bluuebunny/crossref_metadata_2025 huggingface dataset:
Paratextual framing of trans-edited Saudi news reports on the BBC and the Xinhua
(2023)
Authors: BinSultan Nora , Alangari Eithar
Published: November 2024

Since the launch of Vision2030, 2023 has marked significant transformations in Saudi Arabia,
particularly in sports, culture, and technology. This has significantly increased the reporting of international news agencies on
such events, which is, however, absent in the literature of translation studies. There is a notable gap in the literature
regarding the role of mediated paratexts in news trans-editing within the Saudi context. This paper examines how recontextualized
paratexts from Saudi sources contribute to gatekeeping and reframing the perception of Saudi Arabia in global media, focusing on
the comparative case studies on the BBC and the Xinhua. The paper draws on Baker’s (2006) narrative strategies of selective appropriation and
labeling as a methodological framework. The dataset involves a parallel corpus of 304 reports in English and
Arabic from the BBC and the Xinhua published between January and December 2023. The findings
reveal that paratextual frames play a crucial role in shaping how each news agency perceives the transformations in Saudi Arabia
through remarkable reframing in the thematic selections.
Rendition of non-verbal acoustic elements for film audiences with hearing impairments
Authors: Khoshsaligheh Masood , Ahadi Farkoush Nahid , Eriss Azadeh
Published: November 2024

The issue of media accessibility for d/Deaf and hard of hearing (DHH) viewers holds particular importance in
advanced societies, leading to exponential growth in research within the field of subtitling for the d/Deaf and hard of hearing
(SDH). However, limited research has been conducted on media accessibility, especially SDH, resulting in a relatively brief
history of SDH and its quality within Iran. This research aims to determine how non-verbal acoustic elements have been rendered in
subtitle versions of films intended for DHH audiences. Five drama feature films were purposefully selected from the Filimo
platform, the largest video-on-demand (VOD) service in Iran. The study reveals that various strategies were employed in the SDH
versions of the films, and inconsistencies were identified in the presentation methods throughout the films. However, some
strategies conforming to international SDH conventions were identified within the SDH versions of the films. The study underscores
the necessity for further research in this field to enhance the professional standards for producing appropriate SDH in Iran.
A different perspective on epistemics and deontics
Authors: Van De Mieroop Dorien , Stevanovic Melisa , Leinonen Minna , Nevalainen Henri
Published: November 2024

Thus far, few studies have investigated the evaluative points narrators may convey through the sequential features
of reported exchanges in their stories. In this article, we conduct a micro-oriented narrative analysis on how epistemic and
deontic status-stance relations are depicted by narrators in sequences of reported turns. We thus uncover how hierarchies and
potential transgressions between the characters in the storyworld are “shown” rather than “told” to the story recipients, who are
in this way equipped to evaluate the story as a whole and the story characters’ accountability for their interactional behavior in
particular. Furthermore, we argue that the narrators’ discursive set-up of epistemic and deontic relations in these reported
exchanges also displays their emic perspective to them. Therefore we believe that our approach can pave the way
for a novel approach to epistemics and deontics, complementing the insights gained in the conversation-analytic examination of
these phenomena in situ.
Two-Stage Training Method for High-Quality Reconstruction in Single-Pixel Imaging
Authors: Shao 邵 Hui 辉 , Huang 黄 He 何 , Wei 魏 Yu-Xiao 宇霄 , Zhang 张 Hui-Juan 会娟 , Yang 杨 Zhao-Hua 照华 , Yu 余 Yuan-Jin 远金
Published: November 2024

A two-stage training method is proposed to enhance imaging quality and reduce reconstruction time in data-driven single-pixel imaging (SPI) under undersampling conditions. This approach leverages a deep learning algorithm to simulate single-pixel detection and image reconstruction. During the initial training stage, an L
2 regularization constraint is imposed on convolution modulation patterns to determine the optimal initial network weights. In the subsequent stage, a coupled deep learning method integrating coded-aperture design and SPI is adopted, which utilizes backpropagation of the loss function to iteratively optimize both the binarized modulation patterns and imaging network parameters. By reducing the binarization errors introduced by the dithering algorithm, this approach improves the quality of data-driven SPI. Compared with traditional deep-learning SPI methods, the proposed method significantly reduces computational complexity, resulting in accelerated image reconstruction. Experimental and simulation results demonstrate the advantages of the method, including high imaging quality, short image reconstruction time, and simplified training. For an image size of 64 × 64 pixels and 10% sampling rate, the proposed method achieves a peak signal-to-noise ratio of 23.22 dB, structural similarity index of 0.76, and image reconstruction time of approximately 2.57 × 10−4 seconds.
Analysis of DVD diffraction patterns under oblique light illumination
Authors: Tang Xiaogang , Li Zhenbang , Gu Jiangyi , Wang Daju , Wang Yuanxiang , Jing Qingli , Li Shuzhen
Published: November 2024

When an optical disc is illuminated by an incandescent lamp, a bright colored line can be observed on its surface, and the color of the bright line varies with the observer's position. Based on the optical reflection grating structure of a Digital Versatile Disc (DVD), we first establish an optical path model of a source-disc-observer system. Then, we derive an analytic equation describing the relationship between the wavelength of the bright lines and the observation angles by applying the scalar diffraction theory. Finally, an experimental investigation is conducted, confirming the reliability of the theoretical model. This study can be applied to physics experimental teaching in universities, providing a compelling teaching example for students to deeply understand the optical structures and diffraction properties of optical discs.
Charge and thermal transport properties of undoped and doped

T

l

+

(

I

n

3
+

X

2

2
−

)

−

ternary dichalcogenides with a reduced dimensionality: perfect candidates for thermoelectric applications in the mid-temperature region
Authors: Yavuz Mehmed Faruk , Gökçe Serdar , Gür Elif Sena , Mammadov Tofig G , Najafov Arzu I , Salehli Ferid , Akşit Mahmut , Berber Savaş , Mikailzade Faik , Yu Seyidov Mirhasan
Published: November 2024

The present work focuses on a comprehensive investigation of charge and thermal transport properties of thallium–indium-based ternary dichalcogenides with a common chemical formula of

T

l

+

(

I

n

3
+

X

2

2
−

)

−

where X denotes tellurium, selenium or sulfur atoms. Two compounds in the one-dimensional chain form, including pristine TlInSe2 and Fe-doped TlInTe2 as well as a hybrid material constructed from the two-dimensional TlInS2 layered semiconductor diluted with TlFeS2 at % 0.7, have been successfully grown using the Bridgman–Stockbarger technique. X-ray powder diffraction, scanning electron microscopy and energy-dispersive x-ray spectroscopy measurements were performed to characterize the local structure and chemical composition features of all prepared compounds. A strong anisotropy in the charge-carrier transport properties of the samples was observed from

dc

- and

ac

- electrical conductivity measurements made in directions parallel and perpendicular to the layers/chains. This is a fundamental property for realizing high thermoelectric performance in semiconducting materials. As a result, extremely large Seebeck coefficients were revealed upon experimental investigations of the thermoelectric properties of

T

l

+

(

I

n

3
+

X

2

2
−

)

−

samples over a wide temperature range between ∼100 K and ∼800 K. The first-principles density functional theory (DFT) and Boltzmann transport equations were employed to investigate the thermal transport properties of the compounds studied at the atomic scale. A specific interaction between the thallium cation and the sulfur anion was observed in the DFT computation scheme. The developed interaction (more electrostatic than a much weaker van der Waals one) enhances the charge carrier effective mass via flattening of the electronic bands near the band edges and this can give a new path towards Seebeck coefficient enhancement in the traditional mid-temperature range.
Doped-δ-doped transferred electron device for sustained terahertz signal generation
Authors: Ong Duu Sheng
Published: November 2024

The performance of a novel transferred electron device structure aimed at sustaining high-frequency signals in the terahertz (THz) range is investigated. The device uses a highly doped δ-layer to split the n-doped device into two distinct regions, forming a doped-δ-doped configuration. The first region generates high-speed electrons toward the δ-layer, while the second region utilizes negative differential resistance to modulate electron speeds and sustain oscillations. An ensemble self-consistent Monte Carlo model is employed to analyze electron dynamics and THz signal generation in this structure under a constant bias. The design demonstrates superior performance, achieving a fundamental operating frequency of 427 GHz in a 600 nm length InP device, nearly a 50% increase over conventional notch-doped design, while maintaining the current harmonic amplitude. This design achieves higher frequencies without reducing device length and increasing doping density, effectively addressing the trade-off of the Kroemer criterion. The study of the effects of varying doping densities and region lengths on device performance, highlighting the importance of optimizing these parameters to sustain current oscillations and efficiently generate THz signals. This design offers a promising solution for a compact and efficient THz source.
2025 roadmap on 3D nanomagnetism
Authors: Gubbiotti Gianluca , Barman Anjan , Ladak Sam , Bran Cristina , Grundler Dirk , Huth Michael , Plank Harald , Schmidt Georg , van Dijken Sebastiaan , Streubel Robert , Dobrovoloskiy Oleksandr , Scagnoli Valerio , Heyderman Laura , Donnelly Claire , Hellwig Olav , Fallarino Lorenzo , Jungfleisch M Benjamin , Farhan Alan , Maccaferri Nicolò , Vavassori Paolo , Fischer Peter , Tomasello Riccardo , Finocchio Giovanni , Clérac Rodolphe , Sessoli Roberta , Makarov Denys , Sheka Denis D , Krawczyk Maciej , Gallardo Rodolfo , Landeros Pedro , d’Aquino Massimiliano , Hertel Riccardo , Pirro Philipp , Ciubotaru Florin , Becherer Markus , Gartside Jack , Ono Teruo , Bortolotti Paolo , Fernández-Pacheco Amalio
Published: November 2024

The transition from planar to three-dimensional (3D) magnetic nanostructures represents a significant advancement in both fundamental research and practical applications, offering vast potential for next-generation technologies like ultrahigh-density storage, memory, logic, and neuromorphic computing. Despite being a relatively new field, the emergence of 3D nanomagnetism presents numerous opportunities for innovation, prompting the creation of a comprehensive roadmap by leading international researchers. This roadmap aims to facilitate collaboration and interdisciplinary dialogue to address challenges in materials science, physics, engineering, and computing. The roadmap comprises eighteen sections, roughly divided into three blocks. The first block explores the fundamentals of 3D nanomagnetism, focusing on recent trends in fabrication techniques and imaging methods crucial for understanding complex spin textures, curved surfaces, and small-scale interactions. Techniques such as two-photon lithography and focused electron beam-induced deposition enable the creation of intricate 3D architectures, while advanced imaging methods like electron holography and synchrotron x-ray tomography provide nanoscale spatial resolution for studying magnetization dynamics in three dimensions. Various 3D magnetic systems, including coupled multilayer systems, artificial spin-ice, magneto-plasmonic systems, topological spin textures, and molecular magnets are discussed. The second block introduces analytical and numerical methods for investigating 3D nanomagnetic structures and curvilinear systems, highlighting geometrically curved architectures, interconnected nanowire systems, and other complex geometries. Finite element methods are emphasized for capturing complex geometries, along with direct frequency domain solutions for addressing magnonic problems. The final block focuses on 3D magnonic crystals and networks, exploring their fundamental properties and potential applications in magnonic circuits, memory, and spintronics. Computational approaches using 3D nanomagnetic systems and complex topological textures in 3D spintronics are highlighted for their potential to enable faster and more energy-efficient computing.
Order parameter symmetry in superconducting 2H-TaSeS
Authors: Yadav K , Lamba M , Singh M , Yadav M , Kumar A , Patnaik S
Published: November 2024

Superconductors based on transition metal dichalcogenides are of substantial current relevance towards the material realization of topological superconductivity. Here, we report a detailed study on the synthesis and characterization of single crystals of 2H-TaSeS. A superconducting transition is confirmed at

4.15

K

that coexists with a charge-density wave ordering at

66

K

. The temperature dependence of the RF penetration depth indicates s-wave characteristics in the weak-coupling limit. Moderate electronic anisotropy is observed in the upper critical fields. DFT calculations confirm that the most stable structure belongs to the

P

6
3

mc

space group. Negative values in the phonon dispersion curves verify the possibility of coexisting superconductivity with a charge-density wave in 2H-TaSeS. We also study vortex dynamics in this novel superconductor. Overall, our analysis suggests that 2H-TaSeS is a conventional Type-II superconductor without any evidence for topological superconductivity.
Introduction to machine learning potentials for atomistic simulations
Authors: Thiemann Fabian L , O’Neill Niamh , Kapil Venkat , Michaelides Angelos , Schran Christoph
Published: November 2024

Machine learning potentials have revolutionised the field of atomistic simulations in recent years and are becoming a mainstay in the toolbox of computational scientists. This paper aims to provide an overview and introduction into machine learning potentials and their practical application to scientific problems. We provide a systematic guide for developing machine learning potentials, reviewing chemical descriptors, regression models, data generation and validation approaches. We begin with an emphasis on the earlier generation of models, such as high-dimensional neural network potentials and Gaussian approximation potentials, to provide historical perspective and guide the reader towards the understanding of recent developments, which are discussed in detail thereafter. Furthermore, we refer to relevant expert reviews, open-source software, and practical examples—further lowering the barrier to exploring these methods. The paper ends with selected showcase examples, highlighting the capabilities of machine learning potentials and how they can be applied to push the boundaries in atomistic simulations.
Many-body physics of ultracold alkaline-earth atoms with SU(N)-symmetric interactions
Authors: Ibarra-García-Padilla Eduardo , Choudhury Sayan
Published: November 2024

Symmetries play a crucial role in understanding phases of matter and the transitions between them. Theoretical investigations of quantum models with SU(N) symmetry have provided important insights into many-body phenomena. However, these models have generally remained a theoretical idealization, since it is very difficult to exactly realize the SU(N) symmetry in conventional quantum materials for large N. Intriguingly however, in recent years, ultracold alkaline-earth-atom (AEA) quantum simulators have paved the path to realize SU(N)-symmetric many-body models, where N is tunable and can be as large as 10. This symmetry emerges due to the closed shell structure of AEAs, thereby leading to a perfect decoupling of the electronic degrees of freedom from the nuclear spin. In this work, we provide a systematic review of recent theoretical and experimental work on the many-body physics of these systems. We first discuss the thermodynamic properties and collective modes of trapped Fermi gases, highlighting the enhanced interaction effects that appear as N increases. We then discuss the properties of the SU(N) Fermi–Hubbard model, focusing on some of the major experimental achievements in this area. We conclude with a compendium highlighting some of the significant theoretical progress on SU(N) lattice models and a discussion of some exciting directions for future research.
Non-holonomic constraint-assisted GNSS/SINS tight integration navigation method based on a left-invariant extended Kalman filter
Authors: Li Hanxu , Liu Zihao , Li Chonghui , Zheng Yong , Tong Shuai , Chen Shaojie , Gou Wanxiang
Published: November 2024

Misalignment in angle errors often leads to slow convergence and poses challenges in achieving optimal precision in vehicle-integrated navigation systems utilizing integrated global navigation satellite systems (GNSS) and strap-down inertial navigation systems (SINS). Additionally, in cases where GNSS satellite signals are lost, the performance of the non-holonomic constraint (NHC) algorithm can degrade significantly. Hence, in this study, the error state model is redefined by embedding the attitude, velocity, and position within the Lie group space. A novel algorithm based on a left-invariant Kalman filter is developed to assist NHC in GNSS/SINS integration. The proposed method is validated through trials conducted in complex urban environments and open suburban settings, using both loosely and tightly coupled integration modes. The results indicate that the proposed method offers rapid convergence and high precision, even in the presence of heading misalignment errors. In challenging environments, heading accuracy converged to below 0.5° within 50 s, while in open environments, it achieved approximately 0.1°, thus providing reliable, efficient positioning and eliminating the need for expensive attitude alignment procedures.
A distributed factor graph model solving method for cooperative localization of UAV swarms
Authors: Yang Pu , Ye Guo-Yang , Shao Chun-Li , Yang Shuang-Long , Huang Ze-Xia
Published: November 2024

Accurate position information is crucial for unmanned aerial vehicles (UAV) to execute tasks. To balance the contradiction between the payload and localization accuracy of rotary UAVs, a cooperative localization method for UAV swarms based on the factor graph model is studied in this paper. Each UAV is equipped with a local factor graph model. A distributed factor graph model-solving method, AGA-Gauss–Newton conjugate gradient (GNCG), which combines an adaptive genetic algorithm and an improved GNCG algorithm, is proposed. The issue of falling into local optimal solutions was addressed by configuring the crossover and mutation behaviors of the genetic algorithm into an adaptive mode. The Gauss–Newton algorithm (GNQR) was improved using a conjugate gradient iteration, which effectively reduced the operation time of the algorithm. The simulation results indicate that the AGA-GNCG algorithm improves the localization accuracy with respect to the East–North–Up (ENU) frame by 58.8%, 60.6%, and 57.4% relative to the GNQR. Moreover, as the number of UAVs increases, the improved GNCG algorithm exhibits a significant improvement in computational efficiency compared to that of the GNQR algorithm.
R-ATCN: continuous human activity recognition using FMCW radar with temporal convolutional networks
Authors: Chen Jiahao , Gu Minming , Lin Zhiyan
Published: November 2024

The utilization of millimeter-wave radar sensors for continuous human activity recognition technology has garnered significant interest. Prior research predominantly concentrated on recursive neural networks, which often incorporate numerous extraneous information features, hindering the ability to make precise and effective predictions for ongoing activities. In response to this challenge, this paper introduces a dual-dilated one-dimensional temporal convolutional network model with an attention mechanism (R-ATCN). By stacking temporal convolutions to enhance the receptive field without compromising temporal resolution, the R-ATCN effectively captures features. Additionally, the attention mechanism is employed to capture crucial frame information related to activity transitions and overall features. The study gathered 60 data sets from 5 participants utilizing frequency modulated continuous wave radar. It encompassed 8 various activities lasting a total of 52.5 min, with randomized durations and transition times for each activity. To evaluate the performance of the model, this paper also introduces evaluation metrics such as short-time tolerance (STT) score. Experimental results show that the R-ATCN model outperforms other contrastive models in terms of segmental F1-score and STT scores. The effectiveness of the proposed model lies in its ability to accurately identify ongoing human activities within indoor environments.
An adaptive thresholding approach for open set fault diagnosis
Authors: Wang Cheng , Cheng Bingyou , Deng Lili
Published: November 2024

Cross-domain fault diagnosis using deep learning plays a critical role in ensuring the reliability and safety of mechanical systems. However, real-world industrial scenarios often involve unknown fault classes, which introduce significant challenges beyond environmental differences between training and testing phases. These unknown fault classes, which do not appear in the training data, create a cross-domain open set fault diagnosis problem where the target domain includes both known and unknown fault types with distinct distribution characteristics. Traditional domain adaptation methods that align source and target domains often overlook the spatial distribution of each class in the feature space, leading to potential negative transfer and misclassification of unknown faults. To address these challenges, this paper proposes a k-nearest neighbors based adaptive thresholding (KNNAT) method, which dynamically adjusts classification thresholds based on the spatial distribution of each class in the feature space. This approach effectively isolates unknown faults, reducing their impact on domain adaptation and improving the reliability of the diagnostic process. Extensive experiments on the publicly available CWRU bearing and PHM09 datasets demonstrate that the proposed KNNAT method outperforms other state-of-the-art methods, achieving higher accuracy and robustness in identifying known faults while successfully isolating unknown faults. These results highlight the potential of using the KNNAT method to enhance the reliability of mechanical systems in cross-domain fault diagnosis applications.
A degradation evaluation method with the convolutional neural network for the cyclic symmetry rolling bearing
Authors: Hu Congfang , Liu Zhijiang , Xiao Xiangwu , Jin Yufeng , Wang Tao , Zhou Lehong , Su Liang
Published: November 2024

The rolling bearing is a cyclic symmetry engineering component, whose failure is related to the regular operation of the rotating machine. For the purpose of real-time monitoring of operational status and predicting the occurrence of the failure, a rolling bearing performance degradation assessment method based on the convolutional neural network (CNN) was proposed with the XJTU-SY tested acceleration life data set. Firstly, the fault signal characteristics of the rolling bearing were extracted by using an energy decomposition method with a wavelet package to form some data sets. Secondly, the degradation performance of the data is classified by the Fuzzy C-means Algorithm for fuzzy data, and then the extracted characteristic signals were processed with CNNs. Thirdly, a multi-layer nonlinear mapping of training data was firstly formed with CNN, and then the adaptive batch normalization adaptive network was added during the training process, and then the deep learning model was formed as a result. Finally, the degradation degree of the three typical rolling bearing failures was evaluated and predicted with this method. The t-SNE technology was employed to visualize the output of the data distribution in two-dimensional space. Compared with the experiment results, the accuracy of the outer ring, inner ring and rolling body of the rolling bearing are 90.84%, 83.3%, and 80.95% with the fault assessment method, respectively. Thus, the degradation of the cyclic symmetry rolling bearing can be identified effectively and determined by the proposed method in advance.
3-axis force compensated piezo stage combination for high-speed SPM
Authors: Thiesler Jan , Moehler Felix , Marth Harry , Schröder Michael , Korpelainen Virpi , Dai Gaoliang
Published: November 2024

Scanning stages are immanent for scanning probe microscope type tools and highly impact most of their properties, such as scanning speed and positioning accuracy. In this contribution, a serial assembly of an in open-loop operated 3-axes monocrystalline piezo stage with an in closed-loop operated 6-axes polycrystalline piezo stage is presented. The monocrystalline piezo stage is applied to enhance the limited bandwidth of the polycrystalline piezo stage and includes an internal compensation mechanism for reaction forces. The compensation mechanism is designed to operate the scanner as a no retroactive stage by reducing the dynamic scanning motion errors in high-speed application to a minimum. It works for sinusoidal oscillations and shows a frequency dependence. The monocrystalline piezo stage has a position noise of <0.1 nm (1σ) in z-direction, positioning bandwidth >1 kHz and travel range 1 × 1 × 1 μm in x-, y-, z-direction, respectively. The 6-axes piezo stage complements this assembly with a position noise of <0.35 nm (1σ) in x-, y- and z-direction, a closed-loop bandwidth >200 Hz, a rotation range of ±0.5 mrad, and a travel range 45 × 45 × 45 μm in x-, y-, z-direction, respectively.
On three-dimensional flows of viscoelastic fluids of Giesekus type*
Authors: Bulíček Miroslav , Los Tomáš , Málek Josef
Published: November 2024

Viscoelastic rate-type fluids are popular models of choice in many applications involving flows of fluid-like materials with complex micro-structure. A well-developed mathematical theory for the most of these classical fluid models is however missing. The main purpose of this study is to provide a complete proof of long-time and large-data existence of weak solutions to unsteady internal three-dimensional flows of Giesekus fluids subject to a no-slip boundary condition. As a new auxiliary tool, we provide the identification of certain biting limits in the parabolic setting, presented here within the framework of evolutionary Stokes problems. We also generalize the long-time and large-data existence result to higher dimensions, to viscoelastic models with multiple relaxation mechanisms and to viscoelastic models with different type of dissipation.
Diffusion equation quantification: selective enhancement algorithm for bone metastasis lesions in CT images
Authors: Anetai Yusuke , Doi Kentaro , Takegawa Hideki , Koike Yuhei , Yui Midori , Yoshida Asami , Hirota Kazuki , Yoshida Ken , Nishio Teiji , Kotoku Jun’ichi , Nakamura Mitsuhiro , Nakamura Satoaki
Published: November 2024

Objective. Diffusion equation (DE) imaging processing is promising to enhance images showing lesions of bone metastasis (LBM). The Perona–Malik diffusion (PMD) model, which has been widely used and studied, is an anisotropic diffusion processing method to denoise or extract objects from an image effectively. However, the smoothing characteristics of PMD or its related method hinder extraction and enhancement of soft tissue regions of medical image such as computed tomography (CT), typically leaving an indistinct region with ambient tissues. Moreover, PMD expands the border region of the objects. A novel diffusion methodology must be used to enhance the LBM region effectively. Approach. For this study, we originally developed a DE quantification (DEQ) method that uses a filter function to selectively provide modulated diffusion according to the original locations of objects in an image. The structural similarity index measure (SSIM) and Lie derivative image analysis L-value map were used to evaluate image quality and processing. Main results. We determined superellipse function with its order

n
=
4

as a better performing filter for the LBM region. DEQ was found to be more effective at contrasting LBM for various LBM CT images than PMD or its improved models when the filter was a positive exponential similar function. DEQ yields enhancement agreeing with the indications of positron emission tomography despite complex LBM comprising osteoblastic, osteoclastic, mixed tissues, and metal artifacts, which is innovative. Moreover, DEQ retained high quality of image (SSIM> 0.95), and achieved a low mean value of the L-value (<0.001), indicative of our intended selective diffusion compared to other PMD models. Significance. Our method improved the visibility of mixed tissue lesions, which can assist computer visional framework and can help radiologists to produce accurate diagnose of LBM regions which are frequently overlooked in radiology findings because of the various degrees of visibility in CT images.
Dual virtual non-contrast imaging: a Bayesian quantitative approach to determine radiotherapy quantities from contrast-enhanced DECT images
Authors: Beikali Soltani Mohsen , Bouchard Hugo
Published: November 2024

Objective. Contrast agents in computed tomography (CT) scans can compromise the accuracy of dose calculations in radiation therapy planning, especially for particle therapy. This often requires an additional non-contrast CT scan, increasing radiation exposure and introducing potential registration errors. Our goal is to resolve these issues by accurately estimating radiotherapy parameters from dual virtual non-contrast (dual-VNC) images generated by contrast-enhanced dual-energy CT (DECT) scans, while accounting for noise and variability in tissue composition. Approach. A new Bayesian model is introduced to estimate dual-VNC Hounsfield units from contrast-enhanced DECT data. The model defines a prior distribution that describes tissue variations in terms of elemental compositions and mass densities. Multiple reference tissues are used to estimate variations across human tissues. A likelihood distribution is also defined to model the noise contained in CT data. The model is thoroughly validated in a simulated environment including 12 virtual patients under low and high iodine uptake scenarios, while incorporating noise and beam hardening effects. The eigentissue decomposition technique is used to derive elemental compositions and parameters critical for radiotherapy from the dual-VNC images, such as electron density (

ρ

e

), particle stopping power (SPR), and photon energy absorption coefficient (EAC). Main results. The proposed method yields accurate voxelwise estimations for

ρ

e

, SPR, and EAC, with root mean square errors of 3.09%, 3.14%, and 1.34% for highly-enhanced tissues, compared to 5.93%, 6.39%, and 17.11% when the presence of contrast agent is ignored. It also demonstrates robustness to systematic shifts in tissue composition and bandwidth variations in the prior distribution, resulting in overall uncertainties down to 1.13%, 1.33%, and 0.86% for

ρ

e

, SPR, and EAC in soft tissues; 1.17%, 1.32%, and 1.34% in enhanced soft tissues; and 4.34%, 4.00%, and 2.50% in bones. Significance. The proposed method accurately derives radiotherapy parameters from contrast-enhanced DECT data and demonstrates robustness against systematic errors in reference data, highlighting its potential for clinical use.
ML-EM based dual tracer PET image reconstruction with inclusion of prompt gamma attenuation
Authors: Pfaehler Elisabeth , Niekämper Debora , Scheins Jürgen , Shah N Jon , Lerche Christoph W
Published: November 2024

Objective. Conventionally, if two metabolic processes are of interest for image analysis, two separate, sequential positron emission tomography (PET) scans are performed. However, sequential PET scans cannot simultaneously display the metabolic targets. The concurrent study of two simultaneous PET scans could provide new insights into the causes of diseases. Approach. In this work, we propose a reconstruction algorithm for the simultaneous injection of a

β

+

-emitter emitting only annihilation photons and a

β

+

-γ-emitter emitting annihilation photons and an additional prompt γ-photon. As in previous works, the γ-photon is used to identify events originating from the

β

+

-γ-emitter. However, due to e.g. attenuation and down-scatter, the γ-photon is often not detected and not all events can correctly be associated with the

β

+

-γ-emitter as they are detected as double coincidences. In contrast to previous works, we estimate this number of double coincidences with origin in the

β

+

-γ, emitter including the attenuation of the prompt γ, and incorporate this estimation in the forward-projection of the maximum likelihood expectation maximization algorithm. For evaluation, we simulate different scenarios with varying objects and attenuation maps. The nuclide 18F serves as

β

+

-emitter, while 44Sc functions as

β

+

-γ emitter. The performance of the algorithm is assessed by calculating the residual error of the

β

+

-γ-emitter in the reconstructed

β

+

-emitter image. Additionally, the intensity values in the simulated cylinders of the ground truth (GT) and the reconstructed images are compared. Main results. The remaining activity in the

β

+

-emitter image varied from 0.4% to 3.7%. The absolute percentage difference between GT and reconstructed intensity for the pure

β

+

emitter images was found to be between 3.0% and 7.4% for all cases. The absolute percentage difference between the GT and the reconstructed intensity for the

β

+

-γ emitter images ranged from 8.7% to 10.4% for all simulated cases. Significance. These results demonstrate that our approach can reconstruct two separate images with a good quantitation accuracy.
Force lag phenomenon in multi-coil fluid-deficient magnetorheological dampers: experimental investigation and dynamic modeling
Authors: Yang Yang , Li Yuan-Jin , Huang Xing-Huai , Xu Zhao-Dong , Liu Xin-Yu , Wang Li-Xin , Xu Chao , Shahzada Khan , Wan Yu , Ma Jun-Peng
Published: November 2024

Magnetorheological (MR) dampers, renowned for their tunable mechanical properties and energy dissipation capabilities, have been widely implemented in various vibration control systems. Nonetheless, the preparation and infusion process of MR fluids inherently entails the presence of trapped air, and sealing deficiencies can cause fluid leakage during operation, thereby leading to fluid deficiency within the damper’s chamber. Fluid deficiency induces a force lag phenomenon, substantially undermining the damping performance of MR dampers, especially for multi-coil dampers where multiple steps internally restrict the trapped airflow. Existing experimental investigations and mechanical models of MR dampers inadequately consider the force lag phenomenon in fluid-deficient multi-coil MR dampers under multiple modes. Addressing this issue, this study focuses on the experimental investigation and dynamic modeling of the force lag phenomenon in multi-coil MR dampers induced by fluid deficiency. Firstly, performance tests were conducted on a tri-coil MR damper, contrasting force lag phenomenon under varied loading conditions. A dynamic mathematical model was then proposed to characterize this, comprising a series connection of nonlinear spring and hyperbolic tangent elements, in parallel with a damping component, designed to predict multi-coil MR damper behavior under fluid deficiency. Through analyzing experimentally derived force-lag curves, model parameters were determined, leading to the development of a force-lag model for multi-coil fluid-deficient MR dampers. Model calculations were compared with experimental results to verify its efficacy in depicting the altered mechanical properties of multi-coil MR dampers influenced by fluid deficiency. This research furnishes a foundational model for explaining and forecasting the damping performance of multi-coil fluid-deficient MR dampers, facilitating the expanded application of MR damping technology across diverse disciplines.
β-cyclodextrin and carbonyl iron-based supramolecular gels: microstructure and magnetorheology
Authors: Sangitra Surya Narayana , Hotton Claire , Chazapi Ioanna , Paineau Erwan , Pujala Ravi Kumar
Published: November 2024

This study presents the development of supramolecular gels composed of β-cyclodextrin (β-CD) and carbonyl iron (CI) particles, offering an effective combination of enhanced stability and magnetorheological (MR) responsiveness. Unlike conventional CI-based systems prone to rapid sedimentation, the β-CD matrix in this composite prevents particle aggregation, demonstrating sedimentation stability for up to 12 h without visible sedimentation. Microstructural analysis using small-angle x-ray scattering and scanning electron microscopy revealed that the inclusion of CI particles significantly modifies the gel structure, enabling a highly tunable MR response. Rheological tests demonstrated a critical magnetic field (H
C) as low as ∼150 Oe, indicating an early magnetic response, which leads to a significant increase in viscosity and yield stress under magnetic influence. A phase diagram is proposed to explain the underlying MR mechanisms. This novel system offers a new approach to designing magneto-responsive materials with controlled mechanical properties, making them suitable for various smart material applications.
Performance analysis of quantum convolutional layers for image classification
Authors: Xiang Qiu-Yu , Li Dong-Fen , Sun Yu-Chen , Hu Zhi-kang , Yuan Yu-Hang , Hua Xiao-Yu , Zhu Yong-Hao , Fu You , Jiang Yang-Yang
Published: November 2024

In recent years, with the rapid development of quantum computing technology, the fusion of quantum computing and machine learning techniques is becoming a research hotspot in the field of machine learning. This article aims to explore the impact of the depth and width of quantum convolutional layers on image classification tasks in Quantum−Classical Hybrid Convolutional Neural Networks. To this end, a model combining parameterized quantum circuits and classical neural networks is designed, and a series of experiments are conducted on the MNIST dataset to assess the specific effects of different configurations of quantum convolutional layers on model performance. The research results indicate that simply increasing the depth or width of quantum convolutional layers does not guarantee performance improvement and sometimes may even lead to performance degradation. Therefore, when designing quantum convolutional layers, we should make reasonable choices based on the actual needs of the application scenarios. Finally, based on these findings, a multidimensional optimization strategy is proposed to enhance the overall performance of the model. The achievements of this research not only provide important guidance for the design and optimization of Quantum−Classical Hybrid Convolutional Neural Networks but also offer new research perspectives for researchers in the field of quantum machine learning.
Analysis of trapping effects on DC and RF performance in double Π-gate Al0.3Ga0.7N/GaN MOSHEMTs
Authors: Venkateswarlu Rayabarapu , Acharya Bibhudendra , Mishra Guru Prasad
Published: November 2024

This study investigates trap analysis of the DC and RF performance of Al0.3Ga0.7N/GaN Metal-Oxide-Semiconductor high electron mobility transistor (MOSHEMT) with double π-gate technology. The motivation behind double π-gate technology is to evenly distribute the peak electric field and reduce hot electron generation. This gate design helps to lower hot-electron generation across various operating conditions while maintaining device performance, particularly in the lower millimeter-wave frequency range. High dielectric constant HfO2 is used as gate oxide, which helps to lower the gate leakage current. Near the conduction band (CB), the electron quasi-fermi level of 30 meV is achieved. The practical application of HfO2 in AlGaN/GaN HEMTs is limited by its high oxygen permeability, brittleness, and reactivity with moisture and CO2, which can cause mechanical stress and form hafnium carbonate, adversely affecting device performance. Future designs of the double-π gate structure could enhance electrostatic control, reduce short-channel effects, and improve high-frequency performance by scaling down gate dimensions and using high-k or novel dielectric materials. Additionally, optimization for mm-wave and THz applications would help to maintain electron mobility and minimize parasitic capacitance and resistance.
Numerical optimization of all-inorganic CsSnBr3 perovskite solar cells: the observation of 27% power conversion efficiency
Authors: Kheswa B V
Published: November 2024

In this research, we employed SCAPS-1D simulation software to numerically optimize the performance of four CsSnBr3-based perovskite solar cell structures. Specifically, we analyzed the FTO/ZnO/CsSnBr3/rGO/Se, FTO/AlZnO/CsSnBr3/rGO/Se, FTO/LiTiO2/CsSnBr3/rGO/Se, and FTO/WS2/CsSnBr3/rGO/Se configurations. The optimization process focused on adjusting the thicknesses of the electron transport layer, hole transport layer, and perovskite layer, while also evaluating the effects of temperature, series resistance, and shunt resistance on the Jsc, Voc, FF, and PCE. As a result, we achieved PCE of 26.92%, 26.89%, 26.89%, and 26.91% for the FTO/AlZnO, FTO/ZnO, FTO/LiTiO2, and FTO/WS2-based structures, respectively. Furthermore, the PCE obtained for all CsSnBr3-based perovskite solar cell structures outperformed the recently reported ITO/WS2/CsSnBr3/Cu2O/Au perovskite solar cell, which exhibited the highest PCE in the literature, by nearly 5%.
Conformal leaky wave antenna based on gap waveguide technology
Authors: Ansarian A , Dalili Oskouei H R , Zamini H , Mohammadi Shirkolaei M , Farahmand J
Published: November 2024

Due to structural and aerodynamic limitations, conformal antennas are increasingly sought after in antenna design. Gap waveguide technology has also gained traction for its potential in leaky wave antenna development. This paper proposes a novel conformal leaky wave antenna utilizing this emerging gap waveguide technology. Prior research has not extensively explored the radiation characteristics of such antennas on curved and conformal surfaces. This work addresses this gap by investigating the design procedure, radiation mechanism, and optimization methods for leaky wave antennas on conformal surfaces. The proposed antenna achieves a gain of approximately 18 dB and a scanning angle ranging from 1 to 33 degrees within the frequency band of 9.5 GHz to 12 GHz. Fabrication and subsequent measurements of the antenna were conducted, with the results demonstrating good agreement with the simulations.
The stochastic gravitational wave background from primordial gravitational atoms
Authors: Kang Zhaofeng , Li Tianjun , Ye Weitao
Published: November 2024

We propose a scenario of primordial gravitational atoms (PGAs), which may exist in the current and past universe due to spinning primordial black holes (PBHs) and very light bosonic fields. In a monochromatic mass scenario with a sizable dimensionless spin, which may arise in a short matter dominated (MD) era, we analyze the resulting stochastic gravitational wave background (SGWB) signal. Its spectrum is approximately characterized by a rising ∝ f
3 followed by a falling ∝ f
-1 where f is the frequency. Then, we investigate the constraints and prospects of such an SGWB, and find that PGAs with a core mass M
BH ∼ 𝒪(10) M
⊙ and a cloud of light scalar with mass μ ∼ 𝒪 (10-13) eV could yield constraints even stronger than those from bare PBHs. Future detectors such as LISA, Taiji and TianQin are able to explore PGAs over a narrow and elongated strap in the (μ,M
BH) plane, spanning over 10 orders of magnitude for the maximum spin, 10-8
M
⊙ ≲ M
BH ≲ 104
M
⊙, 10-16 eV ≲ μ ≲ 10-3 eV. If the PGA is dressed with a vector cloud, the SGWB signal has a much better opportunity to be probed.
Genetic variability of mud crab Scylla tranquebarica from Malili, South Sulawesi based on mitochondrial 16S rRNA sequence: a preliminary assessment
Authors: Parenrengi A , Gunarto G , Tenriulo A , Sulaeman S , Herlinah H , Suryati E , Lante S
Published: November 2024

Analysis of genetic variability is a very important aspect of providing the genetic database for the domestication purposes of mud crab Scylla tranquebarica. Genetic characterization is needed to be assessed to support the mud crab breeding program. The study aimed to analyze the genetic variation of the mud crab S. tranquebarica broodstock collected from Malili, East Luwu, South Sulawesi, based on the nucleotide sequence of mitochondrial 16S rRNA. The mud crab samples (n=10) were collected from fishermen. The genomic DNA and mtDNA 16S rRNA were isolated by the commercial kits, and the nucleotide sequence was done in the First Base. The sequences of mtDNA were analyzed using Mega-X, BLAST-N, and DnaSP to obtain a consensus sequence, haplotype diversity, nucleotide diversity, haplotype number, and genetic distance. The results of the study revealed the genetic variability of mud crabs from Malili in terms of relatively high haplotype diversity of 0.533, low nucleotide diversity of 0.00096, and two haplotypes. Genetic distance among individuals for intra-population was 0.0010±0.0009. The two haplotypes were highly correlated with the number of clusters observed. Two clusters were detected in which the 1st cluster consisted of six individuals, and the 2nd cluster consisted of four individuals.
Surplus production model for the tuna (Thunnus Sp.) in Makassar Strait Waters
Authors: Hatmar Muh. Aldhy , Nelwan Alfa
Published: November 2024

Tuna (Thunnus Sp.) is a pelagic fish with high potential and economic value in the Makassar Strait. This resource utilizes tuna handlines and purse seine units, and the number of gears has increased. Therefore, effective management is crucial to ensure optimal productivity. This study analyzes the catch and effort at maximum sustainable yield (MSY) of tuna (yellowfin and bigeye) in Majene Regency, West Sulawesi Province, from 2007 to 2022, using catch per unit effort (CPUE) and production data. The analysis includes the equilibrium approach and the Schaefer model to understand the subject comprehensively. In July 2022, the highest tuna catch was 143.47 tons (84.67 tons of yellowfin tuna and 58.8 tons of bigeye tuna). The average CPUE for tuna was 4.7 tons per unit, with a CMSY of 1,113.99 tons per year and an EMSY of 128 units per year. During 2014, 2015, 2019, and 2020, the biomass trend decreased, and overfishing occurred.