Abstract
The interdisciplinary field of immersive learning research is scattered. Combining efforts for better exploration of this field from the different disciplines requires researchers to communicate and coordinate effectively. We call upon the community of immersive learning researchers for planting the Knowledge Tree of Immersive Learning Research, a proposal for a systematization effort for this field, combining both scholarly and practical knowledge, cultivating a robust and ever-growing knowledge base and methodological toolbox for immersive learning. This endeavor aims at promoting evidence-informed practice and guiding future research in the field. This paper contributes with the rationale for three objectives: 1) Developing common scientific terminology amidst the community of researchers; 2) Cultivating a common understanding of methodology, and 3) Advancing common use of theoretical approaches, frameworks, and models.

Abstract
iReceptor Plus will enable researchers around the world to share and analyse huge immunological distributed datasets, from multiple countries, containing sequencing data pertaining to both healthy and sick individuals. Most of the Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) data is currently stored and curated by individual labs, using a variety of tools and technologies.

Abstract
Adaptive immune receptor repertoires (AIRR) are key targets for biomedical research as they record past and ongoing adaptive immune responses. The capacity of machine learning (ML) to identify complex discriminative sequence patterns renders it an ideal approach for AIRR-based diagnostic and therapeutic discovery. So far, widespread adoption of AIRR ML has been inhibited by a lack of reproducibility, transparency and interoperability. immuneML (immuneml.uio.no) addresses these concerns by implementing each step of the AIRR ML process in an extensible, open-source software ecosystem that is based on fully specified and shareable workflows. To facilitate widespread user adoption, immuneML is available as a command-line tool and through an intuitive Galaxy web interface, and extensive documentation of workflows is provided. We demonstrate the broad applicability of immuneML by (1) reproducing a large-scale study on immune state prediction, (2) developing, integrating and applying a novel deep learning method for antigen specificity prediction and (3) showcasing streamlined interpretability-focused benchmarking of AIRR ML.

Abstract
Museums are currently taking on a new dynamic about the provision of services and their relationship with society. This new paradigm was a stimulus for the development of this study that presents a business model for museums with the Usina de Eureka® brand. The development of the model initially investigated the museological environment, in terms of its ability to generate stimuli for the creative cognitive process of its visitors. With promising results arising from case studies carried out in two museums in the Central Region of Portugal, ways were later explored to help the audience in the development of ideas that could give rise to new products, services, environmental solutions, between others. To this end, the business model was created, aiming to be a facilitating interface between the museum's audience and agents interested in the development of innovative ideas.

Abstract
This work presents an extension of a second-order conic programming model (SOCP) to handle the multi-objective optimal power dispatch problem considering the probabilistic nature of some parameters related to power demand and the renewable energy sources (RES) generation, such as wind speed and solar irradiation level. Three objective functions are considered in this study: 1) costs of RES and non-RES generation; 2) active power losses in the transmission system; and, 3) emission pollutant gases produced by fossil fuel-based generating units. The stochastic nature of power demands and RES are developed through a set of representative operational scenarios extracted from historical data and via a scenario reduction technique. The results obtained in the SOCP model are compared with a nonlinear programming (NLP) model to check the robustness and precision of SOCP model. To this, both models are implemented and processed to simulate the optimal flow for the IEEE 57- and 118-bus systems.

Abstract
The remote maintenance of home appliances, like washing machines, air conditioning, and heating system is a complex problem, but with the help of the ongoing developments on Internet of Things, Data Analysis and Artificial Intelligence, the problem can now be tackled with success. This paper mostly focus in presenting the architecture developed within the aim of the SMART-PDM project for the acquisition of data on the operation of home appliances and then it also shows some preliminary results for washing machines, which give some hints on how to fine tune the system to achieve predictive maintenance and condition monitoring.