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Strategic white paper on AI infrastructure for particle, nuclear, and astroparticle physics: insights from JENA and EuCAIF
Get PDFArtificial intelligence (AI) is transforming scientific research, with deep learning methods playing a central role in data analysis, simulations, and signal detection across particle, nuclear, and astroparticle physics. Within the JENA (Joint ECFA, NuPECC, APPEC) communities and as part of the European Coalition for AI in Fundamental Physics initiative, AI integration is advancing steadily. However, broader adoption remains constrained by challenges such as limited computational resources, a lack of expertise, and difficulties in transitioning from research and development to production. This white paper provides a strategic roadmap, informed by a community survey, to address these barriers. It outlines critical infrastructure requirements, prioritises training initiatives, and proposes funding strategies to scale AI capabilities across fundamental physics over the next five years
How STI-centric myopia marginalises DUI innovation in Azerbaijan
Get PDFInnovation policies in post-Soviet economies often prioritise technology-oriented R&D infrastructures while neglecting the production capabilities that sustain incremental learning and adaptation. Yet the mechanisms leading to this outcome are not theorised in the literature. This paper explains how such a selective focus becomes institutionalised through STI-centric policy myopia, a configuration that narrows policy attention to visible, codifiable, and globally recognisable indicators. Using Azerbaijan as an illustrative case, the study draws on policy documents, media and social-media narratives, and a structured survey of senior officials to trace three reinforcing processes: circumscribed policy discourse, which limits the vocabulary through which innovation can be imagined; indicator-driven tunnel vision, which prioritises metrics and global rankings over capability-deepening reforms; and accretive institutional layering, which adds STI-compatible instruments on top of unreformed organisational cores, gradually locking the system into symbolic, high-visibility pathways. Together, these processes stabilise a reform trajectory in which STI-compatible models appear self-evident, while doing–using–interacting (DUI) modes, despite their relevance in sectors such as agriculture, logistics, and processing, remain peripheral to policy design. Recognising this mechanism clarifies why capability-deepening trajectories fail to materialise even when DUI practices exist in the economy, and it sharpens analytical expectations about reform patterns in developing and post-Soviet systems. The paper therefore offers a conceptual lens for understanding the reproduction of STI-oriented policy agendas and for analysing when and how alternative innovation imaginaries might gain institutional traction
Strategies for managing toxicities with high‐dose methotrexate in haematological malignancies: lessons from clinical cases
Get PDFHigh-dose methotrexate (HDMTX) is a key component of induction therapy for a range of cancers, but can lead to serious adverse effects, including acute kidney injury (AKI). We report two cases of HDMTX use in haematological malignancies. Patient 1 was an 8-year-old girl with high-grade NHL. She received 3 courses of HDMTX (3 g/m²) without issue. For her 4th course, she developed AKI with creatinine peaking at 240 μmol/L after HDMTX administration. She required intensified hydration and antihypertensive therapy for fluid overload, received glucarpidase for delayed MTX clearance, required parenteral nutrition for mucositis and ultimately made a full recovery. Patient 2 was a 76-year-old man with primary central nervous system lymphoma. He underwent HDMTX treatment per the MARTA protocol with dose reductions due to renal impairment. He experienced delayed MTX clearance leading to fluid overload, acute liver injury, required prolonged diuretic and antibiotic therapy, and was hospitalised for 3 weeks before recovery. These cases emphasise the need for preemptive supportive care, including prehydration and folinic acid (leucovorin), careful monitoring and early intervention with glucarpidase in the event of toxicities
Reflective or transmissive, is there a superior learning approach? Lessons from mentoring relationships
Get PDFThis study challenges the prevailing assumption that reflective learning approaches are inherently superior to transmissive ones. Specifically, we focus on the professional learning of mentor teachers through an in-depth investigation of mentoring dynamics in the context of Israeli teacher preparation fieldwork and co-teaching. Based on interviews and observations from a ten-case study, we investigate how mentors and mentees shape the nature of their professional learning together. Our analysis examines the dynamics that enabled mentee student teachers to influence their mentors’ professional learning. It demonstrates that mentors benefited from two distinct learning approaches—reflection and reciprocity versus expertise and demonstration—shaped by differing types of actions taken by the STs and their underpinning logics. These findings complicate the discussion regarding reflective and transmissive learning, suggesting that professional learning is not confined to one epistemological ideal but emerges from the alignment of personal dispositions and relational dynamics in practice
First artificial intelligence-based non-invasive framework for data-driven kink defect detection in HTS coils for power applications
Get PDFHigh-temperature superconductor (HTS) coils and windings are fundamental structural elements underpinning a wide range of superconducting technologies and are the crucial components for next-generation superconducting devices across electric transportation and energy systems fields, including fusion reactors for sustainable energy generation, aviation propulsion systems aiming for substantial efficiency gains, and renewable electric power generators integrating superconducting technologies. Nevertheless, the manufacturing processes of these HTS windings commonly introduce localized mechanical stresses that lead to subtle initial bending defects, so-called "kinks", potentially evolving into a risk of catastrophic failure, and significantly compromising operational reliability. Technically, conventional kink detection techniques, such as optical microscopy, magnetic imaging, and metallurgical techniques, performed deployment challenges of invasive inspections, low inspection efficiency, and insufficient sensitivity to subsurface anomalies when handling complex coil geometries. To address these challenges, this study develops the first non-invasive electrical detection method that autonomously identifies incipient kink defects, leveraging an innovative artificial intelligence (AI)-enabled framework that combines frequency domain analysis with adaptive machine learning classifiers. To experimentally validate the proposed framework, controlled kink defects were induced into a Bi-2223 superconducting coil under precise experimental conditions, replicating realistic deformation scenarios encountered during coil winding processes. Voltage signals from coils exhibiting induced kink defects and pristine coils were collected and transformed into discriminative spectral features through effective frequency-domain analysis. These spectral characteristics are further refined by pinpointing essential components, thus achieving effective data dimensionality reduction. Subsequently, a K-Nearest Neighbors classifier, enhanced by adaptive distance metrics and robust cross-validation protocols, was employed, attaining a remarkable detection accuracy of up to 98.9%. Critically, acknowledging practical engineering constraints such as cost efficiency, compatibility with industrial monitoring units, and lower sampling requirements, the developed method successfully maintained an acceptable detection rate. Ultimately, this study establishes the proposed analysis paradigm as a proof-of-concept of a non-invasive diagnostic tool for identifying early-stage mechanical defects. While further validation case studies across different coil designs and operating conditions is required for full industrial generalization, the present study highlights the potential relevance of data-driven electrical diagnostics for improving the manufacturing and maintenance capabilities of HTS coil winding processes. This model contributes to enabling the next generation of superconducting technologies, with prospective applications in electric transportation and energy systems
Challenges in and opportunities for individualizing diabetes technology: a position statement by the European Association for the Study of Diabetes (EASD) and the American Diabetes Association (ADA) Diabetes Technology Working Group
No full textFrom finger-stick blood glucose monitoring and mechanical insulin pens in the 1970s to modern automated insulin delivery systems, rapidly progressing advances in diabetes technology are transforming management options for people with diabetes, particularly those with type 1 diabetes but also, increasingly, people with type 2 diabetes. However, access to life-changing diabetes technologies is neither uniform nor universally covered, and there is no one-size-fits-all approach. In this position statement, we emphasize to health care professionals the importance of supporting individuals with diabetes to access and use the right diabetes technology according to personal needs, capabilities, and preferences. In doing so, we highlight the equal importance of avoiding disparities in the provision of diabetes technology by challenging preconceived barriers, which can be overcome with education and determination. We also make a series of suggestions for action to advance the more widespread adoption of diabetes technology while minimizing the “digital divide.
New insight from modelling galactic deuterons over changing solar activity
Get PDFThe interest in the origin and modulation of cosmic ray deuterons is expected to increase significantly now that observations from AMS-02 and PAMELA detectors have become available. These observations provide a useful tool with which a comprehensive description of the differences in the modulation of deuterons (D) and protons (p) can be made. Observations made by AMS-02 reveal the spectral shape and features of galactic D over the rigidity range 1.92 GV –21.1 GV, whereas those from PAMELA are at a lower rigidity, from 0.75 GV – 2.5 GV. These observations provide interesting surprises leading to subsequent challenges to the established paradigm of the secondary origin of galactic D. In this study a comprehensive 3D numerical model and a set of diffusion and drift coefficients, previously applied to a number of cosmic ray nuclei, together with a newly estimated local interstellar spectrum (LIS) for D based on AMS-02 observations, are used to simulate the modulation of D from 2006 to 2014. These modelling results are compared to published D spectra and the corresponding D/p ratios made by PAMELA over the same period, thereby focussing on modulation during a so-called A < 0 solar magnetic field polarity cycle. A particular objective is to uncover how the D/p ratio evolves at different rigidities over changing solar activity. We find that from July 2006 until December 2009 the p spectra became softer than D spectra indicating that relatively more low rigidity p had reached Earth than D. As solar activity began to pick up after 2009, the contribution of adiabatic energy losses became increasingly evident at lower rigidities, with the modulated p spectra hardening more in comparison with D spectra based on the shapes of their respective LISs. Consequently, the time dependence of the D/p ratio is found to be significant at rigidities below 1 GV, and with a different time-trend than at higher rigidities. The numerical model computations display both qualitative and quantitative compatibility with the observed features of D spectra and D/p ratios from the PAMELA detector
Characterization of heat transfer in 3D CMOS structures using Sideband Scanning Thermal Wave Microscopy
No full textEfficient thermal management is critical for cryogenic CMOS circuits, where local heating can compromise device performance and qubit coherence. Understanding heat flow at the nanoscale in these multilayer architectures requires localized, high-resolution thermal probing techniques capable of accessing buried structures. Here, we introduce a sideband thermal wave detection scheme for Scanning Thermal Microscopy, S-STWM, to probe deeply buried heater structures within CMOS dies. By extracting the phase of propagating thermal waves, this method provides spatially resolved insight into heat dissipation pathways through complex multilayer structures. Our approach enables quantitative evaluation of thermal management strategies, informs the design of cryo-CMOS circuits, and establishes a foundation for in situ thermal characterization under cryogenic operating conditions
scReady - an automated and accessible pipeline for single-cell RNA-Seq preprocessing: empowering novice bioinformaticians
Get PDFThe widespread adoption of single-cell RNA sequencing (scRNAseq) has democratized high-resolution transcriptomic analysis. Although data generation remains costly, many publicly available datasets can be leveraged to address focused research questions. However, the complexity of data processing remains a significant barrier for new bioinformaticians or laboratories without dedicated bioinformatics support. While best practices for scRNAseq analysis have been proposed, their implementation often requires extensive tool installation, testing, and customization. To address these challenges, we developed scReady, a containerized pipeline that automates and standardizes preprocessing for scRNAseq data. Designed for both single machines and high-performance computing clusters, the pipeline ensures flexibility, scalability, and reproducibility. It integrates essential quality control steps, including ambient RNA removal, doublet detection, and cell/gene filtering based on mitochondrial content and customizable thresholds. It also handles CITE-seq data (ADT and HTO). Following normalization and feature selection, scReady outputs a fully processed Seurat object along with diagnostic plots and a comprehensive quality control report. In a representative use case, we applied scReady to integrate synovial tissue scRNA-seq datasets generated using different digestion protocols, starting from Cell Ranger–produced count matrices. With a single command, scReady performed all preprocessing steps and generated integrated UMAP visualizations and quality control metrics consistent with published results. This example illustrates how scReady reduces the coding barrier to reproducible, standardized preprocessing of complex single-cell datasets. By handling the technical execution, scReady empowers researchers to focus their expertise on the critical tasks of parameter selection based on their experimental context and the subsequent biological interpretation
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