bluuebunny/crossref_metadata_2025_split huggingface dataset:
Implementation of a drive cylinder for low collisional experiments on magnetic reconnection
Authors: Gradney P. , Egedal J. , Barnhill I. , Flores-García R. , Greess S. , Kuchta C. , Olson J. , Wallace J. , Yu X. , Forest C. 
Published: December 2023

For most laboratory plasma experiments, Coulomb collisions between the particle species are sufficiently frequent that the particle distribution functions are relaxed to a near-Maxwellian form. This hampers the applicability of such experiments to phenomena observed in tenuous and near-collisionless space plasma. The Terrestrial Reconnection EXperiment (TREX) at the Wisconsin Plasma Physics Laboratory aims to study collisionless reconnection for parameters relevant to the Earth’s magnetosphere. To reduce the role of collisional effects, a reconnection Drive Cylinder has been developed, which increases both the effective system size of the TREX configuration and the rate at which reconnection can be driven. These two effects now permit TREX to reach a kinetic reconnection regime where collisional effects are minimized. The Drive Cylinder is comprised of 12 single loop drive-coils connected in parallel to a 10 kV capacitor bank. Insulated sheets of aluminum are applied to smooth the magnetic fields and enhance the drive efficiency. Following is a description of the technical details and performance of the Drive Cylinder.
Brand transcreation as multimodal configuration
Authors: Wang Junchao , Li Min 
Published: December 2023

AbstractThis article aims at revisiting some key concepts in translation studies by taking into account a new translation or multimodal practice called brand transcreation. Unlike translation proper or brand design proper, brand transcreation involves interlingual and multimodal production in the target culture. It relies on verbal instructions rather than the source text, and intersemiotically, transforms verbal messages into polished multimodal products. Using the systemic-functional sociosemiotic framework, this study delves into the types and dynamic orchestration of semiotic modes involved in two successful brand transcreations from a producer-researcher perspective. The findings reveal that brand transcreation can be defined as a goal-oriented textual production that configures multiple modes and serves specific communicative purposes. In this regard, the concepts of texts, contexts, meaning, and particularly (the categorization of) translation have evolved and require reconsideration.
Conceptualizing health
Authors: Scott Penelope 
Published: December 2023

Abstract
The word ‘health’ is highly polysemous, and many attempts have been made to define its meaning in terms of actual
use and to create a workable and even universal concept of health (Balog
1978; Boruchovitch & Mednick 2002). However, though the meaning of ‘health’
has been debated extensively, as well as the metaphorical conceptualizations of illness (e.g., Sontag 1978), there has been little treatment of how health is metaphorically conceptualized. This article
investigates the meaning of the word ‘health’ in the United States and the United Kingdom, through a search on websites based on
an examination of concordances in the Corpus of Global Web-Based English (GloWbE). It focuses on the senses
emerging from metaphorical cultural conceptualizations. Recent developments in Conceptual Metaphor Theory (Kövecses 2005; Yu 2009) and Cultural Linguistics (Palmer 1996; Sharifian 2011) have increased
the focus on the interaction between cognition and culture. I present an analysis of the conceptual metaphors, proposition
schemas, and image schemas that converge to form a cultural model for health within these speech communities revealing,
for example, that one model sees health in terms of a manageable valuable commodity, which may contribute to health
behaviors such as self-tracking and observation, as discussed by Lupton (2016).
Solving inverse scattering problems via reduced-order model embedding procedures
Authors: Zimmerling Jörn , Druskin Vladimir , Guddati Murthy , Cherkaev Elena , Remis Rob 
Published: December 2023

Abstract
We present a reduced-order model (ROM) methodology for inverse scattering problems in which the ROMs are data-driven, i.e. they are constructed directly from data gathered by sensors. Moreover, the entries of the ROM contain localised information about the coefficients of the wave equation. We solve the inverse problem by embedding the ROM in physical space. Such an approach is also followed in the theory of ‘optimal grids,’ where the ROMs are interpreted as two-point finite-difference discretisations of an underlying set of equations of a first-order continuous system on this special grid. Here, we extend this line of work to wave equations and introduce a new embedding technique, which we call Krein embedding, since it is inspired by Krein’s seminal work on vibrations of a string. In this embedding approach, an adaptive grid and a set of medium parameters can be directly extracted from a ROM and we show that several limitations of optimal grid embeddings can be avoided. Furthermore, we show how Krein embedding is connected to classical optimal grid embedding and that convergence results for optimal grids can be extended to this novel embedding approach. Finally, we also briefly discuss Krein embedding for open domains, that is, semi-infinite domains that extend to infinity in one direction.
Inverse design of dual-band photonic topological insulator beam splitters for efficient light transmission
Authors: Cai Chengxin , Li Xinxin , He Guangchen , Lian Feiyu , Li Mingxing , Wang Qifu , Qin Yao 
Published: December 2023

Abstract
The study of optical topological insulators (PTIs) has revealed intriguing optical properties that diversify the ways in which light can be manipulated, offering significant potential for a wide range of applications. This paper presents a machine learning (ML)-based approach for the reverse design of optical PTIs. Using finite element methods, the paper addresses the challenge of computing the band structure of a dual-band model, enabling the construction of a dataset suitable for ML training. With the goal of maximizing dual-band bandgaps, the study employs the random forest algorithm to predict target parameters and further designs topological edge states. Leveraging these boundary state patterns, two different optical PTI beam splitters are devised, and their transmission coefficients and losses are computed. The results demonstrate that optical devices designed using topological boundary states exhibit enhanced stability and robustness. This approach offers a reliable solution for applications in fields such as optical communication and optical sensing.
Are symmetry protected topological phases immune to dephasing? – a topological electronics perspective
Authors: Midha Siddhant , Jana Koustav , Muralidharan Bhaskaran 
Published: December 2023

Abstract
Harnessing topological phases with their dissipationless edge-channels coupled with the effective engineering of quantum phase transitions is a spinal aspect of topological electronics. The accompanying symmetry protection leads to different kinds of topological edge-channels which include, for instance, the quantum spin Hall (QSH) phase, and the spin quantum anomalous Hall (SQAH) phase. To model realistic devices, it is important to ratify the robustness of the dissipationless edge-channels, which should typically exhibit a perfect quantum of conductance, against various disorder and dephasing. This work is hence devoted to a computational exploration of topological robustness against various forms of dephasing. For this, we employ phenomenological dephasing models under the Keldysh non-equilibrium Green’s function formalism using a model topological device setup on a 2D-Xene platform. Concurrently, we also explicitly add disorder via impurity potentials in the channel and averaging over hundreds of configurations. To describe the extent of robustness, we quantify the decay of the conductance quantum with increasing disorder under different conditions. Our analysis shows that these topological phases are robust to experimentally relevant regimes of momentum dephasing and random disorder potentials. We note that Rashba mixing worsens the performance of the QSH phase and point out a mechanism for the same. Further, we observe that the QSH phase break downs due to spin dephasing, but the SQAH phase remains robust. The SQAH phase shows stark robustness under all the dephasing regimes, and shows promise for realistic device structures for topological electronics applications.
Robust depth-verified RGB-D visual odometry with structural regularities for indoor environments
Authors: Xing Jing , Zhong Qixue , Liu Jian 
Published: December 2023

Abstract
This paper proposes an RGB-D visual odometry method that leverages point, line, plane features and Manhattan structures to achieve robust frame tracking and precise pose estimation, especially in textureless scenes. A validation method is introduced that ensures accurate frame-to-frame rotation estimation by comparing rotation angles computed from multiple Manhattan structures. Depth verification methods involving parameter fitting and outlier removal for point, line, and plane features are implemented by investigating the covariance of sensor depth measurements. We also employ local bundle adjustment in the local mapping thread to refine keyframe poses and landmarks. Comprehensive ablation studies confirm the effectiveness of our contributions. Experimental results on public datasets demonstrate that our method achieves obvious advantages in accuracy and robustness while maintaining real-time performance.
Research on optimization technology of excitation coil in downhole annular flow electromagnetic measurement system
Authors: Ge Liang , Zhou Shuai , Xiao Xiaoting , Zeng Wen , Huang KaiQiang 
Published: December 2023

Abstract
External interference and non-uniform velocity distribution influence the downhole annular flow electromagnetic measurement, reducing detection accuracy. Optimizing the structure of the excitation coil can effectively enhance the uniformity of magnetic field distribution, constituting an essential approach for improving measurement accuracy. Aiming at the excitation coil optimization technology of annular flow electromagnetic measurement system, the study first analyzes and obtains the magnetic field model of the excitation coil based on the principle of annular flow electromagnetic measurement, combined with the theory of vector weight function and puts forward the optimization theory of annular excitation coil. Secondly, the simulation models of rectangular and saddle exciting coils are established using finite element software to analyze the magnetic field distribution of annular flow under different coil and core sizes. The optimal structural parameters of the exciting coils in the annular flow electromagnetic measurement system are determined through evaluation indexes for the magnetic field. Finally, an experimental platform was established for optimizing the electromagnetic measurement of annular flow, and this platform was used to conduct comparative experiments. The experimental results show that the maximum reduction of the optimized system is 3.192%, 1.289%, and 1.813% for the single verification relative value error, single-point verification relative value error, and repeatability compared with the rectangular excitation coil annular flow electromagnetic measurement system. In the flow measurement interval of 25–100 m3 h−1, the single-point verification of the optimized system has a maximum relative display value error of 0.378%, and the measurement accuracy reaches 1.0 level and meets the consistency of the transmission of quantity and value. The research findings hold significant guidance for enhancing the measurement accuracy of the downhole annular flow electromagnetic measurement system.
Application of deep learning in fMRI-based human brain parcellation: a review
Authors: Li Yu , Chen Xun , Ling Qinrui , He Zhiyang , Liu Aiping 
Published: December 2023

AbstractFunctional magnetic resonance imaging (fMRI)-based human brain parcellation reveals brain fundamental organizational principles noninvasively, providing prior guidance for functional analysis and physiological measurement of the brain. Recently, the profound success of deep learning in natural and medical images has attracted an increasing number of deep learning-based investigations for brain parcellation which have accomplished encouraging achievements. This review aims to provide researchers with a comprehensive overview of deep learning-based fMRI brain parcellation and promote the development of this emerging frontier. To begin, we present the history of brain parcellation, emphasizing the current advantages of deep learning in this field. Following a summary of the input modalities, traditional methods, and evaluation criteria for fMRI-based brain parcellation, we comprehensively review the current deep-learning approaches and categorize them based on network architectures. The datasets, models, performance, advantages and limitations are described in detail. Finally, we discuss the current challenges and future directions in methodology, neuroscience, and application, to inspire future investigation in deep learning-based brain parcellation.
A feature extraction method for rotating machinery fault diagnosis based on a target detection index and successive variational mode decomposition
Authors: Cao Chaofan , Zhang Guangtao , Li Zhongliang , Lu Na , Jiang Shuangyun , Wang Lei 
Published: December 2023

Abstract
Rotating machinery feature extraction is critical for the subsequent fault diagnosis and ensuring safe and stable operation. However, the commonly used methods often have limitations, such as the extracted features being redundant or insufficient and the method parameters generally being set based on experience. Although some feature extraction methods apply optimization algorithms for parameter setting, their objective functions are often too simple to obtain favorable fault diagnosis results. To address these issues, a target detection index (TDI) is constructed which can consider the discrimination among features more comprehensively and make the extracted features more sensitive. Furthermore, a fault feature extraction method is proposed based on TDI and successive variational mode decomposition (SVMD). Taking TDI as the objective function, genetic algorithm (GA) is used for the feature selection process and SVMD parameter optimization. The obtained features are then fused and visualized using t-distributed stochastic neighbor embedding and are classified using support vector machines. The Case Western Reserve University data and hydropower generating unit data are employed for method verification. When compared with other decomposition algorithms, the proposed method exhibits great ability at extracting highly sensitive features.
Green to deep-red emissive carbon dot formation by C+ ion implantation on nitrogen beam created self-masked nano-template
Authors: Bhowmick Sudip , Mukherjee Joy , Ghosal Manorama , Nayak Chumki , Satpati Biswarup , Pramanik Goutam , Karmakar Prasanta 
Published: December 2023

Abstract
We report the formation of green to red emissive arrays of carbon dot on silicon-nitride nano-templates by successive implantation of nitrogen and carbon broad ion beams. The patterned nano-templates are formed by 14 keV N2
+ ion-bombardment at grazing incident (70°) on Si. Subsequently, 5 keV C+ ions are implanted at the selective sites of the pyramidal nano-template by taking advantage of the self-masking effect. The nano-pyramidal pattern and the implanted carbon dots at the specific sites are confirmed by atomic force microscopy and cross sectional transmission electron microscopy measurements. The developed carbon dots (CDs) are mostly amorphous and consists of SiC and graphitic nitrogen (CN). G-band and D-band carbons are identified by Raman spectroscopy, while the presence of SiC and CN are detected by XPS measurements. A change of band-gap is observed for C-implanted templates by the UV–vis spectroscopy. Excitation wavelength-dependent photoemission from the dots is found in the green to red region. Maximum intense PL is observed in the green–orange region for excitation wavelength of 425 nm and a redshift of PL with decreasing intensity is observed with the increase of excitation wavelength. The observed photoluminescence is described in terms of the combined effects of quantum confinement, graphitic nitrogen and defect induced additional states formation in the carbon dots. The potential applications of CDs are also addressed.
Bioaerosol size as a potential determinant of airborne E. coli viability under ultraviolet germicidal irradiation and ozone disinfection
Authors: Hao Weixing , Huang Yue-Wern , Wang Yang 
Published: December 2023

Abstract
Ultraviolet germicidal irradiation (UVGI) and ozone disinfection are crucial methods for mitigating the airborne transmission of pathogenic microorganisms in high-risk settings, particularly with the emergence of respiratory viral pathogens such as SARS-CoV-2 and avian influenza viruses. This study quantitatively investigates the influence of UVGI and ozone on the viability of E. coli in bioaerosols, with a particular focus on how E. coli viability depends on the size of the bioaerosols, a critical factor that determines deposition patterns within the human respiratory system and the evolution of bioaerosols in indoor environments. This study used a controlled small-scale laboratory chamber where E. coli suspensions were aerosolized and subjected to varying levels of UVGI and ozone levels throughout the exposure time (2–6 s). The normalized viability of E. coli was found to be significantly reduced by UVGI (60–240 μW s cm−2) as the exposure time increased from 2 to 6 s, and the most substantial reduction of E. coli normalized viability was observed when UVGI and ozone (65–131 ppb) were used in combination. We also found that UVGI reduced the normalized viability of E. coli in bioaerosols more significantly with smaller sizes (0.25–0.5 μm) than with larger sizes (0.5–2.5 μm). However, when combining UVGI and ozone, the normalized viability was higher for smaller particle sizes than for the larger ones. The findings provide insights into the development of effective UVGI disinfection engineering methods to control the spread of pathogenic microorganisms in high-risk environments. By understanding the influence of the viability of microorganisms in various bioaerosol sizes, we can optimize UVGI and ozone techniques to reduce the potential risk of airborne transmission of pathogens.
M3S-Net: multi-modality multi-branch multi-self-attention network with structure-promoting loss for low-dose PET/CT enhancement
Authors: Wang Dong , Jiang Chong , He Jian , Teng Yue , Qin Hourong , Liu Jijun , Yang Xiaoping 
Published: December 2023

Abstract

Objective. PET (Positron Emission Tomography) inherently involves radiotracer injections and long scanning time, which raises concerns about the risk of radiation exposure and patient comfort. Reductions in radiotracer dosage and acquisition time can lower the potential risk and improve patient comfort, respectively, but both will also reduce photon counts and hence degrade the image quality. Therefore, it is of interest to improve the quality of low-dose PET images. Approach. A supervised multi-modality deep learning model, named M3S-Net, was proposed to generate standard-dose PET images (60 s per bed position) from low-dose ones (10 s per bed position) and the corresponding CT images. Specifically, we designed a multi-branch convolutional neural network with multi-self-attention mechanisms, which first extracted features from PET and CT images in two separate branches and then fused the features to generate the final generated PET images. Moreover, a novel multi-modality structure-promoting term was proposed in the loss function to learn the anatomical information contained in CT images. Main results. We conducted extensive numerical experiments on real clinical data collected from local hospitals. Compared with state-of-the-art methods, the proposed M3S-Net not only achieved higher objective metrics and better generated tumors, but also performed better in preserving edges and suppressing noise and artifacts. Significance. The experimental results of quantitative metrics and qualitative displays demonstrate that the proposed M3S-Net can generate high-quality PET images from low-dose ones, which are competable to standard-dose PET images. This is valuable in reducing PET acquisition time and has potential applications in dynamic PET imaging.
Effective DBS treatment improves neural information transmission of patients with disorders of consciousness: an fNIRS study
Authors: Shu Zhilin , Wu Jingchao , Lu Jiewei , Li Haitao , Liu Jinrui , Lin Jianeng , Liang Siquan , Wu Jialing , Han Jianda , Yu Ningbo 
Published: December 2023

Abstract

Objective. Deep brain stimulation (DBS) is a potential treatment that promotes the recovery of patients with disorders of consciousness (DOC). This study quantified the changes in consciousness and the neuromodulation effect of DBS on patients with DOC. Approach. Eleven patients were recruited for this study which consists of three conditions: ‘Pre’ (two days before DBS surgery), ‘Post-On’ (one month after surgery with stimulation), and ‘Post-Off’ (one month after surgery without stimulation). Functional near-infrared spectroscopy (fNIRS) was recorded from the frontal lobe, parietal lobe, and occipital lobe of patients during the experiment of auditory stimuli paradigm, in parallel with the coma recovery scale-revised (CRS-R) assessment. The brain hemodynamic states were defined and state transition acceleration was taken to quantify the information transmission strength of the brain network. Linear regression analysis was conducted between the changes in regional and global indicators and the changes in the CRS-R index. Main results. Significant correlation was observed between the changes in the global transition acceleration indicator and the changes in the CRS-R index (slope = 55.910, p < 0.001, R
2 = 0.732). For the regional indicators, similar correlations were found between the changes in the frontal lobe and parietal lobe indicators and the changes in the CRS-R index (slope = 46.612, p < 0.01, R
2 = 0.694; slope = 47.491, p < 0.01, R
2 = 0.676). Significance. Our study suggests that fNIRS-based brain hemodynamics transition analysis can signify the neuromodulation effect of DBS treatment on patients with DOC, and the transition acceleration indicator is a promising brain functional marker for DOC.