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Crack detection of pavement is a critical task in the periodic survey. Efficient, effective and consistent tracking of the road conditions by identifying and locating crack contributes to establishing an appropriate road maintenance and repair strategy from the promptly informed managers but still remaining a significant challenge. This research seeks to propose practical solutions for targeting the automatic crack detection from images with efficient productivity and cost-effectiveness, thereby improving the pavement performance.

This research applies a novel deep learning method named TransUnet for crack detection, which is structured based on Transformer, combined with convolutional neural networks as encoder by leveraging a global self-attention mechanism to better extract features for enhancing automatic identification. Afterward, the detected cracks are used to quantify morphological features from five indicators, such as length, mean width, maximum width, area and ratio. Those analyses can provide valuable information for engineers to assess the pavement condition with efficient productivity.

In the training process, the TransUnet is fed by a crack dataset generated by the data augmentation with a resolution of 224 × 224 pixels. Subsequently, a test set containing 80 new images is used for crack detection task based on the best selected TransUnet with a learning rate of 0.01 and a batch size of 1, achieving an accuracy of 0.8927, a precision of 0.8813, a recall of 0.8904, an F1-measure and dice of 0.8813, and a Mean Intersection over Union of 0.8082, respectively. Comparisons with several state-of-the-art methods indicate that the developed approach in this research outperforms with greater efficiency and higher reliability.

The developed approach combines TransUnet with an integrated quantification algorithm for crack detection and quantification, performing excellently in terms of comparisons and evaluation metrics, which can provide solutions with potentially serving as the basis for an automated, cost-effective pavement condition assessment scheme.

Given the complex nature of integrated care systems (ICSs), the geographical spread and the large number of organisations involved in partnership delivery, the importance of leadership cannot be overstated. This paper aims to present novel findings from a rapid realist review of ICS leadership in England. The overall review question was: how does leadership in ICSs work, for whom and in what circumstances?

Development of initial programme theories and associated context–mechanism–outcome configurations (CMOCs) were supported by the theory-gleaning activities of a review of ICS strategies and guidance documents, a scoping review of the literature and interviews with key informants. A refined programme theory was then developed by testing these CMOCs against empirical data published in academic literature. Following screening and testing, six CMOCs were extracted from 18 documents. The study design, conduct and reporting were informed by the Realist And Metanarrative Evidence Syntheses: Evolving Standards (RAMESES) training materials (Wong et al., 2013).

The review informed four programme theories explaining that leadership in ICSs works when ICS leaders hold themselves and others to account for improving population health, a sense of purpose is fostered through a clear vision, partners across the system are engaged in problem ownership and relationships are built at all levels of the system.

Despite being a rigorous and comprehensive investigation, stakeholder input was limited to one ICS, potentially restricting insights from varied geographical contexts. In addition, the recent establishment of ICSs meant limited literature availability, with few empirical studies conducted. Although this emphasises the importance and originality of the research, this scarcity posed challenges in extracting and applying certain programme theory elements, particularly context.

This review will be of relevance to academics and health-care leaders within ICSs in England, offering critical insights into ICS leadership, integrating diverse evidence to develop new evidence-based recommendations, filling a gap in the current literature and informing leadership practice and health-care systems.