Infrared Physics & Technology最新文献

{"title":"Adaptive water-mist infrared signature suppression for naval vessels via MPCM-constrained LSTM","authors":"Chaoyi Dong ,&nbsp;Yan Cao ,&nbsp;Yuchen Feng ,&nbsp;Chen Chen ,&nbsp;Jun Yu ,&nbsp;Ziyan Zhu ,&nbsp;Feng Xiong","doi":"10.1016/j.infrared.2026.106489","DOIUrl":"10.1016/j.infrared.2026.106489","url":null,"abstract":"<div><div>Infrared stealth is critical to the survivability and combat effectiveness of modern naval vessels, and fine water-mist spraying, as a mature and effective infrared-stealth technique, reduces ship detectability in the <span><math><mrow><mn>8</mn><mo>∼</mo><mn>12</mn><mspace></mspace><mi>μ</mi><mi>m</mi></mrow></math></span> infrared band through cooling and scattering. However, conventional water-mist systems lack responsiveness to dynamic environments, resulting in unstable stealth performance. To address this issue, an adaptive water-mist infrared-stealth optimisation approach integrating a multi-physics coupling model (MPCM) and a long short-term memory (LSTM) neural network is proposed in this study. First, environmental, navigational and device-level data are collected and fused to construct a unified input state; then, an MPCM is established to simulate the coupled physical processes of ship infrared radiation, temperature distribution and water-mist diffusion, thereby producing physics-constrained high-fidelity labels for training the control model; subsequently, an LSTM model is trained on historical and real-time feature windows to predict the optimal spraying parameters for the next time step; finally, background-difference-ratio-based thresholding is combined with virtual spray optimisation (VSO) to realise a dual closed-loop feedback mechanism. Experimental results indicate that, compared with non-adaptive baseline schemes, the proposed method reduces the peak infrared radiance by <span><math><mrow><mn>18.3</mn><mo>%</mo></mrow></math></span>, decreases the number of extreme hot spots by <span><math><mrow><mn>57.1</mn><mo>%</mo></mrow></math></span>, compresses the target–background temperature difference to <span><math><mrow><mn>7.63</mn><mspace></mspace><mo>°</mo><mi>C</mi></mrow></math></span>, and lowers the total water consumption over 13 h to <span><math><mrow><mspace></mspace><mn>8</mn><mo>,</mo><mn>075</mn><mspace></mspace><msup><mi>m</mi><mn>3</mn></msup></mrow></math></span>. Moreover, the control system operates stably at 1 Hz with an end-to-end latency below 0.451 s, demonstrating that the method simultaneously achieves stronger suppression, reduced water consumption and real-time compliance, thereby providing a feasible route for the engineering deployment of shipborne infrared stealth.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"155 ","pages":"Article 106489"},"PeriodicalIF":3.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147386231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification research based on polarization and Near-Infrared spectrum Dual-Mode image fusion with Multi-Stage overlapping","authors":"Zhong Lv ,&nbsp;Yong Tan ,&nbsp;Xiaojun Yin ,&nbsp;Shaopeng Ren ,&nbsp;Feng Chen ,&nbsp;Ye Zhang ,&nbsp;Jianbo Wang ,&nbsp;Xiaowei Sun ,&nbsp;Haoyang Wu ,&nbsp;Zhicheng Qin ,&nbsp;Zhe Wang","doi":"10.1016/j.infrared.2026.106485","DOIUrl":"10.1016/j.infrared.2026.106485","url":null,"abstract":"<div><div>Distinguishing camouflaged fabrics from similar backgrounds has long been a challenge for conventional imaging technologies. Polarimetric hyperspectral imaging offers a novel solution to this problem; however, the massive volume of raw images generated across multiple polarization states and spectral bands increases the complexity of target recognition. This paper proposes a multi-stage image fusion method that integrates existing techniques—Stokes vector analysis, the Normalized Difference Target Index (NDTI), Effective Guided Image Filtering (EGIF), and Retinex-Based Multiphase (RBMP)—into a novel sequential framework, combining dual-mode information from polarization and near-infrared spectra. The integration enhances the differentiation between targets and backgrounds through coordinated multi-stage feature extraction and fusion.Specifically, the method includes polarization image fusion based on the Stokes vector, background suppression using NDTI, detail enhancement through an improved EGIF algorithm, and brightness correction and target recognition via the RBMP algorithm. Validated through indoor bidirectional reflectance distribution function (BRDF) measurements and multi-angle outdoor experiments, the method effectively identifies camouflaged samples against vegetative backgrounds under various incident and imaging angles. Further testing on public datasets confirms its stability and applicability. This approach demonstrates robust performance for target recognition in grassland environments under multi-angle and varying illumination conditions. Further validation in diverse environments is needed before extending to broader scenarios.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"155 ","pages":"Article 106485"},"PeriodicalIF":3.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147386229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Thermal imaging fault diagnosis of three-phase induction motors using neural networks","authors":"Adam Glowacz","doi":"10.1016/j.infrared.2026.106490","DOIUrl":"10.1016/j.infrared.2026.106490","url":null,"abstract":"<div><div>The article presents a technique for diagnosing faults in three-phase induction motors. It uses two thermal imaging cameras and a novel method called Differences of Color Thermal Images (<em>DoCTI</em>). Eight three-phase induction motors (TPIMs) were analyzed: four 550 W motors and four 500 W motors, under the following conditions: healthy, faulty squirrel cage ring, one broken bar, two broken bars, and three broken bars. Thermographic measurements were conducted with thermal camera vibrations ranging from 0 to 1.2 meters per second squared. A novel feature extraction method for color thermal images (<em>DoCTI</em>) was proposed. Three neural networks, NnetV04, NnetV05, and NnetV06, were presented. Convolutional neural networks were used to analyze the thermal images. High accuracy recognition of motor fault conditions was achieved. The computed results confirm the effectiveness of the proposed approach for the recognition of electrical faults of three-phase induction motors.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"155 ","pages":"Article 106490"},"PeriodicalIF":3.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147386232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Excitation of multi-channel cladding modes based on fan-shaped refractive index modulated long-period fiber gratings","authors":"Xinyi Zhao ,&nbsp;Qingya Peng ,&nbsp;Zuyao Liu ,&nbsp;Fang Wang ,&nbsp;Chengbo Mou ,&nbsp;Yunqi Liu ,&nbsp;Yufang Liu","doi":"10.1016/j.infrared.2026.106494","DOIUrl":"10.1016/j.infrared.2026.106494","url":null,"abstract":"<div><div>We demonstrate a fan-shaped refractive-index-modulated long-period fiber grating (F-LPFG) in standard single-mode fiber, fabricated by CO₂-laser point-shaped exposure. The asymmetric, sector-shaped index perturbation enables the simultaneous coupling of the fundamental core mode to multiple cladding modes with different azimuthal orders (LP₁₄, LP₀₅, LP₁₅, and LP₀₆ mode) in a single grating. The F-LPFG was experimentally characterized for surrounding refractive index, torsion and temperature. The maximum refractive index (RI) sensitivity reaches 7,796.10 nm/RIU in the RI range of 1.445–1.457 RIU, torsional sensitivity is 0.1590 nm/(rad·m⁻¹) over −36 to + 36 rad/m, and temperature sensitivity is 71 pm/°C from 25°C to 110°C. By monitoring multiple resonances and forming a sensitivity matrix, we demonstrate simultaneous demodulation of RI, torsion and temperature. The F-LPFG combines simultaneous multi-parameter measurement capability and a simple structure, and thus offers a compact, scalable approach for multichannel and multi-parameter fiber sensing in demanding application scenarios.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"155 ","pages":"Article 106494"},"PeriodicalIF":3.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147386180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High-order Spatial-Frequency Interaction and Detail Compensation Network for infrared and visible image fusion","authors":"Kanglin Jin,&nbsp;Mengtong Guo,&nbsp;Minghao Piao","doi":"10.1016/j.infrared.2026.106431","DOIUrl":"10.1016/j.infrared.2026.106431","url":null,"abstract":"<div><div>Infrared and visible image fusion aims to generate a fused image that highlights salient targets while preserving fine textures. Existing deep learning-based methods predominantly rely on spatial-domain representations, which fail to fully capture the modality-specific frequency characteristics, leading to suboptimal texture preservation and detail enhancement. Since infrared and visible images exhibit distinct frequency distributions, relying solely on spatial-domain methods is insufficient for achieving high-quality fusion. To overcome this limitation, we propose a novel High-order Spatial-Frequency Interaction and Detail Compensation Network (HSFIDCNet), which jointly exploits spatial and frequency representations for more effective feature fusion. Specifically, the High-order Spatial-Frequency Interaction (HSFI) module enhances cross-domain feature integration, achieving a balanced fusion of global structures and local details, while the Detail Compensation (DC) module strengthens texture representation and highlights salient objects. Extensive experiments on three benchmark datasets (M³FD, LLVIP, and MSRS) against twelve state-of-the-art methods demonstrate that our approach consistently outperforms existing methods, producing fused images with higher contrast and richer textures. In particular, our method achieves the best performance across all three datasets in CC (0.5298, 0.7134, 0.6180), <span><math><msup><mrow><mi>Q</mi></mrow><mrow><mi>A</mi><mi>B</mi><mo>/</mo><mi>F</mi></mrow></msup></math></span> (0.7102, 0.7326, 0.7025), MS-SSIM (0.9573, 0.9696, 0.9778), and <span><math><msub><mrow><mi>Q</mi></mrow><mrow><mi>C</mi><mi>V</mi></mrow></msub></math></span> (478.6155, 267.7829, 203.6782), highlighting its robust and generalizable fusion capability. Code is available at <span><span>https://github.com/sdat-max/HSFIDCNet</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"155 ","pages":"Article 106431"},"PeriodicalIF":3.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146172883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Drone detection network based on RGB-thermal imaging multimodal fusion","authors":"Xingwei Yan ,&nbsp;Kun Liu ,&nbsp;Ji Li ,&nbsp;Yan Zhang ,&nbsp;Yaxiu Zhang ,&nbsp;Chenchen Zhang","doi":"10.1016/j.infrared.2026.106426","DOIUrl":"10.1016/j.infrared.2026.106426","url":null,"abstract":"<div><div>With the rapid proliferation of unmanned aerial vehicles, the issue of their security has gradually become a focal point of research. In infrared target detection tasks, due to the small target size, complex backgrounds, and low contrast, existing methods often rely solely on the internal features of a single modality, lacking the ability to interact with external information, which limits detection performance. To address this issue, this paper proposes a novel multi-modal image detection method, R2TNet, which can directly process misaligned RGB-T images, effectively avoiding the complexity of traditional manual registration. To achieve efficient modality alignment and fusion, this paper designs a supervised bottom-up multimodal alignment module, which adopts a coarse-to-fine layer-wise registration strategy. This effectively alleviates the modality misalignment issue in multimodal images, thereby achieving precise alignment between RGB and infrared features. On this basis, a semantic-guided module is further employed to optimize cross-modal feature fusion using high-level semantic information, significantly improving the accuracy and robustness of target detection. At the same time, a multi-scale gated dynamic fusion module is incorporated to realize fine-grained fusion of multimodal features, further enhancing the model’s adaptability in complex scenarios. Experimental results demonstrate that the proposed R2TNet significantly outperforms existing state-of-the-art bimodal detection methods across multiple evaluation metrics, including Em, Sm, Fm, and MAE, and exhibits stronger robustness and generalization capability in complex backgrounds and small target detection tasks. Moreover, comparative results with unimodal infrared detection methods further validate the advantages of the proposed method in cross-modal fusion detection.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"155 ","pages":"Article 106426"},"PeriodicalIF":3.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146172874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reconstruction-driven and class-balanced domain adaptation network for cross-scene hyperspectral image classification","authors":"Chen Wang ,&nbsp;Jun Li ,&nbsp;Wenpeng Zhang ,&nbsp;Quan Zhang ,&nbsp;Zhen Liu","doi":"10.1016/j.infrared.2026.106435","DOIUrl":"10.1016/j.infrared.2026.106435","url":null,"abstract":"<div><div>Cross-scene hyperspectral image (HSI) classification remains challenging due to domain shifts caused by variations in imaging conditions, atmospheric effects, and sensor characteristics. While unsupervised domain adaptation (UDA) presents a promising solution, conventional methods primarily focus on minimizing the distribution discrepancy between domains. This premature alignment strategy often neglects to first harness rich semantic information from complex HSI data, causing it to struggle with complex domain shifts and be prone to misaligning domain-specific noise with task-relevant features. This issue is further exacerbated by severe class imbalance, which biases the alignment process toward majority classes. This leads to the neglect of minority classes and an increased risk of biased alignment and minority-class degeneration. To address these challenges, a reconstruction-driven and class-balanced domain adaptation network (RBDA-Net) is proposed. Adopting a decoupled strategy, RBDA-Net first employs a self-supervised reconstruction task using a hyperspectral imaging masked autoencoder (HSI-MAE) to learn robust and domain-invariant structural representations, thus providing a noise-resilient feature foundation that mitigates negative transfer. Building upon this foundation, a class-balanced adversarial training (CBAT) module performs domain alignment while concurrently mitigating the impact of class imbalance. By integrating a bi-classifier adversarial framework with fast batch nuclear norm maximization (FBNM), RBDA-Net counteracts the imbalance-induced bias during alignment. This enhances prediction diversity and improves the discriminability of minority classes, critically requiring no prior knowledge of the target domain’s class distribution. Comprehensive experiments on three public cross-scene HSI datasets demonstrate that RBDA-Net significantly outperforms state-of-the-art UDA methods, validating its effectiveness in learning both discriminative and well-balanced representations for cross-domain HSI classification.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"155 ","pages":"Article 106435"},"PeriodicalIF":3.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146172807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Beyond preprocessing and directional bias: Transformer models for robust and efficient cross-instrument NIR calibration in wheat flour analysis","authors":"Jing Liang,&nbsp;Hailong Feng,&nbsp;Yu Xue,&nbsp;Mingyue Huang,&nbsp;Bin Wang,&nbsp;Xiaoxuan Xu,&nbsp;Jing Xu","doi":"10.1016/j.infrared.2026.106448","DOIUrl":"10.1016/j.infrared.2026.106448","url":null,"abstract":"<div><div>Cross-instrument variability remains a key barrier to the scalable application of near-infrared (NIR) spectroscopy in agri-food quality monitoring. This study introduces two Transformer-based calibration transfer models, Transpec and TPDS, designed to enhance spectral alignment across different instruments.</div><div>By combining global attention with localized spectral modeling, the proposed methods reduce reliance on extensive preprocessing and large paired transfer sets. Compared with classical techniques, Transpec and TPDS achieve higher predictive consistency across forward and backward transfers and demonstrate strong performance across multiple flour quality indicators. Their robustness and computational efficiency highlight their potential for real-time deployment in industrial multi-instrument environments. This work establishes a scalable framework for cross-device NIR modeling and contributes to the development of intelligent quality control systems in agricultural processing.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"155 ","pages":"Article 106448"},"PeriodicalIF":3.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146172808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Synergistic optimization of hardness and transmittance in a multilayer DLC/chalcogenide coating system for long-wave infrared As2Se3 windows","authors":"Keyi Li ,&nbsp;Song Chen ,&nbsp;Yimin Chen ,&nbsp;Xiang Shen","doi":"10.1016/j.infrared.2026.106459","DOIUrl":"10.1016/j.infrared.2026.106459","url":null,"abstract":"<div><div>To address the stringent requirements of long-wave infrared (LWIR) imaging systems, namely, exceptional environmental durability and high optical performance, we report the development of an advanced multifunctional anti-reflection (AR) and protective coating system on As₂Se₃ chalcogenide glass. Tailored for the 8–12  μm spectral band, the coating was fabricated via a hybrid deposition approach combining physical vapor deposition (PVD) with radio-frequency plasma-enhanced chemical vapor deposition (RF-PECVD). A key innovation lies in the design of a functionally graded transition layer based on compositionally compatible chalcogenide materials (e.g., Ge-As-Se), which simultaneously enhances interfacial adhesion and enables precise optical impedance matching through accurate thickness control. Systematic optimization of the diamond-like carbon (DLC) top layer revealed that deposition at 600  W RF power, 45 sccm C₄H₁₀ flow rate, and 10  Pa working pressure yields optimal mechanical properties, including a nano-hardness of 16 GPa and an elastic recovery parameter of 83%, a significant improvement over the bare substrate. The fully integrated coating achieves an average transmittance of 91% across the 8–12  μm range and demonstrates outstanding resilience, successfully passing a comprehensive suite of environmental stress tests (including thermal cycling, damp heat, solvent exposure, and abrasion). This work presents a robust, scalable, and technologically viable solution for high-performance AR and protective coatings on chalcogenide glasses, offering significant potential for next-generation infrared optical components in demanding environments.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"155 ","pages":"Article 106459"},"PeriodicalIF":3.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146172812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Memory-Driven Wavelet Network for lightweight infrared image super-resolution","authors":"Tao Jin,&nbsp;Jianyu Huang","doi":"10.1016/j.infrared.2026.106450","DOIUrl":"10.1016/j.infrared.2026.106450","url":null,"abstract":"<div><div>Infrared imaging finds extensive applications in security surveillance, remote sensing, interstellar exploration, and other fields where visible light imaging is limited. However, due to sensor limitations and atmospheric interference, infrared images often suffer from low resolution, severe noise, and poor contrast. To address these challenges, we propose a Memory-Driven Wavelet Network (MDWN) for lightweight infrared image super-resolution. First, we design a Parallel Wavelet Feature Extractor (PWFE) that decomposes input features into multiscale frequency components via wavelet transform, constructing dual path representations that capture complementary low and high frequency details under distinct receptive fields. Second, we propose a Memory-Driven Feature Integration Block (MDFIB), which incorporates a hierarchical memory bank with a progressively increasing number of learnable tokens across network stages. This design enables shallow layers to capture local structural priors, while deeper layers model global semantic representations. The memory tokens act as anchors for cross-region attention, effectively fusing fine-grained local details with long-range contextual information, without resorting to computationally expensive dense pairwise attention. Extensive experiments on multiple infrared benchmark datasets demonstrate that our Memory-Driven Wavelet Network (MDWN) achieves state-of-the-art performance with significantly fewer parameters and lower computational overhead.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"155 ","pages":"Article 106450"},"PeriodicalIF":3.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146172884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}