In metals and alloys, solute segregation at grain boundaries typically undermines cohesion and ductility. Here, we overturn this paradigm by showing that solvent Fe atoms can preferentially enrich low-angle grain boundaries (LAGBs) in a ferrous alloy, dramatically enhancing ductility. Cold rolling and aging generate coherent nanoprecipitates, a high dislocation density, and abundant LAGBs in an austenitic matrix, yielding an ultrahigh tensile yield strength of ∼ 1.74 GPa. Moreover, the solvent Fe enrichment at LAGBs lowers local stacking fault energy and activates austenite-to-martensite transformation under load. This transformation-induced plasticity effect stabilizes plastic flow, enabling a uniform elongation of ∼ 26.2 % despite the alloy’s exceptional strength. Our findings challenge conventional views of segregation and offer a new design strategy for ultra-strong, highly ductile alloys.

This paper presents the eighth edition of the CheckThat! lab, part of the 2025 Conference and Labs of the Evaluation Forum (CLEF). As in previous editions of CheckThat!, the lab offers tasks from the core of the verification pipeline, including check-worthiness, identifying previously fact-checked claims, supporting evidence retrieval, and claim verification as well as auxiliary tasks addressing different facets of individual steps of the pipeline: Task 1 is on identification of subjectivity (a follow-up of the CheckThat! 2024 edition), which is related to the check-worthiness task, Task 2 is on claim normalization, Task 3 addresses fact-checking numerical claims, and Task 4 focuses on scientific web discourse processing. These challenging classification and retrieval problems are offered in different mono-, multi- and crosslingual settings covering more than 20 languages. This year, CheckThat! was one of the most popular labs at CLEF-2025 in terms of team registrations: 177 teams registered, almost half of them actually participating (a total of 83 teams) and 54 submitted system description papers.

Neural-symbolic (NeSy) AI has gained a lot of popularity by enhancing learning models with explicit reasoning capabilities. Both new systems and new benchmarks are constantly introduced and used to evaluate learning and reasoning skills. The large variety of systems and benchmarks, however, makes it difficult to establish a fair comparison among the various frameworks, let alone a unifying set of benchmarking criteria. This paper analyzes the state-of-the-art in benchmarking NeSy systems, studies its limitations, and proposes ways to overcome them. We categorize popular neural-symbolic frameworks into three groups: model-theoretic, proof-theoretic fuzzy, and proof-theoretic probabilistic systems. We show how these three categories have distinct strengths and weaknesses, and how this is reflected in the type of tasks and benchmarks to which they are applied.

Airbus, through its Tech Hub in the Netherlands and Airbus UpNext, is spearheading efforts to accelerate the maturation of critical cryogenic technologies. The ambition? Achieving hydrogen-powered flight in the next decades. As the Airbus Tech Hub aims to catalyse breakthroughs in aerospace technology, ICEFlight is one of its major projects that contributes to shaping the future of aviation. Project ICEFlight (Innovative Cryogenic Electric Flight) is supported by "Luchtvaart in Transitie", a Dutch public-private programme bringing together the aviation sector and local ecosystem. This presentation will detail how ICEFlight, in collaboration with a strong consortium including GKN Aerospace, Cryoworld B.V., Stirling Cryogenics B.V., Futura Composites B.V., the Royal NLR, Delft University of Technology, and University of Twente, will focus on the dual use of liquid hydrogen, as well as fuel as a source for cooling. Key aspects of ICEFlight include: – The development of a specialized cryogenic cooling and electrical distribution system. – Advancing the skills, competences, technologies, and products within the Netherlands for the next generation of aircraft. – The establishment of world-class testing facilities in the Netherlands, led by the Royal NLR, to ensure the reliability and validate the performance of cryogenic systems. Furthermore, the presentation will explain how ICEFlight is connected to Airbus UpNext's CRYOPROP demonstrator, highlighting the coordinated effort to mature superconducting and cryogenic technologies for future hydrogen-powered aircraft. ICEFlight is expected to significantly benefit the Dutch ecosystem by fostering innovation, developing local skills, and strengthening the local supply chain. These developments will all contribute to ultimately positioning the Netherlands as a key player when it comes to the future of aviation.

Minds-On is a novel, theoretically-driven pedagogical approach to support the teaching and learning of science concepts and reasoning skills inupper primary science lessons. Minds-On uses a hands-on – minds-on app- roach interleaving doing experiments with creating interactive concept diagrams. Lessons are fully orchestrated in the online application. Learners complete the lessons independently and receive guidance and feedback from the software. Four lessons have been created combining the following concepts and reasoning skills: seasons/cause-effect, sound/cause-effect, fixings & animals/classificationand bicycle/systems thinking. The approach has been developed using an itera-tive design method with evaluations in each phase either in the classroom or in laboratory conditions. Results from one classroom evaluation (N = 490) showed that both teachers and learners enjoyed working with the lessons, that learners are easily able to complete the lessons within the allocated time and that the app- roach enables learners to effectively apply several types of scientific reasoning and do so more autonomously than in traditional science lessons. Results from the knowledge questionnaires in a later study of the sound lesson (N = 147) showed a significant increase in score compared to before the lesson. In this demonstration
paper, we present the Minds-On approach and associated application, discussing the design process, the theoretical principles on which our approach is based and the preliminary results from recent classroom evaluations.

This study investigates methodological variability across various expert laboratories worldwide, with regards to characterizing the mechanical properties of biological tissues. Two testing rounds were conducted on the specific use case of uniaxial tensile testing of porcine aorta. In the first round, 24 labs were invited to apply their established methods to assess inter-laboratory variability. This revealed significant methodological diversity and associated variability in the stress–stretch results, underscoring the necessity for a standardized approach. In the second round, a consensus protocol was collaboratively developed and adopted by 19 labs in an attempt to minimize variability. This involved standardized sample preparation and uniformity in testing protocol, including the use of a common cutting and thickness measurement tool. Despite protocol harmonization, significant variability persisted across labs, which could not be solely attributed to inherent biological differences in tissue samples. These results illustrate the challenges in unifying testing methods across different research settings, underlining the necessity for further refinement of testing practices. Enhancing consistency in biomechanical experiments is pivotal when comparing results across studies, as well as when using the resulting material properties for in silico simulations in medical research.

Deep learning has proven to be one of the most effective methods in analyzing biological images to extract parameters fundamental for studying physiological functions and pathological conditions. In particular, when coupled with time-lapse microscopy (TLM), deep learning proves particularly effective in studying behaviors involving temporal dynamics. However, TLM videos are often affected by experimental noise and setup limitations, which can lead to inaccurate and poorly reproducible results. Taking advantage of the variational and generative capabilities of Variational Autoencoders (VAEs), we propose VAE-MOTION, a deep learning-based model for the analysis of cardiac contractile dynamics. By incorporating a temporal encoder into its architecture, our model allows the restoration of video quality by removing noise or increasing resolution, while simultaneously extracting accurate contraction-related signals from the latent space. The generation of synthetic videos allowed extensive training of VAE-MOTION, which subsequently validated on real videos from two different cardiac tissue models: 2D monolayers and 3D microtissues. VAE-MOTION was compared to two gold-standard methods in extracting contraction parameters relevant to drug efficacy or toxicity studies, demonstrating its potential for analyzing temporal dynamics in a given phenomenon or process.

Objective
This study aims to develop hip morphology-based radiographic hip osteoarthritis (RHOA) risk prediction models and investigates the added predictive value of hip morphology measurements and the generalizability to different populations.

Methods
We combined data from nine prospective cohort studies participating in the Worldwide Collaboration on OsteoArthritis prediCtion for the Hip (World COACH) consortium. RHOA grades were harmonized, and incident RHOA was defined as hips without definite RHOA at baseline that developed definite RHOA within four to eight years. Baseline hip morphology was quantified with automatically and uniformly determined lateral center edge angle and alpha angle measurements on anteroposterior radiographs. Discriminative performance of generalized linear mixed model (GLMM) definitions with and without hip morphology measurements was determined with stratified cross-validation. With leave-one-cohort-out cross-validation, the generalizability to unseen populations of hip morphology–based GLMMs and random forest (RF) models was evaluated.

Results
From the included 35,984 hips without definite RHOA at baseline, 4.7% developed incident RHOA within four to eight years. The GLMM with cohort-specific intercept, considering baseline demographics, RHOA grade, and hip morphology measurements, showed a mean area under the receiver operating characteristic curve (AUC) of 0.80 (±0.01) in stratified cross-validation. Using a marginal intercept decreased performance by 0.1 in AUC. Similar results were found for a GLMM without hip morphology measurements. Leave-one-cohort-out cross-validation showed comparable discrimination (AUC between 0.56–0.88) and calibration performance for hip morphology-based GLMMs and RF models.

Conclusion
In hips free of definite RHOA, our AUCs for the incident RHOA models showed good predictive performance in similar populations. However, the added predictive value of the morphology measurements was small, and model performance was heterogeneous in leave-one-cohort-out cross-validation.

Future space-based far infrared astronomical observations require background limited detector sensitivities and scalable focal plane array solutions to realize their vast potential in observation speed. In this work, a focal plane array of lens absorber coupled kinetic inductance detectors (KIDs) is proposed to fill this role. The figures of merit and design guidelines for the proposed detector concept are derived by employing a previously developed electromagnetic spectral modeling technique. Two designs operating at central frequencies of 6.98 and 12 THz are studied. A prototype array of the former is fabricated, and its performance is experimentally determined and validated. Specifically, the optical coupling of the detectors to incoherent distributed sources (i.e., normalized throughput) is quantified experimentally with good agreement with the estimations provided by the model. The coupling of the lens absorber prototypes to an incident plane wave, i.e., aperture efficiency, is also indirectly validated experimentally matching the expected value of 54% averaged over two linear polarizations. The noise equivalent power of the KIDs is also measured with limiting value of 8 × 10-20W\√Hz at the bath and radiator temperatures of 130 mK and 2.7 K, respectively, under negligible optical loading.

Recent workshops brought together several developers, educators and users of software packages extending popular languages for spatial data handling, with a primary focus on R, Python and Julia. Common challenges discussed included handling of spatial or spatio-temporal support, geodetic coordinates, in-memory vector data formats, data cubes, inter-package dependencies, packaging upstream libraries, differences in habits or conventions between the GIS and physical modeling communities, and statistical models. The following set of recommendations have been formulated:

(i) considering software problems across data science language silos helps to understand and standardise analysis approaches, also outside the domain of formal standardisation bodies;
(ii) whether attribute variables have block or point support, and whether they are spatially intensive or extensive has consequences for permitted operations, and hence for software implementing those;
(iii) handling geometries on the sphere rather than on the flat plane requires modifications to the logic of simple features,
(iv) managing communities and fostering diversity is a necessary, on-going effort, and
(v) tools for cross-language development need more attention and support.

People around the world seek climate risk information to guide their decisions. For instance, projections about future flood risk inform where households choose to live, how lenders manage credit risks, and which communities receive federal funding. Yet data limitations and fundamental validation challenges raise important concerns about the reliability of such projections. The principles of transparency and reusability help address these concerns by enabling scrutiny of assumptions and methods, development of foundational data and tools, and consistent application of evaluation standards. While there is ongoing debate about how much transparency commercial climate risk services should provide, many expect noncommercial actors to lead the way on operationalizing transparency and reusability to fulfill their knowledge-building role in the climate risk ecosystem. However, despite prominent success stories, we find a substantial gap between principles and practice: Only four percent of the most-cited peer-reviewed climate risk studies in recent years fully share their data and code although this is a widely accepted minimum standard for transparency. We highlight low-cost measures that noncommercial researchers can take now to improve transparency and reusability. We also emphasize that transformative progress requires substantial investment, cross-sector collaboration, and careful consideration of tradeoffs, data rights, and multiple perspectives on equity. We hope this perspective accelerates both immediate actions and longer-term conversations to improve the ability of science to effectively support timely, evidence-based, and sound climate risk management.

Young drivers represent a high-risk group worldwide, with their overrepresentation in road trauma placing substantial pressure on health and economic systems. Their crashes are often linked to risky driving behaviors, accentuating the need for reliable instruments to assess these patterns. The Behavior of Young Novice Drivers Scale (BYNDS) was developed to comprehensively assess multiple dimensions of risky driving behavior in drivers aged 17–29 years; however, it has not yet undergone cross-cultural validation. Aim This study aimed to conduct a comprehensive cross-cultural validation of the BYNDS and examine differences in risky driving behaviors among young drivers from Low- and Middle-Income (LMIC) and High-Income (HIC) countries. Method Data were collected from a cross-sectional sample of n = 3,989 young drivers aged M = 22.25 years, of whom 52 % were male and 48 % female. Participants completed the BYNDS, a 44-item behavioral questionnaire administered across 12 countries (48.6 % LMICs; 51.4 % HICs) spanning five continents. Results The findings indicate that the BYNDS supports a five-factor structure with good fit indices, strong factor loadings, and acceptable reliability, and invariance between countries of different income levels. Furthermore, the validated BYNDS-42 (comprising 42 items distributed across five factors) also showed the ability to distinguish between drivers with and without self-reported crashes or traffic fines. Conclusion This study provides robust evidence supporting the cross-cultural validity and reliability of the BYNDS, reinforcing its value as a tool for assessing young driver behavior. These findings offer empirically grounded insights that can inform behavioral interventions aimed at improving young drivers’ road safety.

Characterisation of the compaction response of reinforcement fabrics is an important component in the design of composite manufacturing processes. To standardise a best practice method, 22 international organisations participated in an exercise to assess the viability and reproducibility of the method discussed in this work. All participants were supplied with the same multiaxial E-glass fibre non-crimp fabric and instructed to measure the compaction stress as a function of the specimen thickness following a set of guidelines. The scatter in results between participants was quantified in terms of the coefficient of variation (CV). The CV of the maximum compaction stress determined at a target specimen thickness of 3 mm (for 10 fabric layers) was 42 % for dry specimens and 46 % for wet specimens, however this was influenced by scatter in the thickness values, which deviated from the target. The CV of the specimen thickness at a compaction stress of 105 Pa was 4 %. In addition, a power law model and a model based on bending of beams were fitted to the compaction curves. Both generally produced fits with high values of the coefficient of determination. The observed level of scatter is thought to be caused by issues with the implementation of the procedures and by variability in the specimen properties, as well as the very steep variation of the force/thickness curve at the required target. The guidelines used here aim to minimise inaccuracies in the test method and will be proposed as a test protocol for standardisation.

Addressing non-idealities in Resistive Random Access Memories (RRAMs) is crucial for their successful commercialization. For example, the inherent resistance drift that occurs during consecutive read operations can induce Read Disturb Faults (RDF), leading to functional errors. This paper analyzes and characterizes the resistance drift and the RDF based on data measurements and presents a physics-based RRAM compact model that incorporates these non-idealities. Additionally, an in-field mitigation scheme is proposed, leveraging bidirectional read operations to balance the resistance. The scheme is implemented and validated through circuit simulations, both for RRAM used as memory and for RRAM-based computation-in-memory microarchitectures for deep neural networks. The results demonstrate that RRAM without any mitigation scheme can start failing after 8,000 consecutive reads, while our mitigation scheme ensures that the memory remains functional even after 10⁶ consecutive reads. Furthermore, the results indicate that using the MNIST dataset as a case study, the accuracy can drop significantly from 86% to as low as 12.5% without any mitigation scheme. In contrast, the proposed mitigation scheme improves this accuracy up to 84.2%.

In this paper, we present the Amsterdam Modeling Suite (AMS), a comprehensive software platform designed to support advanced molecular and materials simulations across a wide range of chemical and physical systems. AMS integrates cutting-edge quantum chemical methods, including Density Functional Theory (DFT) and time-dependent DFT, with molecular mechanics, fluid thermodynamics, machine learning techniques, and more, to enable multi-scale modeling of complex chemical systems. Its design philosophy allows for seamless coupling between components, facilitating simulations that range from small molecules to complex biomolecular and solid-state systems, making it a versatile tool for tackling interdisciplinary challenges, both in industry and in academia. The suite also emphasizes user accessibility, with an intuitive graphical interface, extensive scripting capabilities, and compatibility with high-performance computing environments.

The goal of the NEUROKIT2E project is to create an open-source Deep Learning framework for edge and embedded AI built around an established European value chain. This framework, called AIDGE, supports a wide range of application areas that operate independently and serve a global user community. It provides easy and fast full-stack solutions from Neural Network design and optimization to AI application development all the way down to hardware implementations while enabling code generation for application-specific targets. This platform provides flexibility for academic users in the AI domain to explore and innovate while allowing them the possibility to prototype systems, ensuring their work aligns well with industrial needs. This paper presents the results and achievements of the first part of this three-year project, along with its roadmap and expected outcomes.

Recent discoveries have shown the presence of ribonucleic acid (RNA) on the cell surface, defying the view that RNA only functions intracellularly. However, how RNA is presented on the cell surface and what its biological relevance is are poorly understood. We established Toll-like receptor 7 (TLR7) as a cell-surface RNA (csRNA) probe. Employing it in a genome-wide knockout screening, we identified heparan sulfate (HS) as a crucial factor for csRNA presentation. Cell-surface proximity labeling revealed that HS-associated csRNAs (hepRNAs) are in the vicinity of RNA-binding proteins (RBPs). These observations led us to a model wherein cell-surface HS, RNA, and RBP form ternary complexes, validated by our spatio-selective RNA-protein crosslinking technology in a TLR7-orthogonal manner. We further revealed the identities of hepRNA and found that they can recruit the immune receptor killer cell immunoglobulin-like receptor 2DL5 (KIR2DL5), potentially enhancing receptor-ligand interactions. Employing human cell lines, our findings lay the groundwork for investigating how cell-surface ribonucleoproteins contribute to immune modulation.

Climate change increases the frequency and intensity of heatwaves, amplifying heat-related health risks in cities worldwide. Inequities in heat vulnerability arise from disparities in heat exposure, built and natural environments and population attributes that impact heat sensitivity, and socio-economic determinants of adaptive capability. A lack of internationally consistent and accessible heat vulnerability metrics creates barriers to assessing inequities and benchmarking urban heat vulnerability between cities worldwide. To address this need, we developed the Global Urban Heat Vulnerability Index (GUHVI), applicable to cities worldwide, using open data to identify spatial inequities in heat vulnerability at the neighbourhood scale. Built from an Australia-specific heat vulnerability index, the evidence-informed framework developed for the GUHVI evaluates heat exposure, heat sensitivity and adaptive capability to holistically assess heat vulnerability. Quantitative validation for eight Australian cities demonstrated strengths of the GUHVI in spatial resolution and assessment coverage of the grid-based framework. Qualitative validation for nine diverse cities internationally was performed in collaboration with local subject matter experts with knowledge of each city context. The GUHVI addresses critical gaps in existing methods by enabling systematic and comparable measurement of heat vulnerability in diverse cities internationally. Available through our customizable open-source global indicator software, the GUHVI provides evidence on modifiable risk factors of urban heat vulnerability, to inform targeted adaptation strategies that promote climate resilience and reduce health impacts from heat.

Two-dimensional (2D) semiconductors are emerging as a versatile platform for nanophotonics, offering unprecedented tunability in optical properties through exciton resonance engineering, van der Waals heterostructuring, and external field control. These materials enable active optical modulation, single-photon emission, quantum photonics, and valleytronic functionalities, paving the way for next-generation optoelectronic and quantum photonic devices. However, key challenges remain in achieving large-area integration, maintaining excitonic coherence, and optimizing amplitude-phase modulation for efficient light manipulation. Advances in fabrication, strain engineering, and computational modeling will be crucial to overcoming these limitations. This Perspective highlights recent progress in 2D semiconductor-based nanophotonics, emphasizing opportunities for scalable integration into photonics.

Synthetic cells (SynCells) are artificial constructs designed to mimic cellular functions, offering insights into fundamental biology, as well as promising impact in the fields of medicine, biotechnology, and bioengineering. Achieving a functional SynCell from the bottom up, i.e. by assembling it from molecular components, requires a global collaboration to overcome the many challenges of engineering and assembling life-like modules while addressing biosafety, equity, and ethical concerns in order to guide responsible innovation. Here, we highlight major scientific hurdles, such as the integration of functional modules by ensuring compatibility across diverse synthetic subsystems, and we propose strategies to advance the field.