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This study describes a method that combines synthetic aperture radar (SAR) data with shallow-water modeling to estimate flood hazards at a local level. The method uses particle filtering to integrate flood probability maps derived from SAR imagery with simulated flood maps for various flood return periods within specific river sub-catchments. We tested this method in a section of the Severn River basin in the UK. Our research involves 11 SAR flood observations from ENVISAT ASAR images, an ensemble of 15 particles representing various pre-computed flood scenarios, and 4 masks of spatial units corresponding to different river segmentations. Empirical results yield maps of maximum flood extent with associated return periods, reflecting the local characteristics of the river. The results are validated through a quantitative comparison approach, demonstrating that our method improves the accuracy of flood extent and scenario estimation. This provides spatially distributed return periods in sub-catchments, making flood hazard monitoring effective at a local scale. [ABSTRACT FROM AUTHOR]

Background: Individuals with rare kidney diseases account for 5-10% of people with chronic kidney disease, but constitute more than 25% of patients receiving kidney replacement therapy. The National Registry of Rare Kidney Diseases (RaDaR) gathers longitudinal data from patients with these conditions, which we used to study disease progression and outcomes of death and kidney failure., Methods: People aged 0-96 years living with 28 types of rare kidney diseases were recruited from 108 UK renal care facilities. The primary outcomes were cumulative incidence of mortality and kidney failure in individuals with rare kidney diseases, which were calculated and compared with that of unselected patients with chronic kidney disease. Cumulative incidence and Kaplan-Meier survival estimates were calculated for the following outcomes: median age at kidney failure; median age at death; time from start of dialysis to death; and time from diagnosis to estimated glomerular filtration rate (eGFR) thresholds, allowing calculation of time from last eGFR of 75 mL/min per 1·73 m 2 or more to first eGFR of less than 30 mL/min per 1·73 m 2 (the therapeutic trial window)., Findings: Between Jan 18, 2010, and July 25, 2022, 27 285 participants were recruited to RaDaR. Median follow-up time from diagnosis was 9·6 years (IQR 5·9-16·7). RaDaR participants had significantly higher 5-year cumulative incidence of kidney failure than 2·81 million UK patients with all-cause chronic kidney disease (28% vs 1%; p<0·0001), but better survival rates (standardised mortality ratio 0·42 [95% CI 0·32-0·52]; p<0·0001). Median age at kidney failure, median age at death, time from start of dialysis to death, time from diagnosis to eGFR thresholds, and therapeutic trial window all varied substantially between rare diseases., Interpretation: Patients with rare kidney diseases differ from the general population of individuals with chronic kidney disease: they have higher 5-year rates of kidney failure but higher survival than other patients with chronic kidney disease stages 3-5, and so are over-represented in the cohort of patients requiring kidney replacement therapy. Addressing unmet therapeutic need for patients with rare kidney diseases could have a large beneficial effect on long-term kidney replacement therapy demand., Funding: RaDaR is funded by the Medical Research Council, Kidney Research UK, Kidney Care UK, and the Polycystic Kidney Disease Charity., Competing Interests: Declaration of interests ERM reports support for the current manuscript from VHL UK/Ireland and consulting fees from MSD. SM is chair of OxalEurope. MS reports support for the current manuscript from a Medical Research Council UK Precision Medicine programme grant (MR/R013942/1) and consulting fees from Travere Therapeutics. RJC reports support for the current manuscript from Kidney Research UK. JAS reports support for the current manuscript from Kidney Research UK, Northern Counties Kidney Research Fund, and the Medical Research Council UK (all payments to institution). JAS is Academic Vice President of the UK Kidney Association. FWKT reports support from the National Institute for Health and Care Research Imperial Biomedical Centre. DN is the UK Kidney Association Director of Informatics Research. DPG reports support for the current manuscript from St Peter's Trust for Kidney Bladder and Prostate Research, Medical Research Council, Kidney Research UK, Kidney Care UK, and Polycystic Kidney Disease Charity (all payments to institution). DPG chairs the Rare Diseases Committee of the UK Kidney Association and reports fees for consulting and presenting from Novartis, Alexion, Calliditas, Sanofi, Britannia, and Travere. All other authors declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Tidal flats are some of the most dynamic coastal environments in the world, where traditional coastal mapping and monitoring provide insufficient temporal resolution to reliably map channels and sand flats. Satellite-based Synthetic Aperture Radar (SAR) enables regular cloud-penetrating detection of water flowing through channels within the tidal flats, referred to as tidal channels. This paper presents a method for detecting a path through tidal channels, using satellite imagery, that supports our understanding and safe exploitation of this valuable coastal environment. This approach is the first proposed to identify navigable paths in all conditions, with SAR images susceptible to variation due to weather and tidal conditions. Tidal channels are known to vary in SAR presentation, and we find that tidal flat presentation is also influenced by conditions. The most influential factor is the wind, with high winds causing an inversion in how both tidal flats and tidal channels present in SAR images. The presented method for the automatic detection of tidal channels accounts for this variability by using previous channel paths as a reference to reliably correct imagery and detect the latest path. The final algorithm produces paths with minor errors in 17.6% of images; the error rate increases to 71.7%, with an almost tenfold increase in errors, when the SAR image and paths are not adjusted to account for conditions. This capability has been used to support the Nith Inshore Rescue in attending call-outs from their base in Glencaple, UK, while the insights from monitoring tidal channels for a year demonstrate how periods of high river flow preceded major changes in the channel path. [ABSTRACT FROM AUTHOR]

Coastal sediment grain size is an important factor in determining coastal morphodynamics. In this study, we explore a novel approach for retrieving the median sediment grain size (D50) of gravel-dominated beaches using Synthetic Aperture Radar (SAR) spaceborne imagery. We assessed this by using thirty-six Sentinel-1 (C-band SAR) satellite images acquired in May and June 2022 and 2023, and three NovaSAR (S-band SAR) satellite images acquired in May and June 2022, for three different training sites and one test site across England (the UK). The results from the Sentinel-1 C-band data show strong positive correlations (R2 ≥ 0.75 ) between the D50 and the backscatter coefficients for 15/18 of the resultant models. The models were subsequently used to derive predictions of D50 for the test site, with the models which exhibited the strongest correlations resulting in Mean Absolute Errors (MAEs) in the range 2.26–5.47 mm. No correlation (R2 = 0.04) was found between the backscatter coefficients from the S-band NovaSAR data and D50. These results highlight the potential to derive near-real time estimates of coastal sediment grain size for gravel beaches to better inform coastal erosion and monitoring programs. [ABSTRACT FROM AUTHOR]

For the physical model testing of wave energy converters (WECs) in the wave basin, it is necessary to test the models in a small number of sea states. Previously, the H – T binning method was widely used to determine the sea states that are representative of an ocean area. However, it omitted much useful information such as the wave directionality. In this paper, a novel method, the K -means clustering technique is used in combination with High Frequency (HF) radar measured data from Wave Hub, UK. The results show that K -means clustering method better preserves the characteristics of the ocean area than the binning method. Furthermore, the impact of different regrouping methods on assessing the annual energy output of the model is investigated, by applying the K -means clustering method to a 1:25 two-body hinged raft WEC. It is found that although non-linear performance can be clearly observed in the model both physically and numerically. Due to the fact that most sea states from Wave Hub are out of the non-linearity range of the model, the non-linear effect on the overall performance of the WEC model in this ocean area is limited. It allows the annual energy output to be accurately predicted by using only a small number of representative sea states (defined as K) ≤15, based on K -means clustering method. • K -means method selected representative sea states tested on a physical WEC model. • K -means method is effective in selecting the sea states for WEC model testing. • Representative sea states can obtain accurate annual energy output estimation. • Non-linearity of WEC tested had limited influence on annual energy output estimation. [ABSTRACT FROM AUTHOR]

Background: Disease recurrence remains one of the biggest concerns in patients after resection of pancreatic ductal adenocarcinoma (PDAC). Despite (neo)adjuvant systemic therapy, most patients experience local and/or distant PDAC recurrence within 2 years. High-level evidence regarding the benefits of recurrence-focused surveillance after PDAC resection is missing, and the impact of early detection and treatment of recurrence on survival and quality of life is unknown. In most European countries, recurrence-focused follow-up after surgery for PDAC is currently lacking. Consequently, guidelines regarding postoperative surveillance are based on expert opinion and other low-level evidence. The recent emergence of more potent local and systemic treatment options for PDAC recurrence has increased interest in early diagnosis. To determine whether early detection and treatment of recurrence can lead to improved survival and quality of life, we designed an international randomized trial., Methods: This randomized controlled trial is nested within an existing prospective cohort in pancreatic cancer centers in the Netherlands (Dutch Pancreatic Cancer Project; PACAP) and the United Kingdom (UK) (Pancreas Cancer: Observations of Practice and survival; PACOPS) according to the "Trials within Cohorts" (TwiCs) design. All PACAP/PACOPS participants with a macroscopically radical resection (R0-R1) of histologically confirmed PDAC, who provided informed consent for TwiCs and participation in quality of life questionnaires, are included. Participants randomized to the intervention arm are offered recurrence-focused surveillance, existing of clinical evaluation, serum cancer antigen (CA) 19-9 testing, and contrast-enhanced computed tomography (CT) of chest and abdomen every three months during the first 2 years after surgery. Participants in the control arm of the study will undergo non-standardized clinical follow-up, generally consisting of clinical follow-up with imaging and serum tumor marker testing only in case of onset of symptoms, according to local practice in the participating hospital. The primary endpoint is overall survival. Secondary endpoints include quality of life, patterns of recurrence, compliance to and costs of recurrence-focused follow-up, and the impact on recurrence-focused treatment., Discussion: The RADAR-PANC trial will be the first randomized controlled trial to generate high level evidence for the current clinical equipoise regarding the value of recurrence-focused postoperative surveillance with serial tumor marker testing and routine imaging in patients after PDAC resection. The Trials within Cohort design allows us to study the acceptability of recurrence-focused surveillance among cohort participants and increases the generalizability of findings to the general population. While it is strongly encouraged to offer all trial participants treatment at time of recurrence diagnosis, type and timing of treatment will be determined through shared decision-making. This might reduce the potential survival benefits of recurrence-focused surveillance, although insights into the impact on patients' quality of life will be obtained., Trial Registration: Clinicaltrials.gov, NCT04875325 . Registered on May 6, 2021., (© 2024. The Author(s).)

Differential reflectivity (ZDR$$ {Z}_{\mathrm{DR}} $$) columns were observed using a Met Office three‐dimensional radar composite. An algorithm for automatic detection of ZDR$$ {Z}_{\mathrm{DR}} $$ columns was developed, based on ZDR≥1.0$$ {Z}_{\mathrm{DR}}\ge 1.0 $$ dB and ZH≥10$$ {Z}_{\mathrm{H}}\ge 10 $$ dBZ. Across three case days, detected ZDR$$ {Z}_{\mathrm{DR}} $$ columns were found to precede severe convection in tracked convective cells with a range of lead times from 0 to 20 min depending on the case day. Requiring maxima above 1.4 dB and 30 dBZ of ZDR$$ {Z}_{\mathrm{DR}} $$ and ZH$$ {Z}_{\mathrm{H}} $$ respectively was an appropriate second condition for all three cases although the skill in the early detection of severe convection varied across case days. Despite the high probability of detections, the high false alarm rate accompanied by low critical success index and data latency limit performance based on the three cases considered in this study. Nevertheless, the ability to detect ZDR$$ {Z}_{\mathrm{DR}} $$ columns in operational radar data with a useful lead time prior to severe convection in certain conditions is a promising development towards advancing nowcasting of severe convection in the United Kingdom. [ABSTRACT FROM AUTHOR]

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]

In recent years, trees in European countries have been increasingly endangered by emerging infectious diseases (EIDs). In the United Kingdom (U.K.), this has been observed to affect whole woodlands and forests, threatening the existence of some types of trees. Although quarantine measures have been taken to limit the spreading of such diseases, this has not yet been effectively controlled leading to millions of trees affected by EIDs. Ground-penetrating radar (GPR) has proven effective in identifying critical features on diseased trees for detection of EIDs spread. However, the irregular shape of tree trunks and their complex internal structure represent real challenges for conventional GPR measurements and data processing methodologies. In this research, a dual-polarized GPR system is used to detect internal decay in tree trunks using novel signal processing methodologies. A polarimetric correlation filter based on Bragg scattering on a 3-D Pauli feature vector and an arc-shaped Kirchhoff migration are discussed in detail. The proposed polarimetric correlation filter is utilized to enhance the signal-to-noise ratio (SNR) of B-scans due to bark and tree trunk high-loss properties of tree trunks. Meanwhile, an arc-shaped Kirchhoff migration algorithm is performed to counteract the influence of the bark irregularity. The proposed data processing framework is successfully validated with measurements on a real tree trunk, where cross sections were subsequently cut for comparison purposes. Outcomes from the proposed methodology demonstrate a high consistency with the features observed on the tree trunk cross sections, indicating the reliability of the proposed detection scheme for assessing tree-decay associated with EIDs. [ABSTRACT FROM AUTHOR]

Fireworks and other pyrotechnics are acknowledged as sources of disturbance to wildlife, with evidence that many species react adversely to their sight and sound at discharge. However, how firework releases impact wildlife within a city landscape is poorly understood. Here, we explore the effect of fireworks on urban birds using an L-band staring radar (90-degree sector out to a 5 km range) to capture bird activity derived from flight tracks (i.e. 3D visualisation of individual flying birds built from radar detections) within the city of Birmingham, UK. Comparing the tracks between baseline periods with no fireworks and periods where fireworks are commonly discharged using a null model indicated that birds flew at higher elevations during firework periods (standardised effect sizes of 17.11, 26.54 and 5.83, for Diwali, Bonfire Night, and New Year's Eve, respectively). Birds also flew in more significant numbers (standardised effect sizes of 23.41, 7.98 and 7.19 for Diwali, Bonfire Night, and New Year's Eve, respectively). Therefore, bird activity was elevated during firework events at a time of night when many would otherwise be roosting. Such disturbance may have implications for avian biology since large public firework events occur at colder times of the year in the UK when birds have elevated thermoregulatory costs. [ABSTRACT FROM AUTHOR]

Accurate precipitation estimation with weather radars is essential for hydrological and meteorological applications. The differential reflectivity (ZDR) is a crucial weather radar measurement that helps to improve quantitative precipitation estimates using polarimetric weather radars. However, a system bias between the horizontal and vertical channels generated by the radar produces an offset in ZDR. Existing methods to calibrate ZDR measurements rely on the intrinsic values of the ZDR of natural targets (e.g. drizzle or dry snow) collected at high elevation angles (e.g. higher than 40 ∘ or even at 90 ∘), in which ZDR values close to 0 dB are expected. However, not all weather radar systems can scan at such high elevation angles or point the antenna vertically to collect precipitation measurements passing overhead. Therefore, there is a need to develop new methods to calibrate ZDR measurements using lower-elevation scans. In this work, we present and analyse a novel method for correcting and monitoring the ZDR offset using quasi-vertical profiles computed from scans collected at 9 ∘ elevations. The method is applied to radar data collected through 1 year of precipitation events by two operational C-band polarimetric weather radars in the UK. The proposed method shows a relative error of 0.1 dB when evaluated against the traditional approach based on ZDR measurements collected at 90 ∘ elevations. Additionally, the method is independently assessed using disdrometers located near the radar sites. The results showed a reasonable agreement between disdrometer-derived and radar-calibrated ZDR measurements. [ABSTRACT FROM AUTHOR]

This paper proposes a human activity recognition (HAR) method for frequency-modulated continuous wave (FMCW) radar sensors. The method utilizes a multi-domain feature attention fusion network (MFAFN) model that addresses the limitation of relying on a single range or velocity feature to describe human activity. Specifically, the network fuses time-Doppler (TD) and time-range (TR) maps of human activities, resulting in a more comprehensive representation of the activities being performed. In the feature fusion phase, the multi-feature attention fusion module (MAFM) combines features of different depth levels by introducing a channel attention mechanism. Additionally, a multi-classification focus loss (MFL) function is applied to classify confusable samples. The experimental results demonstrate that the proposed method achieves 97.58% recognition accuracy on the dataset provided by the University of Glasgow, UK. Compared to existing HAR methods for the same dataset, the proposed method showed an improvement of about 0.9–5.5%, especially in the classification of confusable activities, showing an improvement of up to 18.33%. [ABSTRACT FROM AUTHOR]

The first Doppler spectra ever acquired by an atmospheric radar at 200 GHz (G‐band) are presented. The observations were taken during a light precipitation event in May (rain rates <2 mm hr−1) at Chilbolton Observatory, UK, with coincident Ka‐band and W‐band Doppler radar measurements. The collected rain spectra agree with Mie theory predictions: at G‐band they show significant reductions in the spectral power return—as compared to theoretical Rayleigh scattering—corresponding to high Doppler velocities (i.e., large raindrops) with the presence of multiple peaks and "Mie notches" in correspondence to the maxima and minima of the raindrop backscattering cross sections. The first two G‐band Mie troughs correspond to smaller velocities/sizes than the first W‐band Mie notch. These features offered by G‐band radars pave the way toward applying, in rain, Mie notch vertical wind retrievals and multifrequency drop size distribution microphysical retrievals to smaller rain rates and smaller characteristic sizes than ever before. Plain Language Summary: The first Doppler velocity measurements ever acquired by an atmospheric radar at 200 GHz (in the so‐called G‐band) are presented. The observations were taken during a light precipitation event in May (rain rates <2 mm hr−1) at the STFC Chilbolton Observatory, UK, with coincident measurements at 35 and 94 GHz. At high Doppler velocities (i.e., in correspondence to large raindrops) the collected G‐band Doppler velocity spectra show significant non‐Rayleigh effects, as expected from scattering Mie theory when the wavelength is much larger than the scattering target dimension. This is highlighted by the presence of multiple peaks and minima in the spectral power return and by a significant reduction of the spectral power at G‐band compared to the other frequencies. The first two G‐band minima both correspond to smaller velocities/sizes than the first W‐band minimum, a direct consequence of the reduced wavelength of the G‐band radar. These novel features observed by G‐band radars pave the way toward applying, in rain, vertical wind retrievals and multifrequency drop size distribution retrievals to smaller rain rates and smaller characteristic drop sizes than ever before. Key Points: The first Doppler spectra ever acquired by an atmospheric radar at 200 GHz (G‐band) are presentedThe G‐band spectra in rain show the presence of Mie notches, as expected from Mie scattering theoryThe G‐band spectra pave the way toward applying vertical wind and multifrequency DSD retrievals at smaller rain rates than ever before [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]

Weather radars produce high spatial and temporal resolution observations of precipitation events. Over the years, weather radar operators have updated their radar networks to exploit the latest technological advancements. One of the most significant improvements in this matter was providing the radars with polarimetric capabilities, as this allows the radars to gather more detailed information about the precipitation targets' shape, size, phase, and orientation. The radar research community has been working along with these advancements to develop robust algorithms that boost the radar data applications, such as radar quantitative precipitation estimation (QPE), rain microphysics analysis, nowcasting of precipitation or numerical weather prediction based on radar measurements. Some of these algorithms have been implemented in open-source toolboxes that aim to facilitate the processing and quality control of radar data produced by different radar systems. However, these open-source toolboxes have not yet included a chain process tailored to the UK radar research context. This paper presents a TOolbox to process WEather Radar data in PYthon (Towerpy). Towerpy can read, process and display polarimetric radar data from different radar systems, but it is specially customised for data produced by the UK Met Office radar network. Towerpy is built upon robust algorithms that cover various aspects of the radar data quality control (e.g., calibration of radar measurements, identification of non-meteorological echoes, attenuation correction, among others) and the computation of radar rainfall rates. Additionally, a radar processing chain was devised using the Towerpy modules to produce radar rainfall estimates. Raw polarimetric radar measurements collected by the UK Met Office radar network throughout the UK's wettest day on record were used as input for this processing chain. The results confirm that Towerpy is a powerful radar research tool and demonstrate its ability to generate functional radar QPE that can be used to improve operational radar rainfall products. • We present Towerpy, a Python toolbox to process polarimetric weather radar data. • Towerpy provides robust algorithms for radar data quality control and radar QPE. • We set a processing chain that estimates rain rates to show Towerpy's capabilities. [ABSTRACT FROM AUTHOR]

Introduction: Multiple myeloma is a plasma cell malignancy that accounts for 1%-2% of newly diagnosed cancers.At diagnosis, approximately 20% of patients can be identified, using cytogenetics, to have inferior survival (high-risk). Additionally, standard-risk patients, with detectable disease (minimal residual disease (MRD)-positive) postautologus stem cell transplant (ASCT), fare worse compared with those who do not (MRD-negative). Research is required to determine whether a risk-adapted approach post-ASCT could further improve patient outcomes., Methods: RADAR is a UK, multicentre, risk-adapted, response-guided, open-label, randomised controlled trial for transplant-eligible newly diagnosed multiple myeloma patients, using combinations of lenalidomide (R), cyclophosphamide (Cy), bortezomib (Bor), dexamethasone (D) and isatuximab (Isa).Participants receive RCyBorD(x4) induction therapy, followed by high-dose melphalan and ASCT. Post-ASCT, there are three pathways as follows:A phase III discontinuation design to assess de-escalating therapy in standard-risk MRD-negative patients. Participants receive 12 cycles of Isa maintenance. Those who remain MRD-negative are randomised to either continue or stop treatment.A phase II/III multiarm multistage design to test treatment strategies for treatment escalation in standard-risk MRD-positive patients. Participants are randomised to either; R, RBorD(x4) +R, RIsa, or RBorIsaD(x4) + RIsa.A phase II design to assess the activity of intensive treatment strategies in high-risk patients. Participants are randomised to RBorD(x4) +R or RBorIsaD(x4) + RIsa.1400 participants will be registered to allow for 500, 450 and 172 participants in each pathway. Randomisations are equal and treatment is given until disease progression or intolerance., Ethics and Dissemination: Ethical approval was granted by the London-Central Research Ethics Committee (20/LO/0238) and capacity and capability confirmed by the appropriate local research and development department for each participating centre prior to opening recruitment. Participant informed consent is required before trial registration and reconfirmed post-ASCT. Results will be disseminated by conference presentations and peer-reviewed publications., Trial Registration Number: ISCRTN46841867., Competing Interests: Competing interests: ABC, KLR, DAC, AH, CO, LBai, LBar, AP and RL report grants and non-financial support from BMS/Celgene, grants and non-financial support from Merck Sharpe & Dohme, grants and non-financial support from Amgen, grants and non-financial support from Takeda, during the conduct of the trial. DAC also reports travel support from Celgene Corporation. KY, RDT, CB, GJ, GP, MC, BD, DM, GC, HA, KR,SA and AC have no conflicting interests to declare. MC declares, Bristol Myers Squibb- employee, Honoraria/travel support in the last 3 years from, Amgen, BMS/Celgene, Janssen, Takeda, Abbvie. CP declares BMS/Celgene Ad boards and speaker fees, Sanotif—Ad board, speaker fees, conference registration fees. JS reports Carrying out consultancy work (Advisory Board) for Sanofi. And an educational speaking engagement for Celgene/BMS RP declares; Honoraria—Jannsen, BMS, Abbvie, GSK. Consultancy: GSK, Janssen. Meeting support: Janssen, Takeda, BMS. RO declares- Janssen - advisory board, honoraria, Celegene— honoraria, Beigene - advisory board, honoraria, Astra Zeneca—honoraria. MK declares inter-relationships: AbbVie: consultancy; Amgen: honoraria; BMS/Celgene: consultancy, research funding (institution); GSK: consultancy; Janssen: consultancy, research funding (institution); Karyopharm: consultancy; Pfizer: consultancy; SeattleGenetics: consultancy; Takeda: consultancy; Sanofi: honoraria. MD reports owning stock in Abingdon Health. SQ is the founder and CSO of Achilles therapeutics a company developing T cell therapies for solid tumours., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY. Published by BMJ.)

A 5-yr climatology and composite study of precipitation bands associated with extratropical cyclones over the British Isles from April 2017 to March 2022 is constructed. A total of 249 single bands were manually identified from radar network mosaics in association with 167 cyclones identified from surface maps. More bands formed over water near the coast than over inland areas, and most had a meridional orientation. The average lengths of bands at the times of formation and maximum length were 290 and 460 km, respectively; only 20% of bands reached a maximum length exceeding 600 km. The number of bands decreased with increasing duration, with 31% of bands lasting for 2–3 h, with bands lasting more than 10 h uncommon. The bands were classified into six categories, with occluded-frontal bands (19 yr−1), warm-frontal bands (11 yr−1), and cold-frontal bands (10 yr−1) being the most frequent. Occluded-frontal and warm-frontal bands commonly occurred west of Scotland and in the east quadrant relative to their parent cyclones. In contrast, cold-frontal bands commonly occurred southwest of Great Britain and in the south quadrant relative to their parent cyclones. Composites for northwest–southeast occluded-frontal and warm-frontal bands west of Scotland, and southwest–northeast cold-frontal bands southwest of Great Britain, show the different synoptic environments that favor bands. The low-level jet transports moisture into the band and is similar to the location and scale of the composite bands, similar to that of an atmospheric river. These results are compared to previous studies on bands from the United States. Significance Statement: Precipitation bands are lines of heavy precipitation as seen on weather radar. Most studies of bands in extratropical cyclones have occurred in the United States. We examine 5 years of bands in extratropical cyclones over the British Isles to better understand their characteristics. Bands form in preferred geographic regions: offshore of the west coasts of Scotland, Wales, and southwest England. The most common bands are associated with occluded fronts (37% of all bands). The average scale of the bands is associated with the average scale of wind maxima 1–2 km above ground. These results provide a better understanding of the typical characteristics and conditions under which bands form and their geographical variability compared to the United States. [ABSTRACT FROM AUTHOR]

Progress toward habitat protection goals can effectively be performed using satellite imagery and machine-learning (ML) models at various spatial and temporal scales. In this regard, habitat types and landscape structures can be discriminated against using remote-sensing (RS) datasets. However, most existing research in three-dimensional (3D) habitat mapping primarily relies on same/cross-sensor features like features derived from multibeam Light Detection And Ranging (LiDAR), hydrographic LiDAR, and aerial images, often overlooking the potential benefits of considering multi-sensor data integration. To address this gap, this study introduced a novel approach to creating 3D habitat maps by using high-resolution multispectral images and a LiDAR-derived Digital Surface Model (DSM) coupled with an object-based Random Forest (RF) algorithm. LiDAR-derived products were also used to improve the accuracy of the habitat classification, especially for the habitat classes with similar spectral characteristics but different heights. Two study areas in the United Kingdom (UK) were chosen to explore the accuracy of the developed models. The overall accuracies for the two mentioned study areas were high (91% and 82%), which is indicative of the high potential of the developed RS method for 3D habitat mapping. Overall, it was observed that a combination of high-resolution multispectral imagery and LiDAR data could help the separation of different habitat types and provide reliable 3D information. [ABSTRACT FROM AUTHOR]

• Precipitation data from an X-band radar in northern Scotland are assessed. • Assessment against river flow using a distributed hydrological model brings fresh insights. • Lowest usable beam elevation is the main limit on performance in this mountainous area. • Use of dual-polarisation estimators improves performance during summer months. • The X-band radar is suited for filling gaps in the existing C-band radar network. A set of Quantitative Precipitation Estimates (QPEs) from a dual-polarisation X-band radar observation campaign in a mountainous area of Northern Scotland is assessed with reference to observed river flows as well as being compared to estimates from the UK C-band radar and raingauge networks. Employing estimation methods of varying complexity, the X-band QPEs are trialled as alternative inputs to Grid-to-Grid (G2G), a distributed hydrological model, to produce simulated river flows for comparison with observations. This hydrological assessment complements and extends a previous meteorological assessment that used point raingauge data only. Precipitation estimates for two periods over the observation campaign in 2016 (March to April and June to August) are assessed. During the second period, increased incorporation of dual-polarisation variables into the radar processing chain is found to be of considerable benefit, whereas during the first period the low height of the melting layer often restricts their use. As a result of the complex topography in Northern Scotland, the Lowest Usable Elevation (LUE) of the X-band radar observations is found to be a stronger indicator of the hydrological model performance than range from the radar. For catchments with an LUE of less than 3 km, the best X-band QPE typically performs better for modelling river flow than using an estimate from the UK C-band radar network. The hydrological assessment framework used here brings fresh insights into the performance of the different QPEs, as well as providing a stimulus for targeted improvements to dual-polarisation radar-based QPEs that have wider relevance beyond the case study situation. [ABSTRACT FROM AUTHOR]

The National Registry of Rare Kidney Diseases (RaDaR) is a Renal Association (now the UK Kidney Association) initiative to improve patient and clinician knowledge of rare kidney diseases. The RaDaR is the largest rare kidney disease registry in the world and it is a globally unique and extremely rich resource with incredible potential to improve the understanding of these rare conditions [ABSTRACT FROM AUTHOR]

Nowadays, an increasing amount of attention is being devoted towards passive and non-intrusive sensing methods. The prime example is healthcare applications, where on-body sensors are not always an option or in other applications which require the detection and tracking of unauthorized (non-cooperative) targets within a given environment. Therefore, in this paper we present a dataset consisting of measurements obtained from Radio-Frequency (RF) devices. Essentially, the dataset consists of Ultra-Wideband (UWB) data in the form of Channel Impulse Response (CIR), acquired via a Commercial Off-the-Shelf (COTS) UWB equipment. Approximately 1.6 hours of annotated measurements are provided, which are collected in a residential environment. This dataset can be used to passively track a target's location in an indoor environment. Additionally, it can also be used to advance UWB-based Human Activity Recognition (HAR) since three basic human activities were recorded, namely, sitting, standing and walking. We anticipate that such datasets may be utilized to develop novel algorithms and methodologies for healthcare, smart homes and security applications. Measurement(s) Human physical activity • Human location Technology Type(s) Ultra-wideband impulse radar Factor Type(s) Human location • Human physical activity • Monitoring environment • Participant demographics • Radio-frequency signals Sample Characteristic - Organism Homo sapiens Sample Characteristic - Environment residential building Sample Characteristic - Location United Kingdom [ABSTRACT FROM AUTHOR]

Creating native woodland is a policy goal globally, and one strategy to maximize woodland creation benefits in limited space is to target efforts to extend existing woodlands. There is evidence to support spatially targeting habitat creation for biodiversity, however, there is little evidence of how this affects a habitat's structural development. Here, a space‐for‐time study using light detection and ranging (LiDAR) data assesses how the structure of recently created woodlands, are affected by the presence of an adjacent older woodland. Recently created native woodlands were identified across the Isle of Wight UK using historical maps and satellite imagery. Canopy height and foliage height diversity were derived for all woodlands from LiDAR data collected at two different time points (2011 and 2021), and linear models were used to test for any differences in these structural metrics between sites with an adjacent older woodland, and those without. The percentage change in woodland height between the two time points was also tested. In woodlands created adjacent to older woodlands, canopy height was found to be higher by an average of nearly 2 m, and foliage height diversity was found to be on average 4.7% higher, using the 2021 data. Growth rates between 2011 and 2021 were not significantly different between the groups, although young adjacent woodlands grew the most on average. This research shows that creating woodlands adjacent to existing older woodlands reduces the time taken to create tall and to a lesser extent structurally diverse habitat, which may lead to early biodiversity benefits. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive dataset intended to evaluate passive Human Activity Recognition (HAR) and localization techniques with measurements obtained from synchronized Radio-Frequency (RF) devices and vision-based sensors. The dataset consists of RF data including Channel State Information (CSI) extracted from a WiFi Network Interface Card (NIC), Passive WiFi Radar (PWR) built upon a Software Defined Radio (SDR) platform, and Ultra-Wideband (UWB) signals acquired via commercial off-the-shelf hardware. It also consists of vision/Infra-red based data acquired from Kinect sensors. Approximately 8 hours of annotated measurements are provided, which are collected across two rooms from 6 participants performing 6 daily activities. This dataset can be exploited to advance WiFi and vision-based HAR, for example, using pattern recognition, skeletal representation, deep learning algorithms or other novel approaches to accurately recognize human activities. Furthermore, it can potentially be used to passively track a human in an indoor environment. Such datasets are key tools required for the development of new algorithms and methods in the context of smart homes, elderly care, and surveillance applications. Measurement(s) Human physical activity • Human location Technology Type(s) WiFi sensing device • ultra-wideband impulse radar • passive WiFi radar • Kinect motion sensor Factor Type(s) Human location • Human physical activity • Room geometry • Participant demographics • Contactless sensing devices Sample Characteristic - Organism Homo sapiens Sample Characteristic - Environment Office building Sample Characteristic - Location United Kingdom [ABSTRACT FROM AUTHOR]

The start of the shift then involved lively discussions as to how long into the day the snowfall would persist, then amending the forecast temperatures for the next couple of days markedly downwards in view of the snow cover. Although the snow was well forecast for southwest Britain, N. S. Grahame recalls that heavy snow was forecast to extend right into southeast England, but in the end never made it that far east. Greater than expected deepening of the system at short notice led to heavier than anticipated precipitation, meaning that the forecast rain (with wintry precipitation largely confined to hills), rapidly and unexpectedly turned to widespread heavy snow late in the night, with the resulting snow cover persisting for many days subsequently. (a) NWP model forecast, (b) subjective 24-h forecast and (c) rerun of the NWP forecast in research mode after the event. [Extracted from the article]

Soil moisture is important in many hydrological and ecological processes. However, data sets which are currently available have issues with accuracy and resolution. To translate remotely-sensed data to an absolute measure of soil moisture requires mapped estimates of soil hydrological properties and estimates of vegetation properties, and this introduces considerable uncertainty. We present an alternative methodology for producing daily maps of soil moisture over the UK at 2-km resolution ('SMUK'). The method is based on a simple empirical model, calibrated with five years of daily data from cosmic-ray neutron sensors at ~40 sites across the country. The model is driven by precipitation, humidity, a remotely-sensed 'soil water index' satellite product, and soil porosity. The model explains around 70 % of the variance in the daily observations. The spatial variation in the parameter describing the soil water retention (and thereby the response to precipitation) was estimated using daily water balance data from ~1200 catchments with good coverage across the country. The model parameters were estimated by Bayesian calibration using a Markov chain Monte Carlo method, so as to characterise the posterior uncertainty in the parameters and predictions. We found that the simple model could emulate the behaviour of a more complex process-based model. Given the high resolution of the inputs in time and space, the model can predict the very detailed variation in soil moisture which arises from the sporadic nature of precipitation events, including the small-scale and short-term variations associated with orographic and convective rainfall. Predictions over the period 2016 to 2023 demonstrated realistic patterns following the passage of weather fronts and prolonged droughts. The model has negligible computation time, and inputs and predictions are updated daily, lagging approximately one week behind real time. [ABSTRACT FROM AUTHOR]

In the UK, tree, hedgerow, and woodland (THaW) habitats are key havens for biodiversity and support many related ecosystem services. The UK is entering a period of agricultural policy realignment with respect to natural capital and climate change, meaning that now is a critical time to evaluate the distribution, resilience, and dynamics of THaW habitats. The fine‐grained nature of habitats like hedgerows necessitates mapping of these features at relatively fine spatial resolution—and freely available public archives of airborne laser scanning (LiDAR) data at <2 m spatial resolution offer a means of doing so within UK settings. The high cost of LiDAR prohibits use for regular monitoring of THaW change, but space‐borne sensors such as Sentinel‐1 Synthetic Aperture Radar (SAR at ca. 10 m resolution) can potentially meet this need once baseline distributions are established. We address two aims in this manuscript—(1) to rapidly quantify THaW across UK landscapes using LiDAR data and (2) to monitor canopy change intra‐ and inter‐annually using SAR data. We show that workflows applied to airborne LiDAR data can deliver THaW baselines at 2 m resolution, with positional accuracy of >90%. It was also possible to combine LiDAR mapping data and Sentinel‐1 SAR data to rapidly track canopy change through time (i.e., every 3 months) using, cloud‐based processing via Google Earth Engine. The resultant toolkit is also provided as an open‐access web app. The results highlight that whilst nearly 90% of the tallest trees (above 15 m) are captured within the National Forest Inventory (NFI) database only 50% of THaW with a canopy height range of 3–15 m are recorded. Current estimates of tree distribution neglect these finer‐grained features (i.e., smaller or less contiguous THaW canopies), which we argue will account for a significant proportion of landscape THaW cover. [ABSTRACT FROM AUTHOR]

The use of remote measurement technologies (RMTs) across mobile health (mHealth) studies is becoming popular, given their potential for providing rich data on symptom change and indicators of future state in recurrent conditions such as major depressive disorder (MDD). Understanding recruitment into RMT research is fundamental for improving historically small sample sizes, reducing loss of statistical power, and ultimately producing results worthy of clinical implementation. There is a need for the standardisation of best practices for successful recruitment into RMT research. The current paper reviews lessons learned from recruitment into the Remote Assessment of Disease and Relapse- Major Depressive Disorder (RADAR-MDD) study, a large-scale, multi-site prospective cohort study using RMT to explore the clinical course of people with depression across the UK, the Netherlands, and Spain. More specifically, the paper reflects on key experiences from the UK site and consolidates these into four key recruitment strategies, alongside a review of barriers to recruitment. Finally, the strategies and barriers outlined are combined into a model of lessons learned. This work provides a foundation for future RMT study design, recruitment and evaluation. [ABSTRACT FROM AUTHOR]

With the advances of ESA's Sentinel-1 InSAR (Interferometric Synthetic Aperture Radar) mission, there are freely available remote sensing ground deformation observations all over the globe that allow continuous monitoring of natural hazards and structural instabilities. The Digital Environment initiative in the UK aims to include these remote sensing data in the effort at forecasting and mitigating hazards across the UK. In this paper, we present a case study of the Hollin Hill landslide in North Yorkshire where a variety of ground-based geophysical measurements are available for comparison with InSAR data. To include Sentinel-1 data in the UK's Digital Environment, it is important to understand the advantages and limitations of these observations and interpret them appropriately. The Hollin Hill landslide observatory (HHLO) is used by the British Geological Survey to understand landslide processes, and to trial new technologies and methodologies for slope stability characterisation and monitoring. In July 2019, six corner reflectors were installed to improve the coherence of the InSAR measurements. We use Sentinel-1 InSAR data acquired between October 2015 and January 2019 to study the behaviour of this landslide, and find that the line-of-sight component of the down-slope movement is 2.7 mm/year in the descending track, and 7.5–7.7 mm/year in the ascending track. The InSAR measurements also highlight the seasonal behaviour of this landslide. Using InSAR data after the installation of the six corner reflectors, we are able to track the most recent movement on the landslide that occurred in January 2021. This result is in agreement with other ground-based measurements such as tracking of pegs, and soil moisture data derived from electrical resistivity tomography. [ABSTRACT FROM AUTHOR]

In an era of environmental change leading to rising sea levels and increased storminess, there is a need to quantify the volume of beach sediment on the coast of Britain in order to assess the vulnerability to erosion using cheap, easy-to-deploy and non-invasive methods. Horizontal-to-vertical spectral ratio (HVSR) is a technique that uses the natural background seismic 'noise' in order to determine the depth of underlying geological interfaces that have contrasting physical properties. In this study, the HVSR technique was deployed at a number of settings on the coast of England that represented a range of different compositions, geomorphology, and underlying bedrock. We verified the results by comparison to other survey techniques, such as ground-penetrating RADAR, multichannel analysis of surface waves (MASW), and cone penetration tests. At locations where there was sufficient contrast in physical properties of the beach material compared to the underlying bedrock, the beach thickness (and therefore the volume of erodible material) was successfully determined, showing that HVSR is a useful tool to use in these settings. [ABSTRACT FROM AUTHOR]

Coasts are continually changing and remote sensing from satellite has the potential to both map and monitor coastal change at multiple scales. This study aims to assess the application of shorelines extracted from Multi-Spectral Imagery (MSI) and Synthetic Aperture Radar (SAR) from publicly available satellite imagery to map and capture sub-annual to inter-annual shoreline variability. This is assessed at three macro-tidal study sites along the coastline of England, United Kingdom (UK): estuarine, soft cliff environment, and gravel pocket-beach. We have assessed the accuracy of MSI-derived lines against ground truth datum tideline data and found that the satellite derived lines have the tendency to be lower (seaward) on the Digital Elevation Model than the datum-tideline. We have also compared the metric of change derived from SAR lines differentiating between ascending and descending orbits. The spatial and temporal characteristics extracted from SAR lines via Principal Component Analysis suggested that beach rotation is captured within the SAR dataset for descending orbits but not for the ascending ones in our study area. The present study contributes to our understanding of a poorly known aspect of using coastlines derived from publicly available MSI and SAR satellite missions. It outlines a quantitative approach to assess their mapping accuracy with a new non-foreshore method. This allows the assessment of variability on the metrics of change using the Open Digital Shoreline Analysis System (ODSAS) method and to extract complex spatial and temporal information using Principal Component Analysis (PCA) that is transferable to coastline evolution assessments worldwide. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSN) have emerged as a robust and cost-effective solution for buried pipeline monitoring due to the low cost (a maximum of a few tens of UK pounds (GBP)), low power supply capacity (in the order of 1 watt/hour) and small size (centimetre scale) requirements of the wireless sensor nodes. One of the main challenges for WSN deployment, however, is the limited range of underground data communication between the wireless sensor nodes of less than 3 m, which subsequently increases deployment costs for a utility owner for buried pipeline monitoring. A promising alternative to overcome this limitation is using low-frequency (<1 kHz) acoustic signal propagation along the pipe. This paper examines the feasibility of using low-frequency acoustic signal propagation along exposed and buried medium-density polyethylene (MDPE) pipes and makes predictions of the potential distances at which reliable data communication can be achieved. Quantification of the acoustic attenuation was performed using both analytical and numerical models in addition to laboratory and field experiments. The predicted acoustic data communication distance ranged between approximately 18 m for an exposed and approximately 11 m for a buried MDPE pipe. These results demonstrate the feasibility of using low-frequency acoustic signal propagation for achieving reliable wireless underground communication. [ABSTRACT FROM AUTHOR]

Subsurface geonergy can induce ground motion and seismicity, however a scarcity of observations usually obscures the mechanisms underpinning such behaviour. Here, we analyse Interferometric Synthetic Aperture Radar (InSAR) data from ERS, ENVISAT and Sentinel-1 satellites for the period 1995–2017 and interpret ground deformation in the area of the planned Cheshire UK GeoEnergy Observatory ahead of facility contruction. Ground motion is dominated by the compaction of tidal flat deposits overlying two paleo-valleys, trending NNW–SSE. The western paleo-valley experienced faster subsidence rates in the period 1995–2007, whereas the eastern paleo-valley subsided faster in the period 2016–2017. The research highlights how baseline assessment can help differentiate natural variation from any anthropogenic effects associated with the growth of new subsurface technologies. [ABSTRACT FROM AUTHOR]

High-resolution model simulations and radar observations are used to investigate the onset of vortex-genesis in a tornadic narrow cold-frontal rain band (NCFR). The timing and location of vortex-genesis was strongly constrained by a developing frontal wave, which tracked northeast across the United Kingdom and Ireland on 17 October 2011. In the simulations, vortices occurred preferentially during the early stages of wave development and just down-front of the wave centre, where large increases in vertical vorticity occurred in concert with decreases in the cross-frontal confluence. Vortex-genesis ceased as the frontal wave matured, due to the onset of frontal fracture. Two distinct scales of vortex-genesis are documented: primary vortex-genesis on themeso-γ-scale, and secondary vortex-genesis on the miso-scale. We show that horizontal shearing instability is the most likely vortex-genesis mechanism, consistent with previous theoretical work on the stability of vertical vortex strips in the presence of horizontal stretching deformation. Secondary vortices occurred along the braid regions between primary vortices where the shear zone became particularly narrow and intense. In the model, these vortices developed extremely rapidly (from small perturbations to maximum vertical vorticity in 5-15 min) and the strongest exhibited near-surface vertical vorticitymaxima approaching 10-1 s-1. Vortices of both scales were associated with characteristic local perturbations in the NCFR andwe show, by comparisonwith radar reflectivity data, that primary and secondary vortices were likely present in the real NCFR. Tornado reports were associated with small NCFR perturbations like those associated with the secondary vortices in the model simulations. Analysis of the sub-structure of individual simulated vortices suggests that tornado-genesis ismost likely within a region of intense near-surface vertical vorticity stretching at the north or northwest flank of the secondary vortices. [ABSTRACT FROM AUTHOR]

Radar networks hold great promise for monitoring population trends of migrating insects. However, it is important to elucidate the nature of responses to environmental cues. We use data from a mini-network of vertical-looking entomological radars in the southern UK to investigate changes in nightly abundance, flight altitude and behaviour of insect migrants, in relation to meteorological and celestial conditions. Abundance of migrants showed positive relationships with air temperature, indicating that this is the single most important variable influencing the decision to initiate migration. In addition, there was a small but significant effect of moonlight illumination, with more insects migrating on full moon nights. While the effect of nocturnal illumination levels on abundance was relatively minor, there was a stronger effect on the insects' ability to orientate close to downwind: flight headings were more tightly clustered on nights when the moon was bright and when cloud cover was sparse. This indicates that nocturnal illumination is important for the navigational mechanisms used by nocturnal insect migrants. Further, our results clearly show that environmental conditions such as air temperature and light levels must be considered if long-term radar datasets are to be used to assess changing population trends of migrants. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'. [ABSTRACT FROM AUTHOR]

Entanglement in net fisheries (static and drift) is the largest known cause of direct anthropogenic mortality to many small cetacean species, including harbour porpoise (Phocoena phocoena), in UK waters. Despite this, little is known about the behaviour of small cetaceans in proximity to nets.We have developed a passive acoustic monitoring (PAM) system for tracking the fine‐scale three‐dimensional (3D) movements of echolocating cetaceans around actively fishing nets by localising their acoustic clicks. The system consists of two compact four‐channel acoustic recorders with sample‐synchronised sensor packages that use 3D motion tracking technology to accurately log orientation, depth, water temperature and ambient light level. Two recorders were used in tandem, with each one attached to and floating above the net floatline. The system can be deployed during normal fishing operations by a trained researcher or experienced fisheries observer. Recordings were analysed in PAMGuard software and the 3D positions of echolocating animals in the vicinity of the system were calculated using an acoustic particle filter‐based localisation method.We present findings from four deployments in UK waters (each 1–2 days in duration) in which 12 distinct harbour porpoise encounters yielded a sufficient number of detected clicks to track their movements around the net. The tracks show a variety of behaviours, including multiple instances of animals actively foraging in close proximity to the fishing net.We show that a relatively inexpensive PAM system, which is practical to deploy from active fishing vessels, is capable of providing highly detailed data on harbour porpoise behaviour around nets. As harbour porpoises are the one of the most difficult species to localise, this methodology is likely to be suitable for elucidating the behaviour of many other toothed whale species in a variety of situations. [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]

• GPR survey appropriately maps peatland subsurfaces, even across difficult terrain. • LiDAR surface topographies and subsurface profiles can be integrated within a GIS. • Improved measurement of peat thickness contributes to improved estimates of peat volume. • Evaluation of site specific carbon stock can improve regional estimates. Estimations of peatland carbon stocks often use generalised values for peat thickness and carbon content. Ground penetrating radar (GPR), a rapid technique for field data collection, has been increasingly demonstrated as an appropriate method of mapping peat thickness. Light Detection and Ranging (LiDAR) data as a method for understanding peatland surface elevation are also becoming more widely available. Reliable mapping and quantification of site-specific carbon stocks (e.g. upland raised bogs) is therefore, becoming increasingly feasible, providing a valuable contribution to regional, national and potentially global carbon stock assessments. This is particularly important because raised bogs, such as those found in South Wales are considerable carbon stores. They are, however, susceptible to climate warming owing to their southerly location within the UK. Accurate estimates of peatland carbon stocks has broader importance because world-wide peatland carbon stores are significant and threatened by climate change, posing a substantial challenge not only due to climate feedbacks if this stored carbon is released into the atmosphere, but also the impact on the other ecosystem services that they provide. Here, we assess the value of an integrated GPR, LiDAR and Geographic Information System (GIS) approach to improve estimation of regional carbon stocks. We apply the approach to three ombrotrophic raised bogs in South Wales, UK, selected for their conservation value and their topographically-confined raised bog form. GPR and LiDAR are found to be well suited, respectively, to mapping peat thickness at bog scale and surface elevation, thus allowing surface and basal topographies to be evaluated using GIS. In turn, this allows peat volumes to be estimated. For the first time, we record values between 55,200 m3 and 163,000 m3 for the sites considered here. The greater confidence in these peat volume estimates results from the ability to calibrate the GPR velocity using a depth-to-target calibration with peat cores extracted at locations encompassing the deepest bog area. Peat thickness is mapped at the bog scale with near centimetre precision, improving the robustness of subsequent volume calculations and our understanding of the contribution of these small but numerous sites to regional carbon stocks. Our evaluation shows that GPR corresponds well with conventional manual probing but is minimally invasive and therefore less disturbing of sensitive peatland sites, while also offering improved coverage and spatial resolution with less time and cost. In combination with measured bulk density and organic carbon contents, these peat volumes allow carbon stocks to be estimated with greater confidence compared to conventional approaches, having values between 2181 ± 122 tonnes carbon and 6305 ± 351 tonnes carbon at our three sites. [ABSTRACT FROM AUTHOR]

Analysis of ground movement rates along the coastline and upper sections of the Ventnor landslide complex was carried out utilizing Persistent Scatterer Interferometric Synthetic Aperture Radar methods using Sentinel-1 SAR data from 2015 to 2019 (four years). Results were compared with rainfall data, historical ground investigation records and monitoring surveys carried out at Ventnor to relate observations to geology, geomorphology and rainfall. Decomposition of InSAR viewing geometries to vertical and horizontal aligned well with previous ground-based studies. Subsidence of −9.8 mm a−1 at the Lowtherville Graben and heave of +8.5 mm a−1 along the coastline south of Ventnor Park were observed. Decomposition to east-west geometry results showed an eastward displacement of approximately 12.4 mm a−1 along the coastline south of Ventnor Park, and a westward displacement of −3.7 mm a−1 throughout built up sections of Ventnor town, indicating the landslide was displacing more in an eastern direction than vertically. The cause of this movement was investigated by using publicly available intrusive boreholes paired with Persistent Scatterer Interferometry, and a new ground model spanning east-west parallel to the coastline was presented. No evidence of significant ground movement was observed along heavily protected sections of the coastline, suggesting coastal defences comprised of concrete aprons and rip rap appear to be an effective coastal management/landslide stabilisation tool when compared to rip rap alone. The mechanism of this increased stability is likely due to the combination of toe weighting and reduced toe erosion. A lag of approximately 13–20 days was observed between high rainfall events and subsequent peaks in ground displacement, which was shorter than a 29 day lag observed in a previous study. Similar observations of prolonged rainfall resulting in prolonged displacements were also observed. The study demonstrates the capabilities of the PSI methodology in identifying the same ground movements that conventional methods provide. By providing detailed analysis of ground deformation of the Ventnor landslide, we demonstrate small ground movements, validated with existing ground movement surveys. Similar methodology can be applied to coastal landslides in urban environments worldwide, providing a relatively cheap and rapid resource for coastal landslide monitoring. [ABSTRACT FROM AUTHOR]

Background: End-of-life (EoL) care volunteers in hospitals are a novel approach to support patients and their close ones. The iLIVE Volunteer Study supported hospital volunteer coordinators from five European countries to design and implement an EoL care volunteer service on general wards in their hospitals. This study aimed to identify and explore barriers and facilitators to the implementation of EoL care volunteer services in the five hospitals. Methods: Volunteer coordinators (VCs) from the Netherlands (NL), Norway (NO), Slovenia (SI), Spain (ES) and United Kingdom (UK) participated in a focus group interview and subsequent in-depth one-to-one interviews. A theory-inspired framework based on the five domains of the Consolidated Framework for Implementation Research (CFIR) was used for data collection and analysis. Results from the focus group were depicted in radar charts per hospital. Results: Barriers across all hospitals were the COVID-19 pandemic delaying the implementation process, and the lack of recognition of the added value of EoL care volunteers by hospital staff. Site-specific barriers were struggles with promoting the service in a highly structured setting with many stakeholders (NL), negative views among nurses on hospital volunteering (NL, NO), a lack of support from healthcare professionals and the management (SI, ES), and uncertainty about their role in implementation among VCs (ES). Site-specific facilitators were training of volunteers (NO, SI, NL), involving volunteers in promoting the service (NO), and education and awareness for healthcare professionals about the role and boundaries of volunteers (UK). Conclusion: Establishing a comprehensive EoL care volunteer service for patients in non-specialist palliative care wards involves multiple considerations including training, creating awareness and ensuring management support. Implementation requires involvement of stakeholders in a way that enables medical EoL care and volunteering to co-exist. Further research is needed to explore how trust and equal partnerships between volunteers and professional staff can be built and sustained. Trial registration: NCT04678310. Registered 21/12/2020. [ABSTRACT FROM AUTHOR]

Flood inundation forecast maps provide an essential tool to disaster management teams for planning and preparation ahead of a flood event in order to mitigate the impacts of flooding on the community. Evaluating the accuracy of forecast flood maps is essential for model development and improving future flood predictions. Conventional, quantitative binary verification measures typically provide a domain-averaged score, at grid level, of forecast skill. This score is dependent on the magnitude of the flood and the spatial scale of the flood map. Binary scores have limited physical meaning and do not indicate location-specific variations in forecast skill that enable targeted model improvements to be made. A new, scale-selective approach is presented here to evaluate forecast flood inundation maps against remotely observed flood extents. A neighbourhood approach based on the Fraction Skill Score is applied to assess the spatial scale at which the forecast becomes skilful at capturing the observed flood. This skilful scale varies with location and when combined with a contingency map creates a novel categorical scale map, a valuable visual tool for model evaluation and development. The impact of model improvements on forecast flood map accuracy skill scores are often masked by large areas of correctly predicted flooded/unflooded cells. To address this, the accuracy of the flood-edge location is evaluated. The flood-edge location accuracy proves to be more sensitive to variations in forecast skill and spatial scale compared to the accuracy of the entire flood extent. Additionally, the resulting skilful scale of the flood-edge provides a physically meaningful verification measure of the forecast flood-edge discrepancy. The methods are illustrated by application to a case study flood event (with an estimated return period of 120 to 550 years) of the River Wye and River Lugg (UK) in February 2020. Representation errors are introduced where remote sensing observations capture flood extent at different spatial resolutions in comparison with the model. The sensitivity of the verified skilful scale to the resolution of the observations is investigated. Re-scaling and interpolating observations leads to a small reduction in skill score compared with the observation flood map derived at the model resolution. The domain-averaged skilful scale remains the same with slight location-specific variations in skilful scale evident on the categorical scale map. Overall, our novel emphasis on scale, rather than domain-average score, means that comparisons can be made across different flooding scenarios and forecast systems and between forecasts at different spatial scales. • A novel spatial scale-selective approach to evaluate forecast flood maps against Synthetic Aperture Radar data. • Validation of the flood edge gives a physically meaningful measure of prediction accuracy. • Conventional contingency flood maps are improved by including a location specific skilful spatial scale. [ABSTRACT FROM AUTHOR]

The structure and evolution of a cold front that produced a tornadic narrow cold‐frontal rainband (NCFR) over the UK on 20 November 2013 is explored using reanalysis data, high‐resolution model simulations and observations. Physical links are found to exist across a range of spatiotemporal scales, through which the evolving large‐scale flow field exerts an influence on the timing and location of miso‐scale vortex genesis, and therefore the potential for tornado genesis. The synoptic‐scale flow pattern exhibited amplification, consisting of upstream ridge building and downstream trough extension. A prominent jet streak and associated positive potential vorticity (PV) anomaly and tropopause fold moved rapidly southeastward on the rear flank of the extending upper‐level trough, the leading edge of these features eventually overspreading the surface cold front over the UK. Increasing 850 hPa frontogenesis occurred underneath the left exit of the jet streak and associated, intensifying, PV anomaly. A filament of dry, high‐PV air was extruded from the overlying tropopause fold within an intensifying front‐transverse circulation in this region. This dry filament eventually penetrated to low levels immediately behind the front, where it undercut the upper, rearward parts of the wide cold‐frontal precipitation band. Model fields and observations suggest that diabatic cooling, associated with sublimation of solid hydrometeors falling into the dry filament, led to the development of local downdraft and near‐surface divergence maxima and a prominent cold pool immediately behind the surface cold front over central England. Increases in horizontal convergence, updraft speed, and horizontal temperature gradients along the frontal boundary immediately ahead of the cold pool resulted in development of a locally well‐defined cold front and associated NCFR, where the surface front was formerly (and elsewhere continued to be) relatively weak. Tornadoes occurred in association with miso‐scale vortices that developed rapidly along an intensifying vertical vortex sheet at the NCFR. [ABSTRACT FROM AUTHOR]