bluuebunny/crossref_metadata_2025_split huggingface dataset:
Remote teaching data-driven physical modeling through a COVID-19 open-ended data challenge
Authors: Cosentino Lagomarsino Marco , Pacifico Guglielmo , Firmano Valerio , Bella Edoardo , Benzoni Pietro , Grilli Jacopo , Bassetti Federico , Capuani Fabrizio , Cicuta Pietro , Gherardi Marco 
Published: June 2022

Abstract
Physics can be seen as a conceptual approach to scientific problems, a method for discovery, but teaching this aspect of our discipline can be a challenge. We report on a first-time remote teaching experience for a computational physics third-year physics laboratory class taught in the first part of the 2020 COVID-19 pandemic (March–May 2020). To convey a ‘physics of data’ approach to data analysis and data-driven physical modeling we used interdisciplinary data sources, with an openended ‘COVID-19 data challenge’ project as the core of the course. COVID-19 epidemiological data provided an ideal setting for motivating the students to deal with complex problems, where there is no unique or preconceived solution. Our results indicate that such problems yield qualitatively different improvements compared to close-ended projects, as well as point to critical aspects in using these problems as a teaching strategy. By breaking the students’ expectations of unidirectionality, remote teaching provided unexpected opportunities to promote active work and active learning.
An extended primal-dual algorithm framework for nonconvex problems: application to image reconstruction in spectral CT
Authors: Gao Yu , Pan Xiaochuan , Chen Chong 
Published: June 2022

Abstract
Using the convexity of each component of the forward operator, we propose an extended primal-dual algorithm framework for solving a kind of nonconvex and probably nonsmooth optimization problems in spectral computed tomography (CT) image reconstruction. Following the proposed algorithm framework, we present six different iterative schemes or algorithms, and then establish the relationship to some existing algorithms. Under appropriate conditions, we prove the convergence of these schemes for the general case. Moreover, when the proposed schemes are applied to solving a specific problem in spectral CT image reconstruction, namely, total variation regularized nonlinear least-squares problem with nonnegative constraint, we also prove the particular convergence for these schemes by using some special properties. The numerical experiments with densely and sparsely data demonstrate the convergence and accuracy of the proposed algorithm framework in terms of visual inspection of images of realistic anatomic complexity and quantitative analysis with metrics structural similarity, peak signal-to-noise ratio, mean square error and maximum pixel difference. We analyze the computational complexity of these schemes, and discuss the extended applications of this algorithm framework in other nonlinear imaging problems.
MXene-based hydrogels towards the photothermal applications
Authors: Meng Jianqiang , An Ziyue , Liu Yandi , Sun Xiaoyi , Li Juan 
Published: June 2022

Abstract
MXene, a family of two-dimensional transition metal carbides/nitrides, is one of the most attractive photothermal materials due to its unique hydrophilicity, tunable surface activity, and outstanding photothermal conversion performance by absorbing near-infrared light or solar light. The integration of MXene into three-dimensional polymer hydrogels opens up new possibilities for developing advanced material platforms. In this review, we summarize the synthesis of MXene-based hydrogels and highlight their recent progress in photothermal applications. Challenges and perspectives for future research are also outlined.
Fast visual inertial odometry with point–line features using adaptive EDLines algorithm
Authors: Zhao Shenggen , Zhang Tao , Wei Hongyu 
Published: June 2022

Abstract
In mainstream visual inertial odometry (VIO) systems, the method of positional solution by feature point extraction and matching in the image is widely used. However, the tracking accuracy of point features is dependent on the texture richness in the environment. Although many existing algorithms introduce line features in the front end to improve the system’s environmental adaptability, most of them sacrifice system real-time in exchange for higher positioning accuracy. The extraction and matching of line features often require more time, thus failing to meet the real-time requirements of the system for localization. In this paper, we therefore propose a fast VIO fused with point and line features, which enables the system to maintain a high level of positioning robustness in dim and changing light environments with low time cost. The point–line features VIO algorithm is based on adaptive thresholding of EDLines. By adding an adaptive thresholding component to the EDLines algorithm, the robustness of line feature extraction is enhanced to better adapt to changes in ambient lighting. The time needed for line feature extraction is also significantly reduced. A line feature matching algorithm based on geometric information and structural similarity is proposed, which enables fast and accurate line feature matching. The algorithm is compared with point-line visual-inertial odometry and monocular visual-inertial state estimator algorithms on the European robotics challenge dataset and real-world scenes. Many experiments prove that the algorithm has improved in both real time and accuracy.
Analysis and optimization of shielding in measuring helical gear tooth flank by laser interferometry
Authors: Zhu Xindong , Wang Zhaohui , Yang Pengcheng , Zhang Pei , Wang Xian , Zhao Zidan , Meng Jinglin , Wei Xiang 
Published: June 2022

Abstract
Phase shifting laser interferometry is an effective method to measure the form error of a helical gear tooth flank. However, during measurement, part of the measurement light is inevitably shielded by an adjacent tooth or its own structure, and thus cannot be captured by the detector, resulting in the inability to collect a complete set of tooth surface shape information. This problem restrains the practicality of measuring a gear tooth flank by laser interferometry. We propose an analysis method and optimal search algorithm for shielding, to collect a complete set of tooth surface information from a measured gear. First, we propose an analysis method for shielding, based on ray-tracing, which allows the measurable region of helical gear tooth flanks with common parameters to be statistically analyzed. The variation law of the main parameters and a measurable region of the common helical gear tooth surface is expounded. Second, in order to obtain the complete interferogram, we propose an optimal search algorithm for shielding, by changing the relative position between the measured gear and the measurement optical system. Finally, relevant simulation and experiments prove that the proposed analysis and optimization methods are feasible. Furthermore, when the oblique incidence method is used to measure the shielded precision complex surface, the proposed methods of analyzing and optimizing shielding can be used for reference, to realize the complete measurement of the measured surface.
A prediction–correction method for fast and accurate initial displacement field estimation in digital image correlation
Authors: Yang Hongfan , Wang Sihan , Xia Huanxiong , Liu Jianhua , Wang Aimin , Yang Ye 
Published: June 2022

Abstract
Initial displacement estimation is one of the most critical issues in digital image correlation. A better initial value can greatly improve the convergence rate and accuracy of the algorithms with subpixel accuracy. This paper developed an efficient estimation method to yield high-quality initial displacement fields. This method finds the initial displacement of each subset in a prediction–correction way, in which the displacement of the seed point is found by exhaustive search, but the other subsets are first predicted by an extrapolation scheme and then corrected by a monotonous search strategy. This method was tested by extensive experiments and validated by comparing with the well-known exhaustive search and adaptive rood pattern search methods, and then it was combined with the inverse compositional Gauss–Newton algorithm to perform subpixel-optimization experiments. The results demonstrated excellent features of accuracy, effectiveness, and convergence. Finally, we presented a three-dimensional surface reconstruction experiment using the proposed method, obtaining a geometric accuracy with a relative error of 0.016%.
Improvement of endurance and switching speed in Hf1−x
Zr
x
O2 thin films using a nanolaminate structure
Authors: Jang Hojung , Kashir Alireza , Oh Seungyeol , Hwang Hyunsang 
Published: June 2022

Abstract
To improve the endurance and polarization switching speed of Hf1−x
Zr
x
O2 (HZO) ferroelectric films, we designed a 10 nm Hf0.5Zr0.5O2 + ZrO2 (HZZ) nanolaminate structure. Three films with different ZrO2 interlayers thicknesses were compared to find the optimal condition to implement the effect of the topological domain wall which was proposed recently. The HZZ film were deposited by repeatedly stacking ten HZO (∼0.92 nm) and six ZrO2 (∼0.53 nm) layers; they exhibited a dramatic reduction of coercive field without an effective loss of remnant polarization. The endurance at operation voltage increased by more than 100 times compared with that of the solid solution HZO film, and the switching speed was increased by more than two times. The formation of the tetragonal phase-like spacer between the ferroelectric polar regions appears to be the main factor associated with the reduction of the switching barrier and leads to the acceleration of the switching propagation over multiple domains.
Determining Turkish high school students’ misconceptions about electric charge imbalance by using a four-tier misconception test
Authors: Onder-Celikkanli Nuray , Tan Mustafa 
Published: June 2022

Abstract
This study aims to determine Turkish high school students’ misconceptions about the electric charge imbalance by using a four-tier misconception test, where the main questions are accompanied by a question about the reason for the answer, as well as student declarations about their degrees of certainty. A four-tier electric charge imbalance misconception test (4-tier ECIMT) was developed by Onder-Celikkanli (2019) in the context of her dissertation. It consists of 24 questions, each consisting of four tiers. It is limited to (a) electric interactions between charged bodies, (b) electric interactions between a charged body and a neutral body, and (c) electric interactions between two neutral bodies. It was administered to 402 tenth-grade Turkish high school students in Turkey. At the end of the study, some new misconceptions were introduced to the literature. One of the most important ones is ‘the magnitude of the electric force between the two objects is independent of the relative permittivity of the medium between them’.
A deep learning and Monte Carlo based framework for bioluminescence imaging center of mass-guided glioblastoma targeting
Authors: Rezaeifar Behzad , Wolfs Cecile J A , Lieuwes Natasja G , Biemans Rianne , Reniers Brigitte , Dubois Ludwig J , Verhaegen Frank 
Published: June 2022

Abstract

Objective. Bioluminescence imaging (BLI) is a valuable tool for non-invasive monitoring of glioblastoma multiforme (GBM) tumor-bearing small animals without incurring x-ray radiation burden. However, the use of this imaging modality is limited due to photon scattering and lack of spatial information. Attempts at reconstructing bioluminescence tomography (BLT) using mathematical models of light propagation show limited progress. Approach. This paper employed a different approach by using a deep convolutional neural network (CNN) to predict the tumor’s center of mass (CoM). Transfer-learning with a sizeable artificial database is employed to facilitate the training process for, the much smaller, target database including Monte Carlo (MC) simulations of real orthotopic glioblastoma models. Predicted CoM was then used to estimate a BLI-based planning target volume (bPTV), by using the CoM as the center of a sphere, encompassing the tumor. The volume of the encompassing target sphere was estimated based on the total number of photons reaching the skin surface. Main results. Results show sub-millimeter accuracy for CoM prediction with a median error of 0.59 mm. The proposed method also provides promising performance for BLI-based tumor targeting with on average 94% of the tumor inside the bPTV while keeping the average healthy tissue coverage below 10%. Significance. This work introduced a framework for developing and using a CNN for targeted radiation studies for GBM based on BLI. The framework will enable biologists to use BLI as their main image-guidance tool to target GBM tumors in rat models, avoiding delivery of high x-ray imaging dose to the animals.
Deep residual-SVD network for brain image registration
Authors: Cui Kunpeng , Lin Yusong , Liu Yue , Li Yinghao 
Published: June 2022

Abstract

Objective. Medical image registration aims to find the deformation field that can align two images in a spatial position. A medical image registration method based on U-Net architecture has been proposed currently. However, U-Net architecture has few training parameters, which leads to weak learning ability, and it ignores the adverse effects of image noise on the registration accuracy. The article aims at addressing the problem of weak network learning ability and the adverse effects of noisy images on registration. Approach. Here we propose a novel unsupervised 3D brain image registration framework, which introduces the residual unit and singular value decomposition (SVD) denoising layer on the U-Net architecture. Residual unit solves the problem of network degradation, that is, registration accuracy becomes saturated and then degrades rapidly with the increase in network depth. SVD denoising layer uses the estimated model order for SVD-based low-rank image reconstruction. we use Akaike information criterion to estimate the appropriate model order, which is used to remove noise components. We use the exponential linear unit (ELU) as the activation function, which is more robust to noise than other peers. Main results. The proposed method is evaluated on the publicly available brain MRI datasets: Mindboggle101 and LPBA40. Experimental results demonstrate our method outperforms several state-of-the-art methods for the metric of Dice Score. The mean number of folding voxels and registration time are comparable to state-of-the-art methods. Significance. This study shows that Deep Residual-SVD Network can improve registration accuracy. This study also demonstrate that the residual unit can enhance the learning ability of the network, the SVD denoising layer can denoise effectively, and the ELU is more robust to noise.
A systematic study of the contribution of counting statistics to the final lineal energy uncertainty in microdosimetry
Authors: Parisi Gabriele , Schettino Giuseppe , Romano Francesco 
Published: June 2022

Abstract

Objectives. Microdosimetry is proving to be a reliable and powerful tool to be applied in different fields such as radiobiology, radiation protection and hadron therapy. However, accepted standard protocols and codes of practice are still missing. With this regard, a systematic and methodical uncertainty analysis is fundamental to build an accredited uncertainty budget of practical use. This work studied the contribution of counting statistics (i.e. number of events collected) to the final frequency-mean and dose-mean lineal energy uncertainties, aiming at providing guidelines for good experimental and simulation practice. The practical limitation of current technologies and the non-negligible probability of nuclear reactions require careful considerations and nonlinear approaches. Approach. Microdosimetric data were obtained by means of the particle tracking Monte Carlo code Geant4. The uncertainty analysis was carried out relying on a Monte Carlo based numerical analysis, as suggested by the BIPM's ‘Guide to the expression of uncertainty in measurement’. Final uncertainties were systematically investigated for proton, helium and carbon ions at an increasing number of detected events, for a range of different clinical-relevant beam energies. Main results. Rare events generated by nuclear interactions in the detector sensitive volume were found to massively degrade microdosimetric uncertainties unless a very high statistics is collected. The study showed an increasing impact of such events for increasing beam energy and lighter ions. For instance, in the entrance region of a 250 MeV proton beam, about 5 ∗ 107 events need to be collected to obtain a dose-mean lineal energy uncertainty below 10%. Significance. The results of this study help define the necessary conditions to achieve appropriate statistics in computational microdosimetry, pointing out the importance of properly taking into account nuclear interaction events. Their impact on microdosimetric quantities and on their uncertainty is significant and cannot be overlooked, particularly when characterising clinical beams and radiobiological response. This work prepared the ground for deeper investigations involving dedicated experiments and for the development of a method to properly evaluate the counting statistics uncertainty contribution in the uncertainty budget, whose accuracy is fundamental for the clinical transition of microdosimetry.
Quasi-analytical model of surface potential and drain current for trigate negative capacitance FinFET: a superposition approach
Authors: Chauhan Vibhuti , Samajdar Dip Prakash , Bagga Navjeet 
Published: June 2022

Abstract
Negative capacitance (NC) obtained from the ferroelectric polarization switching is a widely adopted approach for the realization of low power, high-performance devices. In this paper, for the first time, we have developed a 3D quasi-analytical model for the surface potential and drain current of the trigate NC-FinFET using the superposition approach. Till date, only double gate (DG) uniformly doped NC-FinFET structures have been explored, which does not reveal the practicality of the device. Therefore, we perform an extensive device evaluation: (a) by solving the Poisson’s equation separately for the side gates (DG) and the top gate to acquire a complete model for trigate FinFET using the superposition principle; (b) to mimic the actual source/drain (S/D) doping, we included Gaussian doping in our proposed model; (c) with the incorporation of the laterally extended gate and S/D underlap. The model data are found in good agreement with the well-calibrated simulation data. We have taken the parabolic approximation method and appropriate boundary conditions to solve the Poisson’s equation.
Collisional microwave heating and wall interaction of an ultracold plasma in a resonant microwave cavity
Authors: van Ninhuijs M A W , Beckers J , Luiten O J 
Published: June 2022

Abstract
Recently, we introduced a resonant microwave cavity as a diagnostic tool for the study of ultracold plasmas (UCPs). This diagnostic allows us to study the electron dynamics of UCPs non-destructively, very fast, and with high sensitivity by measuring the shift in the resonance frequency of a cavity, induced by a plasma. However, in an attempt to theoretically predict the frequency shift using a Gaussian self-similar expansion model, a three times faster plasma decay was observed in the experiment than found in the model. For this, we proposed two causes: plasma–wall interactions and collisional microwave heating. In this paper, we investigate the effect of both causes on the lifetime of the plasma. We present a simple analytical model to account for electrons being lost to the cavity walls. We find that the model agrees well with measurements performed on plasmas with different initial electron temperatures and that the earlier discrepancy can be attributed to electrons being lost to the walls. In addition, we perform measurements for different electric field strengths in the cavity and find that the electric field has a small, but noticeable effect on the lifetime of the plasma. By extending the model with the theory of collisional microwave heating, we find that this effect can be predicted quite well by treating the energy transferred from the microwave field to the plasma as additional initial excess energy for the electrons.
A proposed test of quantum dissipation theory using Kapitza–Dirac electron diffraction
Authors: Puente Raul , Chen Zilin , Batelaan Herman 
Published: June 2022

Abstract
Decoherence can be provided by a dissipative environment as described by the Caldeira–Leggett equation. This equation is foundational to the theory of quantum dissipation. However, no experimental test has been performed that measures for one physical system both the dissipation and the decoherence. Anglin and Zurek predicted that a resistive surface could provide such a dissipative environment for a free electron wave passing close to it. We propose that the electron wave’s coherence and energy loss can be measured simultaneously by using Kapitza–Dirac scattering for varying light intensity.
Dynamics of atoms within atoms
Authors: Tiwari S , Engel F , Wagner M , Schmidt R , Meinert F , Wüster S 
Published: June 2022

Abstract
Recent experiments with Bose–Einstein condensates have entered a regime in which thousands of ground-state condensate atoms fill the Rydberg-electron orbit. After the excitation of a single atom into a highly excited Rydberg state, scattering off the Rydberg electron sets ground-state atoms into motion, such that one can study the quantum-many-body dynamics of atoms moving within the Rydberg atom. Here we study this many-body dynamics using Gross–Pitaevskii and truncated Wigner theory. Our simulations focus in particular on the scenario of multiple sequential Rydberg excitations on the same rubidium condensate which has become the standard tool to observe quantum impurity dynamics in Rydberg experiments. We investigate to what extent such experiments can be sensitive to details in the electron–atom interaction potential, such as the rapid radial modulation of the Rydberg molecular potential, or p-wave shape resonance. We demonstrate that both effects are crucial for the initial condensate response within the Rydberg orbit, but become less relevant for the density waves emerging outside the Rydberg excitation region at later times. Finally we explore the local dynamics of condensate heating. We find that it provides only minor corrections to the mean-field dynamics. Combining all these insights, our results suggest Bose–Einstein condensates as a viable platform for the in situ and real time interrogation of ultra-cold chemistry dynamics involving Rydberg states.