Local Modification Research Articles

Photo‐Arbuzov Reactions as a Broadly Applicable Surface Modification Strategy

AbstractChemical vapor deposition (CVD) polymerization is a commonly used approach in surface chemistry, providing a substrate‐independent platform for bioactive surface functionalization strategies. This work investigates the Arbuzov reaction of halogenated polymer coatings readily available via CVD polymerization, using poly(4‐chloro‐para‐xylylene) (Parylene C) as a model substance. Postpolymerization modification of these coatings via catalyst‐free and UV‐induced Arbuzov reaction using phosphites results in phosphonate‐functionalized polymers. The combination of infrared reflection‐absorption spectroscopy (IRRAS), X‐ray photoelectron spectroscopy (XPS), and time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) provides detailed insights into the reaction progress. Time‐dependent studies suggest that the non‐polar phosphites penetrate deep into the CVD films and react with the polymer film. In addition, ToF‐SIMS, scanning electron microscopy (SEM), and atomic force microscopy (AFM) confirm spatial control of the reaction, resulting in localized chemical and topographical surface modification, recognizable by changes in interference color, fluorescence, and wettability. Preliminary 3D fluorescence spectroscopy investigations indicate tunable near‐infrared emission of these polymer films. This work is the first step toward generating multifunctional polymer coatings based on chemically modifiable, CVD polymers with potential applications in biomaterials, sensors, or optoelectronics.

A prospective re-audit on insulin prescribing and administration practices in a university teaching hospital

Abstract Introduction Insulin is a high-alert medication which can cause significant patient harm if used inappropriately and has been identified as a significant medication safety concern in Galway University Hospitals. The first hospital-wide insulin review[1] was conducted in 2022 to evaluate insulin prescribing/administration practices and identify/develop quality improvements inclusive of: -The appointment of a senior pharmacist with a special interest in diabetes (June 2022), -An updated insulin record (November 2022; previous edition June 2019), -Development/implementation of a bespoke local eLearning module for the safe use of high-alert medications including insulin (March 2023), and -Continuous education on appropriate use for healthcare staff and patients/carers. Aim The aim was to conduct a similar insulin study to assess performance since the previous review, and develop further quality improvements as needed. Methods This study was conducted over one day in June 2023 on 24 wards by 26 interprofessional data collectors. Inclusion criteria comprised inpatients prescribed and/or administered insulin for the previous 72 hours. Exclusion criteria comprised non-admitted patients, short stay wards, critical care, maternity, and psychiatry. The protocol and tool were approved by the local Clinical Audit Committee, piloted with four inpatients, and communicated to all data collectors prior to commencement. Generated data were anonymous and securely stored. Data analysis was carried out using Excel by three independent researchers to confirm reliability of results. Results In total 557 inpatients were reviewed, of which 21% (116/557) had diabetes and 10% (56/557) were prescribed insulin. Nearly three-quarters (73%; 41/56) were prescribed a meal-time supplement, whilst 68% (38/56) were prescribed regular insulin – of which 63% (24/38) comprised prescribing errors, an improvement from 78% (32/41) in 2022. Administration errors comprised 84% (32/38) of records, similar to the 88% (36/41) error rate observed in 2022. Approximately one-fifth (21%; 8/38) of insulin orders were not signed by a prescriber, compared to 12% (5/41) the previous year. More than nine out of ten prescribers clearly documented the correct insulin name, dose, and administration times, and 71% (27/38) documented a medical council registration number, bleep, or name for contact purposes, compared to 59% (24/41) the previous year. Failure to document administration times was noted in 42% (16/38) of instances compared to 46% (19; n=41) the previous year. Insulin was not double-checked by a second independent individual in 21% (8/38) of cases, compared to 29% (12; n=41) in 2022. Conclusion Results have identified an overall practice improvement. Building on recent interventions implemented and incorporating newer strategies will aid in enhanced insulin use. An updated version of the insulin record has been implemented due to the recent discontinuation of the referenced perioperative fluid and the introduction of non-referenced blood ketone monitors. High leverage strategies such as ePrescribing is a current consideration to standardise practices. Conducting a study over a longer period may identify further trends. Strengths include study robustness, interprofessional collaboration, and transferability to other hospitals. Disseminating results and promoting benefits should encourage Irish hospitals to participate in a standardised national annual review to optimise patient care.

MCT-VHD: Multi-modal contrastive transformer for video highlight detection

Autonomous highlight detection aims to identify the most captivating moments in a video, which is crucial for enhancing the efficiency of video editing and browsing on social media platforms. However, current efforts primarily focus on visual elements and often overlook other modalities, such as text information that could provide valuable semantic signals. To overcome this limitation, we propose a Multi-modal Contrastive Transformer for Video Highlight Detection (MCT-VHD). This transformer-based network mainly utilizes video and audio modalities, along with auxiliary text features (if exist) for video highlight detection. Specifically, We enhance the temporal connections within the video by integrating a convolution-based local enhancement module into the transformer blocks. Furthermore, we explore three multi-modal fusion strategies to improve highlight inference performance and employ a contrastive objective to facilitate interactions between different modalities. Comprehensive experiments conducted on three benchmark datasets validate the effectiveness of MCT-VHD, and our ablation studies provide valuable insights into its essential components.

A Bidirectional Non-Coding RNA Promoter Mediates Long-Range Gene Expression Regulation.

Recent evidence suggests that human gene promoters display gene expression regulatory mechanisms beyond the typical single gene local transcription modulation. In mammalian genomes, genes with an associated bidirectional promoter are abundant; bidirectional promoter architecture serves as a regulatory hub for a gene pair expression. However, it has been suggested that its contribution to transcriptional regulation might exceed local transcription initiation modulation. Despite their abundance, the functional consequences of bidirectional promoter architecture remain largely unexplored. This work studies the long-range gene expression regulatory role of a long non-coding RNA gene promoter using chromosome conformation capture methods. We found that this particular bidirectional promoter contributes to distal gene expression regulation in a target-specific manner by establishing promoter-promoter interactions. In particular, we validated that the promoter-promoter interactions of this regulatory element with the promoter of distal gene BBX contribute to modulating the transcription rate of this gene; removing the bidirectional promoter from its genomic context leads to a rearrangement of BBX promoter-enhancer interactions and to increased gene expression. Moreover, long-range regulatory functionality is not directly dependent on its associated non-coding gene pair expression levels.

Rail-STrans: A Rail Surface Defect Segmentation Method Based on Improved Swin Transformer

With the continuous expansion of the transport network, the safe operation of high-speed railway rails has become a crucial issue. Defect detection on the surface of rails is a key part of ensuring the safe operation of trains. Despite the progress of deep learning techniques in defect detection on the rails’ surface, there are still challenges related to various problems, such as small datasets and the varying scales of defects. Based on this, this paper proposes an improved encoder–decoder architecture based on Swin Transformer network, named Rail-STrans, which is specifically designed for intelligent segmentation of high-speed rail surface defects. The problem of a small and black-and-white rail dataset is solved using self-made large and multiple rail surface defect datasets through field shooting, data labelling, and data expansion. In this paper, two Local Perception Modules (LPMs) are added to the encoding network, which helps to obtain local context information and improve the accuracy of detection. Then, the Multiscale Feature Fusion Module (MFFM) is added to the decoding network, which helps to effectively fuse the feature information of defects at different scales in the decoding process and improves the accuracy of defect detection at multiple scales. Meanwhile, the Spatial Detail Extraction Module (SDEM) is added to the decoding network, which helps to retain the spatial detail information in the decoding process and further improves the detection accuracy of small-scale defects. The experimental results show that the mean accuracy of the semantic segmentation of the method proposed in this paper can reach 90.1%, the mean dice coefficient can reach 89.5%, and the segmentation speed can reach 37.83 FPS, which is higher than other networks’ segmentation accuracy. And, at the same time, it can achieve higher efficiency.

Effect of flame characteristics on an isolated ethanol droplet evaporating through stagnation methane/air flames: An experimental and numerical study

A single ethanol droplet evaporation through laminar methane/air stagnation flame is investigated at lean, stoichiometric and rich conditions experimentally and numerically. For the droplets having initial diameters of 20-70μm, the particle Reynolds number is measured via PIV/PTV, resulting in the slip velocity between the droplet and gas flow being small and the surrounding gas being able to carry without any significant convective effects. Evaporation rates, computed from Abramzon–Sirignano for a moving droplet towards a stagnation flame field, satisfy the ones computed from empirically determined evaporation rates from ILIDS measurements as a function of flame temperature, flame speed and flame thickness. Nevertheless, Abramzon–Sirignano model slightly overestimates the vaporization constant for lean cases. The distance traveled by the droplet after leaving the flame zone determines the average critical diameter. Accordingly, droplets larger than 18μm can cross the flame, leading to local modifications in the flame region due to heat loss and vapor diffusion from the droplet. Novelty and Significance Energy conversion in combustion should be carried out by optimizing efficiency, minimizing pollution, and preventing further climate change. The multiphase nature of spray combustion particularly adds further complexity due to the strong coupling of atomization, evaporation, mixing, turbulence, and chemical reactions. Although spray combustion involves a cloud of polydispersed droplets, it is of fundamental importance to understand the dynamics of an isolated droplet and its interactions with the flame front, which can provide indispensable information for spray combustion models. In this study, an experimental and computational framework has been developed to investigate droplet-flame interactions. The dynamics and evaporation characteristics of an isolated droplets interacting with stagnation flames have been determined by coupling several laser diagnostics, while further enhanced with Eulerian–Lagrangian simulations under flame conditions. Thereupon, the outcomes help the design of injectors of spray combustion systems and enhancement of evaporation models.

A single-frame infrared small target detection method based on joint feature guidance

Single-frame infrared small target detection is affected by the low image resolution and small target size, and is prone to the problems of small target feature loss and positional offset during continuous downsampling; at the same time, the sparse features of the small targets do not correlate well with the global-local linkage of the background features. To solve the above problems, this paper proposes an efficient infrared small target detection method. First, this paper incorporates BlurPool in the feature extraction part, which reduces the loss and positional offset of small target features in the process of convolution and pooling. Second, this paper designs an interactive attention deep feature fusion module, which acquires the correlation information between the target and the background from a global perspective, and designs a compression mechanism based on deep a priori knowledge, which reduces the computational difficulty of the self-attention mechanism. Then, this paper designs the context local feature enhancement and fusion module, which uses deep semantic features to dynamically guide shallow local features to realize enhancement and fusion. Finally, this paper proposes an edge feature extraction module for shallow features, which utilizes the complete texture and location information in the shallow features to assist the network to initially locate the target position and edge shape. Numerous experiments show that the method in this paper significantly improves nIoU, F1-Measure and AUC on IRSTD-1k Datasets and NUAA-SIRST Datasets.

Immune-pineal-ocular axis in amphibians: unveiling a novel connection?

Melatonin is a hormone known as an endogenous temporal marker signaling the dark phase of the day. Although the eyes seem to be the main site of melatonin production in amphibians, little information is available about the natural variation in the ocular melatonin levels and its modulation following immune stimulation. We investigated the daily variation of plasma and ocular melatonin levels in bullfrogs (Lithobates catesbeianus) and their modulation following an immune stimulation with lipopolysaccharide (LPS) in yellow cururu toads (Rhinella icterica). For the daily variation, bullfrogs were bled and then euthanized for eye collection every 3h over 24h to determine plasma and ocular melatonin levels. We found a positive correlation between ocular and plasma melatonin levels, with maximum values at night (22h) for both plasma and the eyes. For immune stimulation, yellow cururu toads received an intraperitoneal injection of LPS or saline solution during the day (10h) or at night (22h). Two hours after injection, toads were bled and euthanized for eye collection to obtain plasma and ocular melatonin levels. In addition, the liver and bone marrow were collected to investigate local melatonin modulation. Our results demonstrate that retina light-controlled rhythmic melatonin production is suppressed while liver and bone marrow melatonin levels increase during the inflammatory assemblage in anurans. Interestingly, the LPS injection decreased only ocular melatonin levels, reinforcing the central role of the eyes (i.e., retina) as an essential organ of melatonin production, and a similar role to the pineal gland during the inflammatory response in amphibians. Together, these results point to a possible immune-pineal-ocular axis in amphibians, yet to be fully described in this group.

Synergistic enhancement of strength and ductility in novel solid-stir continuous extrusion: Influence of heterogeneous microstructure and alloy chemistry

Friction stir-derived solid-state processes leverage the synergistic effect of frictional heating and intense shear deformation to achieve localized microstructural modification for the improvement of mechanical properties. In the present work, a novel processing route called solid-stir extrusion (SSE) has been explored for continuous, high-rate extrusion of a high strength non-age-hardenable-AlMgSc alloy. In the current study, loss of strength associated with frictional heating during the process is mitigated as elevated strength is observed in as-extruded condition, thereby eliminating the need for post process heat treatment. The effect of process attributes on mechanical property and microstructure evolution was investigated. The SSE process caused extensive grain refinement and fragmentation of thermally stable Al3Sc intermetallic periodically at shear bands, which contributes to the elevated mechanical strength. The spatial variation in mechanical properties and microstructural heterogeneity was characterized using nanoindentation and microstructural correlation. The current findings indicate the significance of the process-specific alloy design approach to increase performance of material in as-processed condition.

An Investigation of Real-Time Robotic Polishing Motion Planning Using a Dynamical System

When addressing the technical challenges of achieving precise force tracking during the local polishing process of polishing robots, controlling the contact state between the robot and the workpiece surface is essential. To this end, a contact motion-planning strategy based on dynamic systems is designed to generate trajectory routes during local polishing. The trajectory simulation of the local modulation dynamic system is achieved through the employment of the support vector regression (SVR) algorithm with a Gaussian kernel, facilitating the learning process. The feasibility and stability of planning local paths are validated using the local modulation dynamic system. To maintain a constant contact force between the end-effector polishing robot and the workpiece, an integral adaptive impedance control strategy is utilized, enabling the robot’s compliant control. Subsequently, an experimental system for the polishing robot is constructed in order to verify the effectiveness of the motion-planning system. The experimental results demonstrate that the proposed motion-planning approach is applicable in practical polishing processes, ensuring smooth contact and maintaining the desired contact force when scanning nonlinear surfaces, and thus showcasing stability and practicality.

A modified vision transformer architecture with scratch learning capabilities for effective fire detection

Fire is considered to be one of the major influencing factors that cause fatalities, property damage, and economic, and ecological disruption. To perform the early detection of fire using data from vision sensors and prevent or reduce the damage that fires cause, deep learning models have been widely adopted to overcome the limitations of conventional methods. However, mainstream convolutional neural network (CNN) models have limited generalisation abilities in unseen scenarios and struggle to obtain a good trade-off among the accuracy, inference speed, and model size. Currently, vision transformers (ViT) outperform conventional CNN models; however, they are computationally expensive and required more data for training. They provide a limited performance for small and medium-sized datasets, which are very common in the fire scene classification domain. In this work, we employ a novel ViT architecture by combining shifted patch tokenisation and local self-attention modules for efficient fire scene classification and enable the model to learn from scratch even on small and medium-sized datasets. Furthermore, to make the model suitable for real-time inferencing, we modify the transformer encoder and eventually achieve a reduced number of floating-point operations and a reduced model size. Additionally, in this work, a medium-scale fire dataset is developed that contains complex real-world scenarios. Our model is assessed on three benchmark and a self-created datasets using several evaluation metrics, including a novel cross-corpse evaluation metric, as well as a robustness evaluation metric. The experimental results indicate that our model achieved an overwhelmingly better performance compared to existing methods in terms of the accuracy and model complexity.

Attention-based convolution neural network for magnetic tile surface defect classification and detection

Effectively identifying surface defects in magnetic tiles has proven to be highly challenging due to limited sample availability and irrelevant background interference, which also plays a crucial role in significantly influencing the lifespan and reliability of permanent magnet motors. To address these challenges, our study draws inspiration from a comprehensive analysis of the retinal attention mechanism and proposes three guiding criteria: multi-level resolution, what to look for, and where to look at. These criteria are utilized as foundational principles to enhance the representation learning capability of designed neural network structures through the incorporation of the retinal attention mechanism. Subsequently, based on these guiding criteria, we introduce a novel convolutional retinal attention block (CRAB) to learn discriminative and robust feature representations for magnetic tile surface defect classification and detection. The proposed CRAB comprises three modules: multi-resolution module (MRM), global attention aggregation module (GAAM), and local attention aggregation module (LAAM), designed to extract discriminative and robust features by refining meaningful information and suppressing redundant ones. Comprehensive experimental results across image classification and object detection tasks demonstrate that the proposed CRAB outperforms existing methods such as SE, ECA, and CBAM, and can effectively amplify the representation power across various backbone networks, including VGG-16, GoogLeNet, ResNet-18, and ResNet-50. An evaluation on surface defect classification and detection tasks for industrial magnetic tiles further shows that CRAB achieves accuracies of 99.50% and 96.98%, respectively. These results emphasize the promising application prospects of the proposed method in detecting industrial surface defects amid expansive and inconsequential backgrounds. The code of the proposed method is available at: https://github.com/KWflyer/CRAB.

Anomalously low vacancy formation energies and migration barriers at Cu/AlN interfaces from ab initio calculations

It is well known that interfaces in nanomaterials can act as ultra-fast short-circuit diffusion paths, as originating from local structural, chemical and/or electronic modifications at the interface. For example, the interface diffusivity of Cu in Cu/AlN nanomultilayers can be up to two orders of magnitude higher as compared to the bulk, which may promote interfacial premelting of Cu. Extensive ab initio calculations of vacancy formation and migration energies in Cu/AlN nanomultilayers were performed to arrive at the fundamental understanding of such anomalously fast interface diffusion phenomena. It was found that both the metallic Al-terminated interface and the mixed-bonded N-terminated interface promote high atomic interface mobilities by lowering the vacancy formation and vacancy migration energies in the interfacial Cu planes. Moreover, the out-of-plane vacancy migration energies highlight a strong tendency of vacancy segregation toward both interfaces.

Suspended particulate matter influence on dissolved Nd concentration and isotopic composition along GEOTRACES section GP16

The role of suspended particulate matter in the biogeochemical cycling of neodymium (Nd) in the ocean is not well understood. This study reports the measurement of dissolved Nd concentrations and isotopes (<0.2 um), and particulate Nd concentrations along the Eastern Pacific Zonal Transect (EPZT, GP16) as part of the GEOTRACES program. The western part of the EPZT cruise track is influenced by a hydrothermal plume, observed between 2200 and 2800 m, originating at the southern East Pacific Rise. This setting allows for the investigation of the role of particulate matter in Nd biogeochemical cycling and its contribution in modifying the distribution of dissolved Nd. Results show that the highest removal of dissolved Nd (∼ 21 %) was observed near the hydrothermal ridge crest, decreasing to ∼ 8 % at the westernmost station. Here, we report the partition coefficient (Kd) of each particle constituent (e.g., lithics, CaCO3, POM, Opal, Fe-phase, Mn-phase) to show that particle composition plays an important role in Nd removal alongside particle mass. Along the plume, both particulate Mn and Fe contributed to the removal of Nd, with Mn playing the dominant role in sites closest to the vent. At the western stations, below the plume, the concentration of both dissolved and particulate Nd increased with depth, with particulate Nd one to two orders of magnitude lower than measured in the dissolved phase. The increase in dissolved Nd with depth in the interval below the plume could result either from the continuing remineralization of raining particles or the influx of dissolved Nd from bottom sediments. Close to the ridge crest, where the hydrothermal Nd input is at its highest, there is a hint of local modification of dissolved εNd signature, however not large enough to affect the use of εNd as a water mass tracer.

Remote Sensing Image Dehazing via a Local Context-Enriched Transformer

Remote sensing image dehazing is a well-known remote sensing image processing task focused on restoring clean images from hazy images. The Transformer network, based on the self-attention mechanism, has demonstrated remarkable advantages in various image restoration tasks, due to its capacity to capture long-range dependencies within images. However, it is weak at modeling local context. Conversely, convolutional neural networks (CNNs) are adept at capturing local contextual information. Local contextual information could provide more details, while long-range dependencies capture global structure information. The combination of long-range dependencies and local context modeling is beneficial for remote sensing image dehazing. Therefore, in this paper, we propose a CNN-based adaptive local context enrichment module (ALCEM) to extract contextual information within local regions. Subsequently, we integrate our proposed ALCEM into the multi-head self-attention and feed-forward network of the Transformer, constructing a novel locally enhanced attention (LEA) and a local continuous-enhancement feed-forward network (LCFN). The LEA utilizes the ALCEM to inject local context information that is complementary to the long-range relationship modeled by multi-head self-attention, which is beneficial to removing haze and restoring details. The LCFN extracts multi-scale spatial information and selectively fuses them by the the ALCEM, which supplements more informative information compared with existing regular feed-forward networks with only position-specific information flow. Powered by the LEA and LCFN, a novel Transformer-based dehazing network termed LCEFormer is proposed to restore clear images from hazy remote sensing images, which combines the advantages of CNN and Transformer. Experiments conducted on three distinct datasets, namely DHID, ERICE, and RSID, demonstrate that our proposed LCEFormer achieves the state-of-the-art performance in hazy scenes. Specifically, our LCEFormer outperforms DCIL by 0.78 dB and 0.018 for PSNR and SSIM on the DHID dataset.

Transitions of nitrogen optical centers induced by femtosecond laser pulses in treated natural diamond

Diamond is a unique optical material with ultra-hard lattice, which properties can be altered by optically active doping defects combined with carbon interstitials and vacancies. Direct laser writing by focused ultrashort laser pulses enables local modification of color centers in diamonds with excellent three-dimensional positioning accuracy, providing unique capabilities for quantum technologies, magnetometry, laser generation and photoluminescence (PL) micromarking. To shed light on currently unknown underlying atomistic transformations, we exposed electron-irradiated and annealed natural IaAB1-type diamond with an initially high content of various nitrogen-vacancy centers (NV, H3, N3 and H4) to millions of 0.2-ps, 525-nm laser pulses focused inside the sample. The modified areas were characterized by Fourier-transform infrared (FT-IR) and ultraviolet–visible transmission spectroscopy and confocal Raman/PL microspectroscopy with excitation wavelengths of 405 nm and 532 nm. We report on a local decrease in originally strong PL yield of NV, H3 and H4 centers with a monotonic dependence on both pulse energy and exposure time, as well as a rise of H1b and H1c lines in the FT-IR spectra accompanied by a drop in the concentration of H1a center. The results indicate that laser-induced processes make possible both creation and destruction of NV, H3 and H4 centers influenced by their initial concentration and the parameters of laser modification, which can be useful for adjusting of laser writing regimes for PL micromark inscription and other local modification of diamond for photonics applications.

Intensifying electrified flow-through water treatment technologies via local environment modification

Removing high-risk and persistent contaminants from water is challenging, because they typically exist at low concentrations in complex water matrices. Electrified flow-through technologies are viable to overcome the limitations induced by mass transport for efficient contaminant removal. Modifying the local environment of the flow-through electrodes offers opportunities to further improve the reaction kinetics and selectivity for achieving near-complete removal of these contaminants from water. Here, we present state-of-the-art local environment modification approaches that can be incorporated into electrified flow-through technologies to intensify water treatment. We first show methods of nanospace incorporation, local geometry adjustment, and microporous structure optimization that can induce spatial confinement, enhanced local electric field, and microperiodic vortex, respectively, for local environment modification. We then discuss why local environment modification can complement the flow-through electrodes for improving the reaction rate and selectivity. Finally, we outline appropriate scenarios of intensifying electrified flow-through technologies through local environment modification for fit-for-purpose water treatment applications.

Another role of CO-formation catalyst in acidic tandem CO2 electroreduction: Local pH modulator

Another role of CO-formation catalyst in acidic tandem CO2 electroreduction: Local pH modulator

A Hybrid Deep Learning Model Utilizing Cross-Structural Multi-Behavioral Comparative Recommendation for Sustainable Electric Transportation Infrastructure

Ensuring the sustainability of transportation infrastructure for electric vehicles (e-trans) is increasingly imperative in the pursuit of decarbonization goals and addressing the pressing energy shortage. By prioritizing the development and maintenance of resilient e-trans platforms through the optimization of the public charging network, electric vehicle businesses can effectively meet the needs of users, thereby contributing to efforts aimed at improving environmental quality. To achieve this goal, researching the dynamics of vehicle user behaviors plays a crucial role. In this paper, we propose cross-structure multi-behavior contrastive learning for recommendation (C-MBR), which takes into account the dynamic preferences of users, and develops model profiles from the global structure module, local structure module, cross-behavior contrastive learning module, cross-structure contrastive learning module, and model prediction and optimization. C-MBR is mainly designed to learn user preferences from the diversity of users’ behaviors in the process of interacting with the project, so as to grasp the different behavioral intentions of users. The experimental and analytical research is further conducted and validated for dealing with cold start problems. The results indicate that C-MBR has a strong ability to deal with the problem of sparse data. Compared with the ablation experiment, the model performance of C-MBR is significantly enhanced, showing that the C-MBR model can fully apply the information of a global structure and local structure in cross-structure comparative learning and multi-behavioral comparative learning to further alleviate the problem of data sparsity. As a result, the e-trans infrastructure will be significantly enhanced by addressing the issue of data-driven disruption.

Assessing chemostratigraphic and biostratigraphic correlation of Ediacaran phosphorites (Bocaina, Khesen and Doushantuo Formations): Diachronic, local signals of the Neoproterozoic phosphogenic-taphonomic event

The Neoproterozoic phosphogenic event records biotic and biogeochemical changes that seem to indicate intrinsic relationships between phosphogenesis and phosphatic fossil preservation. However, chemo-litho and biostratigraphic correlations conducted on fossiliferous, phosphatic successions of the Doushantuo Formation, South China, evidence a highly heterogeneous record, featuring diachronic depositional ages, variable taxonomic diversity and inconsistent, “noisy” δ13C records. Similar detailed chemo-litho and biostratigraphic correlations targeted in high-grade phosphorite strata have not been performed along Ediacaran units elsewhere. Considering the significance and scarcity of Ediacaran high-grade phosphorite strata, establishing a comprehensive correlation framework within and between those units is fundamental to understanding Ediacaran biogeochemical and biotic evolution. This work aims to integrate new δ13C isotope stratigraphy, and existing Doushantuo-Pertatataka acritarch assemblages and geochronological records from Bocaina (Brazil), Khesen (Mongolia) and Doushantuo (China) Formations. Phosphate rich strata in those units feature inconsistent δ13C signatures characterized by negative values showing large bed-to-bed variations, possibly reflecting local modification of original marine signals by phosphogenesis, suggesting caution against the use in global/regional chemostratigraphic correlation. The record of phosphatized acritarch assemblages shows contrastive taxonomic diversities, and a small number of common taxa including Megasphaera. Whereas the presence of biozonal Doushantuo Formation taxa on Khsesen Fm, resonates with growing views of strong environmental resilience and large temporal ranges of acanthomorph acritarchs throughout the Ediacaran-early Cambrian, the scarcity of similar taxa in the Bocaina Fm may question the reliability of Doushantuo Fm acritarch biozones in other paleogeographic or paleoenvironmental scenarios. Alternatively, the low taxonomic diversity recorded in Bocaina and Khesen Fms may also reflect the effects of the late Ediacaran decline in acritarch diversity. Further, combined evidence of selective paleoenvironmental phosphatization, local geochemical signals, narrow time spans and heterogeneous taxonomic diversity found in phosphatized DPK acanthomorph assemblages indicate the local nature of Ediacaran phosphatic fossil preservation events. Finally, evidence for diachronic preservation between high-grade phosphatic strata and phosphatized microfossils show non-causal relationships between Ediacaran phosphogenesis and phosphatic fossil preservation, highlighting the punctuated, intrinsic local nature of those events.