Crawley Hospital

<strong class="journal-contentHeaderColor">Abstract.</strong> The bottom mixed layer (BML) of the abyssal ocean regulates heat exchange between the deep interior and seafloor, driving water-mass transformation and influencing global circulation. Spatial variability of the BML was examined in the under-sampled abyssal Pacific Ocean using surface-to-seafloor temperature and pressure observations over 4 months in 2023&ndash;24. Given the typical decadal repeat rate of global hydrographic sections, subdecadal variability in the abyssal ocean has remained poorly resolved. While constrained in coverage, our observations contribute towards filling this gap for the central and eastern abyssal Pacific Ocean. Four methods were used to determine the BML thickness, with the threshold method providing the most reliable estimates. The mean BML thickness was 226 &plusmn; 172 with added repeat hydrographic sections providing context and additional data points. At each BML data point we determined the slope, the terrain roughness and the extracted predicted internal tide energy dissipation (over five different low-mode processes and high-mode local processes) at 50 km scales from publicly available datasets. These factors were input into a Random Forest Regressor (RF) model, the first time machine learning techniques have been applied to investigate BML thickness. The RF feature importance scores identified bottom depth, total internal tide energy dissipation, followed by slope, as the strongest predictors of BML thickness, revealing the importance of low-mode internal wave energy losses in this abyssal setting. Targeted and sustained observations near the seafloor at gateway regions of abyssal pathways are vital for understanding energy exchange that influences meridional overturning circulation. Our results highlight a regime where sustained low-mode internal tide energy loss, modulated by topographic slope and depth, governs the BML thickness in the abyssal Pacific. However, the rate at which BML thickness changes over time and the processes that cause these changes remain key unresolved factors.

Abstract Maintaining subsea pipeline integrity is critical for safe and sustainable offshore operations. Traditional inspection methods are costly, time intensive, and constrained by environmental factors, leading to delays in identification and remediation of integrity risks. For example, pipeline Anode inspection currently requires around six months to complete due to manual analysis of the survey data. The paper presents an AI-driven autonomous robotic system designed for high-precision subsea pipeline inspections in the North Sea. It demonstrates efficient detection of pipeline integrity risks through machine learning, reducing processing time and operational expenditure. The system combines computer vision, sonar imaging, and deep learning to autonomously identify pipeline defects, including anchors-marks, corrosion, marine growth, and leaks. The robot leverages a trained YOLO-model for object detection, optimizing inspection based on seabed conditions. Data fusion techniques integrate acoustic, optical, and sensor data to enhance anomaly detection accuracy. The collected inspection data has been streamlined using a cloud-based repository, enabling automated data ingestion, contextualization, and visualization. Machine learning models trained on historical datasets detect deviations with high precision, reducing the time required for defect identification. Field trials with the North Sea operators and their technology centre have validated the system's reliability and effectiveness [1]. Field applications in the North Sea have demonstrated significant improvements in early-stage defect detection, surpassing traditional remotely operated vehicle (ROV) based methods. The AI-driven autonomous system has increased inspection efficiency by reducing manual inspection/Classification of 5000 images from a couple of months to within an hour. The integration of cloud-based analytics has facilitated predictive maintenance strategies, reduced unplanned shutdowns and optimized long-term asset integrity. Automated anomaly detection has minimized manual processing efforts, leading to faster decision-making and intervention. These findings underscore the transformative potential of AI-driven robotics in offshore integrity management and reinforce their suitability for large-scale industry adoption. This paper presents the first implementation of cloud computing and AI-driven autonomous robotics for subsea pipeline inspections in the North Sea. The integration of machine learning with operational decision-making marks a significant advancement in offshore integrity management. By automating data ingestion, contextualization, and anomaly detection, this approach establishes a new benchmark for efficiency, accuracy, and sustainability in subsea inspection. The findings provide valuable insights for industry stakeholders transitioning from conventional inspection methods to AI-powered autonomous solutions.

Internationally, reference dosimetry for clinical proton beams largely follows the guidelines published by the International Atomic Energy Agency (IAEA TRS-398 Rev. 1 (2024). This approach yields a relative standard uncertainty of 1.7% (k= 1) on the absorbed dose to water determined under reference conditions. The new IPEM code of practice presented here, enables the relative standard uncertainty on the absorbed dose to water measured under reference conditions to be reduced to 1.0% (k= 1). This improvement is based on the absorbed dose to water calibration service for proton beams provided by the National Physical Laboratory (NPL), the UK's primary standards laboratory. This significantly reduced uncertainty is achieved through the use of a primary standard level graphite calorimeter to derive absorbed dose to water directly in the clinical department's beam. This eliminates the need for beam quality correction factors (kQ,Q0) as required by the IAEA TRS-398 approach. The portable primary standard level graphite calorimeter, developed over a number of years at the NPL, is sufficiently robust to be useable in the proton beams of clinical facilities both in the UK and overseas. The new code of practice involves performing reference dosimetry measurements directly traceable to the primary standard level graphite calorimeter in a clinical proton beam. Calibration of an ionisation chamber is performed in the centre of a standard test volume (STV) of dose, defined here to be a 10 × 10 × 10 cm volume in water, centred at a depth of 15 cm. Further STVs at reduced and increased depths are also utilised. The designated ionisation chambers are Roos-type plane-parallel chambers. This article provides all the necessary background material, formalism, and specifications of reference conditions required to implement reference dosimetry according to this new code of practice. The Annexes provide a detailed review of ion recombination and how this should be assessed (Annex A1) and detailed work instructions for creating and delivering the STVs (Annex A2).

We present a genome assembly from an individual malePandemis corylana (the Chequered Fruit-tree Tortrix; Arthropoda; Insecta; Lepidoptera; Tortricidae). The genome sequence is 441.6 megabases in span. Most of the assembly is scaffolded into 30 chromosomal pseudomolecules, including the Z sex chromosome. The mitochondrial genome has also been assembled and is 15.53 kilobases in length. Gene annotation of this assembly on Ensembl identified 19,608 protein coding genes.

The pseudoscorpion genus Indohya Beier, 1974 is known to occur in three Gondwanan fragments around the Indian Oceansouthern India, Madagascar and north-western Australiasuggesting that the genus had evolved prior to the breakup of Gondwana and was present on each landmass as they rifted apart during the Mesozoic. The Australian fauna is the most diverse, with nine species previously described from Cape Range and the Kimberley region of north-western Australia. The present study documents the genus Indohya in Australia using a combination of morphology and DNA sequence data. We found a total of 36 species, including 27 new species. The majority of the Pilbara fauna consist of blind troglobites collected from subterranean ecosystems, with an additional three eyed species from epigean habitats. The new species consist of one from Cape Range (I. anastomosa Harvey & Burger, n. sp.), 21 from the Pilbara (I. adlardi Harvey & Burger, n. sp., I. alexanderi Harvey & Burger, n. sp., I. aphana Harvey & Burger, n. sp., I. aquila Harvey & Burger, n. sp., I. arcana Harvey & Burger, n. sp., I. arnoldstrongi Harvey & Burger, n. sp., I. boltoni Harvey & Burger, n. sp., I. cardo Harvey & Burger, n. sp., I. catherineae Harvey & Burger, n. sp., I. cockingi Harvey & Burger, n. sp., I. cribbi Harvey & Burger, n. sp., I. draconis Harvey & Burger, n. sp., I. furtiva Harvey & Burger, n. sp., I. incomperta Harvey & Burger, n. sp., I. jessicae Harvey & Burger, n. sp., I. lynbeazlyeae Harvey & Burger, n. sp., I. morganstrongi Harvey & Burger, n. sp., I. rixi Harvey & Burger, n. sp., I. sagmata Harvey & Burger, n. sp., I. scanloni Harvey & Burger, n. sp. and I. silenda Harvey & Burger, n. sp.) and five from the Kimberley (I. currani Harvey & Burger, n. sp., I. finitima Harvey & Burger, n. sp., I. julianneae Harvey & Burger, n. sp., I. karenae Harvey & Burger, n. sp. and I. sachsei Harvey & Burger, n. sp.). The study is augmented with sequence data from 29 species of Indohya, including all of the 24 species recorded from the Pilbara and Cape Range, and five of the 12 known Kimberley species. Seven clades recovered during the molecular analysis are only represented by nymphs, but we used COI sequence data to diagnose these species in the absence of adult morphological data.

In Magnetic Resonance-Linac (MR-Linac) dosimetry formalisms, a new correction factor, kB,Q , has been introduced to account for corresponding changes to detector readings under the beam quality, Q, and the presence of magnetic field, B. This study aims to develop and implement a Monte Carlo (MC)-based framework for the determination of kB,Q correction factors for a series of ionization chambers utilized for dosimetry protocols and dosimetric quality assurance checks in clinical 1.5T MR-Linacs. Their dependencies on irradiation setup conditions are also investigated. Moreover, to evaluate the suitability of solid phantoms for dosimetry checks and end-to-end tests, changes to the detector readings due to the presence of small asymmetrical air gaps around the detector's tip are quantified. Phase space files for three irradiation fields of the ELEKTA Unity 1.5T/7MV flattening-filter-free MR-Linac were provided by the manufacturer and used as source models throughout this study. Twelve ionization chambers (three farmer-type and nine small-cavity detectors, from three manufacturers) were modeled (including their dead volume) using the EGSnrc MC code package. kB,Q values were calculated for the 10×10cm2 irradiation field and for four cardinal orientations of the detectors' axes with respect to the 1.5T magnetic field. Potential dependencies of kB,Q values with respect to field size, depth, and phantom material were investigated by performing additional simulations. Changes to the detectors' readings due to the presence of small asymmetrical air gaps (0.1 up to 1mm) around the chambers' sensitive volume in an RW3 solid phantom were quantified for three small-cavity chambers and two orientations. For both parallel (to the magnetic field) orientations, kB,Q values were found close to unity. The maximum correction needed was 1.1%. For each detector studied, the kB,Q values calculated for the two parallel orientations agreed within uncertainties. Larger corrections (up to 5%) were calculated when the detectors were oriented perpendicularly to the magnetic field. Results were compared with corresponding ones found in the literature, wherever available. No considerable dependence of kB,Q with respect to field size (down to 3×3cm2 ), depth, or phantom material was noticed, for the detectors investigated. As compared to the perpendicular one, in the parallel to the magnetic field orientation, the air gap effect is minimized but is still considerable even for the smallest air gap considered (0.1mm). For the 10×10cm2 field, magnetic field correction factors for 12 ionization chambers and four orientations were determined. For each detector, the kB,Q value may be also applied for dosimetry procedures under different irradiation parameters provided that the orientation is taken into account. Moreover, if solid phantoms are used, even the smallest asymmetrical air gap may still bias small-cavity chamber response. This work substantially expands the availability and applicability of kB,Q correction factors that are detector- and orientation-specific, enabling more options in MR-Linac dosimetry checks, end-to-end tests, and quality assurance protocols.

Abstract. Knowledge concerning possible inhomogeneities in a data set is of key importance for any subsequent climatological analyses. Well-established relative homogenization methods developed for temperature and precipitation exist but have rarely been applied to snow-cover-related time series. We undertook a homogeneity assessment of Swiss monthly snow depth series by running and comparing the results from three well-established semi-automatic break point detection methods (ACMANT – Adapted Caussinus-Mestre Algorithm for Networks of Temperature series, Climatol – Climate Tools, and HOMER – HOMogenizaton softwarE in R). The multi-method approach allowed us to compare the different methods and to establish more robust results using a consensus of at least two change points in close proximity to each other. We investigated 184 series of various lengths between 1930 and 2021 and ranging from 200 to 2500 m a.s.l. and found 45 valid break points in 41 of the 184 series investigated, of which 71 % could be attributed to relocations or observer changes. Metadata are helpful but not sufficient for break point verification as more than 90 % of recorded events (relocation or observer change) did not lead to valid break points. Using a combined approach (two out of three methods) is highly beneficial as it increases the confidence in identified break points in contrast to any single method, with or without metadata.

Knowledge concerning possible inhomogeneities in a data set is of key importance for any subsequent climatological analyses. Well-established relative homogenization methods developed for temperature and precipitation exist, but with only little experience for snow. We undertook a homogeneity assessment of Swiss snow depth series by running and comparing the results from three well-established semi-automatic break point detection methods (ACMANT, Climatol, and HOMER). Break points identified by each method allowed us to compare the results of the different methods, and by only treating break points as valid if detected in reasonably close proximity by at least two methods, we increased the robustness of the results. We investigated 184 series, of various length between 1930 and 2021 and ranging from 200 to 2500 m a.s.l. and found 45 valid break points. Of those 45, 71 % could be attributed to relocations or observer changes. Metadata are helpful, but not sufficient for break point verification as more than 90 % of recorded events did not lead to valid break points. Using such a combined approach (2 out of 3 methods) is highly beneficial, as it increases the confidence in identified break points in contrast to any single method, with or without metadata.

We study the structural, optical, conductive and piezoresistive properties of d8 iridium dicarbonyl complexes with different β-diketonate ligands.