bluuebunny/crossref_metadata_2025_split huggingface dataset:
Improved performance of BDS-3 time and frequency transfer based on an epoch differenced model with receiver clock estimation
Authors: Zhang Pengfei , Yang Yuanxi , Tu Rui , Gao Yuping , Wang Bing 
Published: January 2024

Abstract
The carrier phase (CP) technique based on the BeiDou Global Satellite Navigation System (BDS-3) has proven to be a crucial spatial tool for remote time and frequency transfer. The current CP technique models the receiver clock offset as a white noise stochastic process and easily absorbs some unmodeled errors, thus compromising the time and frequency transfer performance. To further improve the performance of time and frequency transfer, a new BDS-3 receiver clock estimation algorithm based on the epoch difference (ED) model is presented, and the mathematical principles and applied modes are discussed. The algorithm makes full use of both observations of the current epoch and practical variations of the receiver clock offset, further improving the performance of time and frequency transfer. Five Multi-Global Navigation Satellite System Experiment network stations equipped with various types of receivers and antennas with dual-frequency BDS-3 signals were used to establish four time transfer links (i.e., AMC4–PTBB, BRUX–PTBB, OP71–PTBB, and WTZS–PTBB) to evaluate their effectiveness. The ED model improves all the four time links in terms of noise level, with improvements of 17.0%, 18.3%, 20.3%, and 5.9%, respectively, when compared with the results from a non-ED model. The ED model outputs were better than the raw solutions in terms of frequency stability at all time links, particularly for average time intervals (tau) < 1000 s. The mean improvement was 8.1% for AMC4–PTBB, 16.1% for BRUX–PTBB, 10.0% for OP71–PTBB, and 18.6% for WTZS–PTBB when the average time (tau) was less than 1000 s.
Coronary vessel segmentation in coronary angiography with a multi-scale U-shaped transformer incorporating boundary aggregation and topology preservation
Authors: Wang Guangpu , Zhou Peng , Gao Hui , Qin Zewei , Wang Shuo , Sun Jinglai , Yu Hui 
Published: January 2024

Abstract
Coronary vessel segmentation plays a pivotal role in automating the auxiliary diagnosis of coronary heart disease. The continuity and boundary accuracy of the segmented vessels directly affect the subsequent processing. Notably, during segmentation, vessels with severe stenosis can easily cause boundary errors and breakage, resulting in isolated islands. To address these issues, we propose a novel multi-scale U-shaped transformer with boundary aggregation and topology preservation (UT-BTNet) for coronary vessel segmentation in coronary angiography. Specifically, considering the characteristics of coronary vessels, we first develop the UT-BTNet for coronary vessels segmentation, which combines the advantages of a convolutional neural networks (CNN) and a transformer, and is able to effectively extract the local and global features of angiographic images. Secondly, we innovatively employ boundary loss and topological loss in two stages, in addition to the traditional losses. In the first stage, boundary loss is adopted, which has the effect of boundary aggregation. In the second stage, topological loss is applied to preserve the topology of the vessels, after the network converges. In the experiment, in addition to the two metrics of Dice and intersection over union (IoU), we specifically propose two metrics of boundary intersection over union (BIoU) and Betti error to evaluate boundary accuracy and the continuity of segmentation results. The results show that the Dice is 0.9291, the IoU is 0.8687, the BIoU is 0.5094, and the Betti error is 0.3400. Compared with the other state-of-the-art methods, UT-BTNet achieves better segmentation results, while ensuring the continuity and boundary accuracy of the vessels, indicating its potential clinical value.
Integrating microdosimetric in vitro RBE models for particle therapy into TOPAS MC using the MicrOdosimetry-based modeliNg for RBE ASsessment (MONAS) tool
Authors: Cartechini Giorgio , Missiaggia Marta , Scifoni Emanuele , La Tessa Chiara , Cordoni Francesco G 
Published: January 2024

Abstract

Objective. In this paper, we present MONAS (MicrOdosimetry-based modelliNg for relative biological effectiveness (RBE) ASsessment) toolkit. MONAS is a TOPAS Monte Carlo extension, that combines simulations of microdosimetric distributions with radiobiological microdosimetry-based models for predicting cell survival curves and dose-dependent RBE. Approach. MONAS expands TOPAS microdosimetric extension, by including novel specific energy scorers to calculate the single- and multi-event specific energy microdosimetric distributions at different micrometer scales. These spectra are used as physical input to three different formulations of the microdosimetric kinetic m
odel, and to the generalized stochastic microdosimetric model (GSM2), to predict dose-dependent cell survival fraction and RBE. MONAS predictions are then validated against experimental microdosimetric spectra and in vitro survival fraction data. To show the MONAS features, we present two different applications of the code: (i) the depth-RBE curve calculation from a passively scattered proton SOBP and monoenergetic 12C-ion beam by using experimentally validated spectra as physical input, and (ii) the calculation of the 3D RBE distribution on a real head and neck patient geometry treated with protons. Main results. MONAS can estimate dose-dependent RBE and cell survival curves from experimentally validated microdosimetric spectra with four clinically relevant radiobiological models. From the radiobiological characterization of a proton SOBP and 12C fields, we observe the well-known trend of increasing RBE values at the distal edge of the radiation field. The 3D RBE map calculated confirmed the trend observed in the analysis of the SOBP, with the highest RBE values found in the distal edge of the target. Significance. MONAS extension offers a comprehensive microdosimetry-based framework for assessing the biological effects of particle radiation in both research and clinical environments, pushing closer the experimental physics-based description to the biological damage assessment, contributing to bridging the gap between a microdosimetric description of the radiation field and its application in proton therapy treatment with variable RBE.
First experimental evaluation of count-rate performance for micrometre resolution deep silicon detector
Authors: Jin Zihui , Brunskog Rickard , Danielsson Mats , Persson Mats 
Published: January 2024

Abstract

Objective. An ultra-fine-pitch deep silicon detector has been developed for clinical photon-counting computed tomography (CT). With a small pixel size of 14 × 650 μm2, it has shown potential to reach micrometre spatial resolution in previous simulation studies. A detector prototype with such geometry has been manufactured, and we report on the first experimental evaluation of its count-rate performance. Approach. The measurement was carried out at MAX IV synchrotron laboratory with 35 keV monochromatic x-rays. By inserting tungsten attenuators of 50, 75, 100, 150, 200, 225, 325

μ

m-thicknesses into the beam, the response of the detector to fluence rates from 3.3 × 107 to 1.3 × 1011 mm−2 s−1 was characterized. Main results. The measurement result showed that the detector exhibited count rate linearity up to 6.66 × 108 mm−2 s−1 with 13% count loss and was still functional at count rate up to 2.9 × 1010 mm−2 s−1. A semi-nonparalyzable dead-time model was fitted to the count-rate behaviour of the detector, showing great agreement with the measured data, with an estimated nonparalyzable dead time of 2.9 ns. Significance. This is the first experimental evaluation of the count-rate performance for a deep silicon detector with such small pixel geometry. The results suggest that this type of detector shows the potential to be used at fluence rates encountered in clinical CT with little count loss due to pile-up.
A generalized model for monitor units determination in ocular proton therapy using machine learning: A proof-of-concept study
Authors: Fleury Emmanuelle , Herault Joël , Spruijt Kees , Kouwenberg Jasper , Angellier Gaëlle , Hofverberg Petter , Horwacik Tomasz , Kajdrowicz Tomasz , Pignol Jean-Philippe , Hoogeman Mischa , Trnková Petra 
Published: January 2024

Abstract

Objective. Determining and verifying the number of monitor units is crucial to achieving the desired dose distribution in radiotherapy and maintaining treatment efficacy. However, current commercial treatment planning system(s) dedicated to ocular passive eyelines in proton therapy do not provide the number of monitor units for patient-specific plan delivery. Performing specific pre-treatment field measurements, which is time and resource consuming, is usually gold-standard practice. This proof-of-concept study reports on the development of a multi-institutional-based generalized model for monitor units determination in proton therapy for eye melanoma treatments. Approach. To cope with the small number of patients being treated in proton centers, three European institutes participated in this study. Measurements data were collected to address output factor differences across the institutes, especially as function of field size, spread-out Bragg peak modulation width, residual range, and air gap. A generic model for monitor units prediction using a large number of 3748 patients and broad diversity in tumor patterns, was evaluated using six popular machine learning algorithms: (i) decision tree; (ii) random forest, (iii) extra trees, (iv) K-nearest neighbors, (v) gradient boosting, and (vi) the support vector regression. Features used as inputs into each machine learning pipeline were: Spread-out Bragg peak width, range, air gap, fraction and calibration doses. Performance measure was scored using the mean absolute error, which was the difference between predicted and real monitor units, as collected from institutional gold-standard methods. Main results. Predictions across algorithms were accurate within 3% uncertainty for up to 85.2% of the plans and within 10% uncertainty for up to 98.6% of the plans with the extra trees algorithm. Significance. A proof-of-concept of using machine learning-based generic monitor units determination in ocular proton therapy has been demonstrated. This could trigger the development of an independent monitor units calculation tool for clinical use.
LVONet: automatic classification model for large vessel occlusion based on the difference information between left and right hemispheres
Authors: Ma Yuqi , Chen Shanxiong , Xiong Hailing , Yao Rui , Zhang Wang , Yuan Jiang , Duan Haowei 
Published: January 2024

Abstract

Objective. Stroke is a highly lethal condition, with intracranial vessel occlusion being one of its primary causes. Intracranial vessel occlusion can typically be categorized into four types, each requiring different intervention measures. Therefore, the automatic and accurate classification of intracranial vessel occlusions holds significant clinical importance for assessing vessel occlusion conditions. However, due to the visual similarities in shape and size among different vessels and variations in the degree of vessel occlusion, the automated classification of intracranial vessel occlusions remains a challenging task. Our study proposes an automatic classification model for large vessel occlusion (LVO) based on the difference information between the left and right hemispheres. Approach. Our approach is as follows. We first introduce a dual-branch attention module to learn long-range dependencies through spatial and channel attention, guiding the model to focus on vessel-specific features. Subsequently, based on the symmetry of vessel distribution, we design a differential information classification module to dynamically learn and fuse the differential information of vessel features between the two hemispheres, enhancing the sensitivity of the classification model to occluded vessels. To optimize the feature differential information among similar vessels, we further propose a novel cooperative learning loss function to minimize changes within classes and similarities between classes. Main results. We evaluate our proposed model on an intracranial LVO data set. Compared to state-of-the-art deep learning models, our model performs optimally, achieving a classification sensitivity of 93.73%, precision of 83.33%, accuracy of 89.91% and Macro-F1 score of 87.13%. Significance. This method can adaptively focus on occluded vessel regions and effectively train in scenarios with high inter-class similarity and intra-class variability, thereby improving the performance of LVO classification.
LRENet: a location-related enhancement network for liver lesions in CT images
Authors: Guo Shuli , Wang Hui , Agaian Sos , Han Lina , Song Xiaowei 
Published: January 2024

Abstract

Objective. Liver cancer is a major global health problem expected to increase by more than 55% by 2040. Accurate segmentation of liver tumors from computed tomography (CT) images is essential for diagnosis and treatment planning. However, this task is challenging due to the variations in liver size, the low contrast between tumor and normal tissue, and the noise in the images. Approach. In this study, we propose a novel method called location-related enhancement network (LRENet) which can enhance the contrast of liver lesions in CT images and facilitate their segmentation. LRENet consists of two steps: (1) locating the lesions and the surrounding tissues using a morphological approach and (2) enhancing the lesions and smoothing the other regions using a new loss function. Main results. We evaluated LRENet on two public datasets (LiTS and 3Dircadb01) and one dataset collected from a collaborative hospital (Liver cancer dateset), and compared it with state-of-the-art methods regarding several metrics. The results of the experiments showed that our proposed method outperformed the compared methods on three datasets in several metrics. We also trained the Swin-Transformer network on the enhanced datasets and showed that our method could improve the segmentation performance of both liver and lesions. Significance. Our method has potential applications in clinical diagnosis and treatment planning, as it can provide more reliable and informative CT images of liver tumors.
Ultrafast diffusion tensor imaging based on deep learning and multi-slice information sharing
Authors: Wang Jiechao , Chen Zunquan , Cai Congbo , Cai Shuhui 
Published: January 2024

Abstract

Objective. Diffusion tensor imaging (DTI) is excellent for non-invasively quantifying tissue microstructure. Theoretically DTI can be achieved with six different diffusion weighted images and one reference image, but the tensor estimation accuracy is poor in this case. Increasing the number of diffusion directions has benefits for the tensor estimation accuracy, which results in long scan time and makes DTI sensitive to motion. It would be beneficial to decrease the scan time of DTI by using fewer diffusion-weighted images without compromising reconstruction quality. Approach. A novel DTI scan scheme was proposed to achieve fast DTI, where only three diffusion directions per slice was required under a specific direction switching manner, and a deep-learning based reconstruction method was utilized using multi-slice information sharing and corresponding T
1-weighted image for high-quality DTI reconstruction. A network with two encoders developed from U-Net was implemented for better utilizing the diffusion data redundancy between neighboring slices. The method performed direct nonlinear mapping from diffusion-weighted images to diffusion tensor. Main results. The performance of the proposed method was verified on the Human Connectome Project public data and clinical patient data. High-quality mean diffusivity, fractional anisotropy, and directionally encoded colormap can be achieved with only three diffusion directions per slice. Significance. High-quality DTI-derived maps can be achieved in less than one minute of scan time. The great reduction of scan time will help push the wider application of DTI in clinical practice.
Reconfigurable phononic crystal sensor for liquid detection
Authors: Zhu Tong , Wang Ting-Ting , Zhou Hong-Tao , Wang Yan-Feng , Wang Yue-Sheng 
Published: January 2024

Abstract
We propose a reconfigurable phononic crystal (PnC) for detecting the concentration of solutes in liquids. The designed PnC consists of liquid-filled hollow pillars and connecting bars. The finite element method is used to calculate the transmission spectra and band structures of PnC filled with various liquids. We fabricate 3D printed samples and conduct corresponding experiments. The results show that sound velocity is the key parameter affecting the frequency of the passing band. As the sound velocity increases, the resonance frequency shifts down. For both NaCl solution and ethanol solution, good linear relationships between the resonance frequency and liquid concentration are established. Experimental results show good agreement with simulations, and stable detection capabilities are maintained in the presence of interference. The impact of fabrication tolerances on sensor performance has also been discussed, with a greater impact on sensitivity and a smaller impact on Q-factor. The reconfigurability also shows the potential of the design of multi-liquid PnC sensors.
Modelling and simulation of a commercially available dielectric elastomer actuator
Authors: Sohlbach Lukas , Hobbani Hamza , Blase Christopher , Perez-Peña Fernando , Schmidt Karsten 
Published: January 2024

Abstract
To fully harness the potential of dielectric elastomer actuators (DEAs) in soft robots, advanced control methods are needed. An important groundwork for this is the development of a control-oriented model that can adequately describe the underlying dynamics of a DEA. Existing models commonly focus on custom-made DEAs, simplifying the modelling process due to well-known specifications and actuator structures. However, for commercial actuators, only information from the manufacturer is available, necessitating verification or completion during the modelling process. The aim of this paper is to explore how a commercial stacked silicone-based DEA can be modelled and how complex the model should be to properly replicate the features of the actuator. The static description has demonstrated the suitability of Hooke’s law. In the case of dynamic description, it is shown that no viscoelastic model is needed for control-oriented modelling. However, if all features of the DEA are considered, the generalised Kelvin–Maxwell model with three Maxwell elements shows good results, stability and computational efficiency.
Studies on the Arrott plots of inhomogeneous first order magnetic phase transitions
Authors: Vinod K , Sathyanarayana A T , Gangopadhyay P , Mani Awadhesh 
Published: January 2024

Abstract
First order ferromagnetic to paramagnetic phase transition is studied using the Bean-Rodbell model within the mean-field framework. The presence of sample inhomogeneities is modeled by considering distributions of the Bean-Rodbell parameter. The corresponding Arrott plots with and without distributions of the Bean-Rodbell parameter are generated and compared. Results indicate that in the presence of inhomogeneities, ferromagnetic to paramagnetic phase transition is broadened. The initial negative slopes in the Arrott plots due to first order phase transition changes to positive slopes for temperatures lying in the broadened tail region of the ferromagnetic to paramagnetic phase transitions. However, the initial negative slopes in the paramagnetic regime (for temperatures above the broadened tail region) are preserved even in the presence of sample inhomogeneities. Also, the effect of inhomogeneities on the magnetic entropy change near the first order transition is studied.
Thermodynamics, phase structure and quasinormal modes for AdS Heyward massive black hole
Authors: Singh Bhupendra , Veer Singh Dharm , Kumar Singh Benoy 
Published: January 2024

Abstract
In this paper, we find the new exact AdS black hole (BH) solution in the presence of massive gravity and nonlinear electrodynamics (NED). The obtained black hole solution (known as AdS Heyward massive BH ) interpolates with the AdS Heyward BH in the absence of graviton mass (m) and massive BH when the magnetic charge is switched off. We calculate the exact expression of thermodynamics quantities including local (heat capacity), global (free energy), and dynamical stability (quasinormal modes) of the obtained BH solution. The heat capacity of the BH diverges where the temperature is maximum and free energy is minimum. We also study the extended thermodynamics of the BH when the cosmological constant (Λ) is treated as the thermodynamics pressure (P = –Λ/8π). We analyze the first and second-order phase transition by studying the behavior of Gibbs free energy and these phase transitions are similar to the van der Walls phase transition. The effect of magnetic charge and graviton mass are opposite to each other on critical values.
Adsorption of NO and NO2 on MoSeS/GaN heterojunction: a first-principles study
Authors: Chen Hui , Pang Jianhua , Zhang Jiwei , Wei Guang , Wei Songrui , Wang Kuanyi , Yan Jin 
Published: January 2024

Abstract
The adsorption behaviors of gas molecules such as Cl2, CO, NO, NO2 and SO2 on MoSSe/GaN, as well as their adsorption energies, charge transfer and adsorption distances are investigated based on the first-principles calculation. The large adsorption energies of Cl2, NO, NO2 and SO2 on MoSSe/GaN indicate that the gas molecules have strong interaction forces with MoSSe/GaN. Probing the changes in the electronic structure, the adsorption of NO and NO2 on MoSSe/GaN introduces magnetism into their systems and reduces the band gaps. The magnetism mainly originates from the O atoms in NO and NO2, while the decrease of the band gap is due to the hybridization of orbitals near the bottom of the conduction band and the top of the valence band with the p orbitals of Ga atoms in MoSSe/GaN. This suggests that MoSSe/GaN can be applied to detect NO and NO2 gas molecules and has potential as a semiconductor gas sensor for these gases.
Proca stars with dark photons from spontaneous symmetry breaking of the scalar field dark matter
Authors: San.-Hernandez Leonardo , Matos Tonatiuh 
Published: January 2024

Abstract
Recently, the Scalar Field Dark Matter (SFDM) model (also known as Fuzzy, Wave, Bose-Einstein, Ultra-light
Dark Matter) has gained a lot of attention because it has provided simpler and more natural explanations for various phenomena observed in galaxies, as a natural explanation for the center of galaxies, the number of satellite galaxies around their host and, more recently, a natural explanation for anomalous trajectories of satellite galaxies called Vast Polar Orbits (VPO) observed in various galaxies. In the present work we study the assumption that the SFDM is a type of charged dark boson whose gauge charge is associated with the Dark Photon (DP). Inspired by these results, we study the formation of compact bosonic objects, such as Boson Stars (BS) and focus on the possibility that, due to spontaneous U(1) SFDM symmetry breaking, the DP may acquire mass and form compact objects like Proca Stars (PS). If this is true, we can expect measurable effects on the electromagnetic field of the Standard Model (SM) of particles due to their interaction with the DP on the formation of compact objects.