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Object-based radar rainfall nowcasting is a widely used technique for convective storm prediction. Currently, most existing object-based nowcasting methods primarily focus on predicting cell movements, neglecting the temporal evolution of cell properties such as size, shape, and intensity. Incorporating this evolution is critical for improving predictability in convective storms. While previous studies have used three-dimensional (3D) radar observations to capture vertical changes during convective cell formation, these efforts often analyse or reconstruct specific convective events. Integrating 3D radar information into operational object-based radar rainfall nowcasting remains an open challenge. This research addresses this challenge using deep learning (DL) techniques. More specifically, a DL-based prediction model is developed, which uses 2D and 3D cells' properties retrieved from 3D radar reflectivity data at the current time and across the past 15 min to predict the evolution of these properties over the next 15 min. This model could eventually be integrated into existing object-based nowcasting models. A total of 4708 cell lifecycles, extracted from high-resolution (5-min, 1-km, 24 levels at 0.5 km intervals) 3D radar data across the UK, are used to train the model, and a total of 1177 lifecycles are used for testing. The proposed model is shown to predict the evolution of single-core convective cells effectively, including changes in 2D projected geometry and mean 2D and 3D reflectivity. In particular, by incorporating information on the vertical evolution of convective cores, the prediction errors of mean reflectivity (in both 2D and 3D) can be reduced by approximately 50% at 15-min forecast lead time, as compared to a persistence forecast. Keywords: radar, tracking, convective cell, nowcasting, 3D, deep learning, lstm. • A deep-learning model that predicts single-core convective cell evolution. • Convective core altitudes aid cell mean reflectivity prediction. • Prediction errors of cell reflectivity are reduced by 50% at 15-min lead time. [ABSTRACT FROM AUTHOR]

The UK's tourism and hospitality industries, having the highest concentration of migrant workers compared to other industries, face complex challenges in managing the risks of labour exploitation and modern slavery (MS); issues largely neglected in academic research. New employment models risk weakening workers' power in employment relations, potentially leading to increased vulnerability and exploitation risk. Based on analysing primary and secondary sources, this article examines the nature of these risks. Our findings show the following drivers: power imbalances; possible enforcement gaps; normalisation and moralisation of possible exploitation; and potential threats posed by macro-level political and socioeconomic issues/events; these, including more competitive labour markets, paradoxically also present opportunities. We develop significant, novel theoretical contributions and propose the new '(in)conspicuous exploitariat' concept, encapsulating their (dis)empowerment, and potential future risks/opportunities. The research advances theory, with implications for policy and practice, for both the UK and the international tourism and hospitality industries. [ABSTRACT FROM AUTHOR]

Masonry arch bridges continue to form a pivotal part of the transport networks of the UK and many other countries worldwide. However, their three-dimensional response (3D) under loading has to date been relatively little investigated. Here, details of a large-scale masonry arch bridge load tested to failure under laboratory conditions are presented. The bridge consisted of a brickwork arch barrel, abutments, spandrel walls, and compacted limestone as backfill material. Patch loading was applied to the surface of the backfill at the quarter and three-quarter span points to assess the failure mechanism, load-carrying capacity, and residual strength of the bridge. A range of sensors were employed to capture the 3D bridge response and ground-penetrating radar surveys were performed on both the virgin and load-tested bridge to compare its internal structure. The observed evolution of cracks and post-test deformed geometry demonstrated the presence of both a localised 3D mode of response in the arch barrel in the vicinity of the applied load and a global four-hinge mechanism. Failure-level load tests carried out first at the quarter span and then at the three-quarter span points showed that the load-carrying capacity of the bridge had reduced by only 10 % in the second test, though the stiffness of the bridge had decreased by around 35 %. During the failure-level load tests, significant in-plane and out-of-plane deformations of the spandrel walls were also observed, adding to the much smaller residual deformations recorded in low- and mid-level load tests. • The 3D response of a masonry arch bridge tested in the laboratory investigated. • The bridge was subjected to high levels of load until collapse. • Experimental studies reveal the localised 3D mode of response in the arch barrel. • Finding of this study may provide valuable insights into the assessment and management of existing masonry arch bridges. [ABSTRACT FROM AUTHOR]

• High resolution E. coli data from a mixed UK catchment with field and sewer inputs. • New model for acute impacts using travel times from spatially distributed sources. • Sewer overflow sensors and radar rainfall used to characterize source loadings. • Application for forecasting arrival time of elevated E. coli levels. • Disaggregation of model output shows E.coli source is a function of the rainfall. Surface water quality is frequently impacted by acute rainfall driven pollutant sources such as sewer overflows. Understanding the risk of exposure from faecal pollution from short term impacts is challenging due to a paucity of high-resolution data from river systems. This paper proposes practical modelling approach for forecasting arrival time and durations of elevated E. coli levels based on hydrological routing of catchment source loadings, characterized by distributed and remote sensing techniques (including sewer overflow monitoring). The model is calibrated and validated using new high resolution E. coli datasets from a UK catchment featuring both diffuse field runoff and storm overflow impacts. Hourly/Bihourly sampling of E. coli was undertaken in the river following different rainfall events across a range of seasonal conditions. The model provides a good estimate of arrival times and durations of elevated E. coli periods following rainfall events. Model simulations suggest that key sources in the catchment are event specific, with sewer overflow spills being more significant following short, intense rainfall events. [Display omitted] [ABSTRACT FROM AUTHOR]