Showing papers by "Tsinghua University published in 2022"
University of Exeter1, Max Planck Institute for Biogeochemistry2, Tyndall Centre3, Atlantic Oceanographic and Meteorological Laboratory4, Bjerknes Centre for Climate Research5, University of Maryland, College Park6, CICERO Center for International Climate Research7, Leibniz Institute for Baltic Sea Research8, University of Reading9, Leibniz Institute of Marine Sciences10, Goddard Space Flight Center11, Flanders Marine Institute12, Food and Agriculture Organization13, Alfred Wegener Institute for Polar and Marine Research14, National Oceanic and Atmospheric Administration15, University of East Anglia16, Japan Meteorological Agency17, ETH Zurich18, National Institute for Environmental Studies19, Karlsruhe Institute of Technology20, Laboratoire des Sciences du Climat et de l'Environnement21, Tula Foundation22, Hertie Institute for Clinical Brain Research23, Nanjing University of Information Science and Technology24, Wageningen University and Research Centre25, Tsinghua University26, University of Western Sydney27, Cooperative Institute for Research in Environmental Sciences28, University of Florida29, Center for Neuroscience and Regenerative Medicine30, Woods Hole Research Center31, Michigan State University32, Tianjin University33, Auburn University34, Jilin Medical University35, Max Planck Institute for Meteorology36, Imperial College London37, Centre National de Recherches Météorologiques38, University of Groningen39, Tohoku University40, Ludwig Maximilian University of Munich41, Bank for International Settlements42, Institut Pierre-Simon Laplace43, Environment Canada44, North West Agriculture and Forestry University45, Northwest A&F University46, Pacific Marine Environmental Laboratory47, Stanford University48, Utrecht University49
TL;DR: Friedlingstein et al. as mentioned in this paper presented and synthesized datasets and methodology to quantify the five major components of the global carbon budget and their uncertainties, including fossil CO2 emissions, land use and land-use change data and bookkeeping models.
Abstract: Abstract. Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere in a changing climate is critical to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe and synthesize datasets and methodology to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (EFOS) are based on energy statistics and cement production data, while emissions from land-use change (ELUC), mainly deforestation, are based on land use and land-use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly, and its growth rate (GATM) is computed from the annual changes in concentration. The ocean CO2 sink (SOCEAN) is estimated with global ocean biogeochemistry models and observation-based data products. The terrestrial CO2 sink (SLAND) is estimated with dynamic global vegetation models. The resulting carbon budget imbalance (BIM), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the first time, an approach is shown to reconcile the difference in our ELUC estimate with the one from national greenhouse gas inventories, supporting the assessment of collective countries' climate progress. For the year 2020, EFOS declined by 5.4 % relative to 2019, with fossil emissions at 9.5 ± 0.5 GtC yr−1 (9.3 ± 0.5 GtC yr−1 when the cement carbonation sink is included), and ELUC was 0.9 ± 0.7 GtC yr−1, for a total anthropogenic CO2 emission of 10.2 ± 0.8 GtC yr−1 (37.4 ± 2.9 GtCO2). Also, for 2020, GATM was 5.0 ± 0.2 GtC yr−1 (2.4 ± 0.1 ppm yr−1), SOCEAN was 3.0 ± 0.4 GtC yr−1, and SLAND was 2.9 ± 1 GtC yr−1, with a BIM of −0.8 GtC yr−1. The global atmospheric CO2 concentration averaged over 2020 reached 412.45 ± 0.1 ppm. Preliminary data for 2021 suggest a rebound in EFOS relative to 2020 of +4.8 % (4.2 % to 5.4 %) globally. Overall, the mean and trend in the components of the global carbon budget are consistently estimated over the period 1959–2020, but discrepancies of up to 1 GtC yr−1 persist for the representation of annual to semi-decadal variability in CO2 fluxes. Comparison of estimates from multiple approaches and observations shows (1) a persistent large uncertainty in the estimate of land-use changes emissions, (2) a low agreement between the different methods on the magnitude of the land CO2 flux in the northern extra-tropics, and (3) a discrepancy between the different methods on the strength of the ocean sink over the last decade. This living data update documents changes in the methods and datasets used in this new global carbon budget and the progress in understanding of the global carbon cycle compared with previous publications of this dataset (Friedlingstein et al., 2020, 2019; Le Quéré et al., 2018b, a, 2016, 2015b, a, 2014, 2013). The data presented in this work are available at https://doi.org/10.18160/gcp-2021 (Friedlingstein et al., 2021).
343 citations
TL;DR: In this article, the formation and binding of melt pools is studied, and a comprehensive processing map is proposed that integrates melt pool energy and geometry-related process parameters together, based on which additively manufactured microstructures are developed during and after the solidification of constituent melt pool.
138 citations
TL;DR: Wang et al. as mentioned in this paper proposed a graph convolutional network based on SenticNet to leverage the affective dependencies of the sentence according to the specific aspect, called Sentic GCN.
Abstract: Aspect-based sentiment analysis is a fine-grained sentiment analysis task, which needs to detection the sentiment polarity towards a given aspect. Recently, graph neural models over the dependency tree are widely applied for aspect-based sentiment analysis. Most existing works, however, they generally focus on learning the dependency information from contextual words to aspect words based on the dependency tree of the sentence, which lacks the exploitation of contextual affective knowledge with regard to the specific aspect. In this paper, we propose a graph convolutional network based on SenticNet to leverage the affective dependencies of the sentence according to the specific aspect, called Sentic GCN . To be specific, we explore a novel solution to construct the graph neural networks via integrating the affective knowledge from SenticNet to enhance the dependency graphs of sentences. Based on it, both the dependencies of contextual words and aspect words and the affective information between opinion words and the aspect are considered by the novel affective enhanced graph model. Experimental results on multiple public benchmark datasets illustrate that our proposed model can beat state-of-the-art methods.
127 citations
TL;DR: Wang et al. as mentioned in this paper proposed a graph convolutional network based on SenticNet to leverage the affective dependencies of the sentence according to the specific aspect, called Sentic GCN.
Abstract: Aspect-based sentiment analysis is a fine-grained sentiment analysis task, which needs to detection the sentiment polarity towards a given aspect. Recently, graph neural models over the dependency tree are widely applied for aspect-based sentiment analysis. Most existing works, however, they generally focus on learning the dependency information from contextual words to aspect words based on the dependency tree of the sentence, which lacks the exploitation of contextual affective knowledge with regard to the specific aspect. In this paper, we propose a graph convolutional network based on SenticNet to leverage the affective dependencies of the sentence according to the specific aspect, called Sentic GCN. To be specific, we explore a novel solution to construct the graph neural networks via integrating the affective knowledge from SenticNet to enhance the dependency graphs of sentences. Based on it, both the dependencies of contextual words and aspect words and the affective information between opinion words and the aspect are considered by the novel affective enhanced graph model. Experimental results on multiple public benchmark datasets illustrate that our proposed model can beat state-of-the-art methods.
126 citations
01 Feb 2022
TL;DR: In this article , the formation and binding of melt pools is studied, and a comprehensive processing map is proposed that integrates melt pool energy and geometry-related process parameters together, based on which additively manufactured microstructures are developed during and after the solidification of constituent melt pool.
Abstract: As a revolutionary industrial technology, additive manufacturing creates objects by adding materials layer by layer and hence can fabricate customized components with an unprecedented degree of freedom. For metallic materials, unique hierarchical microstructures are constructed during additive manufacturing, which endow them with numerous excellent properties. To take full advantage of additive manufacturing, an in-depth understanding of the microstructure evolution mechanism is required. To this end, this review explores the fundamental procedures of additive manufacturing, that is, the formation and binding of melt pools. A comprehensive processing map is proposed that integrates melt pool energy- and geometry-related process parameters together. Based on it, additively manufactured microstructures are developed during and after the solidification of constituent melt pool. The solidification structures are composed of primary columnar grains and fine secondary phases that form along the grain boundaries. The post-solidification structures include submicron scale dislocation cells stemming from internal residual stress and nanoscale precipitates induced by intrinsic heat treatment during cyclic heating of adjacent melt pool. Based on solidification and dislocation theories, the formation mechanisms of the multistage microstructures are thoroughly analyzed, and accordingly, multistage control methods are proposed. In addition, the underlying atomic scale structural features are briefly discussed. Furthermore, microstructure design for additive manufacturing through adjustment of process parameters and alloy composition is addressed to fulfill the great potential of the technique. This review not only builds a solid microstructural framework for metallic materials produced by additive manufacturing but also provides a promising guideline to adjust their mechanical properties.
118 citations
TL;DR: The Post-hospitalisation COVID-19 study (PHOSP-COVID) as mentioned in this paper is a prospective, longitudinal cohort study recruiting adults (aged ≥18 years) discharged from hospital with COVID19 across the UK.
118 citations
01 Jan 2022
104 citations
TL;DR: In this article, an arc-shaped piezoelectric sheet between the outer race of rolling bearing and bearing pedestal was installed to scavenge rotational energy from rotating machines.
102 citations
TL;DR: In this paper , dual-atom catalysts (DACs) have attracted extensive attention, as an extension of SACs, and they have higher metal loading and more complex and flexible active sites, thus achieving better catalytic performance and providing more opportunities for electrocatalysis.
Abstract: In recent years, dual-atom catalysts (DACs) have attracted extensive attention, as an extension of single-atom catalysts (SACs). Compared with SACs, DACs have higher metal loading and more complex and flexible active sites, thus achieving better catalytic performance and providing more opportunities for electrocatalysis. This review introduces the research progress in recent years on how to design new DACs to enhance the performance of electrocatalysis. Firstly, the advantages of DACs in increasing metal loading are introduced. Then, the role of DACs in changing the adsorption condition of reactant molecules on metal atoms is discussed. Moreover, the ways in which DACs can reduce the reaction energy barrier of key steps and change the reaction path are explored. Catalytic applications in different electrocatalytic reactions, including the carbon dioxide reduction reaction, oxygen reduction reaction, oxygen evolution reaction, hydrogen evolution reaction, and nitrogen reduction reaction are followed. Finally, a brief summary is made and the key challenges and prospects of DACs are introduced.
102 citations
University of Exeter1, Max Planck Institute for Biogeochemistry2, Tyndall Centre3, Leibniz Institute for Baltic Sea Research4, Atlantic Oceanographic and Meteorological Laboratory5, University of Maryland, College Park6, CICERO Center for International Climate Research7, University of Reading8, Leibniz Institute of Marine Sciences9, Goddard Space Flight Center10, Flanders Marine Institute11, Food and Agriculture Organization12, Alfred Wegener Institute for Polar and Marine Research13, Geophysical Institute14, University of Victoria15, National Oceanic and Atmospheric Administration16, Karlsruhe Institute of Technology17, Laboratoire des Sciences du Climat et de l'Environnement18, Japan Meteorological Agency19, Indiana University20, ETH Zurich21, National Institute for Environmental Studies22, University of East Anglia23, European Commission24, Tula Foundation25, Bjerknes Centre for Climate Research26, Hertie Institute for Clinical Brain Research27, Nanjing University of Information Science and Technology28, Ludwig Maximilian University of Munich29, Auburn University30, Wageningen University and Research Centre31, University of Western Sydney32, Cooperative Institute for Research in Environmental Sciences33, Tsinghua University34, University of Florida35, Center for Neuroscience and Regenerative Medicine36, Woods Hole Research Center37, University of Alaska Fairbanks38, Princeton University39, Michigan State University40, University of Washington41, Appalachian State University42, Sun Yat-sen University43, Imperial College London44, University of Groningen45, University of Tennessee46, Washington University in St. Louis47, Jilin Medical University48, Tohoku University49, Rutgers University50, Centre for Research on Ecology and Forestry Applications51, Institut Pierre-Simon Laplace52, North West Agriculture and Forestry University53, Northwest A&F University54, Pacific Marine Environmental Laboratory55, Xi'an Jiaotong University56, Stanford University57, National Center for Atmospheric Research58, University of Edinburgh59, Max Planck Institute for Meteorology60, Utrecht University61, Oak Ridge National Laboratory62
TL;DR: Friedlingstein et al. as mentioned in this paper presented and synthesized data sets and methodologies to quantify the five major components of the global carbon budget and their uncertainties, including fossil CO2 emissions, land use and land-use change data and bookkeeping models.
Abstract: Abstract. Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere in a changing climate is critical to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe and synthesize data sets and methodologies to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (EFOS) are based on energy statistics and cement production data, while emissions from land-use change (ELUC), mainly deforestation, are based on land use and land-use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly, and its growth rate (GATM) is computed from the annual changes in concentration. The ocean CO2 sink (SOCEAN) is estimated with global ocean biogeochemistry models and observation-based data products. The terrestrial CO2 sink (SLAND) is estimated with dynamic global vegetation models. The resulting carbon budget imbalance (BIM), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the year 2021, EFOS increased by 5.1 % relative to 2020, with fossil emissions at 10.1 ± 0.5 GtC yr−1 (9.9 ± 0.5 GtC yr−1 when the cement carbonation sink is included), and ELUC was 1.1 ± 0.7 GtC yr−1, for a total anthropogenic CO2 emission (including the cement carbonation sink) of 10.9 ± 0.8 GtC yr−1 (40.0 ± 2.9 GtCO2). Also, for 2021, GATM was 5.2 ± 0.2 GtC yr−1 (2.5 ± 0.1 ppm yr−1), SOCEAN was 2.9 ± 0.4 GtC yr−1, and SLAND was 3.5 ± 0.9 GtC yr−1, with a BIM of −0.6 GtC yr−1 (i.e. the total estimated sources were too low or sinks were too high). The global atmospheric CO2 concentration averaged over 2021 reached 414.71 ± 0.1 ppm. Preliminary data for 2022 suggest an increase in EFOS relative to 2021 of +1.0 % (0.1 % to 1.9 %) globally and atmospheric CO2 concentration reaching 417.2 ppm, more than 50 % above pre-industrial levels (around 278 ppm). Overall, the mean and trend in the components of the global carbon budget are consistently estimated over the period 1959–2021, but discrepancies of up to 1 GtC yr−1 persist for the representation of annual to semi-decadal variability in CO2 fluxes. Comparison of estimates from multiple approaches and observations shows (1) a persistent large uncertainty in the estimate of land-use change emissions, (2) a low agreement between the different methods on the magnitude of the land CO2 flux in the northern extratropics, and (3) a discrepancy between the different methods on the strength of the ocean sink over the last decade. This living data update documents changes in the methods and data sets used in this new global carbon budget and the progress in understanding of the global carbon cycle compared with previous publications of this data set. The data presented in this work are available at https://doi.org/10.18160/GCP-2022 (Friedlingstein et al., 2022b).
98 citations
TL;DR: In this paper, the epsilon-based measure data envelopment analysis model with undesirable outputs is applied to estimate TSCDEE for 30 provinces in China from 2010 to 2016.
TL;DR: Wang et al. as discussed by the authors evaluated the carbon reduction changes of commercial building operations in China's 30 provinces during the period 2001-2016, and built a framework of the reduction intensity, amount, and efficiency.
TL;DR: In this article, using dialdehyde waste paper (DAWP) as a cross-linking agent to immobilize persimmon tannin (PT) was first used to remove the U(VI) and Cr(VI), via the "waste control by waste" concept.
TL;DR: POCLib as discussed by the authors proposes orthogonal processing on compression (orthogonal POC), which means that text analytics can be efficiently supported directly on compressed data, regardless of the type of the data processing.
Abstract: Parallel technology boosts data processing in recent years, and parallel direct data processing on hierarchically compressed documents exhibits great promise. The high-performance direct data processing technique brings large savings in both time and space by removing the need for decompressing data. However, its benefits have been limited to data traversal operations; for random accesses, direct data processing is several times slower than the state-of-the-art baselines. This article proposes a novel concept, orthogonal processing on compression (orthogonal POC), which means that text analytics can be efficiently supported directly on compressed data, regardless of the type of the data processing – that is, the type of data processing is orthogonal to its capability of conducting POC. Previous proposals, such as TADOC, are not orthogonal POC. This article presents a set of techniques that successfully eliminate the limitation, and for the first time, establishes the near orthogonal POC feasibility of effectively handling both data traversal operations and random data accesses on hierarchically-compressed data. The work focuses on text data and yields a unified high-performance library, called POCLib. In a ten-node distributed Spark cluster on Amazon EC2, POCLib achieves 3.1× speedup over the state-of-the-art on random data accesses to compressed data, while preserving the capability of supporting traversal operations efficiently and providing large (3.9×) space savings.
TL;DR: In this article , an arc-shaped piezoelectric sheet between the outer race of rolling bearing and bearing pedestal was installed to scavenge rotational energy from rotating machines.
TL;DR: In this article , a defective Co3O4 catalyst was delineated via N doping, leading to a distorted lattice structure, increased active surface oxygen and enhanced oxygen mobility of the catalyst.
TL;DR: In this article, the authors examined the potential peaking pathways of emissions in China's diverse industrial sectors using both regression analysis and Monte Carlo simulation, and found that seven out of the eight sectors are expected to reach their peak emissions before 2040, despite continued economic growth.
TL;DR: In this article , the authors show that a TIR domain adopts distinct oligomers with mutually exclusive NADase and synthetase activity, which is a critical role for them in plant immune responses.
TL;DR: In this article, a high-performance NH4V4O10 cathode with oxygen vacancy and reduced graphene oxide surface modification is presented, which leads to high electronic conductivity, weak electrostatic interaction and low Zn2+ diffusion barrier.
TL;DR: Li et al. as mentioned in this paper employed the Poisson model for panel data to perform an empirical study to confirm the aforementioned questions and showed that public listed companies can acquire the funds needed for green innovation both through internal financing and external financing.
TL;DR: An integrated reduced graphene oxide/polypyrrole hybrid aerogel with highly efficient photodegradation performance and ultrahigh solar-powered water evaporation for simultaneous freshwater production and decontamination from complex wastewater was reported in this paper.
Abstract: Here we report an integrated reduced graphene oxide/polypyrrole hybrid aerogel with highly efficient photodegradation performance and ultrahigh solar-powered water evaporation for simultaneous freshwater production and decontamination from complex wastewater. The nanohybrids were successfully fabricated by the combined hydrothermal reduction and freeze-drying process. The π-π interactions between two components not only prevent the stacking of reduced graphene oxide nanosheets to endow aerogels with abundant water transport channels and ideal mechanical stability, but also facilitate the interactions with organic molecules to realize high removal efficiency toward volatile organic compounds (VOCs). The wide-spectrum light harvesting, photothermal effect and solar-driven photocatalysis in the hybrid aerogel are beneficial for the synergistically enhanced thermal-assisted photodegradation toward VOC-contaminated water with a water evaporation rate of 2.08 kg m−2 h−1 and a phenol removal efficiency of 94.8%. Our findings may help the development of novel functional nanostructures for applications in environmental remediation and solar steam generation.
TL;DR: A quantitative tuning rule for the time-delayed ADRC (TD-ADRC) structure based on the typical first order plus time delay (FOPTD) model is proposed, revealing Relative delay margin is revealed as a critical robustness metric among others.
Abstract: Active disturbance rejection controller (ADRC) has achieved soaring success in motion controls featured by rapid dynamics. However, it turns obstreperous to implement it in the power plant process with considerable time-delay, largely because of the tuning difficulty. To this end, this article proposes a quantitative tuning rule for the time-delayed ADRC (TD-ADRC) structure based on the typical first order plus time delay (FOPTD) model. By compensating the FOPTD process as an integrator plus time delay in low frequencies, the gain parameter of TD-ADRC can be related to a scaled time constant which shapes the closed-loop tracking performance. Bandwidth parameter of extended state observer is scaled as a dimensionless parameter. A sufficient stability condition of TD-ADRC is theoretically derived in terms of the scaled parameter pair, the range of which falls within the practical interest. Relative delay margin is revealed as a critical robustness metric among others, a default pair of scaled parameter setting is recommended as well as an explicit retuning guideline according to the user's preference for performance or robustness. Simulation and laboratory water tank experiment validate the tuning efficacy and a coal mill temperature control test depicts a promising prospective of the proposed method in process control practice.
TL;DR: In this paper, the authors carried out a three-timepoint exposure experiment at 1-, 4-, and 8-week and investigated the colonization dynamics for polyethylene, polypropylene, polystyrene, polyvinyl chloride, and acrylonitrile-butadiene-styrene MP pellets in natural coastal water.
TL;DR: In this article , a synergistic hetero-dihalogenated terminals strategy was systematically employed for the first time to enhance single-crystal packing, boosting the device performance of a Y-BO-FCl:PM6 device with a remarkable PCE of 17.52%.
Abstract: A synergistic hetero-dihalogenated terminals strategy was systematically employed for the first time to enhance single-crystal packing, boosting the device performance of a Y-BO-FCl:PM6 device with a remarkable PCE of 17.52%.
TL;DR: Wang et al. as mentioned in this paper investigated the different emission scales of carbon emission changes of residential and commercial building operations across 30 provinces in China through the carbon Kuznets curve (CKC) model.
TL;DR: In this article, a comprehensive review on the preparation of CDs and composite materials for the detection and adsorption of radioactive ions is presented, focusing on the influence of CDs properties.
01 Feb 2022
TL;DR: An integrated reduced graphene oxide/polypyrrole hybrid aerogel with highly efficient photodegradation performance and ultrahigh solar-powered water evaporation for simultaneous freshwater production and decontamination from complex wastewater was reported in this article .
Abstract: Here we report an integrated reduced graphene oxide/polypyrrole hybrid aerogel with highly efficient photodegradation performance and ultrahigh solar-powered water evaporation for simultaneous freshwater production and decontamination from complex wastewater. The nanohybrids were successfully fabricated by the combined hydrothermal reduction and freeze-drying process. The π-π interactions between two components not only prevent the stacking of reduced graphene oxide nanosheets to endow aerogels with abundant water transport channels and ideal mechanical stability, but also facilitate the interactions with organic molecules to realize high removal efficiency toward volatile organic compounds (VOCs). The wide-spectrum light harvesting, photothermal effect and solar-driven photocatalysis in the hybrid aerogel are beneficial for the synergistically enhanced thermal-assisted photodegradation toward VOC-contaminated water with a water evaporation rate of 2.08 kg m−2 h−1 and a phenol removal efficiency of 94.8%. Our findings may help the development of novel functional nanostructures for applications in environmental remediation and solar steam generation.
TL;DR: It is argued that blockchain is not a panacea for energy systems because blockchain’s component technologies have their own generic issues, and that the application of energy blockchain should be accompanied with improvement measures that conform to practical requirements of energy systems.
TL;DR: In this paper, the authors proposed a theoretical model to explore the impact of social and technical enablers on trust and how trust affects users' continuance intention in the live streaming commerce scenario.
TL;DR: Zhang et al. as discussed by the authors proposed a theoretical model to explore the impact of social and technical enablers on trust and how trust affects users' continuance intention in the live streaming commerce scenario.