People in urban areas frequently use parks for recreation and outdoor activities. Owing to the complexity of the outdoor environment, there have only been a few attempts to understand the effect of the thermal environment on people's use of outdoor spaces. This paper therefore seeks to determine the relationship between the thermal environment, park use and behavioural patterns in an urban area of Sweden. The methods used include structured interviews, unobtrusive observations of the naturally occurring behaviour and simultaneous measurements of thermal comfort variables, i.e., air temperature, air humidity, wind speed and global radiation. The thermal environment is investigated through the mean radiant temperature (Tmrt) and the predicted mean vote (PMV) index. The outcome is compared to the subjective behaviour and thermal sensation of the interviewees. It is found that the thermal environment, access and design are important factors in the use of the park. In order to continue to use the park when the thermal conditions become too cold or too hot for comfort, people improve their comfort conditions by modifying their clothing and by choosing the most supportive thermal opportunities available within the place. The study also shows that psychological aspects such as time of exposure, expectations, experience and perceived control may influence the subjective assessment. Comparison between the thermal sensation of the interviewees and the thermal sensation assessed by the PMV index indicates that steady-state models such as the PMV index may not be appropriate for the assessment of short-term outdoor thermal comfort, mainly because they are unable to analyse transient exposure.

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Universities all over the world have developed Massive Online Open Courses (MOOCs) to attract students and explore new ways of learning. The MOOC "Sustainability in Everyday Life" (SiEL) is currently in its design and early development stage at Chalmers University of Technology. It aims at developing the MOOC participant's capacity to appreciate the complexity of sustainable everyday life by developing skills such as systems thinking and critical reflection on the information flow in public media. This paper aims at sharing first experiences regarding the design and early development of the SiEL MOOC and identifying the role(s) of the teachers and its features during the course design and early development based on these first experiences. An action research approach was used to reach these aims, and the teachers' narratives about these first experiences were used as data source. Three distinct processes (pedagogical, production and interaction) and six roles (owners, teachers, learners, designers, developers and negotiators) were identified. The teachers' roles and the processes and activities taking place during the design and early development are closely linked to each other and need to be carefully considered in order to guarantee a successful MOOC design and development process.

Context. The future deployment of the Square Kilometer Array (SKA) will lead to a massive influx of astronomical data and the automatic detection and characterization of sources will therefore prove crucial in utilizing its full potential. Aims. We examine how existing astronomical knowledge and tools can be utilized in a machine learning-based pipeline to find 3D spectral line sources. Methods. We present a source-finding pipeline designed to detect 21-cm emission from galaxies that provides the second-best submission of SKA Science Data Challenge 2. The first pipeline step was galaxy segmentation, which consisted of a convolutional neural network (CNN) that took an H i cube as input and output a binary mask to separate galaxy and background voxels. The CNN was trained to output a target mask algorithmically constructed from the underlying source catalog of the simulation. For each source in the catalog, its listed properties were used to mask the voxels in its neighborhood that capture plausible signal distributions of the galaxy. To make the training more efficient, regions containing galaxies were oversampled compared to the background regions. In the subsequent source characterization step, the final source catalog was generated by the merging and dilation modules of the existing source-finding software SoFiA, and some complementary calculations, with the CNN-generated mask as input. To cope with the large size of H i cubes while also allowing for deployment on various computational resources, the pipeline was implemented with flexible and configurable memory usage. Results. We show that once the segmentation CNN has been trained, the performance can be fine-tuned by adjusting the parameters involved in producing the catalog from the mask. Using different sets of parameter values offers a trade-off between completeness and reliability.

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