Natural Hazards Research最新文献

{"title":"Performance evaluation and ranking of CMIP6 global climate models over upper blue nile (abbay) basin of Ethiopia","authors":"Jemal Ali Mohammed","doi":"10.1016/j.nhres.2024.06.004","DOIUrl":"10.1016/j.nhres.2024.06.004","url":null,"abstract":"<div><div>The use of Global Climate Models (GCMs) data is the most practical way to conduct studies on climate science. However, performance evaluation and the selection of appropriate GCMs are vital. In this research, the effectiveness of eight selected CMIP6 GCMs in simulating the annual and seasonal rainfall observed over the Ethiopian Upper Blue Nile Basin from 1988 to 2014 was assessed. Five performance metrics (PMs) were used in the study: the correlation coefficient, root mean square error, bias percentage, Kling-Gupta efficiency and Nash-Sutcliffe efficiency. The scores of the various PMs were then combined into one, and the CMIP6 GCMs were ranked using Compromised Programming (CP). The findings from the CP were verified using a spatial, Taylor Diagram (TD), and areal average annual and seasonal evaluations. Even though the PMs produced some contradicting results, the study exhibited that CP was capable to evaluate the CMIP6 GCMs consistently. A regional evaluation of the CMIP6 GCMs relative to the observed data revealed that the best-ranked CMIP6 GCMs by using CP were capable to more accurately replicate the observed annual and seasonal rainfall. The lowest-ranking CMIP6 GCMs were found to have either spatially overvalued or undervalued the amount of rainfall over the basin. The best three CMIP6 GCMs for annual rainfall, according to the results of the CP method, are BCC-CSM2-MR, MIROC6, and NorESM2-MM; for the <em>Kiremt</em> season, the best CMIP6 GCMs are BCC-CSM2-MR, GISS-E2-2-G, and EC-Earth3. INM-CM5-0, MIROC6, and MRI-ESM2-0 ranked highest for <em>Bega</em> season, and EC-Earth3, BCC-CSM2-MR, and MRI-ESM2-0 for <em>Belg</em> season. It is recommended using the above-ranked CMIP6 GCMs to predict the characteristics of rainfall in the UBNB. Furthermore, results suggest that the CMIP6 GCMs be evaluated with a range of PMs across the whole temporal scales and that techniques such as CP be used to identify the best-performing CMIP6 GCMs.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"5 1","pages":"Pages 61-74"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141391501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of potentially susceptible flooding areas leveraging geospatial technology with multicriteria decision analysis in Edo State, Nigeria","authors":"Kesyton Oyamenda Ozegin ,&nbsp;Stephen Olubusola Ilugbo","doi":"10.1016/j.nhres.2024.07.002","DOIUrl":"10.1016/j.nhres.2024.07.002","url":null,"abstract":"<div><div>Floods have claimed lives and devastated societal and ecological systems. Because of their catastrophic tendency and the financial and fatalities they cause, floods have become more and more significant on a global scale in recent years. In Edo State, Nigeria, flooding is a frequent threat that happens annually and seriously damages both lives and property. While the potential of flooding cannot entirely be eliminated, geospatial-based technologies can greatly lessen its effects. In Nigeria's flood-prone Edo State, the study's objectives are to identify inundated places and provide nuanced mapping of the flood risk. To facilitate the determination of the flood risk index (FRI), the study's fundamental flood-predictive features were determined by taking into consideration elevation, slope, distance from the river, rainfall intensity, land use/land cover, soil texture, topographic roughness index, topographic wetness index, normalized difference vegetation index (NDVI), runoff coefficient, aspect, drainage capacity, flow accumulation, the sediment transport index, and the stream power index. The significance of each predictive factor in the analytic hierarchy procedure (AHP) was determined by gathering expert views and perspectives from public entities. A flood threat map was created by processing the gathered data using the AHP and the ArcGIS 10.5 framework. The multicollinearity (MC) estimation was applied to assess the model's predictability. The results of the FRI showed that there were high and extremely severe flood risk zones that affected roughly 26 and 9% of the area, respectively. Flood risks have been identified as predominant in the Edo south region of the study area, which is characterized by low elevation and slope, high drainage capacity, distance from the river, topographic wetness, and index. It showed that the model's resultant vulnerability to flooding maps agreed with past flood occurrences that were previously experienced in the research area, demonstrating the technique's efficacy in locating and mapping locations plagued by flooding. Linear regression (R²) analysis was further conducted on the FRI to evaluate the scientific reliability of the utilized methodology; this shows 0.816 (81.6%) dependability. Consequently, frequent and long-lasting implementation of flooding predictions, warning systems, and mitigation strategies may be achieved.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"5 1","pages":"Pages 109-133"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Global impact of urbanization on ecosystems: A comprehensive bibliometric analysis","authors":"Himadri Soni,&nbsp;Rajiv Kant Yadav,&nbsp;Suresh Kumar Patra","doi":"10.1016/j.nhres.2024.04.001","DOIUrl":"10.1016/j.nhres.2024.04.001","url":null,"abstract":"<div><div>This paper demonstrates a comprehensive assessment of bibliometric analysis of published research on the global impact of urbanization on environmental consequences. Scopus database was used to collect data on the global impact of urbanization on ecosystem research from 2000 to 2023 and scrutinizes the progression of publications, keyword analysis, co-citation of authors, citation of documents, co-authorship of authors, and most efficient and influential authors, countries, and institutions. A total of 4322 research papers (journals) were published during the period (2000–2023). This bibliometric study was carried out with the Vos Viewer software. The study's findings demonstrate that the number of publications has continuously increased between 2000 and 2023. In 2020 and 2022, there were 396 and 635 publications, respectively. The top three influential journals in terms of citations are Remote Sensing of Environment (2415 citations), Ecological Economics (1456 citations), and PLoS (1410). From 2000 to 2023, the most often appearing terms were urbanization (3028), urban area (1111), people (853), land use (769), environmental monitoring (594), environmental effect (570), sustainable development (567), and climate change (534). We created a time series with four periods (2000–2005, 2006–2011, 2012–2017, and 2018–2023) based on the periodic changes, which also shows the trend. In more specific terms, this study demonstrates not just a logical structure but also the development of keywords throughout time. The empirical evidence also shows that while this field initially focused on a few themes, it has since broadened to include the many aspects of urban sustainability. This analysis also identifies the most efficient and influential authors, institutions, and nations in terms of total publishing (TP), proportion of cited publications (PCP), total citation (TC), h-index, and g-index. Yang K is the most prominent author, China and the United States are the most efficient nations, and the Chinese Academy of Science is the most powerful organization. The top two nations by co-authorship are China (40 ​695 citations and 1527 publications) and the United States (41 ​119 citations and 833 publications). Additionally, it is noted that while environmental concerns continue to dominate, basic socioeconomic problems like equality, justice, and public participation are underrepresented. Sustainable development indicators, energy, ecologically friendly infrastructure, water, the use of land, and urban planning are prominent subject topics, with the first three exerting a larger influence in shaping the field's growth. This research may be utilized as a resource for people intrigued in learning more about the evaluation of urban sustainability and its advancement.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"5 1","pages":"Pages 21-35"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140783468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Distribution laws of landslides and theirs influencing factors in the Qiaojia segment of Jinsha River, China","authors":"Liu Chaohai ,&nbsp;Renmao Yuan ,&nbsp;Wang Ying","doi":"10.1016/j.nhres.2024.06.002","DOIUrl":"10.1016/j.nhres.2024.06.002","url":null,"abstract":"<div><div>The landslide disaster database is a prerequisite for regional landslide disaster research, and summarizing and analyzing the distribution pattern and influencing factors of landslide disasters is of great significance for carrying out the susceptibility and hazard assessment. The study area is a typical southwest mountainous area, and geological disasters such as landslides are very serious. A total of 3573 landslides were identified after a combination of image interpretation and field investigation in an area of 8.4 ​km².This paper conducted a spatial analysis to reveal the distribution laws of landslides and analyzed the relationship between landslide and 13 influencing factors such as elevation, slope, slope aspect, topographic relief, soil, land use, lithology, annual average rainfall, ground peak ground acceleration (PGA). It can be concluded that the landslide showed the characteristics of non-uniformity and zonal distribution. A statistics analysis indicates that landslides are significantly correlated with elevation, slope gradient, slope direction, distance from faults, lithology, rivers, highways and so on. Therefore, when constructing engineering in alpine and canyon areas, it is essential to avoid the areas with steep slopes, large height difference, active faults, and the area being distributed by soft or broken hard rock masses to reduce disaster risks.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"5 1","pages":"Pages 48-60"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141394888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing vulnerability of fishermen communities in coastal Bangladesh: A “climate vulnerability index”- based study in Assasuni Upazila, Satkhira, Bangladesh","authors":"Imtiaz Ahmed ,&nbsp;Md. Arif Chowdhury ,&nbsp;Rashed Uz Zzaman ,&nbsp;Syed Labib Ul Islam ,&nbsp;Shamsun Nahar ,&nbsp;Sujit Kumar Roy","doi":"10.1016/j.nhres.2023.12.018","DOIUrl":"10.1016/j.nhres.2023.12.018","url":null,"abstract":"<div><div>Climate Vulnerability Index (CVI) is developed to measure the susceptibility of communities to climate change using a case study. The index includes factors for each of the three aspects of vulnerability, including ‘Exposure’, ‘Sensitivity’, and ‘Adaptive Capability’. Sensitivity is determined by “Health”, “Food”, and “Water”, Adaptive Capability is characterized by “Socio-demographic profile,” “Livelihood strategies,” and “Social networks”, and Exposure is identified by “Natural Disaster” and “Climate Variability”. A study was conducted to investigate the vulnerability of fishermen in Assasuni Upazila, Satkhira, Bangladesh. The study involved individual surveys of randomly identified 100 fishermen from three groups: Gher-based, Ocean-based, and River-based. The findings indicate that the Gher-based fishing community exhibits higher levels of adaptive capacity (0.39), sensitivity (0.57), and exposure (0.74) in comparison to the other two communities. The sub-indicator about the migration of individuals for Gher-based livelihoods exhibits a relatively higher value of 0.85, in contrast to the relatively lower values of 0.23 and 0.11 for river and ocean-based livelihoods, respectively. The utilization of index-based output observations may aid policymakers from national to local levels in identifying and implementing the appropriate adaptation practices that prioritize the welfare of fishing communities residing in the coastal regions of Bangladesh.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"4 4","pages":"Pages 562-572"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139127203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sustainable flood hazard mapping with GLOF: A Google Earth Engine approach","authors":"Subhra Halder,&nbsp;Suddhasil Bose","doi":"10.1016/j.nhres.2024.01.002","DOIUrl":"10.1016/j.nhres.2024.01.002","url":null,"abstract":"<div><div>This study aims to evaluate the efficacy of Google Earth Engine (GEE) in mapping floods and their aftermath, focusing on the recent event caused by cloud burst rainfall and glacial lake outburst flood (GLOF) of Lhonak glacier lake in the Teesta River basin, North Sikkim. The objective is to utilize GEE, coupled with Sentinel-1 Synthetic Aperture Radar (SAR) data and Landsat 9 imagery, for precise remote sensing analysis, flood mapping, and Land Use and Land Cover (LULC) classification. The study employs a comprehensive methodology within the GEE platform, involving the acquisition and preprocessing of Sentinel-1 SAR data to create pre- and post-flood images. The difference between these images is calculated to generate flood maps at five-day intervals, providing a temporal evolution of the flood extent. Additionally, LULC mapping is conducted using Landsat 9 data, contributing to an understanding of pre-flood landscape characteristics. The results and discussion reveal significant impacts on various LULC types, with barren rocks, dense and medium forests, settlements, and agricultural lands experiencing notable effects. This research not only enhances our understanding of GLOFs but also serves as a critical tool for informing disaster management strategies, emphasizing the importance of accurate hazard assessment and the need for holistic approaches to mitigate the cascading effects of such events.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"4 4","pages":"Pages 573-578"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139458220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Irshalwadi landslide in Western Ghats of India: Observations from precursory slope movement, rainfall and soil moisture","authors":"Nirmala Jain,&nbsp;Priyom Roy,&nbsp;Punit Jalan,&nbsp;Tapas R. Martha,&nbsp;Iswar C. Das","doi":"10.1016/j.nhres.2024.01.004","DOIUrl":"10.1016/j.nhres.2024.01.004","url":null,"abstract":"<div><div>The Western Ghats of India, a UNESCO world heritage site, is gradually becoming a hotspot for catastrophic landslides. On July 19, 2023, the entire Irshalwadi village in the Raigad district of Maharashtra, India, was obliterated due to a single catastrophic landslide. Of the 228 residents of the village, the landslide killed 27 people and 57 are still reported to be missing. The landslide occurred on a slope which exhibited no visual precedence of disturbance or creep. However, analysis of potential precursory movements prior to slope failure using the Persistent Scatterer Interferometry (PSI) technique reveals active movement near the base of the slope (in order of ∼12 ​mm/y). Sentinel-2 satellite imagery acquired post-event characterises the landslide as a bifurcated debris flow possibly triggered by heavy rainfall in the region. Peak cumulative rainfall, estimated by the Indian Meteorological Department (IMD), was observed in this region on 17, 18 and July 19, 2023 (∼500) mm and was the highest recorded rainfall in the region during the given period. This caused significant water percolation into the porous basaltic soil, leading to increased soil moisture, as supported by the Soil Moisture Active Passive (SMAP) data. The resultant increase in pore pressure caused the slope material to fail and eventually trigger the landslide.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"4 4","pages":"Pages 579-583"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139391677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unravelling Bangalore's air quality during the second wave: Multifaceted analysis of COVID-19 lockdown impact","authors":"Iranna Gogeri ,&nbsp;Krushna Chandra Gouda ,&nbsp;Aruna Singanahalli ThippaReddy","doi":"10.1016/j.nhres.2024.01.008","DOIUrl":"10.1016/j.nhres.2024.01.008","url":null,"abstract":"<div><div>This study assesses the impact of the second wave COVID-19 lockdown (March–June 2021) on air pollution levels in Bangalore, India, using real-time data from eight CPCB monitoring stations across the city. This research endeavours to dissect the multifaceted impact of the 2021 ​s wave COVID-19 lockdown on Bangalore's air quality. Leveraging real-time data sourced from eight CPCB monitoring stations across the city, the study meticulously delves into diverse analyses to grasp the nuanced implications of the lockdown measures. It scrutinizes changes in six major pollutants: PM_2.5, PM₁₀, CO, NO₂, SO₂, and O₃ during the 2021 lockdown compared to the same period in 2020. The comprehensive studies of different temporal scales were examined such as daily, weekly, and seasonal. The daily and weekly averages were computed to assess the percentage change in air pollutant concentrations during the March to June 2021 lockdown and pre-lockdown phases. The seasonal averages were derived to capture trends in seasonal variations. The examination of air pollution disparities across the city's eight monitoring stations (ST1 to ST8), unravelling localized variations and comprehending the spatial intricacies influencing air quality levels within Bangalore. The comparison of air pollution concentrations between the 2021 and 2020 lockdown periods, offering insights into relative changes in pollutant levels. The concentrations during the 2021 lockdown period (April 27 to June 14) significantly dropped compared to the pre-lockdown phase (March 9 to April 26) by considerable percentages across various pollutants: PM_2.5 (45 %), PM₁₀ (49 %), O₃ (37 %), NO₂ (41 %), SO₂ (5 %), and CO (30 %). The comparative evaluation between air pollutant intensity changes during the common lockdown periods of the first wave (2020) and the second wave (2021) of COVID-19. This contiguity illuminates the shifts in pollution levels and underscores the differing dynamics of air quality between these distinct phases. This research provides comprehensive insights into air quality over Bangalore city during the second wave lockdown. The multifaceted analyses offer a detailed understanding of temporal, spatial, and comparative facets of air pollution dynamics, shedding light on the significant impact of COVID-19 restrictions on urban air quality.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"4 4","pages":"Pages 584-595"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140519895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The role of large language models (AI chatbots) in fire engineering: An examination of technical questions against domain knowledge","authors":"Haley Hostetter ,&nbsp;M.Z. Naser ,&nbsp;Xinyan Huang ,&nbsp;John Gales","doi":"10.1016/j.nhres.2024.06.003","DOIUrl":"10.1016/j.nhres.2024.06.003","url":null,"abstract":"<div><div>This communication presents a short review of chatbot technology and preliminary findings from comparing two recent chatbots, OpenAI’s <em>ChatGPT</em> and Google’s <em>Bard,</em> in the context of fire engineering by evaluating their responses in handling fire safety-related queries. A diverse range of fire engineering questions and scenarios were created and examined, including structural fire design, fire prevention strategies, evacuation, building code compliance, and fire suppression systems. The results reveal some key differences in the performance of the chatbots, with <em>ChatGPT</em> demonstrating a relatively superior performance of 88% (vs. 80% for <em>Bard</em>). Then, this communication highlights the potential for chatbot technology to revolutionize fire engineering practices by providing instant access to critical information while outlining areas for further improvement and research. Evidently, and when it matures, this technology will likely be elemental to our engineers’ practice and education.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"4 4","pages":"Pages 669-688"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143177216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Disaster, risk and crises in tourism and hospitality field: A pathway toward tourism and hospitality management framework for resilience and recovery process","authors":"Li-Wei Liu ,&nbsp;Pahrudin Pahrudin ,&nbsp;Cheng-Yu Tsai ,&nbsp;Lee Hao","doi":"10.1016/j.nhres.2024.06.001","DOIUrl":"10.1016/j.nhres.2024.06.001","url":null,"abstract":"<div><div>This study aims to review the documents in disasters, risk and crises to produce a generic model for developing a holistic framework in the tourism and hospitality industry. This study uses bibliometric analysis and a systematic narrative review of 795 documents from the Web of Science and Scopus databases to determine the number of publications, top authors, top cited documents, top journals, regional distribution, mapping topic trends, the structure of disasters, risk and crises, and the type of disasters, risk and crises. The results find some issues in the disaster and crises such as crises management, resilience, and recovery from disasters, risk, and crises in the tourism and hospitality industry. This study presents a comprehensive framework for tourism and hospitality resilience and recovery process after a disasters and crises.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"4 4","pages":"Pages 653-668"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143177217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}