Photothermal synergistic catalytic systems for treating volatile organic compounds(VOCs)have attracted signif-icant attention due to their energy efficiency and potential to reduce carbon emissions.However,the mechani...Photothermal synergistic catalytic systems for treating volatile organic compounds(VOCs)have attracted signif-icant attention due to their energy efficiency and potential to reduce carbon emissions.However,the mechanism underlying the synergistic reaction remains a critical issue.This study introduces a photothermal synergistic system for the removal of ethyl acetate(EA)by synthesizing Cu-doped OMS-2(denoted as Cu-OMS-2).Under ultraviolet-visible(UV–Vis)irradiation in a flow system,the Cu-OMS-2 catalyst exhibited significantly enhanced performance in the EA degradation process,nearly doubling the effectiveness of pure OMS-2,and increasing carbon dioxide yield by 20%.This exceptional performance is attributed to the synergistic effect of increased oxygen vacancies(OV)at OMS-2 active sites and Cu doping,as confirmed by H2-TPR,O_(2)-TPD,and CO consump-tion measurements.This study clarifies the catalytic mechanism of light-assisted thermocatalysis and offers a novel strategy for designing photothermal catalysts with homogeneous Cu-doped nanorods for VOC removal.展开更多
Debris flow events are frequent in Tajikistan,yet comprehensive investigations at the regional scale are limited.This study integrates remote sensing,Geographic Information System,and machine learning techniques to ev...Debris flow events are frequent in Tajikistan,yet comprehensive investigations at the regional scale are limited.This study integrates remote sensing,Geographic Information System,and machine learning techniques to evaluate debris flow susceptibility and associated hazards across Tajikistan.A dataset comprising 405 documented debris flow points and 14 influencing factors,encompassing geological,climatic-hydrological,and anthropogenic variables,was established.Three machine learning algorithms—Random Forest,Support Vector Machine(SVM),and Multi-layer Perceptron—were applied to generate susceptibility maps and delineate debris flow risk zones.The results indicate that the areas of higher and high susceptibility accounted for 20.43%and 4.41%of the national area,respectively,and were predominantly concentrated along the Zeravshan and Vakhsh river basins.Among the evaluated models,SVM model demonstrated the highest predictive performance.Beyond conventional topographic and environmental controls,drought conditions were identified as a critical factor influencing debris flow occurrence within the arid and semi-arid mountainous regions of Tajikistan.These findings provide a scientific basis for regional debris flow risk management and disaster mitigation planning,and offer practical guidance for selecting conditioning factors in machine-learning-based susceptibility assessments in other dry mountainous environments.展开更多
Vortex-induced vibration(VIV)of an underwater manipulator in pulsating flow presents a notable engineering problem in precise control due to the velocity variation in the flow.This study investigates the VIV response ...Vortex-induced vibration(VIV)of an underwater manipulator in pulsating flow presents a notable engineering problem in precise control due to the velocity variation in the flow.This study investigates the VIV response of an underwater manipulator subjected to pulsating flow,focusing on how different postures affect the behavior of the system.The effects of pulsating parameters and manipulator arrangement on the hydrodynamic coefficient,vibration response,motion trajectory,and vortex shedding behaviors were analyzed.Results indicated that the cross flow vibration displacement in pulsating flow increased by 32.14%compared to uniform flow,inducing a shift in the motion trajectory from a crescent shape to a sideward vase shape.In the absence of interference between the upper and lower arms,the lift coefficient of the manipulator substantially increased with rising pulsating frequency,reaching a maximum increment of 67.0%.This increase in the lift coefficient led to a 67.05%rise in the vibration frequency of the manipulator in the in-line direction.As the pulsating amplitude increased,the drag coefficient of the underwater manipulator rose by 36.79%,but the vibration frequency in the cross-flow direction decreased by 56.26%.Additionally,when the upper and lower arms remained in a state of mutual interference,the cross-flow vibration amplitudes of the upper and lower arms were approximately 1.84 and 4.82 times higher in a circular-elliptical arrangement compared to an elliptical-circular arrangement,respectively.Consequently,the flow field shifted from a P+S pattern to a disordered pattern,disrupting the regularity of the motion trajectory.展开更多
Automated classification of gas flow states in blast furnaces using top-camera imagery typically demands a large volume of labeled data,whose manual annotation is both labor-intensive and cost-prohibitive.To mitigate ...Automated classification of gas flow states in blast furnaces using top-camera imagery typically demands a large volume of labeled data,whose manual annotation is both labor-intensive and cost-prohibitive.To mitigate this challenge,we present an enhanced semi-supervised learning approach based on the Mean Teacher framework,incorporating a novel feature loss module to maximize classification performance with limited labeled samples.The model studies show that the proposed model surpasses both the baseline Mean Teacher model and fully supervised method in accuracy.Specifically,for datasets with 20%,30%,and 40%label ratios,using a single training iteration,the model yields accuracies of 78.61%,82.21%,and 85.2%,respectively,while multiple-cycle training iterations achieves 82.09%,81.97%,and 81.59%,respectively.Furthermore,scenario-specific training schemes are introduced to support diverse deployment need.These findings highlight the potential of the proposed technique in minimizing labeling requirements and advancing intelligent blast furnace diagnostics.展开更多
Reliable traffic flow prediction is crucial for mitigating urban congestion.This paper proposes Attentionbased spatiotemporal Interactive Dynamic Graph Convolutional Network(AIDGCN),a novel architecture integrating In...Reliable traffic flow prediction is crucial for mitigating urban congestion.This paper proposes Attentionbased spatiotemporal Interactive Dynamic Graph Convolutional Network(AIDGCN),a novel architecture integrating Interactive Dynamic Graph Convolution Network(IDGCN)with Temporal Multi-Head Trend-Aware Attention.Its core innovation lies in IDGCN,which uniquely splits sequences into symmetric intervals for interactive feature sharing via dynamic graphs,and a novel attention mechanism incorporating convolutional operations to capture essential local traffic trends—addressing a critical gap in standard attention for continuous data.For 15-and 60-min forecasting on METR-LA,AIDGCN achieves MAEs of 0.75%and 0.39%,and RMSEs of 1.32%and 0.14%,respectively.In the 60-min long-term forecasting of the PEMS-BAY dataset,the AIDGCN out-performs the MRA-BGCN method by 6.28%,4.93%,and 7.17%in terms of MAE,RMSE,and MAPE,respectively.Experimental results demonstrate the superiority of our pro-posed model over state-of-the-art methods.展开更多
To enhance power flow regulation in scenarios involving large-scale renewable energy transmission via high-voltage direct current(HVDC)links and multi-infeed DC systems in load-center regions,this paper proposes a hyb...To enhance power flow regulation in scenarios involving large-scale renewable energy transmission via high-voltage direct current(HVDC)links and multi-infeed DC systems in load-center regions,this paper proposes a hybrid modular multilevel converter–capacitor-commutated line-commutated converter(MMC-CLCC)HVDC transmission system and its corresponding control strategy.First,the system topology is constructed,and a submodule configuration method for the MMC—combining full-bridge submodules(FBSMs)and half-bridge submodules(HBSMs)—is proposed to enable direct power flow reversal.Second,a hierarchical control strategy is introduced,includingMMCvoltage control,CLCC current control,and a coordinationmechanism,along with the derivation of the hybrid system’s power flow reversal characteristics.Third,leveraging the CLCC’s fast current regulation and theMMC’s negative voltage control capability,a coordinated power flow reversal control strategy is developed.Finally,an 800 kV MMC-CLCC hybrid HVDC system is modeled in PSCAD/EMTDC to validate the power flow reversal performance under a high proportion of full-bridge submodule configuration.Results demonstrate that the proposed control strategy enables rapid(1-s transition)and smooth switching of bidirectional power flow without modifying the structure of primary equipment:the transient fluctuation ofDC voltage from the rated value(UdcN)to themaximumreverse voltage(-kUdcN)is less than 5%;the DC current strictly follows the preset characteristic curve with a deviation of≤3%;the active power reverses continuously,and the system maintains stable operation throughout the reversal process.展开更多
The multi-terminal direct current(DC)grid has extinctive superiorities over the traditional alternating current system in integrating large-scale renewable energy.Both the DC circuit breaker(DCCB)and the current flow ...The multi-terminal direct current(DC)grid has extinctive superiorities over the traditional alternating current system in integrating large-scale renewable energy.Both the DC circuit breaker(DCCB)and the current flow controller(CFC)are demanded to ensure the multiterminal DC grid to operates reliably and flexibly.However,since the CFC and the DCCB are all based on fully controlled semiconductor switches(e.g.,insulated gate bipolar transistor,integrated gate commutated thyristor,etc.),their separation configuration in the multiterminal DC grid will lead to unaffordable implementation costs and conduction power losses.To solve these problems,integrated equipment with both current flow control and fault isolation abilities is proposed,which shares the expensive and duplicated components of CFCs and DCCBs among adjacent lines.In addition,the complicated coordination control of CFCs and DCCBs can be avoided by adopting the integrated equipment in themultiterminal DC grid.In order to examine the current flow control and fault isolation abilities of the integrated equipment,the simulation model of a specific meshed four-terminal DC grid is constructed in the PSCAD/EMTDC software.Finally,the comparison between the integrated equipment and the separate solution is presented a specific result or conclusion needs to be added to the abstract.展开更多
基金supported by the Qilu University of Technology(Shandong Academy of Sciences),the Basic Research Project of Science,Education and Industry Integration Pilot Project(No.2022PY047).
文摘Photothermal synergistic catalytic systems for treating volatile organic compounds(VOCs)have attracted signif-icant attention due to their energy efficiency and potential to reduce carbon emissions.However,the mechanism underlying the synergistic reaction remains a critical issue.This study introduces a photothermal synergistic system for the removal of ethyl acetate(EA)by synthesizing Cu-doped OMS-2(denoted as Cu-OMS-2).Under ultraviolet-visible(UV–Vis)irradiation in a flow system,the Cu-OMS-2 catalyst exhibited significantly enhanced performance in the EA degradation process,nearly doubling the effectiveness of pure OMS-2,and increasing carbon dioxide yield by 20%.This exceptional performance is attributed to the synergistic effect of increased oxygen vacancies(OV)at OMS-2 active sites and Cu doping,as confirmed by H2-TPR,O_(2)-TPD,and CO consump-tion measurements.This study clarifies the catalytic mechanism of light-assisted thermocatalysis and offers a novel strategy for designing photothermal catalysts with homogeneous Cu-doped nanorods for VOC removal.
基金supported by the National Natural Science Foundation of China(42361144880)the Science and Technology Program of Xizang Autonomous Region,China(XZ202402ZD0001)the Qinghai Province Basic Research Program Project,China(2024-ZJ-904).
文摘Debris flow events are frequent in Tajikistan,yet comprehensive investigations at the regional scale are limited.This study integrates remote sensing,Geographic Information System,and machine learning techniques to evaluate debris flow susceptibility and associated hazards across Tajikistan.A dataset comprising 405 documented debris flow points and 14 influencing factors,encompassing geological,climatic-hydrological,and anthropogenic variables,was established.Three machine learning algorithms—Random Forest,Support Vector Machine(SVM),and Multi-layer Perceptron—were applied to generate susceptibility maps and delineate debris flow risk zones.The results indicate that the areas of higher and high susceptibility accounted for 20.43%and 4.41%of the national area,respectively,and were predominantly concentrated along the Zeravshan and Vakhsh river basins.Among the evaluated models,SVM model demonstrated the highest predictive performance.Beyond conventional topographic and environmental controls,drought conditions were identified as a critical factor influencing debris flow occurrence within the arid and semi-arid mountainous regions of Tajikistan.These findings provide a scientific basis for regional debris flow risk management and disaster mitigation planning,and offer practical guidance for selecting conditioning factors in machine-learning-based susceptibility assessments in other dry mountainous environments.
基金Supported by the National Natural Science Foundation of China(No.51905211)A Project of the“20 Regulations for New Universities”Funding Program of Jinan(No.202228116).
文摘Vortex-induced vibration(VIV)of an underwater manipulator in pulsating flow presents a notable engineering problem in precise control due to the velocity variation in the flow.This study investigates the VIV response of an underwater manipulator subjected to pulsating flow,focusing on how different postures affect the behavior of the system.The effects of pulsating parameters and manipulator arrangement on the hydrodynamic coefficient,vibration response,motion trajectory,and vortex shedding behaviors were analyzed.Results indicated that the cross flow vibration displacement in pulsating flow increased by 32.14%compared to uniform flow,inducing a shift in the motion trajectory from a crescent shape to a sideward vase shape.In the absence of interference between the upper and lower arms,the lift coefficient of the manipulator substantially increased with rising pulsating frequency,reaching a maximum increment of 67.0%.This increase in the lift coefficient led to a 67.05%rise in the vibration frequency of the manipulator in the in-line direction.As the pulsating amplitude increased,the drag coefficient of the underwater manipulator rose by 36.79%,but the vibration frequency in the cross-flow direction decreased by 56.26%.Additionally,when the upper and lower arms remained in a state of mutual interference,the cross-flow vibration amplitudes of the upper and lower arms were approximately 1.84 and 4.82 times higher in a circular-elliptical arrangement compared to an elliptical-circular arrangement,respectively.Consequently,the flow field shifted from a P+S pattern to a disordered pattern,disrupting the regularity of the motion trajectory.
基金financial support provided by the Natural Science Foundation of Hebei Province,China(No.E2024105036)the Tangshan Talent Funding Project,China(Nos.B202302007 and A2021110015)+1 种基金the National Natural Science Foundation of China(No.52264042)the Australian Research Council(No.IH230100010)。
文摘Automated classification of gas flow states in blast furnaces using top-camera imagery typically demands a large volume of labeled data,whose manual annotation is both labor-intensive and cost-prohibitive.To mitigate this challenge,we present an enhanced semi-supervised learning approach based on the Mean Teacher framework,incorporating a novel feature loss module to maximize classification performance with limited labeled samples.The model studies show that the proposed model surpasses both the baseline Mean Teacher model and fully supervised method in accuracy.Specifically,for datasets with 20%,30%,and 40%label ratios,using a single training iteration,the model yields accuracies of 78.61%,82.21%,and 85.2%,respectively,while multiple-cycle training iterations achieves 82.09%,81.97%,and 81.59%,respectively.Furthermore,scenario-specific training schemes are introduced to support diverse deployment need.These findings highlight the potential of the proposed technique in minimizing labeling requirements and advancing intelligent blast furnace diagnostics.
文摘Reliable traffic flow prediction is crucial for mitigating urban congestion.This paper proposes Attentionbased spatiotemporal Interactive Dynamic Graph Convolutional Network(AIDGCN),a novel architecture integrating Interactive Dynamic Graph Convolution Network(IDGCN)with Temporal Multi-Head Trend-Aware Attention.Its core innovation lies in IDGCN,which uniquely splits sequences into symmetric intervals for interactive feature sharing via dynamic graphs,and a novel attention mechanism incorporating convolutional operations to capture essential local traffic trends—addressing a critical gap in standard attention for continuous data.For 15-and 60-min forecasting on METR-LA,AIDGCN achieves MAEs of 0.75%and 0.39%,and RMSEs of 1.32%and 0.14%,respectively.In the 60-min long-term forecasting of the PEMS-BAY dataset,the AIDGCN out-performs the MRA-BGCN method by 6.28%,4.93%,and 7.17%in terms of MAE,RMSE,and MAPE,respectively.Experimental results demonstrate the superiority of our pro-posed model over state-of-the-art methods.
基金supported by Science and Technology Project of the headquarters of the State Grid Corporation of China(No.5500-202324492A-3-2-ZN).
文摘To enhance power flow regulation in scenarios involving large-scale renewable energy transmission via high-voltage direct current(HVDC)links and multi-infeed DC systems in load-center regions,this paper proposes a hybrid modular multilevel converter–capacitor-commutated line-commutated converter(MMC-CLCC)HVDC transmission system and its corresponding control strategy.First,the system topology is constructed,and a submodule configuration method for the MMC—combining full-bridge submodules(FBSMs)and half-bridge submodules(HBSMs)—is proposed to enable direct power flow reversal.Second,a hierarchical control strategy is introduced,includingMMCvoltage control,CLCC current control,and a coordinationmechanism,along with the derivation of the hybrid system’s power flow reversal characteristics.Third,leveraging the CLCC’s fast current regulation and theMMC’s negative voltage control capability,a coordinated power flow reversal control strategy is developed.Finally,an 800 kV MMC-CLCC hybrid HVDC system is modeled in PSCAD/EMTDC to validate the power flow reversal performance under a high proportion of full-bridge submodule configuration.Results demonstrate that the proposed control strategy enables rapid(1-s transition)and smooth switching of bidirectional power flow without modifying the structure of primary equipment:the transient fluctuation ofDC voltage from the rated value(UdcN)to themaximumreverse voltage(-kUdcN)is less than 5%;the DC current strictly follows the preset characteristic curve with a deviation of≤3%;the active power reverses continuously,and the system maintains stable operation throughout the reversal process.
文摘目的基于4D Flow MRI技术探究急性心肌梗死患者左心室(left ventricular,LV)腔内局部血流动能(kinetic energy,KE)改变。方法纳入30名急性心肌梗死(acute myocardial infarction,AMI)患者和20名对照者。应用4D Flow MRI技术定量评价左心室腔内血流动能,包括左心室平均动能、最小动能、收缩期动能、舒张期动能以及平面内动能(in-plane kinetic energy,In-plane KE)百分比。比较心肌梗死组和对照组之间以及梗死节段与非梗死节段之间血流动能参数的差异。结果与对照组相比,急性心肌梗死组左心室整体平均动能(10.7μJ/mL±3.3 vs 14.7μJ/mL±3.6,P<0.001)、收缩期动能(14.6μJ/mL±5.1 vs 18.9μJ/mL±3.9,P=0.003)及舒张期动能(7.9μJ/mL±2.5 vs 10.6μJ/mL±3.8,P=0.018)均显著降低,其中梗死节段较非梗死节段邻近心腔血流的平均动能降低而收缩期平面内动能百分比增加(49.5μJ/mL±18.7 vs 126.3μJ/mL±50.7,P<0.001;61.8%±11.5 vs 42.9%±14.4,P=0.001)。结论4D Flow MRI技术可定量评价左心室腔内局部血流动能参数。急性心肌梗死后整体心腔血流动能减低,而梗死节段邻近心腔局部血流平面内动能百分比增加。
基金supported in part by Natural Science Foundation of Jiangsu Province under Grant BK20230255Natural Science Foundation of Shandong Province under Grant ZR2023QE281.
文摘The multi-terminal direct current(DC)grid has extinctive superiorities over the traditional alternating current system in integrating large-scale renewable energy.Both the DC circuit breaker(DCCB)and the current flow controller(CFC)are demanded to ensure the multiterminal DC grid to operates reliably and flexibly.However,since the CFC and the DCCB are all based on fully controlled semiconductor switches(e.g.,insulated gate bipolar transistor,integrated gate commutated thyristor,etc.),their separation configuration in the multiterminal DC grid will lead to unaffordable implementation costs and conduction power losses.To solve these problems,integrated equipment with both current flow control and fault isolation abilities is proposed,which shares the expensive and duplicated components of CFCs and DCCBs among adjacent lines.In addition,the complicated coordination control of CFCs and DCCBs can be avoided by adopting the integrated equipment in themultiterminal DC grid.In order to examine the current flow control and fault isolation abilities of the integrated equipment,the simulation model of a specific meshed four-terminal DC grid is constructed in the PSCAD/EMTDC software.Finally,the comparison between the integrated equipment and the separate solution is presented a specific result or conclusion needs to be added to the abstract.
