bluuebunny/crossref_metadata_2025_split huggingface dataset:
Synchronised Video, Motion Capture and Force Plate Dataset for Validating Markerless Human Movement Analysis
Authors: Evans Murray , Needham Laurie , Wade Logan , Parsons Martin , Colyer Steffi , McGuigan Polly , Bilzon James , Cosker Darren 
Published: November 2024

AbstractThe BioCV dataset is a unique combination of synchronised multi-camera video, marker based optical motion capture, and force plate data, observing 15 healthy participants (7 males, 8 females) performing controlled and repeated motions (walking, running, jumping and hopping), as well as photogrammetry scan data for each participant. The dataset was created for the purposes of developing and validating the performance of computer vision based markerless motion capture systems with respect to marker based systems.
DERM12345: A Large, Multisource Dermatoscopic Skin Lesion Dataset with 40 Subclasses
Authors: Yilmaz Abdurrahim , Yasar Sirin Pekcan , Gencoglan Gulsum , Temelkuran Burak 
Published: November 2024

AbstractSkin lesion datasets provide essential information for understanding various skin conditions and developing effective diagnostic tools. They aid the artificial intelligence-based early detection of skin cancer, facilitate treatment planning, and contribute to medical education and research. Published large datasets have partially coverage the subclassifications of the skin lesions. This limitation highlights the need for more expansive and varied datasets to reduce false predictions and help improve the failure analysis for skin lesions. This study presents a diverse dataset comprising 12,345 dermatoscopic images with 40 subclasses of skin lesions, collected in Turkiye, which comprises different skin types in the transition zone between Europe and Asia. Each subgroup contains high-resolution images and expert annotations, providing a strong and reliable basis for future research. The detailed analysis of each subgroup provided in this study facilitates targeted research endeavors and enhances the depth of understanding regarding the skin lesions. This dataset distinguishes itself through a diverse structure with its 5 super classes, 15 main classes, 40 subclasses and 12,345 high-resolution dermatoscopic images.
Evaluation of microbial diversity in the formation water of the producer and marginal wells in bokaro coal field
Authors: Sahu Nishi , Lavania Meeta , Banerjee Dipanjana , Chawla Mansi , Lal Banwari 
Published: November 2024

AbstractThe rise in global energy demand has prompted research on developing strategies for transforming conventional nonrenewable sources to cleaner fuels. Biogenic methane production is a promising source that caters to increasing energy demands. Therefore, research to enhance their production is of great importance. Implementation of successful enhancement strategies requires knowledge of the factors impacting coalbed methane production. The microbial diversity of the formation water in coal seams is the crucial parameter influencing biomethane production. This study explores microbial diversity in the Producing and Marginal wells of Bokaro, India, intending to understand the potential application of microbial-enhanced coalbed methane technology in the marginal wells of this reservoir. The high throughput sequencing analysis revealed the presence of both archaeal and bacterial groups in both well types. The result showed significant differences in the diversity of the samples from the two well groups, suggesting the immense role played by the microbes in producing methane gas. Random forest analysis shows genera Gelria, Methanothermobacter, Thaurea, Youngiibacter, and Proteiniclasticum in the Producing wells while Roseomonas, Rhodobacter, Mycobacterium, Methylobacter, and Bosea in the Marginal wells as the significant contributor in differentiating the overall diversity between the wells of Bokaro. The current study is the first to show microbial uniqueness in coalbed methane wells based on gas production efficiency. It also explores the role of physicochemical factors in framing microbial community structure in the wells. The results provide salient information that will help better understand the impact of microbial diversity on the production of coalbed methane wells of studied coal seams. This knowledge will further aid in exploring the prospects of microbial-enhanced methane in the Marginal wells.
Seaweed liquid extract AS novel sustainable solutions for phycobioremediation plant germination, and feed additive for marine invertebrate copepod
Authors: Ashour Mohamed , Khairy Hanan M. , Bakr Ahmed , Matter Mostafa , Alprol Ahmed E. 
Published: November 2024

AbstractSeveral studies have shown the importance of using seaweed liquid extract (True-Algae-Max, TAM) as a fish feed additive, and fish-water conditioner. In addition, TAM has demonstrated significant growth improvement when used as a plant growth biostimulant. This study investigates whether seaweed liquid extract (TAM) can achieve good results in new experimental fields such as chromium remediation, plant germination, and live feed supplementation for marine invertebrate Copepod (Oithona nana). In this study, several doses of TAM were tested, for the first time, for their impact on the remediation of chromium (Cr6+) ions from aqueous solutions and as an aqua feed additive for marine copepods (Oithona nana). In addition, it has been tested as promising for the seed germination of Fenugreek (Trigonella foenum-graecum) and Faba bean (Vicia faba L.). The most important factors influencing the removal (%) of Cr6+, identified using a two-level Plackett–Burman factorial design, were selected for additional optimization utilizing a rotatable central composite design. The maximum adsorption of Cr6+ was 93.65% under ideal operating circumstances, which included an initial Cr6+ concentration of 60 mg L−1, a temperature of 25 °C, a pH of 3, a TAM biomass of 0.05 g, and a contact time of 60 min at agitation conditions. Plackett–Burman design data shows the significance of each factor and how well the model fits the Cr6+ removal. The results of the germination experiment revealed that the highest significant increase in seed germination was achieved using a TAM level of 0.30 mg mL−1 with V. faba (88%) and 0.03 mg mL−1 with T. foenum-graecum (96.6%). Additionally, compared to the control group, TAM at a level of 0.037 mg mL−1 showed high root length enhancement on V. faba (184%) and T. foenum-graecum (188%). The results of the copepod O. nana feeding additive experiment found that the group fed on starch supplemented with TAM at a level of 0.3 mL L−1, compared to the control group that fed starch only, showed the highest increment in population growth (134.74%), fecundity (270.16%), and population composition of males (133.45%), adults (120.37%), and nauplius (203.18%). Moreover, compared to the control group, the copepod that fed starch supplemented with TAM levels achieved the highest Omega-9 content. In conclusion, it is shown that TAM is a feasible, efficient, and sustainable solution for biodegradable adsorbent for the Cr6+ from aqueous solution, enhances plant seed germination and root length, and is a novel feed additive for marine copepod O. nana, especially in marine invertebrate hatcheries.
Analysis of four long non-coding RNAs for hepatocellular carcinoma screening and prognosis by the aid of machine learning techniques
Authors: Samir Ahmed , Abdeldaim Amira , Mohammed Ammar , Ali Asmaa , Alorabi Mohamed , Hussein Mariam M. , Bakr Yasser Mabrouk , Ibrahim Asmaa Mohamed , Abdelhafiz Ahmed Samir 
Published: November 2024

AbstractHepatocellular carcinoma (HCC) represents a significant health burden in Egypt, largely attributable to the endemic prevalence of hepatitis B and C viruses. Early identification of HCC remains a challenge due to the lack of widespread screening among at-risk populations. The objective of this study was to assess the utility of machine learning in predicting HCC by analyzing the combined expression of lncRNAs and conventional laboratory biomarkers. Plasma levels of four lncRNAs (LINC00152, LINC00853, UCA1, and GAS5) were quantified in a cohort of 52 HCC patients and 30 age-matched controls. The individual diagnostic performance of each lncRNA was assessed using ROC curve analysis. Subsequently, a machine learning model was constructed using Python’s Scikit-learn platform to integrate these lncRNAs with additional clinical laboratory parameters for HCC diagnosis. Individual lncRNAs exhibited moderate diagnostic accuracy, with sensitivity and specificity ranging from 60 to 83% and 53–67%, respectively. In contrast, the machine learning model demonstrated superior performance, achieving 100% sensitivity and 97% specificity. Notably, a higher LINC00152 to GAS5 expression ratio significantly correlated with increased mortality risk. The integration of lncRNA biomarkers with conventional laboratory data within a machine learning framework demonstrates significant potential for developing a precise and cost-effective diagnostic tool for HCC. To enhance the model’s robustness and prognostic capabilities, future studies should incorporate larger cohorts and explore a wider array of lncRNAs.
The influence of geometric nonconformance of the SB4 tension clamps on their strength and elasticity characteristics
Authors: Pieniak Daniel , Guzik Mirosław , Lonkwic Paweł , Lesiak Piotr , Selech Jaroslaw , Krzysiak Zbigniew , Matijosius Jonas , Sejkorova Marie , Kilikevičius Artūras 
Published: November 2024

Abstract
The paper presents comparative laboratory investigations of tension fastener models that attach rails to concrete sleepers. The aim of the paper was to assess the influence of geometric nonconformity of the actual industrial product that meets the PKP Polskie Linie Kolejowe S.A. requirements on the operation of a tension-fastening clamp under stress. Due to the cost and limited possibility of research of the actual industrial product, an additional objective of the research was to validate the usefulness of it in the comparative assessment of the models. In the research, the authors used models of tension fastening clamps manufactured in incremental engineering technology (3D print) on scale 1:2. The properties of the fastening clamps (in their nominal shape) were compared (described in the PKP Polskie Linie Kolejowe S.A. documentation) with the fastening clamps of the actual shape. In the investigation, the authors have confirmed the negative influence of the non-conformance of the shape of the actual fastening clamps with the nominal ones.
Goreisan promotes diuresis by regulating the abundance of aquaporin 2 phosphorylated at serine 269 through calcium-sensing receptor activation
Authors: Ogura Keisuke , Fujitsuka Naoki , Nahata Miwa , Tokita Yohei 
Published: November 2024

AbstractAquaporin 2 (AQP2) contributes to water reabsorption and urine concentration by migrating to the luminal surface of the collecting ducts in an anti-diuretic hormone-stimulated manner, and the signaling pathway involved in AQP2 subcellular localization is a target for arginine vasopressin receptor antagonists (aquaretics). This study investigated the involvement of AQP2 in the diuretic effect and mechanisms of Goreisan (GRS), a traditional Japanese Kampo medicine used to treat conditions such as edema in patients with decreased urination. GRS exerted diuretic effects on desmopressin (DDAVP)-induced decreases in urine output and the level of AQP2 phosphorylated at Serine269 (pSer269-AQP2) in the renal tissues of mice. Furthermore, GRS inhibited the accumulation of pSer269-AQP2 to the luminal side following forskolin stimulation using a 3D culture model of the kidney collecting duct cell line mIMCD-3. GRS induced a transient increase in the intracellular Ca2+ concentration via the calcium-sensing receptor (CaSR) and suppressed the forskolin-stimulated increase in cAMP production. These results suggest that GRS regulates urine volume by modulating the subcellular localization of AQP2 via CaSR.
Flat dispersion at large momentum transfer at the onset of exciton polariton formation
Authors: Nerl Hannah C. , Elyas Khairi , Kochovski Zdravko , Talebi Nahid , Koch Christoph T. , Höflich Katja 
Published: November 2024

AbstractExcitons are quasiparticles, comprised of an electron excited from the valence band and attracted to the hole left behind, that govern transport properties in transition metal dichalcogenides. Excitonic coherence specifically needs to be understood to realise applications based on Bose-Einstein condensation and superfluidity. Here we used momentum-resolved electron energy-loss spectroscopy to obtain the complete energy-momentum dispersion of excitons in thin film and monolayer WSe2 across the entire Brillouin zone, including outside of the light cone and for a large energy-loss range (1.5–4 eV). The measured dispersion of the modes was found to be flat. This suggests that the excitations are at the onset of polaritonic mode formation, propagating in the confinement of nanometer thin and monolayer WSe2. In combination with helium ion microscopy nanopatterning it was possible to probe and control these excitonic modes in thin film WSe2 by modifying the local geometry through nanosized cuts.
A multimodal expert system for the intelligent monitoring and maintenance of transformers enhanced by multimodal language large model fine‐tuning and digital twins
Authors: Zhang Xuedong , Sun Wenlei , Chen Ke , Jiang Renben 
Published: November 2024

AbstractThe development of multimodal large models and digital twin technology is set to revolutionise the methods of intelligent monitoring and maintenance for transformers. To address the issues of low intelligence level, single application mode, and poor human–machine collaboration in traditional transformer monitoring and maintenance methods, an intelligent monitoring and maintenance digital twin multimodal expert reasoning system, fine‐tuned on visual language‐based large models, is proposed. The paper explores the modes and methods for implementing intelligent monitoring and maintenance of transformers based on multimodal data, large models, and digital twin technology. A multimodal language large model (MLLM) framework for intelligent transformer maintenance, grounded on the Large Language and Vision Assistant model, has been designed. To enable large models to understand and reason about image annotation areas, an adaptive grid‐based positional information processor has been designed. To facilitate the compatibility and learning of large models with transformer Dissolved Gas Analysis data, a heterogeneous modality converter based on the Gram–Schmidt angular field has been developed. For the unified modelling and management of multimodal reasoning and comprehensive resource integration in human–machine dialogue, a central linker based on an identity resolution asset management shell has been designed. Subsequently, a visual‐language multimodal dataset for transformer monitoring and maintenance was constructed. Finally, by fine‐tuning parameters, a multimodal expert reasoning system for intelligent transformer monitoring and maintenance was developed. This system not only achieves real‐time monitoring of the transformer's operational status but also generates maintenance strategies intelligently based on operational conditions. The expert system possesses robust human–machine dialogue capabilities and reasoning generation abilities. This research provides a reference for the deep integration of MLLM and digital twin in industrial scenarios, particularly in the application modes of intelligent operation and maintenance for transformers.
Künstliche Intelligenz in der onkologischen Hybridbildgebung
Authors: Feuerecker Benedikt , Heimer Maurice , Geyer Thomas , Fabritius Matthias P. , Gu Sijing , Schachtner Balthasar , Beyer Leonie , Ricke Jens , Gatidis Sergios , Ingrisch Michael , Cyran Clemens C. 
Published: November 2024

Zusammenfassung
Hintergrund Der Stellenwert künstlicher Intelligenz (KI) hat in der
medizinischen Bildgebung in den letzten Jahren deutlich zugenommen. Aufgrund der enormen
Datenmengen und strukturierbaren Aufgaben im diagnostischen Workflow hat die KI in der
onkologischen Hybridbildgebung besonders vielversprechende Anwendungsgebiete für die
Läsionsdetektion, die Läsionscharakterisierung und die Therapiebeurteilung. Vor dem
Hintergrund rasanter Entwicklungen im Bereich des Machine Learning (ML) und des Deep Learning
(DL) ist von einer zunehmenden Bedeutung in der onkologischen Hybridbildgebung auszugehen mit
Potenzial, die klinische Therapiesteuerung und patientenrelevante Ergebnisse zu verbessern.
Methode und Ergebnisse Diese narrative Übersichtsarbeit fasst die Evidenz in verschiedenen aufgabenbezogenen Anwendungen der Bildanalyse von KI im Bereich der onkologischen Hybridbildgebung zusammen. Nach Einführung in das Thema der KI werden ausgewählte Beispiele exploriert, vor dem Hintergrund aktueller Herausforderungen und im Hinblick auf die klinische Relevanz in der Therapiesteuerung diskutiert.

Schlussfolgerung Der Einsatz von KI bietet vielversprechende Anwendungen der Detektion, der Charakterisierung und der longitudinalen Therapiebeurteilung im Bereich der onkologischen Hybridbildgebung. Schlüsselherausforderungen liegen in den Bereichen der Entwicklung von Algorithmen, der Validierung und der klinischen Implementierung.

Kernaussagen Mit der onkologischen Hybridbildgebung werden große
Datenvolumen aus zwei bildgebenden Modalitäten erzeugt, deren strukturierte Analyse komplex
ist.
Für die Datenanalyse werden neue Methoden benötigt, um eine schnelle und kosteneffiziente Beurteilung in allen Aspekten der diagnostischen Wertschöpfungskette zu ermöglichen.
KI verspricht, die diagnostische Auswertung der onkologischen Hybridbildgebung zu
vereinfachen und wesentliche Verbesserungen in Qualität und Effizienz bei der Erkennung,
Charakterisierung und dem longitudinalen Monitoring onkologischer Erkrankungen zu ermöglichen.
Ziel ist, reproduzierbare, strukturierte, quantitative diagnostische Daten für die
evidenzbasierte onkologische Therapiesteuerung zu generieren. Selektierte Anwendungsbeispiele in drei ausgewählten Tumorentitäten (Lungenkarzinom,
Prostatakarzinom, Neuroendokrine Tumore) zeigen, wie KI-gestützte Applikationen einen
wesentlichen Beitrag in der automatisierten Bildanalyse leisten und eine weitere
Individualisierung von Therapien ermöglichen könnten.
The fall of an ellipse in a stratified fluid
Authors: C Hurlen Erik , Llewellyn Smith Stefan G 
Published: November 2024

Abstract
The free fall of an ellipse in an infinite linearly-stratified fluid is investigated using a linear two-dimensional, Boussinesq, diffusionless, inviscid model. The oscillations of the ellipse decay because of radiation damping, but unlike the case of a circular cylinder, the ellipse can also rotate and move horizontally. The resulting equations are solved analytically for some simple cases for which there is little or no rotation. Motions with rotation are studied numerically using a spectral method to solve for the wave field in the fluid.
Sodium alginate-based biochar gel spheres: synthesis, structural analysis, and ofloxacin adsorption efficiency
Authors: Zhao Qingrui , Zhang Hongpei , Zhao Xiaohui , Wang Xiaodan , Zhang Ke , Binguo Zheng , Wang Songchao , Wang Baogui , Guo Fuzhi , Ma Yuqian , Mao Di 
Published: November 2024

Abstract
Composite gel spheres (SA-BC) were prepared by using sodium alginate (SA) as a carrier and adding peanut shell biochar, and their preparation conditions were optimized to evaluate their effectiveness in removing the ofloxacin (OFX) from water. In addition, antibiotic contamination in Chinese urban waters was investigated. The results showed that a variety of antibiotic pollution existed in natural water bodies, among which SA-BC performed excellently in removing OFX from water, with a removal rate of 70.72%, and maintained good adsorption performance after four cycles of use. The experiments demonstrated the general applicability and practical application potential of SA-BC in dynamic adsorption, resistance to ionic interference and removal of heavy metals from water. The fitting results of the adsorption data showed that the adsorption process of OFX by SA-BC was consistent with the Langmuir isothermal model and the pseudo-second-order kinetic model. Further analysis showed that three main adsorption mechanisms existed in the adsorption of OFX by SA-BC, namely, the role of surface functional groups, π–π electron donor–acceptor (EDA) interactions, and electrostatic attraction.
Performance analysis and engineering application of waste rubber crumbs in concrete
Authors: Liu Li 
Published: November 2024

Abstract
This study experimentally analyzed the impact of rubber particle content and KH-570 pretreatment on the workability and mechanical properties of rubberized concrete, as well as their environmental and economic benefits. The results indicated that the addition of rubber reduced workability, but this was mitigated by KH-570 pretreatment. At a 15% rubber content, pretreated rubberized concrete maintained peak stress similar to the control group, but beyond this threshold, peak stress dropped significantly. Compared to the control, RC25 exhibited a 6.9% increase in peak strain, while KRC25 only showed a 4.3% increase; the secant modulus of RC25 decreased by 46.8%, and that of KRC25 by 34.9%. In terms of toughness, the toughness index of RC25 increased by 15.4%, and KRC25 by 8.3%, demonstrating that rubber enhances concrete toughness, albeit with pretreatment affecting the deformability of rubber particles. KH-570 pretreatment significantly reduced the drying shrinkage of concrete, with a 22.7% reduction after 60 days. Rubberized concrete significantly lowered the CO2 emissions per unit volume, with RC25 reducing CO2E by 61.0% compared to the control group, highlighting the effectiveness of rubberized concrete in reducing carbon emissions associated with concrete production.
Mechanisms of forward current transport in vertical nanoscale devices: insights and applications
Authors: Chen Long , Liu Liting , Li Hongfu , Liu Xingqiang , Liu Yuan , Raskin Jean-Pierre , Flandre Denis , Liao Lei , Li Guoli 
Published: November 2024

Abstract
Current transport at metal/semiconductor interface becomes critical to determining ultimate limit in performance of two-dimensional (2D) electronic devices. In this work, we study output characteristics as well as carrier transport of the vertical Schottky-contact 2D transistors and diodes, by experimental measurements and detailed TCAD simulations. Device output current under the forward bias is primarily attributed to thermionic emission (TE) mechanism, then tunneling occurs and becomes the dominant interfacial charge transport in the few-layered MoS2 transistors.While shrinking the vertical channel length from 20 nm to 3.6 and increasing the applied voltage, tunneling ratio rises above 90% for the sub-5 nm scale, indicating the dominated tunneling mechanism. Simultaneously, the Schottky diode loses its rectification ability. Noticeably, Fowler-Nordheim tunneling (FNT) mechanism cannot be accurately identified through the linear slope of ln(I/V2
) versus 1/V (FN-relation) of output current under high electric field, due to the co-existing thermionic current that displays a linear-like feature in the FN-relation plots. The transition from TE to FNT and direct tunneling (DT) regimes can be identified by analyzing the output current components and FN-relation of tunneling current. These results can be employed to understand physical insights and transport limitations of the nanoscale electronics, and to optimize the device design and performance for their ultra-scaled, low-power applications.&#xD;