Our Work

Code, figures, machine-readable outputs, and manuscript source supporting the finite-horizon analysis of synchronized and independent sibling delays in branching claim clusters. The package …

Code, figures, machine-readable outputs, and manuscript source supporting the finite-horizon analysis of synchronized and independent sibling delays in branching claim clusters. The package reproduces the mean, second-factorial moment, portfolio-variance, calibration, phase-type approximation, and nonlinear probability-generating-function calculations reported in the paper.

This dataset contains the earthquake and tremor catalogs for the 2025 Santorini seismo-volcanic crisis described in Soubestre at al. (2026). The earthquake catalog is provided in tabular format using …

This dataset contains the earthquake and tremor catalogs for the 2025 Santorini seismo-volcanic crisis described in Soubestre at al. (2026). The earthquake catalog is provided in tabular format using two files. The "events" file contains one earthquake per line. The "assignments" file describes the associated picks with one pick per line. To match the events and assignments, the columns "idx" in the events file and "event_idx" in the assignments file should be used. Column names for the events and picks use descriptive names. All times are provided in UTC. All coordinates use WGS84 as references. The tremor catalog is split into the shallow and deep clusters described in Soubestre et al. (2026). The tremor catalogs are provided in tabular format with self-descriptive column names.

This report presents a comprehensive benchmarking framework for evaluating machinery used in lithium-ion battery cell production. A detailed catalogue comprising 95 machines—spanning laboratory,…

This report presents a comprehensive benchmarking framework for evaluating machinery used in lithium-ion battery cell production. A detailed catalogue comprising 95 machines—spanning laboratory, pilot, and production scales—was developed using data from public sources, project partners, and commercial platforms. The majority of machines originate from Chinese manufacturers due to greater data availability, while European machines are underrepresented owing to limited specification disclosure. To objectively compare machines across diverse process steps—from slurry mixing to cell aging—a pairwise comparison methodology was employed to assess the relative importance of technical parameters. These weights were then applied in normalization equations to compute performance scores for each machine. A rescaled weighting method was introduced to accommodate incomplete data, ensuring robust comparison even when certain specifications were missing. The resulting framework enables transparent, scalable, and data-driven evaluation of battery production machinery, supporting strategic decision-making and technology benchmarking throughout the project lifecycle.

Abstract This work presents a simple, efficient, and eco‑friendly method for synthesizing Amidoalkyl naphthol derivatives via a one‑pot multicomponent condensation. The reaction employs three easily a…

Abstract This work presents a simple, efficient, and eco‑friendly method for synthesizing Amidoalkyl naphthol derivatives via a one‑pot multicomponent condensation. The reaction employs three easily accessible substrates—2‑naphthol, aromatic aldehydes and urea under reflux in the presence of a biocatalyst prepared from Eucalyptus Albens leaf powder. The biocatalytic strategy significantly shortened reaction times, delivered high yields (75–86%) and avoided the use of corrosive mineral acids or transition‑metal catalysts. The process was evaluated in different solvent proving most effective, affording yield. The resulting compounds were confirmed through melting point analysis and infrared spectroscopy. By utilizing plant‑derived biocatalysts, this protocol adheres to green chemistry principles, offering a non‑toxic, biodegradable, and cost‑effective route to pharmacologically relevant naphthol‑based frameworks.

This dataset was collected as part of a prospective intervention study evaluating the effects of a six-month supervised exercise program on selected hormones involved in glucose homeostasis and appeti…

This dataset was collected as part of a prospective intervention study evaluating the effects of a six-month supervised exercise program on selected hormones involved in glucose homeostasis and appetite regulation in women aged 50 years and older. Participants Sixty women aged 50 years and older participated in the study. 47 women were included in the intervention group and 13 women constituted the control group. Intervention The intervention consisted of supervised exercise sessions performed twice weekly over a period of six months. Participants in the control group maintained their usual lifestyle throughout the study. Data collection Participants were examined twice: at baseline (before the intervention) and after completion of the six-month study period. The following assessments were performed: ·         anthropometric measurements (body height and body weight), ·         body composition assessment using dual-energy X-ray absorptiometry (DXA; Lunar Prodigy, GE Healthcare), ·         dietary assessment using a three-day food records, ·         fasting blood sampling for biochemical analyses. Laboratory analyses Blood samples were collected after an overnight fast. Concentrations of selected biomarkers involved in glucose metabolism and appetite regulation were determined using commercially available MILLIPLEX® Multiplex Assays based on magnetic bead technology. The analysed biomarkers included: glucose, insulin, C-peptide, amylin, glucagon-like peptide-1 (GLP-1), glucose-dependent insulinotropic polypeptide (GIP), ghrelin, leptin, secretin. The Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) was calculated from fasting glucose and insulin concentrations. Data processing The dataset contains anonymized participant-level data collected before and after the intervention. Variables include demographic characteristics, anthropometric measurements, DXA-derived body composition parameters, dietary intake variables, biochemical measurements, and derived variables used in statistical analyses (e.g., changes between baseline and follow-up measurements). All personal identifiers were removed prior to data sharing to ensure participant confidentiality.

IN WHAT WAYS DOES EXPOSURE TO PEER LUXURY CONSUMPTION ON INSTAGRAM SHAPE THE EDUCATIONAL AND CAREER ASPIRATIONS OF TEENAGERS WITH PART-TIME JOBS?This paper examines how exposure to peer luxury posts o…

IN WHAT WAYS DOES EXPOSURE TO PEER LUXURY CONSUMPTION ON INSTAGRAM SHAPE THE EDUCATIONAL AND CAREER ASPIRATIONS OF TEENAGERS WITH PART-TIME JOBS?This paper examines how exposure to peer luxury posts on Instagram relates to the educational and career aspirations of teenagers who hold part-time jobs. It introduces the Aspiration Gap — the tension between a teenager’s current life and the desirable lifestyles they see peers display online — as a proposed mechanism linking social-media exposure to decisions about education versus work. Drawing on an anonymous online survey of 200 adolescents aged 15–18 who work part-time and use Instagram, the study finds that the Aspiration Gap was widely reported and that three factors were associated with its direction: family background, patience (intertemporal discounting), and job type. Education leaning was more common among respondents from families that expected education, those classified as more patient, and those in skill-based jobs (about 70–85%), whereas work leaning was more common among respondents from families that needed money, those classified as more impatient, and those in labour-intensive jobs (about 75–80%). The findings extend the application of Social Comparison Theory and Conspicuous Consumption from emotional responses to reported life decisions. Because the design is cross-sectional and based on self-report, the results describe associations rather than causal effects; future work should test these patterns with experiments and longitudinal studies.

No description provided.

Python programs, pinned dependencies, fixed random seeds, and archived numerical outputs for “Fractional Generator Equations for Diffusion-Perturbed Renewal Risk Models.” The pac…

Python programs, pinned dependencies, fixed random seeds, and archived numerical outputs for “Fractional Generator Equations for Diffusion-Perturbed Renewal Risk Models.” The package supports the numerical studies for fractional generator equations, spectral Green kernels, Gerber–Shiu functions, adjustment coefficients, and ruin simulation. The manuscript is maintained separately so that the new DOI can be inserted after this new record is published.

For decades, pharmaceutical process validation has rested on a relatively narrow set of habits: a fixed qualification protocol, a small handful of conformance batches, and a periodic review of trends …

For decades, pharmaceutical process validation has rested on a relatively narrow set of habits: a fixed qualification protocol, a small handful of conformance batches, and a periodic review of trends to argue that manufacturing is operating consistently. That posture is being challenged. Over roughly the last ten years, manufacturers and regulators have begun exploring how techniques drawn from artificial intelligence — including machine learning, deep learning, artificial neural networks, fuzzy logic, and digital twin modelling — can be brought into every phase of the validation lifecycle. This narrative review gathers the most informative recent literature on the topic and weaves it into a single account aimed at practitioners, quality leaders, and academics alike. United States Food and Drug Administration's three-stage framework — process design, process qualification, and continued process verification — and traces how artificial intelligence is being combined with Process Analytical Technology and Quality by Design to enable real-time monitoring, predictive quality control, and adaptive process understanding. It then turns to the regulatory environment, where instruments such as the International Council for Harmonization Q8–Q13 series, GAMP 5 Second Edition, the FDA's Artificial Intelligence and Machine Learning Action Plan, and the European Union Artificial Intelligence Act are beginning to define how adaptive, data-driven models can be qualified and maintained under Good Manufacturing Practice. Persistent obstacles — model drift, fragmented data, limited explain ability, cyber risk, and the absence of harmonized international guidance — are highlighted as genuine barriers rather than solved problems.

Diese Coffe Lecture wurde im Rahmen der Love Data Week 2025 gehalten. Sie wirft einen Blick auf das Literaturverwaltungsprogramm Zotero und zeigt Ideen, wie Zotero auch als Tool für einige Aufgab…

Diese Coffe Lecture wurde im Rahmen der Love Data Week 2025 gehalten. Sie wirft einen Blick auf das Literaturverwaltungsprogramm Zotero und zeigt Ideen, wie Zotero auch als Tool für einige Aufgaben im Forschungsdatenmanagement in den Geisteswissenschaften eingesetzt werden könnte. FDM-NDS ist ein Verbundprojekt unter dem Dach der Hochschule.digital Niedersachsen und wird im Rahmen von zukunft.niedersachsen, einem Förderprogramm von Niedersächsischem Ministerium für Wissenschaft und Kultur (MWK) und VolkswagenStiftung gefördert.