectral data from three vineyards located in Portugal and in Italy. Red, green, blue and near infrared orthorectified mosaics resulted from the photogrammetric processing of the acquired data. They were then used to calculate RGB and multispectral vegetation indices, as well as a crop surface model (CSM). Three different supervised machine learning (ML) approaches-support vector machine (SVM), random forest (RF) and artificial neural network (ANN)-were trained to classify elements present within each vineyard into one of four classes: grapevine, shadow, soil and other vegetation. The trained models were then used to classify vineyards objects, generated from an object-based image analysis (OBIA) approach, into the four classes. Classification outcomes were compared with an automatic point-cloud classification approach and threshold-based approaches. Results shown that ANN provided a better overall classification performance, regardless of the type of features used. Features based on RGB data showed better performance than the ones based only on multispectral data. However, a higher performance was achieved when using features from both sensors. The methods presented in this study that resort to data acquired from different sensors are suitable to be used in the vineyard classification process. Furthermore, they also may be applied in other land use classification scenarios.

Abstract
This paper proposes a decision support model to define electricity consumers' remuneration structures when providing consumption flexibility, optimized for different load regimes. The proposed model addresses the remuneration of consumers when participating in demand response programs, benefiting or penalizing those who adjust their consumption when needed. The model defines dynamic remuneration values with different natures for the aggregator (e.g. flexibility aggregator or curtailment service provider) and for the consumer. The preferences and perspective of both are considered, by incorporating variables that represent the energy price, the energy production and the flexibility of consumers. The validation is performed using real data from the Iberian market, and results enable to conclude that the proposed model adapts the remuneration values in a way that it is increased according to the consumers' elastic, while being reduced when the generation is higher. Consequently, the model boosts the active consumer participation when flexibility is required, while reaching a solution that represents an acceptable g tradeoff between the aggregators and the consumers. (C) 2022 The Authors. Published by Elsevier Ltd.

Abstract
Stomach cancer is the third deadliest type of cancer in the world (0.86 million deaths in 2017). In 2035, a 20% increase will be observed both in incidence and mortality due to demographic effects if no interventions are foreseen. Upper GI endoscopy (UGIE) plays a paramount role in early diagnosis and, therefore, improved survival rates. On the other hand, human and technical factors can contribute to misdiagnosis while performing UGIE. In this scenario, artificial intelligence (AI) has recently shown its potential in compensating for the pitfalls of UGIE, by leveraging deep learning architectures able to efficiently recognize endoscopic patterns from UGIE video data. This work presents a review of the current state-of-the-art algorithms in the application of AI to gastroscopy. It focuses specifically on the threefold tasks of assuring exam completeness (i.e., detecting the presence of blind spots) and assisting in the detection and characterization of clinical findings, both gastric precancerous conditions and neoplastic lesion changes. Early and promising results have already been obtained using well-known deep learning architectures for computer vision, but many algorithmic challenges remain in achieving the vision of AI-assisted UGIE. Future challenges in the roadmap for the effective integration of AI tools within the UGIE clinical practice are discussed, namely the adoption of more robust deep learning architectures and methods able to embed domain knowledge into image/video classifiers as well as the availability of large, annotated datasets.

Abstract
This paper describes ongoing research to develop a system to automatically generate exercises on document type validation. It aims to support multiple text-based document formalisms, currently including JSON and XML. Validation of JSON documents uses JSON Schema and validation of XML uses both XML Schema and DTD. The exercise generator receives as input a document type and produces two sets of documents: valid and invalid instances. Document types written by students must validate the former and invalidate the latter. Exercises produced by this generator can be automatically accessed in a state-of-the-art assessment system. This paper details the proposed approach and describes the design of the system currently being implemented. © José Paulo Leal, Ricardo Queirós, and Marco Primo.

Abstract
This paper presents an adaptive robust optimization approach to optimal operation of multi-layout energy hubs under uncertainty. In the first step, the multi-layout energy hub concept is presented and discussed comprehensively followed by its required energy management model, but in the deterministic form. In the next step, an adaptive robust optimization approach is developed for the energy management model of multi-layout energy hubs. The uncertainties of energy hub load as well as upstream energy market prices are considered through bounded intervals using polyhedral uncertainty sets. The proposed adaptive-robust multi-layout EHS optimizer (ARMEO) is developed as a tri-level min-max-min optimization problem which cannot be solved directly. To do so, column-and-constraint (C&C) technique is used to recast the tri-level model into a min master problem and a max-min sub-problem. However, the max-min sub-problem is still a bi-level model and cannot be solved directly. To cope, block coordinate descent (BCD) methodology is applied to the sub-problem to iteratively solve the max-min sub-problem. An industrial-based case study is conducted to show the effectiveness of the proposed model in 1) managing multi-layout energy hubs, and 2) provide immunized operational solutions against uncertainties. Based on the results, it is observed that the ARMEO model is subject to a higher operation cost (compared to deterministic model), however, the obtained operating solutions are immunized against the uncertainties. Moreover, it has been shown that the proposed multi-layout EHS model can provide reasonable operating solutions for all layouts of the system as a whole.