Natural Hazards Research最新文献

{"title":"Artificial intelligence and numerical weather prediction models: A technical survey","authors":"Muhammad Waqas ,&nbsp;Usa Wannasingha Humphries ,&nbsp;Bunthid Chueasa ,&nbsp;Angkool Wangwongchai","doi":"10.1016/j.nhres.2024.11.004","DOIUrl":"10.1016/j.nhres.2024.11.004","url":null,"abstract":"<div><div>Can artificial intelligence (AI) models beat traditional numerical weather prediction (NWP) models based on physical principles? The rapid advancement of AI, inherent computational limitations of NWP models, and the lack of access to big data drive this question in terms of resolution and complexity. This survey offers a systematic review of studies that integrate AI with NWP models at various stages of weather and climate modeling. It aims to address key research questions, including the types of forecasting models, the integration of AI into NWP systems, and the comparative efficacy of AI-based approaches versus conventional NWP models. It covered peer-reviewed literature from 2000 to 2024. This technical survey highlights key advancements in the application of AI within NWP modeling in data assimilation, augmentation, pre-processing, adaptive parameter tuning, optimization, uncertainty quantification, extreme event prediction, post-processing, and the interpretation of NWP outputs. While AI demonstrates significant potential in post-processing NWP outputs, pre-processing remains challenging. This survey also presents state-of-the-art AI-based hybrid models and assesses their applicability to weather data. It highlights the promise of AI in potentially replacing traditional NWP models but emphasizes the need for further advancements in model development and application. The study also offers a detailed classification of forecasting models and outlines promising directions for future research.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"5 2","pages":"Pages 306-320"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144221913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning models for enhanced forest-fire prediction at Mount Kilimanjaro, Tanzania: Integrating satellite images, weather data and human activities data","authors":"Cesilia Mambile,&nbsp;Shubi Kaijage,&nbsp;Judith Leo","doi":"10.1016/j.nhres.2024.12.001","DOIUrl":"10.1016/j.nhres.2024.12.001","url":null,"abstract":"<div><div>Forest fires (FFs) are a growing threat to ecosystems and human settlements, particularly in vulnerable regions such as Mount Kilimanjaro, Tanzania. Accurate and timely fire prediction is essential to mitigate these risks and improve fire management strategies. This study develops and evaluates advanced Deep Learning (DL) models for FF prediction by integrating spatiotemporal vegetation indices, environmental data, and human activity indicators. Specifically, Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNNs), and Convolutional Long Short-Term Memory (ConvLSTM) models were employed to analyze Sentinel-2 satellite imagery and weather data, along with anthropogenic factors such as beekeeping, tourism, agriculture, and deforestation rates. Leveraging this diverse, high-dimensional dataset, the ConvLSTM model engineered to capture intricate spatial and temporal relationships delivered superior performance, achieving an AUROC of 0.9785 and Accuracy 98.08%, surpassing the LSTM and CNN models. Integrating human-induced activities with environmental data, these models provide accurate and actionable predictions for fire management in high-risk areas. This study demonstrates the potential of ConvLSTM in developing operational tools for early fire detection, streamlining data-driven decision-making, improving resource allocation, and guiding preventive strategies in fire-prone regions such as Mount Kilimanjaro.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"5 2","pages":"Pages 335-347"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144221914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Surface creep differences and affecting factor along the Xianshuihe fault zone in response to the Wenchuan and Lushan earthquakes","authors":"Guanzhong Liu ,&nbsp;Aichun Liu ,&nbsp;Qin Su ,&nbsp;Lianglong Fan ,&nbsp;Ao Song ,&nbsp;Aipeng Guo ,&nbsp;Yanxing Li","doi":"10.1016/j.nhres.2024.12.008","DOIUrl":"10.1016/j.nhres.2024.12.008","url":null,"abstract":"<div><div>As the southwest and southeast boundaries of the Bayan Har block, the Xianshuihe Fault Zone (XSHFZ) and the Longmenshan Fault Zone (LMSFZ) are likely to be intrinsically related in terms of tectonic activity. Consequently, it is pertinent to consider the potential response of fault creep in the XSHFZ when the LMSFZ encounters different major earthquake disturbances. This paper presents a comparative and analytical study of the fault creep characteristics of the XSHFZ, employing cross-fault short-baseline survey data collected prior to and following the Wenchuan M8.0 and Lushan M7.0 earthquakes. The findings indicate that despite the Wenchuan earthquake having a significantly higher magnitude than the Lushan earthquake and the theoretical static stress change being considerably larger than that of the Lushan earthquake, the absolute velocity and incremental change of fault creep in the XSHFZ before and after the Wenchuan earthquake are less pronounced than those observed before and after the Lushan earthquake. Following the Wenchuan earthquake, the creep rate and nature of the XSHFZ remained largely unchanged, with a slight decrease in the average slip rate. However, in the aftermath of the Lushan earthquake, the XSHFZ exhibited clear signs of \"activation,\" with a notable increase in the average creep rate and the emergence of strong tensional motion. It is hypothesized that the Maerkang, Miyaluo, Minshan, and Huya Fault Zones within the Longmenshan sub-block contributed to the increased complexity of the strong earthquake process, potentially acting as a 'regulating valve' in influencing the redistribution of post-seismic stress and the creep pattern of adjacent faults. Consequently, their roles in regional tectonic activity cannot be overlooked.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"5 2","pages":"Pages 391-398"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144221907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Flood hazard zones prediction using machine-learning-based geospatial approach in lower Niger River basin, Nigeria","authors":"Adedoyin Benson Adeyemi,&nbsp;Akinola Adesuji Komolafe","doi":"10.1016/j.nhres.2025.01.002","DOIUrl":"10.1016/j.nhres.2025.01.002","url":null,"abstract":"<div><div>Flooding has had devastating impacts on lives and properties over the years, caused as a result of climate change, rapid population growth, urbanization, and poor urban planning. The recurring events of this hazard necessitate the development of accurate flood hazard maps to better inform disaster preparedness and mitigation strategies. Therefore, this study aims to integrate Machine Learning Models (MLM) with Geographic Information Systems (GIS) techniques to predict flood hazard zones in the lower Niger River basin in Nigeria. The Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Artificial Neural Networks (ANN) machine learning models were employed to assess flood-prone areas based on twenty (20) influencing factors, categorized into topographic, hydrologic, environmental/anthropogenic, and climatic factors. Based on historical flood events from different sources for the period of 1998–2023 within the study area, data from 1164 flooded and non-flooded points were utilized to train and test the models. Following the evaluation by statistical metrics such as precision, recall, f1-score, overall accuracy, and Receiver Operating Characteristics Area Under the Curve (ROC-AUC), XGBoost was found to have the best performance with an overall accuracy of 91% and ROC-AUC score of 0.89 compared to SVM and ANN with overall accuracy 88% and 85% respectively, and ROC-AUC scores 0.82 and 0.86 respectively. The flood hazard maps showed that areas near the river, particularly in the central and southern part of the basin, including the river confluence areas, are most prone to flooding which is likely to affect critical elements such as croplands, settlements, population centers, and infrastructures. This study provides a foundation to prioritize efforts and resources toward mitigating flood impacts in highly vulnerable areas.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"5 2","pages":"Pages 399-412"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144221908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Seismic capacity assessment of Shikhara style temple with and without traditional seismic enhancement","authors":"Suraj Malla ,&nbsp;Mukil Alagirisamy ,&nbsp;Purushotam Dangol","doi":"10.1016/j.nhres.2024.11.003","DOIUrl":"10.1016/j.nhres.2024.11.003","url":null,"abstract":"<div><div>Brick masonry heritage structures have long been understood as highly vulnerable under earthquake excitation. Due to preservation constraints and considerations, modern materials are not allowed in seismic strengthening or post-earthquake reconstruction of heritage structures. Thus, traditional strengthening techniques are mandatory in heritage structures. The present paper reports the outcome of reconstruction initiative in a Shikhara style temple located in a world heritage site using timber element based seismic enhancement. Using numerical modeling, capacity assessment with and without timber elements is performed. For capacity assessment pushover analysis is performed for both as built and reconstructed models. The results show considerable increase in seismic capacity even with traditional improvement techniques, i.e. vertical timber elements. 18.05% increase in seismic capacity is found when vertical timber elements are used. Also, about 8% increase in the fundamental vibration frequency is observed after strengthening. The paper reports the details of geometrical features, reconstruction aspects, and numerical analysis results comprising modal analysis and capacity assessment.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"5 2","pages":"Pages 299-305"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144221912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advances in earthquake and cascading disasters","authors":"Xiangli He ,&nbsp;Zhaoning Chen ,&nbsp;Qing Yang ,&nbsp;Chong Xu","doi":"10.1016/j.nhres.2025.01.010","DOIUrl":"10.1016/j.nhres.2025.01.010","url":null,"abstract":"<div><div>Earthquakes and cascading disasters have garnered increasing attention. The 2023 Annual Academic Conference of the Committee on Earthquake Hazard Chain, the Seismological Society of China, was successfully held in Beijing. The conference presentations and proceedings covered a wide range of topics beyond seismic disasters, including landslides, ground fissures, avalanches, freeze-thaw processes, land subsidence, dam-break floods, and multiple chain disasters, such as earthquake-landslide, rainfall-landslide-barrier lakes, earthquake-fires, and earthquake-tsunamis. The research directions addressed various aspects, such as disaster database development, formation mechanisms, spatial distribution, identification techniques, prediction methods, monitoring and early warning systems, hazard assessment, mitigation strategies, and post-disaster recovery and reconstruction. In this paper, we systematically categorize and review the conference presentations, focusing on the research status and recent advancements in three major areas: seismic disasters, earthquake-induced cascading disasters, and non-seismically triggered landslides. It aims to provide scientific references for theoretical studies and technological applications in disaster prevention, mitigation, and emergency response. In the future, we should integrate cutting-edge multidisciplinary technologies and theories, leverage data-driven approaches to build a comprehensive database of earthquake disaster chains, enhance precursor monitoring to improve accuracy, advance mechanism research, and develop robust risk assessment systems.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"5 2","pages":"Pages 421-431"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144221341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Shirur landslide of July 2024 triggered by intense rainfall and unchecked development","authors":"Priyajit Kundu ,&nbsp;Varun Menon ,&nbsp;Sreevalsa Kolathayar ,&nbsp;Pruthviraj U","doi":"10.1016/j.nhres.2025.01.005","DOIUrl":"10.1016/j.nhres.2025.01.005","url":null,"abstract":"<div><div>On the morning of July 16, 2024, a significant landslide occurred in Shirur of Uttara Kannada district, Karnataka, India. The landslide claimed seven lives, leaving one person missing and severely disrupting the transport network by blocking National Highway 66. The displaced debris travelled 180m across the highway and into the Gangavali River, causing a significant splash and damaging structures on the opposite bank. The event, characterised by a rotational slip, was triggered by a combination of anthropogenic activities and intense rainfall. The construction of National Highway 66, which involved the removal of the slope's toe without adequate protection for the excavation, significantly destabilised the area. On 15th July, the rain gauge in Ankola recorded rainfall of 260 ​mm. The accumulated rainfall calculated for Shirur using Inverse Distance Weightage (IDW) for the storm period of 4 days was 198 ​mm, which increased the pore water pressure within the soil, weakening its shear strength and leading to slope failure. This incident underscores the need for further analysis and the implementation of appropriate mitigation measures, as the region remains at risk for future landslides. The Shirur landslide serves as a critical reminder of the dynamic nature of such disasters, particularly when human activities exacerbate natural hazards.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"5 2","pages":"Pages 413-420"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144221909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Soil liquefaction potential assessment of ports in Odisha, India","authors":"Satyaprakash Mishra,&nbsp;Arjun Sil,&nbsp;Amit Kumar Das","doi":"10.1016/j.nhres.2024.11.001","DOIUrl":"10.1016/j.nhres.2024.11.001","url":null,"abstract":"<div><div>This study presents a comprehensive assessment of the liquefaction potential at three major port sites in Odisha, India, i.e., Dhamara, Paradeep, and Gopalpur, utilizing well-established methodologies. The evaluation was conducted under the framework of seismic hazard analysis, incorporating site-specific geotechnical data from Standard Penetration Test (SPT) boreholes and considering the maximum estimated earthquake scenarios. The seismic hazard analysis identified varying levels of Peak Ground Acceleration (PGA) across the port sites, with corresponding magnitudes of 7.4 and 6.5 for Dhamara, Paradeep, and Gopalpur, respectively. Liquefaction susceptibility was quantified using the Liquefaction Potential Index (LPI), revealing significant variability across the sites. The Gopalpur port site exhibited a very low to low liquefaction severity, indicating minimal risk under the assessed seismic conditions. In contrast, the Dhamara and Paradeep ports demonstrated a higher susceptibility, with two out of four borehole locations at each site showing high liquefaction severity. This elevated risk is primarily attributed to the presence of saturated silty fine sands in the upper soil layers, which are particularly prone to liquefaction. The findings underscore the critical need for targeted mitigation measures, including ground improvement techniques, at the Dhamara and Paradeep sites to enhance infrastructure stability and safety. The study provides valuable insights into the seismic risks associated with these coastal regions and offers a robust framework for future seismic risk assessments. The outcomes of this research are instrumental in informing the design and implementation of resilient infrastructure in Odisha, thereby contributing to the region's overall seismic safety and disaster preparedness.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"5 2","pages":"Pages 287-298"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144221911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Drought monitoring using the Climate Hazards InfraRed Precipitation with Stations (CHIRPS) in Ethiopia","authors":"Birhan Getachew Tikuye ,&nbsp;Ram L. Ray ,&nbsp;Busnur Manjunatha ,&nbsp;Gebrekidan Worku Tefera ,&nbsp;Sanjita Gurau","doi":"10.1016/j.nhres.2024.12.002","DOIUrl":"10.1016/j.nhres.2024.12.002","url":null,"abstract":"<div><div>Ethiopia has experienced numerous natural disasters, with 110 documented events that include floods, disease outbreaks, droughts, landslides, pest infestations, volcanic eruptions, earthquakes, mass movements, and wildfires over the past 58 years. Among these, drought occurrences are characterized by various factors such as duration, inter-arrival time, peak intensity, frequency, and severity. This study aims to monitor drought patterns in Ethiopia using the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS-v2) satellite rainfall product. A 3 and 12-month timescale Standardized Precipitation Index (SPI) based on a gamma distribution was calculated to evaluate inter-arrival time, peak intensity, frequency, severity, and trends from 1981 to 2021. The performance of CHIRPS data was evaluated compared to gauging stations using the coefficient of determination (R²), root means square error (RMSE), and mean absolute error (MAE) and showed a good agreement was reached. Results reveal an increasing trend in drought across all seasons, including winter, spring, summer, and autumn. However, statistical analysis via the Mann-Kendall trend test indicates that these upward trends are not statistically significant, with computed p-values (0.335, 0.419, 0.384, and 0.207) exceeding the significance level of α ​= ​0.05. Temporal variations in drought indices reveal that certain years, such as 1984, 2010, and 2016, were among the driest periods in Ethiopia, both in terms of annual and seasonal drought severity. In contrast, 1998, 2007, and 2020 were identified as the wettest years and seasons in the country. The SPI-12 monthly index drought characterization shows an average of 4 drought events per year, with a maximum drought duration of 45 months, a maximum magnitude of 80, a frequency of 18%, and a severity of 2.6. The findings highlight the significance of advanced satellite data in accurately characterizing drought conditions, vital for strengthening the country's capacity to adapt to climate variability. By incorporating these insights into national planning and resource management frameworks, Ethiopia can enhance its ability to protect ecosystems, ensure food security, and maintain overall socio-economic stability in the face of the growing threat of drought.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"5 2","pages":"Pages 348-362"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144221916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Climate change-induced shifts in landslide susceptibility in São Sebastião (southeastern Brazil)","authors":"Enner Alcântara ,&nbsp;Cheila Flávia Baião ,&nbsp;Yasmim Carvalho Guimarães ,&nbsp;José Antonio Marengo ,&nbsp;José Roberto Mantovani","doi":"10.1016/j.nhres.2024.11.005","DOIUrl":"10.1016/j.nhres.2024.11.005","url":null,"abstract":"<div><div>Landslides are a pressing natural hazard, particularly in regions prone to extreme weather events, and their frequency is expected to rise due to climate change. This paper investigates landslide susceptibility in São Sebastião, a coastal region in southeastern Brazil, under various climate change scenarios. The study fills a critical gap in understanding how future precipitation changes driven by climate models could affect the area's susceptibility to landslides. Current assessments often overlook the combined effects of environmental variables and land-use dynamics under future climate conditions. To bridge this gap, this research integrates environmental variables, including Soil Moisture Index (SMI), slope degree, saturation, relief dissection, geomorphology, geology, and topographic position index (TPI), with land use and land cover (LULC) data. Scenarios from the Intergovernmental Panel on Climate Change (IPCC) for RCP2.6, RCP4.5, RCP6.0, and RCP8.5 CMIP5 (Climate Models Intercomparison Programme Version 5) models were applied to model the impact of changing precipitation patterns on landslide susceptibility. Using geospatial data and a weighted sum model, susceptibility maps were developed for each climate scenario and validated with a landslide inventory and receiver operating characteristic (ROC) analysis. The findings indicate a notable shift in landslide risk, with scenarios RCP6.0 and RCP8.5 showing significant increases in moderately susceptible areas due to higher precipitation intensities. Frequency Ratio (FR) analysis revealed varying levels of landslide susceptibility across scenarios, with RCP2.6 showing lower probabilities for moderate landslides (FR: 0.007946) compared to higher ratings for RCP4.5, RCP6.0, and RCP8.5 (FR: 1.663156 for high landslides). Slope and TPI emerged as the most influential variables, while land-use types, particularly urban areas and deforestation zones, showed heightened vulnerability in future scenarios.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"5 2","pages":"Pages 321-334"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144221915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}