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Paddy rice methane emissions across Monsoon Asia

Remote Sensing of Environment 2023年 284卷

作者： Ouyang, Zutao Jackson, Robert B. McNicol, Gavin Fluet-Chouinard, Etienne Runkle, Benjamin R.K. Papale, Dario Knox, Sara H. Cooley, Sarah Delwiche, Kyle B. Feron, Sarah Irvin, Jeremy Andrew Malhotra, Avni Muddasir, Muhammad Sabbatini, Simone Alberto, Ma. Carmelita R. Cescatti, Alessandro Chen, Chi-Ling Dong, Jinwei Fong, Bryant N. Guo, Haiqiang Hao, Lu Iwata, Hiroki Jia, Qingyu Ju, Weimin Kang, Minseok Li, Hong Kim, Joon Reba, Michele L. Nayak, Amaresh Kumar Roberti, Debora Regina Ryu, Youngryel Swain, Chinmaya Kumar Tsuang, Benjei Xiao, Xiangming Yuan, Wenping Zhang, Geli Zhang, Yongguang Department of Earth System Science Stanford University StanfordCA United States Woods Institute for the Environment Stanford University StanfordCA United States Precourt Institute for Energy Stanford University StanfordCA United States Department of Earth and Environmental Sciences University of Illinois Chicago ChicagoIL United States Department of Biological and Agricultural Engineering University of Arkansas FayettevilleAR United States University of Tuscia Viterbo Italy IAFES division Viterbo Italy Department of Geography The University of British Columbia VancouverBC Canada Department of Geography University of Oregon EugeneOR United States Department of Environmental Science Policy And Management UC Berkeley BerkeleyCA United States Campus Fryslan University of Groningen Groningen Netherlands Department of Computer Science Stanford University StanfordCA United States Department of Geography University of Zurich Zurich Switzerland International Rice Research Institute Laguna Philippines Ispra Italy Taiwan Agricultural Research Institute Taichung Wufeng Taiwan Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China USDA ARS Delta Water Management Research Unit JonesboroAR United States Fudan University Ministry of Education Key Laboratory for Biodiversity Science and Ecological Engineering and Coastal Ecosystems Research Station of the Yangtze River Estuary Shanghai China / Jiangsu Key Laboratory of Agricultural Meteorology Nanjing University of Information Science and Technology Nanjing China Department of Environmental Science Shinshu University Matsumoto Japan China Meteorological Administration Institute of Atmospheric Environment Shenyang China International Institute for Earth System Sciences Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing University Jiangsu Nanjing210023 China National Center for AgroM

Although rice cultivation is one of the most important agricultural sources of methane (CH4) and contributes ∼8% of total global anthropogenic emissions, large discrepancies remain among estimates of global CH4 emissions from rice cultivation (ranging from 18 to 115 Tg CH4 yr−1) due to a lack of observational constraints. The spatial distribution of paddy-rice emissions has been assessed at regional-to-global scales by bottom-up inventories and land surface models over coarse spatial resolution (e.g., > 0.5°) or spatial units (e.g., agro-ecological zones). However, high-resolution CH4 flux estimates capable of capturing the effects of local climate and management practices on emissions, as well as replicating in situ data, remain challenging to produce because of the scarcity of high-resolution maps of paddy-rice and insufficient understanding of CH4 predictors. Here, we combine paddy-rice methane-flux data from 23 global eddy covariance sites and MODIS remote sensing data with machine learning to 1) evaluate data-driven model performance and variable importance for predicting rice CH4 fluxes;and 2) produce gridded up-scaling estimates of rice CH4 emissions at 5000-m resolution across Monsoon Asia, where ∼87% of global rice area is cultivated and ∼ 90% of global rice production occurs. Our random-forest model achieved Nash-Sutcliffe Efficiency values of 0.59 and 0.69 for 8-day CH4 fluxes and site mean CH4 fluxes respectively, with land surface temperature, biomass and water-availability-related indices as the most important predictors. We estimate the average annual (winter fallow season excluded) paddy rice CH4 emissions throughout Monsoon Asia to be 20.6 ± 1.1 Tg yr−1 for 2001–2015, which is at the lower range of previous inventory-based estimates (20–32 CH4 Tg yr−1). Our estimates also suggest that CH4 emissions from paddy rice in this region have been declining from 2007 through 2015 following declines in both paddy-rice growing area and emission rates per unit

关键词： Climate change

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Infrared and visual image fusion via multi-modal decomposition and PCNN in gradient domain fusion measure 2nd

Infrared and visual image fusion via multi-modal decompositi...

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2nd International Conference on Smart Multimedia, ICSM 2019

作者： Tan, Wei Zhang, Jiajia Qian, Kun Du, Juan Xiang, Pei Zhou, Huixin School of Physics and Optoelectronic Engineering Xidian University Xi’an710071 China Department of Computer Science University of Copenhagen Copenhagen2100 Denmark Shanghai Institute of Spaceflight Control Technology Shanghai201109 China Infrared Detection Technology Research and Development Center of CASC Shanghai201109 China

ISBN: (纸本)9783030544065

Infrared and visual image fusion aims to obtain a complex image which contains more recognizable information. To obtain the complex image, a fusion algorithm via multi-modal decomposition and pulse-coupled neural network (PCNN) in gradient domain fusion measure is proposed. Firstly, the source images are decomposed into three layers through the decomposition model. Then, a gradient domain PCNN fusion measure is employed in the three layers. Finally, the fused image is reconstructed through the three fused layers. Experimental results demonstrate that the proposed algorithm performs effectively in both qualitative and quantitative measures. © Springer Nature Switzerland AG 2020.

关键词： Image fusion

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Science education through project-based learning: A case study

Science education through project-based learning: A case stu...

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2022 International Conference on ENTERprise Information systems, CENTERIS 2022 - International Conference on Project MANagement, ProjMAN 2022 and International Conference on Health and Social Care Information systems and Technologies, HCist 2022

作者： Santos, Carolina Rybska, Eliza Klichowski, Michal Jankowiak, Barbara Jaskulska, Sylwia Domingues, Nuno Carvalho, Diana Rocha, Tânia Paredes, Hugo Martins, Paulo Rocha, João National School of Public Health-NOVA University of Lisbon Avenida Padre Cruz Lisbon1600-560 Portugal Comprehensive Health Research Centre NOVA University of Lisbon Lisbon Portugal Faculty of Biology-Adam Mickiewicz University Uniwersytetu Poznanskiego 6 Poznan61-614 Poland Faculty of Educational Studies-Adam Mickiewicz University Szamarzewskiego 89 Poznan60-568 Poland Instituto Politécnico de Lisboa-Instituto Superior de Engenharia de Lisboa Rua Conselheiro Emidio Navarro 1 Lisbon1959-007 Portugal Institute for Systems and Computer Engineering Technology and Science Rua Dr. Roberto Frias Porto4200-465 Portugal University of Trás-os-Montes e Alto Douro Quinta de Prados Vila Real5001-801 Portugal

Partnerships for Science Education (PAFSE) is a case study of project-based learning applied to real-world problems connected with public health and sustainable development. The EU-funded project organizes science education activities at low secondary level engaging students and school stakeholders (universities, research centres, start-ups, enterprises, governmental organisations, NGOs) in health promotion and disease prevention actions that benefit the health and well-being of the community. Projects leaded by schools are addressed within educational scenarios co-created with partners interested in STEM education and developed under a relevant public health issue through their continuous engagement in open schooling approach. © 2022 The Authors. Published by ELSEVIER B.V.

关键词： Project management

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A Learning Convolutional Neural Network Approach for Network Robustness Prediction

arXiv

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arXiv 2022年

作者： Lou, Yang Wu, Ruizi Li, Junli Wang, Lin Li, Xiang Chen, Guanrong The Department of Computing and Decision Sciences Lingnan University Hong Kong The Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China The College of Computer Science Sichuan Normal University Chengdu610066 China The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201210 China The Department of Control Science and Engineering Tongji University Shanghai200240 China The Department of Electrical Engineering City University of Hong Kong Hong Kong

Network robustness is critical for various societal and industrial networks again malicious attacks. In particular, connectivity robustness and controllability robustness reflect how well a networked system can maintain its connectedness and controllability against destructive attacks, which can be quantified by a sequence of values that record the remaining connectivity and controllability of the network after a sequence of node- or edge-removal attacks. Traditionally, robustness is determined by attack simulations, which are computationally very time-consuming or even practically infeasible. In this paper, an improved method for network robustness prediction is developed based on learning feature representation using convolutional neural network (LFR-CNN). In this scheme, higher-dimensional network data are compressed to lower-dimensional representations, and then passed to a CNN to perform robustness prediction. Extensive experimental studies on both synthetic and real-world networks, both directed and undirected, demonstrate that 1) the proposed LFR-CNN performs better than other two state-of-the-art prediction methods, with significantly lower prediction errors;2) LFR-CNN is insensitive to the variation of the network size, which significantly extends its applicability;3) although LFR-CNN needs more time to perform feature learning, it can achieve accurate prediction faster than attack simulations;4) LFR-CNN not only can accurately predict network robustness, but also provides a good indicator for connectivity robustness, better than the classical spectral measures. © 2022, CC BY.

关键词： Complex networks

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Microplastics and nanoplastics in environment: Sampling, characterization and analytical methods

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Groundwater for Sustainable development 2024年 26卷

作者： Mahapatra, Sarbanee Maity, Jyoti Prakash Singha, Shuvendu Mishra, Tanisha Dey, Gobinda Chandra Samal, Alok Banerjee, Pritam Biswas, Chhanda Chattopadhyay, Sourasis Patra, Rasmi Ranjan Patnaik, Sneha Bhattacharya, Prosun Environmental Science laboratory Department of Chemistry School of Applied Sciences KIIT Deemed to be University Odisha Bhubaneswar751024 India Department of Agricultural Chemistry National Taiwan University Taipei106319 Taiwan Department of Environmental Science University of Kalyani Nadia West Bengal 741235 India Department of Environmental Science Policy and Management University of California University Avenue and Oxford St BerkeleyCA94720 United States Research & Development KIIT Deemed to be University Odisha Bhubaneswar751024 India Department of Computer Science & Application Odisha University of Agriculture & Technology Odisha Bhubaneswar India School of Public Health KIMS Medical College KIIT University Bhubaneswar751024 India Department of Healthcare Administration Asia University ROC Taiwan KTH-International Groundwater Arsenic Research Group Department of Sustainable Development Environmental Science and Engineering KTH Royal Institute of Technology Teknikringen 10B StockholmSE-100 44 Sweden

Microplastics (MPs) and nanoplastics (NPs) have gained increasing attention in environmental research due to their ubiquitousness and potential impacts on natural environments and human health as per the UN Sustainable development Goals (SDGs), particularly SDG-14 to address global threats where at least 12 SDGs, directly/indirectly impacts. Present review is undertaken to highlight the process of breakdown of diverse groups of plastic products in soil, surface water, and groundwater under the influence of different factors (UV, light, heat, microbe, etc.), which are mobilized as MPs/NPs to the surface water, groundwater, air, soil, and living organisms by different natural and anthropogenic processes. Review also highlights a comprehensive overview of the methodology for sampling, characterization, and analysis for these minuscule plastic particles (PPs) in various environmental samples, encompassing surface/subsurface water, sediments, soils, and biological organisms. The collection, extraction, and characterization of MPs/NPs, typically employ filtration processes, wherein a known volume of water is passed through a fine mesh to capture MPs/NPs from water samples. Sediment/soil samples require sieving and density separation techniques to isolate PPs from the surrounding matrix. Biological samples require digestion steps to remove organic matter, leaving behind plastics for analysis. Fourier-transform infrared (FTIR) spectroscopy, Raman spectroscopy, and scanning electron microscopy (SEM) are commonly utilized to determine the polymer composition, size, shape, and surface characteristics of PPs. Quantification involves several approaches, visual counting, image analysis, and spectroscopic techniques. Abundance of MPs/NPs in the respective environmental samples (water, air, soil, etc.) can be determined by comparing the richness (i.e., number/mass) of plastics to the volume or weight of the original sample. Such comprehensive analytical methodologies contribute to

关键词： Sustainable development

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The Use of Social News Curation to Empower Citizens and Journalists: Findings of A Focus Group Study with Professional Curators

The Use of Social News Curation to Empower Citizens and Jour...

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International Conference on computer Supported Cooperative Work in Design

作者： Daniel Schneider António Correia Jano Moreira de Souza Tercio Pacitti Institute of Computer Applications and Research NCE/UFRJ Rio de Janeiro Brazil INESC TEC and University of Tràs-os-Montes e Alto Douro Vila Real Portugal Systems Engineering and Computer Science Program (PESC/COPPE/UFRJ) Rio de Janeiro Brazil

ISBN: (纸本)9781728165981

Citizen engagement in building user-curated narratives of complex or long-lasting news stories has been the key foundation of the design and implementation of the Acropolis virtual environment. Previous user studies have shown, by positive evidence, that this goal can be pragmatically achieved, but the challenge now lies in assessing: a) the extent to which an environment like Acropolis can be used to empower citizens; and b) whether and how the tool could be used to support the work of professional curators. Findings from a focus group study highlighted the tool's potential to engage citizens with news, the usefulness of the environment to build virtual memories, and the convenience of using Acropolis to support professional journalistic work.

关键词： Conferences Buildings Virtual environments Tools Collaborative work

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Double-Free-Layer Magnetic Tunnel Junctions for Probabilistic Bits

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Physical Review Applied 2021年第4期15卷 044049-044049页

作者： Kerem Y. Camsari Mustafa Mert Torunbalci William A. Borders Hideo Ohno Shunsuke Fukami Department of Electrical and Computer Engineering University of California Santa Barbara California 93106 USA Birck Nanotechnology Center Purdue University West Lafayette Indiana 47907 USA Laboratory for Nanoelectronics and Spintronics Research Institute of Electrical Communication Tohoku University Japan Center for Spintronics Research Network Tohoku University Japan Center for Science and Innovation in Spintronics Tohoku University Japan WPI Advanced Institute for Materials Research Tohoku University Japan Center for Innovative Integrated Electronic Systems Tohoku University Japan

Naturally random devices that exploit ambient thermal noise have recently attracted attention as hardware primitives for accelerating probabilistic computing applications. One such approach is to use a low barrier nanomagnet as the free layer of a magnetic tunnel junction (MTJ), the magnetic fluctuations of which are converted to resistance fluctuations in the presence of a stable fixed layer. Here, we propose and theoretically analyze a MTJ with no fixed layers but two free layers that are circularly shaped disk magnets. We use an experimentally benchmarked model that accounts for finite-temperature magnetization dynamics, bias-dependent charge, and spin-polarized currents as well as the dipolar coupling between the free layers. We obtain analytical results for statistical averages of fluctuations that are in good agreement with the numerical model. We find that the free layers with low diameters fluctuate to randomize the resistance of the MTJ in an approximately bias-independent manner. We show how such MTJs can be used to build a binary stochastic neuron (or a p-bit) in hardware. Unlike earlier stochastic MTJs that need to operate at a specific bias point to produce random fluctuations, the proposed design can be random for a wide range of bias values, independent of spin-transfer-torque pinning. Moreover, in the absence of a carefully optimized stabled fixed layer, the symmetric double-free-layer stack can be manufactured using present-day magnetoresistive random-access memory (MRAM) technology by minimal changes to the fabrication process. Such devices can be used as hardware accelerators in energy-efficient computing schemes that require a large throughput of tunably random bits.

关键词： Spintronics Superparamagnetism Magnetic tunnel junctions

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Identification of switched linear systems: Persistence of excitation and numerical algorithms

arXiv

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arXiv 2021年

作者： Mu, Biqiang Chen, Tianshi Cheng, Changming Bai, Er-Wei Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Data Science Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen518172 China State Key Laboratory of Mechanical Systems and Vibration Shanghai Jiaotong University Shanghai China Department of Electrical and Computer Engineering University of Iowa Iowa CityIA52242 United States

This paper investigates two issues on identification of switched linear systems: persistence of excitation and numerical algorithms. The main contribution is a much weaker condition on the regressor to be persistently exciting that guarantees the uniqueness of the parameter sets and also provides new insights in understanding the relation among different subsystems. It is found that for uniquely determining the parameters of switched linear systems, the minimum number of samples needed derived from our condition is much smaller than that reported in the literature. The secondary contribution of the paper concerns the numerical algorithm. Though the algorithm is not new, we show that our surrogate problem, relaxed from an integer optimization to a continuous minimization, has exactly the same solution as the original integer optimization, which is effectively solved by a block-coordinate descent algorithm. Moreover, an algorithm for handling unknown number of subsystems is proposed. Several numerical examples are illustrated to support theoretical analysis. Copyright © 2021, The Authors. All rights reserved.

关键词： Integer programming

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Kidney Tumor Detection and Classification Based on Deep Learning Approaches: A New Dataset in CT Scans

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Journal of Healthcare engineering 2022年第1期2022卷 3861161页

作者： Alzu'Bi, Dalia Abdullah, Malak Hmeidi, Ismail Alazab, Rami Gharaibeh, Maha El-Heis, Mwaffaq Almotairi, Khaled H. Forestiero, Agostino Hussein, Ahmad Mohdaziz Abualigah, Laith Department of Computer Information Systems Jordan University of Science and Technology Irbid2210 Jordan Department of General Surgery and Urology University of Science and Technology Irbid22110 Jordan Department of Diagnostic and Interventional Radiology Faculty of Medicine Jordan University of Science and Technology Irbid2210 Jordan Computer Engineering Department Computer and Information Systems College Umm Al-Qura University Makkah21955 Saudi Arabia Institute for High Performance Computing and Networking CNR CS Rende Italy Deanship of E-Learning and Distance Education Umm Al-Qura University Makkah21955 Saudi Arabia Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Faculty of Information Technology Middle East University Amman11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia

Kidney tumor (KT) is one of the diseases that have affected our society and is the seventh most common tumor in both men and women worldwide. The early detection of KT has significant benefits in reducing death rates, producing preventive measures that reduce effects, and overcoming the tumor. Compared to the tedious and time-consuming traditional diagnosis, automatic detection algorithms of deep learning (DL) can save diagnosis time, improve test accuracy, reduce costs, and reduce the radiologist's workload. In this paper, we present detection models for diagnosing the presence of KTs in computed tomography (CT) scans. Toward detecting and classifying KT, we proposed 2D-CNN models;three models are concerning KT detection such as a 2D convolutional neural network with six layers (CNN-6), a ResNet50 with 50 layers, and a VGG16 with 16 layers. The last model is for KT classification as a 2D convolutional neural network with four layers (CNN-4). In addition, a novel dataset from the King Abdullah University Hospital (KAUH) has been collected that consists of 8,400 images of 120 adult patients who have performed CT scans for suspected kidney masses. The dataset was divided into 80% for the training set and 20% for the testing set. The accuracy results for the detection models of 2D CNN-6 and ResNet50 reached 97%, 96%, and 60%, respectively. At the same time, the accuracy results for the classification model of the 2D CNN-4 reached 92%. Our novel models achieved promising results;they enhance the diagnosis of patient conditions with high accuracy, reducing radiologist's workload and providing them with a tool that can automatically assess the condition of the kidneys, reducing the risk of misdiagnosis. Furthermore, increasing the quality of healthcare service and early detection can change the disease's track and preserve the patient's life. © 2022 Dalia Alzu'bi et al.

关键词： Convolutional neural networks

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Data-driven System Identification of Thermal systems using Machine Learning

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IFAC-PapersOnLine 2021年第7期54卷 162-167页

作者： Ştefan-Cristian Nechita Roland Tóth Koos van Berkel Department of Electrical Engineering Eindhoven University of Technology Eindhoven University of Technology Eindhoven The Netherlands Systems and Control Laboratory Institute for Computer Science and Control Kende u. 13-17 H-1111 Budapest Hungary Research Department ASML Netherlands B.V. 5504 DR Veldhoven The Netherlands

The paper addresses the identification of spatial-temporal mirror surface deformations as a result of laser-based heat load within the lithography process of integrated circuit production. The thermal diffusion and surface deformation are modeled by separation of the spatial-temporal effects using data-driven orthogonal decomposition. A novel tree adjoining grammar (TAG) and sparsity enhanced symbolic-regression-based learning methods are deployed to discover temporal dynamics that connect the spatial variation. The resulting data-driven procedure is applied to automatically synthetise a compact model representation of synthetic thermal effects induced mirror surface deformations.

关键词： Spatial-temporal System Identification Separation of Variables Machine Learning Genetic Programming MIMO System Indentification Tree Adjoining Grammar Equation Discovery Gaussian Proccesses

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