1271. Oka-1 manifoldsAntonio Alarcón, Franc Forstnerič, 2025, izvirni znanstveni članek Povzetek: In this paper we begin a systematic study of the class of complex manifolds which are universal targets of holomorphic maps from open Riemann surfaces. We call them Oka-1 manifolds, by analogy with Oka manifolds that are universal targets of holomorphic maps from Stein manifolds of arbitrary dimension. We prove that every complex manifold which is dominable at most points by spanning tubes of complex lines in affine spaces is an Oka-1 manifold. In particular, a manifold dominable by ${\mathbb C}^n$ at most points is an Oka-1 manifold. We provide many examples of Oka-1 manifolds among compact complex surfaces, including all Kummer surfaces and all elliptic K3 surfaces. We show that the class of Oka-1 manifolds is invariant under Oka-1 maps inducing a surjective homomorphism of fundamental groups; this includes holomorphic fibre bundles with connected Oka fibres. In another direction, we prove that every bordered Riemann surface admits a holomorphic map with dense image in any connected complex manifold. The analogous result is shown for holomorphic Legendrian immersions in an arbitrary connected complex contact manifold.
Ključne besede: Riemann surfaces, complex curves, Oka-1 manifolds, Oka manifolds, K3 surfaces
Objavljeno v DiRROS: 22.08.2025; Ogledov: 228; Prenosov: 118
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1272. Benchmarking sentence encoders in associating indicators with sustainable development goals and targetsAna Gjorgjevikj, Kostadin Mishev, Dimitar Trajanov, Ljupčo Kocarev, 2025, izvirni znanstveni članek Povzetek: The United Nations’ 2030 Agenda for Sustainable Development balances the economic, environmental, and social dimension of sustainable development in 17 Sustainable Development Goals (SDGs), monitored through a well-defined set of targets and global indicators. Although essential for humanity’s future well-being, this monitoring is still challenging due to the variable quality of the statistical data of global indicators compiled at the national level and the diversity of indicators used to monitor sustainable development at the subnational level. Associating indicators other than the global ones with the SDGs/targets may help not only to expand the statistical data, but to better align the efforts toward sustainable development taken at (sub)national level. This article presents a model-agnostic framework for associating such indicators with the SDGs and targets by comparing their textual descriptions in a common representation space. While removing the dependence on the quantity and quality of the statistical data of the indicators, it provides human experts with data-driven suggestions on the complex and not always obvious associations between the indicators and the SDGs/targets. A comprehensive domain-specific benchmarking of a diverse sentence encoder portfolio was performed first, followed by fine-tuning of the best ones on a newly created dataset. Five sets of indicators used at the (sub)national level of governance (around 800 indicators in total) were used for the evaluation. Finally, the influence of 40 factors on the results was analyzed using explainable artificial intelligence (xAI) methods. The results show that 1) certain sentence encoders are better suited to solving the task than others (potentially due to their diverse pre-training datasets), 2) the fine-tuning not only improves the predictive performance over the baselines but also reduces the sensitivity to changes in indicator description length (performance drops even by up to 17% for baseline models as length increases, but remains comparable for fine-tuned models), and 3) better selected training instances have the potential to improve the performance even further (taking into account the limited fine-tuning dataset currently used and the insights from the xAI analysis). Most importantly, this article contributes to filling the existing gap in comprehensive benchmarking of AI models in solving the problem.
Ključne besede: representation learning
Objavljeno v DiRROS: 21.08.2025; Ogledov: 330; Prenosov: 127
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1279. Upcycling of plastic waste into multi-walled carbon nanotubes as efficient organic dye adsorbentGordana Bogoeva Gaceva, Meri Sokolovska, Viktor Stefov, Metodija Najdoski, Sebastijan Kovačič, 2025, izvirni znanstveni članek Povzetek: Multi-walled CNTs with an average diameter of about 80 nm, a length of several micrometers and surface area (SBET) of 100 m2 g–1 were obtained by pyrolysis of low-density polyethylene waste. The potential of the resulting MWCNTs material to purify water containing organic dyes was tested with Bezaktiv Blau HE-RM (BB) and Bezaktiv Rot S-3B (BR) reactive dyes. 200 mg L–1 MWCNT material was used to follow the adsorption of 30 mg L–1, 40 mg L–1, 50 mg L–1 and 60 mg L–1 BB and BR at pH 3 and a temperature of ~25 °C. The results have shown that this material has a high potential as a sorbent, and its adsorption capacity of 257 mg g–1 (for Bezaktiv BlauHE-RM) and 213 mg g–1 (for Bezaktiv Rot) is close to some commercial MWCNTs and functionalized MWCNT-based adsorbents. The adsorption process was very fast, reaching 80–90% of the dye removal in 10–15 minutes, and the equilibrium time was reached in 40–60 minutes. The adsorption isotherm showed that the Langmuir model was more suitable than the Freundlich model for describing the adsorption properties of the pollutants.
Ključne besede: adsorption, anionic dyes, upcycling plastic waste, carbon nanotubes
Objavljeno v DiRROS: 21.08.2025; Ogledov: 386; Prenosov: 170
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1280. Manganese, cobalt, nickel and zinc complexes with pyrazine-2-carboxylic acidSaša Petriček, 2025, izvirni znanstveni članek Ključne besede: complex, zinc, manganese(II), pyrazine-2-carboxylic acid, structure, thermal stability
Objavljeno v DiRROS: 21.08.2025; Ogledov: 326; Prenosov: 169
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