As games are continuously assessing the player, this assessment can be used to adapt the complexity of a game to the proficiency of the player in real time. We performed an experiment to examine the role of dynamic adaptation. In one condition, participants played a version of our serious game for triage training that automatically adapted the complexity level of the presented cases to how well the participant scored previously. Participants in the control condition played a version of the game with no adaptation. The adapted version was significantly more efficient and resulted in higher learning gains per instructional case, but did not lead to a difference in engagement. Adapting games to the proficiency of the player could make serious games more efficient learning tools

Digitalisation in railway networks harnesses digital technologies to optimise operations, leading to enhanced efficiency, and reduced energy consumption. By analysing real-time data, railways can predict maintenance needs, improve passenger experiences, and seamlessly integrate with other transport modes. As societies strive for sustainable transportation solutions, it is imperative to understand and collect digitalisation techniques to enhance efficiency and reduce the ecological footprint of railway networks. This paper serves as a snapshot of the current state of the art addressing the pivotal role of point cloud techniques in advancing railway digitalisation and providing valuable pointers for future research directions. Employing a systematic review approach, our study concentrates exclusively on research centred around railway assets and their digitalisation via point cloud data. We have themed the literature into pre-processing, modelling, and digital twinning. Within this review, we analyse diverse modelling and pre-processing techniques and categorise them for clarity. The digital twin techniques are also collected, though these techniques are scarce in the context of railway infrastructure and point clouds. The paper also presents a compilation of dataset statistics highlighting the scarcity of openly available railway-specific datasets. This scarcity considerably hampers the feasibility of research reproducibility and the comparative analysis of different approaches. Our conclusion reflects on the challenges encountered and proposes a course for future research. Particularly, we conclude that hybrid methodologies that combine machine learning with structure-based techniques hold substantial promise toward creating digital twins, considering the intrinsic characteristics of railway infrastructure.

Having access to accurate and recent digital twins of infrastructure assets benefits the renovation, maintenance, condition monitoring, and construction planning of infrastructural projects. There are many cases where such a digital twin does not yet exist, such as for legacy structures. In order to create such a digital twin, a mobile laser scanner can be used to capture the geometric representation of the structure. With the aid of semantic segmentation, the scene can be decomposed into different object classes. This decomposition can then be used to retrieve cad models from a cad library to create an accurate digital twin. This study explores three deep-learning-based models for semantic segmentation of point clouds in a practical real-world setting: PointNet++, SuperPoint Graph, and Point Transformer. This study focuses on the use case of catenary arches of the Dutch railway system in collaboration with Strukton Rail, a major contractor for rail projects. A challenging, varied, high-resolution, and annotated dataset for evaluating point cloud segmentation models in railway settings is presented. The dataset contains 14 individually labelled classes and is the first of its kind to be made publicly available. A modified PointNet++ model achieved the best mean class Intersection over Union (IoU) of 71% for the semantic segmentation task on this new, diverse, and challenging dataset.

We discuss the design of R3D3, a rolling receptionist robot with the ability to conduct 'double Dutch dialogues': dialogues (in Dutch) that involve, besides a human user, both a robot and a virtual human. R3D3 is intended to assist people when they visit shops, museums, or other establishments by acting as a host or receptionist. In the R3D3 project, we investigate how users can interact with the robot through natural language and nonverbal behaviour. R3D3 uses computer vision to determine user characteristics and combines this with automated speech recognition to analyse users' intentions. The robot and virtual human complement each other's affordances for verbal and nonverbal behaviour. Our preliminary studies have shown that `double Dutch dialogues' require careful attention management through verbal and nonverbal behaviours by the robot and the virtual human, and that a receptionist robot should take the initiative in conversations to fulfil its intentions.