bluuebunny/crossref_metadata_2025_split huggingface dataset:
Transformer neural networks for closed-loop adaptive optics using nonmodulated pyramid wavefront sensors
Authors: Weinberger Camilo , Tapia Jorge , Neichel Benoît , Vera Esteban 
Published: May 2024

Context. The pyramid wavefront sensor (PyWFS) provides the required sensitivity for demanding future adaptive optics (AO) instruments. However, the PyWFS is highly nonlinear and requires the use of beam modulation to successfully close an AO loop under varying atmospheric turbulence conditions. This comes at the expense of a loss in sensitivity.
Aims. We trained, analyzed, and compared the use of deep neural networks (NNs) as nonlinear estimators for the nonmodulated PyWFS, identifying the most suitable NN architecture for a reliable closed-loop AO.
Methods. We developed a novel training strategy for NNs that seeks to accommodate for changes in residual statistics between open and closed loops, plus the addition of noise for robustness purposes. Through simulations, we tested and compared several deep NNs from classical to new convolutional neural networks (CNNs), plus the most recent transformer neural network (TNN; global context visual transformer, GCViT), first for an open loop and then for a closed loop. By identifying and properly retraining the most adequate deep neural net, we tested its simulated performance first in an open loop and then for closing an AO loop at a variety of noise and turbulence conditions. We finally tested the trained NN ability to close a real AO loop for an optical bench.
Results. Using open-loop simulated data, we observe that a TNN (GCViT) largely surpasses any CNN in estimation accuracy in a wide range of turbulence conditions. Moreover, the TNN performs better in a simulated closed loop than CNNs, avoiding estimation issues at the pupil borders. When closing the loop at strong turbulence and low noise, the TNN using nonmodulated PyWFS data is able to close the loop, similar to a PyWFS with 12λ/D of modulation. When the noise is increased, only the TNN is able to close the loop, while the standard linear reconstructor fails even when a modulation is introduced. Using the GCViT, we closed a real AO loop in the optical bench and achieved a Strehl ratio between 0.28 and 0.77 for turbulence conditions corresponding to Fried parameters ranging from 6 to 20 cm, respectively.
Conclusions. Through a variety of simulated and experimental results, we demonstrate that a TNN is the most suitable architecture for extending the dynamic range without sacrificing sensitivity for a nonmodulated PyWFS. It opens the path for using nonmodulated Pyramid WFSs in an unprecedented range of atmospheric and noise conditions.
Confrontation between modelled solar integrated observables and direct observations
Authors: Meunier N. , Lagrange A.-M. , Dumusque X. , Sulis S. 
Published: May 2024

Context. Stellar variability strongly impacts the search for low-mass exoplanets with radial velocity techniques. Two types of planet-free time series can be used to quantify this impact: models and direct solar observations after a subtraction of the Solar System planetary contribution. Making a comparison among these approaches is necessary to improve the models, which can then be used for blind tests across a broad range of conditions.
Aims. Our objective is therefore to validate the amplitude of the convective blueshift in plages used in our previous works, particularly in blind tests, with HARPS-N solar data.
Methods. We applied our model to the structures observed at the time of HARPS-N observations and established a direct comparison between the radial velocity time series. To complete our diagnosis, we also studied the observed radial velocities separately for each diffraction order derived from the individual cross-correlation functions, as well as our line-by-line radial velocities.
Results. We find that our previous model had been underestimating the amplitude of the convective blueshift inhibition by a factor of about 2. A direct estimation of the convective blueshift in the spectra, which is shown to be correlated with the plage filling factor, allows us to explain the difference with previous estimations obtained with MDI/SOHO Dopplergrams, based on the specific properties of the Ni line used in this mission. In addition, we identified several instrumental systematics, in particular, the presence of a 2 m s−1 peak-to-peak signal with a period of about 200 days in radial velocity and bisector. This signal could be due to periodic detector warmups, a systematic dependence of the long-term trend on wavelength that is possibly related to the variability of the continuum over time, and/or an offset in radial velocity after the interruption of several months in October 2017.
Conclusions. A large amplitude in the convective blueshift inhibition of (360 ms−1, namely twice more than in our previous works) must be used when building synthetic times series for blind tests. The presence of instrumental systematics should also be taken into account when using sophisticated methods based on line properties to mitigate stellar activity when searching for very weak signals.
Effect of Postrinsing Times and Methods on Surface Roughness, Hardness, and Polymerization of 3D-Printed Photopolymer Resin
Authors: Katheng Awutsadaporn , Prawatvatchara Wisarut , Tonprasong Watcharapong , Namano Sahaprom , Kongkon Paweena 
Published: May 2024

Abstract
Objectives This in vitro study investigated the effects of different postrinsing times and methods on the surface roughness, surface hardness, and degree of polymerization of materials manufactured via stereolithography (SLA).
Materials and Methods A total of 288 disk-shaped specimens were manufactured using an SLA three-dimensional (3D) printer. The specimens were randomly divided into nine groups (n = 32) based on rinsing times and methods. The groups were categorized into three rinsing methods: automated, ultrasonic, and hand washing, with rinsing times of 5, 10, and 15 minutes using a 99% isopropanol alcohol as a solvent. Linear roughness (Ra) and area roughness (Sa) were measured using a 3D confocal laser microscopy; the roughness morphology was evaluated by using scanning electron microscopy. Vickers hardness (VHN) tests were performed using a Vickers microhardness tester. Fourier-transform infrared spectrometry was used to determine the degree of conversion of treated specimens.
Statistical Analysis Data were statistically analyzed using two-way analysis of variance. The post hoc Tukey tests were conducted to compare the differences between groups (p < 0.05).
Results The choice of the rinsing time and method affected the surface properties of the SLA photopolymer resin. The 15 minutes of ultrasonic method exhibited the highest Ra scores (0.86 ± 0.1 µm), while the 15 minutes of automated method presented the highest Sa scores (1.77 ± 0.35 µm). For the VHN test, the 15 minutes of ultrasonic method displayed the highest VHN score (18.26 ± 1.03 kgf/mm2). For the degree of polymerization, the 15 minutes of automated method was initially identified as the most effective (87.22 ± 6.80).
Conclusion To facilitate the overall surface roughness, surface hardness, and degree of polymerization, the optimal choice of postprocessing rinsing time and method for achieving a clear photopolymer resin was determined to be the ultrasonic method with a rinsing time of 15 minutes.
A first-principles study of the formation and regulation of the electric double layers at Cu (0 0 1)/mineral oil interfaces
Authors: Ren Yuanyang , Zhang Qiankai , Wang Yang , Li Jianfei , Yan Wenchao , Xu Hao , Cheng Chuanhui , Wu Kai 
Published: May 2024

Abstract
Copper-mineral oil interfaces are key components of oil-impregnated power transformers and are commonly believed to be one of their weak points. The formation of an electric double layer (EDL) at this interface as a result of charge accumulation and transfer is crucial to its insulating properties, but a molecular-level understanding of this phenomenon remains unclear. To understand this fundamental aspect, we have investigated the effect of different EDLs on the electric potential and interfacial potential barrier between copper and mineral oil by using first principle calculations. Based on the calculations, the EDL is shown to reduce the interfacial potential barrier and enhance the diffusion of oil molecules at the interface when the copper side is negatively charged and the mineral oil side is positively charged. In contrast, when the copper side is positively charged and the mineral oil side is negatively charged, the corresponding EDL can increase the interfacial potential barrier and reduce the diffusion of oil molecules at this interface. Our findings shed light on the relationship between the structure of EDLs and their electrical properties in oil-impregnated power transformers.
Spectroscopy of phononic switching of magnetization in the terahertz gap
Authors: Gidding Maxime , Davies Carl S , Stupakiewicz Andrzej , Dessmann Nils , Kirilyuk Andrei 
Published: May 2024

Abstract
All-optical schemes for switching magnetization offer a pathway towards the creation of more advanced data-storage technologies, both in terms of recording speed and energy-efficiency. It has previously been shown that picosecond-long optical pulses with central frequencies ranging between 12 and 30 THz are capable of driving magnetic switching in yttrium-iron-garnet films, provided that the excitation frequency matches the characteristic frequency of longitudinal optical phonons. Here, we explore how the phononic mechanism of magnetic switching in three distinct ferrimagnetic iron-garnet films evolves at optical frequencies below 10 THz, within the so-called terahertz gap. We find that at long wavelengths the magnetic switching rather correlates with phonon modes associated with the substrate. Our results show that the process of phononic switching of magnetization, previously discovered in the mid- to far-infrared spectral range, becomes much more complex at frequencies within the terahertz gap.
Fault diagnosis of driving gear in a battery swapping system based on audio features and SRC-Adaboost
Authors: You Xianglong , Wu Hao , Li Jiacheng , You Xiaowen , Zhang Chi , Yuan Hang 
Published: May 2024

Abstract
Because of electrification conditions, key components of battery swapping systems (BSSs) for electric heavy trucks are always damaged by electric erosion, which presents challenges to the safety and efficiency of high-intensity transportation. Due to the special working conditions of a BSS, the fault diagnosis of its driving gear encounters several issues, including reciprocation motion, low and fluctuating speed, and complicated noises. To solve these problems, audio features, including Mel-frequency cepstral coefficients and Gammatone cepstral coefficients, are extracted from the vibration signals. Then, these features are utilized to construct an original dictionary. Next, based on data augmentation and dictionary learning, a robust dictionary is generated from the original dictionary. Finally, with the robust dictionary, sparse representation-based classification is integrated into AdaBoost to achieve accurate fault diagnosis for the driving gear in BSS. The effectiveness of the fault diagnosis scheme is validated based on the monitoring data of the BSS, and the accuracy of fault diagnosis is 99.17%.
A de-texturing model for enhancing the accuracy of centroid positioning
Authors: Wang Yanqi , Xu Zheng , Wang Xiaodong , He Jiaheng , Ren Tongqun 
Published: May 2024

Abstract
In tasks guided by microvision, extracting centroids is a common method for positioning, which is negatively affected by texture. Here, an attention-related de-texturing model is proposed to eliminate the texture of microparts and preserve accurate edges. A network with an attention module called De-texturing Net is built, in which both the transformer and channel attention modules are included. Considering the importance of texture, the additional factor in loss function is constructed based on the Gram matrix difference between target images and generated images. Results show that De-texturing Net can generate de-texturized images with high Peak Signal to Noise Ratio/SSIM, indicating the similarity between de-texturized and target images. Moreover, for the centroid positioning, the error in de-texturized images is significantly lower than the error in original images. This study helps improve the accuracy of centroid positioning due to the de-texturized images with accurate edges.
Roadmap on data-centric materials science
Authors: Bauer Stefan , Benner Peter , Bereau Tristan , Blum Volker , Boley Mario , Carbogno Christian , Catlow C Richard A , Dehm Gerhard , Eibl Sebastian , Ernstorfer Ralph , Fekete Ádám , Foppa Lucas , Fratzl Peter , Freysoldt Christoph , Gault Baptiste , Ghiringhelli Luca M , Giri Sajal K , Gladyshev Anton , Goyal Pawan , Hattrick-Simpers Jason , Kabalan Lara , Karpov Petr , Khorrami Mohammad S , Koch Christoph T. , Kokott Sebastian , Kosch Thomas , Kowalec Igor , Kremer Kurt , Leitherer Andreas , Li Yue , Liebscher Christian H , Logsdail Andrew J , Lu Zhongwei , Luong Felix , Marek Andreas , Merz Florian , Mianroodi Jaber R , Neugebauer Jörg , Pei Zongrui , Purcell Thomas A R , Raabe Dierk , Rampp Markus , Rossi Mariana , Rost Jan-Michael , Saal James , Saalmann Ulf , Sasidhar Kasturi Narasimha , Saxena Alaukik , Sbailò Luigi , Scheidgen Markus , Schloz Marcel , Schmidt Daniel F , Teshuva Simon , Trunschke Annette , Wei Ye , Weikum Gerhard , Xian R Patrick , Yao Yi , Yin Junqi , Zhao Meng , Scheffler Matthias 
Published: May 2024

AbstractScience is and always has been based on data, but the terms ‘data-centric’ and the ‘4th paradigm’ of materials research indicate a radical change in how information is retrieved, handled and research is performed. It signifies a transformative shift towards managing vast data collections, digital repositories, and innovative data analytics methods. The integration of artificial intelligence and its subset machine learning, has become pivotal in addressing all these challenges. This Roadmap on Data-Centric Materials Science explores fundamental concepts and methodologies, illustrating diverse applications in electronic-structure theory, soft matter theory, microstructure research, and experimental techniques like photoemission, atom probe tomography, and electron microscopy. While the roadmap delves into specific areas within the broad interdisciplinary field of materials science, the provided examples elucidate key concepts applicable to a wider range of topics. The discussed instances offer insights into addressing the multifaceted challenges encountered in contemporary materials research.
Construction of sub micro-nano-structured silicon based anode for lithium-ion batteries
Authors: Su Chen , Shodievich Kurbanov Mirtemir , Zhao Yi , Ji Puguang , Zhang Xin , Wang Hua , Zhang Chengwei , Wang Gongkai 
Published: May 2024

Abstract
The significant volume change experienced by silicon (Si) anodes during lithiation/delithiation cycles often triggers mechanical-electrochemical failures, undermining their utility in high-energy-density lithium-ion batteries (LIBs). Herein, we propose a sub micro-nano-structured Si based material to address the persistent challenge of mechanic-electrochemical coupling issue during cycling. The mesoporous Si-based composite submicrospheres (M-Si/SiO2/CS) with a high Si/SiO2 content of 84.6 wt.% is prepared by magnesiothermic reduction of mesoporous SiO2 submicrospheres followed by carbon coating process. M-Si/SiO2/CS anode can maintain a high specific capacity of 740 mAh g−1 at 0.5 A g−1 after 100 cycles with a lower electrode thickness swelling rate of 63%, and exhibits a good long-term cycling stability of 570 mAh g−1 at 1 A g−1 after 250 cycles. This remarkable Li-storage performance can be attributed to the synergistic effects of the hierarchical structure and SiO2 frameworks. The spherical structure mitigates stress/strain caused by the lithiation/delithiation, while the internal mesopores provide buffer space for Si expansion and obviously shorten the diffusion path for electrolyte/ions. Additionally, the amorphous SiO2 matrix not only servers as support for structure stability, but also facilitates the rapid formation of a stable solid electrolyte interphase layer. This unique architecture offers a potential model for designing high-performance Si-based anode for LIBs.
Starch/chitosan nanoparticles bionanocomposite membranes for methylene blue dye removal
Authors: Ilias Hanis Masyithah , Othman Siti Hajar , Shapi’i Ruzanna Ahmad , Yunos Khairul Faezah Md 
Published: May 2024

Abstract
This research aims to develop relatively new membranes from starch biopolymer incorporated with different concentrations (0, 5, 10, 15, 20% w/w of solid starch) of chitosan nanoparticles (CNP) that can be used for water treatment. The membranes were fabricated using the solvent casting method while the CNP was produced using the ionic gelation method. The membranes were characterized in terms of morphology, porosity, water vapor permeability (WVP), and water contact angle. The application of the membranes to treat water was demonstrated on methylene blue solution because methylene blue is a commonly used dye in many industries. It was found that the starch/10% CNP membrane was the optimum membrane for methylene blue dye treatment because the membrane exhibits a smooth surface, high WVP (1.67 × 10−10 g Pa−1 h−1 m−1), high porosity (59.92%), low water contact angle value (44.8°), and resulted in the highest percentage removal of methylene blue (94.0%) after the filtration. After filtration, the starch/10% CNP membrane was still in good condition without breakage. In conclusion, the starch/CNP membranes produced in this study are promising for sustainable and environmentally friendly water treatment, especially for water containing methylene blue dye. This research aligns with current thematic trends in bionanohybrid composite materials utilization, offering innovative solutions for addressing water pollution challenges.
Modulation of polyaniline memristive device switching voltage by nucleotide-free analogue of vitamin B12
Authors: Prudnikov Nikita V , Emelyanov Andrey V , Serenko Maria V , Dereven’kov Ilia A , Maiorova Larissa A , Erokhin Victor V 
Published: May 2024

Abstract
Memristive devices offer essential properties to become a part of the next-generation computing systems based on neuromorphic principles. Organic memristive devices exhibit a unique set of properties which makes them an indispensable choice for specific applications, such as interfacing with biological systems. While the switching rate of organic devices can be easily adjusted over a wide range through various methods, controlling the switching potential is often more challenging, as this parameter is intricately tied to the materials used. Given the limited options in the selection conductive polymers and the complexity of polymer chemical engineering, the most straightforward and accessible approach to modulate switching potentials is by introducing specific molecules into the electrolyte solution. In our study, we show polyaniline (PANI)-based device switching potential control by adding nucleotide-free analogue of vitamin B12, aquacyanocobinamide, to the electrolyte solution. The employed concentrations of this molecule, ranging from 0.2 to 2 mM, enabled organic memristive devices to achieve switching potential decrease for up to 100 mV, thus providing a way to control device properties. This effect is attributed to strong aromatic interactions between PANI phenyl groups and corrin macrocycle of the aquacyanocobinamide molecule, which was supported by ultraviolet-visible spectra analysis.
Dose–toxicity surface histogram-based prediction of radiation dermatitis severity and shape
Authors: Hong Chae-Seon , Park Ye-In , Cho Min-Seok , Son Junyoung , Kim Changhwan , Han Min Cheol , Kim Hojin , Lee Ho , Kim Dong Wook , Choi Seo Hee , Kim Jin Sung 
Published: May 2024

Abstract

Objective. This study aimed to develop a new approach to predict radiation dermatitis (RD) by using the skin dose distribution in the actual area of RD occurrence to determine the predictive dose by grade. Approach. Twenty-three patients with head and neck cancer treated with volumetric modulated arc therapy were prospectively and retrospectively enrolled. A framework was developed to segment the RD occurrence area in skin photography by matching the skin surface image obtained using a 3D camera with the skin dose distribution. RD predictive doses were generated using the dose–toxicity surface histogram (DTH) calculated from the skin dose distribution within the segmented RD regions classified by severity. We then evaluated whether the developed DTH-based framework could visually predict RD grades and their occurrence areas and shapes according to severity. Main results. The developed framework successfully generated the DTH for three different RD severities: faint erythema (grade 1), dry desquamation (grade 2), and moist desquamation (grade 3); 48 DTHs were obtained from 23 patients: 23, 22, and 3 DTHs for grades 1, 2, and 3, respectively. The RD predictive doses determined using DTHs were 28.9 Gy, 38.1 Gy, and 54.3 Gy for grades 1, 2, and 3, respectively. The estimated RD occurrence area visualized by the DTH-based RD predictive dose showed acceptable agreement for all grades compared with the actual RD region in the patient. The predicted RD grade was accurate, except in two patients. Significance. The developed DTH-based framework can classify and determine RD predictive doses according to severity and visually predict the occurrence area and shape of different RD severities. The proposed approach can be used to predict the severity and shape of potential RD in patients and thus aid physicians in decision making.