Deterioration of road and pavement surface conditions is an issue which directly affects the majority of the world today. The complex structure and textural similarities of surface cracks, as well as noise and image illumination variation makes automated detection a challenging task. In this paper, we propose a deep fully convolutional neural network to perform pixel-wise classification of surface cracks on road and pavement images. The network consists of an encoder layer which reduces the input image to a bank of lower level feature maps. This is followed by a corresponding decoder layer which maps the encoded features back to the resolution of the input data using the indices of the encoder pooling layers to perform efficient up-sampling. The network is finished with a classification layer to label individual pixels. Training time is minimal due to the small amount of training/validation data (80 training images and 20 validation images). This is important due to the lack of applicable public data available. Despite this lack of data, we are able to perform image segmentation (pixel-level classification) on a number of publicly available road crack datasets. The network was tested extensively and the results obtained indicate performance in direct competition with that of the current stateof-the-art methods.

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The superior temporal sulcus (STS) and gyrus (STG) are commonly identified to be functionally relevant for multisensory integration of audiovisual (AV) stimuli. However, most neuroimaging studies on AV integration used stimuli of short duration in explicit evaluative tasks. Importantly though, many of our AV experiences are of a long duration and ambiguous. It is unclear if the enhanced activity in audio, visual, and AV brain areas would also be synchronised over time across subjects when they are exposed to such multisensory stimuli. We used intersubject correlation to investigate which brain areas are synchronised across novices for uni- and multisensory versions of a 6-min 26-s recording of an unfamiliar, unedited Indian dance recording (Bharatanatyam). In Bharatanatyam, music and dance are choreographed together in a highly intermodal-dependent manner. Activity in the middle and posterior STG was significantly correlated between subjects and showed also significant enhancement for AV integration when the functional magnetic resonance signals were contrasted against each other using a general linear model conjunction analysis. These results extend previous studies by showing an intermediate step of synchronisation for novices: while there was a consensus across subjects' brain activity in areas relevant for unisensory processing and AV integration of related audio and visual stimuli, we found no evidence for synchronisation of higher level cognitive processes, suggesting these were idiosyncratic.

Blue spaces have been found to have significant salutogenic effects. However, little is known about the mechanisms and pathways that link blue spaces and health. The purpose of this systematic review and meta-analysis is to summarise the evidence and quantify the effect of blue spaces on four hypothesised mediating pathways: physical activity, restoration, social interaction and environmental factors. Following the PRISMA guidelines, a literature search was conducted using six databases (PubMed, Scopus, PsycInfo, Web of Science, Cochrane Library, EBSCOHOST/CINAHL). Fifty studies were included in our systematic review. The overall quality of the included articles, evaluated with the Qualsyst tool, was judged to be very good, as no mediating pathway had an average article quality lower than 70%. Random-effects meta-analyses were conducted for physical activity, restoration and social interaction. Living closer to blue space was associated with statistically significantly higher physical activity levels (Cohen’s d = 0.122, 95% CI: 0.065, 0.179). Shorter distance to blue space was not associated with restoration (Cohen’s d = 0.123, 95% CI: −0.037, 0.284) or social interaction (Cohen’s d = −0.214, 95% CI: −0.55, 0.122). Larger amounts of blue space within a geographical area were significantly associated with higher physical activity levels (Cohen’s d = 0.144, 95% CI: 0.024, 0.264) and higher levels of restoration (Cohen’s d = 0.339, 95% CI: 0.072, 0.606). Being in more contact with blue space was significantly associated with higher levels of restoration (Cohen’s d = 0.191, 95% CI: 0.084, 0.298). There is also evidence that blue spaces improve environmental factors, but more studies are necessary for meta-analyses to be conducted. Evidence is conflicting on the mediating effects of social interaction and further research is required on this hypothesised pathway. Blue spaces may offer part of a solution to public health concerns faced by growing global urban populations.

Conventional surface crack segmentation requires images manually labelled by a trained expert. It is a challenging task as cracks can vary in orientation and size, with some parts of cracks only being one pixel wide. Further, available training data for crack segmentation is sparse. In this work we propose to automate this annotation task, by introducing a fully convolutional U-Net based architecture for semantic segmentation of surface cracks which allows for the use of small datasets through a patch based training process. Our proposed configuration makes use of residual connections inside the convolutional blocks as well as including an attention based gating mechanism between the encoder and decoder section of this architecture, which only propagates relevant activations further. Using our proposed architecture we achieve new state of the art results in two different crack datasets, outperforming the previous best results in two metrics each.