Abstract
Participatory processes are an essential aspect of collaborative planning and decision-making processes, but designing such processes effectively can be quite challenging. This work departs from the assumptions that in sustainable urban mobility planning, the functional urban area needs to be considered, and that citizen engagement is often enacted at the neighborhood level. Under these assumptions, we have examined the experiences of 6 metropolitan cases (Bologna, Nantes, Manchester, Montreal, Christchurch, and Santiago de Chile) and draw insights from their experiences. We conclude this work with some general reflections on the importance of systemic approaches to effectively plan for sustainable transitions in urban mobility.

Abstract
In this paper we present simulations for international kidney exchange programmes (KEPs). KEPs are organised in more than ten countries in Europe to facilitate the exchanges of immunologically incompatible donors. The matching runs are typically conducted in every three months for finding optimal exchanges using hierarchical optimisation with integer programming techniques. In recent years several European countries started to organise international exchanges using different collaboration policies. In this paper we conduct simulations for estimating the benefits of such collaborations with a simulator developed by the team of the ENCKEP COST Action. We conduct our simulations on generated datasets mimicking the practice of the three largest KEPs in Europe, the UK, Spanish and the Dutch programmes. Our main performance measure is the number of transplants compared to the number of registrations to the KEP pools over a 5-year period, however, as a novelty we also analyse how the optimisation criteria play a role in the lexicographic and weighted optimisation policies for these countries. Besides analysing the performances on a single instance, we also conduct large number of simulations to obtain robust findings on the performance of specific national programmes and on the possible benefits of international collaborations.

Abstract

Abstract
This paper leverages the outcomes of the first workshop on HCI Engineering Education [4] to create an online repository where the community can share content relevant to HCI. The repository takes advantage of the functionalities of the Git file versioning system to support presenting and adding content. The paper describes the structure of the repository and the process for adding new content. In addition, we propose an adaptation of the framework for presenting teaching samples, supporting more flexibility in the application of educational material for different teaching objectives. The new presentation format starts with describing a design problem and emphasises the students' applied understanding of conceptual and theoretical knowledge. The presentation format is demonstrated and discussed by the example of an end-user design tool for mobile data collection.

Abstract

Abstract
Pathologies in concrete structures can be visually evidenced on the concrete surface, such as by fissures or cracks, fragmentation of part of the concrete, concrete efflorescence, corrosion stains on the concrete surface, or exposed steel bars, the latter two occurring in reinforced concrete. Therefore, these pathologies can be analyzed via the images of concrete structures. This article proposes a methodology for visually inspecting concrete structures using deep neural networks. This method makes it possible to speed up the detection task and increase its effectiveness by saving time in preparing the identifications to be analyzed and eliminating or reducing errors, such as those resulting from human errors caused by the execution of tedious, repetitive analysis tasks. The methodology was tested to analyze its accuracy. The neural network architecture used for detection was YOLO, versions 4 and 8, which was tested to analyze the gain with migration to a more recent version. The dataset for classification was Ozgnel, which was trained with YOLO version 8, and the detection dataset was CODEBRIM. The use of a dedicated classification dataset allows for a better-trained network for this function and results in the elimination of false positives in the detection stage. The classification achieved 99.65% accuracy.