bluuebunny/crossref_metadata_2025_split huggingface dataset:
Introduction to machine learning potentials for atomistic simulations
Authors: Thiemann Fabian L , O’Neill Niamh , Kapil Venkat , Michaelides Angelos , Schran Christoph 
Published: November 2024

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract

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

Abstract

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

Abstract

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

Abstract
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

Abstract
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.