The rational design of metal single-atom catalysts(SACs)for electrochemical nitrogen reduction reaction(NRR)is challenging.Two-dimensional metal-organic frameworks(2DMOFs)is a unique class of promising SACs.Up to now,...The rational design of metal single-atom catalysts(SACs)for electrochemical nitrogen reduction reaction(NRR)is challenging.Two-dimensional metal-organic frameworks(2DMOFs)is a unique class of promising SACs.Up to now,the roles of individual metals,coordination atoms,and their synergy effect on the electroanalytic performance remain unclear.Therefore,in this work,a series of 2DMOFs with different metals and coordinating atoms are systematically investigated as electrocatalysts for ammonia synthesis using density functional theory calculations.For a specific metal,a proper metal-intermediate atoms p-d orbital hybridization interaction strength is found to be a key indicator for their NRR catalytic activities.The hybridization interaction strength can be quantitatively described with the p-/d-band center energy difference(Δd-p),which is found to be a sufficient descriptor for both the p-d hybridization strength and the NRR performance.The maximum free energy change(ΔG_(max))andΔd-p have a volcanic relationship with OsC_(4)(Se)_(4)located at the apex of the volcanic curve,showing the best NRR performance.The asymmetrical coordination environment could regulate the band structure subtly in terms of band overlap and positions.This work may shed new light on the application of orbital engineering in electrocatalytic NRR activity and especially promotes the rational design for SACs.展开更多
High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well operations.Deep learning-based method for classifying the dynamometer card(DC)of oil wells is an eff...High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well operations.Deep learning-based method for classifying the dynamometer card(DC)of oil wells is an efficient diagnosis method.However,the input of the DC as a two-dimensional image into the deep learning framework suffers from low feature utilization and high computational effort.Additionally,different SRPSs in an oil field have various system parameters,and the same SRPS generates different DCs at different moments.Thus,there is heterogeneity in field data,which can dramatically impair the diagnostic accuracy.To solve the above problems,a working condition recognition method based on 4-segment time-frequency signature matrix(4S-TFSM)and deep learning is presented in this paper.First,the 4-segment time-frequency signature(4S-TFS)method that can reduce the computing power requirements is proposed for feature extraction of DC data.Subsequently,the 4S-TFSM is constructed by relative normalization and matrix calculation to synthesize the features of multiple data and solve the problem of data heterogeneity.Finally,a convolutional neural network(CNN),one of the deep learning frameworks,is used to determine the functioning conditions based on the 4S-TFSM.Experiments on field data verify that the proposed diagnostic method based on 4S-TFSM and CNN(4S-TFSM-CNN)can significantly improve the accuracy of working condition recognition with lower computational cost.To the best of our knowledge,this is the first work to discuss the effect of data heterogeneity on the working condition recognition performance of SRPS.展开更多
The conventional linear time-frequency analysis method cannot achieve high resolution and energy focusing in the time and frequency dimensions at the same time,especially in the low frequency region.In order to improv...The conventional linear time-frequency analysis method cannot achieve high resolution and energy focusing in the time and frequency dimensions at the same time,especially in the low frequency region.In order to improve the resolution of the linear time-frequency analysis method in the low-frequency region,we have proposed a W transform method,in which the instantaneous frequency is introduced as a parameter into the linear transformation,and the analysis time window is constructed which matches the instantaneous frequency of the seismic data.In this paper,the W transform method is compared with the Wigner-Ville distribution(WVD),a typical nonlinear time-frequency analysis method.The WVD method that shows the energy distribution in the time-frequency domain clearly indicates the gravitational center of time and the gravitational center of frequency of a wavelet,while the time-frequency spectrum of the W transform also has a clear gravitational center of energy focusing,because the instantaneous frequency corresponding to any time position is introduced as the transformation parameter.Therefore,the W transform can be benchmarked directly by the WVD method.We summarize the development of the W transform and three improved methods in recent years,and elaborate on the evolution of the standard W transform,the chirp-modulated W transform,the fractional-order W transform,and the linear canonical W transform.Through three application examples of W transform in fluvial sand body identification and reservoir prediction,it is verified that W transform can improve the resolution and energy focusing of time-frequency spectra.展开更多
In overlapping distribution areas of Sorbus pohuashanensis and S.discolor in North China(Mount Tuoliang,Mount Xiling and Mount Baihua),Sorbus indi-viduals were found with pink fruit,which have never been recorded for ...In overlapping distribution areas of Sorbus pohuashanensis and S.discolor in North China(Mount Tuoliang,Mount Xiling and Mount Baihua),Sorbus indi-viduals were found with pink fruit,which have never been recorded for the flora of China.Fourteen morphological characters combined with four chloroplast DNA markers and internal transcribed spacer(ITS)were used to analyze the origin of the Sorbus individuals with pink fruits and their relationship to S.pohuashanensis and S.discolor.PCA,SDA and one-way(taxon)ANOVA of morphological characters provided convincing evidence of the hybrid ori-gin of Sorbus individuals with pink fruits based on a novel morphological character and many intermediate characters.Haplotype analysis based on four cpDNA markers showed that either S.pohuashanensis or S.discolor were maternal parents of Sorbus individuals with pink fruits.Incongru-ence of the position of Sorbus individuals with pink fruits between cpDNA and ITS in cluster trees supported by DNA sequence comparative analysis,implying former hybridiza-tion events between S.pohuashanensis and S.discolor.Mul-tiple hybridization events between S.pohuashanensis and S.discolor might have contributed to the generation of Sorbus individuals with pink fruits.This study has provided insights into hybridization between species of the same genus in sympatric areas,which is of great significance for the study of interspecific hybridization.展开更多
Understanding the evolutionary and ecological processes involved in population differentiation and speciation provides critical insights into biodiversity formation. In this study, we employed 29,865 single nucleotide...Understanding the evolutionary and ecological processes involved in population differentiation and speciation provides critical insights into biodiversity formation. In this study, we employed 29,865 single nucleotide polymorphisms(SNPs) and complete plastomes to examine genomic divergence and hybridization in Gentiana aristata, which is endemic to the Qinghai-Tibet Plateau(QTP) region. Genetic clustering revealed that G. aristata is characterized by geographic genetic structures with five clusters(West, East, Central, South and North). The West cluster has a specific morphological character(i.e., blue corolla) and higher values of FSTcompared to the remaining clusters, likely the result of the geological barrier formed by the Yangtze River. The West cluster diverged from the other clusters in the Early Pliocene;these remaining clusters diverged from one another in the Early Quaternary. Phylogenetic reconstructions based on SNPs and plastid data revealed substantial cyto-nuclear conflicts. Genetic clustering and D-statistics demonstrated rampant hybridization between the Central and North clusters,along the Bayankala Mountains, which form the geological barrier between the Central and North clusters. Species distribution modeling demonstrated the range of G. aristata expanded since the Last Interglacial period. Our findings provide genetic and morphological evidence of cryptic diversity in G. aristata, and identified rampant hybridization between genetic clusters along a geological barrier.These findings suggest that geological barriers and climatic fluctuations have an important role in triggering diversification as well as hybridization, indicating that cryptic diversity and hybridization are essential factors in biodiversity formation within the QTP region.展开更多
Hybridization plays a significant role in biological evolution. However, it is not clear whether ecological contingency differentially influences likelihood of hybridization, particularly at ecological margins where p...Hybridization plays a significant role in biological evolution. However, it is not clear whether ecological contingency differentially influences likelihood of hybridization, particularly at ecological margins where parental species may exhibit reduced fitnesses. Moreover, it is unknown whether future ecosystem change will increase the prevalence of hybridization. Ficus heterostyla and F. squamosa are closely related species co-distributed from southern Thailand to southwest China where hybridization, yielding viable seeds, has been documented. As a robust test of ecological factors driving hybridization, we investigated spatial hybridization signatures based on nuclear microsatellites from extensive population sampling across a widespread contact range. Both species showed high population differentiation and strong patterns of isolation by distance. Admixture estimates exposed asymmetric interspecific gene flow.Signatures of hybridization increase significantly towards higher latitude zones, peaking at the northern climatic margins. Geographic variation in reproductive phenology combined with ecologically challenging marginal habitats may promote this phenomenon. Our work is a first systematic evaluation of such patterns in a comprehensive, latitudinally-based clinal context, and indicates that tendency to hybridize appears strongly influenced by environmental conditions. Moreover, that future climate change scenarios will likely alter and possibly augment cases of hybridization at ecosystem scales.展开更多
The identification and understanding of cryptic intraspecific evolutionary units(lineages) are crucial for planning effective conservation strategies aimed at preserving genetic diversity in endangered species.However...The identification and understanding of cryptic intraspecific evolutionary units(lineages) are crucial for planning effective conservation strategies aimed at preserving genetic diversity in endangered species.However, the factors driving the evolution and maintenance of these intraspecific lineages in most endangered species remain poorly understood. In this study, we conducted resequencing of 77 individuals from 22 natural populations of Davidia involucrata, a “living fossil” dove tree endemic to central and southwest China. Our analysis revealed the presence of three distinct local lineages within this endangered species, which emerged approximately 3.09 and 0.32 million years ago. These divergence events align well with the geographic and climatic oscillations that occurred across the distributional range.Additionally, we observed frequent hybridization events between the three lineages, resulting in the formation of hybrid populations in their adjacent as well as disjunct regions. These hybridizations likely arose from climate-driven population expansion and/or long-distance gene flow. Furthermore, we identified numerous environment-correlated gene variants across the total and many other genes that exhibited signals of positive evolution during the maintenance of two major local lineages. Our findings shed light on the highly dynamic evolution underlying the remarkably similar phenotype of this endangered species. Importantly, these results not only provide guidance for the development of conservation plans but also enhance our understanding of evolutionary past for this and other endangered species with similar histories.展开更多
Geological discontinuity(GD)plays a pivotal role in determining the catastrophic mechanical failure of jointed rock masses.Accurate and efficient acquisition of GD networks is essential for characterizing and understa...Geological discontinuity(GD)plays a pivotal role in determining the catastrophic mechanical failure of jointed rock masses.Accurate and efficient acquisition of GD networks is essential for characterizing and understanding the progressive damage mechanisms of slopes based on monitoring image data.Inspired by recent advances in computer vision,deep learning(DL)models have been widely utilized for image-based fracture identification.The multi-scale characteristics,image resolution and annotation quality of images will cause a scale-space effect(SSE)that makes features indistinguishable from noise,directly affecting the accuracy.However,this effect has not received adequate attention.Herein,we try to address this gap by collecting slope images at various proportional scales and constructing multi-scale datasets using image processing techniques.Next,we quantify the intensity of feature signals using metrics such as peak signal-to-noise ratio(PSNR)and structural similarity(SSIM).Combining these metrics with the scale-space theory,we investigate the influence of the SSE on the differentiation of multi-scale features and the accuracy of recognition.It is found that augmenting the image's detail capacity does not always yield benefits for vision-based recognition models.In light of these observations,we propose a scale hybridization approach based on the diffusion mechanism of scale-space representation.The results show that scale hybridization strengthens the tolerance of multi-scale feature recognition under complex environmental noise interference and significantly enhances the recognition accuracy of GD.It also facilitates the objective understanding,description and analysis of the rock behavior and stability of slopes from the perspective of image data.展开更多
Precisely refining the electronic structure of electrocatalysts represents a powerful approach to further optimize the electrocatalytic performance.Herein,we demonstrate an ingenious d-d orbital hybridization concept ...Precisely refining the electronic structure of electrocatalysts represents a powerful approach to further optimize the electrocatalytic performance.Herein,we demonstrate an ingenious d-d orbital hybridization concept to construct Mo-doped Co_(9)S_(8) nanorod arrays aligned on carbon cloth(CC)substrate(abbreviated as Mo-Co_(9)S_(8)@CC hereafter)as a high-efficiency bifunctional electrocatalyst toward water electrolysis.It has experimentally and theoretically validated that the 4d-3d orbital coupling between Mo dopant and Co site can effectively optimize the H_(2)O activation energy and lower H^(*)adsorption energy barrier,thereby leading to enhanced hydrogen evolution reaction(HER)and oxygen evolution reaction(OER)activities.Thanks to the unique electronic and geometrical advantages,the optimized Mo-Co_(9)S_(8)@CC with appropriate Mo content exhibits outstanding bifunctional performance in alkaline solution,with the overpotentials of 75 and 234 mV for the delivery of a current density of 10 mA cm^(-2),small Tafel slopes of 53.8 and 39.9 mV dec~(-1)and long-term stabilities for at least 32 and 30 h for HER and OER,respectively.More impressively,a water splitting electrolylzer assembled by the self-supported Mo-Co_(9)S_(8)@CC electrode requires a low cell voltage of 1.53 V at 10 mA cm^(-2)and shows excellent stability and splendid reversibility,demonstrating a huge potential for affordable and scalable electrochemical H_(2) production.The innovational orbital hybridization strategy for electronic regulation herein provides an inspirable avenue for developing progressive electrocatalysts toward new energy systems.展开更多
Rational design of efficient and robust earth-abundant alkaline hydrogen evolution reaction(HER)catalysts is a key factor for developing energy conversion technologies.Currently,antiperovskite nitride CuNMn_(3)has gar...Rational design of efficient and robust earth-abundant alkaline hydrogen evolution reaction(HER)catalysts is a key factor for developing energy conversion technologies.Currently,antiperovskite nitride CuNMn_(3)has garnered significant interest due to its remarkable properties such as negative/zero thermal expansion and magnetocaloric effects.However,when utilized as hydrogen evolution catalysts,it encounters large challenge resulting from excessively strong/weak interactions with adsorbed H on Mn/Cu active sites,which leads to low HER activity.In this study,we introduce an asymmetric orbital hybridization strategy in Zn-doped Cu_(1-x)Zn_(x)NMn_(3)by leveraging the localization of Zn electronic states to reconfigure the electronic structures of Cu and Mn,thereby reducing the energy barrier for water dissociation and optimizing Cu and Mn active sites for hydrogen adsorption and H_(2)production.Electrochemical evaluations reveal that Cu_(0.85)Zn_(0.15)NMn_(3)with x=0.15 demonstrates exceptional electrocatalytic activity in alkaline electrolytes.A low overpotential of 52 mV at 10 mA cm^(-2)and outstanding stability over a 150-h test period are achieved,surpassing commercial Pt/C.This research offers a novel strategy for enhancing HER performance by modulating asymmetric hybridization of electron orbitals between multiple metal atoms within a material structure.展开更多
High-entropy catalysts featuring exceptional properties are,in no doubt,playing an increasingly significant role in aprotic lithium-oxygen batteries.Despite extensive effort devoted to tracing the origin of their unpa...High-entropy catalysts featuring exceptional properties are,in no doubt,playing an increasingly significant role in aprotic lithium-oxygen batteries.Despite extensive effort devoted to tracing the origin of their unparalleled performance,the relationships between multiple active sites and reaction intermediates are still obscure.Here,enlightened by theoretical screening,we tailor a high-entropy perovskite fluoride(KCoMnNiMgZnF_(3)-HEC)with various active sites to overcome the limitations of conventional catalysts in redox process.The entropy effect modulates the d-band center and d orbital occupancy of active centers,which optimizes the d–p hybridization between catalytic sites and key intermediates,enabling a moderate adsorption of LiO_(2)and thus reinforcing the reaction kinetics.As a result,the Li–O2 battery with KCoMnNiMgZnF_(3)-HEC catalyst delivers a minimal discharge/charge polarization and long-term cycle stability,preceding majority of traditional catalysts reported.These encouraging results provide inspiring insights into the electron manipulation and d orbital structure optimization for advanced electrocatalyst.展开更多
In situ mRNA hybridization(ISH)is a powerful tool for examining the spatiotemporal expression of genes in shoot apical meristems and flower buds of cucumber.The most common ISH protocol uses paraffin wax;however,embed...In situ mRNA hybridization(ISH)is a powerful tool for examining the spatiotemporal expression of genes in shoot apical meristems and flower buds of cucumber.The most common ISH protocol uses paraffin wax;however,embedding tissue in paraffin wax can take a long time and might result in RNA degradation and decreased signals.Here,we developed an optimized protocol to simplify the process and improve RNA sensitivity.We combined embedding tissue in low melting-point Steedman’s wax with processing tissue sections in solution,as in the whole-mount ISH method in the optimized protocol.Using the optimized protocol,we examined the expression patterns of the CLAVATA3(CLV3)and WUSCHEL(WUS)genes in shoot apical meristems and floral meristems of Cucumis sativus(cucumber)and Arabidopsis thaliana(Arabidopsis).The optimized protocol saved 4–5 days of experimental period compared with the standard ISH protocol using paraffin wax.Moreover,the optimized protocol achieved high signal sensitivity.The optimized protocol was successful for both cucumber and Arabidopsis,which indicates it might have general applicability to most plants.展开更多
Since leaks in high-pressure pipelines transporting crude oil can cause severe economic losses,a reliable leak risk assessment can assist in developing an effective pipeline maintenance plan and avoiding unexpected in...Since leaks in high-pressure pipelines transporting crude oil can cause severe economic losses,a reliable leak risk assessment can assist in developing an effective pipeline maintenance plan and avoiding unexpected incidents.The fast and accurate leak detection methods are essential for maintaining pipeline safety in pipeline reliability engineering.Current oil pipeline leakage signals are insufficient for feature extraction,while the training time for traditional leakage prediction models is too long.A new leak detection method is proposed based on time-frequency features and the Genetic Algorithm-Levenberg Marquardt(GA-LM)classification model for predicting the leakage status of oil pipelines.The signal that has been processed is transformed to the time and frequency domain,allowing full expression of the original signal.The traditional Back Propagation(BP)neural network is optimized by the Genetic Algorithm(GA)and Levenberg Marquardt(LM)algorithms.The results show that the recognition effect of a combined feature parameter is superior to that of a single feature parameter.The Accuracy,Precision,Recall,and F1score of the GA-LM model is 95%,93.5%,96.7%,and 95.1%,respectively,which proves that the GA-LM model has a good predictive effect and excellent stability for positive and negative samples.The proposed GA-LM model can obviously reduce training time and improve recognition efficiency.In addition,considering that a large number of samples are required for model training,a wavelet threshold method is proposed to generate sample data with higher reliability.The research results can provide an effective theoretical and technical reference for the leakage risk assessment of the actual oil pipelines.展开更多
The demand for adopting neural networks in resource-constrained embedded devices is continuously increasing.Quantization is one of the most promising solutions to reduce computational cost and memory storage on embedd...The demand for adopting neural networks in resource-constrained embedded devices is continuously increasing.Quantization is one of the most promising solutions to reduce computational cost and memory storage on embedded devices.In order to reduce the complexity and overhead of deploying neural networks on Integeronly hardware,most current quantization methods use a symmetric quantization mapping strategy to quantize a floating-point neural network into an integer network.However,although symmetric quantization has the advantage of easier implementation,it is sub-optimal for cases where the range could be skewed and not symmetric.This often comes at the cost of lower accuracy.This paper proposed an activation redistribution-based hybrid asymmetric quantizationmethod for neural networks.The proposedmethod takes data distribution into consideration and can resolve the contradiction between the quantization accuracy and the ease of implementation,balance the trade-off between clipping range and quantization resolution,and thus improve the accuracy of the quantized neural network.The experimental results indicate that the accuracy of the proposed method is 2.02%and 5.52%higher than the traditional symmetric quantization method for classification and detection tasks,respectively.The proposed method paves the way for computationally intensive neural network models to be deployed on devices with limited computing resources.Codes will be available on https://github.com/ycjcy/Hybrid-Asymmetric-Quantization.展开更多
In the mining industry,precise forecasting of rock fragmentation is critical for optimising blasting processes.In this study,we address the challenge of enhancing rock fragmentation assessment by developing a novel hy...In the mining industry,precise forecasting of rock fragmentation is critical for optimising blasting processes.In this study,we address the challenge of enhancing rock fragmentation assessment by developing a novel hybrid predictive model named GWO-RF.This model combines the grey wolf optimization(GWO)algorithm with the random forest(RF)technique to predict the D_(80)value,a critical parameter in evaluating rock fragmentation quality.The study is conducted using a dataset from Sarcheshmeh Copper Mine,employing six different swarm sizes for the GWO-RF hybrid model construction.The GWO-RF model’s hyperparameters are systematically optimized within established bounds,and its performance is rigorously evaluated using multiple evaluation metrics.The results show that the GWO-RF hybrid model has higher predictive skills,exceeding traditional models in terms of accuracy.Furthermore,the interpretability of the GWO-RF model is enhanced through the utilization of SHapley Additive exPlanations(SHAP)values.The insights gained from this research contribute to optimizing blasting operations and rock fragmentation outcomes in the mining industry.展开更多
The load types in low-voltage distribution systems are diverse.Some loads have current signals that are similar to series fault arcs,making it difficult to effectively detect fault arcs during their occurrence and sus...The load types in low-voltage distribution systems are diverse.Some loads have current signals that are similar to series fault arcs,making it difficult to effectively detect fault arcs during their occurrence and sustained combustion,which can easily lead to serious electrical fire accidents.To address this issue,this paper establishes a fault arc prototype experimental platform,selects multiple commonly used loads for fault arc experiments,and collects data in both normal and fault states.By analyzing waveform characteristics and selecting fault discrimination feature indicators,corresponding feature values are extracted for qualitative analysis to explore changes in timefrequency characteristics of current before and after faults.Multiple features are then selected to form a multidimensional feature vector space to effectively reduce arc misjudgments and construct a fault discrimination feature database.Based on this,a fault arc hazard prediction model is built using random forests.The model’s multiple hyperparameters are simultaneously optimized through grid search,aiming tominimize node information entropy and complete model training,thereby enhancing model robustness and generalization ability.Through experimental verification,the proposed method accurately predicts and classifies fault arcs of different load types,with an average accuracy at least 1%higher than that of the commonly used fault predictionmethods compared in the paper.展开更多
The amount of oxygen blown into the converter is one of the key parameters for the control of the converter blowing process,which directly affects the tap-to-tap time of converter. In this study, a hybrid model based ...The amount of oxygen blown into the converter is one of the key parameters for the control of the converter blowing process,which directly affects the tap-to-tap time of converter. In this study, a hybrid model based on oxygen balance mechanism (OBM) and deep neural network (DNN) was established for predicting oxygen blowing time in converter. A three-step method was utilized in the hybrid model. First, the oxygen consumption volume was predicted by the OBM model and DNN model, respectively. Second, a more accurate oxygen consumption volume was obtained by integrating the OBM model and DNN model. Finally, the converter oxygen blowing time was calculated according to the oxygen consumption volume and the oxygen supply intensity of each heat. The proposed hybrid model was verified using the actual data collected from an integrated steel plant in China, and compared with multiple linear regression model, OBM model, and neural network model including extreme learning machine, back propagation neural network, and DNN. The test results indicate that the hybrid model with a network structure of 3 hidden layer layers, 32-16-8 neurons per hidden layer, and 0.1 learning rate has the best prediction accuracy and stronger generalization ability compared with other models. The predicted hit ratio of oxygen consumption volume within the error±300 m^(3)is 96.67%;determination coefficient (R^(2)) and root mean square error (RMSE) are0.6984 and 150.03 m^(3), respectively. The oxygen blow time prediction hit ratio within the error±0.6 min is 89.50%;R2and RMSE are0.9486 and 0.3592 min, respectively. As a result, the proposed model can effectively predict the oxygen consumption volume and oxygen blowing time in the converter.展开更多
基金supported by the National Natural Science Foundation of China(21905253,51973200,and 52122308)the Natural Science Foundation of Henan(202300410372)the National Supercomputing Center in Zhengzhou
文摘The rational design of metal single-atom catalysts(SACs)for electrochemical nitrogen reduction reaction(NRR)is challenging.Two-dimensional metal-organic frameworks(2DMOFs)is a unique class of promising SACs.Up to now,the roles of individual metals,coordination atoms,and their synergy effect on the electroanalytic performance remain unclear.Therefore,in this work,a series of 2DMOFs with different metals and coordinating atoms are systematically investigated as electrocatalysts for ammonia synthesis using density functional theory calculations.For a specific metal,a proper metal-intermediate atoms p-d orbital hybridization interaction strength is found to be a key indicator for their NRR catalytic activities.The hybridization interaction strength can be quantitatively described with the p-/d-band center energy difference(Δd-p),which is found to be a sufficient descriptor for both the p-d hybridization strength and the NRR performance.The maximum free energy change(ΔG_(max))andΔd-p have a volcanic relationship with OsC_(4)(Se)_(4)located at the apex of the volcanic curve,showing the best NRR performance.The asymmetrical coordination environment could regulate the band structure subtly in terms of band overlap and positions.This work may shed new light on the application of orbital engineering in electrocatalytic NRR activity and especially promotes the rational design for SACs.
基金We would like to thank the associate editor and the reviewers for their constructive comments.This work was supported in part by the National Natural Science Foundation of China under Grant 62203234in part by the State Key Laboratory of Robotics of China under Grant 2023-Z03+1 种基金in part by the Natural Science Foundation of Liaoning Province under Grant 2023-BS-025in part by the Research Program of Liaoning Liaohe Laboratory under Grant LLL23ZZ-02-02.
文摘High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well operations.Deep learning-based method for classifying the dynamometer card(DC)of oil wells is an efficient diagnosis method.However,the input of the DC as a two-dimensional image into the deep learning framework suffers from low feature utilization and high computational effort.Additionally,different SRPSs in an oil field have various system parameters,and the same SRPS generates different DCs at different moments.Thus,there is heterogeneity in field data,which can dramatically impair the diagnostic accuracy.To solve the above problems,a working condition recognition method based on 4-segment time-frequency signature matrix(4S-TFSM)and deep learning is presented in this paper.First,the 4-segment time-frequency signature(4S-TFS)method that can reduce the computing power requirements is proposed for feature extraction of DC data.Subsequently,the 4S-TFSM is constructed by relative normalization and matrix calculation to synthesize the features of multiple data and solve the problem of data heterogeneity.Finally,a convolutional neural network(CNN),one of the deep learning frameworks,is used to determine the functioning conditions based on the 4S-TFSM.Experiments on field data verify that the proposed diagnostic method based on 4S-TFSM and CNN(4S-TFSM-CNN)can significantly improve the accuracy of working condition recognition with lower computational cost.To the best of our knowledge,this is the first work to discuss the effect of data heterogeneity on the working condition recognition performance of SRPS.
基金Supported by the National Science Foundation of China(42055402)。
文摘The conventional linear time-frequency analysis method cannot achieve high resolution and energy focusing in the time and frequency dimensions at the same time,especially in the low frequency region.In order to improve the resolution of the linear time-frequency analysis method in the low-frequency region,we have proposed a W transform method,in which the instantaneous frequency is introduced as a parameter into the linear transformation,and the analysis time window is constructed which matches the instantaneous frequency of the seismic data.In this paper,the W transform method is compared with the Wigner-Ville distribution(WVD),a typical nonlinear time-frequency analysis method.The WVD method that shows the energy distribution in the time-frequency domain clearly indicates the gravitational center of time and the gravitational center of frequency of a wavelet,while the time-frequency spectrum of the W transform also has a clear gravitational center of energy focusing,because the instantaneous frequency corresponding to any time position is introduced as the transformation parameter.Therefore,the W transform can be benchmarked directly by the WVD method.We summarize the development of the W transform and three improved methods in recent years,and elaborate on the evolution of the standard W transform,the chirp-modulated W transform,the fractional-order W transform,and the linear canonical W transform.Through three application examples of W transform in fluvial sand body identification and reservoir prediction,it is verified that W transform can improve the resolution and energy focusing of time-frequency spectra.
基金This study was supported by the National Natural Science Foundation of China,Grant/Award Number:32071779.
文摘In overlapping distribution areas of Sorbus pohuashanensis and S.discolor in North China(Mount Tuoliang,Mount Xiling and Mount Baihua),Sorbus indi-viduals were found with pink fruit,which have never been recorded for the flora of China.Fourteen morphological characters combined with four chloroplast DNA markers and internal transcribed spacer(ITS)were used to analyze the origin of the Sorbus individuals with pink fruits and their relationship to S.pohuashanensis and S.discolor.PCA,SDA and one-way(taxon)ANOVA of morphological characters provided convincing evidence of the hybrid ori-gin of Sorbus individuals with pink fruits based on a novel morphological character and many intermediate characters.Haplotype analysis based on four cpDNA markers showed that either S.pohuashanensis or S.discolor were maternal parents of Sorbus individuals with pink fruits.Incongru-ence of the position of Sorbus individuals with pink fruits between cpDNA and ITS in cluster trees supported by DNA sequence comparative analysis,implying former hybridiza-tion events between S.pohuashanensis and S.discolor.Mul-tiple hybridization events between S.pohuashanensis and S.discolor might have contributed to the generation of Sorbus individuals with pink fruits.This study has provided insights into hybridization between species of the same genus in sympatric areas,which is of great significance for the study of interspecific hybridization.
基金financial support provided by the Foundation of Henan Educational Committee (22A180024)Natural Science Foundation of Henan Province (232300420212)。
文摘Understanding the evolutionary and ecological processes involved in population differentiation and speciation provides critical insights into biodiversity formation. In this study, we employed 29,865 single nucleotide polymorphisms(SNPs) and complete plastomes to examine genomic divergence and hybridization in Gentiana aristata, which is endemic to the Qinghai-Tibet Plateau(QTP) region. Genetic clustering revealed that G. aristata is characterized by geographic genetic structures with five clusters(West, East, Central, South and North). The West cluster has a specific morphological character(i.e., blue corolla) and higher values of FSTcompared to the remaining clusters, likely the result of the geological barrier formed by the Yangtze River. The West cluster diverged from the other clusters in the Early Pliocene;these remaining clusters diverged from one another in the Early Quaternary. Phylogenetic reconstructions based on SNPs and plastid data revealed substantial cyto-nuclear conflicts. Genetic clustering and D-statistics demonstrated rampant hybridization between the Central and North clusters,along the Bayankala Mountains, which form the geological barrier between the Central and North clusters. Species distribution modeling demonstrated the range of G. aristata expanded since the Last Interglacial period. Our findings provide genetic and morphological evidence of cryptic diversity in G. aristata, and identified rampant hybridization between genetic clusters along a geological barrier.These findings suggest that geological barriers and climatic fluctuations have an important role in triggering diversification as well as hybridization, indicating that cryptic diversity and hybridization are essential factors in biodiversity formation within the QTP region.
基金supported by the National Natural Science Foundation of China (3180031332261123001)+1 种基金Applied Basic Research Foundation of Yunnan Province (202301AT070378, 2019FB034)the “Light of West China” Program of the Chinese Academic of Sciences to J.-F.Huang。
文摘Hybridization plays a significant role in biological evolution. However, it is not clear whether ecological contingency differentially influences likelihood of hybridization, particularly at ecological margins where parental species may exhibit reduced fitnesses. Moreover, it is unknown whether future ecosystem change will increase the prevalence of hybridization. Ficus heterostyla and F. squamosa are closely related species co-distributed from southern Thailand to southwest China where hybridization, yielding viable seeds, has been documented. As a robust test of ecological factors driving hybridization, we investigated spatial hybridization signatures based on nuclear microsatellites from extensive population sampling across a widespread contact range. Both species showed high population differentiation and strong patterns of isolation by distance. Admixture estimates exposed asymmetric interspecific gene flow.Signatures of hybridization increase significantly towards higher latitude zones, peaking at the northern climatic margins. Geographic variation in reproductive phenology combined with ecologically challenging marginal habitats may promote this phenomenon. Our work is a first systematic evaluation of such patterns in a comprehensive, latitudinally-based clinal context, and indicates that tendency to hybridize appears strongly influenced by environmental conditions. Moreover, that future climate change scenarios will likely alter and possibly augment cases of hybridization at ecosystem scales.
基金supported by the Second Tibetan Plateau Scientific Expedition and Research program (No. 2019QZKK0502)Strategic Priority Research Program of Chinese Academy of Sciences (No. XDB31010300)+1 种基金Fundamental Research Funds for the Central UniversitiesInternational Collaboration 111 Program (BP0719040)。
文摘The identification and understanding of cryptic intraspecific evolutionary units(lineages) are crucial for planning effective conservation strategies aimed at preserving genetic diversity in endangered species.However, the factors driving the evolution and maintenance of these intraspecific lineages in most endangered species remain poorly understood. In this study, we conducted resequencing of 77 individuals from 22 natural populations of Davidia involucrata, a “living fossil” dove tree endemic to central and southwest China. Our analysis revealed the presence of three distinct local lineages within this endangered species, which emerged approximately 3.09 and 0.32 million years ago. These divergence events align well with the geographic and climatic oscillations that occurred across the distributional range.Additionally, we observed frequent hybridization events between the three lineages, resulting in the formation of hybrid populations in their adjacent as well as disjunct regions. These hybridizations likely arose from climate-driven population expansion and/or long-distance gene flow. Furthermore, we identified numerous environment-correlated gene variants across the total and many other genes that exhibited signals of positive evolution during the maintenance of two major local lineages. Our findings shed light on the highly dynamic evolution underlying the remarkably similar phenotype of this endangered species. Importantly, these results not only provide guidance for the development of conservation plans but also enhance our understanding of evolutionary past for this and other endangered species with similar histories.
基金supported by the National Natural Science Foundation of China(Grant No.52090081)the State Key Laboratory of Hydro-science and Hydraulic Engineering(Grant No.2021-KY-04).
文摘Geological discontinuity(GD)plays a pivotal role in determining the catastrophic mechanical failure of jointed rock masses.Accurate and efficient acquisition of GD networks is essential for characterizing and understanding the progressive damage mechanisms of slopes based on monitoring image data.Inspired by recent advances in computer vision,deep learning(DL)models have been widely utilized for image-based fracture identification.The multi-scale characteristics,image resolution and annotation quality of images will cause a scale-space effect(SSE)that makes features indistinguishable from noise,directly affecting the accuracy.However,this effect has not received adequate attention.Herein,we try to address this gap by collecting slope images at various proportional scales and constructing multi-scale datasets using image processing techniques.Next,we quantify the intensity of feature signals using metrics such as peak signal-to-noise ratio(PSNR)and structural similarity(SSIM).Combining these metrics with the scale-space theory,we investigate the influence of the SSE on the differentiation of multi-scale features and the accuracy of recognition.It is found that augmenting the image's detail capacity does not always yield benefits for vision-based recognition models.In light of these observations,we propose a scale hybridization approach based on the diffusion mechanism of scale-space representation.The results show that scale hybridization strengthens the tolerance of multi-scale feature recognition under complex environmental noise interference and significantly enhances the recognition accuracy of GD.It also facilitates the objective understanding,description and analysis of the rock behavior and stability of slopes from the perspective of image data.
基金financially supported by the National Natural Science Foundation of China(21972068,22072067,22232004)the High-level Talents Project of Jinling Institute of Technology(jit-b-202164)。
文摘Precisely refining the electronic structure of electrocatalysts represents a powerful approach to further optimize the electrocatalytic performance.Herein,we demonstrate an ingenious d-d orbital hybridization concept to construct Mo-doped Co_(9)S_(8) nanorod arrays aligned on carbon cloth(CC)substrate(abbreviated as Mo-Co_(9)S_(8)@CC hereafter)as a high-efficiency bifunctional electrocatalyst toward water electrolysis.It has experimentally and theoretically validated that the 4d-3d orbital coupling between Mo dopant and Co site can effectively optimize the H_(2)O activation energy and lower H^(*)adsorption energy barrier,thereby leading to enhanced hydrogen evolution reaction(HER)and oxygen evolution reaction(OER)activities.Thanks to the unique electronic and geometrical advantages,the optimized Mo-Co_(9)S_(8)@CC with appropriate Mo content exhibits outstanding bifunctional performance in alkaline solution,with the overpotentials of 75 and 234 mV for the delivery of a current density of 10 mA cm^(-2),small Tafel slopes of 53.8 and 39.9 mV dec~(-1)and long-term stabilities for at least 32 and 30 h for HER and OER,respectively.More impressively,a water splitting electrolylzer assembled by the self-supported Mo-Co_(9)S_(8)@CC electrode requires a low cell voltage of 1.53 V at 10 mA cm^(-2)and shows excellent stability and splendid reversibility,demonstrating a huge potential for affordable and scalable electrochemical H_(2) production.The innovational orbital hybridization strategy for electronic regulation herein provides an inspirable avenue for developing progressive electrocatalysts toward new energy systems.
基金supported by the National Key R&D Program of China(No.2021YFB2800700)National Natural Science Foundation of China(Nos.12274210,62227820,and 12174183)+1 种基金Partial support is from NSF of Jiangsu Province(No.BK20220006)the Fundamental Research Funds for the Central Universities and Jiangsu Key Laboratory of Advanced Techniques for Manipulating Electromagnetic Waves。
文摘Rational design of efficient and robust earth-abundant alkaline hydrogen evolution reaction(HER)catalysts is a key factor for developing energy conversion technologies.Currently,antiperovskite nitride CuNMn_(3)has garnered significant interest due to its remarkable properties such as negative/zero thermal expansion and magnetocaloric effects.However,when utilized as hydrogen evolution catalysts,it encounters large challenge resulting from excessively strong/weak interactions with adsorbed H on Mn/Cu active sites,which leads to low HER activity.In this study,we introduce an asymmetric orbital hybridization strategy in Zn-doped Cu_(1-x)Zn_(x)NMn_(3)by leveraging the localization of Zn electronic states to reconfigure the electronic structures of Cu and Mn,thereby reducing the energy barrier for water dissociation and optimizing Cu and Mn active sites for hydrogen adsorption and H_(2)production.Electrochemical evaluations reveal that Cu_(0.85)Zn_(0.15)NMn_(3)with x=0.15 demonstrates exceptional electrocatalytic activity in alkaline electrolytes.A low overpotential of 52 mV at 10 mA cm^(-2)and outstanding stability over a 150-h test period are achieved,surpassing commercial Pt/C.This research offers a novel strategy for enhancing HER performance by modulating asymmetric hybridization of electron orbitals between multiple metal atoms within a material structure.
基金P.G.acknowledges the financial support from the Youth Foundation of Shandong Natural Science Foundation(No.ZR2023OB230)National Natural Science Foundation(No.22309035)Double First-class Discipline Construction Fund Project of Harbin Institute of Technology at Weihai(No.2023SYLHY11).
文摘High-entropy catalysts featuring exceptional properties are,in no doubt,playing an increasingly significant role in aprotic lithium-oxygen batteries.Despite extensive effort devoted to tracing the origin of their unparalleled performance,the relationships between multiple active sites and reaction intermediates are still obscure.Here,enlightened by theoretical screening,we tailor a high-entropy perovskite fluoride(KCoMnNiMgZnF_(3)-HEC)with various active sites to overcome the limitations of conventional catalysts in redox process.The entropy effect modulates the d-band center and d orbital occupancy of active centers,which optimizes the d–p hybridization between catalytic sites and key intermediates,enabling a moderate adsorption of LiO_(2)and thus reinforcing the reaction kinetics.As a result,the Li–O2 battery with KCoMnNiMgZnF_(3)-HEC catalyst delivers a minimal discharge/charge polarization and long-term cycle stability,preceding majority of traditional catalysts reported.These encouraging results provide inspiring insights into the electron manipulation and d orbital structure optimization for advanced electrocatalyst.
基金supported by the National Natural Science Foundation of China(32002036)。
文摘In situ mRNA hybridization(ISH)is a powerful tool for examining the spatiotemporal expression of genes in shoot apical meristems and flower buds of cucumber.The most common ISH protocol uses paraffin wax;however,embedding tissue in paraffin wax can take a long time and might result in RNA degradation and decreased signals.Here,we developed an optimized protocol to simplify the process and improve RNA sensitivity.We combined embedding tissue in low melting-point Steedman’s wax with processing tissue sections in solution,as in the whole-mount ISH method in the optimized protocol.Using the optimized protocol,we examined the expression patterns of the CLAVATA3(CLV3)and WUSCHEL(WUS)genes in shoot apical meristems and floral meristems of Cucumis sativus(cucumber)and Arabidopsis thaliana(Arabidopsis).The optimized protocol saved 4–5 days of experimental period compared with the standard ISH protocol using paraffin wax.Moreover,the optimized protocol achieved high signal sensitivity.The optimized protocol was successful for both cucumber and Arabidopsis,which indicates it might have general applicability to most plants.
基金The National Key Research and Development Program of China:Design and Key Technology Research of Non-metallic Flexible Risers for Deep Sea Mining(2022YFC2803701)The General Program of National Natural Science Foundation of China(52071336,52374022).
文摘Since leaks in high-pressure pipelines transporting crude oil can cause severe economic losses,a reliable leak risk assessment can assist in developing an effective pipeline maintenance plan and avoiding unexpected incidents.The fast and accurate leak detection methods are essential for maintaining pipeline safety in pipeline reliability engineering.Current oil pipeline leakage signals are insufficient for feature extraction,while the training time for traditional leakage prediction models is too long.A new leak detection method is proposed based on time-frequency features and the Genetic Algorithm-Levenberg Marquardt(GA-LM)classification model for predicting the leakage status of oil pipelines.The signal that has been processed is transformed to the time and frequency domain,allowing full expression of the original signal.The traditional Back Propagation(BP)neural network is optimized by the Genetic Algorithm(GA)and Levenberg Marquardt(LM)algorithms.The results show that the recognition effect of a combined feature parameter is superior to that of a single feature parameter.The Accuracy,Precision,Recall,and F1score of the GA-LM model is 95%,93.5%,96.7%,and 95.1%,respectively,which proves that the GA-LM model has a good predictive effect and excellent stability for positive and negative samples.The proposed GA-LM model can obviously reduce training time and improve recognition efficiency.In addition,considering that a large number of samples are required for model training,a wavelet threshold method is proposed to generate sample data with higher reliability.The research results can provide an effective theoretical and technical reference for the leakage risk assessment of the actual oil pipelines.
基金The Qian Xuesen Youth Innovation Foundation from China Aerospace Science and Technology Corporation(Grant Number 2022JY51).
文摘The demand for adopting neural networks in resource-constrained embedded devices is continuously increasing.Quantization is one of the most promising solutions to reduce computational cost and memory storage on embedded devices.In order to reduce the complexity and overhead of deploying neural networks on Integeronly hardware,most current quantization methods use a symmetric quantization mapping strategy to quantize a floating-point neural network into an integer network.However,although symmetric quantization has the advantage of easier implementation,it is sub-optimal for cases where the range could be skewed and not symmetric.This often comes at the cost of lower accuracy.This paper proposed an activation redistribution-based hybrid asymmetric quantizationmethod for neural networks.The proposedmethod takes data distribution into consideration and can resolve the contradiction between the quantization accuracy and the ease of implementation,balance the trade-off between clipping range and quantization resolution,and thus improve the accuracy of the quantized neural network.The experimental results indicate that the accuracy of the proposed method is 2.02%and 5.52%higher than the traditional symmetric quantization method for classification and detection tasks,respectively.The proposed method paves the way for computationally intensive neural network models to be deployed on devices with limited computing resources.Codes will be available on https://github.com/ycjcy/Hybrid-Asymmetric-Quantization.
基金Projects(42177164,52474121)supported by the National Science Foundation of ChinaProject(PBSKL2023A12)supported by the State Key Laboratory of Precision Blasting and Hubei Key Laboratory of Blasting Engineering,China。
文摘In the mining industry,precise forecasting of rock fragmentation is critical for optimising blasting processes.In this study,we address the challenge of enhancing rock fragmentation assessment by developing a novel hybrid predictive model named GWO-RF.This model combines the grey wolf optimization(GWO)algorithm with the random forest(RF)technique to predict the D_(80)value,a critical parameter in evaluating rock fragmentation quality.The study is conducted using a dataset from Sarcheshmeh Copper Mine,employing six different swarm sizes for the GWO-RF hybrid model construction.The GWO-RF model’s hyperparameters are systematically optimized within established bounds,and its performance is rigorously evaluated using multiple evaluation metrics.The results show that the GWO-RF hybrid model has higher predictive skills,exceeding traditional models in terms of accuracy.Furthermore,the interpretability of the GWO-RF model is enhanced through the utilization of SHapley Additive exPlanations(SHAP)values.The insights gained from this research contribute to optimizing blasting operations and rock fragmentation outcomes in the mining industry.
基金This work was funded by Beijing Key Laboratory of Distribution Transformer Energy-Saving Technology(China Electric Power Research Institute).
文摘The load types in low-voltage distribution systems are diverse.Some loads have current signals that are similar to series fault arcs,making it difficult to effectively detect fault arcs during their occurrence and sustained combustion,which can easily lead to serious electrical fire accidents.To address this issue,this paper establishes a fault arc prototype experimental platform,selects multiple commonly used loads for fault arc experiments,and collects data in both normal and fault states.By analyzing waveform characteristics and selecting fault discrimination feature indicators,corresponding feature values are extracted for qualitative analysis to explore changes in timefrequency characteristics of current before and after faults.Multiple features are then selected to form a multidimensional feature vector space to effectively reduce arc misjudgments and construct a fault discrimination feature database.Based on this,a fault arc hazard prediction model is built using random forests.The model’s multiple hyperparameters are simultaneously optimized through grid search,aiming tominimize node information entropy and complete model training,thereby enhancing model robustness and generalization ability.Through experimental verification,the proposed method accurately predicts and classifies fault arcs of different load types,with an average accuracy at least 1%higher than that of the commonly used fault predictionmethods compared in the paper.
基金financially supported by the National Natural Science Foundation of China (Nos.51974023 and52374321)the funding of State Key Laboratory of Advanced Metallurgy,University of Science and Technology Beijing,China (No.41620007)。
文摘The amount of oxygen blown into the converter is one of the key parameters for the control of the converter blowing process,which directly affects the tap-to-tap time of converter. In this study, a hybrid model based on oxygen balance mechanism (OBM) and deep neural network (DNN) was established for predicting oxygen blowing time in converter. A three-step method was utilized in the hybrid model. First, the oxygen consumption volume was predicted by the OBM model and DNN model, respectively. Second, a more accurate oxygen consumption volume was obtained by integrating the OBM model and DNN model. Finally, the converter oxygen blowing time was calculated according to the oxygen consumption volume and the oxygen supply intensity of each heat. The proposed hybrid model was verified using the actual data collected from an integrated steel plant in China, and compared with multiple linear regression model, OBM model, and neural network model including extreme learning machine, back propagation neural network, and DNN. The test results indicate that the hybrid model with a network structure of 3 hidden layer layers, 32-16-8 neurons per hidden layer, and 0.1 learning rate has the best prediction accuracy and stronger generalization ability compared with other models. The predicted hit ratio of oxygen consumption volume within the error±300 m^(3)is 96.67%;determination coefficient (R^(2)) and root mean square error (RMSE) are0.6984 and 150.03 m^(3), respectively. The oxygen blow time prediction hit ratio within the error±0.6 min is 89.50%;R2and RMSE are0.9486 and 0.3592 min, respectively. As a result, the proposed model can effectively predict the oxygen consumption volume and oxygen blowing time in the converter.
