Abstract
This paper aims to explore the impact of sense of presence and cybersickness on the users' expectancy and perceived similarity between virtual and the corresponding real environments. Two virtual reality setups were tested (non-immersive and immersive) to achieve further conclusions. This research encompassed a quantitative analysis using data collection based on questionnaires, applied to a sample of 45 participants. A virtual experience was conducted (to explore users' cybersickness and sense of presence), followed by a visit to the actual real sites (to determine the degree of perceived similarity between the virtual and the corresponding real environment and if their expectations were fulfilled). Our results show a positive correlation between the global sense of presence and perceived similarity and users' expectancy for the non-immersive VR setup. A positive correlation was also found between the global cybersickness on both perceived similarity and users' expectancy for the immersive VR setup. Implications of such results for virtual tourism are discussed.

Abstract
The use of VR in industrial training contributes to reduce costs and risks, supporting more frequent and diversified use of experiential learning activities, an approach with proven results. In this work, we present an innovative immersive authoring tool for experiential learning in VR-based training. It enables a trainer to structure an entire VR training course in an immersive environment, defining its sub-components, models, tools, and settings, as well as specifying by demonstration the actions to be performed by trainees. The trainees performing the immersive training course have their actions recorded and matched to the ones specified by the trainer.

Abstract
Computer-aided design (CAD) and 3D modeling are similar, but they have different functionalities and applications. CAD is a fundamental tool to create object models, design parts, and create 2D schematics from 3D designed objects that can later be used in manufacturing. Meanwhile, 3D modeling is mostly used in entertainment, to create meshes for animation and games. When there is the necessity of using real-life object models in game engines, a conversion process is required to go from CAD to 3D meshes. Converting from the continuous domain of CAD to the discrete domain of 3D models represents a trade-off between processing cost and visual accuracy, in order to obtain the best user experience. This work explores different methods for the creation of meshes and the reduction of the number of polygons used to represent them. Based on these concepts, an interactive application was created to allow the users to control how the model looks in the game engine, in a simple way, while also optimizing and simplifying the mapping of textures for the generated meshes. This application (CADto3D) generates accurate 3D models based on CAD surfaces while giving the user more control over the final result than other current solutions.

Abstract
This report describes a preliminary study that took place during the second semester of the school year 2019-2020, where suddenly classes had to be held online due to COVID 19 pandemic. Kahoot! a gamified application was used in some of the problem-solving classes of an undergraduate physics course of the integrated masters of the Electrical and Computers Engineering program. The quizzes applied covered rigid body dynamics and thermodynamics, both contents included in the syllabus of the course. The study was planned prior to the pandemic and the necessary adjustments of teaching online altered the goals. A simple analysis of the data obtained with the Kahoot! quizzes is performed and the results are discussed in the context of the positive and negative effects of going online.

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
Index structures were often used to optimise fetch operations to external storage devices (secondary memory). Nowadays, this also holds for increasingly large amounts of data residing in main-memory (primary memory). Within this scope, this work focuses on index structures that efficiently insert, query and delete valid-time data from very large datasets. This work performs a comparative study on the performance of the Interval B+ tree (IB+ tree) and the Improved Interval B+ tree (I2B+ tree): a variant that improves the time-efficiency of the deletion operation by reducing the number of traversed nodes to access siblings. We performed an extensive analysis of the performance of two operations: insertions and deletions, on both index structures, using multiple datasets with growing volumes of data, distinct temporal distributions and tree parameters (time-split alpha and node order). Results confirm that the I2B+ tree globally outperforms the IB+ tree, since, on average, deletion operations are 7% faster, despite insertions requiring 2% more time. Furthermore, results also allowed to determine the key factors that augment the performance difference on deletions between both trees. Copyright © 2021 by Author/s and Licensed by Veritas Publications Ltd., UK.