No evidence for globally coherent warm and cold periods over the preindustrial Common Era.

Earth's climate history is often understood by breaking it down into constituent climatic epochs1. Over the Common Era (the past 2,000 years) these epochs, such as the Little Ice Age2-4, have been characterized as having occurred at the same time across extensive spatial scales5. Although the rapid global warming seen in observations over the past 150 years does show nearly global coherence6, the spatiotemporal coherence of climate epochs earlier in the Common Era has yet to be robustly tested. Here we use global palaeoclimate reconstructions for the past 2,000 years, and find no evidence for preindustrial globally coherent cold and warm epochs. In particular, we find that the coldest epoch of the last millennium-the putative Little Ice Age-is most likely to have experienced the coldest temperatures during the fifteenth century in the central and eastern Pacific Ocean, during the seventeenth century in northwestern Europe and southeastern North America, and during the mid-nineteenth century over most of the remaining regions. Furthermore, the spatial coherence that does exist over the preindustrial Common Era is consistent with the spatial coherence of stochastic climatic variability. This lack of spatiotemporal coherence indicates that preindustrial forcing was not sufficient to produce globally synchronous extreme temperatures at multidecadal and centennial timescales. By contrast, we find that the warmest period of the past two millennia occurred during the twentieth century for more than 98 per cent of the globe. This provides strong evidence that anthropogenic global warming is not only unparalleled in terms of absolute temperatures5, but also unprecedented in spatial consistency within the context of the past 2,000 years.

Encouraging the resumption of economic activity after COVID-19: Evidence from a large scale-field experiment in China.

As the COVID-19 pandemic comes to an end, governments find themselves facing a new challenge: motivating citizens to resume economic activity. What is an effective way to do so? We investigate this question using a field experiment in the city of Zhengzhou, China, immediately following the end of the city's COVID-19 lockdown. We assessed the effect of a descriptive norms intervention providing information about the proportion of participants' neighbors who have resumed economic activity. We find that informing individuals about their neighbors' plans to visit restaurants increases the fraction of participants visiting restaurants by 12 percentage points (37%), among those participants who underestimated the proportion of neighbors who resumed economic activity. Those who overestimated did not respond by reducing restaurant attendance (the intervention yielded no "boomerang" effect); thus, our descriptive norms intervention yielded a net positive effect. We explore the moderating role of risk preferences and the effect of the intervention on subjects' perceived risk of going to restaurants, as well as the contrast with an intervention for parks, which were already perceived as safe. All of these analyses suggest our intervention worked by reducing the perceived risk of going to restaurants.

Customizing CO2 Electroreduction by Pulse-Induced Anion Enrichment.

Electrochemically converting CO2 into specified high-value products is critical for carbon neutral economics. However, governing the product distribution of the CO2 electroreduction on Cu-based catalysts remains challenging. Herein, we put forward an anion enrichment strategy to efficiently dictate the route of *CO reduction by a pulsed electrolysis strategy. Upon periodically applying a positive potential on the cathode, the anion concentration in the vicinity of the electrode increases apparently. By adopting KF, KCl, and KHCO3 as electrolytes, the dominant CO2 electroreduction product on commercial Cu foil can be tuned into CO (53% ± 2.5), C2+ (76.6 ± 2.1%), and CH4 (42.6 ± 2.1%) under pulsed electrolysis. Notably, one can delicately tailor the ratios of CO/CH4, CH4/C2+, and C2+/CO by simply changing the composition of the electrolyte. Density functional theory calculations demonstrate that locally enriched anions can affect the key CO2RR intermediates in different ways owing to their specific electronegativity and volume, which leads to the distinct selectivity. The present study highlights the importance of tuning ionic species at the electrode-electrolyte interface for customizing the CO2 electroreduction products.

Dynamic Reconstructed RuO2 /NiFeOOH with Coherent Interface for Efficient Seawater Oxidation.

Developing efficient oxygen evolution reaction (OER) electrocatalysts for seawater electrolysis is still a big challenge. Herein, a facile one-pot approach is reported to synthesize RuO2 -incorporated NiFe-metal organic framework (RuO2 /NiFe-MOF) with unique nanobrick-nanosheet heterostructure as precatalyst. Driven by electric field, the RuO2 /NiFe-MOF dynamically reconstructs into RuO2 nanoparticles-anchored NiFe oxy/hydroxide nanosheets (RuO2 /NiFeOOH) with coherent interface, during which the dissolution and redeposition of RuO2 are witnessed. Owing to the synergistic interaction between RuO2 and NiFeOOH, the as-reconstructed RuO2 /NiFeOOH exhibits outstanding alkaline OER activity with an ultralow overpotential of 187.6 mV at 10 mA cm-2 and a small Tafel slope of 31.9 mV dec-1 and excellent durability at high current densities of 840 and 1040 mA cm-2 in 1 m potassium hydroxide (KOH). When evaluated for seawater oxidation, the RuO2 /NiFeOOH only needs a low overpotential of 326.2 mV to achieve 500 mA cm-2 and can continuously catalyze OER at 500 mA cm-2 for 100 h with negligible activity degradation. Density function theory calculations reveal that the presence of strong interaction and enhanced charge transfer along the coherent interface between RuO2 and NiFeOOH ensures improved OER activity and stability.

Enhanced CO Affinity on Cu Facilitates CO2 Electroreduction toward Multi-Carbon Products.

Electrochemical CO2 reduction reaction (CO2 RR) is a promising strategy for waste CO2 utilization and intermittent electricity storage. Herein, it is reported that bimetallic Cu/Pd catalysts with enhanced *CO affinity show a promoted CO2 RR performance for multi-carbon (C2+) production under industry-relevant high current density. Especially, bimetallic Cu/Pd-1% catalyst shows an outstanding CO2 -to-C2+ conversion with 66.2% in Faradaic efficiency (FE) and 463.2 mA cm-2 in partial current density. An increment in the FE ratios of C2+ products to CO  for Cu/Pd-1% catalyst further illuminates a preferable C2+ production. In situ Raman spectra reveal that the atop-bounded CO is dominated by low-frequency band CO on Cu/Pd-1% that leads to C2+ products on bimetallic catalysts, in contrast to the majority of high-frequency band CO on Cu that favors the formation of CO. Density function theory calculation confirms that bimetallic Cu/Pd catalyst enhances the *CO adsorption and reduces the Gibbs free energy of the CC coupling process, thereby favoring the formation of C2+ products.

Optimized Sequencing Adaptors Enable Rapid and Real-Time Metagenomic Identification of Pathogens during Runtime of Sequencing.

BACKGROUND: Metagenomic next-generation sequencing (mNGS) offers the promise of unbiased detection of emerging pathogens. However, in indexed sequencing, the sequential paradigm of data acquisition, demultiplexing, and analysis restrain read assignment in advance and real-time analysis, resulting in lengthy turnaround time for clinical metagenomic detection. METHODS: We described the utility of internal-index adaptors with different lengths of barcode in multiplex sequencing. The base composition for each position within these adaptors was well-balanced to ensure nucleotide diversity and optimal sequencing performance and to achieve the early assignment of reads by first sequencing the barcodes. Combined with an automated library preparation device, we delivered a rapid and real-time bioinformatics pathogen identification solution for the Illumina NextSeq platform. The diagnostic performance was evaluated by testing 153 lower respiratory tract specimens using mNGS in comparison to culture, 16S/internal transcribed spacer amplicon sequencing, and additional PCR-based tests. RESULTS: By calculating the average F1 scores of all read lengths under different threshold values, we established the optimal threshold for pathogens identification, and found that 36 bp was the optimal shortest read length for rapid mNGS analysis. Rapid detection had a negative percentage agreement and positive percentage agreement of 100% and 85.1% for bacteria and 97.4% and 80.3% for fungi, when compared to a composite standard. The rapid mNGS solution enabled accurate pathogen identification in about 9.1 to 10.1 h sample-to-answer turnaround time. CONCLUSIONS: Optimized internal index adaptors combined with a real-time analysis pipeline provide a potential tool for a first-line test in critically ill patients.

Effect of different resumption strategies to flatten the potential COVID-19 outbreaks amid society reopens: a modeling study in China.

BACKGROUND: The effect of the COVID-19 outbreak has led policymakers around the world to attempt transmission control. However, lockdown and shutdown interventions have caused new social problems and designating policy resumption for infection control when reopening society remains a crucial issue. We investigated the effects of different resumption strategies on COVID-19 transmission using a modeling study setting. METHODS: We employed a susceptible-exposed-infectious-removed model to simulate COVID-19 outbreaks under five reopening strategies based on China's business resumption progress. The effect of each strategy was evaluated using the peak values of the epidemic curves vis-à-vis confirmed active cases and cumulative cases. Two-sample t-test was performed in order to affirm that the pick values in different scenarios are different. RESULTS: We found that a hierarchy-based reopen strategy performed best when current epidemic prevention measures were maintained save for lockdown, reducing the peak number of active cases and cumulative cases by 50 and 44%, respectively. However, the modeled effect of each strategy decreased when the current intervention was lifted somewhat. Additional attention should be given to regions with significant numbers of migrants, as the potential risk of COVID-19 outbreaks amid society reopening is intrinsically high. CONCLUSIONS: Business resumption strategies have the potential to eliminate COVID-19 outbreaks amid society reopening without special control measures. The proposed resumption strategies focused mainly on decreasing the number of imported exposure cases, guaranteeing medical support for epidemic control, or decreasing active cases.

Source data supported high resolution carbon emissions inventory for urban areas of the Beijing-Tianjin-Hebei region: Spatial patterns, decomposition and policy implications.

This paper developed internationally compatible methods for delineating boundaries of urban areas in China. By integrating emission source data with existing official statistics as well as using rescaling methodology of data mapping for 1 km grid, the authors constructed high resolution emission gridded data in Beijing-Tianjin-Hebei (Jing-Jin-Ji) region in China for 2012. Comparisons between urban and non-urban areas of carbon emissions from industry, agriculture, household and transport exhibited regional disparities as well as sectoral differences. Except for the Hebei province, per capita total direct carbon emissions from urban extents in Beijing and Tianjin were both lower than provincial averages, indicating the climate benefit of urbanization, comparable to results from developed countries. Urban extents in the Hebei province were mainly industrial centers while those in Beijing and Tianjin were more service oriented. Further decomposition analysis revealed population to be a common major driver for increased carbon emissions but climate implications of urban design, economic productivity of land use, and carbon intensity of GDP were both cluster- and sector-specific. This study disapproves the one-size-fits-all solution for carbon mitigation but calls for down-scaled analysis of carbon emissions and formulation of localized carbon reduction strategies in the Jing-Jin-Ji as well as other regions in China.

Evaluating the impact of odors from the 1955 landfills in China using a bottom-up approach.

Landfill odors have created a major concern for the Chinese public. Based on the combination of a first order decay (FOD) model and a ground-level point source Gaussian dispersion model, the impacts from odors emitted from the 1955 landfills in China are evaluated in this paper. Our bottom-up approach uses basic data related to each landfill to achieve a more accurate and comprehensive understanding of impact of landfill odors. Results reveal that the average radius of impact of landfill odors in China is 796 m, while most landfills (46.85%) are within the range of 400-1000 m, in line with the results from previous studies. The total land area impacted by odors has reached 837,476 ha, accounting for 0.09% of China's land territory. Guangdong and Sichuan provinces have the largest land areas impacted by odors, while Tibet Autonomous Region and Tianjin Municipality have the smallest. According to the CALPUFF (California Puff) model and an analysis of social big data, the overall uncertainty of our calculation of the range of odor impacts is roughly -32.88% to 32.67%. This type of study is essential for gaining an accurate and detailed estimation of the affected human population and will prove valuable for addressing the current Not In My Back Yard (NIMBY) challenge in China.

Hybrid optimal design of the eco-hydrological wireless sensor network in the middle reach of the Heihe River Basin, China.

The eco-hydrological wireless sensor network (EHWSN) in the middle reaches of the Heihe River Basin in China is designed to capture the spatial and temporal variability and to estimate the ground truth for validating the remote sensing productions. However, there is no available prior information about a target variable. To meet both requirements, a hybrid model-based sampling method without any spatial autocorrelation assumptions is developed to optimize the distribution of EHWSN nodes based on geostatistics. This hybrid model incorporates two sub-criteria: one for the variogram modeling to represent the variability, another for improving the spatial prediction to evaluate remote sensing productions. The reasonability of the optimized EHWSN is validated from representativeness, the variogram modeling and the spatial accuracy through using 15 types of simulation fields generated with the unconditional geostatistical stochastic simulation. The sampling design shows good representativeness; variograms estimated by samples have less than 3% mean error relative to true variograms. Then, fields at multiple scales are predicted. As the scale increases, estimated fields have higher similarities to simulation fields at block sizes exceeding 240 m. The validations prove that this hybrid sampling method is effective for both objectives when we do not know the characteristics of an optimized variables.

Characteristics of deformation and damage of surrounding rock along the top roadway in the working face of an isolated island and its evolution law.

This study investigates the deformation and damage characteristics of the surrounding rock along the top return mining roadway of an isolated island working face at different stages and reveals its damage mechanism and evolution law. Utilizing a mine in Yangquan City, Shanxi Province, China, as the engineering background, this research employs FLAC 3D numerical simulation and on-site measurements. The findings suggest that the evolution of the plastic zone along the top roadway of the 15,106 island face is largely similar during both the excavation and mining periods. The plastic zones on either side of the roadway are expanding asymmetrically and gradually merging into the plastic zone of the coal pillar. In the destructive stage, the sub-gangs of the roadway are penetrated, indicating the progression into the plastic zone. The investigation points to extensive damage on the larger side of the roadway, the development of fissures, and the significant depth of damage as primary causes of roadway deformation. Moreover, the extent of the plastic zones on both sides of the roadway correlates positively with their relative distance. Continuous monitoring reveals an ongoing increase in roadway displacement, consistent with general observations in coal mining. The results provide valuable insights for optimizing support structures in similar mining environments.

Intraday adaptation to extreme temperatures in outdoor activity.

Linkages between climate and human activity are often calibrated at daily or monthly resolutions, which lacks the granularity to observe intraday adaptation behaviors. Ignoring this adaptation margin could mischaracterize the health consequences of future climate change. Here, we construct an hourly outdoor leisure activity database using billions of cell phone location requests in 10,499 parks in 2017 all over China to investigate the within-day outdoor activity rhythm. We find that hourly temperatures above 30 °C and 35 °C depress outdoor leisure activities by 5% (95% confidence interval, CI 3-7%) and by 13% (95% CI 10-16%) respectively. This activity-depressing effect is larger than previous daily or monthly studies due to intraday activity substitution from noon and afternoon to morning and evening. Intraday adaptation is larger for locations and dates with time flexibility, for individuals more frequently exposed to heat, and for parks situated in urban areas. Such within-day adaptation substantially reduces heat exposure, yet it also delays the active time at night by about half an hour, with potential side effect on sleep quality. Combining empirical estimates with outputs from downscaled climate models, we show that unmitigated climate change will generate sizable activity-depressing and activity-delaying effects in summer when projected on an hourly resolution. Our findings call for more attention in leveraging real-time activity data to understand intraday adaptation behaviors and their associated health consequences in climate change research.

Large-scale spatiotemporal deep learning predicting urban residential indoor PM2.5 concentration.

Indoor PM2.5 pollution is one of the leading causes of death and disease worldwide. As monitoring indoor PM2.5 concentrations on a large scale is challenging, it is urgent to assess population-level exposure and related health risks to develop an easy-to-use and generalized model to predict indoor PM2.5 concentrations and spatiotemporal variations at the global level. Existing machine learning models of indoor PM2.5 are prone to deliver single-point predictions, and their input strategies are not widely applicable. Here, we developed a Bayesian neural network (BNN) model for predicting the distribution of daily average urban residential PM2.5 concentration based on multiple data sources available from nationwide comprehensive sensor-monitoring records in China. The BNN model showed good performance with a 10-fold cross-validation R2 of 0.70, mean-absolute-error of 9.45 µg/m3, root-mean-square error of 13.3 µg/m3, and 95 % prediction interval coverage of 85 %. To demonstrate the application process, this model was applied to predict indoor PM2.5 concentrations on a large spatiotemporal scale. Our modeled population-weighted annual indoor PM2.5 concentration for China in 2019 was 22.8 µg/m3, far exceeding the WHO standard. The validity of the model at the population level can be further bolstered, making it valuable for assessing and managing indoor air pollution-related health risks.

Modulating Electron Density of Ni-N-C Sites by N-doped Ni for Industrial-level CO2 Electroreduction in Acidic Media.

Electro-chemically reducing CO2 in a highly acidic medium is promising for addressing the issue of carbonate accumulation. However, the hydrogen evolution reaction (HER) typically dominates the acidic CO2 reduction. Herein, we construct an efficient electro-catalyst for CO formation based on a core-shell structure, where nitrogen-doped Ni nanoparticles coexist with nitrogen-coordinated Ni single atoms. The optimal catalyst demonstrates a significantly improved CO faradaic efficiency (FE) of 96.7 % in the acidic electrolyte (pH=1) at an industrial-scale current density of 500 mA cm-2 . Notably, the optimal catalyst maintains a high FE of CO exceeding 90 % (current density=500 mA cm-2 ) in the electrolyte with a wide pH range from 0.67 to 14. In-situ spectroscopic characterization and density functional theory calculations show that the local electron density of Ni-N-C sites is enhanced by N-doped Ni particles, which facilitates the formation of *COOH intermediate and the adsorption of *CO. This study demonstrates the potential of a hybrid metal/Ni-N-C interface in boosting acidic CO2 electro-reduction.

Central nitrogen vacancies in polymeric carbon nitride for boosted photocatalytic H2O2 production.

Polymeric carbon nitride (PCN) with vacancies usually exhibits distinguished mass transfer efficiency, outstanding carrier kinetics and excellent photoactivity. Previous studies have revealed the effect of edge vacancies in heptazine units of PCN; however, the roles of central nitrogen vacancies are scarcely investigated. Herein, central nitrogen vacancies polymeric carbon nitride (PCN-NVC) is rationally prepared for photocatalytic H2O2 production with a rate of 25.1 umol/h (λ > 420 nm), which is 3.5 times than that of pristine PCN. Photoelectronic measurements reveal that the central nitrogen vacancies optimize the kinetic process of electron-hole pairs. Density functional theory (DFT) calculations disclose that PCN-NVC displays lower O2 adsorption energy, thereby accelerating the OOH* formation and decreasing the H2O2 generation energy barrier. This work not only provides a strategy for constructing central nitrogen vacancies polymeric carbon nitrogen, but also affords a deep understanding of its roles in photocatalytic H2O2 production.

Quantifying knowledge from the perspective of information structurization.

Scientific literature, as the major medium that carries knowledge between scientists, exhibits explosive growth in the last century. Despite the frequent use of many tangible measures, to quantify the influence of literature from different perspectives, it remains unclear how knowledge is embodied and measured among tremendous scientific productivity, as knowledge underlying scientific literature is abstract and difficult to concretize. In this regard, there has laid a vacancy in the theoretical embodiment of knowledge for their evaluation and excavation. Here, for the first time, we quantify the knowledge from the perspective of information structurization and define a new measure of knowledge quantification index (KQI) that leverages the extent of disorder difference caused by hierarchical structure in the citation network to represent knowledge production in the literature. Built upon 214 million articles, published from 1800 to 2021, KQI is demonstrated for mining influential classics and laureates that are omitted by traditional metrics, thanks to in-depth utilization of structure. Due to the additivity of entropy and the interconnectivity of the network, KQI assembles numerous scientific impact metrics into one and gains interpretability and resistance to manipulation. In addition, KQI explores a new perspective regarding knowledge measurement through entropy and structure, utilizing structure rather than semantics to avoid ambiguity and attain applicability.

Anchoring ultra-small molybdenum oxide species on covalent triazine frameworks for efficient electrochemical nitrogen fixation.

We report a smart ion-exchange strategy to anchor molybdenum oxide particles on charge-modulated conjugated triazine frameworks (Mo/CTF-I) for electrochemically fixing nitrogen. The strong interaction between MoOx and CTF-I is conducive to the activation of the inert N2 molecule in the electro-chemical process. As a result, 5% Mo/CTF-I exhibited an excellent faradaic efficiency of 27.3% and an NH3 yield rate of 7.23 µg h-1 mgcat.-1 at -0.405 V vs. RHE in 0.1 M KOH, surpassing most previous reports.

Single-Atom Anchored Curved Carbon Surface for Efficient CO2 Electro-Reduction with Nearly 100% CO Selectivity and Industrially-Relevant Current Density.

Although single metal atoms on porous carbons (PCs) are widely used in electrochemical CO2 reduction reaction, these systems have long relied on flat graphene-based models, which are far beyond reality because of abundant curved structures in PCs; the effect of curved surfaces has long been ignored. In addition, the selectivity generally decreases under high current density, which severely limits practical application. Herein, theoretical calculations reveal that a single-Ni-atom on a curved surface can simultaneously enhance the total density of states around Fermi level and decrease the energy barrier for *COOH formation, thereby enhancing catalytic activity. This work reports a rational molten salt approach for preparing PCs with ultra-high specific surface area of up to 2635 m2 g-1 . As determined by cutting-edge techniques, a single Ni atom on a curved carbon surface is obtained and used as a catalyst for electrochemical CO2 reduction. The CO selectivity reaches up to 99.8% under industrial-level current density of 400 mA cm-2 , outperforming state-of-the-art PC-based catalysts. This work not only offers a new method for the rational synthesis of single atom catalysts with strained geometry to host rich active sites, but also provides in-depth insights for the origin of catalytic activity of curved structure-enriched PC-based catalysts.

An analysis of impact load and fragmentation dimension to explore energy dissipation patterns in coal crushing.

This research delineates the energy dissipation characteristics in coal crushing under impact loads, leveraging the capabilities of Separated Hopkinson Pressure Bar experimental system. A meticulous examination of both burst-prone and non-burst-prone coal samples during destruction processes was undertaken to decipher the dynamic compression mechanical attributes from perspectives of energy and fragmentatio's fractal dimensions. Burst-prone coal showcases a more pronounced escalation in fragmentation work in comparison to non-burst-prone samples, thereby illustrating a perceptible strain-rate dependent effect correlating with enhanced strain rates. Additionally, it was observed that incident, reflected, and transmitted energy trajectories for both sample categories follow an approximately linear ascendancy, albeit exhibiting diverse magnitudes. Burst-prone coal manifests a more rapid and focused energy growth compared to its non-burst-prone counterpart. When subjected to impact loads, a notable trend was discerned where the fragmentation's fractional dimension escalated persistently with both the incident energy and the crushing work, portraying a prominent growth effect. The insights garnered from this study pave the way for distinguishing between impacted and unimpacted coal samples using energy perspectives and fragmentation's fractal dimensions.

Effects of public-health measures for zeroing out different SARS-CoV-2 variants.

Targeted public health interventions for an emerging epidemic are essential for preventing pandemics. During 2020-2022, China invested significant efforts in strict zero-COVID measures to contain outbreaks of varying scales caused by different SARS-CoV-2 variants. Based on a multi-year empirical dataset containing 131 outbreaks observed in China from April 2020 to May 2022 and simulated scenarios, we ranked the relative intervention effectiveness by their reduction in instantaneous reproduction number. We found that, overall, social distancing measures (38% reduction, 95% prediction interval 31-45%), face masks (30%, 17-42%) and close contact tracing (28%, 24-31%) were most effective. Contact tracing was crucial in containing outbreaks during the initial phases, while social distancing measures became increasingly prominent as the spread persisted. In addition, infections with higher transmissibility and a shorter latent period posed more challenges for these measures. Our findings provide quantitative evidence on the effects of public-health measures for zeroing out emerging contagions in different contexts.