bluuebunny/crossref_metadata_2025_split huggingface dataset:
Diatopic variation as evidence for diachronic changes in the 20th and 21st centuries
Authors: Mihatsch Wiltrud , Vazeilles Ana 
Published: January 2024

Abstract
In Latin American varieties of Spanish there is a series of pragmatic markers that fulfil similar functions.
Tipo, onda and como are mitigators, but they also express
approximation, exemplification, focalisation, discourse structuring and quotation. Pragmatic functions arise in the 20th
century, although approximating como goes back to Old Spanish. Corpus data, metalinguistic comments and
cross-linguistic observations point to the countercultures of the 20th century as a probable trigger leading to
pragmaticalisation. We propose a comparative study of acceptability judgements of the three markers in their Argentinian and
Mexican varieties, contributing to a semantic map showing different stages of pragmaticalisation which, combined with other
data, offers insights into diachronic processes that emerged in the 20th century.
Social indexicality and pragmatic change in the late 20th century
Authors: Ghezzi Chiara 
Published: January 2024

Abstract
This paper considers the role of social indexicality in the pragmatic development of Italian cioè
‘that is’ and tipo ‘type’ in the second half of the 20th century. It analyses listeners’ phone-ins to a radio
station gathered in 1976 and in 2010, together with questionnaires on speakers’ attitudes regarding the use of
cioè and tipo. Firstly, it describes the pragmatic trajectory of the two forms.
Secondly, it discusses how their frequencies and polyfunctionality are subject to rapid changes, and how these changes are
propelled by the social indexicality acquired by the forms. The development of these indexicalities benefits from the
technological innovations of the 20th century, which have a relevant role in the spread of speakers’ interactive
practices.
Phenomenology
Authors: Ostovar-Namaghi Seyyed Ali 
Published: January 2024

Abstract
This chapter aims to explore Phenomenology as
a research methodology. To this end, it will first present its
epistemological, ontological, and axiological underpinnings. It
will then briefly explain its theoretical foundations by
explaining the similarities and differences between
transcendental and interpretative Phenomenology. This is
followed by the principles and properties that differentiate
Phenomenology from other modes of qualitative research.
Subsequently, the purpose of this mode of research is clarified
by explaining and exemplifying the types of research questions
it tackles. More practical aspects of this methodology will then be
explored by illustrating how the data are collected and analysed
and how ethical issues are addressed in the research process and
product. Finally, it will present responses to the critiques
levelled against this research methodology before concluding the
chapter.
Narrative inquiry
Authors: Perrino Sabina M. 
Published: January 2024

Abstract
This chapter describes how narratives can be
useful analytical and methodological tools through a close
analysis of two narrative excerpts that I collected in Senegal
and Northern Italy. Both examples examine how certain
interactional patterns, such as participant transposition and
the co-construction of individuals’ identities and stances, are
enacted and sustained in storytelling practices. These patterns
would not emerge if narratives were not considered as situated
speech events in which speech participants’ interactional moves
(“interactional text”) are as important as the narrative content
(“denotational text”). It is thanks to this
narratives-as-practices approach, versus the more traditional
narratives-as-texts approach, that scholars are able to unveil
participants’ interactional dynamics. The two case studies,
moreover, are fully contextualized and situated.
Slowly rotating ultra-compact Schwarzschild star in the gravastar limit
Authors: Beltracchi Philip , Posada Camilo 
Published: January 2024

Abstract
We reconsider the problem of a slowly rotating homogeneous star, or Schwarzschild star, when its compactness goes beyond the Buchdahl bound and approaches the gravastar limit

R
→
2
M

. We compute surface and integral properties of such configuration by integrating the Hartle–Thorne structure equations for slowly rotating relativistic masses, at second order in angular velocity. In the gravastar limit, we show that the metric of a slowly rotating Schwarzschild star agrees with the Kerr metric, thus, within this approximation, it is not possible to tell a gravastar from a Kerr black hole by any observations from the spacetime exterior to the horizon.
Deep unfolding as iterative regularization for imaging inverse problems
Authors: Cui Zhuo-Xu , Zhu Qingyong , Cheng Jing , Zhang Bo , Liang Dong 
Published: January 2024

Abstract
Deep unfolding methods have gained significant popularity in the field of inverse problems as they have driven the design of deep neural networks (DNNs) using iterative algorithms. In contrast to general DNNs, unfolding methods offer improved interpretability and performance. However, their theoretical stability or regularity in solving inverse problems remains subject to certain limitations. To address this, we reevaluate unfolded DNNs and observe that their algorithmically-driven cascading structure exhibits a closer resemblance to iterative regularization. Recognizing this, we propose a modified training approach and configure termination criteria for unfolded DNNs, thereby establishing the unfolding method as an iterative regularization technique. Specifically, our method involves the joint learning of a convex penalty function using an input-convex neural network to quantify distance to a real data manifold. Then, we train a DNN unfolded from the proximal gradient descent algorithm, incorporating this learned penalty. Additionally, we introduce a new termination criterion for the unfolded DNN. Under the assumption that the real data manifold intersects the solutions of the inverse problem with a unique real solution, even when measurements contain perturbations, we provide a theoretical proof of the stable convergence of the unfolded DNN to this solution. Furthermore, we demonstrate with an example of magnetic resonance imaging reconstruction that the proposed method outperforms original unfolding methods and traditional regularization methods in terms of reconstruction quality, stability, and convergence speed.
Broadband white electroluminescence from a dopant-free OLED comprising pure electromer and electroplex emission
Authors: Barah Dhruvajyoti , Ray Debdutta 
Published: January 2024

Abstract
The utilization of multiple charge transfer complex-based emissions from a bilayer organic device is a low-cost and simple technique to realize white organic light emitting diodes (WOLEDs). In this work, a WOLED structure is presented where a planar heterojunction of 1, 1-bis ((di-4-tolylamino) phenyl) cyclohexane (TAPC)/Bis [2-(diphenylphosphino) phenyl] ether oxide (DPEPO) is employed for white-light generation and thereby eliminating the blend of host and dopants which is typically used as the emissive layer in OLEDs. The WOLED exhibits a broadband emission with a full-width half maxima of 330 nm. A systematic investigation is carried out to interpret the origins of the red (R), yellow (Y), and blue (B) components of the spectrum. It is shown that the electroplex emission originating from TAPC/DPEPO hetero-interface is responsible for the blue emission peak at a wavelength of 488 nm. The electromer emissions from TAPC and DPEPO result in yellow and red emissions with peak intensities at wavelengths 575 nm and 670 nm, respectively. By tuning the relative intensities of the RYB components, the colour of the emitted light from the OLED can be varied and a pure white emission with the Commission International d’Eclairage coordinate of (0.34, 0.36) and colour rendering index of 89 is demonstrated.
Aperiodic dynamical quantum phase transition in multi-band Bloch Hamiltonian and its origin
Authors: Cao Kaiyuan , Guo Hao , Yang Guangwen 
Published: January 2024

Abstract
We investigate the dynamical quantum phase transition (DQPT) in the multi-band Bloch Hamiltonian of the one-dimensional periodic Kitaev model, focusing on quenches from a Bloch band. By analyzing the dynamical free energy and Pancharatnam geometric phase (PGP), we show that the critical times of DQPTs deviate from periodic spacing due to the multi-band effect, contrasting with results from two-band models. We propose a geometric interpretation to explain this non-uniform spacing. Additionally, we clarify the conditions needed for DQPT occurrence in the multi-band Bloch Hamiltonian, highlighting that a DQPT only arises when the quench from the Bloch states collapses the band gap at the critical point. Moreover, we establish that the dynamical topological order parameter, defined by the winding number of the PGP, is not quantized but still exhibits discontinuous jumps at DQPT critical times due to periodic modulation. Additionally, we extend our analysis to mixed-state DQPT and find its absence at non-zero temperatures.
Two-dimensional antiferromagnetic nodal-line semimetal and spin Hall effect in MnC4
Authors: Fernandez H , Gonzalez-Hernandez R , Paez J , Hoat D M , Takeuchi Tan N , Guerrero-Sanchez J , Perez-Tijerina E G 
Published: January 2024

Abstract
Nodal-line semimetals, characterized by Dirac-like crossings along one dimensional k-space lines, represent a unique class of topological materials. In this study, we investigate the intriguing properties of room-temperature antiferromagnetic

M
n

C
4

and its nodal-line features both with and without spin–orbit coupling (SOC). In the absence of SOC, we identify a doubly degenerate Dirac-nodal line, robustly protected by a combination of time-reversal, mirror, and partial-translation symmetries. Remarkably, this nodal line withstands various external perturbations, including isotropic and anisotropic strain, and torsional deformations, due to the ionic-like bonding between Mn atoms and C clusters. With the inclusion of SOC, we observe a distinctive quasi-Dirac-nodal line that emerges due to the interplay between antiferromagnetism and SOC-induced spin-rotation symmetry breaking. Finally, we observed a robust spin Hall conductivity that aligns with the energy range where the quasi-nodal line appears. This study presents a compelling example of a robust symmetry-protected Dirac-nodal line antiferromagnetic monolayer, which has potential for applications in next-generation spintronic devices.
Improved lightweight federated learning network for fault feature extraction of reciprocating machinery
Authors: Zhang Junling , Duan Lixiang , Li Ke , Luo Shilong 
Published: January 2024

Abstract
The working environment of reciprocating machinery is complex, characterized by nonlinear and non-stationary signals. Deep learning can solve the above problems, but it has its own problems such as complex model and large amount of parameters. Additionally, privacy considerations among enterprises prevent data sharing, leading to the emergence of ‘data islands’ and inadequate training of deep learning models. Based on the above analysis, this paper proposes a reciprocating mechanical feature extraction method based on an improved federated lightweight network. A lightweight network SqueezeNet model is used to solve the problems such as long training time of deep learning. By establishing a federated learning framework, the reciprocating mechanical data can be collectively diagnosed across various enterprises, thereby addressing the problem of limited model training caused by insufficient data. Furthermore, to enhance the accuracy of network training and diagnosis, modifications are made to the SqueezeNet network to reduce the number of model parameters while increasing the number and variety of feature extractions. Experimental results demonstrate that when the number of 1 × 1 and 3 × 3 channels is 1–7, the fault diagnosis accuracy is the highest, about 97.96%, which enriches the categories of feature extraction. The number of parameters in In-SqueezeNet is 56% of that in SqueezeNet network model, and the training time is reduced by nearly 15%. The fault diagnosis accuracy is increased from 95.1% to 97.3%, and the diversity of extracted features is increased. Compared with other network models such as ResNet, the improved lightweight federated learning network has a fault diagnosis accuracy of 96.6%, an improvement of 10.6%. At the same time, the training time was reduced to 1982s, a reduction of about 41.5%. The validity of the proposed model is further verified.
A normal tracking differential confocal measurement method for multiple geometric parameters of hemispherical shell resonator with a common reference
Authors: Liu Yuhan , Zhang Xiaocheng , Fu Yuan , Wang Yun , Yao Zhuxian , Zhao Weiqian 
Published: January 2024

Abstract
This paper proposes a normal tracking differential confocal measurement method for the inside and outside surface profiles, shell thickness uniformity, and central asymmetry of inside and outside surfaces of the hemispherical shell resonator (HSR). A differential confocal technique with high-transmittance focusing ability is used to measure a single point on the inside and outside surfaces of the HSR. The normal alignment measurement technique is used to accurately measure the inside and outside surfaces and shell thickness of the HSR with a common reference in one measurement process. The HSR is step-rotated to synchronously collect information on the inside and outside surfaces, and using the differential confocal sensor to measure the different normal-section profiles. The experimental results indicate successful measurement of HSR central asymmetry. The repeated measurement accuracy for the inside and outside surface profiles and thickness uniformity is better than 30 nm.
Carbon nanowall-based gas sensors for carbon dioxide gas detection
Authors: Zhumadilov Rakhymzhan Ye , Yerlanuly Yerassyl , Parkhomenko Hryhorii P , Soltabayev Baktiyar , Orazbayev Sagi A , Bakenov Zhumabay , Ramazanov Tlekkabul S , Gabdullin Maratbek T , Jumabekov Askhat N 
Published: January 2024

Abstract
Carbon nanowalls (CNWs) have attracted significant attention for gas sensing applications due to their exceptional material properties such as large specific surface area, electric conductivity, nano- and/or micro-porous structure, and high charge carrier mobility. In this work, CNW films were synthesized and used to fabricate gas sensors for carbon dioxide (CO2) gas sensing. The CNW films were synthesized using an inductively-coupled plasma (ICP) plasma-enhanced chemical vapor deposition (PECVD) method and their structural and morphological properties were characterized using Raman spectroscopy and electron microscopy. The obtained CNW films were used to fabricate gas sensors employing interdigitated gold (Au) microelectrodes. The gas sensors were fabricated using both direct synthesis of CNW films on interdigitated Au microelectrodes on quartz and also transferring presynthesized CNW films onto interdigitated Au microelectrodes on glass. The CO2 gas-sensing properties of fabricated devices were investigated for different concentrations of CO2 gas and temperature-ranges. The sensitivities of fabricated devices were found to have a linear dependence on the concentration of CO2 gas and increase with temperature. It was revealed that devices, in which CNW films have a maze-like structure, perform better compared to the ones that have a petal-like structure. A sensitivity value of 1.18% was obtained at 500 ppm CO2 concentration and 100 °C device temperature. The CNW-based gas sensors have the potential for the development of easy-to-manufacture and efficient gas sensors for toxic gas monitoring.
Non-contact scintillator imaging dosimetry for total body irradiation in radiotherapy
Authors: Niver Alexander P , Hammer Clifford G , Culberson Wesley S , Jacqmin Dustin , Pogue Brian W 
Published: January 2024

Abstract

Objective. The goal of this work was to assess the potential use of non-contact scintillator imaging dosimetry for tracking delivery in total body irradiation (TBI). Approach. Studies were conducted to measure the time-gated light signals caused by radiation exposure to scintillators that were placed on tissue. The purpose was to assess efficacy in conditions common for TBI, such as the large source to surface distance (SSD) commonly used, the reduced dose rate, the inclusion of a plexiglass spoiler, angle of incidence and effects of peripheral patient support structures. Dose validation work was performed on phantoms that mimicked human tissue optical properties and body geometry. For this work, 1.5 cm diameter scintillating disks were developed and affixed to phantoms under various conditions. A time-gated camera synchronized to the linac pulses was used for imaging. Scintillation intensity was quantified in post processing and the values verified with simultaneous thermolumiescent dosimeter (TLD) measurements. Mean scintillation values in each region were compared to TLD measurements to produce dose response curves, and scatter effects from the spoiler and patient bed were quantified. Main results. The dose determined by scintillators placed in TBI conditions agreed with TLD dose determinations to within 2.7%, and did so repeatedly within 1.0% standard deviation variance. A linear fit between scintillator signal and TLD dose was achieved with an R
2 = 0.996 across several body sites. Scatter from the patient bed resulted in a maximum increase of 19% in dose. Significance. This work suggests that non-contact scintillator imaging dosimetry could be used to verify dose in real time to patients undergoing TBI at the prescribed long SSD and low dose rate. It also has shown that patient transport stretchers can significantly influence surface dose by increasing scatter.
Cone-beam x-ray luminescence computed tomography (CB-XLCT) prototype development and performance evaluation
Authors: Wang Yu-Hong , Jin David Shih-Chun , Wu Tian-Yu , Shen Chieh , Chen Jyh-Cheng , Tseng Snow H , Liu Tse-Ying 
Published: January 2024

Abstract
This study developed a prototype for a rotational cone-beam x-ray luminescence computed tomography (CB-XLCT) system, considering its potential application in pre-clinical theranostic imaging. A geometric calibration method applicable to both imaging chains (XL and CT) was also developed to enhance image quality. The results of systematic performance evaluations were presented to assess the feasibility of commercializing XLCT technology. Monte Carlo GATE simulation was performed to determine the optimal imaging conditions for nanophosphor particles (NPs) irradiated by 70 kV x-rays. We acquired a low-dose transmission x-ray tube and designed a prone positioning platform and a rotating gantry, using mice as targets from commercial small animal μ-CT systems. We then employed the image cross-correlation (ICC) automatic geometric calibration method to calibrate XL and CT images. The performance of the system was evaluated through a series of phantom experiments with a linearity of 0.99, and the contrast-to-noise ratio (CNR) between hydroxyl-apatite (HA) and based epoxy resin is 19.5. The XL images of the CB-XLCT prototype achieved a Dice similarity coefficient (DICE) of 0.149 for a distance of 1 mm between the two light sources. Finally, the final XLCT imaging results were demonstrated using the Letter phantoms with NPs. In summary, the CB-XLCT prototype developed in this study showed the potential to achieve high-quality imaging with acceptable radiation doses for small animals. The performance of CT images was comparable to current commercial machines, while the XL images exhibited promising results in phantom imaging, but further efforts are needed for biomedical applications.
Clinical assessment of deep learning-based uncertainty maps in lung cancer segmentation
Authors: Maruccio Federica Carmen , Eppinga Wietse , Laves Max-Heinrich , Navarro Roger Fonolla , Salvi Massimo , Molinari Filippo , Papaconstadopoulos Pavlos 
Published: January 2024

Abstract

Objective. Prior to radiation therapy planning, accurate delineation of gross tumour volume (GTVs) and organs at risk (OARs) is crucial. In the current clinical practice, tumour delineation is performed manually by radiation oncologists, which is time-consuming and prone to large inter-observer variability. With the advent of deep learning (DL) models, automated contouring has become possible, speeding up procedures and assisting clinicians. However, these tools are currently used in the clinic mostly for contouring OARs, since these systems are not reliable yet for contouring GTVs. To improve the reliability of these systems, researchers have started exploring the topic of probabilistic neural networks. However, there is still limited knowledge of the practical implementation of such networks in real clinical settings. Approach. In this work, we developed a 3D probabilistic system that generates DL-based uncertainty maps for lung cancer CT segmentations. We employed the Monte Carlo (MC) dropout technique to generate probabilistic and uncertainty maps, while the model calibration was evaluated by using reliability diagrams. A clinical validation was conducted in collaboration with a radiation oncologist to qualitatively assess the value of the uncertainty estimates. We also proposed two novel metrics, namely mean uncertainty (MU) and relative uncertainty volume (RUV), as potential indicators for clinicians to assess the need for independent visual checks of the DL-based segmentation. Main results. Our study showed that uncertainty mapping effectively identified cases of under or over-contouring. Although the overconfidence of the model, a strong correlation was observed between the clinical opinion and MU metric. Moreover, both MU and RUV revealed high AUC values in discretising between low and high uncertainty cases. Significance. Our study is one of the first attempts to clinically validate uncertainty estimates in DL-based contouring. The two proposed metrics exhibited promising potential as indicators for clinicians to independently assess the quality of tumour delineation.