Hasil untuk "Law of Europe"

Maria Capua Vetere

Herbert Burkert

L. Montanarella, Panos Panagos

Abstract The new European Green Deal has the ambition to make the European Union the first climate-neutral continent by 2050. The European Commission presented an ambitious package of measures within the Biodiversity Strategy 2030, the Farm to Fork and the European Climate Law including actions to protect our soils. The Farm to Fork strategy addresses soil pollution with 50 % reduction in use of chemical pesticides by 2030 and aims 20 % reduction in fertilizer use plus a decrease of nutrient losses by at least 50%. The Biodiversity Strategy has the ambition to set a minimum of 30 % of the EU’s land area as protected areas, limit urban sprawl, reduce the pesticides risk, bring back at least 10 % of agricultural area under high-diversity landscape features, put forward the 25 % of the EU’s agricultural land as organically farmed, progress in the remediation of contaminated sites, reduce land degradation and plant more than three billion new trees. The maintenance of wetlands and the enhancement of soil organic carbon are also addressed in the European Climate Law. The new EU Soil Observatory will be collecting policy relevant data and developing indicators for the regular assessment and progress towards the ambitious targets of the Green Deal.

N. Charron, L. Dijkstra, V. Lapuente

Jon Cohen, K. Kupferschmidt

After 2 months of mostly waiting and seeing, many countries have suddenly implemented strict measures to slow the spread of coronavirus disease 2019. They had little choice, given the rapid rise in the number of cases and deaths in Europe and the United States. "This is war," President Emmanuel Macron told the French people. But how to fight that war is still under discussion. The hastily introduced measures vary widely between countries and even within countries. That reflects different phases of the epidemic, as well as differences in resources, cultures, governments, and laws. And there9s also confusion about what works best, and how to balance what is necessary with what is reasonable.

I. Law, N. Albert, J. Arbizu et al.

These joint practice guidelines, or procedure standards, were developed collaboratively by the European Association of Nuclear Medicine (EANM), the Society of Nuclear Medicine and Molecular Imaging (SNMMI), the European Association of Neurooncology (EANO), and the working group for Response Assessment in Neurooncology with PET (PET-RANO). Brain PET imaging is being increasingly used to supplement MRI in the clinical management of glioma. The aim of these standards/guidelines is to assist nuclear medicine practitioners in recommending, performing, interpreting and reporting the results of brain PET imaging in patients with glioma to achieve a high-quality imaging standard for PET using FDG and the radiolabelled amino acids MET, FET and FDOPA. This will help promote the appropriate use of PET imaging and contribute to evidence-based medicine that may improve the diagnostic impact of this technique in neurooncological practice. The present document replaces a former version of the guidelines published in 2006 (Vander Borght et al. Eur J Nucl Med Mol Imaging. 33:1374–80, 2006), and supplements a recent evidence-based recommendation by the PET-RANO working group and EANO on the clinical use of PET imaging in patients with glioma (Albert et al. Neuro Oncol. 18:1199–208, 2016). The information provided should be taken in the context of local conditions and regulations.

George Tsebelis

Thi Huyen Trang Nguyen, Niel Hens, Christel Faes

Abstract Background While the COVID-19 pandemic has been burdensome globally, it has fostered extensive data collection at various spatiotemporal resolutions. These data heightened researchers’ interest in investigating multiple facets of the pandemic. In Europe, key factors shaping disease transmission vary among countries, leading to a gap in understanding how the epidemic evolved and spread across countries as a whole. We endeavor to understand the similarities and differences in the spatiotemporal spread of the COVID-19 pandemic across 27 European Union (EU) countries and 3 European Economic Area (EEA) countries between March 2020 and December 2022. Method We utilized a multivariate endemic-epidemic model to conduct a space-time analysis across 30 countries, using weekly aggregated COVID-19 case counts from week 13-2020 to week 50-2022. Our analysis considered the discrepancies in population size, the primary course and three booster vaccine doses - taking into account waning immunity, the Stringency Index as a surrogate for non-pharmaceutical interventions adopted in each country, and the circulation of various viral variants. We employed a power law approximation for spatial interactions between countries. Results We found that within-country transmission was dominant across all countries over almost three years of observation. This work also underscored a basic transmission mechanism, whereby infections introduced by between-country transmission could be of great importance in subsequent local transmission. Furthermore, there were indications of the transition to endemicity since the beginning of 2022, particularly in light of the evolving variants of concern. Conclusion Our study highlighted the benefit of the endemic-epidemic framework to elucidate the COVID-19 disease spread over a large spatial and temporal scale, using a wide range of epidemiological information. Insights derived from this study are beneficial for those interested in seeking an overview of the emergency phase of the COVID-19 pandemic in the EU/EEA region.

Maciej Jońca

The content of most Polish textbooks on Roman law is based on schemes that date back to the 19th century. In the 20th century, Polish jurisprudence was greatly influenced by the doctrine of the German historical school. The manifestation of this remains both the systematics of modern textbooks and their content. However, times have changed. We are witnessing universal processes such as globalization, decolonization, digitization, decodification, etc. Presenting ancient material to students without a modern commentary makes no sense. A Roman law lecture should contribute to building an intercultural dialogue, not unreflectively duplicate content that is tedious at best and annoying at worst. If Roman law is to maintain its status as the most important propaedeutic subject that Polish students learn in the first year of law, it must begin to relate to current realities. Our own cultural identity should be taken into account, and the solutions created by the Romans must be shown to a greater extent in comparative terms.

Thijs L van der Plas, Stephen Law, Michael JO Pocock

The growing demand for scalable biodiversity monitoring methods has fuelled interest in remote sensing data, due to its widespread availability and extensive coverage. Traditionally, the application of remote sensing to biodiversity research has focused on mapping and monitoring habitats, but with increasing availability of large-scale citizen-science wildlife observation data, recent methods have started to explore predicting multi-species presence directly from satellite images. This paper presents a new data set for predicting butterfly species presence from satellite data in the United Kingdom. We experimentally optimise a Resnet-based model to predict multi-species presence from 4-band satellite images, and find that this model especially outperforms the mean rate baseline for locations with high species biodiversity. To improve performance, we develop a soft, supervised contrastive regularisation loss that is tailored to probabilistic labels (such as species-presence data), and demonstrate that this improves prediction accuracy. In summary, our new data set and contrastive regularisation method contribute to the open challenge of accurately predicting species biodiversity from remote sensing data, which is key for efficient biodiversity monitoring.

Kai Hong Law

A selection of new results from the CMS experiment is presented. These results focus on searches for dark-sector particles using Run 2 or Run 3 data. Dedicated data streams were utilised to explore the low-mass parameter space. Machine learning techniques were employed to discriminate between signal and background.

Matthew Aldridge

If you take a superposition of n IID copies of a point process and thin that by a factor of 1/n, then the resulting process tends to a Poisson point process as n tends to infinity. We give a simple proof of this result that highlights its similarity to the law of large numbers and to the law of thin numbers of Harremoës et al.

P. Craig, G. Búrca

Damian Szczepaniak

Xinglei Wang, Tao Cheng, Stephen Law et al.

Existing methods for learning urban space representations from Point-of-Interest (POI) data face several limitations, including issues with geographical delineation, inadequate spatial information modelling, underutilisation of POI semantic attributes, and computational inefficiencies. To address these issues, we propose CaLLiPer (Contrastive Language-Location Pre-training), a novel representation learning model that directly embeds continuous urban spaces into vector representations that can capture the spatial and semantic distribution of urban environment. This model leverages a multimodal contrastive learning objective, aligning location embeddings with textual POI descriptions, thereby bypassing the need for complex training corpus construction and negative sampling. We validate CaLLiPer's effectiveness by applying it to learning urban space representations in London, UK, where it demonstrates 5-15% improvement in predictive performance for land use classification and socioeconomic mapping tasks compared to state-of-the-art methods. Visualisations of the learned representations further illustrate our model's advantages in capturing spatial variations in urban semantics with high accuracy and fine resolution. Additionally, CaLLiPer achieves reduced training time, showcasing its efficiency and scalability. This work provides a promising pathway for scalable, semantically rich urban space representation learning that can support the development of geospatial foundation models. The implementation code is available at https://github.com/xlwang233/CaLLiPer.

Xiaowei Gao, James Haworth, Ilya Ilyankou et al.

Predicting traffic accidents is the key to sustainable city management, which requires effective address of the dynamic and complex spatiotemporal characteristics of cities. Current data-driven models often struggle with data sparsity and typically overlook the integration of diverse urban data sources and the high-order dependencies within them. Additionally, they frequently rely on predefined topologies or weights, limiting their adaptability in spatiotemporal predictions. To address these issues, we introduce the Spatiotemporal Multiview Adaptive HyperGraph Learning (SMA-Hyper) model, a dynamic deep learning framework designed for traffic accident prediction. Building on previous research, this innovative model incorporates dual adaptive spatiotemporal graph learning mechanisms that enable high-order cross-regional learning through hypergraphs and dynamic adaptation to evolving urban data. It also utilises contrastive learning to enhance global and local data representations in sparse datasets and employs an advance attention mechanism to fuse multiple views of accident data and urban functional features, thereby enriching the contextual understanding of risk factors. Extensive testing on the London traffic accident dataset demonstrates that the SMA-Hyper model significantly outperforms baseline models across various temporal horizons and multistep outputs, affirming the effectiveness of its multiview fusion and adaptive learning strategies. The interpretability of the results further underscores its potential to improve urban traffic management and safety by leveraging complex spatiotemporal urban data, offering a scalable framework adaptable to diverse urban environments.

Juha Karvanen, Santtu Tikka

The central challenges in missing data models concern the identifiability of two distributions: the target law and the full law. The target law refers to the joint distribution of the data variables, whereas the full law refers to the joint distribution of the data variables and their corresponding response indicators. However, the relationship between the identifiability of these two distributions and the feasibility of multiple imputation has not been clearly established. We show that imputations can be drawn from the correct conditional distributions for all possible missing data patterns if and only if the full law is identifiable. This result implies that standard multiple imputation methods -- which keep observed values unchanged and replace missing values with imputed values -- are invalid when the target law is identifiable but the full law is not. We demonstrate that alternative imputation strategies, in which certain observed values are also imputed, can enable the estimation of the target law in such cases.

Andrzej Grzybowski, Piotr Brona

Abstract Artificial intelligence (AI)-based medical devices are already commercially available in Europe. The regulations surrounding the introduction and use of medical AI devices in the European Union (EU) are different to those in the USA, and the specifics of European legislature in medical AI are not commonly known. European law classifies medical devices into four classes: I, IIa, IIb, and III, depending on the perceived risk level of the device. Medical devices are certified under independent nongovernment bodies, and some can even self-certify their compliance with EU standards. The European “open” approach is vastly different from the strict perspective of the FDA, as reflected by the number of available medical AI devices. The EU is currently in a transitory period between two regulations, further complicating the legislative landscape. The devices in question deal with extremely sensitive data, collecting, processing, and sending images and diagnoses over the internet. The EU approach puts a large burden of verifying the effectiveness and integrity of the AI device on the consumer, without giving consumers many tools to do that effectively. This highlights the need for effective legislation and oversight from governing bodies, as well as the need for understanding the legalities and limitations of AI devices for those implementing them in clinical practice.

Nikos Koumoutzis

Greece is a unique example of a country member of the Council of Europe that allows for the application of Sharia law by the Mufti on a select part of its citizenry: the members of the Muslim minority in Western Thrace (situated in NE Greece). However, to produce their effects, Mufti decisions need to undergo review and to be declared enforceable by the civil court. The aim of this article is to explore the relevant legal framework arranged in law 4964/2022 and presidential decree 52/2019, whereby the details of such a judicial review are set out. In particular, this article considers the prerequisite of the exequatur to religious adjudication, and then, it goes through all of the levels over which the said review extends, bringing progressively into focus the review of the scope of jurisdiction, the review of compatibility with the Constitution and the European Convention of Human Rights, and the review of some additional issues raised specifically by presidential decree 52/2019 over and above the points just mentioned. A final remark follows in connection with possible errors committed in religious adjudication—errors of law or fact—which remain beyond the reach of the review.