Applied Geomatics最新文献

{"title":"Deep learning and geospatial approaches for post-flood disaster damage assessment: a systematic review","authors":"Leshabana M. Mabina,&nbsp;Frederick M. Mashao,&nbsp;Fhumulani I. Mathivha,&nbsp;Mukovhe V. Singo,&nbsp;Hector Chikoore","doi":"10.1007/s12518-026-00745-8","DOIUrl":"10.1007/s12518-026-00745-8","url":null,"abstract":"<div><p>Extreme precipitation and flooding have demonstrated nature’s overwhelming force against human resilience, triggering geohazards such as landslides. These geohazards frequently cause infrastructure damage, resulting in power outages and loss of communications. This situation highlights the critical need for geospatial technologies to bridge data gaps in assessement of disaster-related damage. However, current literature typically examines landslides, infrastructure damage, and power outages as separate incidents rather than as cascading failures. To address this research gap, our study aimed to synthesize the existing body of knowledge on multi-hazard interactions using a systematic review approach. Adhering to PRISMA guidelines, the study analyzed 162 articles from 2010 to June 2025, sourced from Google Scholar, Springer, and Web of Science. Our findings indicate a growing trend toward the application of machine learning and deep learning in geospatial approaches for damage assessment. Notably, significant scientific contributions in this field predominantly originate from Asia and North America, revealing a geographic gap in research from Africa. In addition, the study discusses the challenges associated with interferometry methods for landslide analysis, classification techniques for building damage assessment, and the use of nighttime light data concerning damage-related power outages. Our findings reveal a significant gap in the absence of an integrated multi-hazard assessment. To bridge this gap, we propose a novel framework for the comprehensive evaluation of the interconnectedness between extreme precipitation, landslides, and infrastructure-induced power outages. This framework provides a roadmap for more resilient disaster management strategies.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 2","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12518-026-00745-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148173633","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":"Crack detection in churches using deep learning","authors":"Matteo Bozzano,&nbsp;Mohamedelmustafa Omer Eid,&nbsp;Serena Cattari,&nbsp;Bianca Federici","doi":"10.1007/s12518-026-00743-w","DOIUrl":"10.1007/s12518-026-00743-w","url":null,"abstract":"<div><p>Post-earthquake damage assessment of churches is a critical task, due to the architectural complexity and the safety risks associated with on-site inspections, which expose surveyors to potentially dangerous structurally compromised areas. Among visible indicators, cracks represent one of the most relevant features for evaluating structural damage. As part of the wider project RAISE (NRRP-ECS00000035), this study proposes an image-based deep learning approach for automatic crack detection in church buildings, with the aim of supporting traditional survey procedures and reducing both inspection time and operator risk. A convolutional neural network based on transfer learning is developed and evaluated using the Xception architecture. The model is first trained on a large benchmark dataset already available of concrete crack images. Image patches are extracted and classified in a binary framework (crack vs. non-crack), and model performance is assessed using standard metrics such as Precision, Recall, and F1-score. While initial training on concrete images yields satisfactory results (98% accuracy), performance drops significantly when applied to church images, highlighting a strong domain shift. Retraining with church-specific data, a custom dataset collected from churches in the Liguria region, including surfaces characterized by frescoes, plaster, and decorative elements, substantially improves performance. It achieves a Precision of 93.87%, a Recall of 88.45%, and an F1-score of 91.08% on frescoed surfaces of the vaults, a fundamental element in the damage survey phase. Additional tests investigate the influence of lighting conditions, image resolution, and capture distance, providing practical guidance for acquisition protocol design. The results demonstrate the potential of deep learning techniques for supporting crack detection in heritage contexts, while also highlighting current limitations related to data variability and patch-level classification. Future developments will focus on dataset expansion, pixel-level segmentation approaches, and integration with standardized damage assessment frameworks.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 2","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12518-026-00743-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148009820","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":"A mobile GIS framework for in-situ measurement of coastal defence conditions: development and application in Kerala, India","authors":"Sai Ganesh Veeravalli,&nbsp;Mridula G. Murali,&nbsp;Satya Kiran Raju Alluri,&nbsp;V. Ramanathan,&nbsp;M V Ramana Murthy","doi":"10.1007/s12518-026-00744-9","DOIUrl":"10.1007/s12518-026-00744-9","url":null,"abstract":"<div><p>Coastal protection structures such as seawalls and groins play a critical role in mitigating coastal erosion and flood risks, yet large-scale, field-based assessments of their condition remain limited due to methodological, logistical, and cost-related constraints. This study presents a mobile GIS framework for the in-situ measurement and classification of coastal defence infrastructure using a web-application developed on Kobo Toolbox, an open-source platform configured to support standardized, georeferenced, and multimedia-enabled data collection in remote or resource-constrained environments. The framework was piloted along Kerala’s 593-kilometre coastline in India, a geomorphologically diverse region characterised by seasonal erosion and high exposure to climate-related hazards. Field deployment during the monsoon season resulted in 1,420 structured survey records of coastal features, supported by over 3,500 handheld GPS observations and approximately 20 gigabytes of photographic documentation collected by trained survey teams. Structures were classified as intact, partially damaged, or totally disintegrated, with condition assessments linked to functional performance metrics such as beach width and sediment deposition patterns. Collected data were centrally managed, validated through expert review, and integrated into GIS environments for spatial analysis and reporting. A qualitative comparison with traditional field surveys, UAV photogrammetry, satellite remote sensing, and citizen-science-based monitoring highlights the framework’s strengths in data consistency, scalability, operation efficiency, and suitability for routine deployment. Designed for use by non-specialist field teams, the proposed framework provides a replicable and cost-effective solution for structured coastal infrastructure monitoring and supports institutional capacity building for long-term shoreline management.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 2","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148009617","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":"Low cost GNSS instruments for monitoring applications using Galileo","authors":"Matteo Cappuccio,&nbsp;Giorgio Del Ciondolo,&nbsp;Luca Tavasci,&nbsp;Luca Poluzzi,&nbsp;Stefano Gandolfi","doi":"10.1007/s12518-026-00740-z","DOIUrl":"10.1007/s12518-026-00740-z","url":null,"abstract":"<div><p>Low-cost multi-frequency GNSS receivers have recently become a viable alternative to geodetic-grade instrumentation for deformation and displacement monitoring applications. Among current GNSS constellations, Galileo offers specific advantages for such applications, including signal authentication through the Open Service Navigation Message Authentication (OSNMA), which enables navigation message authentication and increases the reliability of GNSS-based monitoring system. This study investigates the capability of low-cost GNSS instrumentation to detect millimetric displacements by using only Galileo observations. A dedicated experimental test network composed of five sites was established, where low-cost and geodetic-grade receivers operated simultaneously under identical conditions. At the central monitoring site, controlled three-dimensional displacements were imposed using a calibrated triaxial translation sledge. Three low-cost antennas with similar electronic electrical characteristics but different costs were tested in combination with a dual-frequency u-blox receiver. The collected datasets were processed on a daily basis using both Precise Point Positioning (PPP) and relative positioning approach, relying exclusively on Galileo E1 and E5a signals. The results demonstrate that low-cost GNSS systems can achieve coordinate repeatability comparable to geodetic-grade solutions, particularly for horizontal components. Millimetric horizontal displacements (≥ 2.5 mm) are reliably detected in PPP, while relative positioning enables detection down to 1 mm for short baselines (100 m). Vertical displacement sensitivity is more dependent on antenna quality, highlighting the critical role of antenna design. Overall, the results confirm that low-cost Galileo-based GNSS systems represent a reliable and cost-effective solution for long-term displacement monitoring applications, provided that appropriate antenna choice and processing strategies are adopted.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 2","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12518-026-00740-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148009487","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":"Application of UAV photogrammetry for the mapping of lake terraces on vegetated terrain in the Central European mixed forests zone, Ostrowskie Lake, North-Western Poland","authors":"Robert Kostecki,&nbsp;Mirosław Makohonienko,&nbsp;Julian Chmiel","doi":"10.1007/s12518-026-00741-y","DOIUrl":"10.1007/s12518-026-00741-y","url":null,"abstract":"<div><p>This article presents the use of Unmanned Aerial Vehicle (UAV) photogrammetry for monitoring changes in lake shore topography. The case study focuses on Lake Ostrowskie, where the water level has been declining due to the significant influence of a subsidence sink caused by coal mining activity. The water level has declined by an estimated 4.6 m over the past 60 years. As a result of this gradual decrease, lake terraces have formed parallel to the shoreline, measuring 0.1–0.3 m in height and 0.5–20.0 m in width. UAV-derived products, such as the Digital Elevation Model (DEM) and orthomosaic, have facilitated the identification and spatial mapping of nine lake terraces which have formed since 2004. The identified terraces correspond to former shorelines, which are visible in archival orthophotomaps. The most important advantage of using UAV photogrammetry corrected with Real-Time Kinematic (RTK) field measurements is the high resolution of the resulting products, reaching 1.95 cm per pixel. Such resolutions enable the identification of vegetation height at different stages of the ecological succession, which reflect the relative age of each terrace.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 2","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12518-026-00741-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148009531","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":"From sensitivity to diagnosis: a composite soil degradation index for conservation planning in arid lands","authors":"Mohamed A. E. AbdelRahman,&nbsp;Ali R. A. Moursy,&nbsp;Moatez A. El-Sayed,&nbsp;Alaa H. Abdel-Azeem","doi":"10.1007/s12518-026-00722-1","DOIUrl":"10.1007/s12518-026-00722-1","url":null,"abstract":"<div><p>Land degradation in arid regions is often assessed through sensitivity mapping, which highlights potential risk but fails to quantify the actual severity of soil constraints. Building on previous desertification sensitivity research in Balat, El-Dakhla Oasis, Egypt, this study introduces a diagnostic framework that integrates soil profile analysis with spectral validation to produce a Composite Degradation Index (DI). Thirty georeferenced soil profiles were examined for salinity (ECe), calcium carbonate (CaCO₃), gypsum, gravel content, and texture, each classified into severity levels using FAO and USDA thresholds. These indicators were aggregated into a DI (0–100), delineating four degradation classes: Low, Moderate, High, and Very High. GIS interpolation revealed heterogeneous degradation patterns, with severe multi-factor hotspots concentrated in reclaimed lands. Validation with Sentinel‑2-derived NDVI and albedo confirmed strong correspondence between vegetation decline, surface reflectance, and high DI values (R² = 0.664). Results indicate that 10% of the study area is under very high degradation, 30% high, 50% moderate, and only 10% low. Unlike conventional sensitivity mapping, this status-based diagnosis provides management-oriented outputs, identifying remediation bundles tailored to specific constraints (e.g., drainage for saline soils, micronutrient supplementation for calcareous soils, stabilization for gypsiferous soils, organic amendments for gravelly textures). By bridging soil diagnostics with desertification frameworks, the study advances a reproducible methodology for monitoring degradation severity and supports targeted conservation planning in arid agroecosystems.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 2","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148009395","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":"A unified framework for 3D building surface reconstruction from fused LiDAR and photogrammetric data","authors":"Wahyu Marta Mutiarasari,&nbsp;Muhammad Zulkarnain Abd Rahman,&nbsp;Alias Abdul Rahman,&nbsp;Gurcan Buyuksalih","doi":"10.1007/s12518-026-00748-5","DOIUrl":"10.1007/s12518-026-00748-5","url":null,"abstract":"<div><p>Reliable three-dimensional (3D) building surface reconstruction depends on accurate and comprehensive spatial data; however, acquiring such data remains challenging, particularly for point clouds. Data collected using terrestrial laser scanning (TLS) and unmanned aerial vehicle (UAV)-based LiDAR provide detailed geometric information but are often affected by occlusions, limited viewpoints, and sensor constraints, resulting in incomplete datasets. Although data fusion using the iterative closest point (ICP) algorithm and Laplacian-based enhancement can mitigate these limitations, existing research typically treats fusion and surface reconstruction as separate processes, often applying reconstruction directly to fused data without prior quality assessment. This limits the generation of complete and geometrically consistent 3D building models from heterogeneous datasets. To address this gap, this study proposes a unified framework for 3D building surface reconstruction from fused point clouds derived from TLS, UAV LiDAR, and photogrammetric sources. The workflow applies ICP for alignment, Laplacian-based enhancement for gap reduction, and quantitative evaluation of the enhanced point cloud prior to reconstruction using the Marching Cubes algorithm. The results show an average completeness of 86.9%, with the standard deviation improving from 0.173 m to 0.157 m (roof) and from 0.238 m to 0.191 m (wall). The reconstructed model satisfies level of detail 2 (LoD2) requirements. Although certain architectural features, such as windows and pillars, are preserved, they are not yet semantically structured, indicating potential for extension toward higher LoDs (e.g., LoD3). Overall, the framework highlights the importance of pre-reconstruction enhancement and evaluation and demonstrates its potential for supporting more complete and geometrically consistent 3D building modelling.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 2","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12518-026-00748-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148009529","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":"Ground truthing of dumpsites using remote sensing and machine learning approaches in peri-urban settings","authors":"Veena N Bhajantri,&nbsp;Ashootosh Mandpe","doi":"10.1007/s12518-026-00733-y","DOIUrl":"10.1007/s12518-026-00733-y","url":null,"abstract":"<div><p>The open dumping of waste poses severe environmental and public health hazards when exposed to the atmosphere, yet systematic identification of dumpsites remains challenging for environmental agencies due to data accessibility constraints. This study presents an integrated geospatial framework combining Random Forest (RF) classification and logistic regression analysis using Sentinel-2 and Landsat-8 imagery to detect and characterize waste disposal sites across Madhya Pradesh, India. The RF algorithm achieved 86.49% overall accuracy in automated dumpsite detection, successfully identifying 199 sites, including 60 previously undocumented locations. Spectral-thermal analysis of 36 validated sites revealed distinctive signatures: Land Surface Temperature (LST) ranging 35.47–39.58 °C (mean: 37.30 ± 0.85 °C), Normalized Difference Vegetation Index (NDVI) of 0.04–0.25 (mean: 0.1539 ± 0.0378), and Normalized Difference Built-up Index (NDBI) of -0.06 to 0.12 (mean: 0.0282 ± 0.0511). Logistic regression modelling demonstrated LST’s positive influence on dumpsite classification (88% accuracy), while NDVI exhibited a negative association, reflecting reduced vegetation cover at waste sites. Correlation analysis revealed significant inverse relationships between NDBI-NDVI and NDVI-LST. Multi-criteria threshold classification (NDBI &gt; 0.028, NDVI &lt; 0.15, LST &gt; 37.0 °C) effectively delineated high-risk zones requiring prioritized monitoring. This scalable methodology provides a cost-effective alternative to ground surveys, enabling proactive waste management interventions and environmental protection strategies. However, it lags in analyzing the morphological and compositional attributes of such areas. The further studies will focus on integrating the field investigations with high-resolution remote sensing data to assess characteristics of the identified dumpsite locations.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 2","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148009061","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":"Italian geodetic reference systems: definition and cartographic use","authors":"Riccardo Barzaghi,&nbsp;Valerio Baiocchi","doi":"10.1007/s12518-026-00739-6","DOIUrl":"10.1007/s12518-026-00739-6","url":null,"abstract":"<div><p>The widespread adoption of standardized Coordinate Reference System (CRS) codifications, particularly EPSG (European Petroleum Survey Group, EPSG, 2026) codes, has greatly facilitated the interoperability of geospatial data within GIS environments. However, these codifications were originally conceived for cartographic applications and may exhibit limitations when used in high-precision geodetic contexts. After a general introduction on the definition of reference systems/frames that are used in Italy, this paper analyses the intrinsic constraints of generic CRS definitions—such as EPSG:4326, EPSG, 2026—and the implications of default datum transformation strategies implemented in common GIS software. A specific focus is devoted to the Italian framework, examining transformations between WGS84 and RDN2008 and the official procedures provided by the Istituto Geografico Militare (IGM) through the VertoX software and grid-based methods. Experimental tests comparing transformations performed in QGIS using official NTV2 grids with those obtained via Verto Online reveal discrepancies at the millimetre level. Although negligible for most applications, such differences may become significant in high-precision geodetic and geodynamic studies, highlighting the need for rigorous CRS management in GIS workflows.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 2","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12518-026-00739-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147938332","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":"Extraction and regularization of building footprints from classified LiDAR data: easily accessible tools for their implementation and evaluation","authors":"Renata Ďuračiová,&nbsp;Tomáš Ič,&nbsp;Tadeáš Kotleba,&nbsp;Nikita Yakshin","doi":"10.1007/s12518-026-00731-0","DOIUrl":"10.1007/s12518-026-00731-0","url":null,"abstract":"<div><p>Buildings represent fundamental elements of urban systems, and the management and updating of their representations in spatial information systems are therefore essential. Despite long-term research efforts, the automatic detection and extraction of building footprints from available data sources remain challenging. Difficulties arise from factors such as vegetation cover, close proximity of buildings in dense urban areas, varying levels of detail, inconsistent building definitions across datasets, and irregular geometry of detected footprints. LiDAR data, due to their increasing availability and geometric accuracy, offer significant potential for building footprint detection and updating. This paper aims to automatically or semi-automatically extract, vectorize, and regularize building footprints from classified point clouds, considering the accessibility of commonly used software tools. First, tools available in widely used GIS software environments (ArcGIS and QGIS) are applied, followed by the development of a custom Python-based program incorporating cluster analysis, polygonization, data cleaning, and regularization using available libraries and functions. All three approaches are applied in a case study (Bratislava, Slovakia) and evaluated by the aggregated shape similarity index using manually vectorized building footprints and cadastral data as an external data source. The results demonstrate that the proposed Python-based approach provides reliable and customizable outputs, particularly in contexts where only LiDAR data are available, while also supporting the integration of heterogeneous spatial data sources.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 2","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12518-026-00731-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147937788","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}