bluuebunny/crossref_metadata_2025_split huggingface dataset:
SEY‐Net: Semantic edge Y‐shaped network for pancreas segmentation
Authors: Zhou Bangyuan , Xin Guojiang , Liang Hao , Ding Changsong 
Published: September 2024

AbstractPancreas segmentation has great significance in computer‐aided diagnosis of pancreatic diseases. The small size of the pancreas, high variability in shape, and blurred edges make the task of pancreas segmentation challenging. A new model called SEY‐Net is proposed to solve the above problems, which is a one‐stage model with multi‐inputs. SEY‐Net is composed of three main components. Firstly, the edge information extraction (EIE) module is designed to improve the segmentation accuracy of the pancreas boundary. Then, the SE_ResNet50 is selected as the encoder's backbone to fit the size of the pancreas. Finally, the dual cross‐attention is integrated into the skip connection to better focus on the variable shape of the pancreas. The experimental results shows that the proposed method has better performance and outperforms the other existing state‐of‐the‐art pancreas segmentation methods.
A class of symmetric-hyperbolic PDEs modelling fluid and solid continua
Authors: Boyaval Sébastien 
Published: September 2024

We generalize a new symmetric-hyperbolic system of PDEs proposed in [ESAIM:M2AN 55 (2021) 807-831] for Maxwell fluids to a class of systems that define unequivocally multi-dimensional visco-elastic flows.
Precisely, within a general setting for continuum mechanics, we specify constitutive assumptions i) that ensure the unequivocal definition of motions satisfying widely-admitted physical principles, and ii) that contain [ESAIM:M2AN 55 (2021) 807-831] as one particular realization of those assumptions.
The new class can capture the mechanics of various materials, from solids to viscous fluids, possibly with temperature dependence and heat conduction.
Partnerschaftsstatus und Prävalenz psychischer Störungen bei Frauen
und Männern mit einer Krebserkrankung
Authors: Köditz Anne-Kathrin , Mehnert-Theuerkauf Anja , Ernst Jochen 
Published: September 2024

Zusammenfassung
Hintergrund Patienten mit einer Krebserkrankung sind mit Belastungen
konfrontiert, die das Risiko einer psychischen Störung erhöhen. Eine
Partnerschaft moderiert die psychische Gesundheit der Patienten und kann sich
risikomindernd auswirken. Ziel der vorliegenden Arbeit ist es, den Einfluss des
Partnerschaftsstatus auf die 4-Wochen-Prävalenz psychischer Störungen bei
Krebspatienten und dessen Geschlechtsabhängigkeit darzustellen.
Methodik Im Rahmen der Sekundärdatenanalyse einer multizentrischen
Querschnittstudie wurden N=1.857 Patienten mit einer Krebserkrankung (51,6%
Frauen, Alter 18–75 Jahre, Ø Alter 57 Jahre, 79,7% in Partnerschaft) mit einem
schriftlichen Fragebogen und – zur Diagnostik psychischer Störungen
(4-Wochen-Prävalenz) – mit dem CIDI-O-Interview untersucht. Es werden
Häufigkeiten berechnet und binär-logistische Analysen durchgeführt.
Ergebnisse Personen in Partnerschaft zeigen in den univariaten Analysen im
Merkmal Vorliegen mindestens einer psychischen Störung eine signifikant
geringere Häufigkeit (25,4% vs. 35,3%, p<0,001) und es treten seltener
mehrere Störungen gleichzeitig auf. Auch bezüglich einzelner Störungsklassen
werden partnerschaftsbezogene Unterschiede deutlich. Die multivariate
geschlechtsdifferenzierte Analyse bestätigt diesen Trend. Neben Alter und
Einkommen als signifikante Prädiktoren besteht jedoch nur bei Männern ein
signifikanter protektiver Einfluss der Partnerschaft hinsichtlich des Auftretens
einer psychischen Störung (OR=2,5, p<0,001).
Diskussion Bislang ist der Zusammenhang von Partnerschaftsstatus,
Geschlecht und psychischen Störungen bei Patienten mit Krebs nur ansatzweise
untersucht. Die in unseren Analysen gefundenen Zusammenhänge sollten
weiterverfolgt werden, insbesondere der protektive Aspekt einer Partnerschaft
für das Auftreten einer psychischen Störung.
Schlussfolgerung Im Rahmen psychoonkologischer Versorgung sollte der
Partnerschaftsstatus als möglicher Risikofaktor für das Auftreten psychischer
Störungen, insbesondere bei den Männern, berücksichtigt werden.
Early Challenges: A Case Report of a Premature Baby with Down Syndrome and Uterine Rupture
Authors: Jerbi Bilel , Chourou Hajer , Ben Aziza Rim , Belhadj Ammar Wafa , Kacem Samia , Achour Radhouane 
Published: September 2024

Abstract
Introduction Uterine rupture represents one of the most severe obstetric affections. It is defined as a complete or a partial tearing of the uterine wall. Women with a prior cesarean section are reported to have a higher risk of having this situation. Moreover, maternal death and most of all middle- and long-term adverse consequences remain a great preoccupation. On another scale, neonatal death and ulterior deterioration remain very high, especially in low-income countries.
Case Description A 24-year-old woman with a history of previous cesarean section presented at 35 weeks of gestation with pelvic pain without bleeding. Emergency cesarean section revealed a complete uterine rupture at the scar site from the previous cesarean section. Remarkably, the fetus managed to seal the rupture using the right temporal region, forearm, and right leg, avoiding significant complications. The mother had an uncomplicated postoperative course and was discharged after 48 hours of surveillance.
Conclusion We present with this case an extraordinary case of a uterine rupture where both mother and child had a good outcome. This rare evolution was reported only one time in literature. For this reason, a history of caesarean delivery might present a huge challenge for obstetricians and neonates.
Transfer RNAs are Linked to Ischemic Stroke and Major Bleeding in Patients with End-Stage Kidney Disease
Authors: Nopp Stephan , Königsbrügge Oliver , Schmaldienst Sabine , Säemann Marcus , Pabinger Ingrid , Nossent Anne Yaël , Ay Cihan 
Published: September 2024

Background Patients with end-stage kidney disease (ESKD) are at very high risk for thromboembolism and bleeding. This study aimed to identify small noncoding RNAs (sncRNAs), specifically microRNAs and transfer-RNA (tRNA)-derived fragments (tRFs), as potential novel biomarkers for predicting thromboembolism and bleeding in this high-risk population.
Methods In this sncRNA discovery research, we leveraged the VIVALDI cohort, consisting of 625 ESKD patients on hemodialysis, to conduct two nested case–control studies, each comprising 18 participants. The primary outcomes were ischemic stroke in the first study and major bleeding in the second. Plasma samples were processed using the miND pipeline for RNA-seq analysis to investigate differential expression of microRNAs and tRNA/tRFs between cases and their respective matched controls, with results stringently adjusted for the false discovery rate (FDR).
Results No significant differential expression of microRNAs for either ischemic stroke or major bleeding outcomes was observed in the two nested case–control studies. However, we identified four tRNAs significantly differentially expressed in ischemic stroke cases and seven in major bleeding cases, compared with controls (FDR < 0.1). Coverage plots indicated that specific tRNA fragments (tRFs), rather than full-length tRNAs, were detected, however. Alternative mapping approaches revealed challenges and technical limitations that precluded in-depth differential expression analyses on these specific tRFs. Yet, they also underscored the potential of tRNAs and tRFs as markers for thromboembolism and bleeding.
Conclusion While microRNAs did not show significant differential expression, our study identifies specific tRNAs/tRFs as potential novel biomarkers for ischemic stroke and major bleeding in ESKD patients.
Measurement of Stark-split beam and carbon charge exchange emissions for simultaneous B-field and temperature/rotation analysis at DIII-D
Authors: Albosta R. , Geiger B. , McKee G. , Marchuk O. , Gallenberger T. , Khabanov F. , Den Hartog D. 
Published: September 2024

A set of two newly designed, single-channel Czerny–Turner spectrometers has been deployed at the DIII-D tokamak for measurements of the motional Stark effect (MSE) split beam emission and the C6+ (CVI) carbon charge exchange recombination (CER) emission at high spectral (δλ = 0.13 nm) and temporal (1–5 kHz) resolution. High throughput optics (f/# = 2.8) allow for good signal-to-noise at high time resolution using fast EMCCD detectors. The MSE emission allows for spectral fitting of the magnitude and direction of the local B-field, while the carbon emission yields local ion temperature and toroidal rotation information. To reduce so-called Doppler broadening of the MSE emission, a new channel-specific variable lens-masking approach has been developed. Experimental data collected from the 2023 DIII-D experimental campaign demonstrate the signal quality and instrument fidelity for both diagnostic measurements. Moreover, initial CER data analysis shows a clear evolution of the toroidal rotation during edge localized modes. Initial progress on the advanced MSE model, including a new validated ray-trace model of the DIII-D collection optics, is shown via sensitivity analysis.
An analog of the pendulum/rotor system with elementary solutions
Authors: Robinett R W 
Published: September 2024

Abstract
We explore a novel one-dimensional model potential, V(x), which mimics many aspects of the classic pendulum/rotor system, including low- and high-energy limits which are exactly solvable, as well as a divergent period separating the two. Using energy methods, the classical period, T(E), is evaluated in closed form for all values of the energy (0 < E < ∞) in terms of simple logarithmic functions, in contrast to the pendulum/rotor system where the results require elliptic integrals. We find an unexpected duality between the exact expressions for the period on either side of the divergence. Using both energy methods, and a Newton’s law differential equation approach, we present closed form expressions for trajectory solutions, x(t) and p(t) = mv(t) (or the linear momentum), in terms of hyperbolic functions, which explicitly give the same values for T(E).
Classification and analysis of simple pendulum using artificial neural network approach
Authors: Wadhwa Adya , Wadhwa Ajay 
Published: September 2024

Abstract
We describe an artificial neural network (ANN) for analyzing damped oscillations in a simple pendulum system by using a machine learning (ML) algorithm. We have first shown how to construct a simple ANN consisting of three layers-input, hidden and output, with each layer being composed of neurons representing a relevant feature of the oscillating pendulum. The train and test datasets for the ANN have been taken from the experimental data collected by using the methodology of a previously communicated work. A ML optimization algorithm called stochastic gradient descent has been employed in the neural network to predict the type of pendulum according to the values of the mass, size and damping coefficient of the pendulum.
A microlocal and visual comparison of 2D Kirchhoff migration formulas in seismic imaging*
Authors: Ganster Kevin , Todd Quinto Eric , Rieder Andreas 
Published: September 2024

AbstractThe termKirchhoff migrationrefers to a collection of approximate linearized inversion formulas for solving the inverse problem of seismic tomography which entails reconstructing the Earth’s subsurface from reflected wave fields. A number of such formulas exists, the first dating from the 1950 s. As far as we know, these formulas have not yet been mathematically compared with respect to their imaging properties. This shortcoming is to be alleviated by the present work: we systematically discuss the advantages and disadvantages of the formulas in 2D from a microlocal point of view. To this end we consider the corresponding imaging operators in an unified framework as pseudodifferential or Fourier integral operators. Numerical examples illustrate the theoretical insights and allow a visual comparison of the different formulas.
Symmetry-controlled SrRuO3/SrTiO3/SrRuO3 magnetic tunnel junctions: spin polarization and its relevance to tunneling magnetoresistance
Authors: Samanta Kartik , Tsymbal Evgeny Y 
Published: September 2024

Abstract
Magnetic tunnel junctions (MTJs), that consist of two ferromagnetic electrodes separated by an insulating barrier layer, have non-trivial fundamental properties associated with spin-dependent tunneling. Especially interesting are fully crystalline MTJs where spin-dependent tunneling is controlled by the symmetry group of wave vector. In this work, using first-principles quantum-transport calculations, we explore spin-dependent tunneling in fully crystalline SrRuO3/SrTiO3/SrRuO3 (001) MTJs and predict tunneling magnetoresistance (TMR) of nearly 3000%. We demonstrate that this giant TMR effect is driven by symmetry matching (mismatching) of the incoming and outcoming Bloch states in the SrRuO3 (001) electrodes and evanescent states in the SrTiO3 (001) barrier. We argue that under the conditions of symmetry-controlled transport, spin polarization, whatever definition is used, is not a relevant measure of spin-dependent tunneling. In the presence of diffuse scattering, however, e.g. due to localized states in the band gap of the tunnel barrier, symmetry matching is no longer valid and TMR in SrRuO3/SrTiO3/SrRuO3 (001) MTJs is strongly reduced. Under these conditions, the spin polarization of the interface transmission function becomes a valid measure of TMR. These results provide an important insight into understanding and optimizing TMR in all-oxide MTJs.
ESMDNN-PPI: a new protein–protein interaction prediction model developed with protein language model of ESM2 and deep neural network
Authors: Li Yane , Wang Chengfeng , Gu Haibo , Feng Hailin , Ruan Yaoping 
Published: September 2024

Abstract
Protein–protein interaction (PPI) plays an important role in the biological process. While, there are limitations of long spend time and high labor cost in traditional lab based PPIs detection approaches. Although many computation-based methods have been proposed for prediction of PPIs, achieving high predictive performance and overcoming low generalization performance remain challenging issues. This study developed a novel PPIs prediction method by combining protein language model (PLM) of ESM2 and deep neural network, which show high predictive and generalization performance. Specifically, after protein-coding of protein sequence, the PLM of ESM2 is embedded. Then pre-training weight which trained on human dataset was transferred to other species dataset. The generalization of the model we established is tested on four independent datasets. The results show that values of area under precision–recall curve (AUPR) and area under the receiver operating characteristic (ROC) curve achieved 93.06% and 98.69% on human dataset respectively. AUPR values achieved 87.54%, 84.95%, 81.99%, and 66.23% on datasets of Mus musculus, Drosophila melanogaster, Caenorhabditis elegans and Saccharomyces cerevisiae, which are higher of 13.61%–78.78%, 11.35%–75.71%, 12.76%–73.1% and 11.77%–56.94% than multilayer perceptron + convolutional neural network (MLP + CNN), MLP + gated recurrent unit (GRU) and MLP + CNN + GRU based models on these four dataset respectively. The results indicate that this PPIs prediction method we developed can extract features that better characterize the nature of PPIs with protein sequence, and achieving a high predictive and generalization performance for predicting PPIs.
MAFNet: a two-stage multiple attention fusion network for partial-to-partial point cloud registration
Authors: Chen Xinyu , Luo Jiahui , Ren Yan , Cui Tong , Zhang Meng 
Published: September 2024

Abstract
3D point cloud registration is a critical technology in the fields of visual measurement and robot automation processing. In actual large-scale industrial production, the accuracy of point cloud registration directly affects the quality of automated welding processes. However, most existing methods are confronted with serious challenges such as the failure of partial-to-partial point cloud model registration when facing robot automatic processing guidance and error analysis work. Therefore, this paper proposes a novel two-stage network architecture for point cloud registration, which aims at robot pose adjustment and visual guidance in the field of automated welding by using 3D point cloud data. Specifically, we propose a neighborhood-based multi-head attention module in the coarse registration stage. The neighborhood information of each point can be aggregated through focusing on different weight coefficients of multi-head inputs. Then the spatial structure features which is used to establish the overlapping constraint of point clouds are obtained based on above neighborhood information. In the fine registration stage, we propose the similarity matching removal module based on multiple attention fusion features to explore deeper features from different aspects. By using deep fusion features to guide the similarity calculation, the interference of non-overlapping points is removed to achieve the finer registration. Eventually, we compare and analyze the proposed method with the SOTA ones through several error metrics and overlap estimation experiments based on the ModelNet40 dataset. The test results indicate that our method, relative to other mainstream techniques, achieves lower error rates and the most superior accuracy of 98.61% and recall of 98.37%. To demonstrate the generalization performance of proposed algorithm, extensive experiments on the Stanford 3D Scanning Repository, 7-Scenes and our own scanning dataset using partially overlapping point clouds individually under clean and noisy conditions show the validity and reliability of our proposed registration network.
Monocular visual detection of coal flow rate in scraper conveyor based on template matching background differencing
Authors: Zheng Guangyu , Ding Hua , Dong Xiaoxin , Li Ning , Zhang Junhu , Zhao Junhu , Wang Bao 
Published: September 2024

Abstract
The monitoring of coal flow is a crucial aspect of the intelligent regulation and control of comprehensive mining equipment. In recent years, machine vision technology has become a mainstream method for quickly and efficiently extracting coal flow information. However, the majority of research in this field has focused on belt conveyors, with relatively limited investigation into the use of this technology with scraper conveyors. In order to address the need for monitoring coal flow in scraper conveyors, a monocular visual detection method of coal flow rates based on template matching-background differencing is proposed. First, the region of interet in the images captured using a monocular camera mounted at a specific location is quickly identified using an enhanced template matching method. Second, the image motion region is segmented using interframe and background differencing. Finally, the coal flow rate is calculated on the basis of the number of pixel points in the segmented image. Experimental verification is performed using scraper conveyor test bench and real underground data. The results demonstrate that the proposed coal flow detection method is capable of achieving real-time detection of coal flow in scraper conveyor and provides a theoretical basis for the monitoring of coal flow of the scraper conveyor.
Observation of internal transport barrier evolution in ELMy H-mode plasma in the EAST
Authors: Yu L , Li G S , Wu M Q , Zhou Z , Zhang T , Wu X H , Geng K N , Li G Q , Ye K X , Huang J , Zhou Z Q , Yang S Q , Ma J Y , Wen F , Chu Y Q , Zang Q , Li Y Y , Zhao H L , Gao X 
Published: September 2024

Abstract
Research shows that there is an asynchronous evolution process between the internal transport barriers (ITBs) formed in three channels (ion temperature, electron temperature, and density). Under the condition of a low toroidal magnetic field (

B
t

∼ 1.6 T), the electron density ITB is the easiest to implement through the fueling effect of the first neutral beam injection with the least heating power. However, the

n
e

ITB appears to be independent of heating power. The ion temperature ITB can be formed without reaching the threshold power under the condition of strong E × B shear flow, while maintaining ITBs in ion and electron channels simultaneously requires higher heating power. Moreover, the foot of the

T
i

ITB formed in the strong E × B shear case is located outside that in the fishbone case, while the ITB strength remains almost unchanged. The fishbone activities do not always occur prior to ITB formation, as our previous results have shown, suggesting that there may be no direct relationship between the physical process of ITB triggering and fishbone activities.
The effect of Bi3+ doping on the structural, magnetic, dielectric, optical, and photocatalytic efficiency of Mg-Ni ferrite nanoparticles
Authors: Al-Bassami N S 
Published: September 2024

Abstract
Ferrite nanoparticles with the general formula Mg0.7Ni0.3BixFe2-xO4 (MNB) (0 ≤ x ≤ 0.1, Δx = 0.02) were prepared by the citrate combustion method. X-ray diffraction (XRD) results confirmed the spinel single-phase with crystallite size varied from 30.68 to 43.74 ± 0.01 nm. Scanning electron microscopes with elemental mapping conformed to the nano-nature of the MNB samples with all the constituents present without secondary elements. The sample Mg0.7Ni0.3Bi0.02Fe1.98O4 has the highest saturation magnetization of 31.06 ± 0.01 emu g−1. The sample Mg0.7Ni0.3Bi0.08Fe1.92O4 has the lowest coercivity of 31.06 ± 0.01 G. The high-frequency response of the MNB nanoferrites allows them to be used at frequencies around 6.48± 0.01–6.87± 0.01 GHz. The nanoferrite Mg0.7Ni0.3Bi0.1Fe1.9O4 has notable dielectric parameters at 300 K and 50 Hz: the highest dielectric constant (747.93 with enhancing ratio 371%) and the highest conductivity (26.14 μ(Ω.m)−1 with enhancing ratio 288%). The Mg0.7Ni0.3Bi0.08Fe1.92O4 sample has a loss of 8.65 with an enhancing ratio of 56.79% compared to the loss of the pristine Mg0.7Ni0.3Fe2O4 sample of 15.23. Diffuse reflectance (DR) spectroscopy showed an irregular trend for the band gap values with increasing Bi3+ content, where the nanoferrite Mg0.7Ni0.3Bi0.1Fe1.9O4 had the lowest energy gap of 2 eV. The sample Mg0.7Ni0.3Bi0.1Fe1.9O4 exhibited the maximum photodegradation efficiency (96.16%) for rhodamine B (RhB) dye, with outstanding stability after five cycles (96.16, 95.92, 95.71, 95.56, and 95.23%, respectively). The current work has shown the capability to customize ferrite MNB for soft ferrite applications and to eliminate hazardous RhB from water.