TU Wien

One of the most important principles of J.-Y. Girard's Π 2 1 -logic is induction on dilators. In particular, Girard used this principle to construct his famous functor Λ. He claimed that the totality of Λ is equivalent to the set existence axiom of Π 1 1 -comprehension from reverse mathematics. While Girard provided a plausible description of a proof around 1980, it seems that the very technical details have not been worked out to this day. A few years ago, a loosely related approach led to an equivalence between Π 1 1 -comprehension and a certain Bachmann-Howard principle. The present paper closes the circle. We relate the Bachmann-Howard principle to induction on dilators. This allows us to show that Π 1 1 -comprehension is equivalent to the totality of a functor J due to P. Päppinghaus, which can be seen as a streamlined version of Λ.

• 2 mol% yttria-stabilized zirconia was printed by vat photomolymerization • Increased fracture toughness was observed for 2YSZ compared to 3YSZ • Bending strength of sintered 2YSZ specimens up to 1 GPa were observed • Transformation bands are visible in 2YSZ during and after mechanical loading • Complex shaped parts can be fabricated successfully from 2YSZ 2 mol% yttria-stabilized zirconia (2YSZ) has attracted increasing attention in recent years. Its key characteristic is the lower degree of stabilization compared to 3 mol% yttria-stabilized zirconia (3YSZ), which results in higher fracture toughness. In this study, 2YSZ and 3YSZ samples were fabricated using lithography-based ceramic manufacturing (LCM) and compared with respect to maximum printable wall thickness, microstructure, three-point bending strength (3PB), ball-on-three-balls biaxal strength (B3B), hardness, and fracture toughness. The 2YSZ samples exhibited higher bending strength in both 3PB and B3B tests, as well as higher fracture toughness (889 and 1004 MPa, and 7 MPa m 0.5 , respectively), compared with the 3YSZ material (754 and 969 MPa, and 4.2 MPa m 0.5 ). Microscopy and XRD analyses revealed that these enhanced properties are attributable to the higher phase transformability of 2YSZ. Overall, these results demonstrate that 2YSZ is a promising material for additive manufacturing, particularly for applications requiring high fracture resistance and complex geometries.

Karst springs draining the Hochschwab massif, Eastern Alps, Austria. Accurate forecasting of spring discharge and water quality is crucial for sustainable water resource management. Although machine learning (ML) models have shown considerable potential for forecasting hydrological variables, understanding the underlying processes remains limited. This study aimed to improve the transparency of ML models through an attribution analysis, which explores the contribution of local environmental factors to forecasts. Several ML models were deployed to predict spring discharge and water quality, measured by the spectral absorption coefficient at 254 nm (UV254), up to four days in advance at karst springs. The Deep SHAP method aided in identifying significant seasonal variations in model attributions, showing the most pronounced changes for snow depth, followed by physicochemical variables such as electrical conductivity and other meteorological variables. The Transformer model exhibited the best overall performance. Model uncertainty, assessed through the Deep Ensemble method, is greater in spring and summer, and both the model errors and uncertainties increase with variability of the target variables. To evaluate model applicability for selective water abstraction, we classified UV254 forecasts based on threshold exceedance, achieving high classification accuracy (>95 % for 1-day and >90 % for 2-day forecasts). Integrating Deep SHAP and Deep Ensemble methods enhanced ML transparency. This combined approach provides insights that can inform drinking water management decisions in karst systems. • Presents the first study of time-varying input importance in ML karst forecasts. • Transformer model consistently outperforms LSTM, MARS, and other ML models tested. • Model error and uncertainty increase with higher target variability. • UV254 classification achieves over 90 % accuracy for 1–2 day forecasts. • Model skill differences are linked to catchment size, geology, and processes.

Accessibility-based transport and land-use planning concepts, like the 15-minute city, are often interpreted prescriptively based on a narrow set of actionable elements. While these prescriptions are relatively easy to meet in dense urban contexts, their application beyond such settings risks producing unrealistic goals or adverse effects on sustainability, opportunities and social inclusion. This limits the potential for upscaling accessibility planning across diverse spatial contexts. Drawing on a complexity theory perspective, which highlights that diversity in urban forms arises from the interactions among multiple actors without much scope for centralized control, this paper proposes a condition-based accessibility planning framework. Rather than pursuing static spatial ideals, this approach focuses on shaping the conditions that enable diverse development pathways aligned with contextual dynamics. Planners and policymakers can enhance their grip on autonomously emerging accessibility configurations by creating adaptable transport and land-use systems and by investing in longitudinal participatory practices that support mutual learning among actors and iterative normative assessment of desired development paths. Such a condition-based approach would enable diverse context-specific accessibility configurations while remaining grounded in the core values of accessibility-based planning.

This study focuses on the development of a laser desorption/ionization mass spectrometric method for analyzing carbonaceous chondrites, meteorites that may hold clues to the origin of life. Since carbonaceous chondrites are only available in small quantities, we initially designed an artificial meteorite material (the mineral forsterite) doped with an organic material system (the amino acid tryptophan, the sugar 2-deoxy-D-ribose, and the polycyclic aromatic hydrocarbon triphenylene) as meteorite-like material to develop an LDI-MS method. This simulates a simplified artificial meteoritic composition to study the behavior of organic compounds in an inorganic environment. Experiments with meteorite-like material were performed on four different LDI-MS instruments (reflectron TOF-MS and QTOF-MS) with different performance characteristics (e.g., different lasers and laser repetition rates, different ion source pressure) in positive- and negative-ion mode and compared the data with those obtained on a TOF-SIMS instrument. Real meteoritic samples were also analyzed using our previously developed LDI-MS method and the TOF-SIMS method. For sample preparation, we used an in-house built micro-press, enabling the fixing of both meteorite-like material and real meteoritic splinters onto a target plate. Our unique target system, a universal in-house-designed adapter target holder, facilitated compatibility with all instruments including SIMS from different manufacturers. In the real meteorite samples a variety of elements, including potential detections of rubidium (85Rb:87Rb = 3:1), cesium (133Cs only), and tentatively holmium (165Ho only) was predominantly detected in positive-ion mode. Utilizing LDI with a reflectron TOF-MS in negative-ion mode, we identified distinct carbon clusters ranging from C2 to C13 in the Allende and Jbilet Winselwan meteorites originating most likely from high molecular weight organic carbon compounds. Background analysis confirmed a minimal impact of external contamination with carbon cluster ions validating the authenticity of these findings. No distinct other carbon-containing material could be identified.

Abstract. The Moderate Resolution Imaging Spectroradiometer (MODIS) is one of the most attractive remote sensing datasets used for mapping snow cover. The MODIS product is expected to be replaced by the Visible Infrared Imaging Radiometer Suite (VIIRS) snow cover product in the near future. Therefore, a reliable and accurate evaluation of this product is needed for future hydrological applications. This study aims to assess the mapping accuracy of the VIIRS snow product at the regional scale (i.e., at 631 climate stations in Austria) and within a small experimental catchment, the Jalovecký Creek catchment in northern Slovakia, using extensive snow course measurements conducted at both open and forested sites between January 2012 and December 2020. In the VIIRS snow cover product, the Normalized Difference Snow Index (NDSI) is used to detect snow. A threshold of NDSI (TNDSI) is needed for distinguishing snow from snow-free land. Based on the daily snow depth observations from climate stations/snow course locations, the optimal NDSI threshold (OTNDSI) is first determined through a detailed sensitivity test (100 different TNDSI from 1 to 100 with a step of 1). The overall accuracy (OA) of VIIRS data is then evaluated based on the OTNDSI. The assessment of the OTNDSI/OA is performed for all climate stations/snow profiles, as well as for different groups of stations/snow profiles representing different physiographic and land cover conditions. The findings demonstrate that the classification accuracy for 631 Austrian stations using the optimal thresholds ranges from 52.3 % to 99.3 %, with a median of 92.5 %. The NDSI thresholds vary seasonally and decrease with increasing elevation. The NDSI thresholds fitted to different months and elevations show the smallest differences to OTNDSI in overall accuracy. The NDSI thresholds fitted to different elevations improve regional snow cover mapping by 1.5 % between 900 – 1200 m a.s.l. and by 3.1 % above 1200 m a.s.l. At the catchment scale, the difference is found between open and forested sites, where the mapping accuracy is lower in the forest. VIIRS enables snow mapping accuracy from 71.5 to 95.9 % at the open site (during winter, median OA is up to 98.8 %) and from 58.1 to 93.7 % at the forest site (during winter, median OA is up to 91.5 %). The overall accuracy at the site with the most measurements is 95.9 % (Červenec – open site) and 93.7 % (Červenec – forest site), respectively. The accuracy at the forest is more sensitive to seasonal variation compared to the open area, where the accuracy is more stable and accurate across the year.

Large language models (LLMs) are becoming a common feature in content creation tools, prompting important questions about how design choices influence user trust and engagement in AI-assisted journalism. Beyond output quality, factors such as prompt specificity, model choice, and authorship disclosure are themselves interaction design parameters that influence how users interpret and evaluate AI contributions. Yet, little is known about how these design decisions affect reader perceptions in journalistic contexts. To address this gap, we conducted an experiment with 150 participants who evaluated news articles on the sensitive topic of assisted suicide. The articles systematically varied in authorship (human-written, AI-edited, or AI-generated), stance (pro- or anti-legalization), and prompt specificity (vague, moderate, or highly detailed). Participants rated each article on engagement, subjectivity, and perceived AI involvement, and also provided open-ended justifications for their authorship judgments. Our findings show that prompt specificity and model choice significantly influence perceptions of authorship, underscoring how technical design decisions in AI tools can shape public trust in journalism.