bluuebunny/crossref_metadata_2025_split huggingface dataset:
Self-diffusion of liquid deuterium hydride and liquid tritium
Authors: Prisk T. R. , Hanna S. , Azuah R. T. 
Published: October 2024

We present a quasi-elastic neutron scattering study of liquid deuterium hydride carried out using the Disk Chopper Spectrometer at the National Institute of Standards and Technology. Under saturated vapor pressure, the self-diffusion constant of deuterium hydride obeys an Arrhenius law D=D0exp−EA/kBT, where the prefactor D0 is given by D0=9.5±1.2Å2/ps and the activation energy is given by EA = 58 ± 2 K. We apply the quantum law of corresponding states to the known diffusion constants of the hydrogen isotopologues. From this application, we estimate that D0≈9.1Å2/ps and EA ≈ 75 K in liquid tritium. Young’s theory of quantum-mechanical effects in van der Waals fluids [Young, Phys. Rev. A 23, 1498 (1981)] is shown to apply to the diffusion constants of the liquid hydrogens. Our results underscore the importance of nuclear quantum effects in shaping the properties and behavior of the hydrogen isotopologues.
A molecular dynamics simulation study of EthylChlorophyllide A molecules confined in a SiO2 nanoslit
Authors: Roccatano Danilo , Karki Khadga Jung 
Published: October 2024

This paper investigates the dynamic behavior of EthylChlorophyllide A (EChlideA) molecules in a methanol solution confined within a 4 nm silica nanoslit, using molecular dynamics simulations over a duration of 1 ms. Three systems, containing 1, 2, and 4 solutes, were studied at 298 K. The results demonstrate that EChlideA molecules predominantly adsorb onto the silica surfaces, driven by specific interactions between chlorin ring’s methyl group and the hydroxyl groups of the silica. This adsorption leads to stable binding, particularly in less crowded environments, as indicated by the potential of mean force analysis. Higher molecular concentrations, such as those with four EChlideA molecules, introduce variation in binding strength due to molecular aggregation and complex interactions. The orientation analysis reveals that the chlorin ring tends to align parallel to the surface, requiring rotational adjustments during surface diffusion. In addition, solvent coordination around the Mg ion remains consistent under bulk conditions, although with some variation in higher concentrations. This study also highlights a decrease in linear diffusion and an increase in rotational relaxation times for EChlideA molecules within the confined nanoslit, reflecting the influence of molecular concentration and arrangement on their dynamics. These findings provide valuable insights into the role of surface interactions, molecular orientation, and solvent coordination in confined environments, offering implications for the design of nanoscale systems.
Estimating false alarm rates of sub-dominant quasi-normal modes in GW190521
Authors: Capano Collin D , Abedi Jahed , Kastha Shilpa , Nitz Alexander H , Westerweck Julian , Wang Yi-Fan , Cabero Miriam , Nielsen Alex B , Krishnan Badri 
Published: October 2024

Abstract
A major aim of gravitational wave astronomy is to test observationally the Kerr nature of black holes. The strongest such test, with minimal additional assumptions, is provided by observations of multiple ringdown modes, also known as black hole spectroscopy. For the gravitational wave merger event GW190521, we have previously claimed the detection of two ringdown modes emitted by the remnant black hole. In this paper we provide further evidence for the detection of multiple ringdown modes from this event. We analyse the recovery of simulated gravitational wave signals designed to replicate the ringdown properties of GW190521. We quantify how often our detection statistic reports strong evidence for a sub-dominant

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ringdown mode, even when no such mode is present in the simulated signal. We find this only occurs with a probability ∼0.02, which is consistent with a Bayes factor of

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(1σ uncertainty) found for GW190521. We also quantify our agnostic analysis of GW190521, in which no relationship is assumed between ringdown modes, and find that only 1 in 250 simulated signals without a

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mode yields a result as significant as GW190521. Conversely, we verify that when simulated signals do have an observable

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mode they consistently yield a strong evidence and significant agnostic results. We also find that constraints on deviations from the

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mode on GW190521-like signals with a

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mode are consistent with what was obtained from our previous analysis of GW190521. Our results support our previous conclusion that the gravitational wave signal from GW190521 contains an observable sub-dominant

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mode.
Towards quantum gravity with neural networks: solving the quantum Hamilton constraint of U(1) BF theory
Authors: Sahlmann Hanno , Sherif Waleed 
Published: October 2024

Abstract
In the canonical approach of loop quantum gravity, arguably the most important outstanding problem is finding and interpreting solutions to the Hamiltonian constraint. In this work, we demonstrate that methods of machine learning are in principle applicable to this problem. We consider U(1) BF theory in three dimensions, quantised with loop quantum gravity methods. In particular, we formulate a master constraint corresponding to Hamilton and Gauß constraints using loop quantum gravity methods. To make the problem amenable for numerical simulation we fix a graph and introduce a cutoff on the kinematical degrees of freedom, effectively considering

U
q

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BF theory at a root of unity. We show that the neural network quantum state ansatz can be used to numerically solve the constraints efficiently and accurately. We compute expectation values and fluctuations of certain observables and compare them with exact results or exact numerical methods where possible. We also study the dependence on the cutoff.
A new nonconvex multi-view subspace clustering via learning a clean low-rank representation tensor
Authors: Zhang Xiaoqing , Guo Xiaofeng , Pan Jianyu 
Published: October 2024

Abstract
Recently, low-rank tensor representation has achieved impressive results for multi-view subspace clustering (MSC). The typical MSC methods utilize the tensor nuclear norm as a convex surrogate of the tensor multi-rank to obtain a low-rank representation, which exhibits limited robustness when dealing with noisy and complex data scenarios. In this paper, we introduce an innovative clean low-rank tensor representation approach that combines the idea of tensor robust principal component analysis with a new nonconvex tensor multi-rank approximation regularization. This integration enhances the robustness of the low-rank representation, resulting in improved performance. Furthermore, to better capture the local geometric features, we employ a high-order manifold regularization term. To effectively address our new model, we develop an iterative algorithm that can be proved to converge to the desired Karush-Kuhn-Tucker critical point. The numerical experiments on widely used datasets serve to demonstrate the efficacy and effectiveness of our new method.
Efficient, multimodal, and derivative-free bayesian inference with Fisher–Rao gradient flows
Authors: Chen Yifan , Zhengyu Huang Daniel , Huang Jiaoyang , Reich Sebastian , Stuart Andrew M 
Published: October 2024

Abstract
In this paper, we study efficient approximate sampling for probability distributions known up to normalization constants. We specifically focus on a problem class arising in Bayesian inference for large-scale inverse problems in science and engineering applications. The computational challenges we address with the proposed methodology are: (i) the need for repeated evaluations of expensive forward models; (ii) the potential existence of multiple modes; and (iii) the fact that gradient of, or adjoint solver for, the forward model might not be feasible. While existing Bayesian inference methods meet some of these challenges individually, we propose a framework that tackles all three systematically. Our approach builds upon the Fisher–Rao gradient flow in probability space, yielding a dynamical system for probability densities that converges towards the target distribution at a uniform exponential rate. This rapid convergence is advantageous for the computational burden outlined in (i). We apply Gaussian mixture approximations with operator splitting techniques to simulate the flow numerically; the resulting approximation can capture multiple modes thus addressing (ii). Furthermore, we employ the Kalman methodology to facilitate a derivative-free update of these Gaussian components and their respective weights, addressing the issue in (iii). The proposed methodology results in an efficient derivative-free posterior approximation method, flexible enough to handle multi-modal distributions: Gaussian Mixture Kalman Inversion (GMKI). The effectiveness of GMKI is demonstrated both theoretically and numerically in several experiments with multimodal target distributions, including proof-of-concept and two-dimensional examples, as well as a large-scale application: recovering the Navier–Stokes initial condition from solution data at positive times.
Vital signs detection of moving targets using FMCW radar
Authors: Dai Xiao , Zhang Yuanhui , Luo Jingxue , Liu Kang , Fu Duo 
Published: October 2024

Abstract
Respiratory and heartbeat rates are crucial indicators for human health assessment. Compared to contact-based measurements, millimeter-wave radar detection of these vital signs avoids the discomfort caused by physical contact and better protects personal privacy, making it highly valuable for home health monitoring. The use of millimeter-wave radar for vital sign detection of the human body is mostly focused on targets in a stationary state at present. However, the human body may sway or even move during actual detection. This article proposes a non-contact vital sign detection method for moving targets. Compared with methods for detecting vital signs of stationary targets, detecting vital signs of moving targets requires determining the range bin where the targets are located continuously and extracting target phase information. The noise components such as movement and sway contained in the phase signal need to be removed. In this paper, moving target indication is used to remove static components, an adaptive range bin selection method is proposed to determine the range bin where the targets are located, and range bin selection fluctuation is smoothed using a moving average filter. The wavelet transform is used to decompose the phase signal, remove swaying noise based on autocorrelation function, and reconstruct the life signal for different scale factors. A bandpass filter is used to separate the respiratory and heartbeat signals, and a notch filter is designed to suppress respiratory harmonic signals. The experimental results show that the proposed method can separate vital signs signals from the phase signals of moving targets, achieving detection of respiration and heartbeat rate. The average accuracy of respiration and heartbeat rate detection is 94.7% and 95.5%, respectively.
Label-free quantitative phase imaging and analysis of airborne pollen
Authors: Kumar Anand , Dhawan Sachin , Bhatt Sunil , Saxena Anuj , Khare Mukesh , Dubey Satish Kumar , Mehta Dalip Singh 
Published: October 2024

Abstract
Pollen morphology, involving the physical characteristics of pollen grains from seed plants during reproduction, plays an important role in plant biology, ecology, and evolution. High pollen concentrations in the air can degrade air quality and exacerbate respiratory conditions such as asthma. Understanding pollen morphology and its implications for air quality is significant for mitigating respiratory health risks. Conventionally, fluorescence microscopy is used for pollen imaging, but photobleaching, quenching, and phototoxicity affect the surface morphology and do not provide quantitative data on the pollen grains. For this study, we used bright field (BF) imaging and quantitative phase imaging (QPI), a label-free interferometric microscopy method, to look at differences in the shape of pollen. BF imaging provides information about the shape and size of the different pollens but has a limitation of low contrast. To obtain high-contrast images and quantitative data on the pollen grains without any exogenous agents, we employed QPI and BF imaging in the present study. QPI enables the extraction of detailed information regarding the cell wall, aperture, and thickness of pollen while also maintaining their natural state without the need for chemical treatments. In the present work, we sampled the ambient air from May 2023 to January 2024 on the IIT Delhi campus. Subsequently, QPI and BF imaging have been done for pollen identification and phase analysis of arboreal and non-arboreal pollen. Further, by utilizing the information obtained from BF microscopy and QPI, different species of pollen have been identified, and a pollen calendar has been prepared for exhibiting pollen season throughout the year. To the best of the authors’ knowledge, they have conducted the QPI of airborne pollen grains for the first time; this technique holds great potential for characterizing airborne pollens without the need for staining or sample preparation.
Study on the effects of strain and electrostatic doping on the magnetic anisotropy of GaN/VTe2 van der waals heterostructure
Authors: Xue Junjun , Chen Wei , Hu Shanwen , Chen Zhouyu , Fang Haoyu , Zhi Ting , Shao Pengfei , Cai Qing , Yang Guofeng , Gu Yan , Wang Jin , Chen Dunjun 
Published: October 2024

Abstract
Using a first-principles approach, this study delves into the effects of strain and electrostatic doping on the electronic and magnetic properties of the GaN/VTe2 van der Waals (vdW) heterostructure. The results reveal that when the GaN/VTe2 vdW heterostructure is doped with 0.1 h/0.2 h of electrostatic charge, its magnetization direction undergoes a remarkable reversal, shifting from out-of-plane orientation to in-plane direction. Therefore, we conduct a thorough investigation into the influence of electron orbitals on magnetic anisotropy energy. In addition, as the strain changes from −1% to 1%, the 100% spin polarization region of the GaN/VTe2 vdW heterostructure becomes smaller. It is worth noting that at a doping concentration of 0.1 h, the GaN/VTe2 vdW heterostructure has a Curie temperature of 30 K above room temperature. This comprehensive study provides valuable insights and provides a reference for analyzing the electronic and magnetic properties of low-dimensional systems.
Educational research in physics teacher education: the role of mathematics in physics teaching
Authors: Jacobsson Ebba , Wallenlind Simon , Pendrill Ann-Marie 
Published: October 2024

Abstract
Do student teachers benefit from contact with research, as required in our national teacher education curriculum? This paper describes the experiences of two pre-service teachers, who learned about common challenges encountered in the intersection between mathematics and physics. After a literature survey, they decided to try out a few questions with second-year high-school students (age 17). One of the student teachers constructed a pre- and post-test, confirming earlier results that students perform better on computational tasks. The second pre-service teacher included a few additional questions on a test given by another teacher. Based on the results on the test, four students were selected for interviews. The students interviewed all stated that they spend the majority of their time on calculations, whereas none of them had the habit of reading the textbook to understand the concepts. The interviews also revealed that solving a problem does not guarantee that the student understands the calculation. All students described a strategy of looking for a suitable formula on the sheet provided and just inserting numbers given. Through the test and the interviews, the student teachers gained training in assessment and an increased awareness of conceptual challenges and also of what Physics Education Research can offer.
A low-cost method for quantifying bouncing ball dynamics with smartphone video
Authors: Poonyawatpornkul Jiraporn , Snguanrat Banthita , Chaiworn Panupat 
Published: October 2024

Abstract
This study employed a low-cost method for quantifying bouncing ball dynamics with smartphone video. High-speed video analysis was used to investigate the bouncing behaviour of different balls, including table tennis, rubber, and tennis balls, on a table tennis surface. To examine the effect of surface material on bouncing dynamics, the table tennis ball was also tested on wood and tile surfaces. Key parameters analysed included bounce height decay, the coefficient of restitution (COR), and the bounce half-life. Additionally, we measured the total bouncing time and analysed the relationship between changes in gravitational potential energy, kinetic energy, and total mechanical energy (ME). Our results revealed a significant correlation between the COR and the decay rate of bounce height. Balls with higher COR values, indicative of lower energy loss per bounce, exhibited slower decay rates, resulting in longer bounce half-lives and extended total bounce times. For instance, the table tennis ball on a tile surface demonstrated a high COR of 0.93, a bounce half-life of 4.65 bounces, and a total bounce time of 8.22 ± 0.34 s. This suggests that material properties such as stiffness and elasticity of both the ball and the surface significantly influence the COR and decay rate, beyond just surface smoothness. Furthermore, our analysis of ME changes in the bouncing balls aligns with theoretical predictions, providing insights into the energy transformations occurring during bouncing. This improved understanding can also be a valuable tool for educators, helping teachers and students bridge the gap between theory and real-world observations, leading to a deeper grasp of the concepts.
Intensity vs amplitude of polarised light produced by a polariser
Authors: Goh Ker Liang 
Published: October 2024

Abstract
The intensity of unpolarised light after passing through a polariser is halved. This is a well-documented fact in contemporary textbooks. But how do their amplitudes compare before and after passing through the polariser? This is not mentioned in textbooks, and hence it is our intention to discuss this in this frontline.
Hybrid modality dual-energy imaging aggregating complementary advantages of kV-CT and MV-CBCT: concept proposal and clinical validation
Authors: Qi Junfeng , Yu Shutong , Dong Zhengkun , Liu Jiang , Deng Juan , Mei Guojian , Yin Chuou , Li Qiao , Li Tian , Wang Shi , Zhang Yibao 
Published: October 2024

Abstract

Objective. Megavoltage cone-beam CT (MV-CBCT) is advantageous in metal artifact reduction during image-guided radiotherapy (IGRT), although it is limited by poor soft tissue contrast. This study proposed and evaluated a novel hybrid modality dual-energy (DE) imaging method combining the complementary advantages of kilovoltage CT kV-CT and MV-CBCT. Approach. The kV-CT and MV-CBCT images were acquired on a planning CT scanner and a Halcyon linear accelerator respectively. After rigid registration, images of basis materials were generated using the iterative decomposition method in the volumetric images. The decomposition accuracy was quantitatively evaluated on a Gammex 1472 phantom. The performance of contrast enhancement and metal artifact reduction in virtual monochromatic images were evaluated on both phantom and patient studies. Main results. Using the proposed method, the mean percentage errors for relative electron density (RED) and stopping power ratio were 0.90% and 0.81%, outperforming the clinical single-energy mapping method with mean errors of 1.28% and 1.07%, respectively. The contrasts of soft-tissue insets were enhanced by a factor of 2–3 at 40 keV compared to kV-CT. The standard deviation in the metal artifact area was reduced by ∼67%, from 42 HU (kV-CT) to 14 HU (150 keV monochromatic). The head and neck patient test showed that the percent error of soft-tissue RED in the metal artifact area was reduced from 18.1% (HU-RED conversion) to less than 1.0% (the proposed method), which was equivalent to the maximum dosimetric difference of 28.7% based on the patient-specific plan. Significance. Without hardware modification or extra imaging dose, the proposed hybrid modality method enabled kV–MV DE imaging, providing improved accuracy of quantitative analysis, soft-tissue contrast and metal artifact suppression for more accurate IGRT.
Dual convolution-transformer UNet (DCT-UNet) for organs at risk and clinical target volume segmentation in MRI for cervical cancer brachytherapy
Authors: Kim Gayoung , Viswanathan Akila N , Bhatia Rohini , Landman Yosef , Roumeliotis Michael , Erickson Beth , Schmidt Ehud J , Lee Junghoon 
Published: October 2024

Abstract

Objective. MRI is the standard imaging modality for high-dose-rate brachytherapy of cervical cancer. Precise contouring of organs at risk (OARs) and high-risk clinical target volume (HR-CTV) from MRI is a crucial step for radiotherapy planning and treatment. However, conventional manual contouring has limitations in terms of accuracy as well as procedural time. To overcome these, we propose a deep learning approach to automatically segment OARs (bladder, rectum, and sigmoid colon) and HR-CTV from female pelvic MRI. Approach. In the proposed pipeline, a coarse multi-organ segmentation model first segments all structures, from which a region of interest is computed for each structure. Then, each organ is segmented using an organ-specific fine segmentation model separately trained for each organ. To account for variable sizes of HR-CTV, a size-adaptive multi-model approach was employed. For coarse and fine segmentations, we designed a dual convolution-transformer UNet (DCT-UNet) which uses dual-path encoder consisting of convolution and transformer blocks. To evaluate our model, OAR segmentations were compared to the clinical contours drawn by the attending radiation oncologist. For HR-CTV, four sets of contours (clinical + three additional sets) were obtained to produce a consensus ground truth as well as for inter/intra-observer variability analysis. Main results. DCT-UNet achieved dice similarity coefficient (mean ± SD) of 0.932 ± 0.032 (bladder), 0.786 ± 0.090 (rectum), 0.663 ± 0.180 (sigmoid colon), and 0.741 ± 0.076 (HR-CTV), outperforming other state-of-the-art models. Notably, the size-adaptive multi-model significantly improved HR-CTV segmentation compared to a single-model. Furthermore, significant inter/intra-observer variability was observed, and our model showed comparable performance to all observers. Computation time for the entire pipeline per subject was 12.59 ± 0.79 s, which is significantly shorter than the typical manual contouring time of >15 min. Significance. These experimental results demonstrate that our model has great utility in cervical cancer brachytherapy by enabling fast and accurate automatic segmentation, and has potential in improving consistency in contouring. DCT-UNet source code is available at https://github.com/JHU-MICA/DCT-UNet.