Showing papers by "Lehigh University published in 2020"
New York University1, University of Chicago2, Mackenzie Presbyterian University3, Middlesex University4, University of Kent5, Nicolaus Copernicus University in Toruń6, Harvard University7, Yale University8, Stanford University9, Northwestern University10, University of Sussex11, Utrecht University12, University of California, San Diego13, University of Maryland, College Park14, McGovern Institute for Brain Research15, University of Queensland16, University of Michigan17, California Institute of Technology18, Lehigh University19, University of Regina20, University of Oregon21, Ohio State University22, Massachusetts Institute of Technology23, University of St Andrews24, University of Cambridge25, University of British Columbia26, University of Illinois at Chicago27, University of California, Berkeley28, Carleton University29, VU University Amsterdam30, Cornell University31
TL;DR: Evidence from a selection of research topics relevant to pandemics is discussed, including work on navigating threats, social and cultural influences on behaviour, science communication, moral decision-making, leadership, and stress and coping.
Abstract: The COVID-19 pandemic represents a massive global health crisis. Because the crisis requires large-scale behaviour change and places significant psychological burdens on individuals, insights from the social and behavioural sciences can be used to help align human behaviour with the recommendations of epidemiologists and public health experts. Here we discuss evidence from a selection of research topics relevant to pandemics, including work on navigating threats, social and cultural influences on behaviour, science communication, moral decision-making, leadership, and stress and coping. In each section, we note the nature and quality of prior research, including uncertainty and unsettled issues. We identify several insights for effective response to the COVID-19 pandemic and highlight important gaps researchers should move quickly to fill in the coming weeks and months.
3,223 citations
Stanford University1, Stockholm University2, Texas A&M University3, University of Exeter4, United States Geological Survey5, University of California, Los Angeles6, University of Jyväskylä7, University of Alberta8, University of Toronto9, Umeå University10, University of New Hampshire11, University of Colorado Boulder12, University of Guelph13, Université de Montréal14, Lehigh University15, Northeast Normal University16
TL;DR: This study compiles over 7,000 field observations to present a data-driven map of northern peatlands and their carbon and nitrogen stocks, and uses machine-learning techniques with extensive peat core data to create observation-based maps ofNorthern peatland C and N stocks and to assess their response to warming and permafrost thaw.
Abstract: Northern peatlands have accumulated large stocks of organic carbon (C) and nitrogen (N), but their spatial distribution and vulnerability to climate warming remain uncertain. Here, we used machine-learning techniques with extensive peat core data (n > 7,000) to create observation-based maps of northern peatland C and N stocks, and to assess their response to warming and permafrost thaw. We estimate that northern peatlands cover 3.7 ± 0.5 million km2 and store 415 ± 150 Pg C and 10 ± 7 Pg N. Nearly half of the peatland area and peat C stocks are permafrost affected. Using modeled global warming stabilization scenarios (from 1.5 to 6 °C warming), we project that the current sink of atmospheric C (0.10 ± 0.02 Pg C⋅y-1) in northern peatlands will shift to a C source as 0.8 to 1.9 million km2 of permafrost-affected peatlands thaw. The projected thaw would cause peatland greenhouse gas emissions equal to ∼1% of anthropogenic radiative forcing in this century. The main forcing is from methane emissions (0.7 to 3 Pg cumulative CH4-C) with smaller carbon dioxide forcing (1 to 2 Pg CO2-C) and minor nitrous oxide losses. We project that initial CO2-C losses reverse after ∼200 y, as warming strengthens peatland C-sinks. We project substantial, but highly uncertain, additional losses of peat into fluvial systems of 10 to 30 Pg C and 0.4 to 0.9 Pg N. The combined gaseous and fluvial peatland C loss estimated here adds 30 to 50% onto previous estimates of permafrost-thaw C losses, with southern permafrost regions being the most vulnerable.
294 citations
TL;DR: The role that disorder, perturbations to molecular interactions resulting from sequence, posttranslational modifications, and various regulatory stimuli play on protein LLPS are discussed, with a particular focus on insights that may be obtained from simulation and theory.
Abstract: Biological phase separation is known to be important for cellular organization, which has recently been extended to a new class of biomolecules that form liquid-like droplets coexisting with the surrounding cellular or extracellular environment. These droplets are termed membraneless organelles, as they lack a dividing lipid membrane, and are formed through liquid-liquid phase separation (LLPS). Elucidating the molecular determinants of phase separation is a critical challenge for the field, as we are still at the early stages of understanding how cells may promote and regulate functions that are driven by LLPS. In this review, we discuss the role that disorder, perturbations to molecular interactions resulting from sequence, posttranslational modifications, and various regulatory stimuli play on protein LLPS, with a particular focus on insights that may be obtained from simulation and theory. We finally discuss how these molecular driving forces alter multicomponent phase separation and selectivity.
270 citations
TL;DR: It is clear that the support functionality can play a crucial role in tuning the catalytic activity that is observed and that advanced theory and characterization add greatly to the understanding of these fascinating catalysts.
Abstract: In this review, we discuss selected examples from recent literature on the role of the support on directing the nanostructures of Au-based monometallic and bimetallic nanoparticles. The role of support is then discussed in relation to the catalytic properties of Au-based monometallic and bimetallic nanoparticles using different gas phase and liquid phase reactions. The reactions discussed include CO oxidation, aerobic oxidation of monohydric and polyhydric alcohols, selective hydrogenation of alkynes, hydrogenation of nitroaromatics, CO2 hydrogenation, C–C coupling, and methane oxidation. Only studies where the role of support has been explicitly studied in detail have been selected for discussion. However, the role of support is also examined using examples of reactions involving unsupported metal nanoparticles (i.e., colloidal nanoparticles). It is clear that the support functionality can play a crucial role in tuning the catalytic activity that is observed and that advanced theory and characterization add greatly to our understanding of these fascinating catalysts.
230 citations
TL;DR: This technical study describes all-atom modeling and simulation of a fully glycosylated full-length SARS-CoV-2 spike (S) protein in a viral membrane and makes structures available in CHARMM-GUI COVID-19 Archive so that researchers can use these models to carry out innovative and novel modeling andsimulation research for the prevention and treatment of CO VID-19.
Abstract: This technical study describes all-atom modeling and simulation of a fully glycosylated full-length SARS-CoV-2 spike (S) protein in a viral membrane First, starting from PDB: 6VSB and 6VXX, full-length S protein structures were modeled using template-based modeling, de-novo protein structure prediction, and loop modeling techniques in GALAXY modeling suite Then, using the recently determined most occupied glycoforms, 22 N-glycans and 1 O-glycan of each monomer were modeled using Glycan Reader & Modeler in CHARMM-GUI These fully glycosylated full-length S protein model structures were assessed and further refined against the low-resolution data in their respective experimental maps using ISOLDE We then used CHARMM-GUI Membrane Builder to place the S proteins in a viral membrane and performed all-atom molecular dynamics simulations All structures are available in CHARMM-GUI COVID-19 Archive (http://wwwcharmm-guiorg/docs/archive/covid19) so that researchers can use these models to carry out innovative and novel modeling and simulation research for the prevention and treatment of COVID-19
207 citations
TL;DR: The TDP-43 helical region serves as a short but uniquely tunable module where application of biophysical principles can precisely control assembly and function in cellular and synthetic biology applications of LLPS.
Abstract: Liquid-liquid phase separation (LLPS) is involved in the formation of membraneless organelles (MLOs) associated with RNA processing. The RNA-binding protein TDP-43 is present in several MLOs, undergoes LLPS, and has been linked to the pathogenesis of amyotrophic lateral sclerosis (ALS). While some ALS-associated mutations in TDP-43 disrupt self-interaction and function, here we show that designed single mutations can enhance TDP-43 assembly and function via modulating helical structure. Using molecular simulation and NMR spectroscopy, we observe large structural changes upon dimerization of TDP-43. Two conserved glycine residues (G335 and G338) are potent inhibitors of helical extension and helix-helix interaction, which are removed in part by variants at these positions, including the ALS-associated G335D. Substitution to helix-enhancing alanine at either of these positions dramatically enhances phase separation in vitro and decreases fluidity of phase-separated TDP-43 reporter compartments in cells. Furthermore, G335A increases TDP-43 splicing function in a minigene assay. Therefore, the TDP-43 helical region serves as a short but uniquely tunable module where application of biophysical principles can precisely control assembly and function in cellular and synthetic biology applications of LLPS.
204 citations
TL;DR: In this paper, the authors propose a multi-faceted approach for fashioning theoretical contributions in review articles, which they hope will inspire more authors to develop and submit innovative, original, and high quality theory-building review articles.
Abstract: Reviewing a body of work presents unique opportunities for making a theoretical contribution. Review articles can make readers think theoretically differently about a given field or phenomenon. Yet, review articles that advance theory have been historically under‐represented in Journal of Management Studies. Accordingly, the purpose of this editorial is to propose a multi‐faceted approach for fashioning theoretical contributions in review articles, which we hope will inspire more authors to develop and submit innovative, original, and high‐quality theory‐building review articles. We argue that advancing theory with review articles requires an integrative and generative approach. We propose a non‐exhaustive set of avenues for developing theory with a review article: exposing emerging perspectives, analysing assumptions, clarifying constructs, establishing boundary conditions, testing new theory, theorizing with systems theory, and theorizing with mechanisms. As a journal, Journal of Management Studies is a journal of ideas – new ideas; ideas drawn from reflections on extant theory and ideas with potential to change the way we understand and interpret theory. With this in mind, we think that advancing theory with review articles is an untapped source of new ideas.
199 citations
TL;DR: This work explores a model protein, the disordered N-terminal domain of LAF-1, and highlights how three key features of the sequence control the protein’s propensity to phase-separate, and identifies a region of the RGG domain that has high contact probability and is highly conserved between species; deletion of this region significantly disrupts phase separation in vitro and in vivo.
Abstract: Phase separation of intrinsically disordered proteins (IDPs) commonly underlies the formation of membraneless organelles, which compartmentalize molecules intracellularly in the absence of a lipid membrane. Identifying the protein sequence features responsible for IDP phase separation is critical for understanding physiological roles and pathological consequences of biomolecular condensation, as well as for harnessing phase separation for applications in bioinspired materials design. To expand our knowledge of sequence determinants of IDP phase separation, we characterized variants of the intrinsically disordered RGG domain from LAF-1, a model protein involved in phase separation and a key component of P granules. Based on a predictive coarse-grained IDP model, we identified a region of the RGG domain that has high contact probability and is highly conserved between species; deletion of this region significantly disrupts phase separation in vitro and in vivo. We determined the effects of charge patterning on phase behavior through sequence shuffling. We designed sequences with significantly increased phase separation propensity by shuffling the wild-type sequence, which contains well-mixed charged residues, to increase charge segregation. This result indicates the natural sequence is under negative selection to moderate this mode of interaction. We measured the contributions of tyrosine and arginine residues to phase separation experimentally through mutagenesis studies and computationally through direct interrogation of different modes of interaction using all-atom simulations. Finally, we show that despite these sequence perturbations, the RGG-derived condensates remain liquid-like. Together, these studies advance our fundamental understanding of key biophysical principles and sequence features important to phase separation.
160 citations
American Museum of Natural History1, Instituto Butantan2, George Washington University3, Lehigh University4, University of Adelaide5, South Australian Museum6, Moravian College7, Australian National University8, University of Michigan9, Royal Ontario Museum10, University of Mississippi11, Rutgers University12, Florida State University13, University of São Paulo14, University of Paris15
TL;DR: High-throughput sequence data is used from 289 samples covering 75 families of squamates to address phylogenetic affinities, estimate divergence times, and characterize residual topological uncertainty in the presence of genome scale data to address genomic support for traditional taxonomic groupings Scleroglossa and Macrostomata.
Abstract: Genomics is narrowing uncertainty in the phylogenetic structure for many amniote groups. For one of the most diverse and species-rich groups, the squamate reptiles (lizards, snakes, and amphisbaenians), an inverse correlation between the number of taxa and loci sampled still persists across all publications using DNA sequence data and reaching a consensus on the relationships among them has been highly problematic. In this study, we use high-throughput sequence data from 289 samples covering 75 families of squamates to address phylogenetic affinities, estimate divergence times, and characterize residual topological uncertainty in the presence of genome-scale data. Importantly, we address genomic support for the traditional taxonomic groupings Scleroglossa and Macrostomata using novel machine-learning techniques. We interrogate genes using various metrics inherent to these loci, including parsimony-informative sites (PIS), phylogenetic informativeness, length, gaps, number of substitutions, and site concordance to understand why certain loci fail to find previously well-supported molecular clades and how they fail to support species-tree estimates. We show that both incomplete lineage sorting and poor gene-tree estimation (due to a few undesirable gene properties, such as an insufficient number of PIS), may account for most gene and species-tree discordance. We find overwhelming signal for Toxicofera, and also show that none of the loci included in this study supports Scleroglossa or Macrostomata. We comment on the origins and diversification of Squamata throughout the Mesozoic and underscore remaining uncertainties that persist in both deeper parts of the tree (e.g., relationships between Dibamia, Gekkota, and remaining squamates; among the three toxicoferan clades Iguania, Serpentes, and Anguiformes) and within specific clades (e.g., affinities among gekkotan, pleurodont iguanians, and colubroid families).
160 citations
03 May 2020
TL;DR: Transportation infrastructure plays an important role in supporting the national economy and social well-being. Extreme events have caused terrible physical damages to the transportation infrastruc... as discussed by the authors. But,
Abstract: Transportation infrastructure plays an important role in supporting the national economy and social well-being. Extreme events have caused terrible physical damages to the transportation infrastruc...
147 citations
TL;DR: A novel framework for the quantitative resilience assessment of critical infrastructure, subjected to multiple hazards is proposed, considering the vulnerability of the assets to hazard actions and the rapidity of the damage recovery, including the temporal variability of the hazards.
TL;DR: This work presents a high-throughput protein-ligand complex MD simulations that uses the output from AutoDock Vina to improve docking results in distinguishing active from decoy ligands in DUD-E (directory of useful decoy, enhanced) dataset.
Abstract: Structure-based virtual screening relies on classical scoring functions that often fail to reliably discriminate binders from nonbinders. In this work, we present a high-throughput protein-ligand complex molecular dynamics (MD) simulation that uses the output from AutoDock Vina to improve docking results in distinguishing active from decoy ligands in a directory of useful decoy-enhanced (DUD-E) dataset. MD trajectories are processed by evaluating ligand-binding stability using root-mean-square deviations. We select 56 protein targets (of 7 different protein classes) and 560 ligands (280 actives, 280 decoys) and show 22% improvement in ROC AUC (area under the curve, receiver operating characteristics curve), from an initial value of 0.68 (AutoDock Vina) to a final value of 0.83. The MD simulation demonstrates a robust performance across all seven different protein classes. In addition, some predicted ligand-binding modes are moderately refined during MD simulations. These results systematically validate the reliability of a physics-based approach to evaluate protein-ligand binding interactions.
TL;DR: The development of FF-Converter is presented to prepare Amber simulation inputs with various Amber force fields within the current CHARMM-GUI workflow to support other simulation programs that support the Amber force field.
Abstract: As part of our ongoing efforts to support diverse force fields and simulation programs in CHARMM-GUI, this work presents the development of FF-Converter to prepare Amber simulation inputs with various Amber force fields within the current CHARMM-GUI workflow. The currently supported Amber force fields are ff14SB/ff19SB (protein), Bsc1 (DNA), OL3 (RNA), GLYCAM06 (carbohydrate), Lipid17 (lipid), GAFF/GAFF2 (small molecule), TIP3P/TIP4P-EW/OPC (water), and 12-6-4 ions, and more will be added if necessary. The robustness and usefulness of this new CHARMM-GUI extension are demonstrated by two exemplary systems: a protein/N-glycan/ligand/membrane system and a protein/DNA/RNA system. Currently, CHARMM-GUI supports the Amber force fields only for the Amber program, but we will expand the FF-Converter functionality to support other simulation programs that support the Amber force fields.
TL;DR: It is found that the majority of AI applications focus on the disaster response phase, and challenges to inspire the professional community to advance AI techniques for addressing them in future research are identified.
Abstract: Natural hazards have the potential to cause catastrophic damage and significant socioeconomic loss. The actual damage and loss observed in the recent decades has shown an increasing trend. As a result, disaster managers need to take a growing responsibility to proactively protect their communities by developing efficient management strategies. A number of research studies apply artificial intelligence (AI) techniques to process disaster-related data for supporting informed disaster management. This study provides an overview of current applications of AI in disaster management during its four phases: mitigation, preparedness, response, and recovery. It presents example applications of different AI techniques and their benefits for supporting disaster management at different phases, as well as some practical AI-based decision support tools. We find that the majority of AI applications focus on the disaster response phase. This study also identifies challenges to inspire the professional community to advance AI techniques for addressing them in future research.
TL;DR: After recent large earthquakes, field investigations confirmed that several bridges were severely damaged and collapsed not only due to the earthquake, as an independent hazard, but also to other factors.
Abstract: After recent large earthquakes, field investigations confirmed that several bridges were severely damaged and collapsed not only due to the earthquake, as an independent hazard, but also to...
TL;DR: Grain boundaries can undergo phase-like transitions, called complexion transitions, in which their structure, composition, and properties change discontinuously as temperature, bulk composition, or bulk composition changes as mentioned in this paper.
Abstract: Grain boundaries can undergo phase-like transitions, called complexion transitions, in which their structure, composition, and properties change discontinuously as temperature, bulk composition, an...
George Mason University1, Goddard Space Flight Center2, University of Maryland, Baltimore County3, Université de Montréal4, University of California, Berkeley5, Massachusetts Institute of Technology6, University of New Mexico7, Harvard University8, Vanderbilt University9, University of Kansas10, Queen Mary University of London11, Search for extraterrestrial intelligence12, University of Michigan13, California Institute of Technology14, University of Hawaii at Manoa15, University of Texas at Austin16, University Corporation for Atmospheric Research17, University of Southern Queensland18, Missouri State University19, Georgia State University20, Mississippi State University21, University of Chicago22, Keele University23, University of California, Riverside24, Leiden University25, University of Louisville26, Columbia University27, University of Florida28, Dartmouth College29, Spanish National Research Council30, Lehigh University31, University of Pisa32, Carnegie Institution for Science33, University of New South Wales34, Lowell Observatory35, Nanjing University36, Stony Brook University37
TL;DR: In this paper, the authors reported observations of a planet transiting AU Microscopii (AU Mic b), which has an orbital period of 846 days, an orbital distance of 007-astronomical units, a radius of 04-Jupiter radii, and a mass of less than 18 Jupiter masses at 3σ confidence.
Abstract: AU Microscopii (AU Mic) is the second closest pre-main-sequence star, at a distance of 979 parsecs and with an age of 22 million years1 AU Mic possesses a relatively rare2 and spatially resolved3 edge-on debris disk extending from about 35 to 210 astronomical units from the star4, and with clumps exhibiting non-Keplerian motion5-7 Detection of newly formed planets around such a star is challenged by the presence of spots, plage, flares and other manifestations of magnetic 'activity' on the star8,9 Here we report observations of a planet transiting AU Mic The transiting planet, AU Mic b, has an orbital period of 846 days, an orbital distance of 007 astronomical units, a radius of 04 Jupiter radii, and a mass of less than 018 Jupiter masses at 3σ confidence Our observations of a planet co-existing with a debris disk offer the opportunity to test the predictions of current models of planet formation and evolution
TL;DR: In this paper, the particle breakage and compressibility behavior of sands treated with microbially induced carbonate precipitation (MICP) was investigated using oedometric compression tests.
Abstract: The particle breakage and compressibility behavior of sands treated with microbially induced carbonate precipitation (MICP) has been investigated using oedometric compression tests. The aci...
17 Mar 2020
TL;DR: In this article, it was shown that crystalline topological phases can be materialized in non-crystalline systems and that when weak structural disorder is confined within the interior of the system, it can support amorphous higher-order topological insulators.
Abstract: This paper shows that crystalline topological phases can be materialized in noncrystalline systems. The authors use explicit computation of the corner modes and bulk multipolar invariant and find that when weak structural disorder is confined within the interior of the system it can support amorphous higher-order topological insulators
TL;DR: The simulation approach presented here allows the structural and dynamical properties of biomolecular condensates to be studied in microscopic detail and is generally applicable to single- and multicomponent systems of proteins and nucleic acids involved in LLPS.
Abstract: The formation of membraneless organelles in cells commonly occurs via liquid-liquid phase separation (LLPS) and is in many cases driven by multivalent interactions between intrinsically disordered proteins (IDPs). Investigating the nature of these interactions, and their effect on dynamics within the condensed phase, is therefore of critical importance but very challenging for either simulation or experiment. Here, we study these interactions and their dynamics by pairing a novel multiscale simulation strategy with microsecond all-atom MD simulations of a condensed, IDP-rich phase. We simulate two IDPs this way, the low complexity domain of FUS and the N-terminal disordered domain of LAF-1, and find good agreement with experimental information about average density, water content, and residue-residue contacts. We go significantly beyond what is known from experiments by showing that ion partitioning within the condensed phase is largely driven by the charge distribution of the proteins and-in the cases considered-shows little evidence of preferential interactions of the ions with the proteins. Furthermore, we can probe the microscopic diffusive dynamics within the condensed phase, showing that water and ions are in dynamic equilibrium between dense and dilute phases, and their diffusion is reduced in the dense phase. Despite their high concentration in the condensate, the protein molecules also remain mobile, explaining the observed liquid-like properties of this phase. We finally show that IDP self-association is driven by a combination of nonspecific hydrophobic interactions as well as hydrogen bonds, salt bridges, and π-π and cation-π interactions. The simulation approach presented here allows the structural and dynamical properties of biomolecular condensates to be studied in microscopic detail and is generally applicable to single- and multicomponent systems of proteins and nucleic acids involved in LLPS.
TL;DR: A detailed review of the essential explanation behind graphene nanoantennas experimental proofs for the developments of graphene-based plasmonics antennas, achieving enhanced light–matter interaction by exploiting graphene material conductivity and optical properties is provided.
Abstract: Exceptional advancement has been made in the development of graphene optical nanoantennas. They are incorporated with optoelectronic devices for plasmonics application and have been an active research area across the globe. The interest in graphene plasmonic devices is driven by the different applications they have empowered, such as ultrafast nanodevices, photodetection, energy harvesting, biosensing, biomedical imaging and high-speed terahertz communications. In this article, the aim is to provide a detailed review of the essential explanation behind graphene nanoantennas experimental proofs for the developments of graphene-based plasmonics antennas, achieving enhanced light-matter interaction by exploiting graphene material conductivity and optical properties. First, the fundamental graphene nanoantennas and their tunable resonant behavior over THz frequencies are summarized. Furthermore, incorporating graphene-metal hybrid antennas with optoelectronic devices can prompt the acknowledgment of multi-platforms for photonics. More interestingly, various technical methods are critically studied for frequency tuning and active modulation of optical characteristics, through in situ modulations by applying an external electric field. Second, the various methods for radiation beam scanning and beam reconfigurability are discussed through reflectarray and leaky-wave graphene antennas. In particular, numerous graphene antenna photodetectors and graphene rectennas for energy harvesting are studied by giving a critical evaluation of antenna performances, enhanced photodetection, energy conversion efficiency and the significant problems that remain to be addressed. Finally, the potential developments in the synthesis of graphene material and technological methods involved in the fabrication of graphene-metal nanoantennas are discussed.
TL;DR: In this paper, the authors study sketching in the context of Newton's method for solving finite-sum problems and show that it is a dimensionality reduction technique that has received much attention in the statistics community.
Abstract: Sketching, a dimensionality reduction technique, has received much attention in the statistics community. In this paper, we study sketching in the context of Newton's method for solving finite-sum ...
TL;DR: The proposed model integrates an improved particle swarm optimization with adaptive neurofuzzy inference system (ANFIS) based on the fuzzy C-mean (FCM) clustering method to predict the EPB shield performance during tunnelling.
Abstract: This paper proposes a new computational model to predict the earth pressure balance (EPB) shield performance during tunnelling. The proposed model integrates an improved particle swarm optimization (PSO) with adaptive neurofuzzy inference system (ANFIS) based on the fuzzy C-mean (FCM) clustering method. In particular, the proposed model uses shield operational parameters as inputs and computes the advance rate as the output. Prior to modeling, critical operational parameters are identified through principle component analysis (PCA). The hybrid model is applied to the prediction of the shield performance in the tunnel section of Guangzhou Metro Line 9 in China. The prediction results indicate that the improved PSO-ANFIS model shows high accuracy in predicting the EPB shield performance in terms of the multiobjective fitness function [i.e. root mean square error $(RMSE) = 0.07$ , coefficient of determination ( $R^{2}) = 0.88$ , variance account $(VA) = 0.84$ for testing datasets, respectively]. The good agreement between the actual measurements and predicted values demonstrates that the proposed model is promising for predicting the EPB shield tunnel performance with good accuracy.
TL;DR: It is demonstrated that simple impregnation of the metal precursors onto activated carbon from a low-boiling-point, low-polarity solvent, such as acetone, results in catalysts with an atomic dispersion of cationic metal species.
Abstract: Single-site catalysts can demonstrate high activity and selectivity in many catalytic reactions. The synthesis of these materials by impregnation from strongly oxidizing aqueous solutions or pH-controlled deposition often leads to low metal loadings or a range of metal species. Here, we demonstrate that simple impregnation of the metal precursors onto activated carbon from a low-boiling-point, low-polarity solvent, such as acetone, results in catalysts with an atomic dispersion of cationic metal species. We show the generality of this method by producing single-site Au, Pd, Ru and Pt catalysts supported on carbon in a facile manner. Single-site Au/C catalysts have previously been validated commercially to produce vinyl chloride, and here we show that this facile synthesis method can produce effective catalysts for acetylene hydrochlorination in the absence of the highly oxidizing acidic solvents previously used.
TL;DR: A consensus-based algorithm is presented to achieve proportional power sharing and regulation of weighted geometric mean of bus voltages in dc microgrids with ZIP (constant impedance, constant current, and constant power) loads simultaneously.
Abstract: In dc microgrids, load power sharing and bus voltage regulation are two common control objectives. In this article, a consensus-based algorithm is presented to achieve proportional power sharing and regulation of weighted geometric mean of bus voltages in dc microgrids with ZIP (constant impedance, constant current, and constant power) loads simultaneously. By using the virtue of the Laplacian matrices of undirected connected graphs, a lemma is derived to assist the stability analysis of the dc microgrids. Thus, a sufficient condition that stabilizes the system with ZIP loads is established. In addition, with the help of a distributed voltage regulation error estimator, the tuning of the bus voltages can be realized without the requirement on initial voltage conditions. Through the Lyapunov synthesis, the large-signal stability of the closed-loop system is theoretically ensured for a wide range of load conditions. Finally, simulation studies are performed to validate the merits of the proposed consensus-based algorithm.
TL;DR: Experimental results and density functional theory (DFT) calculations showed that introducing nitrogen atoms into the coal-based activated carbon leads to a rearrangement of the carbon skeleton structure and changes the surface chemical environment.
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TL;DR: A novel design of local differential privacy mechanism for federated learning that makes the local weights update differentially private by adapting to the varying ranges at different layers of a deep neural network, which introduces a smaller variance of the estimated model weights, especially for deeper models.
Abstract: Train machine learning models on sensitive user data has raised increasing privacy concerns in many areas. Federated learning is a popular approach for privacy protection that collects the local gradient information instead of real data. One way to achieve a strict privacy guarantee is to apply local differential privacy into federated learning. However, previous works do not give a practical solution due to three issues. First, the noisy data is close to its original value with high probability, increasing the risk of information exposure. Second, a large variance is introduced to the estimated average, causing poor accuracy. Last, the privacy budget explodes due to the high dimensionality of weights in deep learning models. In this paper, we proposed a novel design of local differential privacy mechanism for federated learning to address the abovementioned issues. It is capable of making the data more distinct from its original value and introducing lower variance. Moreover, the proposed mechanism bypasses the curse of dimensionality by splitting and shuffling model updates. A series of empirical evaluations on three commonly used datasets, MNIST, Fashion-MNIST and CIFAR-10, demonstrate that our solution can not only achieve superior deep learning performance but also provide a strong privacy guarantee at the same time.
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TL;DR: A novel knowledge graphaugmented pre-trained language generation model KG-BART is proposed, which encompasses the complex relations of concepts through the knowledge graph and produces more logical and natural sentences as output and can leverage the graph attention to aggregate the rich concept semantics that enhances the model generalization on unseen concept sets.
Abstract: Generative commonsense reasoning which aims to empower machines to generate sentences with the capacity of reasoning over a set of concepts is a critical bottleneck for text generation. Even the state-of-the-art pre-trained language generation models struggle at this task and often produce implausible and anomalous sentences. One reason is that they rarely consider incorporating the knowledge graph which can provide rich relational information among the commonsense concepts. To promote the ability of commonsense reasoning for text generation, we propose a novel knowledge graph augmented pre-trained language generation model KG-BART, which encompasses the complex relations of concepts through the knowledge graph and produces more logical and natural sentences as output. Moreover, KG-BART can leverage the graph attention to aggregate the rich concept semantics that enhances the model generalization on unseen concept sets. Experiments on benchmark CommonGen dataset verify the effectiveness of our proposed approach by comparing with several strong pre-trained language generation models, particularly KG-BART outperforms BART by 5.80, 4.60, in terms of BLEU-3, 4. Moreover, we also show that the generated context by our model can work as background scenarios to benefit downstream commonsense QA tasks.
TL;DR: A detailed record of vegetation and climate changes over the past 1.74 million years in a lake sediment core from the Zoige Basin, eastern Tibetan Plateau shows three intervals with different orbital- and millennial-scale features superimposed on a stepwise long-term cooling trend.
Abstract: The Tibetan Plateau exerts a major influence on Asian climate, but its long-term environmental history remains largely unknown. We present a detailed record of vegetation and climate changes over the past 1.74 million years in a lake sediment core from the Zoige Basin, eastern Tibetan Plateau. Results show three intervals with different orbital- and millennial-scale features superimposed on a stepwise long-term cooling trend. The interval of 1.74–1.54 million years ago is characterized by an insolation-dominated mode with strong ~20,000-year cyclicity and quasi-absent millennial-scale signal. The interval of 1.54–0.62 million years ago represents a transitional insolation-ice mode marked by ~20,000- and ~40,000-year cycles, with superimposed millennial-scale oscillations. The past 620,000 years are characterized by an ice-driven mode with 100,000-year cyclicity and less frequent millennial-scale variability. A pronounced transition occurred 620,000 years ago, as glacial cycles intensified. These new findings reveal how the interaction of low-latitude insolation and high-latitude ice-volume forcing shaped the evolution of the Tibetan Plateau climate.