bluuebunny/crossref_metadata_2025_split huggingface dataset:
MSCT-UNET: multi-scale contrastive transformer within U-shaped network for medical image segmentation
Authors: Xi Heran , Dong Haoji , Sheng Yue , Cui Hui , Huang Chengying , Li Jinbao , Zhu Jinghua 
Published: December 2023

Abstract

Objective. Automatic mutli-organ segmentation from anotomical images is essential in disease diagnosis and treatment planning. The U-shaped neural network with encoder–decoder has achieved great success in various segmentation tasks. However, a pure convolutional neural network (CNN) is not suitable for modeling long-range relations due to limited receptive fields, and a pure transformer is not good at capturing pixel-level features. Approach. We propose a new hybrid network named MSCT-UNET which fuses CNN features with transformer features at multi-scale and introduces multi-task contrastive learning to improve the segmentation performance. Specifically, the multi-scale low-level features extracted from CNN are further encoded through several transformers to build hierarchical global contexts. Then the cross fusion block fuses the low-level and high-level features in different directions. The deep-fused features are flowed back to the CNN and transformer branch for the next scale fusion. We introduce multi-task contrastive learning including a self-supervised global contrast learning and a supervised local contrast learning into MSCT-UNET. We also make the decoder stronger by using a transformer to better restore the segmentation map. Results. Evaluation results on ACDC, Synapase and BraTS datasets demonstrate the improved performance over other methods compared. Ablation study results prove the effectiveness of our major innovations. Significance. The hybrid encoder of MSCT-UNET can capture multi-scale long-range dependencies and fine-grained detail features at the same time. The cross fusion block can fuse these features deeply. The multi-task contrastive learning of MSCT-UNET can strengthen the representation ability of the encoder and jointly optimize the networks. The source code is publicly available at: https://github.com/msctunet/MSCT_UNET.git.
Optimization of Leaky-ESN parameters based on dragonfly and whale algorithm
Authors: Gong Yuanpeng , Lun Shuxian , Li Ming , Lu Xiaodong 
Published: December 2023

Abstract
In this research, we present a spiral flying mode-based dragonfly algorithm (DA-WOA). Since the traditional Dragonfly algorithm (DA) has a

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flight, the final results obtained are random in nature and have poor global search capability. Changing to a spiral flight pattern not only enlarges the search area but also makes it easy to find the optimal solution. The Swish function is also used to optimize the various flight coefficients of the DA to avoid the gradient descent approaching 0. Adding a fitness coefficient to the final position updating equation can better control the distance between dragonflies and natural enemies and thus find the optimal solution. The convergence speed of the DA-WOA algorithm and the superiority of the global search are verified by 13 test functions. In this paper, DA-WOA is applied to Leaky-ESN to optimize three parameters of the Leaky-ESN system, namely, the leakage rate (α), the spectral radius of the connectivity weight matrix (ρ), and the input scale factor (s

in
). The experimental results demonstrate that DA-WOA has very strong prediction ability as well as very quick convergence speed for the predicted Sin and MG time series.
Development of a Collective Thomson Scattering diagnostic system on SNU X-pinch device
Authors: Lee Jongmin , Kim Jung-Hwa , Park Sungbin , You Yong Sung , Lee Jae-seok , Ghim Y.-c. , Hwang Y.S. 
Published: December 2023

Abstract
The Collective Thomson Scattering (CTS) diagnostic system has been developed for the X-pinch device at Seoul National University. The system is designed to measure various parameters of plasma jets, including plasma temperature and plasma flow velocity. For the flow velocity measurement, the second harmonic Nd:YAG laser and the collection optics are oriented in order to ensure the scattering vector is aligned with the flow direction. The collection optics have been optimized to maximize photon efficiency. Due to the requirement of high spectral dispersion and resolution for the diagnosis of CTS spectra, the spectrometer is designed with a dispersion of 0.004 nm/pixel. The spectral dispersion and resolution of the system has been measured. The CTS diagnostic system will contribute to a deeper understanding of X-pinch plasma dynamics and the development of advanced High Energy Density Plasma (HEDP)-based technologies.
Conceptual study of a two-layer silicon pixel detector to tag the passage of muons from cosmic sources through quantum processors
Authors: Sarıca Ulaşcan 
Published: December 2023

Abstract
Recent studies in quantum computing have shown that quantum
error correction with large numbers of physical qubits are limited
by ionizing radiation from high-energy particles. Depending on the
physical setup of the quantum processor, the contribution of muons
from cosmic sources can constitute a significant fraction of these
interactions. As most of these muons are difficult to stop, we
perform a conceptual study of a two-layer silicon pixel detector to
tag their hits on a solid-state quantum processor instead. With a
typical dilution refrigerator geometry model, we find that
efficiencies greater than 50% are most likely to be achieved if at
least one of the layers is operated at the deep-cryogenic (<1 K)
flanges of the refrigerator. Following this finding, we further
propose a novel research program that could allow the development of
silicon pixel detectors that are fast enough to provide input to
quantum error correction algorithms, can operate at deep-cryogenic
temperatures, and have very low power consumption.
Characterization of iLGADs using soft X-rays
Authors: Liguori Antonio , Barten Rebecca , Baruffaldi Filippo , Bergamaschi Anna , Borghi Giacomo , Boscardin Maurizio , Brückner Martin , Butcher Tim Alexander , Carulla Maria , Centis Vignali Matteo , Dinapoli Roberto , Ebner Simon , Ficorella Francesco , Fröjdh Erik , Greiffenberg Dominic , Hammad Ali Omar , Hasanaj Shqipe , Heymes Julian , Hinger Viktoria , King Thomas , Kozlowski Pawel , Lopez-Cuenca Carlos , Mezza Davide , Moustakas Konstantinos , Mozzanica Aldo , Paternoster Giovanni , Paton Kirsty A. , Ronchin Sabina , Ruder Christian , Schmitt Bernd , Thattil Dhanya , Xie Xiangyu , Zhang Jiaguo 
Published: December 2023

Abstract
Experiments at synchrotron radiation sources and X-ray
Free-Electron Lasers in the soft X-ray energy range
(250 eV–2 keV) stand to benefit from the adaptation of the
hybrid silicon detector technology for low energy photons. Inverse
Low Gain Avalanche Diode (iLGAD) sensors provide an internal gain,
enhancing the signal-to-noise ratio and allowing single photon
detection below 1 keV using hybrid detectors. In addition, an
optimization of the entrance window of these sensors enhances their
quantum efficiency (QE).
In this work, the QE and the gain of a batch of different iLGAD
diodes with optimized entrance windows were characterized using soft
X-rays at the Surface/Interface:Microscopy beamline of the Swiss
Light Source synchrotron. Above 250 eV, the QE is larger than
55% for all sensor variations, while the charge collection
efficiency is close to 100%. The average gain depends on the
gain layer design of the iLGADs and increases with photon energy. A
fitting procedure is introduced to extract the multiplication factor
as a function of the absorption depth of X-ray photons inside the
sensors. In particular, the multiplication factors for electron- and
hole-triggered avalanches are estimated, corresponding to photon
absorption beyond or before the gain layer, respectively.
Beam commissioning of the SARAF Injector and MEBT (protons)
Authors: Weissman L. , Weiss-Babai R. , Barak A. , Choquet A. , Cohen S. , Elimeleh K. , Eliyahu I. , Isakov H. , Gertz I. , Goldberger N. , Goresnic C. , Itzhaki Y. , Kaizer B. , Kreisel A. , Lapin I. , Luner Y. , Paami H. , Perry A. , Polikarpov I. , Reinfeld E. , Rodnizky J. , Shor A. , Shmuely I. , Solomon Y. , Talala Sh. , Tamim N. , Vaintraub S. , Varulker Sh. , Zchut T. , Dumas J. , Chance A. , Chirpaz D. , Darde D. , Desmarchelier G. , Duperrier R. , Ferrand G. , Gaget A. , Gohier F. , Gougnaud F. , Joannem T. , Nadot V. , Pichoff N. , Senee F. , Simon C. , Solenne N. , Uriot D. , Zhao L. 
Published: December 2023

Abstract
The SARAF Phase II cryogenic linear accelerator (linac) is
currently under construction and will be commissioned through
collaborative efforts between SNRC and CEA. The linac will
accelerate a 5 mA pulsed/continuous wave (CW) proton and deuteron
beam, reaching energies of up to 35 MeV and 40 MeV,
respectively. The linac injector consists of Phase I components,
including an ECR ion source, a Low-Energy Beam Transport (LEBT)
line, and a 4-rod Radio Frequency Quadruple (RFQ). Additionally, a
new Medium Energy Beam Transport (MEBT) line has been installed and
integrated into the injector infrastructure. The MEBT includes three
rebuncher resonators, magneto-optical elements, and beam
diagnostics. The MEBT beam diagnostics, as well as the Phase I
D-plate installed downstream, were used for beam
characterization. The downstream-installed Temporary Beam Line (TBL)
and a prototype of the Gallium Indium Liquid Target (GALIT) were
used for commissioning, operating at high beam duty cycles,
including CW mode. The report describes the proton beam
commissioning process of the injector prior to the delivery of the
cryomodules, scheduled for the second half of 2023.
Design and development of MTCA.4-based generic control and data acquisition module for CSNS-II and SAPS-TP
Authors: Long W. , Li X. , Liu Y. , Wu B. , Liu S. , Zhang C. , Chen S. , Zhu J. , Lu Z. , Li X. , Wu J. 
Published: December 2023

Abstract
A new generation of domestically generic control and data
acquisition AMC module has been developed to meet the requirements
of the upgrade and renovation of the RCS LLRF system in the CSNS-II
project, as well as the horizontal testing needs of the
SAPS-TP. This module complies with the MicroTCA.4 standard and
utilizes the Xilinx ZYNQ 7045 SOC as the core data processing
chip. It can simultaneously run FPGA algorithms and the EPICS IOC
remote control application program, serving as an autonomous control
node. The module is equipped with 8 channels of 125 MSPS 16-bit ADC
and 2 channels of 16-bit DAC with a maximum sampling rate of up to
500 MSPS. It also features standard expansion interface, front
optical panel fiber interface, external clock interface, front panel
digital IO interface, and more, catering to the needs of multiple
systems in accelerator facilities such as radio-frequency, beam
diagnostics, and control system. Additionally, the module can
function independently for smaller-scale experimental and testing
systems, such as the LLRF system for horizontal testing of
SAPS-TP. To guarantee the seamless integration and proper operation
of the AMC module within the MicroTCA.4 ecosystem, the MMC mezzanine
card has been developed and thoroughly tested. In order to further
reduce costs for small-scale systems, we optimized the software of
the MMC so that the AMC board can run without the MCH module and
realize self-health monitoring. For common applications, we have
developed a complete software architecture and demo programs,
including FPGA firmware, embedded Linux operating system, kernel
driver modules, and EPICS IOC demo application programs. Detailed
measurements have been conducted on hardware performance such as
SNR, SFDR, channel isolation, as well as software performance
including AXI DMA data bandwidth and system resources utilization,
etc. The measurement results indicate that the module's performance
indicators meet the design requirements.
MXene-GaAs heterojunctions: interface modeling, electronic properties and optical absorption
Authors: Zhou Shuaiqi 
Published: December 2023

Abstract
MXene has gained favor in the field of material research and development due to its excellent two-dimensional structural properties, electronic structure properties, scalability, etc The heterostructures with MXene on one end not only make full use of the characteristics of MXene itself but also have the potential for transformative and application-rich materials when combined with other materials on the opposite end. Inspired by potentials in MXene-contained heterojunctions, this study focuses on the MXene-GaAs heterostructures to better understand their binding characters, structure features, and electron structures. First, the heterostructures (GaAs-Ti3C2O, GaAs-Ti3C2F, and GaAs-Ti3C2OH) are modeled aiming to provide comprehensive insights into their formation. The results reveal that the MXene layer in these heterostructures plays a crucial role in protecting the GaAs crystal, as evidenced by the substantial binding energy observed. Among the three heterostructures, GaAs-Ti3C2OH shows the closest proximity at the interface, attributed to the strong binding between MXene surfaces and Ga atoms. Various analyses, including binding energy calculations, charge polarization evaluations, interface electrostatic potential biases, and electron localization function studies, yield valuable insights into the formation process of these heterojunctions. Moreover, the incorporation of MXene layers enables electron conduction, effectively transforming the heterostructures into Schottky barriers. The density of states (DOS) analysis reveals pronounced peaks near the Fermi levels, indicating excellent electron mobility. Notably, all three heterostructures demonstrate weak magnetic features of the surface GaAs near the Fermi levels, imparted by the MXene layers. Lastly, optical simulations predict an absorption peak located around 4.3 eV for GaAs-Ti3C2OH.
Tabebuia rosea seed extract mediated synthesis of silver nanoparticles with antibacterial, antioxidant, and antiproliferative activities
Authors: Muruganandham Moorthy , Al-Otibi Fatimah Oleyan , Alharbi Raedah Ibrahim , Sivasubramanian Kanagasabapathy , Chaulagain Anon , Velmurugan Palanivel , Basavegowda Nagaraj 
Published: December 2023

Abstract
The green synthesis of silver nanoparticles (AgNPs) using plants has grown in significance recently. The present investigation involved the synthesis of AgNPs utilizing Tabebuia rosea (TR) seeds as a reducing agent. The bioactive potential of the synthesized AgNP was evaluated through antibacterial, antioxidant, and cytotoxicity assays. The confirmation of the formation of AgNPs was achieved through the utilization of UV–vis spectroscopy. The spectroscopic analysis revealed the presence of absorption maxima at 450 nm, which is a distinctive feature of AgNPs. The optimization process for the synthesis of nanoparticles was conducted by varying the pH levels, metal ion (AgNO3), and substrate (Seed extract). The size range of the synthesized nanoparticles was found to be less than 100 nm through the use of scanning electron microscopy (SEM). The profile obtained through energy dispersive x-ray spectroscopy (EDX) analysis of AgNPs exhibited a characteristic optical absorption peak at approximately 3 keV. Further investigation using Fourier transform infrared (FTIR) spectroscopy revealed the involvement of O–H stretching in phenolic compounds and O–H and C=O stretching in carboxylic acids forming AgNPs. The results of the antimicrobial activity assay indicate that the bacteria K. pneumonia exhibited the maximum inhibition zone of 20 ± 0.48 mm, followed by E. faecalis, P. aeruginosa, P. mirabilis, and S. aureus at the highest concentration of 100 mg ml−1, respectively. The DPPH assay findings suggest that the maximum concentration of 500 μg ml−1 of AgNPs exhibited a unique scavenging ability, with a value of 80.98%. Additionally, the application of biologically synthesized AgNPs to treated cells resulted in a cytotoxic effect. The inhibitory concentration (IC50) value of 45  μg ml−1 was determined following a 24 h treatment with human fibroblast cells (L929). Using T. rosea seed to produce AgNPs holds promise for their potential application as nano drugs.
Experimental study of the effect of gas discharge on ionic liquid electrospray
Authors: SHI 石 Wen 文 , YANG 杨 Pengfei 鹏飞 , SONG 宋 Peiyi 培义 , WU 吴 Jian 健 
Published: December 2023

Abstract
Ionic liquid electrospray (ILE) in an atmospheric environment is often accompanied by the gas discharge phenomenon. It interferes with the normal operation of the electrospray and the measurement of experimental parameters. In this study, electrospray experiments were conducted on the ionic liquid EMI-BF4. The observations revealed that the operating modes of the ionic liquid depend on the voltage polarity at high voltages. Additionally, a correspondence between the operating mode of ILE and the current signal in the circuit was established. The shape of the liquid cone formed at the needle tip bore a striking resemblance to the plume of corona discharge, suggesting that the motion trajectory of electrons influenced the curvature of the liquid cone. Steamer theory provided a clear explanation for the change in curvature as the voltage increased.
A finite element-convolutional neural network model (FE-CNN) for stress field analysis around arbitrary inclusions
Authors: Rezasefat Mohammad , Hogan James D 
Published: December 2023

Abstract
This study presents a data-driven finite element-machine learning surrogate model for predicting the end-to-end full-field stress distribution and stress concentration around an arbitrary-shaped inclusion. This is important because the model’s capacity to handle large datasets, consider variations in size and shape, and accurately replicate stress fields makes it a valuable tool for studying how inclusion characteristics affect material performance. An automatized dataset generation method using finite element simulation is proposed, validated, and used for attaining a dataset with one thousand inclusion shapes motivated by experimental observations and their corresponding spatially-varying stress distributions. A U-Net-based convolutional neural network (CNN) is trained using the dataset, and its performance is evaluated through quantitative and qualitative comparisons. The dataset, consisting of these stress data arrays, is directly fed into the CNN model for training and evaluation. This approach bypasses the need for converting the stress data into image format, allowing for a more direct and efficient input representation for the CNN. The model was evaluated through a series of sensitivity analyses, focusing on the impact of dataset size and model resolution on accuracy and performance. The results demonstrated that increasing the dataset size significantly improved the model’s prediction accuracy, as indicated by the correlation values. Additionally, the investigation into the effect of model resolution revealed that higher resolutions led to better stress field predictions and reduced error. Overall, the surrogate model proved effective in accurately predicting the effective stress concentration in inclusions, showcasing its potential in practical applications requiring stress analysis such as structural engineering, material design, failure analysis, and multi-scale modeling.
Correcting for artificial heat in coupled sea ice perturbation experiments
Authors: Fraser-Leach Luke , Kushner Paul , Audette Alexandre 
Published: December 2023

Abstract
A common approach to assessing how polar amplification affects lower latitude climate is to perform coupled ocean-atmosphere experiments in which sea ice is perturbed to a future state. Recent work with a simple 1-dimensional energy balance model (EBM) shows that sea ice perturbation experiments impose an artificial heat flux on the climate system. We explore this effect in a broader range of models and suggest a technique to correct for the artificial heat post-hoc. Our technique successfully corrects for artificial heat in the EBM, and a possible generalization of this approach is developed to correct for artificial heat in an albedo modification experiment in a comprehensive earth system model. Generalizing this technique to sea ice perturbation methodologies that employ a ‘ghost flux’ seen only by the sea ice model would require a better understanding of the effect of ghost fluxes on the surface energy budget. Applying the correction to the comprehensive albedo modification experiment, we find stronger artificial warming than in the EBM. Failing to account for the artificial heat also leads to overestimation of the climate response to sea ice loss (SIL), and can suggest false or artificially strong ‘tugs-of-war’ between low latitude warming and SIL over some fields, for example Arctic surface temperature and zonal wind.
iAMP-Attenpred: a novel antimicrobial peptide predictor based on BERT feature extraction method and CNN-BiLSTM-Attention combination model
Authors: Xing Wenxuan , Zhang Jie , Li Chen , Huo Yujia , Dong Gaifang 
Published: December 2023

Abstract
As a kind of small molecule protein that can fight against various microorganisms in nature, antimicrobial peptides (AMPs) play an indispensable role in maintaining the health of organisms and fortifying defenses against diseases. Nevertheless, experimental approaches for AMP identification still demand substantial allocation of human resources and material inputs. Alternatively, computing approaches can assist researchers effectively and promptly predict AMPs. In this study, we present a novel AMP predictor called iAMP-Attenpred. As far as we know, this is the first work that not only employs the popular BERT model in the field of natural language processing (NLP) for AMPs feature encoding, but also utilizes the idea of combining multiple models to discover AMPs. Firstly, we treat each amino acid from preprocessed AMPs and non-AMP sequences as a word, and then input it into BERT pre-training model for feature extraction. Moreover, the features obtained from BERT method are fed to a composite model composed of one-dimensional CNN, BiLSTM and attention mechanism for better discriminating features. Finally, a flatten layer and various fully connected layers are utilized for the final classification of AMPs. Experimental results reveal that, compared with the existing predictors, our iAMP-Attenpred predictor achieves better performance indicators, such as accuracy, precision and so on. This further demonstrates that using the BERT approach to capture effective feature information of peptide sequences and combining multiple deep learning models are effective and meaningful for predicting AMPs.