Journal of Atmospheric and Solar-Terrestrial Physics最新文献

{"title":"A novel combination forecasting model for short-term wind power","authors":"Na Guo,&nbsp;Hongyu Zheng,&nbsp;Qihuan Zhou,&nbsp;Xinjian Yin","doi":"10.1016/j.jastp.2026.106731","DOIUrl":"10.1016/j.jastp.2026.106731","url":null,"abstract":"<div><div>The accurate prediction of short-term wind power is of great significance for wind power grid integration and grid stability. Short-term wind power is not only related to historical wind power, but also influenced by meteorological factors. This paper proposes a novel combination forecasting model for short-term wind power. The max-relevance min-redundancy feature selection algorithm is used to select meteorological feature data with high correlation and low redundancy. In response to the intermittent and non-stationary characteristics of short-term wind power, variational mode decomposition algorithm is used to decompose short-term wind power, and the generated components reduce the noise and redundancy of the original data. The components obtained by variational mode decomposition are combined with the main features of the extracted meteorological data as inputs to the long short-term memory network, and the outputs of each corresponding long short-term memory network are added to obtain the final prediction result. An improved sparrow search algorithm with better optimization performance is proposed and applied to hyper-parameters optimization of long short-term memory network. Two short-term wind power datasets from different regions and sampling intervals are selected as the research objects. The proposed combination forecasting model showed 28.99 %–89.31 % decrease in RMSE, 30.81 %–86.37 % decrease in MAPE, and 11.07 %–85.38 % decrease in MAE compared with other models on the first dataset. On the second dataset, three indicators decreased by 12.21 %–80.91 %, 50.18 %–87.54 %, and 9.99 %–83.01 %. The comparison results confirmed that the proposed combination forecasting model has high prediction accuracy for short-term wind power while ensuring small system deviations, and its real-time performance can also meet the needs of practical applications.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"279 ","pages":"Article 106731"},"PeriodicalIF":1.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145979915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to ‘Optimized fuzzy logic algorithm for classifying meteorological and non-meteorological echoes in CINRAD/SA data in Poyang lake region’ [J. Atmos. Sol. Terr. Phys., Volume 278, 2026, 106708]","authors":"Landi Zhong ,&nbsp;Haibo Zou ,&nbsp;Xiaoyou Long ,&nbsp;Jiaxin Wang ,&nbsp;Yige Huang","doi":"10.1016/j.jastp.2026.106728","DOIUrl":"10.1016/j.jastp.2026.106728","url":null,"abstract":"","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"279 ","pages":"Article 106728"},"PeriodicalIF":1.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146073906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"M3F-U-Net: A hybrid deep learning model for precipitation forecast correction","authors":"Tong Zhang ,&nbsp;Xing Wang ,&nbsp;Hao Li ,&nbsp;Qiliang Wu ,&nbsp;Dan Geng","doi":"10.1016/j.jastp.2025.106714","DOIUrl":"10.1016/j.jastp.2025.106714","url":null,"abstract":"<div><div>The accuracy of precipitation forecasting exerts significant impacts on socio-economic activities. However, numerical weather prediction (NWP) models often exhibit systematic biases due to the chaotic nature of atmospheric dynamics and localized topographic effects. This study proposes M³F-U-Net, a multi-stream multi-level multi-physics Fusion U-Net designed to decouple and reconstruct atmospheric dynamic, thermodynamic, moisture transport, and topographic forcing processes. A dynamic weighted fusion module is employed to adaptively adjust the importance of physical features, while a residual learning framework is incorporated to achieve precipitation forecast correction. Experiments based on ECMWF model forecasts and ERA5 reanalysis data demonstrate that the model reduces the Mean Squared Error (MSE) and Mean Absolute Error (MAE) by 20.04 % and 14.42 %, respectively. Notably, the Equitable Threat Score (ETS) improves by 6.19 % overall, driven by a significant reduction in false alarms. Subgroup analysis confirms the model's robustness across diverse terrains, achieving an 8.56 % ETS improvement even in complex mountainous regions. Furthermore, the model effectively mitigates nonlinear error accumulation in long-term forecasts (up to 72 h). This research establishes a physically consistent deep learning framework for mitigating systematic biases in NWP-based precipitation forecasts, providing an innovative technical paradigm for numerical model post-processing.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"279 ","pages":"Article 106714"},"PeriodicalIF":1.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145940739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fine-tuning prior knowledge networks for seismic anomaly filtering in Schumann resonance","authors":"Huang Yongming ,&nbsp;Xie Yi ,&nbsp;Que Mingyi ,&nbsp;Lu Yong ,&nbsp;Liu Gaochuan ,&nbsp;Teng Yuntian","doi":"10.1016/j.jastp.2026.106729","DOIUrl":"10.1016/j.jastp.2026.106729","url":null,"abstract":"<div><div>Detecting pre-earthquake anomalies in Schumann Resonance (SR) data is a significant challenge due to the low signal-to-noise ratio, with faint precursor signals often obscured by strong electromagnetic background noise. To address this, this paper proposes a novel, two-stage hybrid filtering method. The approach first uses a one-dimensional convolutional neural network (1D-CNN) to learn the patterns of a robust sliding interquartile range (IQR) detector, thereby acquiring “prior knowledge,” and then applies a fine-tuning stage to the network’s weights to selectively enhance pre-seismic patterns. The method was developed and validated using a multi-year dataset (2013–2021) of SR spectrograms and corresponding seismic events in California. Experimental results demonstrate a significant improvement in signal clarity: the average proportion of anomalies occurring within the 20 days prior to an earthquake increased from 69.91 % before filtering to 83.46 % after, representing a noteworthy average uplift of 13.55 %. This study confirms that our fine-tuned prior knowledge network is an effective approach for enhancing the visibility of potential seismic precursors in noisy SR data, reinforcing the potential of SR as a tool for short-term earthquake studies.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"279 ","pages":"Article 106729"},"PeriodicalIF":1.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146034966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Neural Network (ANN) modeling for CO2 concentration prediction in geothermal fields","authors":"Teddy Miller Samo,&nbsp;Calvine Ominde,&nbsp;Justus Maithya,&nbsp;James Munyithya","doi":"10.1016/j.jastp.2026.106735","DOIUrl":"10.1016/j.jastp.2026.106735","url":null,"abstract":"<div><div>This study explores the application of Artificial Neural Network (ANN) architectures, particularly Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), for forecasting CO₂ concentration trends in geothermal fields. The research is motivated by the growing need to quantify and predict emissions from geothermal power generation—an important renewable energy source whose environmental impacts are often overlooked. Geothermal field measurements were combined with meteorological variables to train and validate the models. Performance was evaluated using mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), coefficient of determination (R²), and bias. The ANN (LSTM–GRU) model demonstrated superior predictive ability, achieving lower RMSE (0.0952 vs. 0.0989), MAE (0.0809 vs. 0.0828), and MAPE (0.20 % vs. 0.21 %), with a higher R² (0.8912 vs. 0.8361) compared to the SARIMA model. Although ANN recorded a slightly higher bias (0.0797 vs. 0.002), its overall performance underscores its effectiveness in modeling complex, non-linear, and temporal patterns of CO₂ concentration. The findings confirm that ANN-based models are more accurate and adaptable than conventional statistical approaches. Their application in geothermal fields provides a robust tool for forecasting emissions, enabling better planning, monitoring, and implementation of environmental strategies to mitigate the contribution of geothermal energy production to greenhouse gas emissions.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"279 ","pages":"Article 106735"},"PeriodicalIF":1.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146034950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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-term air quality forecasting in Korba, India (2025–2047): A hybrid model using 44-year satellite data","authors":"Abhimanyu Kumar Gond,&nbsp;Aarif Jamal,&nbsp;Tarun Verma","doi":"10.1016/j.jastp.2026.106730","DOIUrl":"10.1016/j.jastp.2026.106730","url":null,"abstract":"<div><div>This study employs a hybrid Temporal Convolutional Network (TCN) and Transformer model to forecast air quality trends in Korba, Chhattisgarh, a critically polluted industrial hub dominated by extensive coal mining (including Gevra, Dipka, and Kusmunda mines), multiple thermal power plants, aluminium smelters, and cement production, from 2025 to 2047. Utilizing 44 years of satellite-derived data (1980–2024), the model integrates meteorological variables, vegetation indices (NDVI and NDBI), and coal mining metrics. Under a moderate policy change scenario, it predicts gradually rising pollutant levels: PM₁₀ (32–84 μg/m³), PM₂.₅ (10–33 μg/m³), SO₂ (7.25–12 μg/m³), NO₂ (5.25–9 μg/m³), and AQI (45–110, Moderate per CPCB Delhi standards), with seasonal patterns showing reduced concentrations during monsoon due to rainfall washout and elevated levels in summer and winter owing to limited atmospheric dispersion. The model demonstrated strong performance (R² = 0.75–0.91; RMSE = 1.09–11.91), effectively capturing both short- and long-term trends driven by industrial emissions and environmental factors. A sensitivity analysis further revealed the model's robust response to ±10–20 % variations in key drivers, with the most decisive influence from coal production increases, which could be elevated by 20–30 % and add 10–15 AQI points, while reduced rainfall, higher temperatures, and lower NDVI amplified dust resuspension and secondary pollutant formation. Uncertainty analysis identified high-risk periods, including elevated PM₂.₅ variability in 2025–2026 and AQI in 2033–2038. Validation against ground-truth data from Urja Nagar, Rampur stations, and MODIS Satellite-derived AQI (From January–September 2025; R²: 0.72, 0.61, and 0.58) confirmed forecasted AQI (30–164, mostly Moderate), posing potential respiratory risks to vulnerable groups upon prolonged exposure. These projections highlight escalating public health threats, particularly respiratory and cardiovascular diseases, underscoring the need for urgent interventions, such as stricter emission controls, a transition to renewable energy sources, the adoption of an air quality health index, and enhanced reforestation for dust mitigation. This work offers a robust, data-driven baseline and scalable framework for sustainable air quality management in industrial regions, aligning with India's vision for balanced development by 2047.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"279 ","pages":"Article 106730"},"PeriodicalIF":1.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145979506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Geospatial time series forecasting of air quality across Delhi using ARIMA and LSTM models","authors":"Faizan Tahir Bahadur ,&nbsp;Shagoofta Rasool Shah ,&nbsp;Rama Rao Nidamanuri","doi":"10.1016/j.jastp.2026.106723","DOIUrl":"10.1016/j.jastp.2026.106723","url":null,"abstract":"<div><div>This study compared three forecasting approaches: Classical time-series, ARIMA variants, and Artificial Neural Network (ANN)-based Long Short-Term Memory (LSTM) models for PM2.5 prediction at two Delhi stations (Bawana and Okhla). All models produced strong forecasts and revealed consistently high predictive performance, with accuracy metrics and error values within acceptable ranges, R² values above 0.82 and RMSE between 15 and 30 μg/m³. Okhla generally outperformed Bawana, achieving the highest Classical model R² (0.957) and lowest RMSE (15.70 μg/m³). ARIMA delivered reliable results (Okhla: R² ≈ 0.913, RMSE ≈ 22.49 μg/m³), but performance declined at Bawana (R² ≈ 0.826, RMSE ≈ 29.18 μg/m³), highlighting its sensitivity to station-specific parameters and stationarity assumptions. LSTM architectures outperformed both classical and ARIMA models. ConvLSTM achieved the best overall accuracy (Okhla: R² = 0.891, RMSE = 20.10 μg/m³; Bawana: R² = 0.865, RMSE = 23.74 μg/m³), closely followed by Stacked LSTM (Okhla: R² = 0.889, RMSE = 20.78 μg/m³) and Bi-directional LSTM (Okhla: R² = 0.901, RMSE = 23.31 μg/m³). These models demonstrated stable convergence, minimal overfitting, and robustness against noise. While classical and ARIMA methods remain interpretable and computationally efficient, they struggle with complex nonlinear dependencies and require substantial reconfiguration for new sites. LSTM models, by contrast, adapt easily to other monitoring locations with minor hyperparameter tuning (e.g., learning rate, sequence length, layer depth), making them more scalable for city-wide deployment. In conclusion, although all approaches are viable for PM2.5 forecasting, LSTM-based models, particularly ConvLSTM and Stacked LSTM provide the highest accuracy, robustness, and adaptability, offering a reliable framework for operational air quality management in Delhi. From an applied perspective, the improved short-term predictability offered by LSTM-based models can support early-warning systems, targeted emission-control strategies, and data-driven decision-making for urban air-quality management.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"279 ","pages":"Article 106723"},"PeriodicalIF":1.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145940741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing meteorological drought indices for monitoring agricultural drought using SPEI: a remote sensing approach","authors":"Kanhu Charan Panda ,&nbsp;Pradosh Kumar Paramaguru ,&nbsp;Ram Mandir Singh ,&nbsp;Sudhir Kumar Singh","doi":"10.1016/j.jastp.2026.106726","DOIUrl":"10.1016/j.jastp.2026.106726","url":null,"abstract":"<div><div>Estimating agricultural drought is hindered by limited data availability. Conversely, assessing meteorological drought is very easy as it requires only meteorological data that are easy to obtain. Thus, the study aimed to find meteorological drought indices suitable for assessing agricultural drought in a tropical river basin using remote sensing satellite data. Seven meteorological drought indices, i.e., Standardised Precipitation Index (SPI), Deciles Index (DI), Percent of Normal Index (PNI), Rainfall Anomaly Index (RAI), China-Z Index (CZI), Modified China-Z Index (MCZI), and Z-score Index (ZSI) were compared with an efficient agricultural drought index, viz., Standardised Precipitation Evapotranspiration Index (SPEI) for the Subarnarekha River basin using the NASA POWER satellite data. It was noticed that the CZI had the highest correlation with the 3-month SPEI (R² = 0.88–0.94, followed by the RAI (R² = 0.85–0.90). The meteorological drought indices were divided into three groups based on the ability to replicate SPEI: ‘excellent’ for SPI, CZI, and MCZI; ‘good’ for ZSI and RAI; and ‘poor’ for PNI and DI. Relying solely on correlation coefficients to evaluate drought indices provided erroneous results. However, integrating correlation, RMSE, and statistical distribution enhanced the robustness of the assessment. This approach reclassified MCZI and SPI from “good” to “excellent”. SPI and CZI showed better alignment with SPEI distributions throughout both dry and wet periods, establishing them as the most reliable alternatives for evaluating agricultural drought. The findings emphasize the importance of comprehensive evaluation criteria that incorporate several statistical metrics for reliable drought assessment in tropical and data-scarce regions.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"279 ","pages":"Article 106726"},"PeriodicalIF":1.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145940740","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Forecasting seasonal surface ozone trends using radial basis function networks and ground level monitoring data","authors":"Sharanya Suraboyina ,&nbsp;Gangagni Rao Anupoju ,&nbsp;Anand Polumati","doi":"10.1016/j.jastp.2026.106725","DOIUrl":"10.1016/j.jastp.2026.106725","url":null,"abstract":"<div><div>Accurate forecasting of tropospheric surface ozone (O₃) concentrations is increasingly vital due to rapid urbanization and industrial growth, which have intensified photochemical smog episodes and associated health and environmental risks. Traditional deterministic models often struggle to capture the complex nonlinear relationships among meteorological and precursor variables influencing O₃ formation. Addressing this limitation, the present study introduces a Radial Basis Function Network (RBFN) model for seasonal forecasting of surface O₃ concentrations using real-time ground monitoring data (2014–2017) from an urban air-quality station in Hyderabad, India (TIFR–NBF: Tata Institute of Fundamental Research–National Balloon Facility). The novelty of this work lies in applying RBFNs to capture seasonal and diurnal O₃ variations with high precision, using input parameters including NO, NO₂, NO_x, CO, VOCs, and NMHCs. The RBFN model was trained with 80 % of the dataset and validated using 20 %, and its performance was assessed through Mean Square Error (MSE), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the Willmott's index(WI)Comparative analysis with the nonlinear regression-based Response Surface Methodology (RSM) revealed that the RBFN achieved superior predictive accuracy with lower RMSE (0.0038 vs. 0.0185), MSE (0.000014 vs. 0.000342), MAE (0.0021 vs. 0.0094), and (WI = 0.9999 vs. 0.9812), The model effectively captured seasonal and diurnal O₃ peaks, particularly during summer and pre-monsoon periods. Overall, this study demonstrates the robustness, adaptability, and real-time forecasting potential of RBFNs, underscoring their application in early-warning systems, air-quality management, and policy-driven pollution mitigation strategies for rapidly urbanizing regions.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"279 ","pages":"Article 106725"},"PeriodicalIF":1.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145979914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Degradation behavior of perovskite solar cells under high-intensity and multi band illumination conditions","authors":"Tongsen Yue","doi":"10.1016/j.jastp.2026.106738","DOIUrl":"10.1016/j.jastp.2026.106738","url":null,"abstract":"<div><div>The author investigated the degradation behavior and mechanism of FACs perovskite solar cells under high-intensity (1–7.35 suns) and multi band illumination (ultraviolet, blue, visible light) conditions. The results indicate that the degradation rate of the device is linearly related to the light intensity within the range of 1–4 solar intensities; When there are ≥5 suns, the nonlinear acceleration phenomenon is significant, mainly due to the increase of interface defects and intensified ion migration caused by the photothermal coupling effect. The multi band illumination experiment showed that ultraviolet and blue light had the greatest impact on device stability. After 500 h of ultraviolet light irradiation, PCE decreased by 42.3 %, while blue light decreased by 28.6 %. Optimized packaging (Opt-Enc-M) combined with water cooling system significantly improves stability, with PCE retention rate exceeding 80 % after 1000 h under 5 solar intensities. The research provides a theoretical basis for standardizing accelerated aging testing and improving device stability.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"279 ","pages":"Article 106738"},"PeriodicalIF":1.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146073907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}