Infrared Physics & Technology最新文献

{"title":"Dual-mode NIR spectroscopy integrating characteristic wavelengths and broadband spectra for non-invasive glucose measurement","authors":"Yu He,&nbsp;Xin Wu,&nbsp;Jipeng Huang","doi":"10.1016/j.infrared.2025.106126","DOIUrl":"10.1016/j.infrared.2025.106126","url":null,"abstract":"<div><div>Non-invasive blood glucose measurement technology, with its advantages of convenience and painlessness, shows promise for replacing traditional invasive measurement methods. However, existing techniques still face challenges, including limited accuracy and environmental adaptability. We present a dual-mode near-infrared (NIR) spectroscopy system for blood glucose measurement, featuring: a broadband spectrum measurement mode (900–1,700 nm/1,350–2,150 nm) and a characteristic wavelength measurement mode (940 nm, 1,050 nm, 1,310 nm, and 1,550 nm). Using this system, we collect 1,545 NIR spectral datasets for blood glucose analysis. Different prediction models are compared on the dataset, and the optimal model is selected and deployed in the system. Results demonstrate: In broadband spectrum mode, the Support Vector Regression (SVR) model achieves optimal predictive performance on both the 900–1,700 nm and 1,350–2,150 nm datasets, achieving a Mean Absolute Relative Differences (MARD) of 14.5% and 9.7% respectively. In characteristic wavelength mode, while Random Forest Regression (RFR) shows the best predictive performance with an MARD of 11.0%, the Polynomial Regression (PR) model is ultimately selected for system deployment due to practical implementation considerations, achieving an MARD of 12.8%. In all prediction results, more than 95% of the data points fall within the clinically acceptable error range (zone A and B) of the Clarke Error Grid (CEG), demonstrating strong measurement performance.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"151 ","pages":"Article 106126"},"PeriodicalIF":3.4,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145095074","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":"Local defect resonance enhanced vibrothermography for fatigue crack detection in CF/PEEK composite plate","authors":"Shaoping Deng ,&nbsp;Qin Wei ,&nbsp;Jianguo Zhu ,&nbsp;Guangsan Song ,&nbsp;Haibin Wang ,&nbsp;Jiyuan Guo ,&nbsp;Baogui Zheng ,&nbsp;Jinxiang Song","doi":"10.1016/j.infrared.2025.106148","DOIUrl":"10.1016/j.infrared.2025.106148","url":null,"abstract":"<div><div>Carbon fibre-reinforced polyether ether ketone (CF/PEEK) composites are widely utilized in engineering applications due to their excellent mechanical properties. A rapid and effective non-destructive testing (NDT) technique is essential to ensure the structural integrity of CF/PEEK composites. However, conventional NDT methods still face considerable limitations in achieving high-sensitivity detection of internal fatigue cracks in composites. This research investigates the mechanism of crack local defect resonance (LDR) and extends it to the application of vibrothermography for fatigue crack detection and quantitative assessment. Initially, a vibration measurement combined with modal analysis was conducted to identify the LDR frequencies of fatigue crack in short carbon fibre-reinforced PEEK composite plates. When the specimens are excited at those modal frequencies, the crack is characterized by both out-of-plane and in-plane local resonances. A subsequent vibrothermography experiment with swept excitation further verifies the closely correlation between LDR and the thermal responses induced by the fatigue crack. Comparative experiments demonstrate that the temperature rise at the fatigue crack is higher when excited with the LDR frequency than when excited with the intrinsic frequency of the specimen and the transducer’s operating frequency. Additionally, the proposed method was combined with LDR modal analysis to achieve a quantitative assessment of crack length with a relative deviation of about 8.6%. The LDR-based vibrothermography for fatigue crack detection not only improves the detection sensitivity by enhancing the local heating effect in the defective region but also provides a new and efficient method for defect metrology.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"151 ","pages":"Article 106148"},"PeriodicalIF":3.4,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145095076","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":"SESAM mode-locked Nd,Y,Gd:CaF2 laser delivering 1065 nm femtosecond pulses","authors":"Linxuan Tang ,&nbsp;Yanyan Xue ,&nbsp;Xiaoyue Feng ,&nbsp;Wenxin Li ,&nbsp;Fei Tang ,&nbsp;Zhen Zhang ,&nbsp;Jie Liu ,&nbsp;Jingjing Liu ,&nbsp;Liangbi Su","doi":"10.1016/j.infrared.2025.106157","DOIUrl":"10.1016/j.infrared.2025.106157","url":null,"abstract":"<div><div>A Nd,Y,Gd:CaF₂ disordered crystal was grown using the temperature gradient technique, and its ultrafast laser characteristics were studied. Using a 790 nm fiber coupled laser diode, a stable continuous-wave mode-locked Nd,Y,Gd:CaF₂ laser was achieved at the central wavelength of 1065.5 nm, and its pulse duration was 685 fs. The maximum average output power of 156 mW was obtained for the mode-locked laser, with a pulse repetition rate of 81.06 MHz. These results indicated that Nd,Y,Gd:CaF₂ crystals with broad spectrum are promising for producing femtosecond mode-locked laser around 1 μm.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"151 ","pages":"Article 106157"},"PeriodicalIF":3.4,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145095075","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":"Nonlinear TiC nanosheets for MIR Q-switched laser in Er:GYAP cavity","authors":"Zhentao Wang ,&nbsp;Dunlu Sun ,&nbsp;Guo Chen ,&nbsp;Keqiang Wang ,&nbsp;Huili Zhang ,&nbsp;Jianqiao Luo ,&nbsp;Cong Quan ,&nbsp;Kunpeng Dong ,&nbsp;Yuwei Chen ,&nbsp;Xinjie Li ,&nbsp;Hongyuan Li ,&nbsp;Shiji Dou ,&nbsp;Maojie Cheng","doi":"10.1016/j.infrared.2025.106158","DOIUrl":"10.1016/j.infrared.2025.106158","url":null,"abstract":"<div><div>The multilayer titanium carbide (TiC) nanosheets were successfully synthesized via liquid-phase exfoliation and the nanostructure was confirmed by electron microscopes and Raman peaks shifts. The modulation depth was fitted to be 8.7 % with a saturation transmittance of 88.4 %. The TiC saturable absorbers (SAs) enabled stable passive Q-switched operations with inserting in an Er:GYAP laser cavity, achieving an average output power of 579 mW, a pulse width of 168 ns and a repetition rate of 214 kHz, corresponding to a single pulse energy of 2.7 µJ and a peak power of 16.1 W. The laser spectra of 2730–2830 nm and beam quality factors of 1.61 and 1.63 were determined. This work not only advances the development of compact MIR pulsed lasers but also expands the potential of transition metal carbides in nonlinear photonics.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"151 ","pages":"Article 106158"},"PeriodicalIF":3.4,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145154723","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":"Recent advances in piezoelectric resonant infrared detectors","authors":"Jicong Zhao ,&nbsp;Haiyang Hou ,&nbsp;Yanmeng Dang ,&nbsp;Yanjuan Ma ,&nbsp;Longfei Li ,&nbsp;Haiyan Sun ,&nbsp;Zhipei Sun ,&nbsp;Tengfei Xu","doi":"10.1016/j.infrared.2025.106155","DOIUrl":"10.1016/j.infrared.2025.106155","url":null,"abstract":"<div><div>Uncooled micro-electromechanical systems-based piezoelectric resonant infrared detectors exploit photothermal–piezoelectric coupling for highly sensitive, wavelength-selective detection. Their compact, low-power, and easily integrable design ensures stable performance in noisy environments, enabling advanced infrared sensing and processing in complex conditions. Here, we review two operating mechanisms of piezoelectric resonant infrared detectors and their state of the art results. It provides an overview of piezoelectric resonant infrared detectors fabricated using materials such as gallium nitride (GaN), zinc oxide (ZnO), and lithium niobate (LiNbO₃). Subsequently, we discuss performance enhancements for aluminum nitride (AlN)-based detectors, focusing on infrared absorption, thermal resistance, detection sensitivity, and potential applications. Finally, we present potential challenges facing piezoelectric resonant infrared detectors and outline future research directions.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"152 ","pages":"Article 106155"},"PeriodicalIF":3.4,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145218723","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 of the geographical indication origin of Gastrodia elata Blume based on FT-NIR spectroscopy","authors":"Yingfeng Zhong ,&nbsp;Jieqing Li ,&nbsp;Honggao Liu ,&nbsp;Yuanzhong Wang","doi":"10.1016/j.infrared.2025.106150","DOIUrl":"10.1016/j.infrared.2025.106150","url":null,"abstract":"<div><div>The Yunnan Zhaotong <em>G. elata</em> has high attached edible and medicinal value and has been protected by geographical indication (GI). However, for different <em>G. elata</em> variants, <em>Gastrodia elata</em> Bl. <em>f. glauca</em> S. Chow (GB) exhibits quality characteristics superior to other variants. In different growing conditions, wild <em>G. elata</em> typically displays superior quality characteristics compared to cultivated <em>G. elata</em>. Therefore, accurate certification of origin and variety is a prerequisite for protecting consumer interests. In this framework, we analyzed 418 FT-NIR regional spectra of <em>G. elata</em>. FT-NIR spectroscopy combined with principal component analysis (PCA), binary and multi-class partial least squares discriminant analysis (PLS-DA), and data-driven soft independent modeling of class analogy (DD-SIMCA) techniques were used to discriminate and authenticate Yunnan GI <em>G. elata</em> and distinguish it from wild <em>G. elata</em>. The results showed that PCA was only able to distinguish the <em>Gastrodia elata</em> Bl. <em>f. elata</em> (GR) from the Hanzhong, Shaanxi. For multi-class PLS-DA, it is possible to distinguish wild <em>G. elata</em> and <em>Gastrodia elata</em> Bl. <em>f. viridis</em> (Makino) Makino (GG) from the Hezhang, Guizhou. The binary PLS-DA was able to differentiate between Yunnan GI <em>G. elata</em> and non-Yunnan <em>G. elata</em> as well as GB and Non-GB in Yunnan GI <em>G. elata</em> with 100 % sensitivity and specificity. In addition, DD-SIMCA also verified the reliability of the method. Thus, the method is objective, simple, rapid and can be used for routine analysis of <em>G. elata</em> to verify the identity, species and origin statement of <em>G. elata</em>.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"151 ","pages":"Article 106150"},"PeriodicalIF":3.4,"publicationDate":"2025-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145095069","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":"Lightweight infrared image super-resolution reconstruction network with contrast-driven self-modulation aggregation","authors":"Kui Yuan,&nbsp;Bowen Shen,&nbsp;Huizhou Liu,&nbsp;Juntao Huang,&nbsp;Xuegang Tan","doi":"10.1016/j.infrared.2025.106151","DOIUrl":"10.1016/j.infrared.2025.106151","url":null,"abstract":"<div><div>With the widespread application of infrared imaging technology in various fields, the demand for infrared image resolution is constantly increasing. However, the resolution of infrared images is limited by imaging hardware and environmental conditions. Additionally, current state-of-the-art image super-resolution methods predominantly target visible light imagery, depend on deep network architectures, and demand substantial computational resources and high-end hardware. Therefore, we propose a lightweight infrared image super-resolution reconstruction method based on the contrast-driven self-modulation aggregation network (CDSMANet). Firstly, a core module is designed to decompose infrared images into high-frequency, medium-frequency, and low-frequency components and achieve fusion interactions to drive the extraction of both local and non-local features. It performs a more accurate reconstruction through self-modulation aggregation. Specifically, we generate three branches of different frequency features through feature separation. The medium-frequency and low-frequency branches extract non-local features through non-local self-attention approximation, while the high-frequency branch models local information and extracts local details. Secondly, an adaptive multi-receptive field fusion module (AMF) is developed to integrate these different features, enabling mutual driving of feature extraction. Moreover, a multi-scale convolutional pooling feedforward network (MCPN) is used to further capture deep importance features. Experiments have shown that CDSMANet achieves a good balance between reconstruction performance and computational efficiency on public infrared image datasets.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"151 ","pages":"Article 106151"},"PeriodicalIF":3.4,"publicationDate":"2025-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145095072","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":"BTE-ShapeNet: Background and Target Enhancement with Shape Perception Network for infrared small target detection","authors":"Xiaodong Zhang,&nbsp;Yidan Zhang,&nbsp;Guangfeng Li,&nbsp;Qing Hu","doi":"10.1016/j.infrared.2025.106144","DOIUrl":"10.1016/j.infrared.2025.106144","url":null,"abstract":"<div><div>Infrared small target detection (IRSTD) presents significant challenges due to low contrast, blurred edges, and shape variability of targets in infrared images, which complicate their separation from the background and severely degrade detection performance. To address these challenges, we present a Background and Target Enhancement with Shape Perception Network(BTE-ShapeNet). Specifically, to tackle the issue of insufficient multi-scale feature perception, we design an enhanced scale sensitivity block(SSB) that strengthens the model’s ability to recognize small targets at different scales through multi-scale convolutional features and an adaptive weighting mechanism. Secondly, to address the issues of background complexity and the emergence of false alarms, we propose a background-target attention blocks (BTABs), BTABs refine the separation between background and target features by employing a dual enhancement mechanism for both target and background, and further integrate background and target features through multiple spatial-channel cross-attention transformer blocks, thereby enhancing background suppression capabilities. Additionally, considering the problems of low contrast and blurred edges, we design a shape perception and detail restoration blocks(SPDR), which combines large convolutions and central difference convolutions to effectively enhance the target edge information while preserving its shape characteristics. Experimental results on the IRSTD-1K, NUAA-SIRST, and NUDT-SIRST datasets demonstrate that BTE-ShapeNet outperforms state-of-the-art methods in detection accuracy, particularly under low signal-to-noise ratios and complex backgrounds, significantly improving detection precision while effectively reducing false alarms and miss detection.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"151 ","pages":"Article 106144"},"PeriodicalIF":3.4,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145095068","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":"Multi-scale feature enhancement and cross-dimensional attention mechanism fusion network for hyperspectral image classification","authors":"Xiaoqing Wan ,&nbsp;Kun Hu ,&nbsp;Feng Chen ,&nbsp;Yupeng He ,&nbsp;Hui Liu","doi":"10.1016/j.infrared.2025.106123","DOIUrl":"10.1016/j.infrared.2025.106123","url":null,"abstract":"<div><div>Hyperspectral image (HSI) classification is widely used in various fields but faces significant challenges such as high dimensionality, spatial complexity, and spectral redundancy, which limit classification accuracy. Traditional machine learning methods rely on handcrafted features and struggle with high-dimensional data, while deep learning approaches still encounter difficulties in multi-level feature fusion, global-local collaborative modeling, and efficient cross-dimensional interaction. To address these challenges, this paper proposes the multi-scale feature enhancement and cross-dimensional attention mechanism fusion network (MSFE-CAMF), which integrates three key modules: a multi-scale feature extraction module (MS-FEM), a large-kernel attention and local feature fusion module (LKALFFM), and a cross-dimensional attention module (CDAM). First, the MS-FEM employs a multi-branch 3D convolutional architecture to capture multi-scale spatial dependencies and spectral correlations while maintaining computational efficiency. Additionally, a residual connection mechanism is incorporated to enhance model stability and convergence. Second, the LKALFFM combines large-kernel attention with local feature enhancement, facilitating cross-scale information capture through multi-scale learning. At the same time, it strengthens fine-grained feature sensitivity via local feature fusion, enabling a more refined representation of hyperspectral data. Finally, the CDAM module integrates a cross-dimensional attention mechanism with multi-pooling channel gating (MCG) to enhance multi-dimensional feature modeling of HSI through spatial-channel information interaction, improving classification accuracy and adaptability in complex scenarios. Extensive evaluations on four popular HSI datasets show that, with 10% of the training samples, the proposed method achieves 99.61% overall accuracy on the Houston 2013 dataset, 99.96% on the Salinas dataset, 99.92% on the WHU-Hi-LongKou dataset, and 99.90% on the WHU-Hi-HanChuan dataset. These results demonstrate the competitive performance of our architecture compared to state-of-the-art methods, underscoring its effectiveness and robustness in HSI classification.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"151 ","pages":"Article 106123"},"PeriodicalIF":3.4,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145095070","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":"Hyperspectral imaging combined with NGO-RBFNN for maize variety identification","authors":"Fu Zhang ,&nbsp;Qinghang Chen ,&nbsp;Mengyao Wang ,&nbsp;Baoping Yan ,&nbsp;Ying Xiong ,&nbsp;Yakun Zhang ,&nbsp;Sanling Fu","doi":"10.1016/j.infrared.2025.106141","DOIUrl":"10.1016/j.infrared.2025.106141","url":null,"abstract":"<div><div>Maize is a vital food crop with various varieties cultivated in the world. The market order is significantly threatened by the prevalence of counterfeit and substandard maize seeds. The development of non-destructive methods for accurately identifying maize varieties is necessary. Hyperspectral imaging technology was utilized to acquire spectral data. 540 maize seeds of 6 varieties were divided into training set and test set in a ratio of 2:1. Regions of interest (ROI) with embryo size of 8 × 8 pixels were designated. The average spectral information in the range of 949.43–1709.49 nm was intercepted to eliminate the random noise at both ends of the raw spectral data. Savitzky-Golay (SG) smoothing preprocessing was used on the effective band information, and max normalization (MN) preprocessing was performed on the basis of SG. The characteristic wavelengths were screened using Successive Projections Algorithm (SPA), Competitive Adaptive Reweighted Sampling (CARS) for single screening, and CARS-SPA and CARS + SPA for combined screening. Based on full bands (FB) and characteristic wavelengths, Radial Basis Function Neural Network (RBFNN), Back Propagation Neural Network (BPNN), Extreme Learning Machine (ELM), Random Forest (RF), Support Vector Machine (SVM) were developed. RBFNN were optimized by Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Northern Goshawk Optimization (NGO). The results showed that the (SG + MN)-(CARS + SPA)-NGO-RBFNN model had the best performance with an accuracy of 93.89 % in the test set. The research proved that hyperspectral imaging combined with NGO-RBFNN can effectively identify various maize varieties, which provides a theoretical foundation for the identification of maize varieties.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"151 ","pages":"Article 106141"},"PeriodicalIF":3.4,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145095073","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}