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.

Accurate detection of vascular occlusions during endovascular thrombectomy (EVT) is critical in acute ischemic stroke (AIS). Interpretation of digital subtraction angiography (DSA) sequences poses challenges due to anatomical complexity and time constraints. This work proposes OccluNet, a spatio-temporal deep learning model that integrates YOLOX, a single-stage object detector, with transformer-based temporal attention mechanisms to automate occlusion detection in DSA sequences. We compared OccluNet with a YOLOv11 baseline trained on either individual DSA frames or minimum intensity projections. Two spatio-temporal variants were explored for OccluNet: pure temporal attention and divided space-time attention. Evaluation on DSA images from the MR CLEAN Registry revealed the model’s capability to capture temporally consistent features, achieving precision and recall of 89.02% and 74.87%, respectively. OccluNet significantly outperformed the baseline models, and both attention variants attained similar performance. Source code is available here.

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.

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.

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.

Background
Aggregation of cohort data increases precision for studying neurodegenerative disease pathways, but efforts to combine data and expertise are often hampered by infrastructural, ethical and legal considerations. We aimed to unite various cohort studies in the Netherlands to enhance research infrastructure and facilitate research on dementia etiology and its public health implications.

Methods
The Netherlands Consortium of Dementia Cohorts (NCDC) includes participants with initially no established cognitive impairment from 9 Dutch cohorts: the Amsterdam Dementia Cohort (ADC), Doetinchem Cohort Study (DCS), European Medical Information Framework for Alzheimer’s Disease (EMIF-AD), Longitudinal Aging Study Amsterdam (LASA), the Leiden Longevity Study (LLS), The Maastricht Study, the Memolife substudy of the Lifelines cohort, Rotterdam Study and Second Manifestations of ARTerial disease-Magnetic Resonance (SMART-MR) study. The objectives of NCDC are to improve data infrastructure and access to cohorts related to aging and dementia, investigate the role of Alzheimer’s disease and vascular pathology in the development of dementia and estimate the public health impact of established dementia risk factors by assessing their relative contribution to the population burden of dementia.

Results
We increased the findability, accessibility, interoperability and reusability (FAIR) status of the cohorts through harmonization of data across cohorts, implementation of medical imaging repositories for scan management, implementation of the Personal Health Train infrastructure and provision of meta-data in existing cohort catalogues. We established the ethical and legal frameworks required for federated and pooled analyses and performed the first remote federated data analyses using the Personal Health Train infrastructure. To determine biomarkers of Alzheimer’s disease, endothelial dysfunction and inflammation, 2554 plasma samples were analyzed centrally. Federated, pooled, and coordinated meta-analyses have led to multiple publications in the context of NCDC.

Conclusion
The combination of population-based and clinical cohorts, the coordinated assessment of plasma markers in previously collected samples and implementation and use of the Personal Health Train infrastructure for federated analysis are both feasible and promising for future collaborative efforts.

Recent conversion efficiency breakthroughs in double-junction (tandem) perovskite/crystalline silicon solar cells demand advanced opto-thermo-electrical simulations, that are critical for translating laboratory results into realistic photovoltaic module and system performance. A holistic framework is here developed and presented, combining cell-level simulations, spectral analysis, PV module and PV system modelling. After validating the deployed physics models against measured cells and modules, hourly spectral irradiances for Delft, the Netherlands, and Catania, Italy, are generated and clustered into representative “blue-rich” and “red-rich” spectra. The effects of spectral variations on the current-matching and energy yield of tandem modules are quantified. Realistic module architectures are simulated, integrating dynamic temperature and spectrum data. Temperature coefficients are derived as a function of both irradiance and module temperature, significantly improving upon traditional indoor-derived values. Results show that standard indoor-derived coefficients under-/overestimate values in realistic conditions, highlighting the ultimate need for location-specific power matrixes. This study offers a robust pathway to predict tandem module energy yields across seasons and climates, supporting optimized design choices for industrial production and future PV installations.

Health implications of mobility during pregnancy entail a need to understand pregnant women's activity spaces. We present ActMAP, a framework for quantifying multiple aspects of activity spaces from distinct trips and stays derived from GPS data. We applied ActMAP to data from 238 pregnant women in Barcelona, Spain (2018–2020) and explored weekday, weekend and intraday associations between pregnancy trimester and activity spaces. Activities were more centred around the home later in pregnancy. However, the number of visited places and daily trips remained largely constant throughout pregnancy. By constructing activity spaces from individual trips and stays, ActMAP could provide a framework for GPS-based holistic assessments of mobility.

Purpose
The value of integrating clinical variables, radiomics, and tumor-derived cell-free DNA (cfDNA) for the prediction of survival and response to chemoradiation of patients with resectable esophageal adenocarcinoma is not yet known. Our aim was to investigate if radiomics and cfDNA metrics combined with clinical variables can improve personalized predictions.

Methods and Materials
A cohort of 111 patients with resectable esophageal adenocarcinoma from 2 centers treated with neoadjuvant chemoradiation therapy was used for exploratory retrospective analyses. Models combining the clinical variables of the SOURCE survival model with radiomic features and cfDNA were built using elastic net regression and internally validated using 5-fold cross-validation. Model performance for overall survival (OS) and time to progression (TTP) were evaluated with the C-index and the area under the curve for pathologic complete response.

Results
The best-performing baseline models for OS and TTP were based on the combination of SOURCE-cfDNA that reached a C-index of 0.55 and 0.59 compared with 0.44 to 0.45 with SOURCE alone. The addition of restaging positron emission tomography radiomics to SOURCE was the most promising addition for predicting OS (C-index: 0.65) and TTP (C-index: 0.60). Baseline risk stratification was achieved for OS and TTP by combining SOURCE with radiomics or cfDNA, log-rank P < .01. The best-performing combination model for the prediction of pathologic complete response reached an area under the curve of 0.61 compared with 0.47 with SOURCE variables alone.

Conclusions
The addition of radiomics and cfDNA can improve the performance of an established survival model. External validity needs to be further assessed in future studies together with the optimization of radiomic pipelines.

Rates of relative sea-level rise during the final stage of the last deglaciation, the early Holocene, are key to understanding future ice melt and sea-level change under a warming climate¹. Data about these rates are scarce², and this limits insight into the relative contributions of the North American and Antarctic ice sheets to global sea-level rise during the early Holocene. Here we present an early Holocene sea-level curve based on 88 sea-level data points (13.7–6.2 thousand years ago (ka)) from the North Sea (Doggerland^3,4). After removing the pattern of regional glacial isostatic adjustment caused by the melting of the Eurasian Ice Sheet, the residual sea-level signal highlights two phases of accelerated sea-level rise. Meltwater sourced from the North American and Antarctic ice sheets drove these two phases, peaking around 10.3 ka and 8.3 ka with rates between 8 mm yr⁻¹ and 9 mm yr⁻¹. Our results also show that global mean sea-level rise between 11 ka and 3 ka amounted to 37.7 m (2σ range, 29.3–42.2 m), reconciling the mismatch that existed between estimates of global mean sea-level rise based on ice-sheet reconstructions and previously limited early Holocene sea-level data. With its broad spatiotemporal coverage, the North Sea dataset provides critical constraints on the patterns and rates of the late-stage deglaciation of the North American and Antarctic ice sheets, improving our understanding of the Earth-system response to climate change.

For the practical diagnosis of inflammatory respiratory diseases, achieving sensitive and rapid NO sensing at the parts per billion level, all at room temperature, is of great significance. Herein, we developed a chemiresistor gas sensor with a sheet-on-sheet structure composed of an amorphous Cu-hemin MOF with reduced graphene oxide (rGO) nanosheets. The SEM images show that the Cu-hemin MOF/rGO composite exhibits a two-dimensional sheet-like structure. Due to its nanosized architecture, the Cu-hemin MOF exhibits a significant number of active sites for efficient NO detection. The Cu-hemin MOF/rGO composite material exhibited excellent NO sensing performance, including high sensitivity (Ra/Rg = 1.06, 50 ppb), reliable repeatability, high selectivity, and fast response/recovery (43 s/367 s, 10 ppm). The mechanism study revealed that the formation of the MOF altered the hemin dimer’s structure, resulting in the release of additional Fe(III)–N4 active sites and improved sensitivity. Moreover, the incorporation of rGO significantly boosted the conductivity of Cu-hemin MOFs. Using this two-dimensional sheet-like material, a mask-type sensor was also prepared and verified to be effective as a flexible and wearable sensing device for parts per billion level exhaled NO detection.

Older adults often experience a decline in functional abilities, affecting their independence and mobility at home. Wearable lower-limb exoskeletons (LLEs) have the potential to serve as both assistive devices to support mobility and training tools to enhance physical capabilities. However, active end-user involvement is crucial to ensure LLEs align with users’ needs and preferences. This study employed a co-design methodology to explore home-based LLE requirements from the perspectives of older adults with mobility impairments and physiotherapists. Four older adults with self-reported mobility limitations participated by creating personas to represent different user needs and experiences (i.e., PERCEPT methodology), alongside four experienced physiotherapists who contributed their professional insights. As assistive devices, LLEs were seen as valuable for promoting independence, supporting mobility, and facilitating social participation, with essential activities including shopping, toileting, and outdoor walking. Physiotherapists expressed enthusiasm for integrating LLEs into remote rehabilitation programs, particularly to improve strength, balance, coordination, and walking speed. Key design considerations included a lightweight, discreet device that is easy to don and doff and comfortable for extended wear. Physiotherapists highlighted the potential of digital monitoring to assess physical parameters and personalize therapy. Fatigue emerged as a significant challenge for older adults, reinforcing the need for assistive LLEs to alleviate exhaustion and enhance functional independence. A shortlist of LLE features was drafted and scored, covering activity and design applications. These findings provide valuable insights into the design and usability of home-based LLEs, offering a foundation for developing devices that improve acceptance, usability, and long-term impact on healthy ageing.

More than ten ergot alkaloids comprising both natural and semi-synthetic products are used to treat various diseases. The central C ring forms the core pharmacophore for ergot alkaloids, giving them structural similarity to neurotransmitters, thus enabling their modulation of neurotransmitter receptors. The haem catalase chanoclavine synthase (EasC) catalyses the construction of this ring through complex radical oxidative cyclization. Unlike canonical catalases, which catalyse H2O2 disproportionation, EasC and its homologues represent a broader class of catalases that catalyse O2-dependent radical reactions. We have elucidated the structure of EasC by cryo-electron microscopy, revealing a nicotinamide adenine dinucleotide phosphate (reduced) (NADPH)-binding pocket and a haem pocket common to all haem catalases, with a unique homodimeric architecture that is, to our knowledge, previously unobserved. The substrate prechanoclavine unprecedentedly binds in the NADPH-binding pocket, instead of the previously suspected haem-binding pocket, and two pockets were connected by a slender tunnel. Contrary to the established mechanisms, EasC uses superoxide rather than the more generally used transient haem iron–oxygen complexes (such as compounds I, II and III), to mediate substrate transformation through superoxide-mediated cooperative catalysis of the two distant pockets. We propose that this reactive oxygen species mechanism could be widespread in metalloenzyme-catalysed reactions.

Coherent precipitation-hardened alloys often struggle to achieve both ultrahigh strength and exceptional ductility due to their limited resistance to dislocation motion and vulnerability to glide plane softening. Here, we tackle these challenges by introducing multicomponent precipitates with much increased antiphase boundary (APB) energy. In a model Ni₃Al-type (L1₂) precipitation-hardened face-centered cubic (FCC) NiCo-based alloy, we incorporate multiple elements at the Al sublattice sites within the precipitates, reducing antisite defects and enhancing ordering degree. This process yields multicomponent precipitates with an ultrahigh APB energy (~308 ± 14 millijoules per square meter), which notably strengthens the alloy. Moreover, the exceptionally high APB energy transforms the deformation mechanism from dislocation shearing to stacking fault shearing, thereby avoiding glide plane softening. These result in a tensile yield strength of 1616 ± 9 megapascals, an ultimate tensile strength of 2155 ± 22 megapascals, and a uniform elongation of 10.1 ± 0.3% for the alloy.

The ability to identify individual protein molecules using Surface-Enhanced Raman Scattering (SERS) spectroscopy, without the need for labelling, is a significant advancement in biomedical diagnostics. However, the inherently small Raman scattering cross-section of most (bio) molecules necessitates significant signal amplification for successful detection, particularly at the single-molecule level. A novel approach is introduced for fabricating plasmonic nanopores suitable for sequential Raman readout, namely the recording of Raman spectra from portions of molecules, which are progressively flowing into plasmonic hot spots. The method is based on Capillary-Assisted Particle Assembly (CAPA)of gold nanoparticles (Au NPs), thus ensuring high stability and cost-effectiveness. By electrophoretically driving polypeptides through these nanopores, real-time Raman detection is achieved with a Single-Photon Avalanche Diode (SPAD) camera, attaining single-molecule detection at 1 nm concentration with 100 microsecond resolution. Statistical analysis of translocation times, photon scattering rates, and spectral data confirms a linear correlation between dwell time, molecular length, and Raman signal intensity. On average, a photon scattering of 6 photons/amino acid and a translocation time of 7 µs per amino acid is recorded. These results demonstrate the feasibility of sequential Raman readout, overcoming key limitations of SERS. This method represents a significant step toward label-free, high-resolution molecular identification, with future potential applications in protein identification.

Seawater desalination has become an essential part of the drinking water supply of many arid countries in the world – including Cyprus and the wider Mediterranean – Middle Eastern region. In this study, the microinvertebrate fauna near the outfall sites of 2 seawater desalination plants, Dhekelia and Larnaca, in Cyprus was assessed by microscopy-based classical taxonomy and eDNA metabarcoding. The results show impact of desalination brine discharge on marine benthic communities. The community structure and the impact of abiotic factors was more prominent in the sediments than the water, and the alpha diversity results from metabarcoding were consistent with the results from microscopy but differed taxonomically.

Allocating students to projects is a commonplace task in computing education. These decisions underpin student-supervisor allocation, the formation of tutee and capstone groups, and pair programming. These allocations play a critical role for individual learner outcomes and the success of collaborative interventions. For example, imbalance in either gender, ethnicity, or nationality can negatively impact learner outcomes. Despite the critical importance of these allocation choices, we see little consensus on how these are implemented. The allocation task can be challenging and time-consuming for instructors of even moderately-sized classes, and the fairness implications can be difficult to assess. Inadvertently, an instructor may allocate in a way that amplifies existing biases or disproportionately harms those from disadvantaged or protected groups. From students' perspectives, a lack of transparency on the allocation process may also lead to issues of trust. The Working Group will undertake a study of allocation practices by bringing together educational and ML literature to develop and evaluate the fairness of allocation methods, and develop educator guidelines to promote pedagogically grounded allocation practices.

Electrode–electrolyte interphases are critical determinants of the reversibility and longevity of lithium (Li)-metal batteries (LMBs). However, upon cycling, the inherently delicate interphases, formed from electrolyte decomposition, become vulnerable to chemomechanical degradation and corrosion, resulting in rapid capacity loss and thus short battery life. Here, we present a comprehensive analysis of the complex interplay between the thermodynamic and kinetic properties of interphases on Li-metal anodes, providing insights into interphase design to address these challenges. Direct measurements of ion-transport kinetics across various electrolyte chemistries reveal that interphases with high Li-ion mobility are essential for achieving dense Li deposits. Conversely, sluggish ion transport generates high-surface-area Li deposits that induce Li random stripping and the accumulation of isolated Li deposits. Surprisingly, interphases that support long cycle life do not necessarily require the formation of dense Li deposits but must avoid possible electrochemical/chemical reactions between the Li-metal deposits and electrolytes’ components. By that, in some specific electrolyte systems, isolated Li deposits can recover and electrically rejoin the active Li anodes’ mass. These findings challenge conventional understanding and establish new principles for designing durable LMBs, demonstrating that even with commercial carbonate-based electrolytes, LiNi0.8Co0.1Mn0.1O2||Cu cells can achieve high reversibility.