Ocean Modelling最新文献

{"title":"The response of East China Sea internal tides to ocean warming: intensified while maintaining seasonal contrasts","authors":"Bingtian Li ,&nbsp;Haidong Pan ,&nbsp;Dong Jiang ,&nbsp;Shujiang Li","doi":"10.1016/j.ocemod.2026.102696","DOIUrl":"10.1016/j.ocemod.2026.102696","url":null,"abstract":"<div><div>The internal tide (IT) in the East China Sea (ECS) originates predominantly at the ECS shelf break and along ridges near the Ryukyu Island chain. Stratification in the ECS varies seasonally, contributing to corresponding changes in ITs. In recent decades, continual ocean warming has intensified stratification and may modulate IT. However, the effects of warming on the ITs in the ECS, including the seasonality, remain unclear. In this study, the evolution of IT energy and tidal mixing in response to ocean warming is explored using numerical simulations. The results indicate that ocean warming generally amplifies IT conversion and dissipation. However, the seasonal contrast in IT energetics is preserved because the stratification maintains its seasonal variability in a warming climate. Moreover, the contrast in tidal mixing across seasons also remains relatively stable. These findings may advance the understanding of hydrodynamic changes in response to warming.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"201 ","pages":"Article 102696"},"PeriodicalIF":2.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146171474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"sciCUN: A deep learning model for daily sea surface current fields inference—A case study of the Gulf of Riga","authors":"Amirhossein Barzandeh,&nbsp;Ilja Maljutenko,&nbsp;Sander Rikka,&nbsp;Urmas Raudsepp","doi":"10.1016/j.ocemod.2026.102693","DOIUrl":"10.1016/j.ocemod.2026.102693","url":null,"abstract":"<div><div>The precise forecasting of sea surface currents is essential for applications including navigation, pollution management, and ecosystem monitoring. Conventional high-resolution hydrodynamic models, such as NEMO, provide detailed short-term forecasts; however, they are computationally intensive and resource-demanding. To address these challenges, we introduce sciCUN: a deep-learning model for <u><strong>s</strong></u>urface <u><strong>c</strong></u>urrent <u><strong>i</strong></u>nference using <u><strong>C</strong></u>NN-<u><strong>U</strong></u>-<u><strong>N</strong></u>et. As a case study, we used sciCUN to forecast daily current fields in the Gulf of Riga. During the training process, the model learns how the atmospheric forcing of the next day affects the fields of previous sea surface currents. sciCUN was trained from 1993 to 2019 and evaluated over a 4-year (2020–2023) prediction performance test. The results of the performance evaluations showed that somewhat less accurate predictions were mostly found in coastal regions close to river mouths and along the Baltic Sea border in the Irbe Strait, where, in contrast to hydrodynamic models, the data-driven modeling process did not apply boundary conditions. Nevertheless, sciCUN showed good predictive performance throughout its four-year testing period, achieving an average Euclidean distance of 2.30 cm/s between its prediction outputs and the original data. Furthermore, sciCUN obtained an average component-wise MAE of 1.45 cm/s and an average correlation coefficient of 0.92. sciCUN further demonstrated its ability to predict dominant daily surface current patterns through additional SOM analyses, using various clustering grid sizes to classify daily surface current maps into groups ranging from two to twelve prototypes. When the cluster size was reduced to two, focusing on the most dominant and distinctive patterns, sciCUN-predicted outputs achieved 97% accuracy in matching the correct cluster. By increasing the clustering grid size to categorize daily sea surface current maps into 12 prototypes, sciCUN still achieved 87% accuracy. Notably, most mismatches occurred between clusters whose prototypes exhibited closely resembling internal patterns. These results show that sciCUN is a computationally efficient and reliable way to emulate daily sea surface current forecasts.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"201 ","pages":"Article 102693"},"PeriodicalIF":2.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146171909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Latitudinal dependence of circulation seasonality in the South China Sea and its response to ENSO","authors":"Zhe Guo ,&nbsp;Zhiqiang Liu ,&nbsp;Zhongya Cai","doi":"10.1016/j.ocemod.2026.102691","DOIUrl":"10.1016/j.ocemod.2026.102691","url":null,"abstract":"<div><div>Surface circulation in the South China Sea (SCS), primarily driven by water exchange through the Luzon Strait and regional wind forcing, exhibits a strong seasonal cycle, typically intensifying in winter and weakening in summer. However, this seasonality varies significantly across the basin, reflecting complex interactions between local dynamics and external forcing. Using satellite altimetry and numerical simulations, this study identifies a latitudinal dependence in the timing of surface circulation transitions. From south to north, the decay phase, when mean kinetic energy declines from its seasonal peak, becomes progressively longer, while the growth phase shortens. Energy budget analysis reveals that in the northern SCS, mean kinetic energy is sustained longer due to joint contributions from local wind power and external kinematic energy (KE) input. In contrast, the southern SCS experiences a rapid drop in KE, driven primarily by a sharp decline in wind power. This spatial pattern also varies interannually, modulated by the El Niño–Southern Oscillation (ENSO). In the south, decay phase duration is positively correlated with ENSO strength, largely due to ENSO-driven variations in wind stress. In the north, ENSO influences wind stress and Kuroshio intrusion in opposite ways, resulting in a negative correlation between ENSO and decay time. These findings enhance our understanding of how large-scale climate variability modulates marginal sea circulation and offer new insights for improving regional ocean modeling.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"201 ","pages":"Article 102691"},"PeriodicalIF":2.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146081499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Island morphological influences on landslide-generated tsunami propagation and resonance: A case study in the Xisha Islands, South China Sea","authors":"Ao Shen ,&nbsp;Qiliang Sun ,&nbsp;Cheng Zhang ,&nbsp;Xingxing Wang ,&nbsp;Qing Wang","doi":"10.1016/j.ocemod.2026.102694","DOIUrl":"10.1016/j.ocemod.2026.102694","url":null,"abstract":"<div><div>Tsunamis generated by submarine landslides pose significant threats to coastal populations and infrastructure. Their hazards are greatly related to wave propagation processes, such as wave refraction, reflection, and resonance, which can lead to unexpected wave elevations and catastrophic damage. Consequently, a comprehensive understanding of wave propagation and associated influencing factors is essential for enhancing tsunami prediction accuracy and mitigating hazards. In this study, based on the parameter constraints derived from multibeam bathymetric data, we modeled a realistic landslide scenario and associated tsunamis in the Xisha Islands of the South China Sea (SCS) by the coupled models of NHWAVE (Non-hydrostatic Wave Model) and FUNWAVE-TVD (Fully Nonlinear Wave model - Total Variation Diminishing). Through wavelet transform and model decomposition, we concentrated on investigating the energy variations and wave interactions during the wave propagation processes. Additionally, we analyzed the impact of island resonance on tsunami elevation. Our study highlights that landslide-generated tsunamis can excite wave resonance between islands, leading to sustained influence. By decomposing and analyzing the time series data, we identify two components of tsunamis: characteristic component 1 (CC1) and characteristic component 2 (CC2). CC1 is characterized by low-amplitude, high-frequency, short-period waves; conversely, CC2 consists of high-amplitude, low-frequency, long-period waves. CC1 represents the propagated tsunami waves that are directly generated by landslides; whereas CC2 encompasses those influenced by wave refraction and reflection during wave propagation. This study quantifies the contributions of refraction and reflection effects on tsunamis during wave propagation by using variance ratios and the Pearson correlation coefficients. The findings provide quantitative evidence elucidating how island morphologies influence the propagation of landslide-generated tsunamis. This study indicates that submarine landslide-generated tsunami represents a substantial geohazard for the islands, and will be also helpful for the tsunami hazard assessment in the regions where there are complex morphologies (e.g., islands and bays).</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"201 ","pages":"Article 102694"},"PeriodicalIF":2.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146171906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Geometry-adaptive and feature-modulating dynamic segmentation network for detection of ocean eddies","authors":"Jiayi Song,&nbsp;Yunhua Zhang,&nbsp;Xiao Dong,&nbsp;Yixuan Li","doi":"10.1016/j.ocemod.2026.102698","DOIUrl":"10.1016/j.ocemod.2026.102698","url":null,"abstract":"<div><div>Accurate detection of mesoscale oceanic eddies from satellite altimetry data is crucial for understanding ocean dynamics and climate processes. Eddies’ variable morphology and dense distribution in ocean flow fields present significant challenges for automated identification systems. While multi-scale segmentation networks have advanced eddy detection capabilities, conventional approaches using fixed sampling patterns inadequately capture irregular eddy geometries and produce boundary artifacts in regions of closely-packed eddies. This paper introduces a novel dual-adaptive feature extraction framework that addresses these limitations through two complementary mechanisms. First, our Geometry-Responsive Sampling Module (GRSM) implements bounded deformable convolutions that dynamically adjust sampling positions by learning optimal displacement fields constrained within physically meaningful ranges. This enables adaptive matching to irregular eddy boundaries across different scales. Second, our Spatial Weighting Module (SWM) employs dedicated convolution branches to generate context-aware sampling weights that enhance eddy boundary features while suppressing redundant information, effectively resolving boundary delineation uncertainties in closely-packed eddy fields. Experiments across oceanographically distinct regions validate the framework’s robustness and transferability. Compared to established methods such as SegNet, FCN-8s, EddyNet, PSPNet, and DeepLabv3+, our approach demonstrates superior performance, with improvements of 1.3–2.9% in mean Intersection over Union (mIoU) and 0.8–2.4% in mean Dice Coefficient (mDice) over the leading baselines. The method achieves 89.52% mIoU and 92.78% mDice on the South China Sea-Northwest Pacific dataset, and maintains 87.05% mIoU and 92.61% mDice on the South Atlantic dataset. The consistent performance across regions with contrasting eddy dynamics, scale distributions, and flow regimes demonstrates the framework’s capability to accurately segment mesoscale eddies under diverse oceanographic conditions.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"201 ","pages":"Article 102698"},"PeriodicalIF":2.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146171905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Numerical modeling of large spherical microplastics transport and deposition using CFD-DEM","authors":"Mostafa Bigdeli ,&nbsp;Abdolmajid Mohammadian ,&nbsp;Abolghasem Pilechi","doi":"10.1016/j.ocemod.2026.102697","DOIUrl":"10.1016/j.ocemod.2026.102697","url":null,"abstract":"<div><div>The transport and deposition patterns of microplastics (MPs) in water are significantly influenced by the hydraulic characteristics of the surrounding fluid flow, including the water level, flow velocity, and the bathymetric and geometric conditions of the water bodies. Accordingly, this study aims to employ an Eulerian-Lagrangian numerical modeling approach to illuminate spherical MPs transport and deposition patterns by comparing different scenarios applied to flow and channel geometry. Various test scenarios were simulated to assess the impact of various variables, including the flow velocity (<em>u_f</em>), depth of turbulent flows (<em>w</em>), deepening of the channel bed (<em>d</em>), and slopes applied to the bottom of the channel bed (<em>s</em>). The utilized numerical model is based on the unresolved computational fluid dynamics (CFD) model coupled with discrete element method (DEM) solver, which was validated against laboratory experimental measurements. The results demonstrated that the CFD-DEM could simulate MPs transport and deposition with an accuracy of over 63 %. Additionally, the model effectively captured the effects of hydraulic parameters (<em>w, u_f, d</em>, and <em>s</em>) on MPs deposition. It was concluded that CFD-DEM model is highly sensitive to the values of the sliding and rolling friction coefficients, and the number of particles considered for running simulations. Furthermore, the demonstrated sensitivity of the model to specific parameters offers a foundation for optimizing future modeling efforts and enhancing predictive capabilities in environmental and hydraulic engineering applications.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"201 ","pages":"Article 102697"},"PeriodicalIF":2.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146171903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of intertidal habitats on storm-induced flooding in estuaries: Application to the Thames Estuary, UK","authors":"Renjie Zhu ,&nbsp;Wei Zhang ,&nbsp;Xiaoyan Wei","doi":"10.1016/j.ocemod.2026.102702","DOIUrl":"10.1016/j.ocemod.2026.102702","url":null,"abstract":"<div><div>Intertidal habitats have been rapidly reduced worldwide due to land reclamation. To mitigate risks related to flooding, erosion, and biodiversity loss, intertidal restoration and creation are increasingly implemented worldwide. Nevertheless, the mechanisms through which these habitats affect estuarine flooding are still underexplored. This study developed a three-dimensional, idealized semi-analytical model to systematically examine intertidal effects on extreme high water levels (EWL) in estuaries. As a case study, the model was applied to the idealized Thames Estuary (UK) during the storm Xaver, the strongest North Sea surge in the last seven decades. Our results show that semi-diurnal tides and lower-frequency surges (with periods greater than ∼24 h) are the main drivers of EWL in the idealized Thames Estuary during this storm. The semi-diurnal tides and surges in the estuary are strongly amplified and delayed by intertidal habitats, and these phase delays increase with increasing tide/surge frequency. Intertidal effects on lower-frequency tides and surges are minor. Intertidal habitats increase EWL by more than 0.1 m during the storm Xaver, which is mostly controlled by habitat-induced changes in the semi-diurnal tidal amplitudes and surge phases. Although the Thames Barrier closure effectively reduces flood risks upstream during this storm, it increases EWL by ∼0.2 m in the outer estuary. Also, channel shallowing can reverse intertidal effects from raising to lowering EWL. Our findings reveal that intertidal habitats play a variable role across different tide and surge constituents, providing a critical scientific basis for understanding uncertainties associated with their effectiveness in estuarine flood risk mitigation.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"201 ","pages":"Article 102702"},"PeriodicalIF":2.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146171475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Long-lead daily sea surface salinity prediction using time-series CCI L4 SSS satellite data and a temporal convolutional deep learning model","authors":"Dandan Li ,&nbsp;Changjiang Xiao ,&nbsp;Min Huang ,&nbsp;Qiquan Yang ,&nbsp;Xiong Xu ,&nbsp;Yingbing Liu","doi":"10.1016/j.ocemod.2026.102692","DOIUrl":"10.1016/j.ocemod.2026.102692","url":null,"abstract":"<div><div>Accurate long-lead daily Sea Surface Salinity prediction remains a significant challenge, primarily attributed to complex oceanic dynamics and the cumulative propagation of prediction errors over extended time frames. Existing methodologies, encompassing classical machine learning approaches and recurrent deep learning architectures, struggle to balance computational efficiency with the modeling of long-range temporal dependencies in SSS time series. This study introduces a temporal convolutional network (TCN)-based model to address these challenges, leveraging dilated causal convolutions to model multi-scale SSS dynamics while mitigating error accumulation in long-lead forecasting. The proposed deep learning model enables the automatic capture and modeling of SSS temporal dependencies using only historical time-series SSS data from satellite remote sensing. Comprehensive experiments conducted at eight geographically dispersed sites in the Indian Ocean, utilizing European Space Agency (ESA) Climate Change Initiative (CCI) Level 4 (L4) SSS satellite data, demonstrate that the proposed model outperforms both baseline machine learning and deep learning models, demonstrating its superior capability for long-lead daily SSS prediction.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"201 ","pages":"Article 102692"},"PeriodicalIF":2.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146081501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"InWaveSR: Topography-aware super-resolution network for internal solitary waves","authors":"Xinjie Wang ,&nbsp;Zhongrui Li ,&nbsp;Peng Han ,&nbsp;Chunxin Yuan ,&nbsp;Jiexin Xu ,&nbsp;Zhiqiang Wei ,&nbsp;Jie Nie","doi":"10.1016/j.ocemod.2026.102700","DOIUrl":"10.1016/j.ocemod.2026.102700","url":null,"abstract":"<div><div>The effective utilization of observational data is frequently hindered by insufficient resolution. To address this problem, we present a new spatio-temporal super-resolution (STSR) model, called <em>InWaveSR</em>. It is built on a deep learning framework with physical restrictions and can efficiently generate high-resolution data from low-resolution input, especially for data featuring internal solitary waves (ISWs). To increase generality and interpretation, the model <em>InWaveSR</em> uses the non-hydrostatic primitive equations with eddy viscosity and diffusivity as the constraint, ensuring that the output results are physically consistent. In addition, the proposed model incorporates an <em>HF-ResBlock</em> component that combines the attention mechanism and the Fast Fourier Transform (FFT) method to improve the performance of the model in capturing high-frequency characteristics. Simultaneously, in order to enhance the adaptability of the model to complicated bottom topography, an edge sampling and numerical pre-processing method are carried out to optimize the training process. In evaluations, the proposed <em>InWaveSR</em> achieved a peak signal-to-noise ratio (PSNR) of <span><math><mrow><mn>37</mn><mo>.</mo><mn>8</mn><mspace></mspace></mrow></math></span>dB, demonstrating superiority over traditional interpolation and previous neural network methods. This highlights its effectiveness and reliability in reconstructing high-resolution ISW structures with physically consistent dynamics.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"201 ","pages":"Article 102700"},"PeriodicalIF":2.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146171907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Suppression of submesoscale currents by a tropical cyclone","authors":"Bijit K. Kalita ,&nbsp;Shrikant M. Pargaonkar ,&nbsp;P.N. Vinayachandran","doi":"10.1016/j.ocemod.2026.102695","DOIUrl":"10.1016/j.ocemod.2026.102695","url":null,"abstract":"<div><div>Using high-resolution submesoscale-permitting Regional Ocean Modelling System (ROMS) simulations with and without river runoff, this study investigates how a category 5 tropical cyclone modulates submesoscale currents (SMC) in the Bay of Bengal (BoB). The analysis reveals a three-stage sequence: pre-storm genesis of SMC, their suppression during the passage of the cyclone, and partial post-storm recovery. Before the storm, offshore advection of a river plume by a cyclonic eddy generated sharp lateral buoyancy gradients and velocity shear along the plume edges that sustained fronts, filaments, and ageostrophic secondary circulations through frontogenesis and mixed-layer instabilities. During the in-storm stage, strong winds and vertical mixing disrupted these buoyancy gradients, reversed energy conversion from potential to kinetic energy, and diminished frontal jets as wind-stress-driven frictional forcing overwhelmed buoyancy control. After landfall, weaker but reappeared buoyancy gradients once again supported frontogenesis and the gradual re-emergence of SMC. These findings demonstrate a novel cyclone-plume-SMC coupling in the BoB with implications for vertical exchanges, cyclone intensity, and biogeochemical responses in freshwater-dominated basins.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"201 ","pages":"Article 102695"},"PeriodicalIF":2.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146171908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}