The stable nanobubbles adhered to mineral surfaces may facilitate their efficient separation via flotation in the mining industry.However,the state of nanobubbles on mineral solid surfaces is still elusive.In this stu...The stable nanobubbles adhered to mineral surfaces may facilitate their efficient separation via flotation in the mining industry.However,the state of nanobubbles on mineral solid surfaces is still elusive.In this study,molecular dynamics(MD)simulations are employed to examine mineral-like model surfaces with varying degrees of hydrophobicity,modulated by surface charges,to elucidate the adsorption behavior of nanobubbles at the interface.Our findings not only contribute to the fundamental understanding of nanobubbles but also have potential applications in the mining industry.We observed that as the surface charge increases,the contact angle of the nanobubbles increases accordingly with shape transformation from a pancake-like gas film to a cap-like shape,and ultimately forming a stable nanobubble upon an ordered water monolayer.When the solid–water interactions are weak with a small partial charge,the hydrophobic gas(N_(2))molecules accumulate near the solid surfaces.However,we have found,for the first time,that gas molecules assemble a nanobubble on the water monolayer adjacent to the solid surfaces with large partial charges.Such phenomena are attributed to the formation of a hydrophobic water monolayer with a hydrogen bond network structure near the surface.展开更多
Micro-expressions(ME)recognition is a complex task that requires advanced techniques to extract informative features fromfacial expressions.Numerous deep neural networks(DNNs)with convolutional structures have been pr...Micro-expressions(ME)recognition is a complex task that requires advanced techniques to extract informative features fromfacial expressions.Numerous deep neural networks(DNNs)with convolutional structures have been proposed.However,unlike DNNs,shallow convolutional neural networks often outperform deeper models in mitigating overfitting,particularly with small datasets.Still,many of these methods rely on a single feature for recognition,resulting in an insufficient ability to extract highly effective features.To address this limitation,in this paper,an Improved Dual-stream Shallow Convolutional Neural Network based on an Extreme Gradient Boosting Algorithm(IDSSCNN-XgBoost)is introduced for ME Recognition.The proposed method utilizes a dual-stream architecture where motion vectors(temporal features)are extracted using Optical Flow TV-L1 and amplify subtle changes(spatial features)via EulerianVideoMagnification(EVM).These features are processed by IDSSCNN,with an attention mechanism applied to refine the extracted effective features.The outputs are then fused,concatenated,and classified using the XgBoost algorithm.This comprehensive approach significantly improves recognition accuracy by leveraging the strengths of both temporal and spatial information,supported by the robust classification power of XgBoost.The proposed method is evaluated on three publicly available ME databases named Chinese Academy of Sciences Micro-expression Database(CASMEII),Spontaneous Micro-Expression Database(SMICHS),and Spontaneous Actions and Micro-Movements(SAMM).Experimental results indicate that the proposed model can achieve outstanding results compared to recent models.The accuracy results are 79.01%,69.22%,and 68.99%on CASMEII,SMIC-HS,and SAMM,and the F1-score are 75.47%,68.91%,and 63.84%,respectively.The proposed method has the advantage of operational efficiency and less computational time.展开更多
On May 14th,following the U.S.adjustment of additional tariffs on Chinese goods,American buyers began stockpiling in earnest.Many cross-border e-commerce companies also received a surge of orders.At 7 PM,a bustling Ha...On May 14th,following the U.S.adjustment of additional tariffs on Chinese goods,American buyers began stockpiling in earnest.Many cross-border e-commerce companies also received a surge of orders.At 7 PM,a bustling Hangzhou-based cross-border e-commerce company was alive with multiple languages echoing through its live-streaming rooms as backend order numbers climbed steadily.展开更多
In this manuscript,we propose an analytical equivalent linear viscoelastic constitutive model for fiber-reinforced composites,bypassing general computational homogenization.The method is based on the reduced-order hom...In this manuscript,we propose an analytical equivalent linear viscoelastic constitutive model for fiber-reinforced composites,bypassing general computational homogenization.The method is based on the reduced-order homogenization(ROH)approach.The ROH method typically involves solving multiple finite element problems under periodic conditions to evaluate elastic strain and eigenstrain influence functions in an‘off-line’stage,which offers substantial cost savings compared to direct computational homogenization methods.Due to the unique structure of the fibrous unit cell,“off-line”stage calculation can be eliminated by influence functions obtained analytically.Introducing the standard solid model to the ROH method enables the creation of a comprehensive analytical homogeneous viscoelastic constitutive model.This method treats fibrous composite materials as homogeneous,anisotropic viscoelastic materials,significantly reducing computational time due to its analytical nature.This approach also enables precise determination of a homogenized anisotropic relaxation modulus and accurate capture of various viscoelastic responses under different loading conditions.Three sets of numerical examples,including unit cell tests,three-point beam bending tests,and torsion tests,are given to demonstrate the predictive performance of the homogenized viscoelastic model.Furthermore,the model is validated against experimental measurements,confirming its accuracy and reliability.展开更多
In this paper,we delve into a generalized higher order Camassa-Holm type equation,(or,an ghmCH equation for short).We establish local well-posedness for this equation under the condition that the initial data uo belon...In this paper,we delve into a generalized higher order Camassa-Holm type equation,(or,an ghmCH equation for short).We establish local well-posedness for this equation under the condition that the initial data uo belongs to the Sobolev space H'(R)for some s>2.In addition,we obtain the weak formulation of this equation and prove the existence of both single peakon solution and a multi-peakon dynamic system.展开更多
In this paper,we give a complete characterization of all self-adjoint domains of odd order differential operators on two intervals.These two intervals with all four endpoints are singular(one endpoint of each interval...In this paper,we give a complete characterization of all self-adjoint domains of odd order differential operators on two intervals.These two intervals with all four endpoints are singular(one endpoint of each interval is singular or all four endpoints are regulars are the special cases).And these extensions yield"new"self-adjoint operators,which involve interactions between the two intervals.展开更多
Alloying transition metals with Pt is an effective strategy for optimizing Pt-based catalysts toward the oxygen reduction reaction(ORR).Atomic ordered intermetallic compounds(IMC)provide unique electronic and geometri...Alloying transition metals with Pt is an effective strategy for optimizing Pt-based catalysts toward the oxygen reduction reaction(ORR).Atomic ordered intermetallic compounds(IMC)provide unique electronic and geometrical effects as well as stronger intermetallic interactions due to the ordered arrangement of metal atoms,thus exhibiting superior electrocata-lytic activity and durability.However,quantitatively analyzing the ordering degree of IMC and exploring the correlation between the ordering degree and ORR activity remains extremely challenging.Herein,a series of ternary Pt_(2)NiCo interme-tallic catalysts(o-Pt_(2)NiCo)with different ordering degree were synthesized by annealing temperature modulation.Among them,the o-Pt_(2)NiCo which annealed at 800℃for two hours exhibits the highest ordering degree and the optimal ORR ac-tivity,which the mass activity of o-Pt_(2)NiCo is 1.8 times and 2.8 times higher than that of disordered Pt_(2)NiCo alloy and Pt/C.Furthermore,the o-Pt_(2)NiCo still maintains 70.8%mass activity after 30,000 potential cycles.Additionally,the ORR activity test results for Pt_(2)NiCo IMC with different ordering degree also provide a positive correlation between the ordering degree and ORR activity.This work provides a prospective design direction for ternary Pt-based electrocatalysts.展开更多
Higher-order band topology not only enriches our understanding of topological phases but also unveils pioneering lower-dimensional boundary states,which harbors substantial potential for next-generation device applica...Higher-order band topology not only enriches our understanding of topological phases but also unveils pioneering lower-dimensional boundary states,which harbors substantial potential for next-generation device applications.The distinct electronic configurations and tunable attributes of two-dimensional materials position them as a quintessential platform for the realization of second-order topological insulators(SOTIs).This article provides an overview of the research progress in SOTIs within the field of two-dimensional electronic materials,focusing on the characterization of higher-order topological properties and the numerous candidate materials proposed in theoretical studies.These endeavors not only enhance our understanding of higher-order topological states but also highlight potential material systems that could be experimentally realized.展开更多
In this paper,a new technique is introduced to construct higher-order iterative methods for solving nonlinear systems.The order of convergence of some iterative methods can be improved by three at the cost of introduc...In this paper,a new technique is introduced to construct higher-order iterative methods for solving nonlinear systems.The order of convergence of some iterative methods can be improved by three at the cost of introducing only one additional evaluation of the function in each step.Furthermore,some new efficient methods with a higher-order of convergence are obtained by using only a single matrix inversion in each iteration.Analyses of convergence properties and computational efficiency of these new methods are made and testified by several numerical problems.By comparison,the new schemes are more efficient than the corresponding existing ones,particularly for large problem sizes.展开更多
The dorsal and ventral visual streams have been considered to play distinct roles in visual processing for action:the dorsal stream is assumed to support real-time actions,while the ventral stream facilitates memory-g...The dorsal and ventral visual streams have been considered to play distinct roles in visual processing for action:the dorsal stream is assumed to support real-time actions,while the ventral stream facilitates memory-guided actions.However,recent evidence suggests a more integrated function of these streams.We investigated the neural dynamics and functional connectivity between them during memory-guided actions using intracranial EEG.We tracked neural activity in the inferior parietal lobule in the dorsal stream,and the ventral temporal cortex in the ventral stream as well as the hippocampus during a delayed action task involving object identity and location memory.We found increased alpha power in both streams during the delay,indicating their role in maintaining spatial visual information.In addition,we recorded increased alpha power in the hippocampus during the delay,but only when both object identity and location needed to be remembered.We also recorded an increase in theta band phase synchronization between the inferior parietal lobule and ventral temporal cortex and between the inferior parietal lobule and hippocampus during the encoding and delay.Granger causality analysis indicated dynamic and frequency-specific directional interactions among the inferior parietal lobule,ventral temporal cortex,and hippocampus that varied across task phases.Our study provides unique electrophysiological evidence for close interactions between dorsal and ventral streams,supporting an integrated processing model in which both streams contribute to memory-guided actions.展开更多
Dear Editor,In this letter,a constrained networked predictive control strategy is proposed for the optimal control problem of complex nonlinear highorder fully actuated(HOFA)systems with noises.The method can effectiv...Dear Editor,In this letter,a constrained networked predictive control strategy is proposed for the optimal control problem of complex nonlinear highorder fully actuated(HOFA)systems with noises.The method can effectively deal with nonlinearities,constraints,and noises in the system,optimize the performance metric,and present an upper bound on the stable output of the system.展开更多
Sign language dataset is essential in sign language recognition and translation(SLRT). Current public sign language datasets are small and lack diversity, which does not meet the practical application requirements for...Sign language dataset is essential in sign language recognition and translation(SLRT). Current public sign language datasets are small and lack diversity, which does not meet the practical application requirements for SLRT. However, making a large-scale and diverse sign language dataset is difficult as sign language data on the Internet is scarce. In making a large-scale and diverse sign language dataset, some sign language data qualities are not up to standard. This paper proposes a two information streams transformer(TIST) model to judge whether the quality of sign language data is qualified. To verify that TIST effectively improves sign language recognition(SLR), we make two datasets, the screened dataset and the unscreened dataset. In this experiment, this paper uses visual alignment constraint(VAC) as the baseline model. The experimental results show that the screened dataset can achieve better word error rate(WER) than the unscreened dataset.展开更多
No-wash bioassays based on nanoparticles are used widely in biochemical procedures because of their responsive sensing and no need forwashing processes.Essential for no-wash biosensing are the interactions between nan...No-wash bioassays based on nanoparticles are used widely in biochemical procedures because of their responsive sensing and no need forwashing processes.Essential for no-wash biosensing are the interactions between nanoparticles and biomolecules,but it is challenging toachieve controlled bioconjugation of molecules on nanomaterials.Reported here is a way to actively improve nanoparticle-based no-washbioassays by enhancing the binding between biomolecules and gold nanoparticles via acoustic streaming generated by a gigahertz piezoelectricnanoelectromechanical resonator.Tunable micro-vortices are generated at the device-liquid interface,thereby accelerating the internalcirculating flow of the solution,bypassing the diffusion limitation,and thus improving the binding between the biomolecules and goldnanoparticles.Combined with fluorescence quenching,an enhanced and ultrafast no-wash biosensing assay is realized for specific proteins.The sensing method presented here is a versatile tool for different types of biomolecule detection with high efficiency and simplicity.展开更多
With the widespread application of Internet of Things(IoT)technology,the processing of massive realtime streaming data poses significant challenges to the computational and data-processing capabilities of systems.Alth...With the widespread application of Internet of Things(IoT)technology,the processing of massive realtime streaming data poses significant challenges to the computational and data-processing capabilities of systems.Although distributed streaming data processing frameworks such asApache Flink andApache Spark Streaming provide solutions,meeting stringent response time requirements while ensuring high throughput and resource utilization remains an urgent problem.To address this,the study proposes a formal modeling approach based on Performance Evaluation Process Algebra(PEPA),which abstracts the core components and interactions of cloud-based distributed streaming data processing systems.Additionally,a generic service flow generation algorithmis introduced,enabling the automatic extraction of service flows fromthe PEPAmodel and the computation of key performance metrics,including response time,throughput,and resource utilization.The novelty of this work lies in the integration of PEPA-based formal modeling with the service flow generation algorithm,bridging the gap between formal modeling and practical performance evaluation for IoT systems.Simulation experiments demonstrate that optimizing the execution efficiency of components can significantly improve system performance.For instance,increasing the task execution rate from 10 to 100 improves system performance by 9.53%,while further increasing it to 200 results in a 21.58%improvement.However,diminishing returns are observed when the execution rate reaches 500,with only a 0.42%gain.Similarly,increasing the number of TaskManagers from 10 to 20 improves response time by 18.49%,but the improvement slows to 6.06% when increasing from 20 to 50,highlighting the importance of co-optimizing component efficiency and resource management to achieve substantial performance gains.This study provides a systematic framework for analyzing and optimizing the performance of IoT systems for large-scale real-time streaming data processing.The proposed approach not only identifies performance bottlenecks but also offers insights into improving system efficiency under different configurations and workloads.展开更多
The exponential expansion of the Internet of Things(IoT),Industrial Internet of Things(IIoT),and Transportation Management of Things(TMoT)produces vast amounts of real-time streaming data.Ensuring system dependability...The exponential expansion of the Internet of Things(IoT),Industrial Internet of Things(IIoT),and Transportation Management of Things(TMoT)produces vast amounts of real-time streaming data.Ensuring system dependability,operational efficiency,and security depends on the identification of anomalies in these dynamic and resource-constrained systems.Due to their high computational requirements and inability to efficiently process continuous data streams,traditional anomaly detection techniques often fail in IoT systems.This work presents a resource-efficient adaptive anomaly detection model for real-time streaming data in IoT systems.Extensive experiments were carried out on multiple real-world datasets,achieving an average accuracy score of 96.06%with an execution time close to 7.5 milliseconds for each individual streaming data point,demonstrating its potential for real-time,resourceconstrained applications.The model uses Principal Component Analysis(PCA)for dimensionality reduction and a Z-score technique for anomaly detection.It maintains a low computational footprint with a sliding window mechanism,enabling incremental data processing and identification of both transient and sustained anomalies without storing historical data.The system uses a Multivariate Linear Regression(MLR)based imputation technique that estimates missing or corrupted sensor values,preserving data integrity prior to anomaly detection.The suggested solution is appropriate for many uses in smart cities,industrial automation,environmental monitoring,IoT security,and intelligent transportation systems,and is particularly well-suited for resource-constrained edge devices.展开更多
In the contemporary digital landscape,the proliferation of information has led to an increasing diversity of channels through which consumers obtain information,resulting in a gradual transformation of shopping habits...In the contemporary digital landscape,the proliferation of information has led to an increasing diversity of channels through which consumers obtain information,resulting in a gradual transformation of shopping habits.Consumers now frequently rely on external sources to make well-informed purchasing decisions,leading to the emergence of live shopping as a prominent avenue for gathering product information and completing transactions.E-commerce live streaming has experienced rapid growth,leveraging its ability to generate traffic and capture consumer attention.The integration of content and live streaming not only meets users’psychological needs but also facilitates seamless communication between buyers and sellers.From the perspective of content marketing typologies,this paper examines content marketing across three key dimensions:informational content,entertainment content,and emotional content.It further explores the impact of content marketing on consumers’purchase intentions within the context of e-commerce live streaming.展开更多
With technological advancements,high-speed rail has emerged as a prevalent mode of transportation.During travel,passengers exhibit a growing demand for streaming media services.However,the high-speed mobile networks e...With technological advancements,high-speed rail has emerged as a prevalent mode of transportation.During travel,passengers exhibit a growing demand for streaming media services.However,the high-speed mobile networks environment poses challenges,including frequent base station handoffs,which significantly degrade wireless network transmission performance.Improving transmission efficiency in high-speed mobile networks and optimizing spatiotemporal wireless resource allocation to enhance passengers’media experiences are key research priorities.To address these issues,we propose an Adaptive Cross-Layer Optimization Transmission Method with Environment Awareness(ACOTM-EA)tailored for high-speed rail streaming media.Within this framework,we develop a channel quality prediction model utilizing Kalman filtering and an algorithm to identify packet loss causes.Additionally,we introduce a proactive base station handoffstrategy to minimize handoffrelated disruptions and optimize resource distribution across adjacent base stations.Moreover,this study presents a wireless resource allocation approach based on an enhanced genetic algorithm,coupled with an adaptive bitrate selection mechanism,to maximize passenger Quality of Experience(QoE).To evaluate the proposed method,we designed a simulation experiment and compared ACOTM-EA with established algorithms.Results indicate that ACOTM-EA improves throughput by 11%and enhances passengers’media experience by 5%.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.12022508,12074394,and 22125604)Shanghai Supercomputer Center of ChinaShanghai Snowlake Technology Co.Ltd.
文摘The stable nanobubbles adhered to mineral surfaces may facilitate their efficient separation via flotation in the mining industry.However,the state of nanobubbles on mineral solid surfaces is still elusive.In this study,molecular dynamics(MD)simulations are employed to examine mineral-like model surfaces with varying degrees of hydrophobicity,modulated by surface charges,to elucidate the adsorption behavior of nanobubbles at the interface.Our findings not only contribute to the fundamental understanding of nanobubbles but also have potential applications in the mining industry.We observed that as the surface charge increases,the contact angle of the nanobubbles increases accordingly with shape transformation from a pancake-like gas film to a cap-like shape,and ultimately forming a stable nanobubble upon an ordered water monolayer.When the solid–water interactions are weak with a small partial charge,the hydrophobic gas(N_(2))molecules accumulate near the solid surfaces.However,we have found,for the first time,that gas molecules assemble a nanobubble on the water monolayer adjacent to the solid surfaces with large partial charges.Such phenomena are attributed to the formation of a hydrophobic water monolayer with a hydrogen bond network structure near the surface.
基金supported by the Key Research and Development Program of Jiangsu Province under Grant BE2022059-3,CTBC Bank through the Industry-Academia Cooperation Project,as well as by the Ministry of Science and Technology of Taiwan through Grants MOST-108-2218-E-002-055,MOST-109-2223-E-009-002-MY3,MOST-109-2218-E-009-025,and MOST431109-2218-E-002-015.
文摘Micro-expressions(ME)recognition is a complex task that requires advanced techniques to extract informative features fromfacial expressions.Numerous deep neural networks(DNNs)with convolutional structures have been proposed.However,unlike DNNs,shallow convolutional neural networks often outperform deeper models in mitigating overfitting,particularly with small datasets.Still,many of these methods rely on a single feature for recognition,resulting in an insufficient ability to extract highly effective features.To address this limitation,in this paper,an Improved Dual-stream Shallow Convolutional Neural Network based on an Extreme Gradient Boosting Algorithm(IDSSCNN-XgBoost)is introduced for ME Recognition.The proposed method utilizes a dual-stream architecture where motion vectors(temporal features)are extracted using Optical Flow TV-L1 and amplify subtle changes(spatial features)via EulerianVideoMagnification(EVM).These features are processed by IDSSCNN,with an attention mechanism applied to refine the extracted effective features.The outputs are then fused,concatenated,and classified using the XgBoost algorithm.This comprehensive approach significantly improves recognition accuracy by leveraging the strengths of both temporal and spatial information,supported by the robust classification power of XgBoost.The proposed method is evaluated on three publicly available ME databases named Chinese Academy of Sciences Micro-expression Database(CASMEII),Spontaneous Micro-Expression Database(SMICHS),and Spontaneous Actions and Micro-Movements(SAMM).Experimental results indicate that the proposed model can achieve outstanding results compared to recent models.The accuracy results are 79.01%,69.22%,and 68.99%on CASMEII,SMIC-HS,and SAMM,and the F1-score are 75.47%,68.91%,and 63.84%,respectively.The proposed method has the advantage of operational efficiency and less computational time.
文摘On May 14th,following the U.S.adjustment of additional tariffs on Chinese goods,American buyers began stockpiling in earnest.Many cross-border e-commerce companies also received a surge of orders.At 7 PM,a bustling Hangzhou-based cross-border e-commerce company was alive with multiple languages echoing through its live-streaming rooms as backend order numbers climbed steadily.
基金support by the National Key R&D Program of China(Grant No.2023YFA1008901)the National Natural Science Foundation of China(Grant Nos.11988102,12172009)is gratefully acknowledged.
文摘In this manuscript,we propose an analytical equivalent linear viscoelastic constitutive model for fiber-reinforced composites,bypassing general computational homogenization.The method is based on the reduced-order homogenization(ROH)approach.The ROH method typically involves solving multiple finite element problems under periodic conditions to evaluate elastic strain and eigenstrain influence functions in an‘off-line’stage,which offers substantial cost savings compared to direct computational homogenization methods.Due to the unique structure of the fibrous unit cell,“off-line”stage calculation can be eliminated by influence functions obtained analytically.Introducing the standard solid model to the ROH method enables the creation of a comprehensive analytical homogeneous viscoelastic constitutive model.This method treats fibrous composite materials as homogeneous,anisotropic viscoelastic materials,significantly reducing computational time due to its analytical nature.This approach also enables precise determination of a homogenized anisotropic relaxation modulus and accurate capture of various viscoelastic responses under different loading conditions.Three sets of numerical examples,including unit cell tests,three-point beam bending tests,and torsion tests,are given to demonstrate the predictive performance of the homogenized viscoelastic model.Furthermore,the model is validated against experimental measurements,confirming its accuracy and reliability.
文摘In this paper,we delve into a generalized higher order Camassa-Holm type equation,(or,an ghmCH equation for short).We establish local well-posedness for this equation under the condition that the initial data uo belongs to the Sobolev space H'(R)for some s>2.In addition,we obtain the weak formulation of this equation and prove the existence of both single peakon solution and a multi-peakon dynamic system.
基金Supported by NSFC (No.12361027)NSF of Inner Mongolia (No.2018MS01021)+1 种基金NSF of Shandong Province (No.ZR2020QA009)Science and Technology Innovation Program for Higher Education Institutions of Shanxi Province (No.2024L533)。
文摘In this paper,we give a complete characterization of all self-adjoint domains of odd order differential operators on two intervals.These two intervals with all four endpoints are singular(one endpoint of each interval is singular or all four endpoints are regulars are the special cases).And these extensions yield"new"self-adjoint operators,which involve interactions between the two intervals.
基金supported by the National Natural Science Foundation(22279036)the Innovation and Talent Recruitment Base of New Energy Chemistry and Device(B21003).
文摘Alloying transition metals with Pt is an effective strategy for optimizing Pt-based catalysts toward the oxygen reduction reaction(ORR).Atomic ordered intermetallic compounds(IMC)provide unique electronic and geometrical effects as well as stronger intermetallic interactions due to the ordered arrangement of metal atoms,thus exhibiting superior electrocata-lytic activity and durability.However,quantitatively analyzing the ordering degree of IMC and exploring the correlation between the ordering degree and ORR activity remains extremely challenging.Herein,a series of ternary Pt_(2)NiCo interme-tallic catalysts(o-Pt_(2)NiCo)with different ordering degree were synthesized by annealing temperature modulation.Among them,the o-Pt_(2)NiCo which annealed at 800℃for two hours exhibits the highest ordering degree and the optimal ORR ac-tivity,which the mass activity of o-Pt_(2)NiCo is 1.8 times and 2.8 times higher than that of disordered Pt_(2)NiCo alloy and Pt/C.Furthermore,the o-Pt_(2)NiCo still maintains 70.8%mass activity after 30,000 potential cycles.Additionally,the ORR activity test results for Pt_(2)NiCo IMC with different ordering degree also provide a positive correlation between the ordering degree and ORR activity.This work provides a prospective design direction for ternary Pt-based electrocatalysts.
基金supported by the National Natu-ral Science Foundation of China(Grants No.12174220 and No.12074217)the Shandong Provincial Science Foundation for Excellent Young Scholars(Grant No.ZR2023YQ001)+1 种基金the Taishan Young Scholar Program of Shandong Provincethe Qilu Young Scholar Pro-gram of Shandong University.
文摘Higher-order band topology not only enriches our understanding of topological phases but also unveils pioneering lower-dimensional boundary states,which harbors substantial potential for next-generation device applications.The distinct electronic configurations and tunable attributes of two-dimensional materials position them as a quintessential platform for the realization of second-order topological insulators(SOTIs).This article provides an overview of the research progress in SOTIs within the field of two-dimensional electronic materials,focusing on the characterization of higher-order topological properties and the numerous candidate materials proposed in theoretical studies.These endeavors not only enhance our understanding of higher-order topological states but also highlight potential material systems that could be experimentally realized.
基金Supported by the National Natural Science Foundation of China(12061048)NSF of Jiangxi Province(20232BAB201026,20232BAB201018)。
文摘In this paper,a new technique is introduced to construct higher-order iterative methods for solving nonlinear systems.The order of convergence of some iterative methods can be improved by three at the cost of introducing only one additional evaluation of the function in each step.Furthermore,some new efficient methods with a higher-order of convergence are obtained by using only a single matrix inversion in each iteration.Analyses of convergence properties and computational efficiency of these new methods are made and testified by several numerical problems.By comparison,the new schemes are more efficient than the corresponding existing ones,particularly for large problem sizes.
基金supported by European Union–Next Generation EU(LX22NPO5107(MEYS))the Czech Science Foundation(20-21339S)+2 种基金the Grant Agency of Charles University(GAUK 248122 and 272221)ERDF-Project Brain Dynamics(CZ.02.01.01/00/22_008/0004643)the Ministry of Health of the Czech Republic Project NU21J-08-00081.
文摘The dorsal and ventral visual streams have been considered to play distinct roles in visual processing for action:the dorsal stream is assumed to support real-time actions,while the ventral stream facilitates memory-guided actions.However,recent evidence suggests a more integrated function of these streams.We investigated the neural dynamics and functional connectivity between them during memory-guided actions using intracranial EEG.We tracked neural activity in the inferior parietal lobule in the dorsal stream,and the ventral temporal cortex in the ventral stream as well as the hippocampus during a delayed action task involving object identity and location memory.We found increased alpha power in both streams during the delay,indicating their role in maintaining spatial visual information.In addition,we recorded increased alpha power in the hippocampus during the delay,but only when both object identity and location needed to be remembered.We also recorded an increase in theta band phase synchronization between the inferior parietal lobule and ventral temporal cortex and between the inferior parietal lobule and hippocampus during the encoding and delay.Granger causality analysis indicated dynamic and frequency-specific directional interactions among the inferior parietal lobule,ventral temporal cortex,and hippocampus that varied across task phases.Our study provides unique electrophysiological evidence for close interactions between dorsal and ventral streams,supporting an integrated processing model in which both streams contribute to memory-guided actions.
基金supported in part by the National Natural Science Foundation of China(62173255,62188101)Shenzhen Key Laboratory of Control Theory and Intelligent Systems(ZDSYS20220330161800001)
文摘Dear Editor,In this letter,a constrained networked predictive control strategy is proposed for the optimal control problem of complex nonlinear highorder fully actuated(HOFA)systems with noises.The method can effectively deal with nonlinearities,constraints,and noises in the system,optimize the performance metric,and present an upper bound on the stable output of the system.
基金supported by the National Language Commission to research on sign language data specifications for artificial intelligence applications and test standards for language service translation systems (No.ZDI145-70)。
文摘Sign language dataset is essential in sign language recognition and translation(SLRT). Current public sign language datasets are small and lack diversity, which does not meet the practical application requirements for SLRT. However, making a large-scale and diverse sign language dataset is difficult as sign language data on the Internet is scarce. In making a large-scale and diverse sign language dataset, some sign language data qualities are not up to standard. This paper proposes a two information streams transformer(TIST) model to judge whether the quality of sign language data is qualified. To verify that TIST effectively improves sign language recognition(SLR), we make two datasets, the screened dataset and the unscreened dataset. In this experiment, this paper uses visual alignment constraint(VAC) as the baseline model. The experimental results show that the screened dataset can achieve better word error rate(WER) than the unscreened dataset.
基金the financial support received from the National Natural Science Foundation of China(Grant No.62174119)the 111 Project (Grant No.B07014)the Foundation for Talent Scientists of Nanchang Institute for Microtechnology of Tianjin University
文摘No-wash bioassays based on nanoparticles are used widely in biochemical procedures because of their responsive sensing and no need forwashing processes.Essential for no-wash biosensing are the interactions between nanoparticles and biomolecules,but it is challenging toachieve controlled bioconjugation of molecules on nanomaterials.Reported here is a way to actively improve nanoparticle-based no-washbioassays by enhancing the binding between biomolecules and gold nanoparticles via acoustic streaming generated by a gigahertz piezoelectricnanoelectromechanical resonator.Tunable micro-vortices are generated at the device-liquid interface,thereby accelerating the internalcirculating flow of the solution,bypassing the diffusion limitation,and thus improving the binding between the biomolecules and goldnanoparticles.Combined with fluorescence quenching,an enhanced and ultrafast no-wash biosensing assay is realized for specific proteins.The sensing method presented here is a versatile tool for different types of biomolecule detection with high efficiency and simplicity.
基金funded by the Joint Project of Industry-University-Research of Jiangsu Province(Grant:BY20231146).
文摘With the widespread application of Internet of Things(IoT)technology,the processing of massive realtime streaming data poses significant challenges to the computational and data-processing capabilities of systems.Although distributed streaming data processing frameworks such asApache Flink andApache Spark Streaming provide solutions,meeting stringent response time requirements while ensuring high throughput and resource utilization remains an urgent problem.To address this,the study proposes a formal modeling approach based on Performance Evaluation Process Algebra(PEPA),which abstracts the core components and interactions of cloud-based distributed streaming data processing systems.Additionally,a generic service flow generation algorithmis introduced,enabling the automatic extraction of service flows fromthe PEPAmodel and the computation of key performance metrics,including response time,throughput,and resource utilization.The novelty of this work lies in the integration of PEPA-based formal modeling with the service flow generation algorithm,bridging the gap between formal modeling and practical performance evaluation for IoT systems.Simulation experiments demonstrate that optimizing the execution efficiency of components can significantly improve system performance.For instance,increasing the task execution rate from 10 to 100 improves system performance by 9.53%,while further increasing it to 200 results in a 21.58%improvement.However,diminishing returns are observed when the execution rate reaches 500,with only a 0.42%gain.Similarly,increasing the number of TaskManagers from 10 to 20 improves response time by 18.49%,but the improvement slows to 6.06% when increasing from 20 to 50,highlighting the importance of co-optimizing component efficiency and resource management to achieve substantial performance gains.This study provides a systematic framework for analyzing and optimizing the performance of IoT systems for large-scale real-time streaming data processing.The proposed approach not only identifies performance bottlenecks but also offers insights into improving system efficiency under different configurations and workloads.
基金funded by the Ongoing Research Funding Program(ORF-2025-890)King Saud University,Riyadh,Saudi Arabia and was supported by the Competitive Research Fund of theUniversity of Aizu,Japan.
文摘The exponential expansion of the Internet of Things(IoT),Industrial Internet of Things(IIoT),and Transportation Management of Things(TMoT)produces vast amounts of real-time streaming data.Ensuring system dependability,operational efficiency,and security depends on the identification of anomalies in these dynamic and resource-constrained systems.Due to their high computational requirements and inability to efficiently process continuous data streams,traditional anomaly detection techniques often fail in IoT systems.This work presents a resource-efficient adaptive anomaly detection model for real-time streaming data in IoT systems.Extensive experiments were carried out on multiple real-world datasets,achieving an average accuracy score of 96.06%with an execution time close to 7.5 milliseconds for each individual streaming data point,demonstrating its potential for real-time,resourceconstrained applications.The model uses Principal Component Analysis(PCA)for dimensionality reduction and a Z-score technique for anomaly detection.It maintains a low computational footprint with a sliding window mechanism,enabling incremental data processing and identification of both transient and sustained anomalies without storing historical data.The system uses a Multivariate Linear Regression(MLR)based imputation technique that estimates missing or corrupted sensor values,preserving data integrity prior to anomaly detection.The suggested solution is appropriate for many uses in smart cities,industrial automation,environmental monitoring,IoT security,and intelligent transportation systems,and is particularly well-suited for resource-constrained edge devices.
文摘In the contemporary digital landscape,the proliferation of information has led to an increasing diversity of channels through which consumers obtain information,resulting in a gradual transformation of shopping habits.Consumers now frequently rely on external sources to make well-informed purchasing decisions,leading to the emergence of live shopping as a prominent avenue for gathering product information and completing transactions.E-commerce live streaming has experienced rapid growth,leveraging its ability to generate traffic and capture consumer attention.The integration of content and live streaming not only meets users’psychological needs but also facilitates seamless communication between buyers and sellers.From the perspective of content marketing typologies,this paper examines content marketing across three key dimensions:informational content,entertainment content,and emotional content.It further explores the impact of content marketing on consumers’purchase intentions within the context of e-commerce live streaming.
基金substantially supported by the National Natural Science Foundation of China under Grant No.62002263in part by Tianjin Municipal Education Commission Research Program Project under 2022KJ012Tianjin Science and Technology Program Projects:24YDTPJC00630.
文摘With technological advancements,high-speed rail has emerged as a prevalent mode of transportation.During travel,passengers exhibit a growing demand for streaming media services.However,the high-speed mobile networks environment poses challenges,including frequent base station handoffs,which significantly degrade wireless network transmission performance.Improving transmission efficiency in high-speed mobile networks and optimizing spatiotemporal wireless resource allocation to enhance passengers’media experiences are key research priorities.To address these issues,we propose an Adaptive Cross-Layer Optimization Transmission Method with Environment Awareness(ACOTM-EA)tailored for high-speed rail streaming media.Within this framework,we develop a channel quality prediction model utilizing Kalman filtering and an algorithm to identify packet loss causes.Additionally,we introduce a proactive base station handoffstrategy to minimize handoffrelated disruptions and optimize resource distribution across adjacent base stations.Moreover,this study presents a wireless resource allocation approach based on an enhanced genetic algorithm,coupled with an adaptive bitrate selection mechanism,to maximize passenger Quality of Experience(QoE).To evaluate the proposed method,we designed a simulation experiment and compared ACOTM-EA with established algorithms.Results indicate that ACOTM-EA improves throughput by 11%and enhances passengers’media experience by 5%.
