Tom Benhamou, James Cummings, Gabriel Goldberg
et al.
We introduce a new class of ultrafilters which generalizes the well-known class of simple $P$-point ultrafilters. We prove that for any well-founded $σ$-directed partial order $\mathbb{D}$ there is a mild forcing extension where there is an ultrafilter $U$ on $ω$ with a base $\mathcal{B}$ such that $(\mathcal{B},\supseteq^*)\cong \mathbb{D}$. On a measurable cardinal we prove a similar result: relative to a supercompact cardinal, it is consistent that $κ$ is supercompact, and for a $κ^+$-directed well-founded poset $\mathbb{D}$, there is a ${<}κ$-directed closed $κ^+$-cc forcing extension where there is a \emph{normal} ultrafilter $U$ on $κ$ with a base $\mathcal{B}$ such that $(\mathcal{B},\supseteq^*)\cong \mathbb{D}$. These are optimal results in the class of $P$-points and realize every potential structure of a $P$-point. We apply our constructions to obtain ultrafilters with controlled Tukey-type, in particular, an ultrafilter with non-convex Tukey and depth spectra is presented, answering questions from \cite{Benhamou_2024}. Our construction also provides new models where $\mathfrak{u}_κ<2^κ$, answering questions from \cite{Benhamou_Goldberg2025}.
Tanmay Khule, Stefan Marksteiner, Jose Alguindigue
et al.
In modern automotive development, security testing is critical for safeguarding systems against increasingly advanced threats. Attack trees are widely used to systematically represent potential attack vectors, but generating comprehensive test cases from these trees remains a labor-intensive, error-prone task that has seen limited automation in the context of testing vehicular systems. This paper introduces STAF (Security Test Automation Framework), a novel approach to automating security test case generation. Leveraging Large Language Models (LLMs) and a four-step self-corrective Retrieval-Augmented Generation (RAG) framework, STAF automates the generation of executable security test cases from attack trees, providing an end-to-end solution that encompasses the entire attack surface. We particularly show the elements and processes needed to provide an LLM to actually produce sensible and executable automotive security test suites, along with the integration with an automated testing framework. We further compare our tailored approach with general purpose (vanilla) LLMs and the performance of different LLMs (namely GPT-4.1 and DeepSeek) using our approach. We also demonstrate the method of our operation step-by-step in a concrete case study. Our results show significant improvements in efficiency, accuracy, scalability, and easy integration in any workflow, marking a substantial advancement in automating automotive security testing methodologies. Using TARAs as an input for verfication tests, we create synergies by connecting two vital elements of a secure automotive development process.
Nishit Patel, Hoang-Ha Nguyen, Jet van de Geest
et al.
Physical inactivity significantly contributes to obesity and other non-communicable diseases, yet efforts to increase population-wide physical activity levels have met with limited success. The built environment plays a pivotal role in encouraging active behaviors like walking. Walkability indices, which aggregate various environmental features, provide a valuable tool for promoting healthy, walkable environments. However, a standardized, high-resolution walkability index for Europe has been lacking. This study addresses that gap by developing a standardized, high-resolution walkability index for the entire European region. Seven core components were selected to define walkability: walkable street length, intersection density, green spaces, slope, public transport access, land use mix, and 15-minute walking isochrones. These were derived from harmonized, high-resolution datasets such as Sentinel-2, NASA's elevation models, OpenStreetMap, and CORINE Land Cover. A 100 m x 100 m hierarchical grid system and advanced geospatial methods, like network buffers and distance decay, were used at scale to efficiently model real-world density and proximity effects. The resulting index was weighted by population and analyzed at different spatial levels using visual mapping, spatial clustering, and correlation analysis. Findings revealed a distinct urban-to-rural gradient, with high walkability scores concentrated in compact urban centers rich in street connectivity and land use diversity. The index highlighted cities like Barcelona, Berlin, Munich, Paris, and Warsaw as walkability leaders. This standardized, high-resolution walkability index serves as a practical tool for researchers, planners, and policymakers aiming to support active living and public health across diverse European contexts.
The European Union aims to achieve climate-neutrality by 2050, with interim 2030 targets including 55% greenhouse gas emissions reduction compared to 1990 levels, 10 Mt p.a. of a domestic green H2 production, and 50 Mt p.a. of domestic CO2 injection capacity. To support these targets, Projects of Common and Mutual Interest (PCI-PMI) - large infrastructure projects for electricity, hydrogen and CO2 transport, and storage - have been identified by the European Commission. This study focuses on PCI-PMI projects related to hydrogen and carbon value chains, assessing their long-term system value and the impact of pipeline delays and shifting policy targets using the sector-coupled energy system model PyPSA-Eur. Our study shows that PCI-PMI projects enable a more cost-effective transition to a net-zero energy system compared to scenarios without any pipeline expansion. Hydrogen pipelines help distribute affordable green hydrogen from renewable-rich regions in the north and southwest to high-demand areas in central Europe, while CO2 pipelines link major industrial emitters with offshore storage sites. Although these projects are not essential in 2030, they begin to significantly reduce annual system costs by more than EUR 26 billion from 2040 onward. Delaying implementation beyond 2040 could increase system costs by up to EUR 24.2 billion per year, depending on the extent of additional infrastructure development. Moreover, our results show that PCI-PMI projects reduce the need for excess wind and solar capacity and lower reliance on individual CO2 removal technologies, such as Direct Air Capture, by 13 to 136 Mt annually, depending on the build-out scenario.
Generation planning approaches face challenges in managing the incompatible mathematical structures between stochastic production simulations for reliability assessment and optimization models for generation planning, which hinders the integration of reliability constraints. This study proposes an approach to embedding reliability verification constraints into generation expansion planning by leveraging a weighted oblique decision tree (WODT) technique. For each planning year, a generation mix dataset, labeled with reliability assessment simulations, is generated. An WODT model is trained using this dataset. Reliability-feasible regions are extracted via depth-first search technique and formulated as disjunctive constraints. These constraints are then transformed into mixed-integer linear form using a convex hull modeling technique and embedded into a unit commitment-integrated generation expansion planning model. The proposed approach is validated through a long-term generation planning case study for the Electric Reliability Council of Texas (ERCOT) region, demonstrating its effectiveness in achieving reliable and optimal planning solutions.
Electrical grid congestion has emerged as an immense challenge in Europe, making the forecasting of load and its associated metrics increasingly crucial. Among these metrics, peak load is fundamental. Non-time-resolved models of peak load have their advantages of being simple and compact, and among them Velander's formula (VF) is widely used in distribution network planning. However, several aspects of VF remain inadequately addressed, including year-ahead prediction, scaling of customers, aggregation, and, most importantly, the lack of probabilistic elements. The present paper proposes a quantile interpretation of VF that enables VF to learn truncated cumulative distribution functions of peak loads with multiple quantile regression under non-crossing constraints. The evaluations on non-residential customer data confirmed its ability to predict peak load year ahead, to fit customers with a wide range of electricity consumptions, and to model aggregations of customers. A noteworthy finding is that for a given electricity consumption, aggregations of customers have statistically larger peak loads than a single customer.
How are new technologies like generative AI quickly adopted and used by executive and managerial leaders to create value in organizations? A survey of INSEAD's global alumni base revealed several intriguing insights into perceptions and engagements with generative AI across a broad spectrum of demographics, industries, and geographies. Notably, there's a prevailing optimism about the role of generative AI in enhancing productivity and innovation, as evidenced by the 90% of respondents being excited about its time-saving and efficiency benefits. Analysis revealed different attitudes about adoption and use across demographic variables. Younger respondents are significantly more excited about generative AI and more likely to be using it at work and in personal life than older participants. Those in Europe have a somewhat more distant view of generative AI than those in North America in Asia, in that they see the gains more likely to be captured by organizations than individuals, and are less likely to be using it in professional and personal contexts than those in North America and Asia. This may also be related to the fact that those in Europe are more likely to be working in Financial Services and less likely to be working in Information Technology industries than those in North America and Asia. Despite this, those in Europe are more likely to see AGI happening faster than those in North America, although this may reflect less interaction with generative AI in personal and professional contexts. These findings collectively underscore the complex and multifaceted perceptions of generative AI's role in society, pointing to both its promising potential and the challenges it presents.
Giorgio Baiamonte, Carmelo Agnese, Carmelo Cammalleri
et al.
The modelling of the occurrence of rainfall dry and wet spells (ds and ws, respectively) can be jointly conveyed using the inter-arrival times (it). While the modelling of it has the advantage of requiring a single fitting for the description of all rainfall time characteristics (including wet and dry chains, an extension of the concept of spells), the assumption on the independence and identical distribution of the renewal times it implicitly imposes a memoryless property on the derived ws, which may not be true in some cases. In this study, two different methods for the modelling of rainfall time characteristics at station scale have been applied: i) a direct method (DM) that fits the discrete Lerch distribution to it records, and then derives ws and ds (as well as the corresponding chains) from the it distribution; and ii) an indirect method (IM) that fits the Lerch distribution to the ws and ds records separately, relaxing the assumptions of the renewal process. The results of this application over six stations in Europe, characterized by a wide range of rainfall regimes, highlight how the geometric distribution does not always reasonably reproduce the ws frequencies, even when it are modelled by the Lerch distribution well. Improved performances are obtained with the IM, thanks to the relaxation of the assumption on the independence and identical distribution of the renewal times. A further improvement on the fittings is obtained when the datasets are separated into two periods, suggesting that the inferences may benefit for accounting for the local seasonality.
El primer editor de la Fazienda de Ultramar, Moshé Lazar, fue el primero en establecer que el material bíblico de la obra provenía de un original hebreo. Para demostrar su hipótesis incluyó en su pionera edición de 1965 numerosas notas a pie estableciendo correspondencias entre determinados pasajes o palabras de la Fazienda y de la Biblia hebrea. Sin embargo, Lazar a menudo obvió aquellas partes donde la obra sigue la Vulgata, ocultando así las correspondencias entre ambas obras. En el presente artículo se demostrará que las contribuciones de la Vulgata a la Fazienda son mucho mayores de lo que se ha aceptado, sugiriendo que los pasajes del Viejo Testamento de la Fazienda provienen de dos fuentes distintas: la Biblia hebrea y la Vulgata latina.
Vitamin-V is a 2023-2025 Horizon Europe project that aims to develop a complete RISC-V open-source software stack for cloud services with comparable performance to the cloud-dominant x86 counterpart and a powerful virtual execution environment for software development, validation, verification, and test that considers the relevant RISC-V ISA extensions for cloud deployment.
Paulina Tedesco, Alex Lenkoski, Hannah C. Bloomfield
et al.
A transition to renewable energy is needed to mitigate climate change. In Europe, this transition has been led by wind energy, which is one of the fastest growing energy sources. However, energy demand and production are sensitive to meteorological conditions and atmospheric variability at multiple time scales. To accomplish the required balance between these two variables, critical conditions of high demand and low wind energy supply must be considered in the design of energy systems. We describe a methodology for modeling joint distributions of meteorological variables without making any assumptions about their marginal distributions. In this context, Gaussian copulas are used to model the correlated nature of cold and weak-wind events. The marginal distributions are modeled with logistic regressions defining two sets of binary variables as predictors: four large-scale weather regimes and the months of the extended winter season. By applying this framework to ERA5 data, we can compute the joint probabilities of co-occurrence of cold and weak-wind events on a high-resolution grid (0.25 deg). Our results show that a) weather regimes must be considered when modeling cold and weak-wind events, b) it is essential to account for the correlations between these events when modeling their joint distribution, c) we need to analyze each month separately, and d) the highest estimated number of days with compound events are associated with the negative phase of the North Atlantic Oscillation (3 days on average over Finland, Ireland, and Lithuania in January, and France and Luxembourg in February) and the Scandinavian Blocking pattern (3 days on average over Ireland in January and Denmark in February). This information could be relevant for application in sub-seasonal to seasonal forecasts of such events.
Maarten Bijleveld van Lexmond, J. Bonmatin, D. Goulson
et al.
In July 2009, a group of entomologists and ornithologists met at Notre Dame de Londres, a small village in the French department of Herault, as a result of an international enquiry amongst entomologists on the catastrophic decline of insects (and arthropods in general) all over Europe. They noted that a perceptible and gradual decline of insects, as part of the general impoverishment of the natural environment, had set in from the 1950s onwards. Amongstmany others, they recognized as root causes of this decline the intensification of agriculture with its accompanying loss of natural habitats and massive use of pesticides and herbicides, the manifold increase in roads and motorized traffic as well as a continent-wide nocturnal light pollution and nitrogen deposition. They equally agreed that a further degradation of the situation, a steeper decline in insect populations, had started in the decade 1990–2000. This first began inwestern Europe, followed by eastern and southern Europe, is nowadays apparent in the scarcity of insects splattered on windscreens of motorcars and squashed against their radiators and is best documented in the decline of butterflies and the global disorders amongst honey bees. They concluded that these phenomena reflected the now general collapse of Europe’s entomofauna. They also noted that the massive collapse of different species, genera and families of arthropods coincided with the severe decline of populations of different insectivorous bird species up to now considered as “common” such as swallows and starlings. On the basis of existing studies and numerous observations in the field as well as overwhelming circumstantial evidence, they came to the hypothesis that the new generation of pesticides, the persistent, systemic and neurotoxic neonicotinoids and fipronil, introduced in the early 1990s, are likely to be responsible at least in part for these declines. They, therefore, issued the Appeal of Notre Dame de Londres under the heading “No Silent Spring again” referring to Rachel Carson’s book “Silent Spring” then published almost half a century ago:
This article focuses on nationals from Ghana who have lost interest in pursuing migration dreams to Europe and North America after failed attempts to migrate. Many less experienced youths who attempt to migrate to Europe and North America face challenges such as strict immigration laws, high cost of financing migration plans, or illegal recruiters. Some risk their lives through dangerous routes to achieve their migration goals. The negative consequences recorded are numerous, including death en route to Europe and North America. Using life stories, this article lets failed migrants recount the frustration, wasted resources and years spent to fulfil their migration dreams. It discusses individual factors such as experiences that affect the decision not to pursue migration dreams despite the culture of migration in their communities. The article concludes that strict immigration policies in Europe and North America have restricted international migration among less experienced and less skilled youth in Ghana, leading to personal decisions not to migrate but adjust to the conditions at home, and later describing their stay as a preferred decision.
Widely used European land cover maps such as CORINE are produced at medium spatial resolutions (100 m) and rely on diverse data with complex workflows requiring significant institutional capacity. We present a high resolution (10 m) land cover map (ELC10) of Europe based on a satellite-driven machine learning workflow that is annually updatable. A Random Forest classification model was trained on 70K ground-truth points from the LUCAS (Land Use/Cover Area frame Survey) dataset. Within the Google Earth Engine cloud computing environment, the ELC10 map can be generated from approx. 700 TB of Sentinel imagery within approx. 4 days from a single research user account. The map achieved an overall accuracy of 90% across 8 land cover classes and could account for statistical unit land cover proportions within 3.9% (R2 = 0.83) of the actual value. These accuracies are higher than that of CORINE (100 m) and other 10-m land cover maps including S2GLC and FROM-GLC10. We found that atmospheric correction of Sentinel-2 and speckle filtering of Sentinel-1 imagery had minimal effect on enhancing classification accuracy (< 1%). However, combining optical and radar imagery increased accuracy by 3% compared to Sentinel-2 alone and by 10% compared to Sentinel-1 alone. The conversion of LUCAS points into homogenous polygons under the Copernicus module increased accuracy by <1%, revealing that Random Forests are robust against contaminated training data. Furthermore, the model requires very little training data to achieve moderate accuracies - the difference between 5K and 50K LUCAS points is only 3% (86 vs 89%). At 10-m resolution, the ELC10 map can distinguish detailed landscape features like hedgerows and gardens, and therefore holds potential for aerial statistics at the city borough level and monitoring property-level environmental interventions (e.g. tree planting).