Sodium-ion batteries have emerged as promising alternatives to lithium-ion batteries due to their abundant raw material reserves,low cost,enhanced safety,and environmental sustainability.Na_(2)Fe_(2)OS_(2),featuring a...Sodium-ion batteries have emerged as promising alternatives to lithium-ion batteries due to their abundant raw material reserves,low cost,enhanced safety,and environmental sustainability.Na_(2)Fe_(2)OS_(2),featuring a layered anti-perovskite structure,has attracted significant interest for its high capacity and facile synthesis.In this study,density functional theory calculations were performed to systematically investigate the phase stability,ionic conductivity,and voltage characteristics of Na_(2)Fe_(2)OS_(2)as a model system for anti-perovskite layered cathode materials.The compound exhibits excellent phase stability,and its equilibrium potential was calculated for the series Na_(x)Fe_(2)OCh_(2)(0<±<2)(where Ch represents chalcogenides).Naion transport analysis using the climbing image nudged elastic band method reveals a relatively low migration barrier(~0.47eV)along a dingonal pathway,indicating efficient Na^(+)mobility.To expand the materials design space,we systematically explored the effects of substituting Fe with various transition metals and replacing S with Se in NaaTM_(2)OCh_(2)structures.Among the variants studied,Na_(2)Mn_(2)OS_(2) demonstrates the most favorable combination of high voltage(~2.51V),robust phase stability,and superior energy density(~427 W-h/kg).This comprehensive comparison of transition metal substitutions provides vnluable insights for the rational design and experimental development of next-generation anti-perovskite layered cathode materials for sodium-ion batteries.展开更多
Accurate recognition of flight deck operations for carrier-based aircraft, based on operation trajectories, is critical for optimizing carrier-based aircraft performance. This recognition involves understanding short-...Accurate recognition of flight deck operations for carrier-based aircraft, based on operation trajectories, is critical for optimizing carrier-based aircraft performance. This recognition involves understanding short-term and long-term spatial collaborative relationships among support agents and positions from long spatial–temporal trajectories. While the existing methods excel at recognizing collaborative behaviors from short trajectories, they often struggle with long spatial–temporal trajectories. To address this challenge, this paper introduces a dynamic graph method to enhance flight deck operation recognition. First, spatial–temporal collaborative relationships are modeled as a dynamic graph. Second, a discretized and compressed method is proposed to assign values to the states of this dynamic graph. To extract features that represent diverse collaborative relationships among agents and account for the duration of these relationships, a biased random walk is then conducted. Subsequently, the Swin Transformer is employed to comprehend spatial–temporal collaborative relationships, and a fully connected layer is applied to deck operation recognition. Finally, to address the scarcity of real datasets, a simulation pipeline is introduced to generate deck operations in virtual flight deck scenarios. Experimental results on the simulation dataset demonstrate the superior performance of the proposed method.展开更多
Combining the advantages of high efficiency,low-pressure drop,and large throughput,the pore arrayenhanced tube-in-tube microchannel(PA-TMC) is a promising microreactor for industrial applications.However,most of the m...Combining the advantages of high efficiency,low-pressure drop,and large throughput,the pore arrayenhanced tube-in-tube microchannel(PA-TMC) is a promising microreactor for industrial applications.However,most of the mass transfer takes place in the upstream pore region,while the contribution of the downstream annulus is limited.In this work,helical wires were introduced into the annulus by adhering to the outer surface of the inner tube.Mixing behavior and mass transfer of liquid-liquid twophase flow in PA-TMC with different helical wires have been systematically studied by a combination of experiments and volume of fluid(VOF) method.The introduction of helical wires improves the overall volumetric mass transfer coefficient KLa by up to 133% and the mass transfer efficiency E by up to 117%.The simulation results show that the helical wire brings extra phase mixing regions and increases the specific interface area,while accelerating the fluid flow and expanding the area of enhanced turbulent dissipation rate.Influences of helical wires in various configurations are compared by the comprehensive index I concerning the pressure drop and mass transfer performance simultaneously and a new correlation between KLa and specific energy consumption φ is proposed.This research deepens the understanding of the mixing behavior and mass transfer in the PA-TMCs and provides practical experience for the process intensification of microchannel reactors.展开更多
The Heterogeneous Capacitated Vehicle Routing Problem(HCVRP),which involves efficiently routing vehicles with diverse capacities to fulfill various customer demands at minimal cost,poses an NP-hard challenge in combin...The Heterogeneous Capacitated Vehicle Routing Problem(HCVRP),which involves efficiently routing vehicles with diverse capacities to fulfill various customer demands at minimal cost,poses an NP-hard challenge in combinatorial optimization.Recently,reinforcement learning approaches such as 2D Array Pointer Networks(2D-Ptr)have demonstrated remarkable speed in decision-making by modeling multiple agents’concurrent choices as a sequence of consecutive actions.However,these learning-based models often struggle with generalization,meaning they cannot seamlessly adapt to new scenarios with varying numbers of vehicles or customers without retraining.Inspired by the potential of multi-teacher knowledge distillation to harness diverse knowledge from multiple sources and craft a comprehensive student model,we propose to enhance the generalization capability of 2D-Ptr through Multiple Teacher-forcing Knowledge Distillation(MTKD).We initially train 12 unique 2D-Ptr models under various settings to serve as teacher models.Subsequently,we randomly sample a teacher model and a batch of problem instances,focusing on those where the chosen teacher performed best.This teacher model then solves these instances,generating high-reward action sequences to guide knowledge transfer to the student model.We conduct rigorous evaluations across four distinct datasets,each comprising four HCVRP instances of varying scales.Our empirical findings underscore the proposed method superiority over existing learning-based methods in terms of both computational efficiency and solution quality.展开更多
Accurate wind speed measurements on maritime vessels are crucial for weather forecasting,sea state prediction,and safe navigation.However,vessel motion and challenging environmental conditions often affect measurement...Accurate wind speed measurements on maritime vessels are crucial for weather forecasting,sea state prediction,and safe navigation.However,vessel motion and challenging environmental conditions often affect measurement precision.To address this issue,this study proposes an innovative framework for correcting and predicting shipborne wind speed.By integrating a main network with a momentum updating network,the proposed framework effectively extracts features from the time and frequency domains,thereby allowing for precise adjustments and predictions of shipborne wind speed data.Validation using real sensor data collected at the Qingdao Oceanographic Institute demonstrates that the proposed method outperforms existing approaches in single-and multi-step predictions compared to existing methods,achieving higher accuracy in wind speed forecasting.The proposed innovative approach offers a promising direction for future validation in more realistic maritime onboard scenarios.展开更多
The Dynamical Density Functional Theory(DDFT)algorithm,derived by associating classical Density Functional Theory(DFT)with the fundamental Smoluchowski dynamical equation,describes the evolution of inhomo-geneous flui...The Dynamical Density Functional Theory(DDFT)algorithm,derived by associating classical Density Functional Theory(DFT)with the fundamental Smoluchowski dynamical equation,describes the evolution of inhomo-geneous fluid density distributions over time.It plays a significant role in studying the evolution of density distributions over time in inhomogeneous systems.The Sunway Bluelight II supercomputer,as a new generation of China’s developed supercomputer,possesses powerful computational capabilities.Porting and optimizing industrial software on this platform holds significant importance.For the optimization of the DDFT algorithm,based on the Sunway Bluelight II supercomputer and the unique hardware architecture of the SW39000 processor,this work proposes three acceleration strategies to enhance computational efficiency and performance,including direct parallel optimization,local-memory constrained optimization for CPEs,and multi-core groups collaboration and communication optimization.This method combines the characteristics of the program’s algorithm with the unique hardware architecture of the Sunway Bluelight II supercomputer,optimizing the storage and transmission structures to achieve a closer integration of software and hardware.For the first time,this paper presents Sunway-Dynamical Density Functional Theory(SW-DDFT).Experimental results show that SW-DDFT achieves a speedup of 6.67 times within a single-core group compared to the original DDFT implementation,with six core groups(a total of 384 CPEs),the maximum speedup can reach 28.64 times,and parallel efficiency can reach 71%,demonstrating excellent acceleration performance.展开更多
Investigating highly effective electrocatalysts for high-temperature proton exchange membrane fuel cells(HT-PEMFC)requires the resistance to phosphate acid(PA)poisoning at cathodic oxygen reduction reaction(ORR).Recen...Investigating highly effective electrocatalysts for high-temperature proton exchange membrane fuel cells(HT-PEMFC)requires the resistance to phosphate acid(PA)poisoning at cathodic oxygen reduction reaction(ORR).Recent advancements in catalysts have focused on alleviating phosphoric anion adsorption on Pt-based catalysts with modified electronic structure or catalytic interface and developing Fe-N-C based catalysts with immunity of PA poisoning.Fe-N-C-based catalysts have emerged as promising alternatives to Pt-based catalysts,offering significant potential to overcome the characteristic adsorption of phosphate anion on Pt.An overview of these developments provides insights into catalytic mechanisms and facilitates the design of more efficient catalysts.This review begins with an exploration of basic poisoning principles,followed by a critical summary of characterization techniques employed to identified the underlying mechanism of poisoning effect.Attention is then directed to endeavors aimed at enhancing the HT-PEMFC performance by well-designed catalysts.Finally,the opportunities and challenges in developing the anti-PA poisoning strategy and practical HT-PEMFC is discussed.Through these discussions,a comprehensive understanding of PA-poisoning bottlenecks and inspire future research directions is aim to provided.展开更多
Through three millennia of practice,Traditional Chinese Medicine(TCM)has evolved by integrating knowledge from diverse disciplines,forging a distinct developmental path that respects its ancient foundations while inco...Through three millennia of practice,Traditional Chinese Medicine(TCM)has evolved by integrating knowledge from diverse disciplines,forging a distinct developmental path that respects its ancient foundations while incorporating innovation.TCM has achieved significant breakthroughs in elucidating its theoretical foundations using contemporary scientific methodologies,through the implementation of modernization initiatives over the past three decades.The TCM modernization program has yielded continuous innovations,propelling TCM into a high-quality development stage across both clinical practice and industrial applications.Notably,these advances have enhanced global recognition and adoption of TCM.展开更多
As Internet ofThings(IoT)technologies continue to evolve at an unprecedented pace,intelligent big data control and information systems have become critical enablers for organizational digital transformation,facilitati...As Internet ofThings(IoT)technologies continue to evolve at an unprecedented pace,intelligent big data control and information systems have become critical enablers for organizational digital transformation,facilitating data-driven decision making,fostering innovation ecosystems,and maintaining operational stability.In this study,we propose an advanced deployment algorithm for Service Function Chaining(SFC)that leverages an enhanced Practical Byzantine Fault Tolerance(PBFT)mechanism.The main goal is to tackle the issues of security and resource efficiency in SFC implementation across diverse network settings.By integrating blockchain technology and Deep Reinforcement Learning(DRL),our algorithm not only optimizes resource utilization and quality of service but also ensures robust security during SFC deployment.Specifically,the enhanced PBFT consensus mechanism(VRPBFT)significantly reduces consensus latency and improves Byzantine node detection through the introduction of a Verifiable Random Function(VRF)and a node reputation grading model.Experimental results demonstrate that compared to traditional PBFT,the proposed VRPBFT algorithm reduces consensus latency by approximately 30%and decreases the proportion of Byzantine nodes by 40%after 100 rounds of consensus.Furthermore,the DRL-based SFC deployment algorithm(SDRL)exhibits rapid convergence during training,with improvements in long-term average revenue,request acceptance rate,and revenue/cost ratio of 17%,14.49%,and 20.35%,respectively,over existing algorithms.Additionally,the CPU resource utilization of the SDRL algorithmreaches up to 42%,which is 27.96%higher than other algorithms.These findings indicate that the proposed algorithm substantially enhances resource utilization efficiency,service quality,and security in SFC deployment.展开更多
To address the challenge of missing modal information in entity alignment and to mitigate information loss or bias arising frommodal heterogeneity during fusion,while also capturing shared information acrossmodalities...To address the challenge of missing modal information in entity alignment and to mitigate information loss or bias arising frommodal heterogeneity during fusion,while also capturing shared information acrossmodalities,this paper proposes a Multi-modal Pre-synergistic Entity Alignmentmodel based on Cross-modalMutual Information Strategy Optimization(MPSEA).The model first employs independent encoders to process multi-modal features,including text,images,and numerical values.Next,a multi-modal pre-synergistic fusion mechanism integrates graph structural and visual modal features into the textual modality as preparatory information.This pre-fusion strategy enables unified perception of heterogeneous modalities at the model’s initial stage,reducing discrepancies during the fusion process.Finally,using cross-modal deep perception reinforcement learning,the model achieves adaptive multilevel feature fusion between modalities,supporting learningmore effective alignment strategies.Extensive experiments on multiple public datasets show that the MPSEA method achieves gains of up to 7% in Hits@1 and 8.2% in MRR on the FBDB15K dataset,and up to 9.1% in Hits@1 and 7.7% in MRR on the FBYG15K dataset,compared to existing state-of-the-art methods.These results confirm the effectiveness of the proposed model.展开更多
Variations in ocean mixed layer depth(MLD)show a significant impact on energy balance in the global climate systems and marine ecosystems.At present,the accuracy of modeling MLD,especially in the region with complex o...Variations in ocean mixed layer depth(MLD)show a significant impact on energy balance in the global climate systems and marine ecosystems.At present,the accuracy of modeling MLD,especially in the region with complex ocean dynamics,remains a challenge,thus calling for an emergency using artificial intelligence approach to improve the assessment of the MLD.A novel convolutional neural network model was developed based on a dual-attention module(DA-CNN)to estimate the MLD in the Bay of Bengal(BoB)by integrating multi-source remote sensing data and Argo gridded data.Compared with the original CNN model,the DA-CNN model exhibits superior performance with notable improvements in the annual average root mean square error(RMSE)and R2 values by 13.0%and 8.4%,respectively,while more accurately capturing the seasonal variations in MLD.Moreover,the results using the DA-CNN model show minimum RMSE and maximum R2 values,in comparison to the calculation by the random forest,artificial neural network model,and the hybrid coordinate ocean model.Accordingly,our findings suggest that the newly developed DA-CNN model provides an effective advantage in studying the MLD and the associated ocean processes.展开更多
In this paper,a typical experiment is carried out based on a high-resolution air-sea coupled model,namely,the coupled ocean-atmosphere-wave-sediment transport(COAWST)model,on both heterogeneous many-core(SW)and homoge...In this paper,a typical experiment is carried out based on a high-resolution air-sea coupled model,namely,the coupled ocean-atmosphere-wave-sediment transport(COAWST)model,on both heterogeneous many-core(SW)and homogenous multicore(Intel)supercomputing platforms.We construct a hindcast of Typhoon Lekima on both the SW and Intel platforms,compare the simulation results between these two platforms and compare the key elements of the atmospheric and ocean modules to reanalysis data.The comparative experiment in this typhoon case indicates that the domestic many-core computing platform and general cluster yield almost no differences in the simulated typhoon path and intensity,and the differences in surface pressure(PSFC)in the WRF model and sea surface temperature(SST)in the short-range forecast are very small,whereas a major difference can be identified at high latitudes after the first 10 days.Further heat budget analysis verifies that the differences in SST after 10 days are mainly caused by shortwave radiation variations,as influenced by subsequently generated typhoons in the system.These typhoons generated in the hindcast after the first 10 days attain obviously different trajectories between the two platforms.展开更多
In underground engineering,the detection of structural cracks on tunnel surfaces stands as a pivotal task in ensuring the health and reliability of tunnel structures.However,the dim and dusty environment inherent to u...In underground engineering,the detection of structural cracks on tunnel surfaces stands as a pivotal task in ensuring the health and reliability of tunnel structures.However,the dim and dusty environment inherent to under-ground engineering poses considerable challenges to crack segmentation.This paper proposes a crack segmentation algorithm termed as Focused Detection for Subsurface Cracks YOLOv8(FDSC-YOLOv8)specifically designed for underground engineering structural surfaces.Firstly,to improve the extraction of multi-layer convolutional features,the fixed convolutional module is replaced with a deformable convolutional module.Secondly,the model’s receptive field is enhanced by introducing a multi-branch convolutional module,improving the extraction of shallow features for small targets.Next,the Dynamic Snake Convolution module is incorporated to enhance the extraction capability for slender and weak cracks.Finally,the Convolutional Block Attention Module(CBAM)module is employed to achieve better target determination.The FDSC-YOLOv8s algorithm’s mAP50 and mAP50-95 reach 96.5%and 66.4%,according to the testing data.展开更多
LiBH_(4) containing 18.5 wt.%H_(2) is an attractive high-capacity hydrogen storage material,however,it suffers from high operation temperature and poor reversibility.Herein,a novel and low-cost bifunctional additive,w...LiBH_(4) containing 18.5 wt.%H_(2) is an attractive high-capacity hydrogen storage material,however,it suffers from high operation temperature and poor reversibility.Herein,a novel and low-cost bifunctional additive,waxberry-like Fe_(3)O_(4) secondary nanospheres assembled from ultrafine primary Fe_(3)O_(4) nanoparticles,is synthesized,which exhibits significant destabilization and bidirectional catalyzation towards(de)hydrogenation of LiBH_(4).With an optimized addition of 30 wt.% waxberry-like Fe_(3)O_(4),the system initiated dehydrogenation below 100℃ and released a total of 8.1 wt.%H_(2) to 400℃.After 10 cycles,a capacity retention of 70% was achieved,greatly superior to previously reported oxides-modified systems.The destabilizing and catalyzing mechanisms of waxberry-like Fe_(3)O_(4) on LiBH_(4) were systematically analyzed by phase and microstructural evolutions during dehydrogenation and hydrogenation cycling as well as density functional theory(DFT)calculations.The present work provides new insights in developing advanced nano-additives with unique structural and multifunctional designs towards LiBH4 hydrogen storage.展开更多
Background Cotton fiber is a model tissue for studying microtubule-associated proteins(MAPs).The Xklp2(TPX2)proteins that belong to the novel MAPs member mainly participate in the formation and development of microtub...Background Cotton fiber is a model tissue for studying microtubule-associated proteins(MAPs).The Xklp2(TPX2)proteins that belong to the novel MAPs member mainly participate in the formation and development of microtubule(MT).However,there is a lack of studies concerning the systematic characterization of the TPX2 genes family in cotton.Therefore,the identification and portrayal of G.hirsutum TPX2 genes can provide key targets for molecular manipula-tion in the breeding of cotton fiber improvement.Result In this study,TPX2 family genes were classified into two distinct subclasses TPXLs and MAP genes WAVE DAMP-ENED2-LIKE(WDLs)and quite conservative in quantity.GhWDL3 was significantly up-regulated in 15 days post anthe-sis fibers of ZRI-015(an upland cotton with longer and stronger fiber).GhWDL3 promotes all stem hairs to become straight when overexpressed in Arabidopsis,which may indirectly regulate cotton fiber cell morphology during fiber development.Virus induced gene silencing(VIGS)results showed that GhWDL3 inhibited fiber cell elongation at fiber development periods through regulating the expression of cell wall related genes.Conclusion These results reveal that GhWDL3 regulated cotton fiber cell elongation and provide crucial information for the further investigation in the regulatory mechanisms/networks of cotton fiber length.展开更多
High-temperature polymer electrolyte membrane fuel cells (HT-PEMFCs) show excellent application prospects due to its enhanced tolerance of hydrogen impurity.However,the sluggish electrode kinetics caused by its ineffi...High-temperature polymer electrolyte membrane fuel cells (HT-PEMFCs) show excellent application prospects due to its enhanced tolerance of hydrogen impurity.However,the sluggish electrode kinetics caused by its inefficient electrocatalytic interface and proton transfer severely restricts its performance.To overcome the sluggish electrode kinetics,the ethylenediamine tetramethylenephosphonic acid(EDTMPA) was successfully incorporated into the catalysts layer to regulate the phosphoric acid (PA) distribution to boost the electrocatalytic reaction interface and proton transfer,thus increasing the output power and stability of HT-PEMFCs.The hydrophilic H_(2)PO_(4)^(-) and electron donor N atom of EDTMPA could efficiently decrease the absorption of PA on the catalyst surface and facilitate proton transportation in the membrane electrode,as demonstrated by our experiments.The fuel cell assembled with the prepared membrane electrode shows a high reactivity of 1175 mW cm^(-2)and excellent stability,which is much better than the past reference report.The results of this work provide new insights into the utilization of small molecules with phosphate groups to enhance phosphate tolerance and proton conduction,and there is also a further improvement in the reactivity,durability,and utilization of the electrocatalysts in HT-PEMFCs.展开更多
Time-synchronous-averaging(TSA)is based on the idea of denoising by averaging,and it extracts the periodic components of a quasiperiodic signal and keeps the extracted waveform undistorted.This paper studies the mathe...Time-synchronous-averaging(TSA)is based on the idea of denoising by averaging,and it extracts the periodic components of a quasiperiodic signal and keeps the extracted waveform undistorted.This paper studies the mathematical properties of TSA,where three propositions are given to reveal the nature of TSA.This paper also proposes a TSA-spectrum based on super-resolution analysis and it decomposes a signal without using any base function.In contrast to discrete Fourier transform spectrum(DFT-spectrum),which is a spectrum in frequency domain,TSA-spectrum is a period-based spectrum,which can present more details of the cross effects between different periodic components of a quasiperiodic signal.Finally,a case study is carried out using bearing fault analysis to illustrate the performance of TSA-spectrum,where the rotation speed fluctuation of the shaft is estimated,which is about 0.12 ms difference.The extracted fault signals are presented and some insights are provided.We believe that this paper can provide new motivation for TSA-spectrum to be widely used in applications involving quasiperiodic signal processing(QSP).展开更多
Depression is a common mental health disorder.With current depression detection methods,specialized physicians often engage in conversations and physiological examinations based on standardized scales as auxiliary mea...Depression is a common mental health disorder.With current depression detection methods,specialized physicians often engage in conversations and physiological examinations based on standardized scales as auxiliary measures for depression assessment.Non-biological markers-typically classified as verbal or non-verbal and deemed crucial evaluation criteria for depression-have not been effectively utilized.Specialized physicians usually require extensive training and experience to capture changes in these features.Advancements in deep learning technology have provided technical support for capturing non-biological markers.Several researchers have proposed automatic depression estimation(ADE)systems based on sounds and videos to assist physicians in capturing these features and conducting depression screening.This article summarizes commonly used public datasets and recent research on audio-and video-based ADE based on three perspectives:Datasets,deficiencies in existing research,and future development directions.展开更多
One of the fundamental driving forces in the materials science community is the hunt for new materials with specific properties that meet the requirements of rapidly evolving technology.
基金supported by the National Natural Science Foundation of China(Grant Nos.12404264 and 22209067)Shenzhen Basic Research Program(Natural Science Foundation)Key Project of Basic Research(Grant No.JCYJ20241202123916023)Shenzhen Science and Technology Program(Grant No.KQTD20200820113047086)。
文摘Sodium-ion batteries have emerged as promising alternatives to lithium-ion batteries due to their abundant raw material reserves,low cost,enhanced safety,and environmental sustainability.Na_(2)Fe_(2)OS_(2),featuring a layered anti-perovskite structure,has attracted significant interest for its high capacity and facile synthesis.In this study,density functional theory calculations were performed to systematically investigate the phase stability,ionic conductivity,and voltage characteristics of Na_(2)Fe_(2)OS_(2)as a model system for anti-perovskite layered cathode materials.The compound exhibits excellent phase stability,and its equilibrium potential was calculated for the series Na_(x)Fe_(2)OCh_(2)(0<±<2)(where Ch represents chalcogenides).Naion transport analysis using the climbing image nudged elastic band method reveals a relatively low migration barrier(~0.47eV)along a dingonal pathway,indicating efficient Na^(+)mobility.To expand the materials design space,we systematically explored the effects of substituting Fe with various transition metals and replacing S with Se in NaaTM_(2)OCh_(2)structures.Among the variants studied,Na_(2)Mn_(2)OS_(2) demonstrates the most favorable combination of high voltage(~2.51V),robust phase stability,and superior energy density(~427 W-h/kg).This comprehensive comparison of transition metal substitutions provides vnluable insights for the rational design and experimental development of next-generation anti-perovskite layered cathode materials for sodium-ion batteries.
基金co-supported by the National Key Research and Development Program of China(No. 2021YFB3301504)the National Natural Science Foundation of China (Nos. 62072415, 62036010, 42301526, 62372416 and 62472389)the National Natural Science Foundation of Henan Province, China (No. 242300421215)
文摘Accurate recognition of flight deck operations for carrier-based aircraft, based on operation trajectories, is critical for optimizing carrier-based aircraft performance. This recognition involves understanding short-term and long-term spatial collaborative relationships among support agents and positions from long spatial–temporal trajectories. While the existing methods excel at recognizing collaborative behaviors from short trajectories, they often struggle with long spatial–temporal trajectories. To address this challenge, this paper introduces a dynamic graph method to enhance flight deck operation recognition. First, spatial–temporal collaborative relationships are modeled as a dynamic graph. Second, a discretized and compressed method is proposed to assign values to the states of this dynamic graph. To extract features that represent diverse collaborative relationships among agents and account for the duration of these relationships, a biased random walk is then conducted. Subsequently, the Swin Transformer is employed to comprehend spatial–temporal collaborative relationships, and a fully connected layer is applied to deck operation recognition. Finally, to address the scarcity of real datasets, a simulation pipeline is introduced to generate deck operations in virtual flight deck scenarios. Experimental results on the simulation dataset demonstrate the superior performance of the proposed method.
基金the National Natural Science Foundation of China(22208320)the Science and Technology Program of Henan Province(212102210044)The Henan Association for Science and Technology Youth Talent Support Program(2022HYTP026).
文摘Combining the advantages of high efficiency,low-pressure drop,and large throughput,the pore arrayenhanced tube-in-tube microchannel(PA-TMC) is a promising microreactor for industrial applications.However,most of the mass transfer takes place in the upstream pore region,while the contribution of the downstream annulus is limited.In this work,helical wires were introduced into the annulus by adhering to the outer surface of the inner tube.Mixing behavior and mass transfer of liquid-liquid twophase flow in PA-TMC with different helical wires have been systematically studied by a combination of experiments and volume of fluid(VOF) method.The introduction of helical wires improves the overall volumetric mass transfer coefficient KLa by up to 133% and the mass transfer efficiency E by up to 117%.The simulation results show that the helical wire brings extra phase mixing regions and increases the specific interface area,while accelerating the fluid flow and expanding the area of enhanced turbulent dissipation rate.Influences of helical wires in various configurations are compared by the comprehensive index I concerning the pressure drop and mass transfer performance simultaneously and a new correlation between KLa and specific energy consumption φ is proposed.This research deepens the understanding of the mixing behavior and mass transfer in the PA-TMCs and provides practical experience for the process intensification of microchannel reactors.
基金in part by the National Science Foundation of China under Grant No.62276238in part by the National Science Foundation for Distinguished Young Scholars of China under Grant No.62325602in part by the Natural Science Foundation of Henan,China under Grant No.232300421095.
文摘The Heterogeneous Capacitated Vehicle Routing Problem(HCVRP),which involves efficiently routing vehicles with diverse capacities to fulfill various customer demands at minimal cost,poses an NP-hard challenge in combinatorial optimization.Recently,reinforcement learning approaches such as 2D Array Pointer Networks(2D-Ptr)have demonstrated remarkable speed in decision-making by modeling multiple agents’concurrent choices as a sequence of consecutive actions.However,these learning-based models often struggle with generalization,meaning they cannot seamlessly adapt to new scenarios with varying numbers of vehicles or customers without retraining.Inspired by the potential of multi-teacher knowledge distillation to harness diverse knowledge from multiple sources and craft a comprehensive student model,we propose to enhance the generalization capability of 2D-Ptr through Multiple Teacher-forcing Knowledge Distillation(MTKD).We initially train 12 unique 2D-Ptr models under various settings to serve as teacher models.Subsequently,we randomly sample a teacher model and a batch of problem instances,focusing on those where the chosen teacher performed best.This teacher model then solves these instances,generating high-reward action sequences to guide knowledge transfer to the student model.We conduct rigorous evaluations across four distinct datasets,each comprising four HCVRP instances of varying scales.Our empirical findings underscore the proposed method superiority over existing learning-based methods in terms of both computational efficiency and solution quality.
基金supported by the Major Innovation Project for the Integration of Science,Education,and Industry of Qilu University of Technology(Shandong Academy of Sciences)(Nos.2023HYZX01,2023JBZ02)the Open Project of Key Laboratory of Computing Power Network and Information Security,Ministry of Education,Qilu University of Technology(Shandong Academy of Sciences)(No.2023ZD007)+2 种基金the Talent Research Projects of Qilu University of Technology(Shandong Academy of Sciences)(No.2023RCKY136)the Technology and Innovation Major Project of the Ministry of Science and Technology of China(No.2022ZD0118600)the Jinan‘20 New Colleges and Universities’Funded Project(No.202333043)。
文摘Accurate wind speed measurements on maritime vessels are crucial for weather forecasting,sea state prediction,and safe navigation.However,vessel motion and challenging environmental conditions often affect measurement precision.To address this issue,this study proposes an innovative framework for correcting and predicting shipborne wind speed.By integrating a main network with a momentum updating network,the proposed framework effectively extracts features from the time and frequency domains,thereby allowing for precise adjustments and predictions of shipborne wind speed data.Validation using real sensor data collected at the Qingdao Oceanographic Institute demonstrates that the proposed method outperforms existing approaches in single-and multi-step predictions compared to existing methods,achieving higher accuracy in wind speed forecasting.The proposed innovative approach offers a promising direction for future validation in more realistic maritime onboard scenarios.
基金supported by National Key Research and Development Program of China under Grant 2024YFE0210800National Natural Science Foundation of China under Grant 62495062Beijing Natural Science Foundation under Grant L242017.
文摘The Dynamical Density Functional Theory(DDFT)algorithm,derived by associating classical Density Functional Theory(DFT)with the fundamental Smoluchowski dynamical equation,describes the evolution of inhomo-geneous fluid density distributions over time.It plays a significant role in studying the evolution of density distributions over time in inhomogeneous systems.The Sunway Bluelight II supercomputer,as a new generation of China’s developed supercomputer,possesses powerful computational capabilities.Porting and optimizing industrial software on this platform holds significant importance.For the optimization of the DDFT algorithm,based on the Sunway Bluelight II supercomputer and the unique hardware architecture of the SW39000 processor,this work proposes three acceleration strategies to enhance computational efficiency and performance,including direct parallel optimization,local-memory constrained optimization for CPEs,and multi-core groups collaboration and communication optimization.This method combines the characteristics of the program’s algorithm with the unique hardware architecture of the Sunway Bluelight II supercomputer,optimizing the storage and transmission structures to achieve a closer integration of software and hardware.For the first time,this paper presents Sunway-Dynamical Density Functional Theory(SW-DDFT).Experimental results show that SW-DDFT achieves a speedup of 6.67 times within a single-core group compared to the original DDFT implementation,with six core groups(a total of 384 CPEs),the maximum speedup can reach 28.64 times,and parallel efficiency can reach 71%,demonstrating excellent acceleration performance.
文摘Investigating highly effective electrocatalysts for high-temperature proton exchange membrane fuel cells(HT-PEMFC)requires the resistance to phosphate acid(PA)poisoning at cathodic oxygen reduction reaction(ORR).Recent advancements in catalysts have focused on alleviating phosphoric anion adsorption on Pt-based catalysts with modified electronic structure or catalytic interface and developing Fe-N-C based catalysts with immunity of PA poisoning.Fe-N-C-based catalysts have emerged as promising alternatives to Pt-based catalysts,offering significant potential to overcome the characteristic adsorption of phosphate anion on Pt.An overview of these developments provides insights into catalytic mechanisms and facilitates the design of more efficient catalysts.This review begins with an exploration of basic poisoning principles,followed by a critical summary of characterization techniques employed to identified the underlying mechanism of poisoning effect.Attention is then directed to endeavors aimed at enhancing the HT-PEMFC performance by well-designed catalysts.Finally,the opportunities and challenges in developing the anti-PA poisoning strategy and practical HT-PEMFC is discussed.Through these discussions,a comprehensive understanding of PA-poisoning bottlenecks and inspire future research directions is aim to provided.
文摘Through three millennia of practice,Traditional Chinese Medicine(TCM)has evolved by integrating knowledge from diverse disciplines,forging a distinct developmental path that respects its ancient foundations while incorporating innovation.TCM has achieved significant breakthroughs in elucidating its theoretical foundations using contemporary scientific methodologies,through the implementation of modernization initiatives over the past three decades.The TCM modernization program has yielded continuous innovations,propelling TCM into a high-quality development stage across both clinical practice and industrial applications.Notably,these advances have enhanced global recognition and adoption of TCM.
基金supported by the National Natural Science Foundation of China under Grant 62471493 and 62402257partially supported by the Natural Science Foundation of Shandong Province under Grant ZR2023LZH017,ZR2024MF066 and 2023QF025+2 种基金partially supported by the Open Research Subject of State Key Laboratory of Intelligent Game(No.ZBKF-24-12)partially supported by the Foundation of Key Laboratory of Education Informatization for Nationalities(Yunnan Normal University),the Ministry of Education(No.EIN2024C006)partially supported by the Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE(No.202306).
文摘As Internet ofThings(IoT)technologies continue to evolve at an unprecedented pace,intelligent big data control and information systems have become critical enablers for organizational digital transformation,facilitating data-driven decision making,fostering innovation ecosystems,and maintaining operational stability.In this study,we propose an advanced deployment algorithm for Service Function Chaining(SFC)that leverages an enhanced Practical Byzantine Fault Tolerance(PBFT)mechanism.The main goal is to tackle the issues of security and resource efficiency in SFC implementation across diverse network settings.By integrating blockchain technology and Deep Reinforcement Learning(DRL),our algorithm not only optimizes resource utilization and quality of service but also ensures robust security during SFC deployment.Specifically,the enhanced PBFT consensus mechanism(VRPBFT)significantly reduces consensus latency and improves Byzantine node detection through the introduction of a Verifiable Random Function(VRF)and a node reputation grading model.Experimental results demonstrate that compared to traditional PBFT,the proposed VRPBFT algorithm reduces consensus latency by approximately 30%and decreases the proportion of Byzantine nodes by 40%after 100 rounds of consensus.Furthermore,the DRL-based SFC deployment algorithm(SDRL)exhibits rapid convergence during training,with improvements in long-term average revenue,request acceptance rate,and revenue/cost ratio of 17%,14.49%,and 20.35%,respectively,over existing algorithms.Additionally,the CPU resource utilization of the SDRL algorithmreaches up to 42%,which is 27.96%higher than other algorithms.These findings indicate that the proposed algorithm substantially enhances resource utilization efficiency,service quality,and security in SFC deployment.
基金partially supported by the National Natural Science Foundation of China under Grants 62471493 and 62402257(for conceptualization and investigation)partially supported by the Natural Science Foundation of Shandong Province,China under Grants ZR2023LZH017,ZR2024MF066,and 2023QF025(for formal analysis and validation)+1 种基金partially supported by the Open Foundation of Key Laboratory of Computing Power Network and Information Security,Ministry of Education,Qilu University of Technology(Shandong Academy of Sciences)under Grant 2023ZD010(for methodology and model design)partially supported by the Russian Science Foundation(RSF)Project under Grant 22-71-10095-P(for validation and results verification).
文摘To address the challenge of missing modal information in entity alignment and to mitigate information loss or bias arising frommodal heterogeneity during fusion,while also capturing shared information acrossmodalities,this paper proposes a Multi-modal Pre-synergistic Entity Alignmentmodel based on Cross-modalMutual Information Strategy Optimization(MPSEA).The model first employs independent encoders to process multi-modal features,including text,images,and numerical values.Next,a multi-modal pre-synergistic fusion mechanism integrates graph structural and visual modal features into the textual modality as preparatory information.This pre-fusion strategy enables unified perception of heterogeneous modalities at the model’s initial stage,reducing discrepancies during the fusion process.Finally,using cross-modal deep perception reinforcement learning,the model achieves adaptive multilevel feature fusion between modalities,supporting learningmore effective alignment strategies.Extensive experiments on multiple public datasets show that the MPSEA method achieves gains of up to 7% in Hits@1 and 8.2% in MRR on the FBDB15K dataset,and up to 9.1% in Hits@1 and 7.7% in MRR on the FBYG15K dataset,compared to existing state-of-the-art methods.These results confirm the effectiveness of the proposed model.
基金Supported by the Ministry of Science and Technology of the People’s Republic of China(No.2019 YFE 0125000)the National Natural Science Foundation of China(No.42376032)。
文摘Variations in ocean mixed layer depth(MLD)show a significant impact on energy balance in the global climate systems and marine ecosystems.At present,the accuracy of modeling MLD,especially in the region with complex ocean dynamics,remains a challenge,thus calling for an emergency using artificial intelligence approach to improve the assessment of the MLD.A novel convolutional neural network model was developed based on a dual-attention module(DA-CNN)to estimate the MLD in the Bay of Bengal(BoB)by integrating multi-source remote sensing data and Argo gridded data.Compared with the original CNN model,the DA-CNN model exhibits superior performance with notable improvements in the annual average root mean square error(RMSE)and R2 values by 13.0%and 8.4%,respectively,while more accurately capturing the seasonal variations in MLD.Moreover,the results using the DA-CNN model show minimum RMSE and maximum R2 values,in comparison to the calculation by the random forest,artificial neural network model,and the hybrid coordinate ocean model.Accordingly,our findings suggest that the newly developed DA-CNN model provides an effective advantage in studying the MLD and the associated ocean processes.
基金This work is supported by the National Key Research and Development Plan program of the Ministry of Science and Technology of China(No.2016YFB0201100)Additionally,this work is supported by the National Laboratory for Marine Science and Technology(Qingdao)Major Project of the Aoshan Science and Technology Innovation Program(No.2018ASKJ01-04)the Open Fundation of Key Laboratory of Marine Science and Numerical Simulation,Ministry of Natural Resources(No.2021-YB-02).
文摘In this paper,a typical experiment is carried out based on a high-resolution air-sea coupled model,namely,the coupled ocean-atmosphere-wave-sediment transport(COAWST)model,on both heterogeneous many-core(SW)and homogenous multicore(Intel)supercomputing platforms.We construct a hindcast of Typhoon Lekima on both the SW and Intel platforms,compare the simulation results between these two platforms and compare the key elements of the atmospheric and ocean modules to reanalysis data.The comparative experiment in this typhoon case indicates that the domestic many-core computing platform and general cluster yield almost no differences in the simulated typhoon path and intensity,and the differences in surface pressure(PSFC)in the WRF model and sea surface temperature(SST)in the short-range forecast are very small,whereas a major difference can be identified at high latitudes after the first 10 days.Further heat budget analysis verifies that the differences in SST after 10 days are mainly caused by shortwave radiation variations,as influenced by subsequently generated typhoons in the system.These typhoons generated in the hindcast after the first 10 days attain obviously different trajectories between the two platforms.
基金This research was funded by the National Key R&D Program of China(Project:Key Technologies and Equipment for Multi-View Stereoscopic Disaster Detection and Emergency Response to Derived Disasters in Underground Spaces,2022YFC3005600)the National Natural Science Foundation of China(52378402)+2 种基金Shandong Provincial Natural Science Foundation Youth Project(ZR2022QE021 and ZR202211100077)Shandong Province Higher Education Young Innovative Team Project(2022KJ037)State Key Laboratory of Precision Blasting and Hubei Key Laboratory of Blasting Engineering,Jianghan University(PBSKL2022C03),funding from Shandong Railway Investment Holding Group Co.,Ltd.(“Key Technologies for Rapid and Intelligent Construction of Large Section High-Speed Railway Tunnels in Low Mountain and Hilly Areas”and“Intelligent Construction Trolley Equipment and Key Technologies for the Lining of Ultra-Long Open Tunnel Sections”).
文摘In underground engineering,the detection of structural cracks on tunnel surfaces stands as a pivotal task in ensuring the health and reliability of tunnel structures.However,the dim and dusty environment inherent to under-ground engineering poses considerable challenges to crack segmentation.This paper proposes a crack segmentation algorithm termed as Focused Detection for Subsurface Cracks YOLOv8(FDSC-YOLOv8)specifically designed for underground engineering structural surfaces.Firstly,to improve the extraction of multi-layer convolutional features,the fixed convolutional module is replaced with a deformable convolutional module.Secondly,the model’s receptive field is enhanced by introducing a multi-branch convolutional module,improving the extraction of shallow features for small targets.Next,the Dynamic Snake Convolution module is incorporated to enhance the extraction capability for slender and weak cracks.Finally,the Convolutional Block Attention Module(CBAM)module is employed to achieve better target determination.The FDSC-YOLOv8s algorithm’s mAP50 and mAP50-95 reach 96.5%and 66.4%,according to the testing data.
基金supported by the National Natural Science Foundation of China(No.52071287)the Natural Science Foundation of Zhejiang Province(No.LZ23E010002)the Basic and Applied Basic Research Foundation of Guangdong Province(Nos.2021A1515110676,2022A1515011832).
文摘LiBH_(4) containing 18.5 wt.%H_(2) is an attractive high-capacity hydrogen storage material,however,it suffers from high operation temperature and poor reversibility.Herein,a novel and low-cost bifunctional additive,waxberry-like Fe_(3)O_(4) secondary nanospheres assembled from ultrafine primary Fe_(3)O_(4) nanoparticles,is synthesized,which exhibits significant destabilization and bidirectional catalyzation towards(de)hydrogenation of LiBH_(4).With an optimized addition of 30 wt.% waxberry-like Fe_(3)O_(4),the system initiated dehydrogenation below 100℃ and released a total of 8.1 wt.%H_(2) to 400℃.After 10 cycles,a capacity retention of 70% was achieved,greatly superior to previously reported oxides-modified systems.The destabilizing and catalyzing mechanisms of waxberry-like Fe_(3)O_(4) on LiBH_(4) were systematically analyzed by phase and microstructural evolutions during dehydrogenation and hydrogenation cycling as well as density functional theory(DFT)calculations.The present work provides new insights in developing advanced nano-additives with unique structural and multifunctional designs towards LiBH4 hydrogen storage.
基金supported by the National Key Research and Development Program of China(2022YFD1200300)China Agriculture Research System(CARS-15-01).
文摘Background Cotton fiber is a model tissue for studying microtubule-associated proteins(MAPs).The Xklp2(TPX2)proteins that belong to the novel MAPs member mainly participate in the formation and development of microtubule(MT).However,there is a lack of studies concerning the systematic characterization of the TPX2 genes family in cotton.Therefore,the identification and portrayal of G.hirsutum TPX2 genes can provide key targets for molecular manipula-tion in the breeding of cotton fiber improvement.Result In this study,TPX2 family genes were classified into two distinct subclasses TPXLs and MAP genes WAVE DAMP-ENED2-LIKE(WDLs)and quite conservative in quantity.GhWDL3 was significantly up-regulated in 15 days post anthe-sis fibers of ZRI-015(an upland cotton with longer and stronger fiber).GhWDL3 promotes all stem hairs to become straight when overexpressed in Arabidopsis,which may indirectly regulate cotton fiber cell morphology during fiber development.Virus induced gene silencing(VIGS)results showed that GhWDL3 inhibited fiber cell elongation at fiber development periods through regulating the expression of cell wall related genes.Conclusion These results reveal that GhWDL3 regulated cotton fiber cell elongation and provide crucial information for the further investigation in the regulatory mechanisms/networks of cotton fiber length.
基金financially supported by the National Key R&D Program of China (2021YFA 1500900)the National Natural Science Foundation of China (Grant No.:22425021, 22102053)+5 种基金the Provincial Natural Science Foundation of Hunan (2024JJ2012)the Science and Technology Innovation Program of Hunan Province (Grant Nos.2022RC1036)the Top ten Technological Breakthrough Projects in Hunan Province (2023GK1050)the Guangdong Basic and Applied Basic Research Foundation (2024A1515012889)the Shenzhen Science and technology program (JCYJ20210324122209025)the Major Program of the Natural Science Foundation of Hunan Province(2021JC0006)。
文摘High-temperature polymer electrolyte membrane fuel cells (HT-PEMFCs) show excellent application prospects due to its enhanced tolerance of hydrogen impurity.However,the sluggish electrode kinetics caused by its inefficient electrocatalytic interface and proton transfer severely restricts its performance.To overcome the sluggish electrode kinetics,the ethylenediamine tetramethylenephosphonic acid(EDTMPA) was successfully incorporated into the catalysts layer to regulate the phosphoric acid (PA) distribution to boost the electrocatalytic reaction interface and proton transfer,thus increasing the output power and stability of HT-PEMFCs.The hydrophilic H_(2)PO_(4)^(-) and electron donor N atom of EDTMPA could efficiently decrease the absorption of PA on the catalyst surface and facilitate proton transportation in the membrane electrode,as demonstrated by our experiments.The fuel cell assembled with the prepared membrane electrode shows a high reactivity of 1175 mW cm^(-2)and excellent stability,which is much better than the past reference report.The results of this work provide new insights into the utilization of small molecules with phosphate groups to enhance phosphate tolerance and proton conduction,and there is also a further improvement in the reactivity,durability,and utilization of the electrocatalysts in HT-PEMFCs.
基金supported by the National Natural Science Foundation of China(Nos.52008198,51425804,U20A20283,and U1813222)the Shenzhen International Cooperation Research Program(No.GJHZ20200731095009029)+2 种基金the Shenzhen Science and Technology Program(Nos.RCBS20210609103823048 and KJZD20230923114916032)the Foundation of the Department of Science and Technology of Guangdong Province(No.2019TQ05Z654)the Guangdong Provincial Key Laboratory of Construction Robotics and Intelligent Construction(No.2022KSYS013),China.
文摘Time-synchronous-averaging(TSA)is based on the idea of denoising by averaging,and it extracts the periodic components of a quasiperiodic signal and keeps the extracted waveform undistorted.This paper studies the mathematical properties of TSA,where three propositions are given to reveal the nature of TSA.This paper also proposes a TSA-spectrum based on super-resolution analysis and it decomposes a signal without using any base function.In contrast to discrete Fourier transform spectrum(DFT-spectrum),which is a spectrum in frequency domain,TSA-spectrum is a period-based spectrum,which can present more details of the cross effects between different periodic components of a quasiperiodic signal.Finally,a case study is carried out using bearing fault analysis to illustrate the performance of TSA-spectrum,where the rotation speed fluctuation of the shaft is estimated,which is about 0.12 ms difference.The extracted fault signals are presented and some insights are provided.We believe that this paper can provide new motivation for TSA-spectrum to be widely used in applications involving quasiperiodic signal processing(QSP).
基金Supported by Shandong Province Key R and D Program,No.2021SFGC0504Shandong Provincial Natural Science Foundation,No.ZR2021MF079Science and Technology Development Plan of Jinan(Clinical Medicine Science and Technology Innovation Plan),No.202225054.
文摘Depression is a common mental health disorder.With current depression detection methods,specialized physicians often engage in conversations and physiological examinations based on standardized scales as auxiliary measures for depression assessment.Non-biological markers-typically classified as verbal or non-verbal and deemed crucial evaluation criteria for depression-have not been effectively utilized.Specialized physicians usually require extensive training and experience to capture changes in these features.Advancements in deep learning technology have provided technical support for capturing non-biological markers.Several researchers have proposed automatic depression estimation(ADE)systems based on sounds and videos to assist physicians in capturing these features and conducting depression screening.This article summarizes commonly used public datasets and recent research on audio-and video-based ADE based on three perspectives:Datasets,deficiencies in existing research,and future development directions.
基金supported by the National Natural Science Foundation of China(Grant Nos.21701043,21825201 and U19A2017)the Provincial Natural Science Foundation of Hunan(2019GK2031)+1 种基金the Open Project Program of Key Laboratory of Low Dimensional Materials&Application Technology(Xiangtan University),Ministry of Education,China(No.KF20180202)the China Postdoctoral Science Foundation(Grant Nos.2019 M662766,2019 M662759,2020 M682549,and 2020 M672473)。
文摘One of the fundamental driving forces in the materials science community is the hunt for new materials with specific properties that meet the requirements of rapidly evolving technology.
基金supported by Major International(Regional)Joint Research Project of the National Natural Science Foundation of China(61320106011)National High Technology Research and Development Program of China(863 Program)(2014AA052802)National Natural Science Foundation of China(61573224)
