检索结果-内蒙古大学图书馆

作者： C X Wang J Liu Y P Li J Zhao X M Kong College of Fundamental Research Beijing Polytechnic School of Environmental Science and Engineering Xiamen University of Technology School of Environmental Beijing Normal University Research Scientist Environmental Systems Engineering ProgramFaculty of Engineering and Applied ScienceUniversity of Regina

Incorporating uncertainty assessment into hydrological simulation is of vital significance for providing valuable information for conserving and restoring the ecology environment in arid and semiarid regions. In this study, a sparse polynomial chaos expansions method was developed to quantify hydrological model parameter uncertainties on model performance in Kaidu river basin, China. A four dimension two order polynomial chaos expansions model was built and the effect of four parameters were quantified based on the coefficients of the polynomial chaos expansions model. Results indicated that precipitation in summer has more significant influence on model output than that in other seasons. High Sobol sensitivity indices values（0.22 in spring, 0.17 in summer, 0.21 in autumn and 0.29） for the interaction of precipitation and maximum capacity for fast store demonstrate that they are the major factors affecting runoff generation. These results can help reveal the flow processes and provide valuable information for water resources management.

关键词：

来源：评论

学校读者我要写书评

暂无评论

COVID-19 Impacts on Household Solid Waste Generation in Latin America - a Participatory Approach

SSRN

引用

SSRN 2022年

作者： Requena-Sanchez, Norvin Carbonel, Dalia Moonsammy, Stephan Demel, Larissa Vallester, Erick Velásquez, Diana Cervantes, Jessica Alejandra Toledo Núñez, Verónica Livier Díaz García, Rosario Vásquez Cruz, Melissa Santa Visbal, Elsy Ng, Kelvin Tsun Wai Solid Waste Technical Team Faculty of Environmental Engineering National University of Engineering 210 Túpac Amaru Ave Rímac Lima25 Peru National University of Engineering 210 Túpac Amaru Ave Rímac Lima25 Peru Department of Environmental Studies Faculty of Earth and Environmental Sciences University of Guyana P. O. Box 10 1110 Turkeyen Campus Georgetown Guyana United Nations Development Program Casa de las Naciones Unidas Edificio # 129 Ciudad del Saber Ciudad de Panamá Panama Technological University of Panama Avenida Universidad Tecnológica de Panamá Vía Puente Centenario Campus Metropolitano Víctor Levi Sasso Panama National Autonomous University of Honduras Bulevar Suyapa M.D.C Tegucigalpa Honduras University of Guadalajara Av. Juárez No. 976 Colonia Centro Jalisco GuadalajaraC.P. 44100 Mexico Daniel Alcides Carrion National University Av. Los Próceres 703 Yanacancha Cerro de Pasco Peru Intercultural National University Fabiola Salazar Leguia from Bagua Jirón Ancash N° 520 Amazonas Bagua Peru Simón Bolívar University Litoral Headquarters Camurí Grande Edo. Vargas Parroquia Naiguatá La Guaira Venezuela Environmental Systems Engineering Faculty of Engineering and Applied Science University of Regina 3737 Wascana Parkway Saskatchewan S4S 0A2 Canada

The Covid-19 pandemic has greatly impacted Latin America, the continent with the highest number of cases and Covid-related deaths. Strict confinement conditions at the beginning of the pandemic put to a halt recycling activities and augmented the consumption of plastic as a barrier to stop the spread of the virus. In Latin America the lack of data to understand the waste management dynamics difficult the adjustment of waste management strategies to cope with the Covid-19. As a novel contribution to the waste management data gap for Latin America, this study uses a virtual and participatory methodology that collects and generates information on household solid waste generation and composition. We present the results obtained between June and November 2021 in six countries in the Latin America region, with a total of 503 participants. Participants indicated that the pandemic motivated them to initiate or increase waste reduction (41%), waste separation (40%) and waste recovery (33%) activities. 43% of participants perceived and increase on their total volume of waste;however, the quantitative data showed a decrease on household waste generation in Peru (-31%), Honduras (-25%) and Venezuela (-82%). No changes in waste composition were observed. Despite the limited sample size, this data provides a much-needed approximation of household waste generation and composition in a pandemic situation during 2021. © 2022, The Authors. All rights reserved.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

Finding shortest and nearly shortest path nodes in large substantially incomplete networks

arXiv

引用

arXiv 2022年

作者： Kitsak, Maksim Ganin, Alexander Elmokashfi, Ahmed Cui, Hongzhu Eisenberg, Daniel A. Alderson, David L. Korkin, Dmitry Linkov, Igor Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands Network Science Institute Northeastern University BostonMA02115 United States Department of Systems and Information Engineering University of Virginia CharlottesvilleVA22904 United States U.S. Army Engineer Research and Development Center Contractor Concord MA01742 United States Simula Metropolitan Center for Digital Engineering Oslo Norway Bioinformatics and Computational Biology Program Worcester Polytechnic Institute WorcesterMA01609 United States Institute for Genomic Medicine Columbia University Medical Center New YorkNY United States Operations Research Department Naval Postgraduate School MontereyCA93943 United States Data Science Program Worcester Polytechnic Institute WorcesterMA01609 United States Computer Science Department Worcester Polytechnic Institute WorcesterMA01609 United States U.S. Army Engineer Research and Development Center Environmental Laboratory Concord MA01742 United States

Dynamic processes on networks, be it information transfer in the Internet, contagious spreading in a social network, or neural signaling, take place along shortest or nearly shortest paths. Unfortunately, our maps of most large networks are substantially incomplete due to either the highly dynamic nature of networks, or high cost of network measurements, or both, rendering traditional path finding methods inefficient. We find that shortest paths in large real networks, such as the network of protein-protein interactions (PPI) and the Internet at the autonomous system (AS) level, are not random but are organized according to latent-geometric rules. If nodes of these networks are mapped to points in latent hyperbolic spaces, shortest paths in them align along geodesic curves connecting endpoint nodes. We find that this alignment is sufficiently strong to allow for the identification of shortest path nodes even in the case of substantially incomplete networks. We demonstrate the utility of latent-geometric path finding in problems of cellular pathway reconstruction and communication security. Copyright © 2022, The Authors. All rights reserved.

关键词： Proteins

来源：评论

学校读者我要写书评

暂无评论

Explainable Machine Learning for Anaerobic Digestion: A Critical Review

SSRN

引用

SSRN 2022年

作者： Li, Wangliang Gupta, Rohit Zhang, Le Liu, Hongtao Zhang, Zhikai Hou, Jiayi You, Siming Ok, Yong Sik James Watt School of Engineering University of Glasgow GlasgowG12 8QQ United Kingdom Nanoengineered Systems Laboratory UCL Mechanical Engineering University College London LondonWC1E 7JE United Kingdom Wellcome EPSRC Centre for Interventional and Surgical Sciences University College London LondonW1W 7TS United Kingdom Department of Resources and Environment School of Agriculture and Biology Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China Institute of Geographic Science and Natural Resources Research Chinese Academy of Sciences Beijing100101 China CAS Key Laboratory of Green Process and Engineering Institute of Process Engineering Chinese Academy of Sciences Beijing100190 China School of Water Resources and Environment Hebei GEO University Hebei Shijiazhuang050031 China Korea Biochar Research Center APRU Sustainable Waste Management Program Division of Environmental Science and Ecological Engineering Korea University Seoul02841 Korea Republic of

Anaerobic digestion (AD) is a traditional process to achieve energy recovery from a variety of organic waste streams. However, the AD performance in terms of biogas production and process stability relies on the cooperation of the functional microbial communities and the operating parameters. The latest progress in machine learning for AD processes including optimization, parametric prediction, perturbation detection, and long-term monitoring were critically reviewed. The explainable data-driven models including black-box machine learning (ML) models and nature-inspired optimization techniques were compared, while summarizing their applications. The review also highlighted the challenges, perspectives, and opportunities for future research of applying ML for AD processes. © 2022, The Authors. All rights reserved.

关键词： Optimization

来源：评论

学校读者我要写书评

暂无评论

Adaptive Coordination Offsets for Signalized Arterial Intersections using Deep Reinforcement Learning

arXiv

引用

arXiv 2020年

作者： Diaz, Keith Anshilo Dailisan, Damian Sharaf, Umang Santos, Carissa Gan, Qijian Uy, Francis Aldrine Lim, May T. Bayen, Alexandre M. The School of Civil Environmental and Geological Engineering Mapua University Manila Philippines The National Institute of Physics University of the Philippines Diliman Philippines The Analytics Computing and Complex Systems Laboratory Asian Institute of Management Philippines The Electrical Engineering and Computer Sciences department at University of California Berkeley United States The PATH program in the Institute of Transportation Studies University of California Berkeley United States The Electrical Engineering and Computer Sciences Department The Department of Civil and Environmental Engineering The Institute of Transportation Studies University of California Berkeley United States

Coordinating intersections in arterial networks is critical to the performance of urban transportation systems. Deep reinforcement learning (RL) has gained traction in traffic control research along with data-driven approaches for traffic control systems. To date, proposed deep RL-based traffic schemes control phase activation or duration. Yet, such approaches may bypass low-volume links for several cycles to optimize the network-level traffic flow. Here we propose a deep RL framework that dynamically adjusts offsets based on traffic states and preserves the planned phase timings and order derived from model-based methods. This framework improves arterial coordination while maintaining phase order and timing predictability. Using a validated and calibrated traffic model, we trained the policy of a deep RL agent that aims to reduce travel delays in the network. To evaluate the resulting policy, we compared its performance against the phase offsets deployed along a segment of Huntington Drive in the city of Arcadia. The resulting policy dynamically readjusts phase offsets in response to changes in traffic demand. Simulation results show that the proposed deep RL agent outperformed the baseline on average, effectively reducing delay time by 13.21% in the AM Scenario, 2.42% in the Noon scenario, and 6.2% in the PM scenario when offsets are adjusted in 15-minute intervals. Finally, we also show the robustness of our agent to extreme traffic conditions, such as demand surges in off-peak hours and localized traffic incidents. Copyright © 2020, The Authors. All rights reserved.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

INTRODUCTION

引用

Bulletin of the American Meteorological Society 2024年第8期105卷 S1-S11页

作者： Boyer, T. Blunden, J. Dunn, R.J.H. NOAA/NESDIS National Centers for Environmental Information Silver Spring Maryland NOAA/NESDIS National Centers for Environmental Information Asheville North Carolina Met Office Hadley Centre Exeter United Kingdom European Centre for Medium-Range Weather Forecasts Reading United Kingdom University of Maryland College Park Maryland Scripps Institution of Oceanography University of California San Diego La Jolla California NOAA/NWS National Centers for Environmental Prediction Climate Prediction Center College Park Maryland Brandenburg University of Technology (BTU) Cottbus-Senftenberg Germany Servicio Meteorológico Nacional Buenos Aires Argentina NOAA/OAR Physical Sciences Laboratory Boulder Colorado European Centre for Medium-Range Weather Forecasts Bonn Germany Center for Geophysical Research and School of Physics University of Costa Rica San José Costa Rica Department of Meteorology and National Centre for Earth Observation University of Reading Reading United Kingdom Centro Nacional de Monitoramento e Alertas de Desastres Naturais CEMADEN São Paulo Brazil Université Grenoble Alpes Institut des Géosciences de l’Environnement IRD CNRS Grenoble INP Grenoble France Hampton University Hampton Virginia Seychelles Meteorological Authority Mahe Seychelles Section for Glaciers Ice and Snow Norwegian Water Resources and Energy Directorate Oslo Norway National Research Institute for Agriculture Food and Environment (INRAE) CARRTEL Université Savoie Mont Blanc Thonon les Bains France Graduate School of Agriculture Osaka Metropolitan University Sakai Japan Science Faculty Universidad Nacional de Colombia Bogotá Colombia University of Bremen Bremen Germany NOAA Global Monitoring Laboratory Boulder Colorado Servicio Nacional de Meteorología e Hidrología del Perú Lima Perú Centro de Investigaciones sobre Desertificación – Spanish National Research Council (CSIC-UV-GVA) Valencia Spain Hydro-Climate Extremes Lab (H-CEL) Ghent University Ghent Belgium Department o

In 2023, La Niña conditions that generally prevailed in the eastern Pacific Ocean from mid-2020 into early 2023 gave way to a strong El Niño by October. Atmospheric concentrations of Earth’s major greenhouse gases—carbon dioxide, methane, and nitrous oxide—all increased to record-high levels. The annual global average carbon dioxide concentration in the atmosphere rose to 419.3±0.1 ppm, which is 50% greater than the pre-industrial level. The growth from 2022 to 2023 was 2.8 ppm, the fourth highest in the record since the 1960s. The combined short-term effects of El Niño and the long-term effects of increasing levels of heat-trapping gases in the atmosphere contributed to new records for many essential climate variables reported here. The annual global temperature across land and oceans was the highest in records dating as far back as 1850, with the last seven months (June–December) having each been record warm. Over land, the globally averaged temperature was also record high. Dozens of countries reported record or near-record warmth for the year, including China and continental Europe as a whole (warmest on record), India and Russia (second warmest), and Canada (third warmest). Intense and widespread heatwaves were reported around the world. In Vietnam, an all-time national maximum temperature record of 44.2°C was observed at Tuong Duong on 7 May, surpassing the previous record of 43.4°C at Huong Khe on 20 April 2019. In Brazil, the air temperature reached 44.8°C in Araçuaí in Minas Gerais on 20 November, potentially a new national record and 12.8°C above normal. The effect of rising temperatures was apparent in the cryosphere, where snow cover extent by June 2023 was the smallest in the 56-year record for North America and seventh smallest for the Northern Hemisphere overall. Heatwaves contributed to the greatest average mass balance loss for Alpine glaciers around the world since the start of the record in 1970. Due to rapid volume loss beginning in 2021, St. A

关键词：

来源：评论

学校读者我要写书评

暂无评论

The 2022 report of the Lancet Countdown on health and climate change: health at the mercy of fossil fuels (vol 400, pg 1619, 2022)

引用

LANCET 2022年第10365期400卷 1766-1766页

作者： Romanello, M. Di Napoli, C. Drummond, P. Institute for Global Health University College London London UK School of Agriculture Policy and Development University of Reading Reading UK Institute for Sustainable Resources University College London London UK Department of Global Health Centre for Health and the Global Environment University of Washington Seattle WA USA UCL Energy Institute University College London London UK Department of Health Sciences University of York York UK Department of Meteorology University of Reading Reading UK Institute for Risk and Disaster Reduction University College London London UK School of Earth and Environment University of Leeds Leeds UK Centre on Climate Change and Planetary Health London School of Hygiene & Tropical Medicine London UK School of Population Health University of Melbourne Melbourne VIC Australia Department of Earth System Science Tsinghua University Beijing China Mercator Research Institute on Global Commons and Climate Change Berlin Germany Department of Environment Climate Change and Health World Health Organization Geneva Switzerland Institute of Environmental Sciences University of Geneva Geneva Switzerland Cardiovascular Epidemiology Unit Department of Public Health & Primary Care University of Cambridge Cambridge UK School of Government University of Birmingham Birmingham UK Economic Analysis of Climate Impacts and Policy Division Centro Euro-Mediterraneo sui Cambiamenti Climatici Venice Italy Institute for Environmental Design and Engineering University College London London UK Natural Resources Institute University of Greenwich London UK Department of Environmental Health Sciences and Yale Center on Climate Change and Health Yale University New Haven CT USA Department of Civil and Environmental Engineering Northeastern University Boston MA USA Department of Fish and Wildlife Conservation Virginia Polytechnic Institute and State University Blacksburg VA USA Department of Geography University College London London UK NU

With advancements in the science of detection and attribution studies, the influence of climate change over many events has now been quantified. Because of the rapidly increasing temperatures, vulnerable populations (adults older than 65 years, and children younger than one year of age) were exposed to 3·7 billion more heatwave days in 2021 than annually in 1986–2005 (indicator 1.1.2), and heat-related deaths increased by 68% between 2000–04 and 2017–21 (indicator 1.1.5), a death toll that was significantly exacerbated by the confluence of the COVID-19 pandemic. Despite the local cooling and overall health benefits of urban greenspaces, only 277 (27%) of 1038 global urban centres were at least moderately green in 2021 (indicator 2.2.3), and the number of households with air conditioning increased by 66% from 2000 to 2020, a maladaptive response that worsens the energy crisis and further increases urban heat, air pollution, and greenhouse gas emissions. [...]millions of people do not have access to the energy needed to keep their homes at healthy temperatures, preserve food and medication, and meet the seventh Sustainable Development Goal (to ensure access to affordable, reliable, sustainable, and modern energy for all). Aggravating this situation even further, governments continue to incentivise fossil fuel production and consumption: 69 (80%) of 86 countries reviewed had net-negative carbon prices (ie, provided a net subsidy to fossil fuels) for a net total of US$400 billion

关键词：

来源：评论

学校读者我要写书评

暂无评论

Correction for Thompson et al., "Fatty Acid and Alcohol Metabolism in Pseudomonas putida: Functional Analysis Using Random Barcode Transposon Sequencing"

引用

Applied and environmental Microbiology 2021年第8期87卷 1-1页

作者： Thompson, Mitchell G. Incha, Matthew R. Pearson, Allison N. Schmidt, Matthias Sharpless, William A. Eiben, Christopher B. Cruz-Morales, Pablo Blake-Hedges, Jacquelyn M. Liu, Yuzhong Adams, Catharine A. Haushalter, Robert W. Krishna, Rohith N. Lichtner, Patrick Blank, Lars M. Mukhopadhyay, Aindrila Deutschbauer, Adam M. Shih, Patrick M. Keasling, Jay D. Joint BioEnergy Institute EmeryvilleCalifornia United States Biological Systems and Engineering Division Lawrence Berkeley National Laboratory BerkeleyCalifornia United States Department of Plant Biology University of California DavisCalifornia United States Department of Plant and Microbial Biology University of California BerkeleyCalifornia United States RWTH Aachen University Aachen Germany Joint Program in Bioengineering University of California BerkeleyCalifornia United States Centro de Biotecnología FEMSA Instituto Tecnológico y de Estudios Superiores de Monterrey Monterrey Mexico Department of Chemistry University of California BerkeleyCalifornia United States Environmental Genomics and Systems Biology Division Lawrence Berkeley National Laboratory BerkeleyCalifornia United States Environmental and Genomics and Systems Biology Division Lawrence Berkeley National Laboratory BerkeleyCalifornia United States Department of Chemical and Biomolecular Engineering University of California BerkeleyCalifornia United States Institute for Quantitative Biosciences University of California BerkeleyCalifornia United States The Novo Nordisk Foundation Center for Biosustainability Technical University of Denmark Lyngby Denmark Center for Synthetic Biochemistry Institute for Synthetic Biology Shenzhen Institutes for Advanced Technologies Shenzhen China

来源：评论

学校读者我要写书评

暂无评论

Spatiotemporal dynamics of ecosystem fires and biomass burning-induced carbon emissions in China over the past two decades

引用

Geography and Sustainability 2020年第1期1卷 47-58页

作者： Anping Chen Rongyun Tang Jiafu Mao Chao Yue Xiran Li Mengdi Gao Xiaoying Shi Mingzhou Jin Daniel Ricciuto Sam Rabin Phillippe Ciais Shilong Piao Department of Biology Colorado State UniversityFort CollinsCO 80523USA Graduate Degree Program in Ecology Colorado State UniversityFort CollinsCO 80523USA Department of Industrial and Systems Engineering University of TennesseeKnoxvilleTN 37996USA Environmental Sciences Division and Climate Change Science Institute Oak Ridge National LaboratoryOak RidgeTN 37831USA State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau Northwest Agriculture&Forestry UniversityYanglingShanxi 712100China College of Urban and Environmental Sciences Central China Normal UniversityWuhanHubei 430079China Sino-French Institute for Earth System Science College of Urban and Environmental SciencesPeking UniversityBeijing 100871China Atmospheric Environmental Research(IMK-IFU) Institute of Meteorology and Climate ResearchKarlsruhe Institute of Technology(KIT)Kreuzeckbahnstraße 19Garmisch-Partenkirchen 82467Germany LSCE UMR CEA-CNRSBat.709CEL’Orme des MerisiersGif-sur-Yvette 91191France

Fire is a major type of disturbance that has important influences on ecosystem dynamics and carbon *** our understanding of ecosystem fires and their carbon cycle consequences is still limited,largely due to the difficulty of large-scale fire monitoring and the complex interactions between fire,vegetation,climate,and anthropogenic ***,using data from satellite-derived fire observations and ecosystem model simulations,we performed a comprehensive investigation of the spatial and temporal dynamics of China’s ecosystem fire disturbances and their carbon emissions over the past two decades(1997–2016).Satellite-derived results showed that on average about 3.47-4.53×10^(4) km^(2) of the land was burned annually during the past two decades,among which annual burned forest area was about 0.81-1.25×10^(4) km^(2),accounting for 0.33-0.51%of the forest area in *** burning emitted about 23.02 TgC per *** to satellite products,simulations from the Energy Exascale Earth System Land Model(ELM)strongly overestimated China’s burned area and fire-induced carbon *** burned area and fire-induced carbon emissions were high for boreal forest in Northeast China’s Daxing’anling region and subtropical dry forest in South Yunnan,as revealed by both the satellite product and the model *** results suggest that climate and anthropogenic factors play critical roles in controlling the spatial and seasonal distribution of China’s ecosystem fire *** findings highlight the importance of multiple complementary approaches in assessing ecosystem fire disturbance and its carbon *** studies are required to improve the methods of observing and modelling China’s ecosystem fire disturbances,which will provide valuable information for fire management and ecosystem sustainability in an era when both human activities and the natural environment are rapidly changing.

关键词： Fire emission Burned area Fire models China

来源：评论

学校读者我要写书评

暂无评论

Agricultural All-Terrain Vehicle Safety: Hazard Control Methods Using the Haddon Matrix

引用

Journal of Agromedicine 2021年第4期26卷 420-435页

作者： Farzaneh Khorsandi Paul Ayers Gerene Denning Charles Jennissen Dee Jepsen Melvin Myers Stephen Oesch Michael Pate David J. White a Agricultural Safety and Health Engineering Assistant Specialist in Cooperative Extension Defpartment of Biological and Agricultural Engineering University of California Davis b Professor Department of Biosystems Engineering and Soil Science University of Tennessee Knoxville c Emeritus Research Scientist in Emergency Medicine Department of Emergency Medicine University of Iowa Carver College of Medicine d MD Clinical Professor of Pediatrics and Emergency Medicine Department of emergency medicine University of Iowa Carver College of Medicine e Associate Professor / State Agricultural Safety Leader Department of Food Agricultural and Biological Engineering agricultural safety and health program The Ohio State University f Associate Professor (Adjunct) Environmental Health Emory University Emory Rollins School of Public Health Atlanta Georgia g Retired highway and motor vehicle safety attorney h Associate Professor Applied Sciences Technology & Education Agricultural Systems Technology and Education program Utah State University i PhD Professor of Social and Behavioral Health Sciences College of Public Health and Human Sciences School of Social and Behavioral Health Sciences and Extension 4-H Youth Development Oregon State University

ABSTRACTABSTRACTAll-terrain vehicle (ATV) crashes are one of the leading causes of death and injury in agriculture across the United States. Use of ATVs is highly prevalent in rural areas and has seen an increase in their application for a variety of agricultural work tasks. Empirical research on the use of these vehicles for agricultural tasks and associated injuries is limited, especially in the United States. Moreover, little is known about the risk factors associated with ATV-related injuries while doing farm work. A comprehensive review was conducted to evaluate the current injury burden of ATV use in agriculture, the need for future research, and possible solutions related to agricultural ATV safety. Potential injury prevention approaches are evaluated based on the hierarchy of control, including elimination or substitution (using side-by-side vehicles instead of ATVs or modifying ATV design), engineering control (operator protection devices), administrative authority (regulations and standards in the United States and around the world), training, and use of personal protective equipment. In addition, vehicle nomenclature, risk factors, and crash mechanisms are reviewed. Recommendations to decrease the likelihood of ATV crashes and injuries are provided.

关键词： Agriculture all-terrain vehicle ATV quad bike safety

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：