bluuebunny/crossref_metadata_2025_split huggingface dataset:
Automated Integration of AI Results into Radiology Reports Using Common Data Elements
Authors: Mehdiratta Garv , Duda Jeffrey T. , Elahi Ameena , Borthakur Arijitt , Chatterjee Neil , Gee James , Sagreiya Hersh , Witschey Walter R. T. , Kahn Charles E. 
Published: January 2025

Abstract
Integration of artificial intelligence (AI) into radiology practice can create opportunities to improve diagnostic accuracy, workflow efficiency, and patient outcomes. Integration demands the ability to seamlessly incorporate AI-derived measurements into radiology reports. Common data elements (CDEs) define standardized, interoperable units of information. This article describes the application of CDEs as a standardized framework to embed AI-derived results into radiology reports. The authors defined a set of CDEs for measurements of the volume and attenuation of the liver and spleen. An AI system segmented the liver and spleen on non-contrast CT images of the abdomen and pelvis, and it recorded their measurements as CDEs using the Digital Imaging and Communications in Medicine Structured Reporting (DICOM-SR) framework to express the corresponding labels and values. The AI system successfully segmented the liver and spleen in non-contrast CT images and generated measurements of organ volume and attenuation. Automated systems extracted corresponding CDE labels and values from the AI-generated data, incorporated CDE values into the radiology report, and transmitted the generated image series to the Picture Archiving and Communication System (PACS) for storage and display. This study demonstrates the use of radiology CDEs in clinical practice to record and transfer AI-generated data. This approach can improve communication among radiologists and referring providers, harmonize data to enable large-scale research efforts, and enhance the performance of decision support systems. CDEs ensure consistency, interoperability, and clarity in reporting AI findings across diverse healthcare systems.
Effect of forest loss and fragmentation per se on arboreal and ground mammals of the Lacandon rainforest, Mexico
Authors: Martínez-Ruiz Marisela , Arroyo-Rodríguez Víctor , San-José Miriam , Arce-Peña Norma , Cudney-Valenzuela Sabine , Galán-Acedo Carmen 
Published: January 2025

Abstract
Understanding the effects of forest loss and fragmentation per se (independent of forest loss) on wildlife is urgently needed to design biodiversity-friendly landscape scenarios, particularly for forest-specialist species, such as many ground and arboreal tropical mammals. As this topic remains contentious, we assessed the species-specific response of 14 arboreal and ground mammals to landscape-scale forest loss and fragmentation measured across different scales in the Lacandon rainforest, Mexico. Surprisingly, most species (6 of 14 species, 43%) were weakly related to forest loss, or positively associated with it (7 of 14, 50%), likely because in this young agricultural frontier some individuals can crowd in the remaining forest patches. Only the Geoffroy’s spider monkey was negatively impacted by forest loss. We did not find evidence of extinction thresholds (nonlinear responses to forest loss) in any species. Only in four species fragmentation per se provided a slightly better fit to the data, but as expected, its effect was non-significant. Our multiscale analysis revealed that the scale of effect of forest loss and fragmentation was independent of body mass and habitat use (arboreal vs. ground). Taken together, our findings suggest that landscape composition is more important than configuration, and highlight the conservation value of the studied landscapes for arboreal and ground mammals. In fact, they add to growing evidence indicating that, on a per-area basis, a piece of forest land in a highly deforested landscape has a similar conservation value to that of a more forested one.
Rage Against the Dying of the Light: Employees’ Moral Outrage, Anger Expression, and Generalized Well-Being
Authors: Giacalone Robert A. , Valentine Sean R. , Yin Bingqing , Promislo Mark D. 
Published: January 2025

Abstract
Past work demonstrates that ethical ideologies and behaviors are associated with the physical and psychological well-being of employees. Drawing from ethical impact theory and related research, this investigation utilizes two studies of working adults to examine the relationships among employees’ moral outrage, anger expression, and generalized well-being, with the latter construct being operationalized with multi-variable measures of ill-being, positive life outlook, and health consciousness. In the first study, two dimensions of moral outrage were identified, with results indicating that these components variably related to the ill-being/well-being measures and similar health behaviors. In the second study, anger expression was accounted for, thus disentangling it from the moral outrage dimensions, with results demonstrating that such anger changed the nature of some relationships between the moral outrage dimensions and the well-being outcomes. Overall, anger expression was associated more closely with negative outcomes, while the moral outrage dimensions were more closely associated with positive outcomes. These findings suggest that moral outrage may be cathartic, which contributes to further theory development and enhances understanding of the complex relationships among individual moral outrage, anger expression, and well-being. The practical considerations of the results are discussed, and the study’s limitations and suggestions for future research directions are highlighted.
Dinosaurs, Mushrooms, and Geometric Forms - Conversations with Young Children About Natural Science and Mathematics
Authors: Skalstad Ingunn , Bjerknes Anne-Line , Svendsen Siv 
Published: January 2025

Abstract
In this case study, we explore how early childhood education and care (ECEC) teachers can foster children’s participation in conversations about natural science and mathematics and what factors influence it. Data consisted of 29 conversations between ECEC teachers and children. The results showed that eight of the conversations were open dialogues in which the teachers and children participated equally. In 19 of the conversations, the ECEC teachers led the conversation, and the children, to various degrees, were active and engaged participants. Two conversations were led by the children. The results showed that the children participated more actively in conversations about natural science than mathematics, and that multisensory artifacts and physical activities enhanced their participation in the conversations. The study suggests that ECEC settings should provide more opportunities for multisensory experiences and physical activities, and that teachers need more guidance on how to explain concepts and phenomena, within natural science and mathematics, to children of different ages.
Ill-informed Consensus or Truthful Disagreement? How Argumentation Styles and Preference Perceptions Affect Deliberation Outcomes in Groups with Conflicting Stakes
Authors: Stein Jonas , Romeijn Jan-Willem , Mäs Michael 
Published: January 2025

Abstract
In groups where members deliberate with limited information, consensus can emerge where, under complete information, fundamental disagreement would prevail. Using an agent-based model, we explore the factors contributing to group consensus by comparing argumentation styles in two types of groups: agents in groups of advocates communicate arguments for options perceived as personally beneficial. Agents in groups of diplomats do the same but avoid disagreement in that they bring up arguments supporting a second-best option whenever their interaction partner perceives to benefit the least from what the sender finds best. Results show that consensus depends on argumentation style, but also on what members initially perceive as preferred. Diplomats are more likely to form consensus when initial perceptions accurately align with full information preferences, which diverge within the group. Conversely, and perhaps counterintuitively, in the presence of inaccurate initial perceptions, groups of advocates converge while diplomats part in disagreement. Our results imply that the ideal argumentation style must be considered carefully in light of both the desired outcome and the initial information distribution: when conflicting stakes produce a trade-off between consensus and truthful perceptions, polite versus selfish ways of deliberation may produce one or the other outcome, depending on the initial information members are equipped with.
Plastics underground: microplastic pollution in South African freshwater caves and associated biota
Authors: Mutshekwa Thendo , Motitsoe Samuel N. , Naidoo Trishan , Majingo Zamabhisi , Mlambo Musa C. 
Published: January 2025

Abstract
Microplastics (MPs) have been characterised in South African rivers, lakes, and the marine environment, yet we know less about MPs in subterranean environments. In this study, we assessed MP pollution in the sediment, subsurface water, and resident freshwater amphipod, Sternophysinx species across six South African subterranean cave systems. We hypothesised that MP pollution will increase with human visitations and activities in and around selected subterranean caves. We found MPs in sediments, subsurface waters, and amphipod species ranging from 4.9 ± 1.2 to 25.0 ± 6.9 particles/kg–1, 2.7 ± 0.7 to 15.0 ± 1.7 particles/L–1 and 2.1 ± 0 to 9.8 ± 3.1 particles/dry mass, respectively, with polypropylene being the most abundant polymer according to FTIR analysis. White fibres were dominant in sediments and water samples, whereas blue fibres were dominant in amphipods. Our results supported the hypothesis that MPs densities were correlated with human visitation and activities in and around the caves. The presence of MPs in subterranean caves presents a biodiversity and conservation threat to endemic and understudied cave-dwelling aquatic invertebrates, due to MPs ability to be transferable between trophic levels causing physiological constraints.
Performance Evaluation of PρT-SAFT, PρT-PC-SAFT, PC-SAFT, and CPA Equations of State for Predicting Density, Thermal Expansion Coefficient, Isothermal Compressibility, Isobaric Heat Capacity, Speed of Sound, and Saturated Vapor Pressure of Three Pure Ethylene Glycols and Their Mixtures
Authors: Pakravesh Arash , Mohammadi Amir H. , Richon Dominique 
Published: January 2025

Abstract
Ethylene glycols are a group of versatile industrial solvents with broad applications across various sectors. Accurate thermodynamic modeling of these compounds is essential for enhancing their utilization and optimizing industrial processes. Among the advanced models available, the Statistical Associating Fluid Theory (SAFT) type equation of state (EoS) stands out for its effectiveness in capturing the thermodynamic behavior of complex fluids. This study employs the PρT-SAFT, PρT-PC-SAFT, PC-SAFT, and CPA EoSs to model pure monoethylene glycol (MEG), diethylene glycol (DEG), triethylene glycol (TEG), and their mixtures. Furthermore, the predictive capabilities of these models are critically evaluated for polyethylene glycol 400 (PEG 400). The performance of the PρT-SAFT, PρT-PC-SAFT, PC-SAFT, and CPA EoSs was evaluated for predicting key thermodynamic properties, including density, thermal expansion coefficient, isothermal compressibility, isobaric heat capacity, speed of sound, and saturated vapor pressure, for pure MEG, DEG, TEG, and PEG 400. Among the models, the PρT-SAFT demonstrated superior accuracy in modeling their properties. Subsequently, the volumetric properties and vapor–liquid equilibrium data of binary mixtures of MEG, DEG, and TEG were predicted using the same EoSs, without incorporating any binary interaction parameters. Under these conditions, the PρT-SAFT achieved the highest accuracy. Furthermore, predictions of the volumetric properties for the ternary mixture of MEG, DEG, and TEG also indicated that the PρT-SAFT outperformed the other models. The overall average absolute deviation percentages for the PρT-SAFT, PρT-PC-SAFT, PC-SAFT, and CPA EoSs across all examined thermodynamic properties and systems were 7.0, 8.2, 22.2, and 30.2, respectively, confirming the robustness of the PρT-SAFT.
Leadership Dynamics in Teams: The Reciprocity of Shared and Empowering Leadership
Authors: Klasmeier Kai N. , Güntner Amelie V. , Schleu Joyce Elena 
Published: January 2025

Abstract
Until now, leadership has mostly been considered a unidirectional process centered on leaders influencing their followers. However, more recent theoretical developments indicate that leadership is a dynamic team process, valuing the contributions of leaders and followers alike. In this pre-registered study, we investigate the reciprocal interplay between shared leadership and formal team leadership (i.e., empowering leadership). We assume that leaders generally interpret their team’s shared leadership as an investment to reach for common goals. Accordingly, their team’s effort should improve leader trust in the team, which in turn should increase empowering leadership behavior of the leader. Furthermore, we propose that this indirect relationship is first-stage moderated by leader self-efficacy. We invited 721 team members and their leaders nested in 169 teams to join a three-wave longitudinal study. Results from Bayesian cross-lagged panel modeling support our predicted mediation effect while also showing the established reverse effect of empowering leadership on shared leadership. The moderation effect was not supported. This study provides insights into the dynamic and reciprocal interplay between shared and formal leadership. Our findings underline that formal leaders do not perceive shared leadership as a threat but instead value the team’s effort reflected in higher trust and decision latitude.
Quantum MEP Hydrodynamical Model for Charge Transport
Authors: Camiola V. D. , Romano V. , Vitanza G. 
Published: January 2025

Abstract
A well known procedure to get quantum hydrodynamical models for charge transport is to resort to the Wigner equations and deduce the hierarchy of the moment equations as in the semiclassical approach. If one truncates the moment hierarchy to a finite order, the resulting set of balance equations requires some closure assumption because the number of unknowns exceed the number of equations. In the classical and semiclassical kinetic theory a sound approach to get the desired closure relations is that based on the Maximum Entropy Principle (MEP) (Jaynes in Phys Rev 106:620–630, 1957) [see Camiola et al. (Charge transport in low dimensional semiconductor structures, the maximum entropy approach. Springer, Cham, 2020) for charge transport in semiconductors]. In Romano (J Math Phys 48:123504, 2007) a quantum MEP hydrodynamical model has been devised for charge transport in the parabolic band approximation by introducing quantum correction based on the equilibrium Wigner function (Wigner in Phys Rev 40:749–749, 1932). An extension to electron moving in pristine graphene has been obtained in Luca and Romano (in: Atti della Accademia Peloritana dei Pericolanti—Classe di Scienze Fisiche, Matematiche e Naturali, [S.l.], p. A5, 2018, https://doi.org/10.1478/AAPP.96S1A5). Here we present a quantum hydrodynamical model which is valid for a general energy band considering a closure of the moment system deduced by the Wigner equation resorting to a quantum version of MEP. Explicit formulas for quantum correction at order

$$\hbar ^2$$

ħ
2

are obtained with the aid of the Moyal calculus for silicon and graphene removing the limitation that the quantum corrections are based on the equilibrium Wigner function as in Romano (J Math Phys 48:123504, 2007), Luca and Romano (in: Atti della Accademia Peloritana dei Pericolanti—Classe di Scienze Fisiche, Matematiche e Naturali, [S.l.], p. A5, 2018, https://doi.org/10.1478/AAPP.96S1A5). As an application, quantum correction to the mobilities are deduced.
RGCVAE: relational graph conditioned variational autoencoder for molecule design
Authors: Rigoni Davide , Navarin Nicolò , Sperduti Alessandro 
Published: January 2025

Abstract
Identifying molecules that exhibit some pre-specified properties is a difficult problem to solve. In the last few years, deep generative models have been used for molecule generation. Deep Graph Variational Autoencoders are among the most powerful machine learning tools with which it is possible to address this problem. However, existing methods struggle to capture the true data distribution and tend to be computationally expensive. In this work, we propose RGCVAE, an efficient and effective Graph Variational Autoencoder based on: (i) an encoding network exploiting a new powerful Relational Graph Isomorphism Network; (ii) a novel probabilistic decoding component. Compared to several State-of-the-Art VAE methods on two widely adopted datasets, RGCVAE shows State-of-the-Art molecule generation performance while being significantly faster to train. The Python code implementing RGCVAE is openly accessible for download at: https://github.com/drigoni/RGCVAE.
Masked autoencoder for multiagent trajectories
Authors: Rudolph Yannick , Brefeld Ulf 
Published: January 2025

Abstract
Automatically labeling trajectories of multiple agents is key to behavioral analyses but usually requires a large amount of manual annotations. This also applies to the domain of team sport analyses. In this paper, we specifically show how pretraining transformer models improves the classification performance on tracking data from professional soccer. For this purpose, we propose a novel self-supervised masked autoencoder for multiagent trajectories to effectively learn from only a few labeled sequences. Our approach builds upon a factorized transformer architecture for multiagent trajectory data and employs a masking scheme on the level of individual agent trajectories. As a result, our model allows for a reconstruction of masked trajectory segments while being permutation equivariant with respect to the agent trajectories. In addition to experiments on soccer, we demonstrate the usefulness of the proposed pretraining approach on multiagent pose data from entomology. In contrast to related work, our approach is conceptually much simpler, does not require handcrafted features and naturally allows for permutation invariance in downstream tasks.
Adaptive transformer modelling of density function for nonparametric survival analysis
Authors: Zhang Xin , Mehta Deval , Hu Yanan , Zhu Chao , Darby David , Yu Zhen , Merlo Daniel , Gresle Melissa , van der Walt Anneke , Butzkueven Helmut , Ge Zongyuan 
Published: January 2025

Abstract
Survival analysis holds a crucial role across diverse disciplines, such as economics, engineering and healthcare. It empowers researchers to analyze both time-invariant and time-varying data, encompassing phenomena like customer churn, material degradation and various medical outcomes. Given the complexity and heterogeneity of such data, recent endeavors have demonstrated successful integration of deep learning methodologies to address limitations in conventional statistical approaches. However, current methods typically involve cluttered probability distribution function (PDF), have lower sensitivity in censoring prediction, only model static datasets, or only rely on recurrent neural networks for dynamic modelling. In this paper, we propose a novel survival regression method capable of producing high-quality unimodal PDFs without any prior distribution assumption, by optimizing novel Margin-Mean-Variance loss and leveraging the flexibility of Transformer to handle both temporal and non-temporal data, coined UniSurv. Extensive experiments on several datasets demonstrate that UniSurv places a significantly higher emphasis on censoring compared to other methods.
iSOUP-SymRF: Symbolic feature ranking with random forests in online multi-target regression and multi-label classification
Authors: Osojnik Aljaž , Panov Panče , Džeroski Sašo 
Published: January 2025

Abstract
The task of feature ranking has received considerable attention across various predictive modelling tasks in the batch learning scenario, but not in the online learning setting. Available methods that estimate feature importances on data streams have so far predominantly focused on ranking the features for the tasks of classification and occasionally multi-label classification. We propose a novel online feature ranking method for online multi-target regression iSOUP-SymRF, which estimates feature importance scores based on the positions at which a feature appears in the trees of a random forest of iSOUP-Trees, and additionally extend it to task of online feature ranking for multi-label classification. By utilizing iSOUP-Trees, which can address multiple structured output prediction tasks on data streams, iSOUP-SymRF promises feature ranking across a variety of online structured output prediction tasks. We examine the ranking convergence of iSOUP-SymRF in terms of the methods’ parameters, the size of the ensemble and the number of selected features, as well as their stability under different random seeds. Furthermore, to show the utility of iSOUP-SymRF and its rankings we use them in conjunction with two state-of-the-art online multi-target regression and multi-label classification methods, iSOUP-Tree and AMRules, and analyze the impact of adding features according to the rankings obtained from iSOUP-SymRF.
Uncovering temporal patterns in visualizations of high-dimensional data
Authors: Poličar Pavlin G. , Zupan Blaž 
Published: January 2025

Abstract
With the increasing availability of high-dimensional data, analysts often rely on exploratory data analysis to understand complex data sets. A key approach to exploring such data is dimensionality reduction, which embeds high-dimensional data in two dimensions to enable visual exploration. However, popular embedding techniques, such as t-SNE and UMAP, typically assume that data points are independent. When this assumption is violated, as in time-series data, the resulting visualizations may fail to reveal important temporal patterns and trends. To address this, we propose a formal extension to existing dimensionality reduction methods that incorporates two temporal loss terms that explicitly highlight temporal progression in the embedded visualizations. Through a series of experiments on both synthetic and real-world datasets, we demonstrate that our approach effectively uncovers temporal patterns and improves the interpretability of the visualizations. Furthermore, the method improves temporal coherence while preserving the fidelity of the embeddings, providing a robust tool for dynamic data analysis.