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This paper explores the development of a smart Structural Health Monitoring (SHM) platform tailored for long-span bridge monitoring, using the Forth Road Bridge (FRB) as a case study. It discusses the selection of smart sensors available for real-time monitoring, the formulation of an effective data strategy encompassing the collection, processing, management, analysis, and visualization of monitoring data sets to support decision-making, and the establishment of a cost-effective and intelligent sensor network aligned with the objectives set through comprehensive communication with asset owners. Due to the high data rates and dense sensor installations, conventional processing techniques are inadequate for fulfilling monitoring functionalities and ensuring security. Cloud-computing emerges as a widely adopted solution for processing and storing vast monitoring data sets. Drawing from the authors' experience in implementing long-span bridge monitoring systems in the UK and China, this paper compares the advantages and limitations of employing cloud- computing for long-span bridge monitoring. Furthermore, it explores strategies for developing a robust data strategy and leveraging artificial intelligence (AI) and digital twin (DT) technologies to extract relevant information or patterns regarding asset health conditions. This information is then visualized through the interaction between physical and virtual worlds, facilitating timely and informed decision-making in managing critical road transport infrastructure. [ABSTRACT FROM AUTHOR]

Distributed optical fibre sensing (DOFS)-based strain measurement systems are now routinely deployed across infrastructure health monitoring applications. However, there are still practical performance and measurement issues associated with the fibre's attachment method, particularly with thermoplastic pipeline materials (e.g., high-density polyethylene, HDPE) and adhesive affixment methods. In this paper, we introduce a new optical fibre installation method that utilises a hot-weld encapsulation approach that fully embeds the fibre onto the pipeline's plastic surface. We describe the development, application and benefits of the new embedment approach (as compared to adhesive methods) and illustrate its practical performance via a full-scale, real-world, dynamic loading trial undertaken on a 1.8 m diameter, 6.4 m long stormwater pipeline structure constructed from composite spiral-wound, steel-reinforced, HDPE pipe. The optical frequency domain reflectometry (OFDR)-based strain results show how the new method improves strain transference and dynamic measurement performance and how the data can be easily interpreted, in a practical context, without the need for complex strain transfer functions. Through the different performance tests, based on UK rail-road network transport loading conditions, we also show how centimetre- to metre-scale strain variations can be clearly resolved at the frequencies and levels consistent with transport- and construction-based, buried infrastructure loading scenarios. [ABSTRACT FROM AUTHOR]

Pulse oximeters are widely used in hospitals and homes for measurement of blood oxygen saturation level (SpO2) and heart rate (HR). Concern has been raised regarding a possible bias in obtaining pulse oximeter measurements from different fingertips and the potential effect of skin pigmentation (white, brown, and dark). In this study, we obtained 600 SpO2 measurements from 20 volunteers using three UK NHS-approved commercial pulse oximeters alongside our custom-developed sensor, and used the Munsell colour system (5YR and 7.5YR cards) to classify the participants' skin pigmentation into three distinct categories (white, brown, and dark). The statistical analysis using ANOVA post hoc tests (Bonferroni correction), a Bland–Altman plot, and a correlation test were then carried out to determine if there was clinical significance in measuring the SpO2 from different fingertips and to highlight if skin pigmentation affects the accuracy of SpO2 measurement. The results indicate that although the three commercial pulse oximeters had different means and standard deviations, these differences had no clinical significance. [ABSTRACT FROM AUTHOR]

Various facial colour cues were identified as valid predictors of facial attractiveness, yet the conventional univariate approach has simplified the complex nature of attractiveness judgement for real human faces. Predicting attractiveness from colour cues is difficult due to the high number of candidate variables and their inherent correlations. Using datasets from Chinese subjects, this study proposed a novel analytic framework for modelling attractiveness from various colour characteristics. One hundred images of real human faces were used in experiments and an extensive set of 65 colour features were extracted. Two separate attractiveness evaluation sets of data were collected through psychophysical experiments in the UK and China as training and testing datasets, respectively. Eight multivariate regression strategies were compared for their predictive accuracy and simplicity. The proposed methodology achieved a comprehensive assessment of diverse facial colour features and their role in attractiveness judgements of real faces; improved the predictive accuracy (the best-fit model achieved an out-of-sample accuracy of 0.66 on a 7-point scale) and significantly mitigated the issue of model overfitting; and effectively simplified the model and identified the most important colour features. It can serve as a useful and repeatable analytic tool for future research on facial impression modelling using high-dimensional datasets. [ABSTRACT FROM AUTHOR]