bluuebunny/crossref_metadata_2025_split huggingface dataset:
Analysis of guidance and levitation forces between HTS magnets and conductive tubes for Hyperloop
Authors: Choi Suyong , Lee Changyoung , Lim Jungyoul 
Published: March 2024

High-temperature superconducting (HTS) magnets combined with linear synchronous motors and electrodynamic suspension (EDS) are considered as one of the most suitable technologies for Hyperloop. However, HTS magnets on pods generate strong magnetic fields, inducing eddy currents on conductive tubes when pods undergo movement through conductive tubes. The induced eddy currents affect HTS magnets, leading to electromagnetic (EM) drag, guidance, and levitation forces on the pods, thus reducing the propulsion efficiency and dynamic stability of pods. This study continues the existing research on EM drag forces between HTS magnet and tube for Hyperloop by comprehensively analyzing the EM guidance and levitation forces between HTS magnets and conductive tubes. Importantly, the full-scale 3D finite element analysis (FEA) simulations show that different steel tubes, such as AISI 1010 and high-manganese (Hi-Mn) steels, should be adopted, depending on the operating velocities, v, to avoid attractive forces in the guidance direction decreasing horizontal stiffnesses, For example, Hi-Mn tubes generating repulsive guidance forces are adopted when v is below 300 km/h while AISI 1010 tubes are used when v is over 300 km/h to minimize the construction cost of vacuum tubes to guarantee the increase in kx. For the proposed concept, the effect of the different tube connections on guidance and levitation forces is confirmed by the full-scale 3D FEA simulation. Moreover, levitation forces generated between HTS magnets and conductive tubes are nearly 0.5% of the EDS forces of pods, i.e., 200 kN over v of 150 km/h in the levitation direction. Therefore, the effect on vertical stiffnesses, ky, might be limited. In the end, to validate the proposed tube connections, the effect of the two different tube connections on guidance and levitation forces is confirmed by the full-scale 3D FEA simulation, and it was found that the sudden change in the guidance and levitation forces could be low enough to be neglected in the acceleration (from Hi-Mn to AISI 1010 steel tubes) and deceleration (from AISI 1010 to Hi-Mn steel tubes) regions.
Physics based optical modeling of iron disulfide thin films
Authors: Zaka Awais , Abdul Hadi Sabina , Pal Pratibha , Kumar Dayanand , El-Atab Nazek , Alhassan Saeed , Nayfeh Ammar 
Published: March 2024

In this work, physics based optical modeling is carried out using iron disulfide thin films deposited by using a plasma-assisted, radio frequency-powered technique. Iron disulfide is a transition metal dichalcogenide material, exhibiting a variety of unique and excellent characteristics. Various characterization techniques are employed to examine the growth rate, film thickness, and behavior of as-grown iron disulfide thin films. Furthermore, the physics based optical modeling was performed using a combination of experimental techniques and computer modeling approaches. The analyzed thin films exhibit a bandgap of around 1.16 eV. The theoretically calculated values of absorbance, transmission, and reflectance show a good match with the experimental measurements. Moreover, a physics based optical model is developed based on the experimental data and is used to calculate the external quantum efficiency and the optically generated current density of the iron disulfide films to provide insight into its use as an absorber layer.
Demographics and reproductive characteristics of threatened Malabar pufferfish (Carinotetraodon travancoricus) from Western Ghats hotspot, India
Authors: Renjithkumar Chelapurath Radhakrishnan , Roshni Kuttanelloor 
Published: March 2024

Context Carinotetraodon travancoricus is a threatened ornamental aquarium fish endemic to Western Ghats (WG) biodiversity hotspot of India and indiscriminate collection of this species resulted in a severe population decline. Aims To understand the impacts of wild collection of C. travancoricus for aquarium-fish trade, information on their population status and reproductive characteristics is required. Methods Specimens for the present study were collected from the Chalakudy River of WG and growth, mortality, exploitation and reproductive characteristics of C. travancoricus were investigated. Key results The natural mortality of the species was higher than the fishing mortality. The peak spawning period of the species was from May to September. Length at first maturity (L50) for males and females was estimated to be 18.4 and 18.1 mm respectively. Conclusions The stock of C. travancoricus in the Chalakudy River may be in near full exploitation under current harvesting strategy. The lower value of length of first capture than size at first maturity indicated that C. travancoricus is likely to be captured before it matures and breeds, subsequently contributing to declining recruitment. Implications A combination of different strategies (closed fishing seasons, mesh-size regulation and no-fishing areas) will improve sustainable management and conservation of species.
Machine learning for structural design models of continuous beam systems via influence zones
Authors: Gallet Adrien , Liew Andrew , Hajirasouliha Iman , Smyl Danny 
Published: March 2024

Abstract
This work develops a machine learned structural design model for continuous beam systems from the inverse problem perspective. After demarcating between forward, optimisation and inverse machine learned operators, the investigation proposes a novel methodology based on the recently developed influence zone concept which represents a fundamental shift in approach compared to traditional structural design methods. The aim of this approach is to conceptualise a non-iterative structural design model that predicts cross-section requirements for continuous beam systems of arbitrary system size. After generating a dataset of known solutions, an appropriate neural network architecture is identified, trained, and tested against unseen data. The results show a mean absolute percentage testing error of 1.6% for cross-section property predictions, along with a good ability of the neural network to generalise well to structural systems of variable size. The CBeamXP dataset generated in this work and an associated python-based neural network training script are available at an open-source data repository to allow for the reproducibility of results and to encourage further investigations.
Vision transformer-based electronic nose for enhanced mixed gases classification
Authors: Du Haiying , Shen Jie , Wang Jing , Li Qingyu , Zhao Long , He Wanmin , Li Xianrong 
Published: March 2024

Abstract
The classification of mixed gases is one of the major functions of the electronic nose. To address the challenges associated with complex feature construction and inadequate feature extraction in gas classification, we propose a classification model for gas mixtures based on the vision transformer (ViT). The whole-process signals of the sensor array are taken as input signals in the proposed classification model, and self-attention mechanism is employed for the fusion of global information and adaptive feature extraction to make full use of the dependence of responses at different stages of the whole-process signals to improve the model’s classification accuracy. Our model exhibited a remarkable accuracy (96.66%) using a dataset containing acetone, methanol, ammonia, and their binary mixtures. In comparison, experiments conducted by support vector machine and a one-dimensional deep convolutional neural network model demonstrated classification accuracy of 90.56% and 92.75%, respectively. Experimental results indicate that the ViT gas classification model can be effectively combined with multi-channel time series data from the sensor array using the self-attention mechanism, thereby improving the accuracy of mixed gases classification. This advancement can be expected to become a standard method for classifying mixed gases.
Temporal and spatial flow field reconstruction from low-resolution PIV data and pressure probes using physics-informed neural networks
Authors: Lai Bozhen , Liu Yingzheng , Wen Xin 
Published: March 2024

Abstract
In this paper, we present an innovative approach using physics-informed neural networks to reconstruct high-frequency, full-field flows, including the pressure field, by integrating sparse, noisy, low-temporal-resolution particle image velocimetry (PIV) data with high-temporal-resolution pressure probe data. This method effectively leverages the spatial richness of PIV data and the temporal abundance of pressure probe data, offering a complementary spatial and temporal data fusion. The incorporation of physical laws via equation constraints enables the neural network to accurately learn and predict complex fluid dynamics, achieving comprehensive flow field reconstructions. Rigorous testing across various flow types—periodic, non-periodic, and complex—against critical parameters like sampling interval (SI), number of grid points per snapshot (NGPIS), and number of pressure probes (NPP) has demonstrated remarkable accuracy. The results show reconstruction errors for velocity components (u, v) and pressure (p) below 5% with sufficient data, and around 10% for v and p, and below 5% for u in data-limited scenarios. A case study with SI = 30, NGPIS = 2000, NPP = 5 underscores the enhanced robustness and accuracy of random sampling, especially under various noisy conditions. Thus, this approach shows significant potential for temporal and spatial reconstruction of flow fields.
Multi-scale adversarial learning with difficult region supervision learning models for primary tumor segmentation
Authors: Zheng Shenhai , Sun Qiuyu , Ye Xin , Li Weisheng , Yu Lei , Yang Chaohui 
Published: March 2024

Abstract

Objective. Recently, deep learning techniques have found extensive application in accurate and automated segmentation of tumor regions. However, owing to the variety of tumor shapes, complex types, and unpredictability of spatial distribution, tumor segmentation still faces major challenges. Taking cues from the deep supervision and adversarial learning, we have devised a cascade-based methodology incorporating multi-scale adversarial learning and difficult-region supervision learning in this study to tackle these challenges. Approach. Overall, the method adheres to a coarse-to-fine strategy, first roughly locating the target region, and then refining the target object with multi-stage cascaded binary segmentation which converts complex multi-class segmentation problems into multiple simpler binary segmentation problems. In addition, a multi-scale adversarial learning difficult supervised UNet (MSALDS-UNet) is proposed as our model for fine-segmentation, which applies multiple discriminators along the decoding path of the segmentation network to implement multi-scale adversarial learning, thereby enhancing the accuracy of network segmentation. Meanwhile, in MSALDS-UNet, we introduce a difficult region supervision loss to effectively utilize structural information for segmenting difficult-to-distinguish areas, such as blurry boundary areas. Main results. A thorough validation of three independent public databases (KiTS21, MSD’s Brain and Pancreas datasets) shows that our model achieves satisfactory results for tumor segmentation in terms of key evaluation metrics including dice similarity coefficient, Jaccard similarity coefficient, and HD95. Significance. This paper introduces a cascade approach that combines multi-scale adversarial learning and difficult supervision to achieve precise tumor segmentation. It confirms that the combination can improve the segmentation performance, especially for small objects (our codes are publicly availabled on https://zhengshenhai.github.io/).
Hybrid-supervised deep learning for domain transfer 3D protoacoustic image reconstruction
Authors: Lang Yankun , Jiang Zhuoran , Sun Leshan , Xiang Liangzhong , Ren Lei 
Published: March 2024

Abstract

Objective. Protoacoustic imaging showed great promise in providing real-time 3D dose verification of proton therapy. However, the limited acquisition angle in protoacoustic imaging induces severe artifacts, which impairs its accuracy for dose verification. In this study, we developed a hybrid-supervised deep learning method for protoacoustic imaging to address the limited view issue. Approach. We proposed a Recon-Enhance two-stage deep learning method. In the Recon-stage, a transformer-based network was developed to reconstruct initial pressure maps from raw acoustic signals. The network is trained in a hybrid-supervised approach, where it is first trained using supervision by the iteratively reconstructed pressure map and then fine-tuned using transfer learning and self-supervision based on the data fidelity constraint. In the enhance-stage, a 3D U-net is applied to further enhance the image quality with supervision from the ground truth pressure map. The final protoacoustic images are then converted to dose for proton verification. Main results. The results evaluated on a dataset of 126 prostate cancer patients achieved an average root mean squared errors (RMSE) of 0.0292, and an average structural similarity index measure (SSIM) of 0.9618, out-performing related start-of-the-art methods. Qualitative results also demonstrated that our approach addressed the limit-view issue with more details reconstructed. Dose verification achieved an average RMSE of 0.018, and an average SSIM of 0.9891. Gamma index evaluation demonstrated a high agreement (94.7% and 95.7% for 1%/3 mm and 1%/5 mm) between the predicted and the ground truth dose maps. Notably, the processing time was reduced to 6 s, demonstrating its feasibility for online 3D dose verification for prostate proton therapy. Significance. Our study achieved start-of-the-art performance in the challenging task of direct reconstruction from radiofrequency signals, demonstrating the great promise of PA imaging as a highly efficient and accurate tool for in
vivo 3D proton dose verification to minimize the range uncertainties of proton therapy to improve its precision and outcomes.
Energy-dependent implementation of secondary electron emission models in continuum kinetic sheath simulations
Authors: Bradshaw Kolter , Srinivasan Bhuvana 
Published: March 2024

Abstract
The plasma-material interactions present in multiple fusion and propulsion concepts between the flow of plasma through a channel and a material wall drive the emission of secondary electrons. This emission is capable of altering the fundamental structure of the sheath region, significantly changing the expected particle fluxes to the wall. The emission spectrum is separated into two major energy regimes, a peak of elastically backscattered primary electrons at the incoming energy, and cold secondary electrons inelastically emitted directly from the material. The ability of continuum kinetic simulations to accurately represent the secondary electron emission is limited by relevant models being formulated in terms of monoenergetic particle interactions which cannot be applied directly to the discrete distribution function. As a result, rigorous implementation of energy-dependent physics is often neglected in favor of simplified, constant models. We present here a novel implementation of semi-empirical models in the boundary of continuum kinetic simulations which allows the full range of this emission to be accurately captured in physically-relevant regimes.
On the criteria for superconducting superlattices using CaCuO2
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Authors: Ikeda Ai , Krockenberger Yoshiharu , Taniyasu Yoshitaka , Yamamoto Hideki 
Published: March 2024

Abstract
The manifestation of superconductivity in cuprate superconductors is concerted to copper-oxygen planes. To maintain charge neutrality, the copper-oxygen planes are embedded between materials, that are electronically insulating. Here, we introduce BaBiO3, LaCrO3, EuFeO3, and Ca2Fe2O5 as candidate materials. We use molecular beam epitaxy as a synthesis tool for the development of artificial superconducting superlattices in combination with infinte-layer CaCuO2. We find that (1) layer lattice matching, (2) thermodynamic compatibility and (3) absence of diffusion of any element other than Cu into CuO2 planes are key criteria to realize superconductivity. For brown-millerite Ca2Fe2O5/CaCuO2 superlattices, criteria 1–3 are met and superconductivity emerges.
Reliable ferroelectricity down to cryogenic temperature in wake-up free Hf0.5Zr0.5O2 thin films by thermal atomic layer deposition
Authors: Wu Shuyu , Cao Rongrong , Jiang Hao , Li Yu , Zhang Xumeng , Yang Yang , Wang Yan , Wei Yingfen , Liu Qi 
Published: March 2024

Abstract
The performance and reliability of ferroelectric thin films at temperatures around a few Kelvin are critical for their application in cryo-electronics. In this work, TiN/Hf0.5Zr0.5O2/TiN capacitors that are free from the wake-up effect are investigated systematically from room temperature (300 K) to cryogenic temperature (30 K). We observe a consistent decrease in permittivity (ε
r) and a progressive increase in coercive electric field (E
c) as temperatures decrease. Our investigation reveals exceptional stability in the double remnant polarization (2P
r) of our ferroelectric thin films across a wide temperature range. Specifically, at 30 K, a 2P
r of 36 µC/cm2 under an applied electric field of 3.0 MV/cm is achieved. Moreover, we observed a reduced fatigue effect at 30 K in comparison to 300 K. The stable ferroelectric properties and endurance characteristics demonstrate the feasibility of utilizing HfO2 based ferroelectric thin films for cryo-electronics applications.
A novel one-time-programmable memory unit based on Schottky-type p-GaN diode
Authors: Feng Chao , Dai Xinyue , Jiang Qimeng , Huang Sen , Fan Jie , Wang Xinhua , Liu Xinyu 
Published: March 2024

Abstract
In this work, a novel one-time-programmable memory unit based on a Schottky-type p-GaN diode is proposed. During the programming process, the junction switches from a high-resistance state to a low-resistance state through Schottky junction breakdown, and the state is permanently preserved. The memory unit features a current ratio of more than 103, a read voltage window of 6 V, a programming time of less than 10−4 s, a stability of more than 108 read cycles, and a lifetime of far more than 10 years. Besides, the fabrication of the device is fully compatible with commercial Si-based GaN process platforms, which is of great significance for the realization of low-cost read-only memory in all-GaN integration.
Visible-to-near-infrared photodetectors based on SnS/SnSe2 and SnSe/SnSe2 p−n heterostructures with a fast response speed and high normalized detectivity
Authors: Zhu Xinfa , Duan Weishuai , Meng Xiancheng , Jia Xiyu , Zhang Yonghui , Zhou Pengyu , Wang Mengjun , Zheng Hongxing , Fan Chao 
Published: March 2024

Abstract
The emergent two-dimensional (2D) material, tin diselenide (SnSe2), has garnered significant consideration for its potential in image capturing systems, optical communication, and optoelectronic memory. Nevertheless, SnSe2-based photodetection faces obstacles, including slow response speed and low normalized detectivity. In this work, photodetectors based on SnS/SnSe2 and SnSe/SnSe2 p−n heterostructures have been implemented through a polydimethylsiloxane (PDMS)−assisted transfer method. These photodetectors demonstrate broad-spectrum photoresponse within the 405 to 850 nm wavelength range. The photodetector based on the SnS/SnSe2 heterostructure exhibits a significant responsivity of 4.99 × 103 A∙W−1, normalized detectivity of 5.80 × 1012 cm∙Hz1/2∙W−1, and fast response time of 3.13 ms, respectively, owing to the built-in electric field. Meanwhile, the highest values of responsivity, normalized detectivity, and response time for the photodetector based on the SnSe/SnSe2 heterostructure are 5.91 × 103 A∙W−1, 7.03 × 1012 cm∙Hz1/2∙W−1, and 4.74 ms, respectively. And their photodetection performances transcend those of photodetectors based on individual SnSe2, SnS, SnSe, and other commonly used 2D materials. Our work has demonstrated an effective strategy to improve the performance of SnSe2-based photodetectors and paves the way for their future commercialization.
A numerical investigation of effective optical characteristics of dielectric mixture layer on Si sphere by FDTD method
Authors: Fujita Kazuaki , Kuramoto Naoki 
Published: March 2024

Abstract
This paper reports on a numerical simulation of the optical characteristics of a dielectric mixture layer formed on a Si substrate using the finite-difference time-domain (FDTD) method. This study investigated the validity of using effective medium approximation (EMA) layers in thin mixture film problems with the optical constants of bulk materials. The complex reflection coefficients of Si substrates with randomly distributed thin mixture films fabricated from water/SiO2 and water/carbonaceous materials were numerically evaluated at normal and oblique incidences of plane electromagnetic waves via the FDTD principle, and the possible effects of the mixture layers on the effective optical characteristics were investigated. We observed that Bruggeman’s model based on EMA provides a good prediction of the behaviour of the plane wave reflected by the Si surface with randomly distributed mixture layers. The results also suggested that mixture layers have effects under oblique incidence, but they have no significant impact under normal incidence.
Development and validation of fully open-source R2S shutdown dose rate capabilities in OpenMC
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Authors: Peterson Ethan E. , Romano Paul K. , Shriwise Patrick C. , Myers Patrick A. 
Published: March 2024

Abstract
We present the first fully open-source capabilities for shutdown dose rate (SDR) calculations of fusion energy facilities based on the Rigorous 2-Step (R2S) methodology. These capabilities have been implemented in the OpenMC Monte Carlo particle transport code, building on its existing capabilities while also leveraging new features that have been added to the code to support SDR calculations, such as decay photon source generation. Each of the individual physics components in the R2S workflow—neutron transport, activation, decay photon source generation, and photon transport—have been verified through code-to-code comparisons with MCNP6.2 and FISPACT-II 4.0. These comparisons generally demonstrate excellent agreement between codes for each of the physics components. The full cell-based R2S workflow was validated by performing a simulation of the first experimental campaign from the Frascati Neutron Generator (FNG) ITER dose rate benchmark problem from the Shielding INtegral Benchmark Archive and Database (SINBAD). For short cooling times, the dose calculated by OpenMC agrees with the experimental measurements within the stated experimental uncertainties. For longer cooling times, an overprediction of the shutdown dose was observed relative to experiment, which is consistent with previous studies in the literature. Altogether, these features constitute a combination of capabilities in a single, open-source codebase to provide the fusion community with a readily-accessible option for SDR calculations and a platform for rapidly analyzing the performance of fusion technology.