Waste Management Bulletin最新文献

{"title":"Valorisation of organic wastes through black soldier fly (Hermetia illucens) Larvae: Impacts on Growth, nutritional Composition, and bioconversion efficiency","authors":"Pawan Chapagaee ,&nbsp;Sandesh Thapa ,&nbsp;Sushil Shrestha ,&nbsp;Bigyan Puri ,&nbsp;Anup Ghimire ,&nbsp;Dipak Raj Bist ,&nbsp;Lokendra Khatri ,&nbsp;Adhiraj Kunwar","doi":"10.1016/j.wmb.2025.100278","DOIUrl":"10.1016/j.wmb.2025.100278","url":null,"abstract":"<div><div>Organic waste accumulation poses a significant environmental challenge, necessitating effective waste management strategies. The black soldier fly serves as a beneficial insect, aiding in waste reduction and animal feed production, while its frass contributes to sustainable soil improvement. Therefore, this study is aimed to investigate the bioconversion efficiency, growth performance, longevity, waste reduction, and nutritional composition of <em>Hermetia illucens</em> (Black soldier fly; BSF) larvae reared on five different urban organic wastes: restaurant waste, vegetable waste, fruit waste, kitchen waste, and butchery chicken waste following completely randomized design (CRD) with five urban waste treatment and four replications. The results revealed the highest larval growth rate and bioconversion found on restaurant waste 12.02 ± 0.47 mg/day and 6.97 ± 0.15 % respectively. Larva reared on butchery chicken waste showed highest larval mortality (76.02 ± 0.42 %) and life cycle duration (57 days). Also, the highest decomposition rate of waste was found on kitchen waste (0.73 ± 0.02) and fruit waste (0.72 ± 0.009). The highest waste reduction rate was found on kitchen waste (73.66 ± 2.70 %) and restaurant waste (62 ± 0.94 %). Larvae reared on restaurant waste exhibited highest crude protein content (37 ± 0.44 %DM), whereas highest crude fat was found on larva reared on butchery chicken waste (47.4 ± 0.64 %DM). The study highlights how BSF can efficiently decrease waste quantities and transform nutrient-balanced urban organic wastes into high-value biomass. The most promising substrates for large-scale BSF rearing and circular bio economy applications in developing nations like Nepal were found to be kitchen and restaurant wastes.</div></div>","PeriodicalId":101276,"journal":{"name":"Waste Management Bulletin","volume":"4 1","pages":"Article 100278"},"PeriodicalIF":0.0,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884249","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":"Household food waste across multiple food groups in Dodoma, Tanzania: A Multinomial Probit approach","authors":"Denis M. Silayo ,&nbsp;Mary Kulwijila ,&nbsp;Abiud J. Bongole","doi":"10.1016/j.wmb.2025.100276","DOIUrl":"10.1016/j.wmb.2025.100276","url":null,"abstract":"<div><div>Food waste (FdW) undermines food security both directly and indirectly by disrupting sustainable food systems. Understanding the root causes of FdW and its effects across multiple dimensions is crucial. Despite extensive global research, studies focusing on Household Food Waste (HFdW) in Tanzania remain limited. This study addresses that gap by analyzing the determinants of FdW generation across combinations of Food Groups (FGs) in Dodoma, Tanzania. A cross-sectional survey of 402 households was conducted to collect data on FdW-related behaviors and preferences. Principal Component Analysis (PCA) with varimax rotation was applied to reduce dimensionality, revealing that Cereals, Legumes &amp; Pulses, and Roots &amp; Tubers contributed most strongly to the retained components that capture the main variation in HFdW patterns. Each group was then dichotomized into high or low waste based on the median, and the resulting binary indicators were combined to create eight possible FdW patterns, representing all combinations of waste intensity across the three groups. These FdW patterns formed the dependent variable in a Multinomial Probit Regression Model (MPRM). The model revealed that demographic factors such as higher education, female-headed, and older households were associated with lower HFdW. Behavioural practices, including meal planning and leftover reuse, also reduced waste, while attitudinal factors such as greater awareness of FdW impacts further reinforced this effect. In contrast, weaker perceptions of money value were linked to higher FdW levels. The findings highlight the importance of behavioral and contextual factors in shaping HFdW. Policymakers should consider targeted strategies such as meal planning support, storage and handling education, and gender-responsive interventions to reduce FdW and enhance food security in Tanzania and other low-resource settings.</div></div>","PeriodicalId":101276,"journal":{"name":"Waste Management Bulletin","volume":"4 1","pages":"Article 100276"},"PeriodicalIF":0.0,"publicationDate":"2025-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884127","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":"Tracking the plastic footprint: a bibliometric mapping of microplastics research in Asia (2015–2025)","authors":"Ronilo P. Antonio","doi":"10.1016/j.wmb.2025.100274","DOIUrl":"10.1016/j.wmb.2025.100274","url":null,"abstract":"<div><div>The growing crisis of microplastic pollution poses serious environmental and public health challenges, yet despite Asia’s central role in plastic leakage and research growth, its scholarly landscape over the past decade remains unmapped. This study addresses this gap by conducting a comprehensive bibliometric analysis of 3,797 Scopus-indexed articles published between 2015 and 2025, employing an integrated approach that combines performance analysis, co-citation analysis, and co-word analysis using VOSviewer software. The results reveal an exponential surge in research output since 2019, with China, India, and Indonesia emerging as key contributors. Citation analysis highlights seminal works on atmospheric transport, soil contamination, and human ingestion as influential drivers of the field. Co-citation mapping identifies four intellectual clusters, mainly foundational conceptual frameworks, environmental pathways, global plastics accounting, and ecological impacts. Meanwhile co-word analysis uncovers three thematic domains: pollution sources and detection, ecological and biological effects, and environmental fate and transport. Overlay visualization further demonstrates a temporal shift from early descriptive and methodological studies toward interdisciplinary, solution-oriented research integrating ecological risk assessment and human health concerns. By synthesizing a decade of scholarship, this study provides a critical evidence base for guiding future research priorities and informs risk assessment strategies, policy design, and global governance efforts aimed at mitigating the escalating microplastics crisis.</div></div>","PeriodicalId":101276,"journal":{"name":"Waste Management Bulletin","volume":"4 1","pages":"Article 100274"},"PeriodicalIF":0.0,"publicationDate":"2025-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884248","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":"Scoping mushroom cultivation in the Northern Territory: Applying a circular economy approach","authors":"Waseem Ahmed ,&nbsp;Yujuan Li ,&nbsp;Edward Mwando ,&nbsp;Kamaljit Sangha ,&nbsp;Tham Dong ,&nbsp;Cheng-Yuan Xu","doi":"10.1016/j.wmb.2025.100275","DOIUrl":"10.1016/j.wmb.2025.100275","url":null,"abstract":"","PeriodicalId":101276,"journal":{"name":"Waste Management Bulletin","volume":"4 1","pages":"Article 100275"},"PeriodicalIF":0.0,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884128","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 evolving landscape of AI-driven risk management in the biogas production: A systematic and bibliometric review","authors":"Mohamed Abourida ,&nbsp;Michael Short ,&nbsp;Oleksiy V. Klymenko ,&nbsp;Noor M. Khamis ,&nbsp;Charf Mahammedi ,&nbsp;M.K.S. Al-Mhdawi ,&nbsp;Abdel-Hamed Sakr","doi":"10.1016/j.wmb.2025.100271","DOIUrl":"10.1016/j.wmb.2025.100271","url":null,"abstract":"<div><div>This review presents the first combined systematic and bibliometric review synthesising artificial intelligence (AI)-driven approaches to risk management in biogas production within wastewater treatment plants (WWTPs), with emphasis on decision-optimisation and operational safety. Seven academic databases: Scopus, Web of Science, IEEE Xplore, ScienceDirect, SpringerLink, Taylor &amp; Francis, and Google Scholar were systematically searched from 2015 to March 2025, and screening followed PRISMA 2020 guidelines. Of 3,716 retrieved records, 109 studies met the inclusion criteria. Bibliometric mapping (VOSviewer) and qualitative synthesis identified five thematic clusters: (i) biogas process safety, (ii) IoT integration and renewable-energy, (iii) optimisation and supply-chain resilience, (iv) AI-driven decision-support frameworks, and (v) advanced machine-learning techniques. The analysis reveals a marked increase in publications since 2020, reflecting a shift from conceptual modelling toward applied digital risk solutions. Europe and China remain leading contributors, although collaboration networks are fragmented and methodological heterogeneity persists. Full-scale validation of AI models in operational WWTP-based biogas plants remains limited, with most studies relying on laboratory experiments, simulations, or pilot-scale data. Constraints include publication bias, database coverage, English-language restrictions, inconsistent performance metrics, and limited access to long-term Supervisory Control and Data Acquisition (SCADA) Systems datasets. The review demonstrates that AI-driven methods have significant potential to improve safety, operational efficiency, and regulatory assurance in biogas facilities. However, achieving practical and scalable implementation will require rigorous multi-site validation, standardised evaluation indicators, integration of explainable AI, and alignment with plant-level risk-governance frameworks.</div></div>","PeriodicalId":101276,"journal":{"name":"Waste Management Bulletin","volume":"4 1","pages":"Article 100271"},"PeriodicalIF":0.0,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884129","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":"Detecting volume changes in municipal solid waste landfill using airborne laser scanning","authors":"O. Brovkina ,&nbsp;M. Pikl ,&nbsp;F. Zemek ,&nbsp;J. Michálek","doi":"10.1016/j.wmb.2025.100272","DOIUrl":"10.1016/j.wmb.2025.100272","url":null,"abstract":"<div><div>Accurate and operative monitoring of municipal solid waste (MSW) landfills is critical for operational safety, spatial planning, and regulatory compliance. Traditional point-based surveying methods are precise, but they are limited in spatial density coverage and hence, efficiency. The objective of the study is to evaluate the application of airborne laser scanning (ALS) for detecting volume changes at operational MSW landfill in the Czech Republic. Specifically, the study determines optimal spatial resolution of digital terrain model (DTM) from ALS for estimation of landfill volume, estimates uncertainties related to slope steepness and different vegetation cover affecting the accuracy of ALS-derived DTM, and formalizes and applies the method for detecting landfill volume changes using ALS. Two ALS datasets (10 points/m²) were collected in a five-month interval and processed at multiple spatial resolutions (0.3 m to 1.5 m). GPS reference points were measured for ALS data co-registration and to assess the accuracy of ALS-derived elevations. Positional errors and their propagation into elevation errors were quantified, and vegetation-induced uncertainties were considered. Results indicate that DTM resolutions of 0.3–0.8 m provide the most reliable estimates of volume change, especially in heterogeneous areas such as vegetated slopes. Differences in the standard deviation (SD) of elevation changes for selected areas at the landfill between the DTM resolution of 0.3 m and coarser resolutions were minimal for stable surfaces such as roads and compacted waste (0.01–0.02 m), but higher for vegetated areas, where the SD increased by up to 0.10 m due to surface roughness and variable laser penetration through the canopy. Comparison with independent GPS reference points showed that finer DTMs (0.3 and 0.5 m) reduced both bias and variability (mean differences ≤ 0.23 m, SD ≤ 0.24 m), whereas coarser DTMs (0.8 and 1.5 m) increased systematic errors due to surface smoothing and vegetation-induced misclassification. The findings recommend acquiring ALS data in early spring or late autumn, when vegetation cover is minimal and the influence of canopy on DTM accuracy is reduced. The study presents a novel workflow integrating ALS data with error modeling to improve landfill monitoring protocols. While the workflow was demonstrated on a specific site, it has potential for adaptation and application in other MSW landfills.</div></div>","PeriodicalId":101276,"journal":{"name":"Waste Management Bulletin","volume":"4 1","pages":"Article 100272"},"PeriodicalIF":0.0,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145694292","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":"Heavy metals in the soil, water, plants, and river sediments of Bangladesh: A synthesis","authors":"Sharmin Aktar Hasi ,&nbsp;Samiul Ahsan Jyoti ,&nbsp;Jagadish Chandra Joardar ,&nbsp;Milton Halder","doi":"10.1016/j.wmb.2025.100266","DOIUrl":"10.1016/j.wmb.2025.100266","url":null,"abstract":"<div><div>Heavy metals (HMs) contamination is an emerging environmental and public health issues in south Asian country like Bangladesh. HMs from various anthropogenic sources accumulate in the soil, are taken up by plants and entering our food chain, which causes health disorder. However, this study synthesizes updated data on heavy metals contamination in agricultural soils, water, sediments, and food crops across the major industrial areas of Bangladesh from 2010 to 2024. We found that the agricultural soils surrounding of the Dhaka city is highly contaminated with Mn, Zn, Cu, Pb, Cr, Ni, As, Cd. Heavy metals content in water from Buriganaga and Sytalokka river of Dhaka city was greater in compared to countryside river. Similarly, the plants growing near Dhaka city were highly contaminated with HMs (Zn, Mn, Cu, Pb, Ni, Cr), which were <em>Oryza sativa</em> and <em>Amaranthus dubius</em>. Furthermore, in case of sediments, Sytalokka river sediment followed by Buriganga were contained higher content of heavy metals like Cd, Pb, Ni, Zn, Cr and Cu in compared to the other rivers across the Bangladesh. However, different remediation strategies like application of organic amendments, phytoremediation, bioremediation are currently using across the Bangladesh to limit the plant uptake and food chain contamination. This synthesis will provide baseline information and first nationwide comparison of heavy metals data among the different regions of Bangladesh that can be used by policymakers for risk prioritization over the country. Furthermore, this study underscores the urgent need for policy interventions to mitigate heavy metal pollution and ensure sustainable food safety.</div></div>","PeriodicalId":101276,"journal":{"name":"Waste Management Bulletin","volume":"3 4","pages":"Article 100266"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145683866","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":"Artificial intelligence in waste management systems: Applications, challenges, and prospects","authors":"Imane Belyamani","doi":"10.1016/j.wmb.2025.100269","DOIUrl":"10.1016/j.wmb.2025.100269","url":null,"abstract":"<div><div>Despite global recognition of the climate crisis, greenhouse gas emissions are projected to rise by 8.8 % by 2030, primarily due to inadequate planning, poor implementation, and insufficient financial support. While international initiatives such as the ’Waste to Zero’ coalition launched at the 28th Conference of the Parties to the UNFCCC (COP 28) highlight the urgency of advancing decarbonization and the circularity of waste systems, this review focuses on how artificial intelligence (AI) can accelerate that transformation. It systematically explores the role of AI in advancing waste management practices, with a focus on predictive analytics, route optimization, and machine learning-based material classification. Beyond summarizing existing approaches, it proposes a multilayer framework that connects data sensing, planning, sorting, and treatment within an adaptive lifecycle perspective. The review further examines existing gaps related to policy support, infrastructure readiness, data standardization, and scalability. While numerous studies demonstrate the potential of AI to improve operational efficiency and material recovery, real-world implementation remains limited by economic, regulatory, and technological barriers. To address these limitations, a strategic roadmap is presented, integrating technical innovation with governance and investment pathways to enhance implementation potential. By consolidating these insights, the study offers a comprehensive synthesis that clarifies how AI can strengthen circularity across the waste lifecycle and guide the sector toward scalable, evidence-based sustainability transitions.</div></div>","PeriodicalId":101276,"journal":{"name":"Waste Management Bulletin","volume":"3 4","pages":"Article 100269"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145617494","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":"Managing process-safety risks in wastewater-based biogas: human–organisational drivers and PSMS implications for waste management operations in the UK","authors":"Mohamed Abourida ,&nbsp;Abdel-Hamed Sakr ,&nbsp;Noor M. Khamis ,&nbsp;Michael Short ,&nbsp;Oleksiy V. Klymenko","doi":"10.1016/j.wmb.2025.100267","DOIUrl":"10.1016/j.wmb.2025.100267","url":null,"abstract":"<div><div>Biogas production within wastewater treatment plants (WWTPs) provides renewable energy recovery but also introduces complex process-safety challenges arising from the interplay of human, organisational, and technical factors. This study systematically evaluates how these dimensions interact to influence process-safety outcomes in UK wastewater-based biogas facilities, addressing a gap where prior safety research has largely prioritised the examination of technical and operational failures in the petrochemical and chemical sectors. A mixed-methods design was adopted, integrating a PRISMA-guided literature review, a structured national survey of industry professionals (n = 90), and analysis of historical incident reports (n = 63). Triangulated quantitative and qualitative data were examined using correlation and regression analyses to identify interdependencies among risk drivers. Findings reveal that gas-leak risk shows the strongest correlation with general site risks (r = 0.857, p &lt; 0.001), followed by human and organisational factors (HOFs) (r = 0.768, p &lt; 0.001) and process factors (r = 0.733, p &lt; 0.001). The regression model (R² = 0.754) confirms that site-level governance (e.g., maintenance discipline, leadership visibility, and resource sufficiency) exerts the greatest influence on incident probability. These findings demonstrate that technical safeguards alone are insufficient without robust organisational and behavioural integration within Process Safety Management Systems (PSMS). The study demonstrates that HOFs directly shape safety outcomes in WWTPs, presenting a novel empirical contribution to literature, emphasising predictive maintenance, competence-based training, proactive reporting, and AI-enabled monitoring within PSMS as enablers of safer and more sustainable biogas recovery in wastewater operations.</div></div>","PeriodicalId":101276,"journal":{"name":"Waste Management Bulletin","volume":"3 4","pages":"Article 100267"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145617495","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":"An analytical framework for medical waste forecasting using machine learning: Paving the path toward zero waste in healthcare","authors":"S M Shahinur Rahman,&nbsp;Md Ruhul Amin,&nbsp;A M Almas Shahriyar Azad,&nbsp;Md. Ariful Haque,&nbsp;Md. Limonur Rahman Lingkon","doi":"10.1016/j.wmb.2025.100270","DOIUrl":"10.1016/j.wmb.2025.100270","url":null,"abstract":"<div><div>This study presents a robust framework for accurately forecasting medical waste (MW) generation in healthcare settings, addressing escalating environmental and public health concerns. Using machine learning (ML) models, including Linear Regression (LR), Support Vector Regression (SVR), Long Short-Term Memory (LSTM), and Random Forest (RF), the research integrates critical patient-related variables, such as indoor and outdoor patient counts, surgeries, and deaths. Based on eight years of comprehensive data (2016–2023) from the medical colleges in Rajshahi, Bangladesh, the LR model showed the most reliable outcomes, achieving <span><math><msup><mrow><mi>R</mi></mrow><mn>2</mn></msup></math></span> values between 0.91 and 0.95 for all waste categories and outperforming others with mean absolute errors (<em>MAE</em>) as low as 1.39 kg for recyclable waste. Projections reveal a significant 50 % surge in general waste by 2030, reaching approximately 987.75 tons, while infectious waste will grow from 66.54 to 735.23 tons. Monte Carlo simulations quantified variability across waste categories, demonstrating fluctuations within <span><math><mrow><mo>±</mo><mn>5</mn><mo>%</mo></mrow></math></span> of mean values, ensuring robustness. Finally, the research represents a novel policy recommendation for zero waste generation at the study location, which advocates for category-specific MW management strategies, aligning with Sustainable Development Goals (SDGs) 3 (Good Health and Well-being), 12 (Responsible Consumption and Production), and 13 (Climate Action). This work introduces a novel precedent for incorporating artificial intelligence into healthcare operations to attain zero waste generation while tackling public health and environmental issues. Thus, this work offers an innovative methodology that integrates machine learning to link patient data with waste generation, providing practical insights for resource optimization and operational planning.</div></div>","PeriodicalId":101276,"journal":{"name":"Waste Management Bulletin","volume":"4 1","pages":"Article 100270"},"PeriodicalIF":0.0,"publicationDate":"2025-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145694291","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}