Composite Dataset Research Articles

Simulation of semiconductor wafer dicing induced faults on chips and their application as augmentation method for a deep learning based visual inspection system

Abstract In semiconductor wafer dicing, one particular area of interest is the process of visual inspection to detect manufacturing defects that occur throughout the manufacturing process. Emerging defect patterns are typically in the micrometer range, translating to barely visible defects in pixel size on high-resolution imagery. The availability of rarely occurring defects is generally limited due to the labor-intensive nature of the related, highly specialized annotation task. Therefore, this contribution proposes a hybrid system for wafer and chip image data synthesis utilizing wafer and dicing path templates for the synthetic generation of large quantities of labeled flawless and, rarely, faulty chip and dicing street imagery. These are utilized for subsequent deep learning based defect detection and classification by employing a residual neural network as the core classifier for our visual inspection system. Our results show promising prospects when the original image data are supplemented with synthesized images by creating so-called composite data sets. Compared to the system’s baseline on the original data set, an F1-score-based relative improvement of up to 3.98 times was achieved. Furthermore, a novel synthetic-composite leave-one-out cross-validation (SC-LOOCV) method is proposed as a means to analyze the quality of our synthesized data for each specific wafer type. Based on these experiments, we scored a relative improvement of up to 5.99. For all our wafer types, overall relative improvement factors of 1.99 (composite) and 2.83 (SC-LOOCV) highlight the benefits of our realized system.

Nutritional intelligence in the food system: Combining food, health, data and AI expertise.

Transformative change is needed across the food system to improve health and environmental outcomes. As food, nutrition, environmental and health data are generated beyond human scale, there is an opportunity for technological tools to support multifactorial, integrated, scalable approaches to address the complexities of dietary behaviour change. Responsible technology could act as a mechanistic conduit between research, policy, industry and society, enabling timely, informed decision making and action by all stakeholders across the food system. Domain expertise in food, nutrition and health should always be integrated into both the development and continuous deployment of AI-powered nutritional intelligence (NI) to ensure it is responsible, accurate, safe, useable and effective. Dietary behaviours are complex and improving diet-related health outcomes requires socio-cultural-demographic considerations within the design and deployment of NI tools. This article describes existing examples of NI within the food system and future opportunities. Human-in-the-loop approaches with food, health and nutrition experts involved at all stages including data acquisition, processing, output validation and ongoing quality assurance are essential to ensure evidence-based practice. The same ethical considerations should apply in this domain as in any other (e.g. privacy, inclusivity, robustness, transparency and accountability) and responsible practice must encompass rigorous standards and alignment with regulatory frameworks. Critical today and in the future is accessibility to appropriate high-quality food compositional data sets, which include up-to-date information on commercially available products that reflect the constantly evolving food landscape to realise the potential of responsible AI to help address the existing food system challenges.

Constrained least squares simplicial-simplicial regression

Simplicial-simplicial regression refers to the regression setting where both the responses and predictor variables lie within the simplex space, i.e. they are compositional. For this setting, constrained least squares, where the regression coefficients themselves lie within the simplex, is proposed. The model is transformation-free but the adoption of a power transformation is straightforward, it can treat more than one compositional datasets as predictors and offers the possibility of weights among the simplicial predictors. Among the model’s advantages are its ability to treat zeros in a natural way and a highly computationally efficient algorithm to estimate its coefficients. Resampling based hypothesis testing procedures are employed regarding inference, such as linear independence, and equality of the regression coefficients to some pre-specified values. The strategy behind the formulation of the new model implemented is related to an existing methodology, that is of the same spirit, showcasing how other similar models can be employed as well. Finally, the performance of the proposed technique and its comparison to the existing methodology takes place using simulation studies and real data examples.

Aquatic-terrestrial linkages drive contrasting biodiversity patterns in tropical and temperate forests.

Riparian ecosystems harbour unique biodiversity because of their close interconnections with adjacent aquatic ecosystems. Yet, how aquatic ecosystems influence terrestrial biodiversity over different spatial scales is poorly understood, particularly in the tropics. We conducted field campaigns to collect 235 terrestrial invertebrate assemblages along 150 m transects from 47 streams in both Brazil and the UK, compiling one of the largest known datasets of riparian invertebrate community composition at multiple spatial scales. Invertebrate densities increased towards water in both regions. In Brazil, this was driven by an increase in spiders, with a corresponding decrease in non-predators, resulting in higher predator : prey ratios near water. In the UK, non-predator densities increased towards water, decreasing predator : prey ratios. While pairwise dissimilarity increased with distance from water in both regions, β-diversity was significantly higher in tropical assemblages, with more β-diversity explained by turnover. Spider community composition was significantly structured by distance from water in the Brazilian sites, suggesting tropical assemblages were influenced more by emerging aquatic prey, with a distinct spider community replacing other predators, with possible top-down control of terrestrial prey. High turnover-driven dissimilarity among tropical assemblages suggests that Brazilian riparian ecosystems are better managed at the landscape scale, with an emphasis on in-stream measures preventing disruption of aquatic resource subsidies.

Divergent resource‐use strategies of encroaching shrubs: Can traits predict encroachment success in tallgrass prairie?

Abstract Changes in climate and land management over the last half‐century have favoured woody plants native to grasslands and led to the rapid expansion of woody species. Despite this being a global phenomenon, it is unclear why some woody species have rapidly expanded while others have not. We assessed whether the most abundant woody encroaching species in tallgrass prairie have common growth forms and physiology or unique traits that differentiate their resource‐use strategies. We characterized the abundance, above‐ground carbon allocation, and leaf‐level physiological and structural traits of seven woody encroaching species in tallgrass prairie that span an order of magnitude in abundance. To identify species‐specific increases in abundance, we used a 34‐year species composition dataset at Konza Prairie Biological Station (Central Great Plains, USA). We then compared biomass allocation and leaf‐level traits to determine differences in carbon and water use strategies among species. While all focal species increased in abundance over time, encroachment in this system is primarily driven by three species: Cornus drummondii, Prunus americana and Rhus glabra. The most dominant species, Cornus drummondii, had the most extreme values for several traits, including the lowest leaf:stem mass ratios, lowest photosynthetic capacity and highest turgor loss point. Two of the most abundant species, Cornus drummondii and Rhus glabra, had opposing growth forms and resource‐use strategies. These species had significantly different above‐ground carbon allocation, leaf‐level drought tolerance and photosynthetic capacity. There were surprisingly few interspecific differences in specific leaf area and leaf dry matter content, suggesting these traits were poor predictors of species‐level encroachment. Synthesis. Woody encroaching species in tallgrass prairie encompass a spectrum of growth forms and leaf physiology. Two of the most abundant woody species fell at opposite ends of this spectrum. Our results suggest niche differences among a community of woody species facilitate the rapid encroachment by a few species. This study shows that woody encroaching species do not conform to a ‘one‐size‐fits‐all’ strategy, and a diversity of growth forms and physiological strategies may make it more challenging to reach management goals that aim to conserve or restore grassland communities.

Land plants, sediment composition, granitic magma compositions and environments of formation

Earth’s biosphere and lithosphere continually interact, and life’s metabolic processes influence atmospheric composition, the weathering of rocks and how rivers deliver sediments to ocean and intracontinental basins. Recently, other authors noted a correlation between the oxygen isotopic compositions and crustal residencies of arc magmatic rocks. This correlation appeared at about 430 Ma and is attributed to the rise of deep-rooted land plants, which increased clay-mineral production and input to ocean basins, by slowing sediment transfer across continents, stabilising soils, and thereby enhancing weathering time and promoting clay formation. Granites, which form much of the continental crust, contain components derived through partial melting of sediments that can have varying chemical maturity, and the major-element chemical features and isotopic compositions of the granites reflects this. In a global dataset of granite compositions, dating from 500 Ma, major-element markers that reflect original source clay content do not reveal the stepwise increase in sediment clay content inferred for the sources of arc magmas at 430 Ma, and only increase consistently from approximately 200 Ma. This demonstrates that although granite compositions reflect the plant influence, they are not principally formed through subduction processes in magmatic arcs but rather in back-arc environments, and do not receive the immediate transcontinental sediment input that characterises ocean basins. The lag in the granite response occurs because there is commonly a substantial time gap between arc magmatism and granite genesis. This is because a subsequent orogeny is generally required to partially melt older back-arc rocks, including sediments, to produce granitic magmas.

TPE-xgboost for explainable predictions of concrete compressive strength considering compositions, and mechanical and microstructure properties of testing samples

The use of machine learning (ML) for predicting concrete compressive strength (CCS) has shown promising and accurate results, making it a valuable tool in the field. However, efficient prediction requires not only a robust ML approach but also a comprehensive and well-curated dataset. This study addresses this challenge by investigating an extensive and integrated dataset of (1) concrete compositions (mixture proportions) and (2) testing conditions (mechanical and microstructure properties of concrete testing samples), containing 1525 observations relating to 39 parameters. On algorithms side, this research proposes novel tree-structured parzen estimator based extreme gradient boosting (TPE-xgboost) for accurate and confident CCS predictions. Moreover, SHapley Additive exPlanations (SHAP) analysis was performed to provide a comprehensive understanding of feature importance, dependencies, and interactions. Compared to prior research, this study demonstrates significant improvements in CCS prediction accuracy for separate investigations on concrete compositions (3.77%) and mechanical and microstructure properties (28.57%), while maintaining reasonable accuracy for the integrated dataset despite its complexity and sparseness. SHAP analysis reveals key influential factors, including age and water-to-binder ratio in concrete composition, and peak load and height of testing samples, as well as microstructural characteristics such as mean values of global autocorrelation length and its integral range. This research provides valuable insights into model optimization, predictive performance, and feature dependencies and interactions, contributing to the development of more accurate and reliable CCS prediction models.

Intelligent target recognition method for infrared/laser composite fuze

Abstract In the context of complex environment, the intelligent target recognition method for infrared/laser composite imaging fuze encounters the following challenges: 1) The dearth of infrared/laser composite imaging fuze data hampers the advancement and implementation of deep learning technology in fuze target recognition; 2) The extant infrared/laser composite imaging fuze target recognition methods exhibit suboptimal recognition accuracy in complex terrain and ground conditions, which predisposes them to early or false explotions. To address the aforementioned issues, we have constructed infrared/laser composite imaging target and background dataset in diverse scenes, terrains and features, which provides effective data support for the intelligent recognition of composite imaging fuze. Additionally, we have designed a cross-modal fusion attention mechanism and an invariant feature learning method. The method facilitates the fusion of multimodal complementary information and the intelligent recognition of multi-scale and multi-morphology targets in complex scenes. Furthermore, they enhance the accuracy of the composite imaging fuze, enabling it to adapt to complex terrains. This enhances the combat effectiveness of composite imaging fuze in complex environments, while also offering technical support for the intelligentization of target recognition systems in future ballistic fuze.

MCCA-VNet: A Vit-Based Deep Learning Approach for Micro-Expression Recognition Based on Facial Coding.

In terms of facial expressions, micro-expressions are more realistic than macro-expressions and provide more valuable information, which can be widely used in psychological counseling and clinical diagnosis. In the past few years, deep learning methods based on optical flow and Transformer have achieved excellent results in this field, but most of the current algorithms are mainly concentrated on establishing a serialized token through the self-attention model, and they do not take into account the spatial relationship between facial landmarks. For the locality and changes in the micro-facial conditions themselves, we propose the deep learning model MCCA-VNET on the basis of Transformer. We effectively extract the changing features as the input of the model, fusing channel attention and spatial attention into Vision Transformer to capture correlations between features in different dimensions, which enhances the accuracy of the identification of micro-expressions. In order to verify the effectiveness of the algorithm mentioned, we conduct experimental testing in the SAMM, CAS (ME) II, and SMIC datasets and compared the results with other former best algorithms. Our algorithms can improve the UF1 score and UAR score to, respectively, 0.8676 and 0.8622 for the composite dataset, and they are better than other algorithms on multiple indicators, achieving the best comprehensive performance.

Sample Article for Pree Reviewer

Background Large research consortia can generate tremendous amounts of biological information, including high-resolution soil, vegetation, and climate data. While this knowledge stock holds invaluable potential for answering evolutionary and ecological questions, making these data exploitable for modelling remains a daunting task due to the many processing steps required for synthesis. This might result in many researchers to fall back to a handful of ready-to-use data sets, potentially at the expense of statistical power and scientific rigour. In a push for a more stringent approach, we introduce BEpipeR, an R pipeline that allows for the streamlined synthesis of plot-based Biodiversity Exploratories data. Methods BEpipeR was designed with flexibility and ease of use in mind. For instance, users simply choose between aggregating forest or grassland data, or a combination thereof, effectively allowing them to process any experimental plot data of this research consortium. Additionally, instead of coding, they parse most processing information in a user-friendly way through parameter sheets. Processing includes, among others, the creation of a spatially explicit plot-ID template, data wrangling, quality control, plot-wise aggregations, the calculation of derived metrics, data joining to a large composite data set, and metadata compilation. Results With BEpipeR, we provide a feature-rich pipeline that allows users to process Biodiversity Exploratories data in a flexible and reproducible way. This pipeline might serve as a starting point for aggregating the numerous data sets of this and potentially similar research consortia. In this way, it might be a primer for the construction of consortia-wide composite data sets that take full advantage of the consortia’s rich information stocks, ultimately boosting the visibility and participation of individual research projects. Conclusions The BEpipeR permits the user-friendly processing and plot-wise aggregation of Biodiversity Exploratories data. With modifications, this framework may be easily adopted by other research consortia.

A Comprehensive Evaluation of Iris Segmentation on Benchmarking Datasets.

Iris is one of the most widely used biometric modalities because of its uniqueness, high matching performance, and inherently secure nature. Iris segmentation is an essential preliminary step for iris-based biometric authentication. The authentication accuracy is directly connected with the iris segmentation accuracy. In the last few years, deep-learning-based iris segmentation methodologies have increasingly been adopted because of their ability to handle challenging segmentation tasks and their advantages over traditional segmentation techniques. However, the biggest challenge to the biometric community is the scarcity of open-source resources for adoption for application and reproducibility. This review provides a comprehensive examination of available open-source iris segmentation resources, including datasets, algorithms, and tools. In the process, we designed three U-Net and U-Net++ architecture-influenced segmentation algorithms as standard benchmarks, trained them on a large composite dataset (>45K samples), and created 1K manually segmented ground truth masks. Overall, eleven state-of-the-art algorithms were benchmarked against five datasets encompassing multiple sensors, environmental conditions, demography, and illumination. This assessment highlights the strengths, limitations, and practical implications of each method and identifies gaps that future studies should address to improve segmentation accuracy and robustness. To foster future research, all resources developed during this work would be made publicly available.

Chemical Composition and Bioactivity Dataset Integration to Identify Antiproliferative Compounds in Phyllanthus Plants.

Phyllanthus species are renowned in traditional medicine for their diverse therapeutic properties, including potential anticancer activities. This study explored the antiproliferative potential of six Phyllanthus species by integrating chemical composition with bioactivity assays to identify key antiproliferative compounds. The integration of liquid chromatography-mass spectrometry (LC-MS)-based chemical composition data with antiproliferative activity against three cancer cell lines-PC-3 (prostate adenocarcinoma), SiHa (cervical carcinoma), and A549 (lung carcinoma)-as well as a normal mouse fibroblast line (L929) was performed by covariate analysis. These compounds were subsequently isolated and structurally characterized using spectroscopic methods. Through covariate statistics, seven m/z features were found to be plausible active compounds, and after isolation, they were related to butyrolactone and arylnaphthalide lignans. Among the active isolates, an unreported compound, (+)-phyllanlathyrin 6, was discovered in the aerial part of Phyllanthus lathyroides. The isolated compounds exhibited moderate to good antiproliferative activity (IC50 < 20 µM) with selectivity to SiHa, validating the covariate-based identification approach. These findings highlight the potential of Phyllanthus species as sources of novel anticancer agents, with specific arylnaphthalide lignans showing promising cytotoxic effects that could be further developed into therapeutic leads. Additionally, this study underscores the value of combining advanced analytical techniques with bioactivity testing to uncover bioactive compounds from natural sources. The results contribute to the growing body of evidence supporting the therapeutic relevance of Phyllanthus species and provide a foundation for future drug development efforts targeting cancer treatment.

BEpipeR: a user-friendly, flexible, and scalable data synthesis pipeline for the Biodiversity Exploratories and other research consortia

Background Large research consortia can generate tremendous amounts of biological information, including high-resolution soil, vegetation, and climate data. While this knowledge stock holds invaluable potential for answering evolutionary and ecological questions, making these data exploitable for modelling remains a daunting task due to the many processing steps required for synthesis. This might result in many researchers to fall back to a handful of ready-to-use data sets, potentially at the expense of statistical power and scientific rigour. In a push for a more stringent approach, we introduce BEpipeR, an R pipeline that allows for the streamlined synthesis of plot-based Biodiversity Exploratories data. Methods BEpipeR was designed with flexibility and ease of use in mind. For instance, users simply choose between aggregating forest or grassland data, or a combination thereof, effectively allowing them to process any experimental plot data of this research consortium. Additionally, instead of coding, they parse most processing information in a user-friendly way through parameter sheets. Processing includes, among others, the creation of a spatially explicit plot-ID template, data wrangling, quality control, plot-wise aggregations, the calculation of derived metrics, data joining to a large composite data set, and metadata compilation. Results With BEpipeR, we provide a feature-rich pipeline that allows users to process Biodiversity Exploratories data in a flexible and reproducible way. This pipeline might serve as a starting point for aggregating the numerous data sets of this and potentially similar research consortia. In this way, it might be a primer for the construction of consortia-wide composite data sets that take full advantage of the consortia’s rich information stocks, ultimately boosting the visibility and participation of individual research projects. Conclusions The BEpipeR pipeline permits the user-friendly processing and plot-wise aggregation of Biodiversity Exploratories data. With modifications, this framework may be easily adopted by other research consortia.

Development and evaluation of a novel framework to enhance k-NN algorithm’s accuracy in data sparsity contexts

This paper presents a novel framework for implementing the k-NN algorithm, designed to enhance its accuracy in contexts with sparse data. The framework addresses limitations in the algorithm’s training process by optimizing data structures. It employs composite datasets generated from the initial data using a data-driven fuzzy Analytic Hierarchy Process weighting scheme. This approach is designed to enhance the informational content in the initial datasets, thus reducing the entropy and implementation uncertainty. The framework was evaluated using 75 publicly available datasets and 3 generated datasets, demonstrating significant accuracy improvements across various k-parameter values. The findings were rigorously generalized using non-parametric hypothesis tests; while the resulting sensitivity was assessed by applying different distance metrics. By enhancing informational content, the composite data structures contribute to both accuracy improvements and scalability, particularly in data-sparse contexts. This relationship underscores the critical role of entropy in enhancing the performance of explainable machine learning algorithms, providing a valuable and interpretable tool for transforming data structures in sparse data environments.

Cassava disease detection using a lightweight modified soft attention network.

Cassava is a high-carbohydrate crop that is at risk of viral infections. The production rate and quality of cassava crops are affected by several diseases. However, the manual identification of diseases is challenging and requires considerable time because of the lack of field professionals and the limited availability of clear and distinct information. Consequently, the agricultural management system is seeking an efficient and lightweight method that can be deployable to edged devices for detecting diseases at an early stage. To address these issues and accurately categorize different diseases, a very effective and lightweight framework called CDDNet has been introduced. We used MobileNetV3Small framework as a backbone feature for extracting optimized, discriminating, and distinct features. These features are empirically validated at the early intermediate stage. Additionally, we modified the soft attention module to effectively prioritize the diseased regions and enhance significant cassava plant disease-related features for efficient cassava disease detection. Our proposed method achieved accuracies of 98.95%, 97.03%, and 98.25% on Cassava Image Dataset, Cassava Plant Disease Merged (2019-2020) Dataset, and the newly created Cassava Plant Composite Dataset, respectively. Furthermore, the proposed technique outperforms previous state-of-the-art methods in terms of accuracy, parameter count, and frames per second values, ultimately making the proposed CDDNet the best one for real-time processing. Our findings underscore the importance of a lightweight and efficient technique for cassava disease detection and classification in a real-time environment. Furthermore, we highlight the impact of modified soft attention on model performance. © 2024 The Author(s). Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Rapid shifts in grassland communities driven by climate change

Many terrestrial plant communities, especially forests, have been shown to lag in response to rapid climate change. Grassland communities may respond more quickly to novel climates, as they consist mostly of short-lived species, which are directly exposed to macroclimate change. Here we report the rapid response of grassland communities to climate change in the California Floristic Province. We estimated 349 vascular plant species’ climatic niches from 829,337 occurrence records, compiled 15 long-term community composition datasets from 12 observational studies and 3 global change experiments, and analysed community compositional shifts in the climate niche space. We show that communities experienced significant shifts towards species associated with warmer and drier locations at rates of 0.0216 ± 0.00592 °C yr−1 (mean ± s.e.) and −3.04 ± 0.742 mm yr−1, and these changes occurred at a pace similar to that of climate warming and drying. These directional shifts were consistent across observations and experiments. Our findings contrast with the lagged responses observed in communities dominated by long-lived plants and suggest greater biodiversity changes than expected in the near future.

Quantifying age-specific household contacts in Aotearoa New Zealand for infectious disease modelling.

Accounting for population age structure and age-specific contact patterns is crucial for accurate modelling of human infectious disease dynamics and impact. A common approach is to use contact matrices, which estimate the number of contacts between individuals of different ages. These contact matrices are frequently based on data collected from populations with very different demographic and socio-economic characteristics from the population of interest. Here we use a comprehensive household composition dataset based on Aotearoa New Zealand census and administrative data to construct a household contact matrix and a synthetic population that can be used for modelling. We investigate the behaviour of a compartment-based and an agent-based epidemic model parametrized using these data, compared with a commonly used contact matrix that was constructed by projecting international data onto New Zealand's population. We find that using the New Zealand household data, either in a compartment-based model or in an agent-based model, leads to lower attack rates in older age groups compared with using the projected contact matrix. This difference becomes larger when household transmission is more dominant relative to non-household transmission. We provide electronic versions of the synthetic population and household contact matrix for other researchers to use in infectious disease models.

Drone Detection Performance Evaluation via Real Experiments with Additional Synthetic Darkness

Detecting drones is increasingly challenging, particularly when developing passive and low-cost defense systems capable of countering malicious attacks in environments with high levels of darkness and severe weather conditions. This research addresses the problem of drone detection under varying darkness levels by conducting an extensive study using deep learning models. Specifically, the study evaluates the performance of three advanced models: Yolov8, Vision Transformers (ViT), and Long Short-Term Memory (LSTM) networks. The primary focus is on how these models perform under synthetic darkness conditions, ranging from 20% to 80%, using a composite dataset (CONNECT-M) that simulates nighttime scenarios. The methodology involves applying transfer learning to enhance the base models, creating Yolov8-T, ViT-T, and LSTM-T variants. These models are then tested across multiple datasets with varying darkness levels. The results reveal that all models experience a decline in performance as darkness increases, as measured by Precision-Recall and ROC Curves. However, the transfer learning-enhanced models consistently outperform their original counterparts. Notably, Yolov8-T demonstrates the most robust performance, maintaining higher accuracy across all darkness levels. Despite the general decline in performance with increasing darkness, each model achieves an accuracy above 0.6 for data subjected to 60% or greater darkness. The findings highlight the challenges of drone detection under low-light conditions and emphasize the effectiveness of transfer learning in improving model resilience. The research suggests further exploration into multi-modal systems that combine audio and optical methods to enhance detection capabilities in diverse environmental settings.

An innovative approach for QoS-aware web service composition using whale optimization algorithm

With the proliferation of services and the vast amount of data produced by the Internet, numerous services with comparable functionalities but varying Quality of Service (QoS) attributes are potential candidates for meeting user needs. Consequently, the selection of the most suitable services has become increasingly challenging. To address this issue, a synthesis of multiple services is conducted through a composition process to create more sophisticated services. In recent years, there has been a growing interest in QoS uncertainty, given its potential impact on determining an optimal composite service, where each service is characterized by multiple QoS properties (e.g., response time and cost) that are frequently subject to change primarily due to environmental factors. Here, we introduce a novel approach that depends on the Multi-Agent Whale Optimization Algorithm (MA-WOA) for web service composition problem. Our proposed algorithm utilizes a multi-agent system for the representation and control of potential services, utilizing MA-WOA to identify the optimal composition that meets the user’s requirements. It accounts for multiple quality factors and employs a weighted aggregation function to combine them into a cohesive fitness function. The efficiency of the suggested method is evaluated using a real and artificial web service composition dataset (comprising a total of 52,000 web services), with results indicating its superiority over other state-of-the-art methods in terms of composition quality and computational effectiveness. Therefore, the proposed strategy presents a feasible and effective solution to the web service composition challenge, representing a significant advancement in the field of service-oriented computing.

Vertical transport of anthropogenic lead by reversible scavenging in the South Atlantic Ocean

Stable lead (Pb) isotopes have been regarded as tracers of ocean circulation, both in the present time and geological past. Here we present a new dataset of seawater Pb concentrations and isotope compositions for ten depth profiles from the South Atlantic Ocean (GEOTRACES cruises GA02 and GA10). By comparing Pb isotope data collected on the two cruises, and by modelling the distribution of Pb with an extended optimum multiparameter analysis, we find evidence of vertical transport of anthropogenic Pb pollution due to reversible scavenging. Surface to depth transfer of polluted Pb is aided by high suspended particulate matter loads at the Brazil – Malvinas Confluence and along ∼40°S in the South Atlantic. Overall, our findings caution the use of Pb isotope ratios as ventilation tracers in the South Atlantic and emphasize the importance of particle-seawater interaction for biogeochemical cycles.