Infrared Physics & Technology最新文献

{"title":"TMCN: Text-guided Mamba-CNN dual-encoder network for infrared and visible image fusion","authors":"Jianming Zhang ,&nbsp;Xiangnan Shi ,&nbsp;Zhijian Feng ,&nbsp;Yan Gui ,&nbsp;Jin Wang","doi":"10.1016/j.infrared.2025.105895","DOIUrl":"10.1016/j.infrared.2025.105895","url":null,"abstract":"<div><div>Infrared and visible image fusion (IVF) combines the complementary advantages of two images from different physical imaging methods to create a new image with richer information. To better address issues such as weak texture details, low contrast, and poor visual perception of overexposed and underexposed areas, we propose a text-guided Mamba-CNN dual-encoder network (TMCN). Firstly, to leverage the feature extraction capabilities of Mamba and CNN, we design a pre-training network to train a Mamba-based encoder, a CNN-based encoder, and a decoder. The structures of these encoders are also used in the image fusion stage. Then, we introduce a hybrid Mamba-CNN dual-encoder to extract global and local features from infrared and visible images, resulting in four distinct types of feature information. Secondly, we design a global fusion block (GFB) via the Mamba-based encoder, and a local fusion block (LFB) via the CNN-based encoder, to fuse the global and local features of the two modalities, respectively. Following these fusion blocks, we introduce text semantic information and utilize its stable and targeted characteristics to better solve the above problems. Therefore, we propose a plug-and-play text-guided block (TB) that first uses a CLIP-based text encoder to encode the input text, and then exploits feed-forward neural network (FFN) to extract two parameters for subsequent linear transformations, which reflect the text-guided mechanism. Finally, numerous experiments demonstrate that our method achieves excellent performance in IVF and has strong versatility. Furthermore, our method enhances the performance of downstream tasks such as object detection and semantic segmentation. The code will be available at <span><span>https://github.com/XiangnanShi-CSUST/TMCN</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"149 ","pages":"Article 105895"},"PeriodicalIF":3.1,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143935503","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":"A SEDEA-based approach for evaluating the effectiveness of thermal camouflage using image and spectral data","authors":"Ngoc Son Nguyen ,&nbsp;Anh Tuan Nguyen ,&nbsp;Tien Bao Tran ,&nbsp;Huu Khanh Vu ,&nbsp;Duc Hieu Vu ,&nbsp;Manh Thang Nguyen","doi":"10.1016/j.infrared.2025.105872","DOIUrl":"10.1016/j.infrared.2025.105872","url":null,"abstract":"<div><div>Using military camouflage technologies in modern warfare to avoid or reduce the ability of detection from opponents has rapidly developed all over the world. The method to evaluate camouflage effectiveness is investigated to ensure the practical applicability of camouflage products. Because of sensitive information, no universally accepted or reliable standards exist for measuring camouflage effectiveness domestically or internationally. This absence of standards has led to various assessment methods, broadly categorized into subjective and objective evaluations. Each approach has certain advantages and restrictions, with neither proving unequivocally superior. A comprehensive evaluation of camouflage effectiveness is a complex issue that requires considering multiple critical factors such as the characteristics of surveillance equipment, targets, background environment, atmosphere conditions, and observers. This study introduces a novel objective evaluation framework utilizing the new Data Envelopment Analysis (DEA) model, incorporating a newly developed set of assessment indicators derived from the target’s thermal image data and radiometric spectral data. This model utilizes 11 evaluation indicators, including eight characteristic indices derived from thermal images captured by a thermal camera and three spectral feature indices obtained from data collected by a spectroradiometer. Experimental analysis on multiple camouflage outfit samples demonstrate that, compared to traditional DEA models, the Super-Efficiency DEA model offers a more reliable ranking system, effectively categorizing the effectiveness of camouflage outfits. The results also suggest that the Super-Efficiency DEA model can serve as a valuable tool for standardizing and enhancing the objectivity of camouflage effectiveness assessments in the future.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"149 ","pages":"Article 105872"},"PeriodicalIF":3.1,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143943587","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":"NOC-YOLO: An exploration to enhance small-target vehicle detection accuracy in aerial infrared images","authors":"Yuhao Zhang ,&nbsp;Zhenhua Dai ,&nbsp;Cunshu Pan ,&nbsp;Gaofeng Zhang ,&nbsp;Jin Xu","doi":"10.1016/j.infrared.2025.105905","DOIUrl":"10.1016/j.infrared.2025.105905","url":null,"abstract":"<div><div>Infrared imaging technology enhances the visibility of vehicles in the dark from an aerial perspective. However, the loss of texture and boundary features of targets in infrared images impedes the accurate detection of small-target vehicles. To address this issue, we propose Nocturne-You Only Look Once (NOC-YOLO), using YOLOv10n as the baseline model, aimed at improving the detection accuracy of small-target vehicles in drone-captured infrared images. We introduce three key improvements to the baseline model, incorporating attention mechanisms and multi-scale feature fusion to enhance the interaction between features. Extensive experiments on the DroneVehicle dataset demonstrate NOC-YOLO’s superiority: it achieves state-of-the-art mAP50 (79.5 %) and mAP50-95 (63.2 %), surpassing YOLOv10n by 1.4 % in mAP50, respectively, with only a 10 % increase in parameters and 12 % higher GFLOPs. Compared to non-YOLO methods, NOC-YOLO achieves significant performance improvements while maintaining exceptional lightweight characteristics, making it highly suitable for real-time deployment in resource-constrained environments. This balance of high precision and efficiency provides robust technical support for safety–critical applications like nighttime driving trajectory extraction and collision risk analysis.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"149 ","pages":"Article 105905"},"PeriodicalIF":3.1,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143935505","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":"DFLMF-ISTD: Infrared small object detection network based on decoupled feature learning and multi-scale feature fusion","authors":"Ning Li,&nbsp;Daozhi Wei,&nbsp;Shucai Huang,&nbsp;Xirui Xue","doi":"10.1016/j.infrared.2025.105851","DOIUrl":"10.1016/j.infrared.2025.105851","url":null,"abstract":"<div><div>Infrared small object detection is widely used in small maneuvering object awareness and high threatening object detection and recognition. In recent years, the introduction of deep learning methods has greatly improved the detection performance of infrared small objects. However, the presence of clutter in infrared small object images (low signal-to-noise ratio, SNR) and the lack of shape and texture information for the objects lead to a decrease in detection performance in complex environments. As such, in this article, an infrared small object detection network based on decoupled feature learning and multi-scale feature fusion is proposed. First, utilizing disentangled feature learning, we construct Reversible Column Networks (Revcol) with C3 modules to get RevcolC3 to alleviate the issues of complex feature extraction and the loss of small-scale object information. Second, a new lighted attention spatial pyramid pooling (LASP) module is proposed. By convolving the features extracted from the backbone and performing two consecutive pooling operations, a large kernel separated attention (LSKA) mechanism is introduced to process spatial and channel information separately. This enhances the model’s multi-feature extraction capabilities while reducing computational complexity. Finally, a novel lightweight three-dimensional multi-scale feature fusion (LTDMF) module is designed to efficiently utilize the correlations between three-level pyramid feature maps and effectively extract infrared small object features. This enhances the network’s ability to detect objects while maintaining the same model size. The proposed methodology is rigorously evaluated for its feasibility and reliability on the benchmark SIRST and IRSTD-1k datasets. The experimental results indicate that the proposed methodology outperforms current state-of-the-art (SOTA) infrared small object detection techniques under conditions of complex environments, small infrared object scales, and the absence of discernible texture and shape features.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"149 ","pages":"Article 105851"},"PeriodicalIF":3.1,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143943586","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":"Machine learning-driven strength prediction for solid waste-based backfill materials using infrared radiation indices","authors":"Arienkhe Endurance Osemudiamhen ,&nbsp;Liqiang Ma ,&nbsp;Ichuy Ngo ,&nbsp;Guanghui Cao","doi":"10.1016/j.infrared.2025.105898","DOIUrl":"10.1016/j.infrared.2025.105898","url":null,"abstract":"<div><div>The resilience of underground mining systems is deeply associated with the unconfined compressive strength (UCS) of backfill components, which is key to securing safety, operational performance, and ecological balance. This investigation presents a data-centric methodology that amalgamates machine learning (ML) techniques with infrared radiation (IR) indices, specifically Average Infrared Radiation Temperature (AIRT) and Variance of Infrared Radiation Temperature (VIRT), aimed at improving the precision of UCS predictions. A thorough dataset of 180 samples focused on backfill materials taken from solid waste underwent a careful analytical methodology using progressive machine learning models, including Light Gradient Boosting Machine (LightGBM), Artificial Neural Network (ANN), Random Forest Regression (RFR), and Least Squares Support Vector Machine (LSSVM),<!--> <!-->where LightGBM yielded stellar outcomes, reaching an R² score of 0.9276 and reflecting notably small error margins, as underlined by a mean absolute error (MAE) of 0.5618 and a root mean square error (RMSE) of 0.8803. The variance accounted for (VAF) was calculated at 0.9342, with the relative standard residual (RSR) recorded at 1.002. The prediction interval (PI) was defined as [2.0178, 5.9030], alongside a normal mean bias error (NMBE) of −0.0327. The employed methodology incorporated stringent experimental protocols, encompassing sample preparation, curing processes, UCS evaluations, and thermal imaging analyses to capture the dynamic responses elicited during loading. A user-centric graphical user interface (GUI) was engineered employing Tkinter, facilitating real-time UCS forecasts and expeditious decision-making within mining contexts. This study accentuates the transformative capacity of synergizing IR metrics with ML, presenting a replicable framework for integrating empirical testing with computational modeling. It emphasizes the necessity for larger, more heterogeneous datasets to bolster model robustness while fostering sustainable backfill design and environmentally conscientious mining practices.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"149 ","pages":"Article 105898"},"PeriodicalIF":3.1,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143931865","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":"Rapid discrimination of Chinese rice Wine (Huangjiu) from various regions using Benchtop-NIR and Micro-NIR spectrometers in conjunction with chemometrics","authors":"Qiang Hu ,&nbsp;Xinxin Feng ,&nbsp;Lan Wang ,&nbsp;Lili Zhang ,&nbsp;Xueping Chen ,&nbsp;Yanyun Wang ,&nbsp;Guangfa Xie ,&nbsp;Qi Peng","doi":"10.1016/j.infrared.2025.105904","DOIUrl":"10.1016/j.infrared.2025.105904","url":null,"abstract":"<div><div>Near Infrared (NIR) technology plays a crucial role in food authenticity and origin identification. This study evaluates benchtop-NIR and Micro-NIR technologies, combined with chemometrics, to differentiate Huangjiu (Chinese rice wine) origins. Both models demonstrated strong performance, with Micro-NIR excelling in identification and prediction over benchtop-NIR. Spectral data from Huangjiu across various regions were used to develop a chemometric discrimination model. Benchtop-NIR’s database utilized first derivative preprocessing, while the sub-database employed second derivative and vector normalization. Micro-NIR used Savitzky-Golay (S.Golay), Standard Normal Variate (SNV), and Normalize Peak preprocessing, achieving 100% accuracy. Benchtop-NIR’s Q² was 0.939, R²X [1] 0.738, R²X [2] 0.202, and R² 0.94. Micro-NIR outperformed with Q² at 0.983, R²X [1] 0.981, R²X [2] 0.00627, and R² at 0.988. This study significantly advances the geographical identification of Huangjiu using NIR technology.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"149 ","pages":"Article 105904"},"PeriodicalIF":3.1,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143927780","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":"Novel piezo-controlled ATR-FTIR microspectroscopy for in-situ monitoring of electrochemical reaction in battery models","authors":"Jitraporn Vongsvivut ,&nbsp;Sailin Liu ,&nbsp;Ruizhi Zhang ,&nbsp;Alan Easdon ,&nbsp;Wei Kong Pang ,&nbsp;Zaiping Guo","doi":"10.1016/j.infrared.2025.105899","DOIUrl":"10.1016/j.infrared.2025.105899","url":null,"abstract":"<div><div>Attenuated total reflection Fourier transform infrared (ATR-FTIR) technique has become indispensable for surface-specific molecular analysis. At the Australian Synchrotron’s Infrared Microspectroscopy (IRM) beamline, we have advanced this technique by developing a novel piezo-controlled ATR-FTIR device designed for in-situ monitoring of electrochemical reactions in battery models. This piezo-controlled ATR-FTIR system incorporates high-precision piezoelectric linear translation stages, enabling sub-micron positioning and a gentle approach to engage samples with step intervals as small as 50 nm. By capturing high-quality spectral data during charge–discharge cycles of zinc ion batteries (ZIBs) at a controlled 100 nm distance from the electrode surface, the system overcomes common spectral artifacts associated with traditional reflectance setups and provides genuine interfacial chemical information without disrupting ongoing reactions. Combining the unique ability to monitor interfacial chemistry with its precision and reproducibility, this piezo-controlled ATR-FTIR device expands the analytical potential of synchrotron-FTIR microspectroscopy, offering transformative insights into the formation of solid electrolyte interphase and solvation mechanisms. The applications in ZIBs demonstrated in this study highlight the capability of the piezo-controlled ATR-FTIR technique for understanding critical interfacial processes that underpin energy storage performance and catalysis research, setting a new standard for synchrotron-FTIR studies of dynamic interfacial phenomena.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"149 ","pages":"Article 105899"},"PeriodicalIF":3.1,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143921864","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":"Composite structure of Ge/ZnO multilayer films with Au metasurface for enhanced infrared reflection and compatible microwave transparency","authors":"Xiaofeng Qi,&nbsp;Mengke Qiao,&nbsp;Pingan Chen,&nbsp;Yingli Zhu,&nbsp;Xiangcheng Li","doi":"10.1016/j.infrared.2025.105901","DOIUrl":"10.1016/j.infrared.2025.105901","url":null,"abstract":"<div><div>Suppressing target infrared radiation signatures and enhancing their environment-adaptive camouflage capability have remained key research objectives in infrared thermal management technologies, this study proposes a metasurface-based composite multilayer film (CMF) composed of germanium (Ge)/zinc oxide (ZnO) multilayers (MLF) and an Au square periodic array (ASPA), achieving high infrared reflectivity and high microwave transmittance through synergistic design. For the infrared band, alternating Ge/ZnO layers with optimized thicknesses satisfy Bragg reflection conditions, leveraging constructive interference between high/low refractive index materials to achieve broadband high reflectivity. The ultrathin Au periodic array acts as an auxiliary reflector, suppressing transmission losses via the intrinsic infrared reflectivity of metals and dense subwavelength arrangement, thereby broadening the reflection bandwidth. For the microwave band, specially designed ultrathin Au metasurfaces achieve impedance matching with free space, demonstrating high-efficiency microwave transmission. Experimental and simulation results demonstrate that the proposed composite structure achieves average infrared reflectance of 94.1 % in the 3–5 µm band and 89.1 % in the 8–14 µm band, while exhibiting efficient transmission characteristics exceeding 85 % in the 2–15 GHz frequency range and maintaining relatively high transmittance efficiency of 75–85 % in the 15–18 GHz band. By integrating infrared interference and microwave metasurface resonance, this work resolves the incompatibility of conventional materials in thermal management and communication, offering a novel approach to designing multifunctional integrated satellite materials for thermal control and signal transmission.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"149 ","pages":"Article 105901"},"PeriodicalIF":3.1,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143927779","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":"Determination of refractive index of crystalline silicon in the infrared region on the basis of interference pattern observed in thick slab","authors":"Jiří Vohánka,&nbsp;Jiří Večeře,&nbsp;Daniel Franta,&nbsp;Ivan Ohlídal","doi":"10.1016/j.infrared.2025.105889","DOIUrl":"10.1016/j.infrared.2025.105889","url":null,"abstract":"<div><div>The precise values of the refractive index of crystalline silicon are determined in the infrared region based on the measurements of the interference pattern in 0.25 mm thick wafer. The interference pattern observed for one particular incidence angle allows us to determine the optical thickness precisely, however, the wafer thickness and refractive index, whose product gives the optical thickness, can be determined with much worse accuracy. This limitation could be overcome by using several incidence angles because if the dependence of the period of interference pattern on the incidence angle is considered, it is possible to determine both the thickness and refractive index with high accuracy. The FTIR infrared ellipsometer is used for measurements at oblique incidence angles, while the FTIR spectrophotometer is utilized for measurements at near-normal incidence. To correctly interpret the experimental data, it is necessary to consider the influence of the finite spectral resolution and beam divergence of the instruments and the thickness non-uniformity of the sample. These effects significantly alter the observed interference patterns. The formulae needed to accomplish this task are derived in this work. The values of the refractive index determined using the proposed method for the crystalline silicon show differences smaller than <span><math><mrow><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mo>−</mo><mn>3</mn></mrow></msup></mrow></math></span> from the values obtained by the minimum deviation method.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"149 ","pages":"Article 105889"},"PeriodicalIF":3.1,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143912623","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":"A PCNN model for infrared pedestrian segmentation in complex environments","authors":"Honggui Cao ,&nbsp;Bo Ye ,&nbsp;Yangkun Zou ,&nbsp;Zhizhen Zhu ,&nbsp;Zijie Wan ,&nbsp;Shsoda Yin","doi":"10.1016/j.infrared.2025.105897","DOIUrl":"10.1016/j.infrared.2025.105897","url":null,"abstract":"<div><div>Pulse Coupled Neural Network (PCNN) is widely used in infrared pedestrian image segmentation. Due to the complex background, heat source interference, uneven brightness distribution of human targets and easy aliasing with the background in infrared pedestrian images, the segmentation of human targets is incomplete and the segmentation effect is poor. An improved PCNN algorithm for segmenting infrared pedestrian targets in complex environments and a new idea of preferentially segmenting background regions are proposed. The infrared pedestrian image is represented as a graph structure, and the superpixels in the infrared image are represented as nodes of the graph. The score of each node becoming the background region is evaluated by analyzing the structure of the graph, and the score is mapped to the original image as the connection strength of the model. Correspondingly, the dynamic threshold is set as the clustering center of the image background, and the background region of the image is prioritized for classification and output. Infrared pedestrian images with complex environment can be effectively segmented by the model, especially for infrared images with more heat source interference in the background.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"149 ","pages":"Article 105897"},"PeriodicalIF":3.1,"publicationDate":"2025-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143916197","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}