European Commission Joint Research Centre Research Articles

Mapping the Bibliometrics Landscape of AI in Medicine: Methodological Study.

Artificial intelligence (AI), conceived in the 1950s, has permeated numerous industries, intensifying in tandem with advancements in computing power. Despite the widespread adoption of AI, its integration into medicine trails other sectors. However, medical AI research has experienced substantial growth, attracting considerable attention from researchers and practitioners. In the absence of an existing framework, this study aims to outline the current landscape of medical AI research and provide insights into its future developments by examining all AI-related studies within PubMed over the past 2 decades. We also propose potential data acquisition and analysis methods, developed using Python (version 3.11) and to be executed in Spyder IDE (version 5.4.3), for future analogous research. Our dual-pronged approach involved (1) retrieving publication metadata related to AI from PubMed (spanning 2000-2022) via Python, including titles, abstracts, authors, journals, country, and publishing years, followed by keyword frequency analysis and (2) classifying relevant topics using latent Dirichlet allocation, an unsupervised machine learning approach, and defining the research scope of AI in medicine. In the absence of a universal medical AI taxonomy, we used an AI dictionary based on the European Commission Joint Research Centre AI Watch report, which emphasizes 8 domains: reasoning, planning, learning, perception, communication, integration and interaction, service, and AI ethics and philosophy. From 2000 to 2022, a comprehensive analysis of 307,701 AI-related publications from PubMed highlighted a 36-fold increase. The United States emerged as a clear frontrunner, producing 68,502 of these articles. Despite its substantial contribution in terms of volume, China lagged in terms of citation impact. Diving into specific AI domains, as the Joint Research Centre AI Watch report categorized, the learning domain emerged dominant. Our classification analysis meticulously traced the nuanced research trajectories across each domain, revealing the multifaceted and evolving nature of AI's application in the realm of medicine. The research topics have evolved as the volume of AI studies increases annually. Machine learning remains central to medical AI research, with deep learning expected to maintain its fundamental role. Empowered by predictive algorithms, pattern recognition, and imaging analysis capabilities, the future of AI research in medicine is anticipated to concentrate on medical diagnosis, robotic intervention, and disease management. Our topic modeling outcomes provide a clear insight into the focus of AI research in medicine over the past decades and lay the groundwork for predicting future directions. The domains that have attracted considerable research attention, primarily the learning domain, will continue to shape the trajectory of AI in medicine. Given the observed growing interest, the domain of AI ethics and philosophy also stands out as a prospective area of increased focus.

EMODnet Biology: Unlocking European marine biodiversity data

EMODnet Biology (hosted and coordinated by the Flanders Marine Institute (VLIZ)) is one of the seven themes within the European Marine Observation and Data network (EMODnet). The EMODnet Biology consortium aims to facilitate the accessibility and usage of marine biodiversity data. With the principle of "collect once, use many times" at its core, EMODnet Biology fosters collaboration across various sectors, including research, policy-making, industry, and individual citizens, to enhance knowledge sharing and inform decision-making. EMODnet Biology focuses on providing free and open access to comprehensive historical and recent data on the occurrence of marine species and their traits in all European regional seas. It achieves this through partnerships and collaboration with diverse international initiatives, such as the World Register of Marine Species (WoRMS), Marine Regions and the European node of the Ocean Biodiversity Information System (EurOBIS) among others. By promoting the usage of the Darwin Core Standard (Wieczorek et al. 2012), EMODnet Biology fosters data interoperability and ensures seamless integration with wider networks such as the Global Biodiversity Information Facility (GBIF) and the Ocean Biodiversity Information System (OBIS), serving as a significant data provider of the latter, as it is responsible for most of its data generated in Europe. Since its inception, EMODnet Biology has undertaken actions covering various areas, including providing access to marine biological data with spatio-temporal, taxonomic, environmental- and sampling-related information among others; developing an exhaustive data quality control tool based on the Darwin Core standard, the British Oceanographic Data Centre and Natural Environment Research Council Vocabulary Server (BODC NVS2) parameters and other controlled vocabularies used; creating and providing training courses to guide data providers; performing gap analyses to identify data quality and coverage shortcomings; creating and publishing marine biological distribution maps for various species or species groups; and interacting with international and European initiatives, projects and organizations. providing access to marine biological data with spatio-temporal, taxonomic, environmental- and sampling-related information among others; developing an exhaustive data quality control tool based on the Darwin Core standard, the British Oceanographic Data Centre and Natural Environment Research Council Vocabulary Server (BODC NVS2) parameters and other controlled vocabularies used; creating and providing training courses to guide data providers; performing gap analyses to identify data quality and coverage shortcomings; creating and publishing marine biological distribution maps for various species or species groups; and interacting with international and European initiatives, projects and organizations. Furthermore, EMODnet Biology contributes to the overall EMODnet initiative, which covers multidisciplinary data and products. Thanks to the use of standard protocols and tools across disciplines, EMODnet Biology products can contribute to multidisciplinary analysis of pressures and impacts on key marine species and habitats, and, lastly, support a better management and planning of the maritime space. In conclusion, EMODnet Biology plays a pivotal role in biodiversity informatics by providing users with a wealth of accessible and reusable marine biodiversity data and products. Its collaborative approach, extensive partnerships, and adherence to the FAIR (Findable, Accessible, Interoperable, Reusable) data principles (Wilkinson et al. 2016) as well as to the Infrastructure for Spatial Information in Europe (INSPIRE) metadata technical guidelines (European Commission Joint Research Centre 2013) and the Open Geospatial Consortium (OGC) standards make it a valuable resource for advancing knowledge, informing policies, and supporting sustainable management of marine ecosystems.

Monitoring the consumption footprint of countries to support policy‐making: An assessment of data availability in Germany

AbstractThe European Green Deal and the German Resource Efficiency Programme both aim at decoupling resource consumption and associated environmental burdens from economic growth. Monitoring the progress of such policies requires robust estimates of environmental pressures and impacts, both from a domestic and a footprint perspective. Building on the life cycle assessment‐based consumption footprint (CoF) indicator, developed by the European Commission Joint Research Centre, we assess the environmental impacts of Germany's consumption in the areas of food, mobility, housing, household goods, and appliances during the period 2010–2018. A comparison between European and national consumption statistics revealed some differences in terms of data composition, granularity, consumption intensities, and calculated environmental impacts. Using national data sources results in slightly lower environmental impacts (e.g., due to differences in the assessment scope of national statistics) and requires some data preparation to match the CoF indicator. Emerging consumption trends can be highlighted using national data. Both data sources converge on main trends: Germany transgresses the safe operating space for several impact categories, with consumption of food, household goods, and mobility being the main drivers. Domestic impacts have decreased over time at the expense of outsourcing environmental pressures and impacts to other countries. The CoF indicator could complement resource monitoring frameworks and might be further aligned to the national context using country‐level consumption statistics and life cycle inventory data.

#3356 COVID-19 PANDEMIC EFFECT ON HOSPITALIZATION OF HEMODIALYSIS PATIENTS

Abstract Background and Aims During the COVID-19 pandemic many facets of our lives were affected, including the healthcare systems worldwide. We aimed to assess the hospitalization rates of hemodialysis (HD) patients with confirmed COVID-19 infection (COVID-19+) versus other patients (i.e., patients with no documented COVID-19 infection in the European Clinical Database (EuCliD®)) in 2020 and their relation to HD patients from the pre-pandemic era in 2019. Method We included 63,216 HD patients treated in 2019-2020 in NephroCare centers from 23 countries from EuCliD® in the Europe, Middle East, and Africa (EMEA) region. Monthly hospitalization rates (hospital admission per 10,000 patients) were estimated separately for COVID-19+ and other HD patients in 2020, and HD patients treated in 2019, with COVID-19+ status as a time-varying variable. The number of COVID-19 cases and deaths were extracted from data released by the European Commission Joint Research Centre (ECJRC)[1]. The correlation between the monthly hospitalization rates and numbers of COVID-19 cases and deaths in general population (GP) were evaluated. Results Characteristics of the approximately 42,000-43,000 monthly treated HD patients were comparable between 2019 and 2020 (Table 1). The hospitalization rates were much higher for COVID-19+ HD patients throughout 2020 compared to other HD patients in 2020 and HD patients in 2019. The hospitalization rates were highest in the spring of 2020 for COVID-19+ HD patients. However, in 2020 other HD patients’ hospitalization rates were lower than the 2019 HD patients’ hospitalization rates. Interestingly the hospitalization rates of other HD patients in 2020 dipped even more below hospitalizations for HD patients in 2019 following the GP pandemic waves (Figure 1). Hospital admission for both COVID-19+ and other HD patients were inversely correlation with the pandemic conditions in GP (Figure 2; spearman correlation coefficients [ρ] with COVID-19 cases/deaths in GP, -0.77/-0.22 and -0.29/-0.64, respectively, for COVID-19+ and other HD patients). Conclusion Our study indicated the COVID-19 pandemic had an impact on hospitalization rates of both HD patients with a COVID-19 diagnosis and those patients without a documented COVID-19 infection. Potential reasons of decreased hospitalization rates in patients without a documented COVID-19 diagnosis could be healthcare resource constraints or regarding patient concerns about exposure to COVID-19 in hospital settings [2].

A Transformer-based method to reduce cloud shadow interference in automatic lake water surface extraction from Sentinel-2 imagery

Lake extraction from remote sensing images is an important approach to monitoring water resources. It provides guidance for aiding in maintaining natural ecological balance through the artificial adjustment of lake water resources. Cloud shadows are common interference factors in lake extraction from optical images. In this paper, we proposed a deep learning network based on a novel Transformer architecture to extract lakes from Sentinel-2 imagery. To determine how training datasets affect misclassifications of cloud shadows, we produced five training datasets that contained different proportions of cloud shadows to train the network; the highest proportion of cloud shadows among them was 4%. We found that, when the training dataset contained 4% cloud shadows, the obtained model could effectively reduce the misclassification of cloud shadows and improve the accuracy of lake extraction, and with this model, the evaluation results over the validation dataset achieved an overall accuracy (OA) of 0.9954 and a Kappa of 0.9568. In addition, the Transformer-based network was applied in the endorheic basin of Tibetan Plateau to further evaluate the generalization ability of the network over a large area. The Global Surface Water (GSW) dataset from the European Commission Joint Research Centre was used as a reference to validate our results in this area. Our experiment and results demonstrated the underlying potential of the Transformer-based network for improving image classification of optical imagery by reducing cloud shadow inferences.

HiLPD-GEE: high spatial resolution land productivity dynamics calculation tool using Landsat and MODIS data

ABSTRACT Land productivity is one of the sub-indicators for measuring SDG 15.3.1. Land Productivity Dynamics (LPD) is the most popular approach for reporting this indicator at the global scale. A major limitation of existing products of LPD is the coarse spatial resolution caused by remote sensing data input, which cannot meet the requirement of fine-scale land degradation assessment. To resolve this problem, this study developed a tool (HiLPD-GEE) to calculate 30 m LPD by fusing Landsat and MODIS data based on Google Earth Engine (GEE). The tool generates high-quality fused Normalized Difference Vegetation Index (NDVI) dataset for LPD calculation through gap filling and Savitzky–Golay filtering (GF-SG) and then uses the method recommended by the European Commission Joint Research Centre (JRC) to calculate LPD. The tool can calculate 30 m LPD in any spatial range within any time window after 2013, supporting global land degradation monitoring. To demonstrate the applicability of this tool, the LPD product was produced for African Great Green Wall (GGW) countries. The analysis proves that the 30 m LPD product generated by HiLPD-GEE could reflect the land productivity change effectively and reflect more spatial details. The results also provide an important insight for the GGW initiative.

Sustainable Development Goals (SDGs), Socio Energy and Socio Sustainability

Purpose: Year 2020 until 2021, there are increase of documents based on Sustainable Development Goals (SDGs), Socio Energy documents published in SCOPUS.COM. Subject area Documents, first Environmental Science, followed Energy, and Social Sciences. Documents by funding sponsor, Compare the document counts for up to 15 funding sponsors, dominant European Commission, Documents by affiliation, Compare the document counts for up to 15 affiliations, dominant University of KwaZulu-Natal. 
 Design/methodology/approach: Documents per year by source, dominant Sustainability Switzerland journal, Documents by country or territory, Compare the document counts for up to 15 countries/territories, first United Kingdom, followed Nigeria and United States. In ASIA first China, followed Japan and India. In ASEAN first Malaysia and Thailand, there are no Indonesia, weakness. There are increase of documents based on Sustainable Development Goals (SDGs), Socio Sustainability documents published in SCOPUS.COM.
 Findings: Documents by funding sponsor, Compare the document counts for up to 15 funding sponsor. First Chinese Academy of Sciences, followed National Natural Science Foundation of China, Centre for International Forestry Research, Deutsche Forschungsgemeinschaft, Directorate-General for the Environment, and Engineering and Physical. Documents per year by source, first Sustainability Switzerland journal, followed International Journal Of Sustainable Development And World Ecology, and International Organisations Research Journal. Compare the document counts for up to 10 sources. Documents by affiliation, Compare the document counts for up to 15 affiliations, first Chinese Academy of Sciences. University of Leeds, followed University of Southampton, European Commission Joint Research Centre, Universitas Indonesia, Institut Teknologi Bandung, and Universitas Sumatera Utara. Documents by country or territory, Compare the document counts for up to 15 countries/territories, first United Kingdom, second South Africa, third Italy. First in ASIA China, First in ASEAN Indonesia. Subject area Documents, first Social Sciences, and Environmental Science.
 Paper type: Research paper

Nuclear data activities at GELINA

Over the last decade, efforts were made to improve the performance of the experimental set-ups at the Geel Electron Linear Accelerator (GELINA) neutron time-of-flight facility of the European Commission Joint Research Centre (EC-JRC). These efforts, which result in an improved quality of neutroninduced cross section data for many reaction channels like elastic, inelastic, capture, fission, etc., relate to the accelerator, the measurement setups and the data reduction and analysis procedures. This paper presents a summary of the data produced in the last years at GELINA for nuclear energy applications. Most of the work has been performed as part of the EUFRAT open-access program.

The past and the future of the GAINS spectrometer @ GELINA

GAINS (Gamma Array for Inelastic Neutron Scattering), currently installed at the GELINA (Geel Electron Linear Accelerator) neutron source of the EC-JRC (European Commission-Joint Research Centre), is one of the best instruments available worldwide for measuring high resolution, low uncertainty neutron inelastic cross-section data relevant for both fundamental research and nuclear physics applications (like Generation IV facilities). It features 12 HPGe detectors coupled with a 235U fission chamber for neutron flux monitoring. The paper presents a short history of the GAINS development over the years and reviews some of the main experimental results together with plans for future experiments.

All-Weather and Superpixel Water Extraction Methods Based on Multisource Remote Sensing Data Fusion

The high spatial and temporal resolution of water body data offers valuable guidance for disaster monitoring and assessment. These data can be employed to quickly identify water bodies, especially small water bodies, and to accurately locate affected areas, which is significant for protecting people’s lives and property. However, the application of optical remote sensing is often limited by clouds and fog during actual floods. In this paper, water extraction methods of the multisource data fusion model (MDFM) and superpixel water extraction model (SWEM) are proposed, in which the MDFM fuses optical and synthetic aperture radar (SAR) images, and all-weather water extraction is achieved by using spectral information of optical images, texture information and the good penetration performance of SAR images. The SWEM further improves the accuracy of the water boundary with superpixel decomposition for extracted water boundaries using the fully constrained least squares (FCLS) method. The results show that the correlation coefficient (r) and area accuracy (Parea) of the MDFM and SWEM are improved by 2.22% and 9.20% (without clouds), respectively, and 3.61% and 18.99% (with clouds), respectively, compared with the MDFM, and 41.54% and 85.09% (without clouds), respectively, and 32.31% and 84.31% (with clouds), respectively, compared with the global surface water product of the European Commission Joint Research Centre’s Global Surface Water Explorer (JRC-GSWE). The MDFM and SWEM can extract water bodies with all weather and superpixel and improve the temporal and spatial resolution of water extraction, which has obvious advantages.

Cultural participation in Cultural and Creative Cities: Positive regional outcomes and potential congestion concerns

AbstractFollowing the hype that has been given to culture as trigger and enhancer of local economic performance in the last 20 years, the cultural character of cities is considered as a strategic strength and opportunity that can favour the economic system of the entire regions in which the cities are located. According to this perspective, this paper explores the role of cultural participation, intended as individuals' involvement in cultural activities, in affecting regional output. Exploiting a regional production function and data from the Cultural and Creative Cities Monitor (CCCM) provided by the European Commission Joint Research Centre (JRC), the effect of urban cultural participation on the regional economy is econometrically investigated. Potential congestion effects possibly related to (excessive) cultural tourism are also examined. Cultural participation in Cultural and Creative Cities emerges as a strategic resource for development, although some congestion mechanisms could reduce its positive effect. Related policy implications are thoroughly discussed.

Agriculture from Areas Facing Natural or Other Specific Constraints (ANCs) in Poland, Its Characteristics, Directions of Changes and Challenges in the Context of the European Green Deal

Increasing the European Union’s (EU) current efforts to protect the natural environment, especially in the context of protecting agricultural soils, is one of its basic challenges. The European Commission (EC) is now ready and willing to strongly strengthen protection of the natural environment. Activities are primarily supported by the European Green Deal (EGD) strategy from 2019 and its thematic strategies, as well as the currently revised Common Agricultural Policy (CAP) for 2023–2027. Further direct support for farms from Areas facing Natural or other Specific constraints (ANCs support), compensating for them at least some of the additional costs and lost income related to the difficulties for agricultural production, will be one of the most important actions of the future CAP in the context of the protection of agricultural soils. It should be added that, in recent years, all EU Member States, including Poland (in 2019), have carried out a mandatory new delimitation of ANCs according to the same rules established by the EC Joint Research Center (EC JRC) under the CAP 2014–2020. The aim of the study is to characterize the organizational situation of Polish agriculture, to indicate the direction and strength of the changes taking place in it, as well as the main determinants of its further functioning in areas (communes) with a particularly high saturation of current ANCs, as compared to other areas, with and without ANCs. It is also important to present the economic consequences and development opportunities for farms conducting agricultural production in these areas. The results of the study show the condition of Polish agriculture and changes taking place in this sector in areas with different saturation and specificity of ANCs related to challenges for environmental protection included in the EGD strategy and the current and future CAP. The research results will also provide an important basis for policymakers in undertaking effective agricultural support in ANCs and for the scientific community to develop agricultural research that can meet the most pressing challenges faced in the areas. It should be noted that the study emphasizes the significance of the new institutional economics, the postulates of which can provide solid support for the actions taken to better protect ANCs.

Monitoring and Predicting Channel Morphology of the Tongtian River, Headwater of the Yangtze River Using Landsat Images and Lightweight Neural Network

The Tongtian River is the source of the Yangtze River and is a national key ecological reserve in China. Monitoring and predicting the changes and mechanisms of the Tongtian River channel morphology are beneficial to protecting the “Asian Water Tower”. This study aims to quantitatively monitor and predict the accretion and erosion area of the Tongtian River channel morphology during the past 30 years (1990–2020). Firstly, the water bodies of the Tongtian River were extracted and the accretion and erosion areas were quantified using 1108 Landsat images based on the combined method of three water-body indices and a threshold, and the surface-water dataset provided by the European Commission Joint Research Centre. Secondly, an intelligent lightweight neural-network model was constructed to predict and analyze the accretion and erosion area of the Tongtian River. Results indicate that the Tongtian River experienced apparent accretion and erosion with a total area of 98.3 and 94.9 km2, respectively, during 1990–2020. The braided (meandering) reaches at the upper (lower) Tongtian River exhibit an overall trend of accretion (erosion). The Tongtian River channel morphology was determined by the synergistic effect of sediment-transport velocity and streamflow. The lightweight neural network well-reproduced the complex nonlinear processes in the river-channel morphology with a final prediction error of 0.0048 km2 for the training session and 4.6 km2 for the test session. Results in this study provide more effective, reasonable, and scientific decision-making aids for monitoring, protecting, understanding, and mining the evolution characteristics of rivers, especially the complex change processes of braided river channels in alpine regions and developing countries.

Global crop calendars of maize and wheat in the framework of the WorldCereal project

ABSTRACT Crop calendars provide valuable information on the timing of important stages of crop development such as the planting or Start of Season (SOS) and harvesting dates or End of Season (EOS). This information is critical for many crop monitoring applications such as crop-type mapping, crop condition monitoring, and crop yield estimation and forecasting. Spatially detailed information on the crop calendars provides an important asset in this respect, as it allows the algorithms to account for specific local circumstances while also maximizing their robustness and global applicability. Existing global crop calendar products, as produced by the Group on Earth Observations’ Global Agricultural Monitoring (GEOGLAM) Crop Monitor, the United States Department of Agriculture Foreign Agricultural Service (USDA-FAS), the Food and Agriculture Organization (FAO), and the European Commission Joint Research Center’s Anomaly hot Spots of Agricultural Production (ASAP), generally provide this information only at national or subnational level. In this work, we present gridded SOS and EOS maps for wheat and maize that represent the crop calendars’ spatial variability at 0.5° spatial resolution. These maps are generated in the framework of WorldCereal, which is a European Space Agency (ESA) funded project whose cropland and crop-type wheat and maize algorithms at global scale and at 10 m spatial resolution require this information. The proposed maps are built leveraging the above noted global products (Crop Monitor, USDA-FAS, FAO, ASAP) whose datasets are combined into a baseline map and sampled to train a Random Forest algorithm based on climatic and geographic data. Their evaluation against test data from the baseline maps set aside for validation purposes show a good performance with SOS (EOS) R 2 of 0.87 (0.92) and a root mean square error (RMSE) of 27 (26) days for wheat, showing the lowest errors (RMSE <15 days) in North America, Central Europe, South Africa, and Australia, all critical areas for global wheat production and trade. Meanwhile, the largest errors (RMSE between 40 and 60 days) occurred in regions of South America close to the Amazon Forest and in Africa close to the Congo Basin. In the case of maize, the SOS (EOS) evaluation shows an R 2 of 0.88 (0.79) and an RMSE of 24 (28) days for maize, with the best performing regions (RMSE < 15 days) located in the Northern Hemisphere, South Africa, and Australia, important areas for global maize production and trade. Meanwhile, the worst performing regions were in Brazil, Saudi Arabia and India. Additionally, the crop calendars were evaluated using a simple Land Surface Phenology (LSP) model based on Sentinel-2 and Landsat 8 Earth Observation data from Sentinel-2 and Landsat 8 over known wheat and maize fields. The results show a SOS (EOS) R 2 of 0.75 (0.88) and an RMSE of 25 (18) days for wheat and SOS (EOS) R 2 of 0.80 (0.88) and an RMSE of 35 (24) days for maize. Therefore, the presented calendars present an advancement over the existing crop calendar products in terms of capturing spatial coverage and variability and reporting their accuracy.

Domain-Specific Common Data Elements for Rare Disease Registration: Conceptual Approach of a European Joint Initiative Toward Semantic Interoperability in Rare Disease Research.

BackgroundWith hundreds of registries across Europe, rare diseases (RDs) suffer from fragmented knowledge, expertise, and research. A joint initiative of the European Commission Joint Research Center and its European Platform on Rare Disease Registration (EU RD Platform), the European Reference Networks (ERNs), and the European Joint Programme on Rare Diseases (EJP RD) was launched in 2020. The purpose was to extend the set of common data elements (CDEs) for RD registration by defining domain-specific CDEs (DCDEs).ObjectiveThis study aims to introduce and assess the feasibility of the concept of a joint initiative that unites the efforts of the European Platform on Rare Disease Registration Platform, ERNs, and European Joint Programme on Rare Diseases toward extending RD CDEs, aiming to improve the semantic interoperability of RD registries and enhance the quality of RD research.MethodsA joint conference was conducted in December 2020. All 24 ERNs were invited. Before the conference, a survey was communicated to all ERNs, proposing 18 medical domains and requesting them to identify highly relevant choices. After the conference, a 3-phase plan for defining and modeling DCDEs was drafted. Expected outcomes included harmonized lists of DCDEs.ResultsAll ERNs attended the conference. The survey results indicated that genetic, congenital, pediatric, and cancer were the most overlapping domains. Accordingly, the proposed list was reorganized into 10 domain groups and recommunicated to all ERNs, aiming at a smaller number of domains.ConclusionsThe approach described for defining DCDEs appears to be feasible. However, it remains dynamic and should be repeated regularly based on arising research needs.

Application of Spectral Index-Based Logistic Regression to Detect Inland Water in the South Caucasus

The Caucasus is a diverse region with many climate zones that range from subtropical lowlands to mountainous alpine areas. The region is marked by irrigated croplands fed by irrigation canals, heavily vegetated wetlands, lakes, and reservoirs. In this study, we demonstrate the development of an improved surface water map based on a global water dataset to get a better understanding of the spatial distribution of small water bodies. First, we used the global water product from the European Commission Joint Research Center (JRC) to generate training data points by stratified random sampling. Next, we applied the optimal probability cut-off logistic regression model to develop surface water datasets for the entire Caucasus region, covering 19 Landsat tiles from May to October 2019. Finally, we used 6745 manually classified points (3261 non-water, 3484 water) to validate both the newly developed water dataset and the JRC global surface water dataset using an estimated proportion of area error matrix to evaluate accuracy. Our approach produced surface water extent maps with higher accuracy (89.2%) and detected 392 km2 more water than the global product (86.7% accuracy). We demonstrate that the newly developed method enables surface water detection of small ponds and lakes, flooded agricultural fields, and narrow irrigation channels, which are particularly important for mosquito-borne diseases.

Putting Science into Standards workshop on standards for organ-on-chip.

The European Commission Joint Research Centre and the European Standardization Organizations CEN and CENELEC organized the "Putting Science into Standards" workshop, focusing on organ-on-chip technologies. The workshop, held online on 28-29 April, 2021, aimed at identifying needs and priorities for standards development and suggesting possible ways forward.

Development and testing of a Delayed Gamma-ray Spectroscopy instrument utilizing Cf-252 neutrons evaluated for nuclear safeguards applications

The Japan Atomic Energy Agency (JAEA) is collaborating with the European Commission Joint Research Centre (EC/JRC) to develop delayed gamma-ray spectroscopy (DGS). Toward this, the JAEA Integrated Support Center for Nuclear Nonproliferation and Nuclear Security (ISCN) designed the Delayed Gamma-ray Test Spectrometer (DGTS) that uses a 252Cf source (DGTS-C). This instrument is used to refine the design for a compact, induced-fission safeguards technique to supplement current nuclear material accountancy in reprocessing facilities. Experiments were performed at PERLA laboratory of the EC/JRC Ispra, Italy site to evaluate the neutron-irradiation and associated instrumentation capabilities to obtain delayed gamma-ray spectral signatures of assayed nuclear material. Neutron count-rate measurements were performed using a 3He detector and confirmed with MCNP6 models that showed a fluence of ∼87.1×10−4 n cm−2 nsrc−1. Short time-pattern interrogations of various 235U and 239Pu samples resulted in quantifiable delayed gamma-ray peaks from fission products. The peak intensities and spectral signatures resulted in fissile nuclide distinction and linear mass-correlation results. Consequently, we confirm the practicality of this JAEA/ISCN instrument design for DGS interrogation safeguards capabilities.

Evoqua Water Technologies Corp, USA

The Japan Atomic Energy Agency (JAEA) is collaborating with the European Commission Joint Research Centre (EC/JRC) to develop delayed gamma-ray spectroscopy (DGS). Toward this, the JAEA Integrated Support Center for Nuclear Nonproliferation and Nuclear Security (ISCN) designed the Delayed Gamma-ray Test Spectrometer (DGTS) that uses a 252Cf source (DGTS-C). This instrument is used to refine the design for a compact, induced-fission safeguards technique to supplement current nuclear material accountancy in reprocessing facilities. Experiments were performed at PERLA laboratory of the EC/JRC Ispra, Italy site to evaluate the neutron-irradiation and associated instrumentation capabilities to obtain delayed gamma-ray spectral signatures of assayed nuclear material. Neutron count-rate measurements were performed using a 3He detector and confirmed with MCNP6 models that showed a fluence of ∼87.1×10−4 n cm−2 nsrc−1. Short time-pattern interrogations of various 235U and 239Pu samples resulted in quantifiable delayed gamma-ray peaks from fission products. The peak intensities and spectral signatures resulted in fissile nuclide distinction and linear mass-correlation results. Consequently, we confirm the practicality of this JAEA/ISCN instrument design for DGS interrogation safeguards capabilities.

OpenACC. An open database of car-following experiments to study the properties of commercial ACC systems

Adaptive Cruise Control (ACC) systems are becoming increasingly available as a standard equipment in modern commercial vehicles. Their penetration rate in the fleet is constantly increasing, as well as their use, especially under freeway conditions. At the same time, limited information is openly available on how these systems actually operate and their differences depending on the vehicle manufacturer or model. This represents an important gap because as the number of ACC vehicles on the road increases, traffic dynamics on freeways may change accordingly, and new collective phenomena, which are only marginally known at present, could emerge. Yet, as ACC systems are introduced as comfort options and their operation is entirely under the responsibility of the driver, vehicle manufacturers do not have explicit requirements to fulfill nor they have to provide any evidence about their performances. As a result, any safety implication connected to their interactions with other road users escapes any monitoring and opportunity of improvement.This work presents a set of experimental car-following campaigns, providing an overview of the behavior of commercial ACC systems under different driving conditions. Furthermore, the suggestion of a unified data structure across the different tests facilitates comparison between the different campaigns, vehicles, systems and specifications. The complete data is published as an open-access database (OpenACC), available to the research community. As more test campaigns will be carried out, OpenACC will evolve accordingly.The activity is performed in the framework of the openData policy of the European Commission Joint Research Centre with the objective to engage the whole scientific community towards a better understanding of the properties of ACC vehicles in view of anticipating their possible impacts on traffic flow and prevent possible problems connected to their widespread introduction. In this light, OpenACC, over time, also aims at becoming a reference point to study if and how the parameters of such systems need to be regulated, how homogeneously they behave, how new ACC car-following models should be designed for traffic microsimulation purposes and what are the key differences between ACC systems and human drivers.