Carbonaceous material has attracted much attention in the application of sodium-ion batteries(SIBs)anode.However,sluggish reaction kinetics and structure stability impede the application.Therefore,a stacked layered su...Carbonaceous material has attracted much attention in the application of sodium-ion batteries(SIBs)anode.However,sluggish reaction kinetics and structure stability impede the application.Therefore,a stacked layered sulfur-carbon complex with long-chain C–S_(x)–C bond(M-SC-S)is prepared.The layered structure ensures structural stability,and long-chain C–S_(x)–C bond expanding interlayer spacing boosts facile Na+diffusion.When assembled into cells,a high-quality solid-electrolyte interphase film would be formed due to a good match between the M-SC-S electrode and ether electrolyte.Moreover,an electrochemical activation process would happen between the Cu current collector and proper S-doped electrode material to in-situ form Cu_(2)S.The formation of Cu_(2)S in active material can not only provide more active sites for sodium storage and enhance pseudo-capacitance,but also reinforce the electrode/current collector interface and decrease the interfacial transfer resistance for rapid Na+kinetics.The synergistic effect of structure design and interface engineering optimizes the sodium storage system.Thus,the M-SC-S electrode delivers an excellent cyclic performance(321.6 mAh g^(−1)after 1000 cycles at 2 A g^(−1)with a capacity retention rate of 97.4%)and good rate capability(282.8 mAh g^(−1)after 4000 cycles even at a high current density of 10 A g^(−1)).The full cell also has an impressive cyclic performance(151.4 mAh g^(−1)after 500 cycles at 0.5 A g^(−1)).展开更多
Network Intrusion Detection System(NIDS)detection of minority class attacks is always a difficult task when dealing with attacks in complex network environments.To improve the detection capability of minority-class at...Network Intrusion Detection System(NIDS)detection of minority class attacks is always a difficult task when dealing with attacks in complex network environments.To improve the detection capability of minority-class attacks,this study proposes an intrusion detection method based on a two-layer structure.The first layer employs a CNN-BiLSTM model incorporating an attention mechanism to classify network traffic into normal traffic,majority class attacks,and merged minority class attacks.The second layer further segments the minority class attacks through Stacking ensemble learning.The datasets are selected from the generic network dataset CIC-IDS2017,NSL-KDD,and the industrial network dataset Mississippi Gas Pipeline dataset to enhance the generalization and practical applicability of the model.Experimental results show that the proposed model achieves an overall detection accuracy of 99%,99%,and 95%on the CIC-IDS2017,NSL-KDD,and industrial network datasets,respectively.It also significantly outperforms traditional methods in terms of detection accuracy and recall rate for minority class attacks.Compared with the single-layer deep learning model,the two-layer structure effectively reduces the false alarm rate while improving the minority-class attack detection performance.The research in this paper not only improves the adaptability of NIDS to complex network environments but also provides a new solution for minority-class attack detection in industrial network security.展开更多
The electrocatalytic reduction of nitric oxide for ammonia synthesis(NORR)is a key green energy conversion technology.Its efficiency relies on high-performance electrocatalysts to enhance both ammonia yield(Y_(NH3))an...The electrocatalytic reduction of nitric oxide for ammonia synthesis(NORR)is a key green energy conversion technology.Its efficiency relies on high-performance electrocatalysts to enhance both ammonia yield(Y_(NH3))and Faradaic efficiency(F_(NH3)).However,conventional experimental methods for screening high-activity NORR catalysts often entail high resource consumption and time costs.Machine learning combined with SHAP feature analysis was employed to establish a stacked ensemble model that integrates multiple algorithms,to allow for a systematic investigation of the key descriptors governing NORR performance based on an experimental dataset.Evaluation of eight model algorithms revealed that the Stacked-SVR model achieved an R^(2)of 0.9223 and an RMSE of 0.0608 for predicting on the test set,whereas the Stacked-RF model achieved an R^(2)of 0.9042 and an RMSE of 0.0900 for predicting.The stacked ensemble model integrates the strengths of individual algorithms and demonstrates strong NORR prediction performance while avoiding overfitting.SHAP feature analysis results revealed that the Cu content in the catalyst composition has the most significant impact on catalytic performance.Moreover,the combination of the wet chemical reduction synthesis,a carbon fiber(CF)conductive substrate,and HCl electrolyte is more favorable for enhancing catalytic activity.Additionally,moderately lowering the working potential,controlling the electrolyte volume at low to medium levels,reducing catalyst loading,and increasing electrolyte concentration were found to synergistically enhance both and.展开更多
Subsurface reservoirs commonly exhibit layered structures.Conventional amplitude variation with angle(AVA)inversion,which relies on the Zoeppritz equation and its approximations,often fails to accurately estimate elas...Subsurface reservoirs commonly exhibit layered structures.Conventional amplitude variation with angle(AVA)inversion,which relies on the Zoeppritz equation and its approximations,often fails to accurately estimate elastic parameters because it assumes single-interface models and ignores multiple reflections and transmission losses.To address these limitations,this study proposes a novel prestack time-frequency domain joint inversion method that utilizes the reflection matrix method(RMM)as the forward operator.The RMM accurately simulates wave propagation in layered media,while the joint inversion framework minimizes the misfit between observed and synthetic data in both the time and frequency domains.By incorporating Bayesian theory to optimize the inversion process,the method effectively balances contributions from both time-domain waveforms and frequency-domain spectral information through a weighting factor.Tests on both synthetic data and field data demonstrate that the proposed method outperforms conventional AVA inversion and time-domain waveform inversion in accuracy and robustness.Furthermore,the method demonstrates good robustness against variations in initial models,random noise,and coherent noise interference.This study provides a practical and effective approach for high-precision reservoir characterization,with potential applications in complex layered media.展开更多
NiFe-layered double hydroxides(NiFe-LDHs)are among the most promising earth-abundant electrocatalysts for the oxygen evolution reaction(OER)in alkaline media.However,their practical application is hindered by intrinsi...NiFe-layered double hydroxides(NiFe-LDHs)are among the most promising earth-abundant electrocatalysts for the oxygen evolution reaction(OER)in alkaline media.However,their practical application is hindered by intrinsic activity limitations and poor stability,primarily due to the asymmetric adsorption of oxygen intermediates.To overcome this,the binding strength must be synergistically tuned to a moderate level to optimize catalytic performance.Here,we engineered NiFeCoCr LDH through Co doping to enhance electrical conductivity and controlled Cr leaching to introduce cationic vacancies for modulating intermediate binding strength in NiFe LDH.X-ray absorption near-edge structure and extended X-ray absorption fine structure analyses reveal that NiFe-LDH with Co doping and Cr vacancies modulates the Ni oxidation state and local coordination environment,leading to a balanced electronic structure and enhanced structural complexity around the Ni sites.Additionally,these vacancies can trap OH^(-)/H_(2)O species,which can serve as a reservoir for OH^(-) transfer,facilitating the rapid formation of OER intermediates and enhancing catalytic performance at high current densities.As a result,V_(Cr)-NiFeCo LDH achieves 1.6 A cm^(-2)current density at 1.7 V vs.RHE while maintaining stable operation for over 1000 h at 500 mA cm^(-2).Density functional theory(DFT)calculations validate the synergistic effects of Co doping and Cr-induced vacancies on intermediate binding energies and improved OER kinetics.Overall,this work presents a rational design strategy to simultaneously enhance the activity and durability of NiFe-based OER catalysts for their application in high-performance alkaline water electrolysis.展开更多
基金supported by the Key Research and Development Program of Wuhan(2025010102030005)the National Nature Science Foundation of Jiangsu Province(BK20221259)。
文摘Carbonaceous material has attracted much attention in the application of sodium-ion batteries(SIBs)anode.However,sluggish reaction kinetics and structure stability impede the application.Therefore,a stacked layered sulfur-carbon complex with long-chain C–S_(x)–C bond(M-SC-S)is prepared.The layered structure ensures structural stability,and long-chain C–S_(x)–C bond expanding interlayer spacing boosts facile Na+diffusion.When assembled into cells,a high-quality solid-electrolyte interphase film would be formed due to a good match between the M-SC-S electrode and ether electrolyte.Moreover,an electrochemical activation process would happen between the Cu current collector and proper S-doped electrode material to in-situ form Cu_(2)S.The formation of Cu_(2)S in active material can not only provide more active sites for sodium storage and enhance pseudo-capacitance,but also reinforce the electrode/current collector interface and decrease the interfacial transfer resistance for rapid Na+kinetics.The synergistic effect of structure design and interface engineering optimizes the sodium storage system.Thus,the M-SC-S electrode delivers an excellent cyclic performance(321.6 mAh g^(−1)after 1000 cycles at 2 A g^(−1)with a capacity retention rate of 97.4%)and good rate capability(282.8 mAh g^(−1)after 4000 cycles even at a high current density of 10 A g^(−1)).The full cell also has an impressive cyclic performance(151.4 mAh g^(−1)after 500 cycles at 0.5 A g^(−1)).
文摘董志塬地区位于黄土高原中心地带,滑坡灾害频发,亟需明确滑坡易发性分区,以支持该区域滑坡隐患的科学防控。因此,本文以董志塬为研究区,选取高程、坡向和NDVI等12个影响因素作为评价因子,基于频率比(frequency ratio,FR)模型,结合随机森林(random forest,RF)与人工神经网络(artificial neural network,ANN)模型开展滑坡静态易发性评价,并分析各因子对评价精度的贡献。结果表明,FRRF和FR-ANN模型的曲线下面积(area under the curve,AUC)值分别为0.922和0.918,表明FR-RF模型在董志塬滑坡易发性评价中的精度更高。坡度、坡向和道路密度对滑坡易发性的贡献率分别为16.7%、15.3%和1.4%。为克服地形复杂和数据更新滞后的问题,本文将FR-RF模型的易发性结果与InSAR Stacking结果相结合,将静态滑坡易发性评价精度由6.9%提升到8.1%。动态易发性结果表明,董志塬滑坡高易发区主要分布于河流沿岸,占总面积的6.5%,该区域的滑坡数量占总滑坡数的23.6%,滑坡密度15.7个/km^(2)。低易发区主要位于远离河流的中部区域,占总面积的81.7%,滑坡数量占总滑坡数的57.8%,滑坡密度4.7个/km^(2)。本研究通过融合InSAR Stacking方法,解决了静态滑坡易发性评价数据更新滞后问题,减少了假阴性错误,为传统滑坡易发性评价赋予了时效性,可以实现董志塬滑坡易发性动态评价,为灾害防治提供了重要数据支持。
基金supported by the Institute of Information&Communications Technology Planning&Evaluation(IITP)—Innovative Human Resource Development for Local Intellectualization program grant funded by the Korea government(MSIT)(IITP-2025-RS-2022-00156334)in part by Liaoning Province Nature Fund Project(2024-BSLH-214).
文摘Network Intrusion Detection System(NIDS)detection of minority class attacks is always a difficult task when dealing with attacks in complex network environments.To improve the detection capability of minority-class attacks,this study proposes an intrusion detection method based on a two-layer structure.The first layer employs a CNN-BiLSTM model incorporating an attention mechanism to classify network traffic into normal traffic,majority class attacks,and merged minority class attacks.The second layer further segments the minority class attacks through Stacking ensemble learning.The datasets are selected from the generic network dataset CIC-IDS2017,NSL-KDD,and the industrial network dataset Mississippi Gas Pipeline dataset to enhance the generalization and practical applicability of the model.Experimental results show that the proposed model achieves an overall detection accuracy of 99%,99%,and 95%on the CIC-IDS2017,NSL-KDD,and industrial network datasets,respectively.It also significantly outperforms traditional methods in terms of detection accuracy and recall rate for minority class attacks.Compared with the single-layer deep learning model,the two-layer structure effectively reduces the false alarm rate while improving the minority-class attack detection performance.The research in this paper not only improves the adaptability of NIDS to complex network environments but also provides a new solution for minority-class attack detection in industrial network security.
文摘The electrocatalytic reduction of nitric oxide for ammonia synthesis(NORR)is a key green energy conversion technology.Its efficiency relies on high-performance electrocatalysts to enhance both ammonia yield(Y_(NH3))and Faradaic efficiency(F_(NH3)).However,conventional experimental methods for screening high-activity NORR catalysts often entail high resource consumption and time costs.Machine learning combined with SHAP feature analysis was employed to establish a stacked ensemble model that integrates multiple algorithms,to allow for a systematic investigation of the key descriptors governing NORR performance based on an experimental dataset.Evaluation of eight model algorithms revealed that the Stacked-SVR model achieved an R^(2)of 0.9223 and an RMSE of 0.0608 for predicting on the test set,whereas the Stacked-RF model achieved an R^(2)of 0.9042 and an RMSE of 0.0900 for predicting.The stacked ensemble model integrates the strengths of individual algorithms and demonstrates strong NORR prediction performance while avoiding overfitting.SHAP feature analysis results revealed that the Cu content in the catalyst composition has the most significant impact on catalytic performance.Moreover,the combination of the wet chemical reduction synthesis,a carbon fiber(CF)conductive substrate,and HCl electrolyte is more favorable for enhancing catalytic activity.Additionally,moderately lowering the working potential,controlling the electrolyte volume at low to medium levels,reducing catalyst loading,and increasing electrolyte concentration were found to synergistically enhance both and.
基金the sponsorship of National Natural Science Foundation of China(42325403)Deep Earth Probe and Mineral Resources Exploration-National Science and Technology Major Project of China(2024ZD1004201)。
文摘Subsurface reservoirs commonly exhibit layered structures.Conventional amplitude variation with angle(AVA)inversion,which relies on the Zoeppritz equation and its approximations,often fails to accurately estimate elastic parameters because it assumes single-interface models and ignores multiple reflections and transmission losses.To address these limitations,this study proposes a novel prestack time-frequency domain joint inversion method that utilizes the reflection matrix method(RMM)as the forward operator.The RMM accurately simulates wave propagation in layered media,while the joint inversion framework minimizes the misfit between observed and synthetic data in both the time and frequency domains.By incorporating Bayesian theory to optimize the inversion process,the method effectively balances contributions from both time-domain waveforms and frequency-domain spectral information through a weighting factor.Tests on both synthetic data and field data demonstrate that the proposed method outperforms conventional AVA inversion and time-domain waveform inversion in accuracy and robustness.Furthermore,the method demonstrates good robustness against variations in initial models,random noise,and coherent noise interference.This study provides a practical and effective approach for high-precision reservoir characterization,with potential applications in complex layered media.
基金supported by the Natural Science Foundation of China Grant No.52272289 and 5240223,and JSPS(Japan Society for the Promotion of Science)of Grant No.22K19088,23H00313,24H02202,and 24H02205。
文摘NiFe-layered double hydroxides(NiFe-LDHs)are among the most promising earth-abundant electrocatalysts for the oxygen evolution reaction(OER)in alkaline media.However,their practical application is hindered by intrinsic activity limitations and poor stability,primarily due to the asymmetric adsorption of oxygen intermediates.To overcome this,the binding strength must be synergistically tuned to a moderate level to optimize catalytic performance.Here,we engineered NiFeCoCr LDH through Co doping to enhance electrical conductivity and controlled Cr leaching to introduce cationic vacancies for modulating intermediate binding strength in NiFe LDH.X-ray absorption near-edge structure and extended X-ray absorption fine structure analyses reveal that NiFe-LDH with Co doping and Cr vacancies modulates the Ni oxidation state and local coordination environment,leading to a balanced electronic structure and enhanced structural complexity around the Ni sites.Additionally,these vacancies can trap OH^(-)/H_(2)O species,which can serve as a reservoir for OH^(-) transfer,facilitating the rapid formation of OER intermediates and enhancing catalytic performance at high current densities.As a result,V_(Cr)-NiFeCo LDH achieves 1.6 A cm^(-2)current density at 1.7 V vs.RHE while maintaining stable operation for over 1000 h at 500 mA cm^(-2).Density functional theory(DFT)calculations validate the synergistic effects of Co doping and Cr-induced vacancies on intermediate binding energies and improved OER kinetics.Overall,this work presents a rational design strategy to simultaneously enhance the activity and durability of NiFe-based OER catalysts for their application in high-performance alkaline water electrolysis.
