Sustainable Cities and Society最新文献

{"title":"Collaborative optimization of particulate matter pollution reduction between urban transport and pocket parks: A mobile monitoring and novel inverse network DEA approach","authors":"Jiqiang Zhao ,&nbsp;Le Zhan ,&nbsp;Hongmei Zhao ,&nbsp;Lijun Cheng ,&nbsp;Hongdi He","doi":"10.1016/j.scs.2026.107171","DOIUrl":"10.1016/j.scs.2026.107171","url":null,"abstract":"<div><div>Although pocket parks play an important role in mitigating traffic-related pollution and improving urban microenvironments, the resource allocation pathways for their collaborative emission reduction with transportation systems remain unclear. For the first time, this study integrates micro-scale mobile monitoring with an Inverse Network Data Envelopment Analysis (DEA) framework to systematically characterize the collaborative emission reduction mechanism between traffic emissions and pocket park purification, and to propose an efficiency-constrained optimal resource allocation pathway. Using 30 representative pocket parks in Yuhang District, Hangzhou, as case studies, a two-stage network DEA model linking traffic emissions and park purification was constructed and further extended into an inverse network DEA model to optimally estimate input–output adjustments under different emission reduction targets. The results reveal that: (1) particulate matter (PM) concentrations exhibit a distinct spatial attenuation pattern within parks, with the strongest reduction occurring within 10 meters of the road edge; (2) output-oriented efficiency is generally higher than input-oriented efficiency, indicating that enhancing collaborative emission reduction performance depends more on activating output potential and management efficiency rather than simply increasing inputs; and (3) under constant system efficiency, achieving a 5% increase in PM reduction requires an average 5.71% increase in input resources, among which greening ratio demonstrates the highest marginal benefit and serves as a key driver for improving synergistic performance. This study extends the application of inverse DEA models to the micro-scale environmental domain and provides a methodological framework and empirical evidence for refined resource allocation in transportation–green space collaborative emission reduction.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"138 ","pages":"Article 107171"},"PeriodicalIF":12.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147398995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Trend-based satellite monitoring of urban landscape dynamics and modeling cooling-connectivity networks in Tehran metropolis","authors":"Peyman Karami","doi":"10.1016/j.scs.2026.107181","DOIUrl":"10.1016/j.scs.2026.107181","url":null,"abstract":"<div><div>Understanding the temporal dynamics and spatial connectivity of urban green spaces (UGSs) and urban indicators is crucial for mitigating the adverse effects of Urban Heat Islands (UHIs). This study applies a novel time-based approach to examine Tehran’s landscape transformation by analyzing summer averages (2013–2023) of multiple indices: Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), and Vegetation Health Index (VHI). Temporal trends were detected via the Mann–Kendall test, while spatial overlaps were assessed via the Kappa coefficient. Circuit theory quantified UGS connectivity, focusing on areas with rising VHI. Results revealed UHI cores in western and southwestern Tehran, associated with industrial land uses and LST trends. Although NDVI exhibited widespread positive trends, VHI improvements were spatially constrained, indicating a disconnect between vegetation cover and ecological health. Machine learning models identified spatial drivers of current flow, with Random Forest achieving high accuracy (R² = 0.83). Southern districts showed high resistance to cooling flows due to linear built-up morphologies, whereas northern zones exhibited enhanced connectivity driven by woody vegetation and clustered green nodes. These findings highlight the necessity of functional UGSs and suggest that spatial planning prioritize vegetative networks and thermal equity to enhance urban resilience.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"138 ","pages":"Article 107181"},"PeriodicalIF":12.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147399069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatial-socioeconomic semantic integration of remote sensing and urban planning maps for improved urban thermal environment characterization","authors":"Yu Kang ,&nbsp;Zhe Zhang ,&nbsp;Jiating Li ,&nbsp;Zongying Zhao ,&nbsp;Dongxue Li ,&nbsp;Jiahua Yu ,&nbsp;Jingjing Zhang ,&nbsp;Xinjia Zhang ,&nbsp;Qiao Hu ,&nbsp;Yixue Chen ,&nbsp;Jiajing Chen ,&nbsp;Lin Li","doi":"10.1016/j.scs.2026.107205","DOIUrl":"10.1016/j.scs.2026.107205","url":null,"abstract":"<div><div>Rapid urbanization and climate change intensified the spatiotemporal heterogeneity of urban thermal environments (UTEs). Conventional UTE studies primarily focus on physical and land cover indicators, neglecting the underlying socioeconomic fabrics that describe human–environment interactions and drive thermal heterogeneity. This study develops a CNN (Convolutional Neural Networks) intelligent framework for socioeconomic indicator simulation, using for accurate UTE modeling. Socioeconomic indicators are intelligently derived from land use attributes contained in publicly available urban planning maps (UPMs), overcoming the long-standing data scarcity in UTE studies. These indicators were represented as probabilistic maps, offering spatially explicit and interpretable socioeconomic representations. The framework was first applied in Zhengzhou, Henan Province, China, and was further validated in an adjacent city, Kaifeng. Results indicate that the method achieves 0.61 to 0.69 test <span><math><msup><mi>R</mi><mn>2</mn></msup></math></span>values across different urban contexts and timings, demonstrating robust and stable generalization. Feature importance analyses further proved that these socioeconomic indicators contribute 48.3% to 54.6% to the UTE modeling across the four seasons, implying the high importance of the socioeconomic indicators on UTE modeling. Ultimately, block-level UTE responses were derived to identify high-risk areas, with quantitative land use optimization strategies established. The framework translates UTE risk diagnostics into concrete spatial governance strategies, shifting from qualitative guidance toward data-driven risk mitigation. It provides urban planners with a quantitative tool to assess block-level thermal responses to land use patterns and to optimize urban form for improved thermal comfort and climate resilience in metropolitan areas.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"138 ","pages":"Article 107205"},"PeriodicalIF":12.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147399436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bottom-up mapping of urban building stock high-resolution energy profiles: A case study of the City of Melbourne, Australia","authors":"Ziying Sun,&nbsp;Ruijie Liu,&nbsp;Ben Liu,&nbsp;Zijun Ye,&nbsp;Victor Wei-Chung Chang,&nbsp;Jin Zhou","doi":"10.1016/j.scs.2026.107206","DOIUrl":"10.1016/j.scs.2026.107206","url":null,"abstract":"<div><div>This study presents a high-resolution, bottom-up urban building energy model for the Australian context, underpinned by a comprehensive archetype library and demonstrated using the City of Melbourne as a case study. Addressing the national gap in spatially explicit and temporally granular simulations, the model computes energy demand from the individual building level up to the city scale using 79 archetypes spanning typologies, construction eras, and height classes. To enable large-scale implementation of the model, an automated workflow was established to extract geometric building information, integrate non-geometric building attributes, and run simulations with EnergyPlus as the core engine. The city-wide annual energy demand is estimated at 4,876 GWh. Across typologies, average annual energy use intensities (kWh·m⁻²) are highest for hospitals/clinics (376.2), followed by retails (213.0), commercial accommodations (210.5), houses/townhouses (143.0), offices (136.6), educational/research buildings (86.0), and residential apartments (73.8). Compared with reported references from yearly energy records, official reports, and public datasets, the simulated city-wide total is 7.9% higher than the reference value. At the typology level, deviations generally fall within ±20.0%, except for retails (30.5%). Overall, the findings reveal notable typological and spatial disparities: offices and residential apartments jointly account for over half of city-wide demand, while Melbourne CBD, Docklands, Parkville, and Southbank are major energy hotspots, collectively contributing around 70.0% of the city-wide total. At finer temporal resolutions, city-wide demand fluctuates between 7.2 and 18.5 GWh daily and ranges from 0.2 to 1.5 GWh hourly, exhibiting patterns characteristic of a temperate climate and a pronounced diurnal cycle aligned with occupancy and operational schedules. A Monte Carlo based uncertainty analysis further suggests model robustness, with uncertainty driven by input variability well bounded within 25.2% across typologies and 20.8% across spatial scales. By generating fine-grained, end-use-specific energy profiles, the model provides a solid analytical foundation for building energy planning and management, with potential to support Australia’s net-zero transition, and offers an extensible platform for future methodological advancements and scenario-based research.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"138 ","pages":"Article 107206"},"PeriodicalIF":12.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147399437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Urban heat & cool island: Investigating Pune’s (India) thermal dynamics using MODIS and Landsat 8–9 data","authors":"Payal More ,&nbsp;Dhaarna","doi":"10.1016/j.scs.2026.107174","DOIUrl":"10.1016/j.scs.2026.107174","url":null,"abstract":"<div><div>The urban thermal dynamics of a city comprise of Urban Heat Island Effect (UHI) and the Urban Cool Island Effect (UCI) that influence the local climate. This research employs a spatial and comparative approach to analyze thermal variations in Pune, India, by studying the conditions across three distinct years-2016, 2020, and 2024, to capture yearly variations influenced by changes in human activity and environmental dynamics, particularly during the 2020 period marked by the COVID-19 lockdown in India. To evaluate the urban thermal patterns, the research integrates four indices (i) Land Surface Temperature (LST), (ii) Normalized Difference Vegetation Index (NDVI), (iii) Normalized Difference Moisture Index (NDMI), and (iv) Normalized Difference Anthropogenic Impervious Surface Index (NDAISI). The spatial mapping has been done using Q-GIS, presenting diurnal and seasonal patterns for all indices. Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat 8–9 satellite data were utilized for April (summer) and November (winter) for the analysis. Statistical correlation techniques have been used to evaluate the interrelationship between LST with the other three indices. To capture the temporal disparity in heat retention and dissipation, it assesses interactions between these indices with diurnal and seasonal variations. Surface Urban Heat Island Intensity (SUHII) variation has been plotted using a Box and Whisker plot. The result suggests that Pune shows the UCI effect during the day, while the UHI effect during the night. During UCI (daytime), the results indicate a positive correlation between LST and NDAISI, whereas NDVI and NDMI exhibit strong negative correlations with LST, highlighting their cooling effects. The study highlights intra-urban thermal variation as an urban heat spread (UHS) effect. These findings provide valuable insights for urban planners and policymakers in developing heat action plans and climate-resilient urban strategies for Pune and similar cities.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"138 ","pages":"Article 107174"},"PeriodicalIF":12.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Infrastructure access and availability as determinants of community vulnerability: A spatial analysis of 733 districts in India","authors":"Srijith Balakrishnan ,&nbsp;Shivam Srivastava ,&nbsp;Chirag Kothari","doi":"10.1016/j.scs.2026.107182","DOIUrl":"10.1016/j.scs.2026.107182","url":null,"abstract":"<div><div>As natural disasters increase in frequency and severity, infrastructure planning must account for how existing deficits and disparities shape community vulnerability and recovery. While regional vulnerability and resilience frameworks consider infrastructure systems as core determinants, the interaction between infrastructure characteristics, such as access and availability, and their influence on community vulnerability remain underexplored, particularly in the Global South. To this end, we leverage open-source geospatial data to construct granular datasets for 733 districts in India and apply statistical methods to assess relationships between community and infrastructure-related characteristics. Specifically, we consider three dimensions of infrastructure at the district level: (a) regional critical infrastructure availability, (b) social infrastructure density, and (c) household-level essential utility access. Geographic distributions and distinct profiles of infrastructure characteristics, are identified by employing clustering algorithms on composite indicators, while spatial regression models evaluate the association between community vulnerability and infrastructure dimensions. Findings suggest that access to essential utilities is the strongest factor associated with reduced vulnerability, with a significant interaction between critical infrastructure availability and utility access, indicating potential synergistic effects. Regions with below-average infrastructure provisions are associated with higher community vulnerability and may therefore warrant greater resource investment to achieve equitable vulnerability reduction outcomes. This study provides empirical evidence of synergistic effects using district-level geospatial analysis, supporting coordinated infrastructure development at network and household levels to enhance community capabilities and resilience.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"138 ","pages":"Article 107182"},"PeriodicalIF":12.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147399003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantifying the synergy effects of climate policy portfolios: Evidence from China","authors":"Panpan Meng ,&nbsp;Kui Liu ,&nbsp;Zheyi Xia ,&nbsp;Wenguang Chen","doi":"10.1016/j.scs.2026.107198","DOIUrl":"10.1016/j.scs.2026.107198","url":null,"abstract":"<div><div>The integration of diverse policy instruments has emerged as a critical frontier in global climate governance, yet the logic governing the interaction of heterogeneous instruments remains underexplored. Leveraging China’s staggered implementation of the Low-Carbon City Pilot (LCP) and the Smart City Pilot (SCP) as a quasi-natural experiment, this study investigates their joint impact on urban pollution and carbon reduction efficiency (PCRE). Drawing on system innovation theory, this study finds that the interaction between the LCP and SCP generates a significant policy synergy effect (PSE) rather than trade-offs, thereby amplifying the effectiveness of individual interventions. Crucially, the empirical results uncover a distinct asymmetry in policy sequencing: implementing the LCP prior to the SCP yields significantly larger PCRE gains. Mechanism analysis indicates that this synergy is mediated by green technological innovation (GTI) and energy utilization structure (EUS). Furthermore, the results reveal that PSE exhibits significant temporal lag effects and demonstrates SCP-dominated spatial spillover. These findings advance the understanding of urban policy synergies, offering practical insights for optimizing complex climate policy portfolios.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"138 ","pages":"Article 107198"},"PeriodicalIF":12.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147399065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An LLM-driven estimation framework for estimating regional travel structures based on spatiotemporal multi-modal travel data","authors":"Huapeng Shen ,&nbsp;Jiancheng Weng ,&nbsp;Pengfei Lin","doi":"10.1016/j.scs.2026.107196","DOIUrl":"10.1016/j.scs.2026.107196","url":null,"abstract":"<div><div>Accurately characterizing regional travel structures (the distribution of travel modes) is essential for developing targeted interventions to promote green mobility and inform sustainable urban policies. However, despite considerable emphasis on optimizing regional travel structures to promote sustainable urban development, existing methods frequently overlook crucial aspects such as spatial heterogeneity and dynamic travel behaviors, limiting their effectiveness in guiding adaptive urban planning strategies. To address this gap, this study proposes a novel framework based on a fine-tuned large language model (LLM) to estimate regional travel structures. By transforming multimodal data, the task is reformulated as a text-to-text estimation problem using Llama3, enhanced through domain knowledge and chain-of-thought prompting. Applied to 335 street-level units in Beijing, the proposed model significantly improves estimation accuracy, reducing RMSE and MAE by 18%–25% compared to baseline methods. The results uncover distinct spatial patterns, with core districts heavily reliant on public transit, whereas suburban regions show greater dependence on private cars. The proposed framework shows advanced AI’s potential to offer interpretable and precise decision-support tools for optimizing urban transportation systems.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"138 ","pages":"Article 107196"},"PeriodicalIF":12.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147399067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The robustness benefit of disorder: A global typological analysis of road network morphology and urban performance","authors":"Lin Wang ,&nbsp;Jaeyoung Jay Lee ,&nbsp;Junjie Hu","doi":"10.1016/j.scs.2026.107204","DOIUrl":"10.1016/j.scs.2026.107204","url":null,"abstract":"<div><div>This study investigates the non-linear relationship between urban road network morphology and operational performance, challenging the traditional planning paradigm that geometric order and grid-like regularity universally equate to operational efficiency. Utilizing a dataset of 70 globally diverse cities, the research employs a multi-stage quantitative framework. First, K-means clustering is applied to stratify cities into distinct typologies based on structural and macroeconomic attributes. Second, Generalized Additive Models integrated with SHAP interpretation are utilized to deconstruct the non-linear, context-dependent determinants of network directional entropy. Third, the study applies Copula models to quantify the dependence structures between morphology and performance metrics, complemented by targeted attack simulations to assess dynamic topological robustness. Empirical results reveal a significant positive correlation between higher directional entropy and superior steady-state performance, specifically in reducing commute times and <span><math><mrow><mi>C</mi><msub><mi>O</mi><mn>2</mn></msub></mrow></math></span> emissions. However, this relationship exhibits fundamental heterogeneity: while high-entropy, fine-grained networks derive resilience from topological redundancy, whereas hierarchical networks primarily derive resilience from capacity-driven efficiency rather than topological redundancy. These findings indicate that network optimization is strictly typology-dependent, necessitating context-specific planning strategies rather than universal geometric standardization.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"138 ","pages":"Article 107204"},"PeriodicalIF":12.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147399434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From hazard coupling to targeted governance: Decoding compound heat-flood risks in high-density urban areas through HEVA system combined with the LCZ framework","authors":"Jialu Gao,&nbsp;Jin Bai,&nbsp;Hongxuan Lan,&nbsp;Hongchi Zhang,&nbsp;Fei Guo,&nbsp;Peisheng Zhu,&nbsp;Xiaodong Lu","doi":"10.1016/j.scs.2026.107172","DOIUrl":"10.1016/j.scs.2026.107172","url":null,"abstract":"<div><div>The ongoing rise in global temperatures has intensified the instability of the climate system, leading to more frequent extreme weather events with broader impacts. The interplay of multiple disaster types poses significant threats to public health and results in substantial economic losses. Some cities are hit by both heavy rains and floods during the hot season. The public has been seriously affected by the lack of coping measures. The risk of heat and flood in the high-density urban area of Wuhan is assessed by establishing the HEVA assessment system. The LCZ maps were generated using RS combined with the GIS method, and the compound heat-flood risk was assessed using copula function and XGBoost-SHAP method. The results indicate that: (1) The high heat risk in Wuhan's urban area is primarily concentrated in the southern parts of Jiangan, Jianghan and Qiaokou districts. The Lowest risk in the lakes and rivers region. The high flood risk areas are mainly located in the southern side of Jiangan, Jianghan and Qiaokou Districts, with the risk showing a scattered distribution;(2) The results of the compound heat-flood risk show that high values are in the central area of the city due to high construction density and frequent population activities, while the surrounding area of the city shows risk suppression,(3) The relative contribution of heat risk is greater than that of flood risk in the city. On the basis of the results of the study and the LCZ framework, targeted prevention and control strategies at the block scale are proposed.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"138 ","pages":"Article 107172"},"PeriodicalIF":12.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147399439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}