bluuebunny/crossref_metadata_2025_split huggingface dataset:
Deep reinforcement learning for active flow control in a turbulent separation bubble
Authors: Font Bernat , Alcántara-Ávila Francisco , Rabault Jean , Vinuesa Ricardo , Lehmkuhl Oriol 
Published: February 2025

Abstract
The control efficacy of deep reinforcement learning (DRL) compared with classical periodic forcing is numerically assessed for a turbulent separation bubble (TSB). We show that a control strategy learned on a coarse grid works on a fine grid as long as the coarse grid captures main flow features. This allows to significantly reduce the computational cost of DRL training in a turbulent-flow environment. On the fine grid, the periodic control is able to reduce the TSB area by 6.8%, while the DRL-based control achieves 9.0% reduction. Furthermore, the DRL agent provides a smoother control strategy while conserving momentum instantaneously. The physical analysis of the DRL control strategy reveals the production of large-scale counter-rotating vortices by adjacent actuator pairs. It is shown that the DRL agent acts on a wide range of frequencies to sustain these vortices in time. Last, we also introduce our computational fluid dynamics and DRL open-source framework suited for the next generation of exascale computing machines.
Automated identification of bulk structures, two-dimensional materials, and interfaces using symmetry-based clustering
Authors: Denell Thea , Himanen Lauri , Scheidgen Markus , Draxl Claudia 
Published: February 2025

Abstract
With the rapidly increasing amount of materials data being generated in a variety of projects, efficient and accurate classification of atomistic structures is essential. A current barrier to effective database queries lies in the often ambiguous, inconsistent, or completely missing classification of existing data, highlighting the need for standardized, automated, and verifiable classification methods. This work proposes a robust solution for identifying and classifying a wide spectrum of materials through an iterative technique, called symmetry-based clustering (SBC). Because SBC is not a machine learning-based method, it requires no prior training. Instead, it identifies clusters in atomistic systems by automatically recognizing common unit cells. We demonstrate the potential of SBC to provide automated, reliable classification and to reveal well-known symmetry properties of various materials. Even noisy systems are shown to be classifiable, showing the suitability of our algorithm for real-world data applications. The software implementation is provided in the open-source Python package, MatID, exploiting synergies with popular atomic-structure manipulation libraries and extending the accessibility of those libraries through the NOMAD platform.
A spatial inventory of freshwater macroinvertebrate occurrences in the Guineo-Congolian biodiversity hotspot
Authors: Akindele Emmanuel O. , Adedapo Abiodun M. , Akinpelu Oluwaseun T. , Kowobari Esther D. , Folorunso Oluwatosin C. , Fagbohun Ibrahim R. , Oladeji Tolulope A. , Aliu Olanrewaju O. , Adenola Oluwatobiloba S. , Adu Babasola W. , Arimoro Francis O. , Ogbogu Sylvester S. , Domisch Sami 
Published: February 2025

Abstract
The Guineo-Congolian region, extending from Guinea in West Africa to the central part of Africa, is considered an important biodiversity hotspot in the Afrotropics. Aside from the underreporting and underestimation of freshwater ecosystems, the challenges regarding incorrect coordinates and taxonomical inaccuracies in freshwater species occurrence data pose another major hurdle that may hinder freshwater conservation efforts in the hotspot. Hence, for any biogeographic analysis, species distribution modelling or conservation initiative, it is crucial to use datasets that are, to the largest possible extent, free of spatial and taxonomic errors. We present the final output of 8,809 occurrences consisting of 4 phyla, eight classes, 32 orders, and 1,104 species. We also added the Hydrography90m stream network attributes to the macroinvertebrate occurrence records, such that the data spans across 2,890 sub-catchments and Strahler stream orders 1–12. These records are considered valid and can be used for biogeographic analysis of freshwater macroinvertebrates in this important yet understudied freshwater biodiversity hotspot.
Australian compilation of seismic-derived bathymetry
Authors: Lebrec Ulysse , Paumard Victorien , Denudt Juliette , Du Réau Catherine , Lang Simon C. , Bailleul Julien 
Published: February 2025

Abstract
This article presents a national seismic-derived bathymetric compilation based on the integration of 253 3D seismic surveys. Individual surveys were combined to produce four regional compilations covering an area of 267,000 km2, with a spatial resolution of 30 × 30 m and a vertical accuracy of 5 m + 5%d. The production of the dataset is based on the integration of seismic survey first returns with seismic vessel echosounder measurements. Following the extraction of the depth soundings, all data points were converted from time to depth using synthetic velocity profiles and filtered to remove erroneous records. The seismic survey’s first returns were corrected using navigation depth soundings to account for geometric distortions. All depth values were reduced to WGS84 and EGM2008 datum. A comparison of the seismic-derived bathymetry with multibeam echosounder surveys suggests that where a thin layer of loose sediments overlies a lithified substratum, the seismic first return captures the top of the substratum.
LeaData a novel reference data of leather images for automatic species identification
Authors: Varghese Anjli , Jawahar Malathy , Prince A. Amalin 
Published: February 2025

Abstract
In the leather industry, the mammalian skins of buffalo, cow, goat, and sheep are the permissible materials for leather-making. They serve the trade of quality leather products; hence, the knowledge of animal species in leather is inevitable. The traditional identification techniques are prone to ambiguous predictions due to insufficient reference studies. Indeed, leather image analysis with big data can pave the way for automatic and objective analysis with accurate prediction. This study focuses on creating novel and unique leather image data, LeaData. The objective is to automatically determine species from grain surface analysis. Hence, it employs a simple, cheaper, handheld digital microscope for leather image acquisition. The magnifying parameter 47
$$\times$$
captures the species-unique grain patterns distributed over the leather surface. In total, the LeaData encloses 38,172 images of four species from 137 leather samples. This big data spans leather images with theoretically ideal and practically non-ideal grain patterns. It also includes images of grain patterns varying over different body parts. Thus, the novel LeaData is an adequately larger pool of leather images with diverse behavior. The motive is to establish a smart leather species identification technique that can be easily accessible by leather specialists, customs officials, and leather product manufacturers. Hence, this paper solely creates the bigger LeaData and presents its different versions to the digital image processing and computer vision research community. This digitized source of permissible leather species helps enable digitization in leather technology for species identification. In turn, in maintaining biodiversity preservation and consumer protection.
Three new species of Mesobiotus (Eutardigrada: Macrobiotidae) from Sweden with an updated phylogeny of the genus
Authors: Atherton Sarah , Hulterström Jens , Guidetti Roberto , Jönsson K. Ingemar 
Published: February 2025

Abstract
Three new species of Mesobiotus (Tardigrada: Eutardigrada: Macrobiotidae) are described from Skåne County in the southernmost region of Sweden. All three species are distinguished morphologically and through differences in DNA sequences as supported by PTP and mPTP analyses. With the addition of Mesobiotus bockebodicus sp. nov., M. skanensis sp. nov., and M. zelmae sp. nov., the number of nominal species of Macrobiotidae in Sweden has increased to 26, 73% of which have been documented from Skåne. Finally, new morphological details and DNA sequences are presented for Mesobiotus emiliae, a new record is presented of M. mandalori from Sweden, and the phylogenetic relationships within the genus is reconstructed using previously published and new 18S and COI gene sequences.
Exploring the prognostic role of microbial and genetic markers in lung squamous cell carcinoma
Authors: Yang Fan , Jia Xiaodong , Ma Zihuan , Liu Siyao , Liu Chunzi , Chen Dan , Wang Xiuju , Qian Niansong , Ma Hui 
Published: February 2025

Abstract
Despite advances in diagnostic and therapeutic strategies, the prognosis of lung squamous cell carcinoma (LUSC) patients remains poor, and the potential of microbiome-based prognostic biomarkers and therapeutic targets remains largely unexplored. LUSC patient data from The Cancer Genome Atlas (TCGA), including microbial genus level abundance data and RNA sequencing (RNA-Seq) data, were used as a training dataset. Two other independent datasets GSE19188 and GSE157009 serve as validation datasets. A microbiome-based risk score (RS) model was constructed by univariate Cox regression analysis combined with the least absolute contraction and selection operator (LASSO) regression. 18 microbial genera were found to be significantly associated with RFS in LUSC patients. The microbial signature built with these microbial genera, exhibited robust predictive accuracy in both the training and validation datasets. Furthermore, hub mRNA between high- and low-risk groups were selected by XGBOOST and intersect with mRNAs screened by univariate Cox regression analysis, finally identifying four mRNA significantly associated with LUSC prognosis. This study reveals a complex interplay between the lung microbiome and genetic biomarkers, and identifies specific microbial-based and mRNA associated with prognosis in LUSC. These findings provide a basis for future studies aimed to elucidate the mechanisms underlying these associations and provide potential biomarkers for guiding treatment decisions and improving patient outcomes.
Unique composition and evolutionary histories of large low velocity provinces
Authors: Panton James , Davies J. Huw , Koelemeijer Paula , Myhill Robert , Ritsema Jeroen 
Published: February 2025

Abstract
The two “large low velocity provinces” (LLVPs) are broad, low seismic wave speed anomalies in Earth’s lower mantle beneath Africa and the Pacific Ocean. Recent research suggests they contain relatively dense subducted oceanic crust (SOC), but the relative concentration of this recycled material within them is an open question. Using simulations of 3-D global mantle circulation over the past 1 Gyr, we find that two antipodal LLVPs develop naturally as a consequence of Earth’s recent subduction history and the gravitational settling and stirring of SOC. Shear-wave velocity reductions in the two LLVPs are similar due to the dominating influence of temperature over composition. However, the formation histories are distinct. Circum-Pacific subduction of oceanic lithosphere has continuously replenished the Pacific LLVP with relatively young SOC since 300 Ma, while the African LLVP comprises older, well-mixed material. Our models suggest the Pacific LLVP stores up to 53% more SOC produced in the last 1.2 Gyr than the African LLVP, potentially making the Pacific domain denser and less buoyant.
Real world federated learning with a knowledge distilled transformer for cardiac CT imaging
Authors: Tölle Malte , Garthe Philipp , Scherer Clemens , Seliger Jan Moritz , Leha Andreas , Krüger Nina , Simm Stefan , Martin Simon , Eble Sebastian , Kelm Halvar , Bednorz Moritz , André Florian , Bannas Peter , Diller Gerhard , Frey Norbert , Groß Stefan , Hennemuth Anja , Kaderali Lars , Meyer Alexander , Nagel Eike , Orwat Stefan , Seiffert Moritz , Friede Tim , Seidler Tim , Engelhardt Sandy 
Published: February 2025

Abstract
Federated learning is a renowned technique for utilizing decentralized data while preserving privacy. However, real-world applications often face challenges like partially labeled datasets, where only a few locations have certain expert annotations, leaving large portions of unlabeled data unused. Leveraging these could enhance transformer architectures’ ability in regimes with small and diversely annotated sets. We conduct the largest federated cardiac CT analysis to date (n = 8, 104) in a real-world setting across eight hospitals. Our two-step semi-supervised strategy distills knowledge from task-specific CNNs into a transformer. First, CNNs predict on unlabeled data per label type and then the transformer learns from these predictions with label-specific heads. This improves predictive accuracy and enables simultaneous learning of all partial labels across the federation, and outperforms UNet-based models in generalizability on downstream tasks. Code and model weights are made openly available for leveraging future cardiac CT analysis.
Efficient hysteresis characterization and prediction in 3D‐printed magnetic materials using deep learning
Authors: Lo Giudice Michele , Salvini Alessandro , Stella Marco , Sargeni Fausto , Licciardi Silvia , Ala Guido , Romano Pietro , Bertolini Vittorio , Faba Antonio 
Published: February 2025

AbstractThis research proposes a data processing pipeline employing Fourier analysis and deep neural networks to replicate the phenomenon of magnetic hysteresis, in particular frequency components derived from experimental data gathered using a newly developed 3D‐printed material. The characterisation of hysteresis is essential for enhancing material performance and constructing precise models to anticipate material behaviour under diverse operating circumstances, especially in 3D‐printed materials where properties can be meticulously regulated to ensure successful applications. The experimental signals were used for training and testing a neural network, exploiting Fourier coefficients to condense signals into the frequency components. This compression extracts fewer parameters and thus reduces and optimises the resources required by the neural network. It also improves the generalisation performance of the model, allowing it to make more accurate predictions on unseen data. This therefore optimises traditional modelling that requires a complete representation of hysteresis loops in the time domain, which must be addressed with the use of complex neural networks and large datasets. The experimental results show lower computational costs during the prediction process and a smaller memory footprint. Furthermore, the proposed model is easily adaptable for the loss estimation in different types of materials and input signals.
S2k-Leitlinie „Harninkontinenz bei geriatrischen Patienten“ – Teil 4: Medikamentöse Therapie
Authors: Wiedemann Andreas , Becher Klaus , Bojack Barbara , Drews Claudia , Kirschner-Hermanns Ruth 
Published: February 2025

Zusammenfassung
Einleitung 1/2023 wurde das dritte Update der seit 2005 bei der AWMF (Arbeitsgemeinschaft wissenschaftlich-medizinischer Fachgesellschaften) gelisteten und fortlaufend aktualisierten Leitlinie „Harninkontinenz bei geriatrischen Patienten – Diagnostik und Therapie“ durch die interdisziplinäre Arbeitsgruppe „Harninkontinenz“ der Deutschen Gesellschaft für Geriatrie (DGG) publiziert. Aus dieser Leitlinie, die als offizielle Leitlinie DGG akkreditiert ist, stellt der vorliegende Artikel das Kapitel „Medikamentöse Therapie“ dar.
Methodik In einem strukturierten Bewertungsprozess identifizierte eine Literaturrecherche zunächst die vorhandene Literatur im Kontext des „geriatrischen Patienten“, wie er als zumeist über 70-jährig und multimorbid oder über 80jährig durch die Fachgesellschaften definiert ist. Primäre Berücksichtigung fanden randomisierte, doppelblinde, Placebokontrollierte Studien sowie bereits vorhandene Leitlinien zum Thema. Wo keine solchen Untersuchungen vorlagen oder aus methodischen Gründen prinzipiell nicht durchführbar sind, wurden auch Publikationen anderer Designs (nicht randomisierte Untersuchungen, Fallkontrollstudien) zur Leitlinienerstellung herangezogen. Die daraus resultierenden Leitlinienempfehlungen wurden einem strukturierten Abstimmungsprozess unterzogen und abschließend Deligierten relevanter Fachgesellschaften vorgelegt.
Ergebnisse Die medikamentöse Therapie der Überaktiven Blase, der Belastungsharninkontinenz und der Überlaufinkontinenz sowie die unspezifische medikamentöse Therapie mit Antidiuretika wurde vor dem besonderen Hintergrund des vulnerablen, potentiell kognitiv eingeschränkten und im Rahmen der Multimorbidität und Multimedikation durch Medikamenteninteraktionen besonders gefährdeten Patienten betrachtet. Dabei fand das relevante Nebenwirkungsprofil der eingesetzten Substanzen besondere Berücksichtigung.
Schlussfolgerungen Eine medikamentöse Therapie der Harninkontinenz bedarf bei den besonders vulnerablen, häufig multimedizierten Patienten eine besonders sorgfältige Indikationsstellung und Überwachung in Kenntnis des spezifischen Risiko- und Nebenwirkungsprofils der eingesetzten Substanzen. Es ist ständig zu überprüfen, ob auch nicht-medikamentöse Interventionen zu ähnlichen Ergebnissen führen; dies gilt insbesondere dann, wenn potentielle Nebenwirkungen der medikamentösen Therapie vor dem Hintergrund eines speziellen Gefährdungspotentials des geriatrischen Patienten zu berücksichtigen sind.
Shoulder Dystocia. Guideline of the DGGG, OEGGG and SGGG (S2k-Level, AWMF Registry No. 015/098, 10/2024)
Authors: Jakubowski Peter , Abele Harald , Bamberg Christian , Bogner Gerhard , Desery Katharina , Fazelnia Claudius , Jückstock Julia , Hamza Amr Sherif , Heihoff-Klose Anne , Janning Luise , Köbke Andrea , Kyvernitakis Ioannis , Lütje Wolf , Reister Frank , Reitter Anke , Seeger Sven , Seehafer Peggy , Springer Laila , Valet Axel , Wallwiener Stephanie , Kimmich Nina 
Published: February 2025

Abstract
Purpose This is an official guideline of the German Society for Gynecology and Obstetrics (Deutsche Gesellschaft für Gynäkologie und Geburtshilfe, DGGG), the Austrian Society for Gynecology and Obstetrics (Österreichische Gesellschaft für Gynäkologie und Geburtshilfe, ÖGGG) and the Swiss Society for Gynecology and Obstetrics (Schweizerische Gesellschaft für Gynäkologie und Geburtshilfe, SGGG). Shoulder dystocia is a rare but feared obstetric complication with potentially far-reaching medical consequences for mother and child. The purpose of this guideline is to standardize the processes which provide individual obstetric solutions for shoulder dystocia in accordance with current scientific knowledge and current clinical practice. The aim is also to emphasize that no matter how good the medical care, shoulder dystocia and its associated complications cannot be entirely prevented and are not fully controllable.
Methods Representative members from different medical specialties were commissioned by the guidelines programm of the DGGG, OEGGG and SGGG to develop this S2k-guideline using a structured consensus process.
Recommendations The guideline provides recommendations about the definition, diagnosis, epidemiology, risk factors and prevention, logistics, and measures to treat shoulder dystocia including an algorithm for action, and the associated complications, documentation requirements, debriefing, forensic aspects, education, training and simulation as well as follow-up discussions on the shoulder dystocia event.
Risk for Imminent Delivery in Preeclampsia Based on the sFlt-1/PlGF Ratio: Do We Need New Cut-Offs?
Authors: Enengl Sabine , Oppelt Peter , Stelzl Patrick , Scharnreitner Iris , Altmann Reinhard , Grienberger Julia , Hermann Philipp , Wagner Helga , Rezk-Füreder Marlene , Damian Ilinca , Maghörndl Lena 
Published: February 2025

AbstractIt is still difficult to predict the outcome of preeclampsia and determine the individual procedure with regards to the time of birth. Cut-offs of the sFlt-1/PlGF ratio with a high risk for imminent delivery have been previously published and analyzed by our study group, but could not be confirmed. The aim of the current study is to re-evaluate the described cut-off values again in a new period of time.We performed a retrospective analysis (IRB 1279/2020) including all preeclampsia patients delivering in our department over a 3-year period. Patients were divided into 2 groups – gestational week 24+0–33+6 with an s-Flt1/PlGF > 655.2 and 34+0–37+0 weeks with an sFlt-1/PlGF > 201 and were compared with preeclampsia patients of the same weeks with sFlt-1/PlGF values below the described cut-offs. Correlation between sFlt-1/PlGF ratio and time to delivery was assessed.The association between sFlt-1/PlGF above the threshold and delivery within 48 h is significant for the high ratio early group (p < 0.01) but not for the high ratio late group (p = 0.62). In the early group, 60% of patients with sFlt-1/PlGF > 655.2 but only 8% in the low ratio group delivered within 48 h. In both the early and the late preeclampsia group, a high number of patients remained pregnant even though they showed elevated ratios.High sFlt-1/PlGF ratios seem to correlate with a shorter pregnancy duration to some extent. Nevertheless, not all patients need to be delivered within 48 h, so the decision should never be based on the laboratory test alone.
Dispelling stigma and enduring bias: exploring the perception of esports participation among young women
Authors: Tang Di , Peng Bo , Ma Ruisi , Sum Kim-wai Raymond 
Published: February 2025

Abstract
This study examined how female players engage with Honor of Kings (HOK), a mobile Multiplayer Online Battle Arena (MOBA) game, focusing on the facilitating and inhibiting factors shaping their participation in this traditionally male-dominated domain. Semi-structured interviews were conducted with 31 female players (aged 18–29). The analysis primarily focused on how social factors, attitudes, and perceived behavioral control influence their gaming experience in HOK. Additionally, thematic analysis was utilized to explore themes related to the experiences of female participation in HOK, societal perceptions of esports, gender biases, and online toxicity. The results indicate that HOK has successfully broadened female participation in esports through social media integration and esthetic appeal. However, traditional cultural norms and masculine gaming culture, manifested in gender discrimination and online harassment, significantly challenge women’s inclusive participation in the esports ecosystem. This study is the first to explore women’s experiences as primary players in mobile esports, providing fresh insights into gender dynamics in esports. The research expands understanding of the relationships between game design, social integration, and cultural barriers in esports participation.
Effect of Al3+ substitution on structural, morphological, magnetic, optical, and functional study of ZnFe2O4 nanoparticles
Authors: Sundararajan M. , Dahlous Kholod , Ramesh M. , Isaac R. S. Rimal , Yuvaraj S. , Rajabathar Jothi Ramalingam , Dash Chandra Sekhar , Udhaya P. Aji 
Published: February 2025

ZnFe1−xAlxO4 (x = 0.0, 0.1, 0.3, and 0.5) samples were synthesized by the combustion method. The x-ray diffraction pattern confirms that the ferrite samples have a cubic inverse spinel structure, and Rietveld refinement adds more weight to this conclusion. Furthermore, the space group is Fd3̄m. Using Rietveld analysis, the oxygen location, lattice parameter, and cation distribution were found, proving that Al doped ZnFe2O4 ferrites were present in every sample. According to structural research, when the Al3+ concentration increases, the crystallite size decreases (15–29 nm) but the lattice parameter increases (8.399–8.418 Å). The FE-SEM images are agglomerated for all the ferrite samples. The estimated energy gap (1.83–2.05 eV) increases as the doping concentration is increased. The FT-IR spectrum revealed the formation of Al doped ZnFe2O4 nanoparticles, and vibrating sample magnetometry showed the ferromagnetic behavior.