Abstract

Abstract
Often, real-life problems require modelling several response variables together. This work analyses a multivariate linear regression model when the data are censored. Censoring distorts the correlation structure of the underlying variables and increases the bias of the usual estimators. Thus, we propose three methods to deal with multivariate data under left censoring, namely Expectation Maximization (EM), DataAugmentation (DA) and Gibbs Sampler with Data Augmentation (GDA). Results from a simulation study showthat both DA and GDA estimates are consistent for low and moderate correlation. Under high correlation scenarios, EM estimates present a lower bias.

Abstract
Many organizations' performance and survival challenges need dynamic capabilities and technology to speed the development of those capabilities. Companies are constantly visiting the strategies used in learning as a crucial element in preparing their workforce for the accelerated changes. Learning Technologies stand as a facilitator of these challenges, which is why they are so important. There is still a good margin of exploration in the field of the learning technologies. The reality is that a reduced number of studies explore the technology as important in accelerating innovation, performance, and competitiveness. The present research will focus on the strategic implementation of learning technologies. The approach we chose to solve this problem is to develop guidelines that support the strategy for implementing technology in the learning field. The approach will allow us to relate the strategy with the challenges and the impact the organization is expected to achieve.

Abstract
Building a researchers affinity network through the automatic processing of their publications allows us to gain a perspective that goes beyond the networks established through co-authorship. The definition of the importance of each researcher is defined upon their bibliographic production volume, i.e., number of publications, and also upon their centrality in the general network of researchers. In fact, the centrality of a researcher in a network reveals its importance in communication flows with other researchers, thus assuming that communication between researchers is itself a relevant factor for organizational life and in its production. Both network and centrality concepts are better interpreted in a graphical way. In this study, we explore the workflow that will provide these visualizations and focus in the empirical selection of the most appropriate centrality measure. We also propose a centrality visualization method that facilitates the interpretation of the selected measures

Abstract
Currently, radiologists face an excessive workload, which leads to high levels of fatigue, and consequently, to undesired diagnosis mistakes. Decision support systems can be used to prioritize and help radiologists making quicker decisions. In this sense, medical content-based image retrieval systems can be of extreme utility by providing well-curated similar examples. Nonetheless, most medical content-based image retrieval systems work by finding the most similar image, which is not equivalent to finding the most similar image in terms of disease and its severity. Here, we propose an interpretability-driven and an attention-driven medical image retrieval system. We conducted experiments in a large and publicly available dataset of chest radiographs with structured labels derived from free-text radiology reports (MIMIC-CXR-JPG). We evaluated the methods on two common conditions: pleural effusion and (potential) pneumonia. As ground-truth to perform the evaluation, query/test and catalogue images were classified and ordered by an experienced board-certified radiologist. For a profound and complete evaluation, additional radiologists also provided their rankings, which allowed us to infer inter-rater variability, and yield qualitative performance levels. Based on our ground-truth ranking, we also quantitatively evaluated the proposed approaches by computing the normalized Discounted Cumulative Gain (nDCG). We found that the Interpretability-guided approach outperforms the other state-of-the-art approaches and shows the best agreement with the most experienced radiologist. Furthermore, its performance lies within the observed inter-rater variability.

Abstract
This paper proposes two novel strategies for determining the bilateral trading preferences of households participating in a fully Peer-to-Peer (P2P) local energy market. The first strategy matches between surplus power supply and demand of participants, while the second is based on the distance between them in the network. The impact of bilateral trading preferences on the price and amount of energy traded is assessed for the two strategies. A decentralized fully P2P energy trading market is developed to generate the results in a day-ahead setting. After that, a permissioned blockchain-smart contract platform is used for the implementation of the decentralized P2P trading market on a digital platform. Actual data from a residential neighborhood in the Netherlands, with different varieties of distributed energy resources, is used for the simulations. Results show that in the two strategies, the energy procurement cost and grid interaction of all participants in P2P trading are reduced compared to a baseline scenario. The total amount of P2P energy traded is found to be higher when the trading preferences are based on distance, which could also be considered as a proxy for energy efficiency in the network by encouraging P2P trading among nearby households. However, the P2P trading prices in this strategy are found to be lower. Further, a comparison is made between two scenarios: with and without electric heating in households. Although the electrification of heating reduces the total amount of P2P energy trading, its impact on the trading prices is found to be limited.