Excess dinitrogen (ΔN2) is the net accumulation of N2 produced by nitrate-reducing processes in the water column and sediments of oxygen-deficient marine systems. Therefore, biological production and physical processes shape its spatial distribution. This study investigates ΔN2 distribution below the halocline in the Baltic Sea using a coupled biogeochemical model evaluated against observations from the Bornholm, Gdańsk, and Gotland Deeps during 2020–2021. Vertical distributions of temperature, salinity, oxygen (O2), nutrients, N2/Ar ratios, and ΔN2 were compared to assess model performance and identify controls on N2 production. A persistent spatial gradient in ΔN2 was observed, with highest concentrations in the Gotland Basin, intermediate levels in the Gdańsk Basin, and lowest values in the Bornholm Basin. These differences correspond to variations in stratification, ventilation, and organic matter (OM) accumulation. Seasonal variability was strongest in the Gdańsk Basin, where elevated winter ΔN2 suggests enhanced nitrogen-loss processes supported by OM export and mixing-driven nutrient redistribution. In contrast, the Gotland Basin exhibited relatively stable, persistently reducing conditions, while the Bornholm Basin showed strong interannual variability related to episodic North Sea inflows. The weak relationship between ΔN2 and modeled denitrification rates (ranging from �0.0010 to 0.0200 𝜇mol kg−1 day−1) indicates that ΔN2 should not be interpreted as a direct proxy for pelagic denitrification. Rather, it reflects the cumulative effects of both pelagic and benthic denitrification, together with physical transport processes. By comparison, denitrification rates were more strongly associated with particulate organic carbon (POC) availability. The reported ΔN2 patterns are representative of the investigated post-inflow period rather than a long-term Baltic Sea baseline.
In this study, the initial and fatigue-evolved yield surfaces of Laser Powder Bed Fusion (LPBF)-fabricated stainless steel (SS) grade 316 L were experimentally characterised for three build orientations: horizontal (XY), inclined at 45° (ZX45), and vertical (Z). Uniaxial tensile tests revealed that XY printed specimen exhibit higher mechanical properties than those printed in other orientations. Initial yield surfaces were determined in axial-shear stress space using sixteen proportional loading paths and were fitted using isotropic (Tresca and Huber-von Mises-Hencky) and anisotropic (Hill48 and Szczepiński) yield criteria. The results demonstrated clear deviations from isotropic yielding, with the Szczepiński criterion providing the best agreement with the experimental yield points. Axial stress-controlled fatigue tests were subsequently performed with periodic interruptions at intervals of 20,000 cycles. At each interruption, the yield point was measured to capture the progressive evolution of the yield surface. The results showed orientation-dependent distortional hardening, characterised primarily by asymmetric evolution of axial tensile and compressive yield strengths, while shear yielding remained largely unchanged. Microstructural analysis confirmed that this evolution correlates with orientation-dependent changes in lattice misorientation and accumulated plastic strain.
Fossil fungal ascospores assignable to the genus Potamomyces have been recovered from Early Pliocene deposits at the Gray Fossil Site, a Fossil-Lagersta & uml;tte in Tennessee, USA. Characterized by four prominent equatorial verrucae, these ascospores closely resemble the fossil-species Potamomyces invaginatus, P. batii, and P. pontidiensis, as well as the extant species P. armatisporus. Modern species of Potamomyces are primarily saprotrophic fungi that occur on decaying wood in freshwater and brackish environments, as well as in moist terrestrial habitats. During the Pliocene, Potamomyces formed part of the late Neogene wetland ecosystem associated with sinkhole ponds at the Gray Fossil Site, which developed under a warm temperate to subtropical humid climate. The remains described here represent the northernmost known occurrence of Potamomyces in North America, based on both fossil and extant records. These findings further suggest that Potamomyces appears to have considerable potential as a non-pollen palynomorph proxy for reconstructing past environments and climates.
We quantified excess N2 accumulation below the halocline of the Baltic Proper using in situ N2/Ar measurements combined with N2/Ar estimates derived from the three-dimensional ERGOM–MOM biogeochemical model. The observed temporal increase in excess N2 was subsequently converted to nitrogen loss. The analysis focused on the post-2014 Major Baltic Inflow period, when deep-water ventilation effects had largely subsided and stratification with progressive oxygen depletion was re-established. Both observations and model simulations showed increasing excess N2 below the halocline, particularly in the Eastern Gotland Basin (EGB). Observation-based estimates of nitrogen loss amounted to approximately 80.8 ± 22.7 kt N yr−1 in the EGB, ∼15.6 ± 7.8 kt N yr−1 in the Bornholm Basin (BB), and ∼9.3 ± 4.1 kt N yr−1 in the Gdańsk Basin (GB). Model-derived estimates were consistently lower, but basin-specific comparisons revealed no significant differences between observational and temporally matched model-derived excess N2 accumulation trends. The obtained values of net fixed nitrogen loss fall within the lower range of published Baltic Sea estimates, likely because the N2/Ar approach reflects integrated net accumulation of dissolved N2 rather than gross instantaneous denitrification rates. These results demonstrate that deep-water excess N2 accumulation provides an integrative basis for estimating first-order net fixed nitrogen loss in stratified marine systems such as the Baltic Sea.
Explainable artificial intelligence (XAI) has predominantly focused on generating model-centric explanations that approximate the behavior of black-box models. However, such explanations often overlook a fundamental aspect of interpretability: different users require different explanations depending on their goals, preferences, and cognitive constraints. Although recent work has explored user-centric and personalized explanations, most existing approaches rely on heuristic adaptations or implicit user modeling, lacking a principled framework for representing and learning individual preferences. In this paper, we consider Preference-Based Explainable Artificial Intelligence (PREF-XAI), a novel perspective that reframes explanation as a preference-driven decision problem. Within PREF-XAI, explanations are not treated as fixed outputs, but as alternatives to be evaluated and selected according to user-specific criteria. In the PREF-XAI perspective, here we propose a methodology that combines rule-based explanations with formal preference learning. User preferences are elicited through a ranking of a small set of candidate explanations and modeled via an additive utility function inferred using robust ordinal regression. Experimental results on real-world datasets show that PREF-XAI can accurately reconstruct user preferences from limited feedback, identify highly relevant explanations, and discover novel explanatory rules not initially considered by the user. Beyond the proposed methodology, this work establishes a connection between XAI and preference learning, opening new directions for interactive and adaptive explanation systems.
