bluuebunny/crossref_metadata_2025_split huggingface dataset:
Automatic multi-LiDAR calibration and refinement method
Authors: Wei Pengjin , Yan Guohang , Li Yikang , Fang Kun , Cai Xinyu , Liu Wei , Yang Jie 
Published: April 2024

Abstract
Sensor-based environmental perception is a crucial component of autonomous driving systems. To perceive the surrounding environment better, an intelligent system would utilize multiple LiDARs (3D Light Detection and Ranging). The accuracy of the perception largely depends on the quality of the sensor calibration. This research aims to develop a robust, fast, automatic, and accurate calibration strategy for multiple LiDAR systems. Our proposed multi-LiDAR calibration method consists of two stages: rough and refinement calibration. In the first stage, sensors are roughly calibrated from an arbitrary initial position using a deep neural network that does not rely on prior information or constraints on the initial sensor pose. In the second stage, we propose the octree-based refinement, an optimization method that considers sensor noise and prioritization. Our strategy is robust, fast, and not restricted to any environment. Additionally, we collected two datasets consisting of both real-world and simulated scenarios. Our experimental results from both datasets demonstrate the reliability and accuracy of our method. All the related datasets and codes are open-sourced on the GitHub website at https://github.com/OpenCalib/LiDAR2LiDAR.
Immersive visualisation for a large metrological data set
Authors: Babic B , Coleman V A , Herrmann J 
Published: April 2024

Abstract
A purpose of scientific visualisation is to facilitate the extraction of information within underlying data. Visualisation of a large data set requires an appropriate visualisation method where, instead of focusing on expected features, all acquired data are treated on an equal footing. Here, we identify critical differences between raw and processed data acquired from displacement measurements at the nanoscale. We illustrate the strength of immersive data visualisation in rapidly and intuitively identifying non-obvious data trends, supported by correlation with the associated measurement uncertainties. These insights can be directly leveraged for optimisation of instrumentation and measurement protocols, application of appropriate measurement corrections, and iterative experimental design changes.
Relative humidity measurements of a thin-film humidity sensor in condensing conditions in the temperature range from −40 °C to 5 °C
Authors: Lee Sang-Wook , Lee Young-Suk , Choi Byung Il , Kim Yong-Gyoo , Kim Sunghun 
Published: April 2024

Abstract
In condensing conditions, metastable states such as supersaturation of water vapour and supercooling of liquid water are commonly observed in the free atmosphere. This study investigates the response of a polymeric thin-film humidity sensor under condensing conditions at various temperatures (−40 °C, −20 °C, −5 °C, and 5 °C) and different relative humidity (RH) levels. To ensure precise control of RH, a saturator-based humidity generator operating in a two-temperature mode is utilized. The condensing conditions are achieved in two ways: by increasing water vapour pressure (WVP) at a fixed temperature and by decreasing the temperature while maintaining a constant WVP. In general, when measuring RH under condensing conditions, the sensor indicates a temporary supersaturation state with an RH peak exceeding 100% before the onset of condensation. Subsequently, the RH value exhibits a delayed decrease when exposed to a non-condensing RH level. The experiments with a constant WVP demonstrate a lower likelihood of ice condensation compared to those with a constant temperature. This study demonstrates the measurement capability and behaviour of thin-film humidity sensors above 100% RH in the supersaturation states that are commonly observed in the free atmosphere at meteorologically-relevant temperatures.
UV photodetectors based on W-doped ZnO thin films
Authors: Jalal R , Ozel K , Atilgan A , Yildiz A 
Published: April 2024

Abstract
W-doped ZnO thin films deposited on Si substrates with (100) orientation by sol–gel spin coating method at temperature 500 °C. W/Zn atomic ratio varies from 0% to 4%. Then, the UV detection performance analysis of p–n heterojunction UV photodetectors based on W-doped ZnO/Si is analyzed. The current–voltage curves of W-doped ZnO/Si are investigated in dark and exhibit diode-like rectifying behavior. Among doped ZnO/Si, sample with atomic ratio of W/Zn = 2% is the best candidate to study photodetector characteristics in UV range. The resulting device exhibits a rectification ratio RR of 5587 at ±5 V, a higher responsivity of 3.84 A W−1 and a photosensitivity value of 34 at 365 nm under 0.5 mW cm−2. The experimental findings reveal that the UV detection performance of the heterojunction-based photodetectors strongly dependent on the properties of metal oxide layer. The main goal of this work is to investigate the effect of W doping on the performance of ZnO/Si based photodetectors. Based on our results, it is observed that 2 at% of W dopant is the optimum amount of doping for high performance photodetector of ZnO:W/Si heterojunction thanks to the suppressed recombination ratio and enhanced carrier separation properties in the depletion zone.
Phonon energy dissipation in friction between black phosphorus layers
Authors: Dong Yun , Wang Jinguang , Rui Zhiyuan , Yang Futian , Tang Xinyi , Tao Yi , Liu Yifan , Shi Bo 
Published: April 2024

Abstract
Herein, we employ molecular dynamics simulations to decode the friction properties and phonon energy dissipation between black phosphorus layers. The observations reveal the influence of three factors, temperature, velocity, and normal load, on the friction force of monolayer/bilayer black phosphorus. Specifically, friction is negatively correlated with layer thickness and temperature, and positively correlated with velocity and normal load. The change in friction force is further explained in terms of frictional energy dissipation, and supplemented by the height of potential barriers as well as the number of excited phonons. From the phonon spectrum analysis, the phonon number at the contact interface is found to be higher than that at the non-contact interface. This is due to the larger distance of the contact interface atoms deviate from their equilibrium positions, resulting in higher total energy generated by more intense oscillations, and therefore contributes greater to friction.
Mott memristor based stochastic neurons for probabilistic computing
Authors: Fida Aabid Amin , Mittal Sparsh , Khanday Farooq Ahmad 
Published: April 2024

Abstract
Many studies suggest that probabilistic spiking in biological neural systems is beneficial as it aids learning and provides Bayesian inference-like dynamics. If appropriately utilised, noise and stochasticity in nanoscale devices can benefit neuromorphic systems. In this paper, we build a stochastic leaky integrate and fire (LIF) neuron, utilising a Mott memristor’s inherent stochastic switching dynamics. We demonstrate that the developed LIF neuron is capable of biological neural dynamics. We leverage these characteristics of the proposed LIF neuron by integrating it into a population-coded spiking neural network and a spiking restricted Boltzmann machine (sRBM), thereby showcasing its ability to implement probabilistic learning and inference. The sRBM achieves a software-comparable accuracy of 87.13%. Unlike CMOS-based probabilistic neurons, our design does not require any external noise sources. The designed neurons are highly energy efficient and ultra-compact, requiring only three components: a resistor, a capacitor and a memristor device.
A novel non-chemically amplified resist based on polystyrene-iodonium derivatives for electron beam lithography
Authors: Cui Xuewen , Zhang Siliang , Cong Xue , Gao Jiaxing , Wu Yurui , Guo Xudong , Hu Rui , Wang Shuangqing , Chen Jinping , Li Yi , Du Wenna , Yang Guoqiang 
Published: April 2024

Abstract
To break the resolution limitation of traditional resists, more work is needed on non-chemically amplified resists (non-CARs). Non-CARs based on iodonium salt modified polystyrene (PS-I) were prepared with controllable molecular weight and structure. The properties of the resist can be adjusted by the uploading of iodonium salts on the polymer chain, the materials with a higher proportion of iodonium salts show better lithography performance. By comparing contrast curves and quality of the lithographic patterns, the optimum developing condition of 4-methyl-2-pentanone and ethyl alcohol (v:v = 1:7) was selected. The high-resolution stripes of 15 nm half-pitch (HP) can be achieved by PS-I0.58 in e-beam lithography (EBL). PS-I0.58 shows the advanced lithography performance in the patterns of 16 nm HP and 18 nm HP stripes with low line edge roughness (3.0 nm and 2.4 nm). The resist shows excellent potential for further pattern transfer, the etch selectivity of resist PS-I0.58 to the silicon was close to 12:1. The lithographic mechanism of PS-I was investigated by experimental and theoretical calculation, which indicates the polarity of materials changes results in the solubility switch. This work provides a new option and useful guidelines for the development of high-resolution resist.
LymphoDose: a lymphocyte dose estimation framework—application to brain radiotherapy
Authors: de Kermenguy François , Benzazon Nathan , Maury Pauline , Vauclin Rémi , M’hamdi Meissane , Cifliku Vjona , Limkin Elaine , Diallo Ibrahima , Morel Daphné , Milewski Candice , Clémenson Céline , Mondini Michele , Deutsch Eric , Robert Charlotte 
Published: April 2024

Abstract

Objective. Severe radiation-induced lymphopenia occurs in 40% of patients treated for primary brain tumors and is an independent risk factor of poor survival outcomes. We developed an in-silico framework that estimates the radiation doses received by lymphocytes during volumetric modulated arc therapy brain irradiation. Approach. We implemented a simulation consisting of two interconnected compartmental models describing the slow recirculation of lymphocytes between lymphoid organs (

M
1

) and the bloodstream (

M

2

). We used dosimetry data from 33 patients treated with chemo-radiation for glioblastoma to compare three cases of the model, corresponding to different physical and biological scenarios: (H1) lymphocytes circulation only in the bloodstream i.e. circulation in

M

2

only; (H2) lymphocytes recirculation between lymphoid organs i.e. circulation in

M

1

and

M

2

interconnected; (H3) lymphocytes recirculation between lymphoid organs and deep-learning computed out-of-field (OOF) dose to head and neck (H&N) lymphoid structures. A sensitivity analysis of the model’s parameters was also performed. Main results. For H1, H2 and H3 cases respectively, the irradiated fraction of lymphocytes was 99.8 ± 0.7%, 40.4 ± 10.2% et 97.6 ± 2.5%, and the average dose to irradiated pool was 309.9 ± 74.7 mGy, 52.6 ± 21.1 mGy and 265.6 ± 48.5 mGy. The recirculation process considered in the H2 case implied that irradiated lymphocytes were irradiated in the field only 1.58 ± 0.91 times on average after treatment. The OOF irradiation of H&N lymphoid structures considered in H3 was an important contribution to lymphocytes dose. In all cases, the estimated doses are low compared with lymphocytes radiosensitivity, and other mechanisms could explain high prevalence of RIL in patients with brain tumors. Significance. Our framework is the first to take into account OOF doses and recirculation in lymphocyte dose assessment during brain irradiation. Our results demonstrate the need to clarify the indirect effects of irradiation on lymphopenia, in order to potentiate the combination of radio-immunotherapy or the abscopal effect.
Iterative reconstruction for limited-angle CT using implicit neural representation
Authors: Lee Jooho , Baek Jongduk 
Published: April 2024

Abstract

Objective. Limited-angle computed tomography (CT) presents a challenge due to its ill-posed nature. In such scenarios, analytical reconstruction methods often exhibit severe artifacts. To tackle this inverse problem, several supervised deep learning-based approaches have been proposed. However, they are constrained by limitations such as generalization issue and the difficulty of acquiring a large amount of paired CT images. Approach. In this work, we propose an iterative neural reconstruction framework designed for limited-angle CT. By leveraging a coordinate-based neural representation, we formulate tomographic reconstruction as a convex optimization problem involving a deep neural network. We then employ differentiable projection layer to optimize this network by minimizing the discrepancy between the predicted and measured projection data. In addition, we introduce a prior-based weight initialization method to ensure the network starts optimization with an informed initial guess. This strategic initialization significantly improves the quality of iterative reconstruction by stabilizing the divergent behavior in ill-posed neural fields. Our method operates in a self-supervised manner, thereby eliminating the need for extensive data. Main results. The proposed method outperforms other iterative and learning-based methods. Experimental results on XCAT and Mayo Clinic datasets demonstrate the effectiveness of our approach in restoring anatomical features as well as structures. This finding was substantiated by visual inspections and quantitative evaluations using NRMSE, PSNR, and SSIM. Moreover, we conduct a comprehensive investigation into the divergent behavior of iterative neural reconstruction, thus revealing its suboptimal convergence when starting from scratch. In contrast, our method consistently produced accurate images by incorporating an initial estimate as informed initialization. Significance. This work showcases the feasibility to reconstruct high-fidelity CT images from limited-angle x-ray projections. The proposed methodology introduces a novel data-free approach to enhance medical imaging, holding promise across various clinical applications.
Full waveform inversion using frequency shift envelope-based global correlation norm for ultrasound computed tomography
Authors: Wu Yun , Yan Weicheng , Liu Zhaohui , Zhang Qiude , Zhou Liang , Song Junjie , Qiu Wu , Ding Mingyue , Yuchi Ming 
Published: April 2024

Abstract
Many studies have been carried out on ultrasound computed tomography (USCT) for its ability to offer quantitative measurements of tissue sound speed. Full waveform inversion (FWI) is a technique for reconstructing high-resolution sound speed images by iteratively minimizing the difference between the observed ultrasound data and the synthetic data based on the waveform equation. However, FWI suffers from cycle-skipping, which usually causes FWI convergence at a local minimum. Cycle-skipping occurs when the phase difference between the observed data and the synthetic data exceeds half a cycle. The simplest way to avoid cycle-skipping is to use low-frequency information for reconstruction. Nevertheless, in imaging systems, the response bandwidth of the probe is limited, and reliable low-frequency information often exceeds the response band. Therefore, it is a challenge to perform FWI imaging and avoid cycle-skipping problems without low-frequency information. In this paper, we propose a frequency shift envelope-based global correlation norm (FSEGCN), where an artificial source wavelet with a lower frequency is adopted to calculate synthetic data. FSEGCN compared with FWI, envelope inversion (EI), global correlation norm (GCN), envelope-based global correlation norm (EGCN) through concentric circle phantom without low-frequency information. The experimental results demonstrated the capability of the proposed method to recover the sound speed close to the exact model in the absence of low-frequency information, whereas FWI, EI, GCN, and EGCN cannot. Experiments on phantoms of the human head and calf show that artificial source wavelets can reduce image artifacts and enhance reconstruction robustness, when original low-frequency information is absent.
SPFS: SNR peak-based frequency selection method to alleviate resolution degradation in MPI real-time imaging
Authors: Shan Shihao , Zhang Chenglong , Cheng Min , Qi Yafei , Yu Dexin , Wildgruber Moritz , Ma Xiaopeng 
Published: April 2024

Abstract

Objective. The primary objective of this study is to address the reconstruction time challenge in magnetic particle imaging (MPI) by introducing a novel approach named SNR-peak-based frequency selection (SPFS). The focus is on improving spatial resolution without compromising reconstruction speed, thereby enhancing the clinical potential of MPI for real-time imaging. Approach. To overcome the trade-off between reconstruction time and spatial resolution in MPI, the researchers propose SPFS as an innovative frequency selection method. Unlike conventional SNR-based selection, SPFS prioritizes frequencies with signal-to-noise ratio (SNR) peaks that capture crucial system matrix information. This adaptability to varying quantities of selected frequencies enhances versatility in the reconstruction process. The study compares the spatial resolution of MPI reconstruction using both SNR-based and SPFS frequency selection methods, utilizing simulated and real device data. Main results. The research findings demonstrate that the SPFS approach substantially improves image resolution in MPI, especially when dealing with a limited number of frequency components. By focusing on SNR peaks associated with critical system matrix information, SPFS mitigates the spatial resolution degradation observed in conventional SNR-based selection methods. The study validates the effectiveness of SPFS through the assessment of MPI reconstruction spatial resolution using both simulated and real device data, highlighting its potential to address a critical limitation in the field. Significance. The introduction of SPFS represents a significant breakthrough in MPI technology. The method not only accelerates reconstruction time but also enhances spatial resolution, thus expanding the clinical potential of MPI for various applications. The improved real-time imaging capabilities of MPI, facilitated by SPFS, hold promise for advancements in drug delivery, plaque assessment, tumor treatment, cerebral perfusion evaluation, immunotherapy guidance, and in vivo cell tracking.
Deep learning and radiomics-based approach to meningioma grading: exploring the potential value of peritumoral edema regions
Authors: Zhang Zhuo , Miao Ying , Wu JiXuan , Zhang Xiaochen , Ma Quanfeng , Bai Hua , Gao Qiang 
Published: April 2024

Abstract

Objective. To address the challenge of meningioma grading, this study aims to investigate the potential value of peritumoral edema (PTE) regions and proposes a unique approach that integrates radiomics and deep learning techniques. Approach. The primary focus is on developing a transfer learning-based meningioma feature extraction model (MFEM) that leverages both vision transformer (ViT) and convolutional neural network (CNN) architectures. Additionally, the study explores the significance of the PTE region in enhancing the grading process. Main results. The proposed method demonstrates excellent grading accuracy and robustness on a dataset of 98 meningioma patients. It achieves an accuracy of 92.86%, precision of 93.44%, sensitivity of 95%, and specificity of 89.47%. Significance. This study provides valuable insights into preoperative meningioma grading by introducing an innovative method that combines radiomics and deep learning techniques. The approach not only enhances accuracy but also reduces observer subjectivity, thereby contributing to improved clinical decision-making processes.
Interaction of atmospheric pressure helium plasma jet with non-planar substrates: path selectivity of surface ionization wave
Authors: Liu Guoqiang , Zhou Jiateng , Xia Yang , Wang Yuxin , Liu Dongping 
Published: April 2024

Abstract
Most surfaces treated by atmospheric pressure plasma jets (APPJs) in practical applications are notably three-dimensional. However, non-planar surfaces exhibit a diverse array of geometries, such as variations in curvature, roughness, and texture, complicating the prediction of surface ionization waves (SIWs) propagation behavior across varied surface shapes, in the absence of sufficient experimental data. In this study, we made measurements of APPJ interactions with the non-planar substrates using the spatio-temporal resolved image method. Non-planar substrates encompassed wavy surfaces, arrayed hemispheres, and randomly textured raised surfaces. We tracked the morphology and velocity of SIW propagation over these surfaces. The results indicate that the SIW propagation on non-planar surfaces is significantly influenced by surface geometry and displays path selectivity, i.e. the SIW tends to propagate along valleys. The average propagation velocity of the SIW increases with the increasing radius of the wavy surface, as well as with the increased height of the arrayed hemispheres. This is attributable to the surface geometry constraining the dispersion of the SIW, causing it to concentrate and propagate in a singular direction. Moreover, the surface geometry markedly affects the distribution of the plasma treatment area, with the plasma inclined to enter valleys (where the light emission is significantly stronger than that of peaks) and to closely adhere to hemispherical surfaces. These patterns suggest a potential positive impact on treating skin surfaces such as pores, reducing bacteria in wrinkles, and addressing pimples.
Advancing 4D printing through designing interlocking blocks: enhancing deformation uniformity in active composite structures
Authors: Benyahia Kheira , Seriket Hichem , Blanquer Sébastien , Gomes Samuel , Bodaghi Mahdi , André Jean-Claude , Zhou Kun , Qi H Jerry , Demoly Frédéric 
Published: April 2024

Abstract
Interlocking assemblies have been explored to address large assembly and complex parts and are now integral to additive manufacturing (AM) for creating objects with dissimilar materials and multiple properties. 4D printing technology, which combines smart materials (SMs) with AM, aligns with this approach by enabling the creation of objects that can change shape or properties when exposed to external stimuli. As 4D printing trends towards strategically arranging active and passive materials for improved control and performance, challenges arise due to the limited compatibility of existing 3D printers with the required SM properties. To address this issue, interlocking blocks of dissimilar materials can be printed and then assembled into a desired shape. This work aims to generalize the applicability of the interlocking block assembly approach. This will be achieved by improving the deformation uniformity in a 4D multi-material interlocked assembly. This paper also addresses limitations that can occur due to the interfaces between interlocking blocks, such as lack of deformation and contact continuity. Thus, it will be a question of customizing the shapes of the blocks in the early stages of assembly generation, considering SMs and their potential transformations. Finally, this approach is illustrated with an example, shedding light on the practical implications.