This paper investigates the path-following control problem with obstacle avoidance of autonomous surface vehicles in the presence of actuator faults,uncertainty and external disturbances.Autonomous surface vehicles in...This paper investigates the path-following control problem with obstacle avoidance of autonomous surface vehicles in the presence of actuator faults,uncertainty and external disturbances.Autonomous surface vehicles inevitably suffer from actuator faults in complex sea environments,which may cause existing obstacle avoidance strategies to fail.To reduce the influence of actuator faults,an improved artificial potential function is constructed by introducing the lower bound of actuator efficiency factors.The nonlinear state observer,which only depends on measurable position information of the autonomous surface vehicle,is used to address uncertainties and external disturbances.By using a backstepping technique and adaptive mechanism,a path-following control strategy with obstacle avoidance and fault tolerance is designed which can ensure that the tracking errors converge to a small neighborhood of zero.Compared with existing results,the proposed control strategy has the capability of obstacle avoidance and fault tolerance simultaneously.Finally,the comparison results through simulations are given to verify the effectiveness of the proposed method.展开更多
Robots are widely used,providing significant convenience in daily life and production.With the rapid development of artificial intelligence and neuromorphic computing in recent years,the realization of more intelligen...Robots are widely used,providing significant convenience in daily life and production.With the rapid development of artificial intelligence and neuromorphic computing in recent years,the realization of more intelligent robots through a pro-found intersection of neuroscience and robotics has received much attention.Neuromorphic circuits based on memristors used to construct hardware neural networks have proved to be a promising solution of shattering traditional control limita-tions in the field of robot control,showcasing characteristics that enhance robot intelligence,speed,and energy efficiency.Start-ing with introducing the working mechanism of memristors and peripheral circuit design,this review gives a comprehensive analysis on the biomimetic information processing and biomimetic driving operations achieved through the utilization of neuro-morphic circuits in brain-like control.Four hardware neural network approaches,including digital-analog hybrid circuit design,novel device structure design,multi-regulation mechanism,and crossbar array,are summarized,which can well simulate the motor decision-making mechanism,multi-information integration and parallel control of brain at the hardware level.It will be definitely conductive to promote the application of memristor-based neuromorphic circuits in areas such as intelligent robotics,artificial intelligence,and neural computing.Finally,a conclusion and future prospects are discussed.展开更多
Photocatalytic CO_(2)reduction to produce high value-added carbon-based fuel has been proposed as a promising approach to mitigate global warming issues.However,the conversion efficiency and product selectivity are st...Photocatalytic CO_(2)reduction to produce high value-added carbon-based fuel has been proposed as a promising approach to mitigate global warming issues.However,the conversion efficiency and product selectivity are still low due to the sluggish dynamics of transfer processes involved in proton-assisted multi-electron reactions.Lowering the formation energy barriers of intermediate products is an effective method to enhance the selectivity and productivity of final products.In this study,we aim to regulate the surface electronic structure of Bi_(2)WO_(6)by doping surface chlorine atoms to achieve effective photocatalytic CO_(2)reduction.Surface Cl atoms can enhance the absorption ability of light,affect its energy band structure and promote charge separation.Combined with DFT calculations,it is revealed that surface Cl atoms can not only change the surface charge distribution which affects the competitive adsorption of H_(2)O and CO_(2),but also lower the formation energy barrier of intermediate products to generate more intermediate*COOH,thus facilitating CO production.Overall,this study demonstrates a promising surface halogenation strategy to enhance the photocatalytic CO_(2)reduction activity of a layered structure Bi-based catalyst.展开更多
With the rapid growth of manuscript submissions,finding eligible reviewers for every submission has become a heavy task.Recommender systems are powerful tools developed in computer science and information science to d...With the rapid growth of manuscript submissions,finding eligible reviewers for every submission has become a heavy task.Recommender systems are powerful tools developed in computer science and information science to deal with this problem.However,most existing approaches resort to text mining techniques to match manuscripts with potential reviewers,which require high-quality textual information to perform well.In this paper,we propose a reviewer recommendation algorithm based on a network diffusion process on a scholar-paper multilayer network,with no requirement for textual information.The network incorporates the relationship of scholar-paper pairs,the collaboration among scholars,and the bibliographic coupling among papers.Experimental results show that our proposed algorithm outperforms other state-of-the-art recommendation methods that use graph random walk and matrix factorization and methods that use machine learning and natural language processing,with improvements of over 7.62%in recall,5.66%in hit rate,and 47.53%in ranking score.Our work sheds light on the effectiveness of multilayer network diffusion-based methods in the reviewer recommendation problem,which will help to facilitate the peer-review process and promote information retrieval research in other practical scenes.展开更多
The estimation of pain intensity is critical for medical diagnosis and treatment of patients.With the development of image monitoring technology and artificial intelligence,automatic pain assessment based on facial ex...The estimation of pain intensity is critical for medical diagnosis and treatment of patients.With the development of image monitoring technology and artificial intelligence,automatic pain assessment based on facial expression and behavioral analysis shows a potential value in clinical applications.This paper reports a framework of convolutional neural network with global and local attention mechanism(GLA-CNN)for the effective detection of pain intensity at four-level thresholds using facial expression images.GLA-CNN includes two modules,namely global attention network(GANet)and local attention network(LANet).LANet is responsible for extracting representative local patch features of faces,while GANet extracts whole facial features to compensate for the ignored correlative features between patches.In the end,the global correlational and local subtle features are fused for the final estimation of pain intensity.Experiments under the UNBC-McMaster Shoulder Pain database demonstrate that GLA-CNN outperforms other state-of-the-art methods.Additionally,a visualization analysis is conducted to present the feature map of GLA-CNN,intuitively showing that it can extract not only local pain features but also global correlative facial ones.Our study demonstrates that pain assessment based on facial expression is a non-invasive and feasible method,and can be employed as an auxiliary pain assessment tool in clinical practice.展开更多
Alzheimer’s disease(AD)is a complex,progressive neurodegenerative disorder.The subtle and insidious onset of its pathogenesis makes early detection of a formidable challenge in both contemporary neuroscience and clin...Alzheimer’s disease(AD)is a complex,progressive neurodegenerative disorder.The subtle and insidious onset of its pathogenesis makes early detection of a formidable challenge in both contemporary neuroscience and clinical practice.In this study,we introduce an advanced diagnostic methodology rooted in theMed-3D transfermodel and enhanced with an attention mechanism.We aim to improve the precision of AD diagnosis and facilitate its early identification.Initially,we employ a spatial normalization technique to address challenges like clarity degradation and unsaturation,which are commonly observed in imaging datasets.Subsequently,an attention mechanism is incorporated to selectively focus on the salient features within the imaging data.Building upon this foundation,we present the novelMed-3D transfermodel,designed to further elucidate and amplify the intricate features associated withADpathogenesis.Our proposedmodel has demonstrated promising results,achieving a classification accuracy of 92%.To emphasize the robustness and practicality of our approach,we introduce an adaptive‘hot-updating’auxiliary diagnostic system.This system not only enables continuous model training and optimization but also provides a dynamic platform to meet the real-time diagnostic and therapeutic demands of AD.展开更多
Carbon fiber composites,characterized by their high specific strength and low weight,are becoming increasingly crucial in automotive lightweighting.However,current research primarily emphasizes layer count and orienta...Carbon fiber composites,characterized by their high specific strength and low weight,are becoming increasingly crucial in automotive lightweighting.However,current research primarily emphasizes layer count and orientation,often neglecting the potential of microstructural design,constraints in the layup process,and performance reliability.This study,therefore,introduces a multiscale reliability-based design optimization method for carbon fiber-reinforced plastic(CFRP)drive shafts.Initially,parametric modeling of the microscale cell was performed,and its elastic performance parameters were predicted using two homogenization methods,examining the impact of fluctuations in microscale cell parameters on composite material performance.A finite element model of the CFRP drive shaft was then constructed,achieving parameter transfer between microscale and macroscale through Python programming.This enabled an investigation into the influence of both micro and macro design parameters on the CFRP drive shaft’s performance.The Multi-Objective Particle Swarm Optimization(MOPSO)algorithm was enhanced for particle generation and updating strategies,facilitating the resolution of multi-objective reliability optimization problems,including composite material layup process constraints.Case studies demonstrated that this approach leads to over 30%weight reduction in CFRP drive shafts compared to metallic counterparts while satisfying reliability requirements and offering insights for the lightweight design of other vehicle components.展开更多
Photocatalytic and photoelectrochemical water splitting using semiconductor materials are effective approaches for converting solar energy into hydrogen fuel.In the past few years,a series of photocatalysts/photoelect...Photocatalytic and photoelectrochemical water splitting using semiconductor materials are effective approaches for converting solar energy into hydrogen fuel.In the past few years,a series of photocatalysts/photoelectrocatalysts have been developed and optimized to achieve efficient solar hydrogen production.Among various optimization strategies,the regulation of spin polarization can tailor the intrinsic optoelectronic properties for retarding charge recombination and enhancing surface reactions,thus improving the solar-to-hydrogen(STH)efficiency.This review presents recent advances in the regulation of spin polarization to enhance spin polarized-dependent solar hydrogen evolution activity.Specifically,spin polarization manipulation strategies of several typical photocatalysts/photoelectrocatalysts(e.g.,metallic oxides,metallic sulfides,non-metallic semiconductors,ferroelectric materials,and chiral molecules)are described.In the end,the critical challenges and perspectives of spin polarization regulation towards future solar energy conversion are briefly provided.展开更多
Integrated data and energy transfer(IDET)is capable of simultaneously delivering on-demand data and energy to low-power Internet of Everything(Io E)devices.We propose a multi-carrier IDET transceiver relying on superp...Integrated data and energy transfer(IDET)is capable of simultaneously delivering on-demand data and energy to low-power Internet of Everything(Io E)devices.We propose a multi-carrier IDET transceiver relying on superposition waveforms consisting of multi-sinusoidal signals for wireless energy transfer(WET)and orthogonal-frequency-divisionmultiplexing(OFDM)signals for wireless data transfer(WDT).The outdated channel state information(CSI)in aging channels is employed by the transmitter to shape IDET waveforms.With the constraints of transmission power and WDT requirement,the amplitudes and phases of the IDET waveform at the transmitter and the power splitter at the receiver are jointly optimised for maximising the average directcurrent(DC)among a limited number of transmission frames with the existence of carrier-frequencyoffset(CFO).For the amplitude optimisation,the original non-convex problem can be transformed into a reversed geometric programming problem,then it can be effectively solved with existing tools.As for the phase optimisation,the artificial bee colony(ABC)algorithm is invoked in order to deal with the nonconvexity.Iteration between the amplitude optimisation and phase optimisation yields our joint design.Numerical results demonstrate the advantage of our joint design for the IDET waveform shaping with the existence of the CFO and the outdated CSI.展开更多
Lithium-sulfur batteries(LSBs)have been regarded as one of the promising candidates for the next-generation“lithium-ion battery beyond”owing to their high energy density and due to the low cost of sulfur.However,the...Lithium-sulfur batteries(LSBs)have been regarded as one of the promising candidates for the next-generation“lithium-ion battery beyond”owing to their high energy density and due to the low cost of sulfur.However,the main obstacles encountered in the commercial implementation of LSBs are the notorious shuttle effect,retarded sulfur redox kinetics,and uncontrolled dendrite growth.Accordingly,single-atom catalysts(SACs),which have ultrahigh catalytic efficiency,tunable coordination configuration,and light weight,have shown huge potential in the field of LSBs to date.This review summarizes the recent research progress of SACs applied as multifunctional components in LSBs.The design principles and typical synthetic strategies of SACs toward effective Li–S chemistry as well as the working mechanism promoting sulfur conversion reactions,inhibiting the lithium polysulfide shuttle effect,and regulating Li+nucleation are comprehensively illustrated.Potential future directions in terms of research on SACs for the realization of commercially viable LSBs are also outlined.展开更多
Lithium-ion batteries(LIBs)require separators with high performance and safety to meet the increasing demands for energy storage applications.Coating electrochemically inert ceramic materials on conventional polyolefi...Lithium-ion batteries(LIBs)require separators with high performance and safety to meet the increasing demands for energy storage applications.Coating electrochemically inert ceramic materials on conventional polyolefin separators can enhance stability but comes at the cost of increased weight and decreased capacity of the battery.Herein,a novel separator coated with lithium iron phosphate(LFP),an active cathode material,is developed via a simple and scalable process.The LFP-coated separator exhibits superior thermal stability,mechanical strength,electrolyte wettability,and ionic conductivity than the conventional polyethylene(PE)separator.Moreover,the LFP coating can actively participate in the electrochemical reaction during the charge-discharge process,thus enhancing the capacity of the battery.The results show that the LFP-coated separator can increase the cell capacity by 26%,and improve the rate capability by 29%at 4 C compared with the conventional PE separator.The LFP-coated separator exhibits only 1.1%thermal shrinkage at 140°C,a temperature even above the melting point of PE.This work introduces a new strategy for designing separators with dual functions for the next-generation LIBs with improved performance and safety.展开更多
2D MXenes are highly attractive for fabricating high-precision gas sensors operated at room temperature(RT)due to their high surface-to-volume ratio.However,the limited selectivity and low sensitivity are still long-s...2D MXenes are highly attractive for fabricating high-precision gas sensors operated at room temperature(RT)due to their high surface-to-volume ratio.However,the limited selectivity and low sensitivity are still long-standing challenges for their further applications.Herein,the self-assembly of 0D-2D heterostructure for highly sensitive NO_(2) detection was achieved by integrating ZnO nanoparticles on Ti_(3)C_(2)Tx MXene-derived TiO_(2) nanosheets(designated as ZnO@MTiO_(2)).ZnO nanoparticles can not only act as spacers to prevent the restacking of MTiO_(2) nanosheets and ensure effective transfer for gas molecules,but also enhance the sensitivity of the sensor the through trapping effect on electrons.Meanwhile,MTiO_(2) nanosheets facilitate gas diffusion for rapid sensor response.Benefiting from the synergistic effect of individual components,the ZnO@MTiO_(2)0D-2D heterostructure-based sensors revealed remarkable sensitivity and excellent selectivity to low concentration NO_(2) at RT.This work may facilitate the sensing application of MXene derivative and provide a new avenue for the development of high-performance gas sensors in safety assurance and environmental monitoring.展开更多
Convolutional neural networks(CNNs)are well suited to bearing fault classification due to their ability to learn discriminative spectro-temporal patterns.However,gathering sufficient cases of faulty conditions in real...Convolutional neural networks(CNNs)are well suited to bearing fault classification due to their ability to learn discriminative spectro-temporal patterns.However,gathering sufficient cases of faulty conditions in real-world engineering scenarios to train an intelligent diagnosis system is challenging.This paper proposes a fault diagnosis method combining several augmentation schemes to alleviate the problem of limited fault data.We begin by identifying relevant parameters that influence the construction of a spectrogram.We leverage the uncertainty principle in processing time-frequency domain signals,making it impossible to simultaneously achieve good time and frequency resolutions.A key determinant of this phenomenon is the window function's choice and length used in implementing the shorttime Fourier transform.The Gaussian,Kaiser,and rectangular windows are selected in the experimentation due to their diverse characteristics.The overlap parameter's size also influences the outcome and resolution of the spectrogram.A 50%overlap is used in the original data transformation,and±25%is used in implementing an effective augmentation policy to which two-stage regular CNN can be applied to achieve improved performance.The best model reaches an accuracy of 99.98%and a cross-domain accuracy of 92.54%.When combined with data augmentation,the proposed model yields cutting-edge results.展开更多
Aqueous Zinc-based energy storage devices are considered as one of the potential candidates in future power technologies.Nevertheless,poor low temperature performance and uncontrollable Zn dendrite growth lead to the ...Aqueous Zinc-based energy storage devices are considered as one of the potential candidates in future power technologies.Nevertheless,poor low temperature performance and uncontrollable Zn dendrite growth lead to the limited energy storage capability.Herein,an anti-hydrolysis,cold-resistant,economical,safe,and environmentally friendly electrolyte is developed by utilizing water,ethylene glycol(EG),and ZnCl_(2)with high ionic conductivity(7.9 mS cm^(-1)in glass fiber membrane at-20℃).The spectra data and DFT calculations show the competitive coordination of EG and Cl-to induce a unique solvation configuration of Zn^(2+),conducive to effectively inhibiting the hydrolysis of Zn^(2+),suppressing the dendrite growth,and broadening the working voltage range and temperature range of ZnCl_(2)electrolyte.The isotope tracing data confirm that Cl^(-)could effectively destroy the ZnO passivation film,promoting the formation of Zn nuclei and improving its reaction activity.Compared to the corresponding ZnSO4electrolyte,the Cu/Zn half-cell with the ZnCl_(2)electrolyte exhibits a stable cycle life of more than 1600 h at-20℃,even at the current density of 5 mA cm^(-2).The assembled Zn-ion hybrid capacitor possesses an average capacity of 42.68 m A h g^(-1)under-20℃at a current density of 5 A g^(-1),3.5 times than that of the modified ZnSO4electrolyte.Our work proposes a new approach for optimizing aqueous electrolytes to meet low temperature energy storage applications.展开更多
With the help of the first principle calculation,the solid-state reaction experiment was conducted to investigate the alteration in the sintering and the microwave dielectric properties of Mg_(3)B_(2)O_(6)ceramic with...With the help of the first principle calculation,the solid-state reaction experiment was conducted to investigate the alteration in the sintering and the microwave dielectric properties of Mg_(3)B_(2)O_(6)ceramic with many Zn^(2+)substitutions.These properties were characterized using the scanning electron microscopy,network analyzer,X-ray diffraction,Raman spectroscopy,energy-dispersive spectroscopy,and thermomechanical and differential-thermal analyses.The coexistence of Mg_(3)B_(2)O_(6),Mg_(2)B_(2)O_(5)and ZnO ceramics could be observed with increasing Zn^(2+)addition,and the lattice distortion occurred in the Mg_(2)B_(2)O_(5)and Mg_(3)B_(2)O_(6)ceramics due to the substitution of Mg^(2+)with Zn^(2+).The electron density and the bond property of the MgO_(6)octahedron changed,and a quantitative method was used to discuss the variation in sintering,substitution and phase formation properties.The densification window was decreased to 1100℃,and the dielectric properties improved with the formation of a three-phase borate solid solution(dielectric constant=6.73,quality factor=112,000 GHz at 16 GHz(Q=7000),temperature coefficient of resonant frequency=-61.2 ppm℃^(-1),and relative density=97.0%).展开更多
At present,knowledge embedding methods are widely used in the field of knowledge graph(KG)reasoning,and have been successfully applied to those with large entities and relationships.However,in research and production ...At present,knowledge embedding methods are widely used in the field of knowledge graph(KG)reasoning,and have been successfully applied to those with large entities and relationships.However,in research and production environments,there are a large number of KGs with a small number of entities and relations,which are called sparse KGs.Limited by the performance of knowledge extraction methods or some other reasons(some common-sense information does not appear in the natural corpus),the relation between entities is often incomplete.To solve this problem,a method of the graph neural network and information enhancement is proposed.The improved method increases the mean reciprocal rank(MRR)and Hit@3 by 1.6%and 1.7%,respectively,when the sparsity of the FB15K-237 dataset is 10%.When the sparsity is 50%,the evaluation indexes MRR and Hit@10 are increased by 0.8%and 1.8%,respectively.展开更多
Stock market forecasting has drawn interest from both economists and computer scientists as a classic yet difficult topic.With the objective of constructing an effective prediction model,both linear and machine learni...Stock market forecasting has drawn interest from both economists and computer scientists as a classic yet difficult topic.With the objective of constructing an effective prediction model,both linear and machine learning tools have been investigated for the past couple of decades.In recent years,recurrent neural networks(RNNs)have been observed to perform well on tasks involving sequence-based data in many research domains.With this motivation,we investigated the performance of long-short term memory(LSTM)and gated recurrent units(GRU)and their combination with the attention mechanism;LSTM+Attention,GRU+Attention,and LSTM+GRU+Attention.The methods were evaluated with stock data from three different stock indices:the KSE 100 index,the DSE 30 index,and the BSE Sensex.The results were compared to other machine learning models such as support vector regression,random forest,and k-nearest neighbor.The best results for the three datasets were obtained by the RNN-based models combined with the attention mechanism.The performances of the RNN and attention-based models are higher and would be more effective for applications in the business industry.展开更多
In this study,we investigated the abatement of volatile organic compounds(VOCs)by the atmospheric pressure microwave plasma torch(AMPT).To study the treatment efficiency of AMPT,we used the toluene and water-based var...In this study,we investigated the abatement of volatile organic compounds(VOCs)by the atmospheric pressure microwave plasma torch(AMPT).To study the treatment efficiency of AMPT,we used the toluene and water-based varnish to simulate VOCs,respectively.By measuring the compounds and contents of the mixture gas before/after the microwave plasma process,we have calculated the treatment efficiency of AMPT.The experimental results show that the treatment efficiency of AMPT for toluene with a concentration of 17.32×10^(4) ppm is up to 60 g/kWh with the removal rate of 86%.For the volatile compounds of water-based varnish,the removal efficiency is up to 97.99%.We have demonstrated the higher potential for VOCs removal of the AMPT process.展开更多
It is crucial to efficiently separate and transport photo-induced charge carriers for the effective implementation of photocatalysis toward environmental remediation.A rational design strategy is proposed to validate ...It is crucial to efficiently separate and transport photo-induced charge carriers for the effective implementation of photocatalysis toward environmental remediation.A rational design strategy is proposed to validate such proposition through the construction of an interfacial structure in the form of LDH/Zn_(2)SnO_(4) heterostructures in this research.The interfacial charge transfer on LDH/Zn_(2)SnO_(4) is greatly promoted via the unique charge transfer pathway,as characterized by transient photocurrent responses,X-ray photoelectron spectroscopy,electron paramagnetic resonance spectrum,and photoluminescence analysis.As such,it contributes to the generation of reactive oxygen species(ROS)and the activation of reactants for the mineralization of toluene.According to the in situ DRIFTS spectra analysis,the accumulation of benzoic acid takes place possibly through the partial oxidation of the methyl group on toluene at the interface of the LDH/Zn 2 SnO 4 heterostructure.This process can greatly promote the photocatalytic oxidation of toluene with the enhanced ring-opening efficiency.The LDH/Zn 2 SnO 4 is thus demonstrated as superior photocatalyst against toluene(removal efficiency of 89.5%;mineralization of 83.1%;and quantum efficiency of 4.55×10^(−6) molecules/photon).As such,the performance of this composite far exceeds that of their individual components(e.g.,P25,pure Mg-Al LDH,or Zn_(2)SnO_(4)).This study is expected to offer a new path to the interfacial charge transfer mechanism based on the design of highly efficient photocatalysts for air purification.展开更多
基金the National Natural Science Foundation of China(51939001,52171292,51979020,61976033)Dalian Outstanding Young Talents Program(2022RJ05)+1 种基金the Topnotch Young Talents Program of China(36261402)the Liaoning Revitalization Talents Program(XLYC20-07188)。
文摘This paper investigates the path-following control problem with obstacle avoidance of autonomous surface vehicles in the presence of actuator faults,uncertainty and external disturbances.Autonomous surface vehicles inevitably suffer from actuator faults in complex sea environments,which may cause existing obstacle avoidance strategies to fail.To reduce the influence of actuator faults,an improved artificial potential function is constructed by introducing the lower bound of actuator efficiency factors.The nonlinear state observer,which only depends on measurable position information of the autonomous surface vehicle,is used to address uncertainties and external disturbances.By using a backstepping technique and adaptive mechanism,a path-following control strategy with obstacle avoidance and fault tolerance is designed which can ensure that the tracking errors converge to a small neighborhood of zero.Compared with existing results,the proposed control strategy has the capability of obstacle avoidance and fault tolerance simultaneously.Finally,the comparison results through simulations are given to verify the effectiveness of the proposed method.
文摘Robots are widely used,providing significant convenience in daily life and production.With the rapid development of artificial intelligence and neuromorphic computing in recent years,the realization of more intelligent robots through a pro-found intersection of neuroscience and robotics has received much attention.Neuromorphic circuits based on memristors used to construct hardware neural networks have proved to be a promising solution of shattering traditional control limita-tions in the field of robot control,showcasing characteristics that enhance robot intelligence,speed,and energy efficiency.Start-ing with introducing the working mechanism of memristors and peripheral circuit design,this review gives a comprehensive analysis on the biomimetic information processing and biomimetic driving operations achieved through the utilization of neuro-morphic circuits in brain-like control.Four hardware neural network approaches,including digital-analog hybrid circuit design,novel device structure design,multi-regulation mechanism,and crossbar array,are summarized,which can well simulate the motor decision-making mechanism,multi-information integration and parallel control of brain at the hardware level.It will be definitely conductive to promote the application of memristor-based neuromorphic circuits in areas such as intelligent robotics,artificial intelligence,and neural computing.Finally,a conclusion and future prospects are discussed.
基金supported by the National Natural Science Foundation of China(Grant No.51708078)Natural Science Foundation of Chongqing(Grant No.CSTB2022NSCQ-MSX0815)+2 种基金Science and Technology Research Program of Chongqing Municipal Education Commission(Grant No.KJQN202200542)the Chongqing Innovative Research Group Project(Grant No.CXQT21015)Foundation of Chongqing Normal University(22XLB022).
文摘Photocatalytic CO_(2)reduction to produce high value-added carbon-based fuel has been proposed as a promising approach to mitigate global warming issues.However,the conversion efficiency and product selectivity are still low due to the sluggish dynamics of transfer processes involved in proton-assisted multi-electron reactions.Lowering the formation energy barriers of intermediate products is an effective method to enhance the selectivity and productivity of final products.In this study,we aim to regulate the surface electronic structure of Bi_(2)WO_(6)by doping surface chlorine atoms to achieve effective photocatalytic CO_(2)reduction.Surface Cl atoms can enhance the absorption ability of light,affect its energy band structure and promote charge separation.Combined with DFT calculations,it is revealed that surface Cl atoms can not only change the surface charge distribution which affects the competitive adsorption of H_(2)O and CO_(2),but also lower the formation energy barrier of intermediate products to generate more intermediate*COOH,thus facilitating CO production.Overall,this study demonstrates a promising surface halogenation strategy to enhance the photocatalytic CO_(2)reduction activity of a layered structure Bi-based catalyst.
基金Project supported by the National Natural Science Foundation of China(Grant No.T2293771)the New Cornerstone Science Foundation through the XPLORER PRIZE.
文摘With the rapid growth of manuscript submissions,finding eligible reviewers for every submission has become a heavy task.Recommender systems are powerful tools developed in computer science and information science to deal with this problem.However,most existing approaches resort to text mining techniques to match manuscripts with potential reviewers,which require high-quality textual information to perform well.In this paper,we propose a reviewer recommendation algorithm based on a network diffusion process on a scholar-paper multilayer network,with no requirement for textual information.The network incorporates the relationship of scholar-paper pairs,the collaboration among scholars,and the bibliographic coupling among papers.Experimental results show that our proposed algorithm outperforms other state-of-the-art recommendation methods that use graph random walk and matrix factorization and methods that use machine learning and natural language processing,with improvements of over 7.62%in recall,5.66%in hit rate,and 47.53%in ranking score.Our work sheds light on the effectiveness of multilayer network diffusion-based methods in the reviewer recommendation problem,which will help to facilitate the peer-review process and promote information retrieval research in other practical scenes.
基金supported by the National Natural Science Foundation of China under Grant No.62276051the Natural Science Foundation of Sichuan Province under Grant No.2023NSFSC0640Medical Industry Information Integration Collaborative Innovation Project of Yangtze Delta Region Institute under Grant No.U0723002。
文摘The estimation of pain intensity is critical for medical diagnosis and treatment of patients.With the development of image monitoring technology and artificial intelligence,automatic pain assessment based on facial expression and behavioral analysis shows a potential value in clinical applications.This paper reports a framework of convolutional neural network with global and local attention mechanism(GLA-CNN)for the effective detection of pain intensity at four-level thresholds using facial expression images.GLA-CNN includes two modules,namely global attention network(GANet)and local attention network(LANet).LANet is responsible for extracting representative local patch features of faces,while GANet extracts whole facial features to compensate for the ignored correlative features between patches.In the end,the global correlational and local subtle features are fused for the final estimation of pain intensity.Experiments under the UNBC-McMaster Shoulder Pain database demonstrate that GLA-CNN outperforms other state-of-the-art methods.Additionally,a visualization analysis is conducted to present the feature map of GLA-CNN,intuitively showing that it can extract not only local pain features but also global correlative facial ones.Our study demonstrates that pain assessment based on facial expression is a non-invasive and feasible method,and can be employed as an auxiliary pain assessment tool in clinical practice.
基金funded by the National Natural Science Foundation of China(No.62076044)Scientific Research Foundation of Chongqing University of Technology(No.2020ZDZ015).
文摘Alzheimer’s disease(AD)is a complex,progressive neurodegenerative disorder.The subtle and insidious onset of its pathogenesis makes early detection of a formidable challenge in both contemporary neuroscience and clinical practice.In this study,we introduce an advanced diagnostic methodology rooted in theMed-3D transfermodel and enhanced with an attention mechanism.We aim to improve the precision of AD diagnosis and facilitate its early identification.Initially,we employ a spatial normalization technique to address challenges like clarity degradation and unsaturation,which are commonly observed in imaging datasets.Subsequently,an attention mechanism is incorporated to selectively focus on the salient features within the imaging data.Building upon this foundation,we present the novelMed-3D transfermodel,designed to further elucidate and amplify the intricate features associated withADpathogenesis.Our proposedmodel has demonstrated promising results,achieving a classification accuracy of 92%.To emphasize the robustness and practicality of our approach,we introduce an adaptive‘hot-updating’auxiliary diagnostic system.This system not only enables continuous model training and optimization but also provides a dynamic platform to meet the real-time diagnostic and therapeutic demands of AD.
基金supported by the S&T Special Program of Huzhou(Grant No.2023GZ09)the Open Fund Project of the ShanghaiKey Laboratory of Lightweight Structural Composites(Grant No.2232021A4-06).
文摘Carbon fiber composites,characterized by their high specific strength and low weight,are becoming increasingly crucial in automotive lightweighting.However,current research primarily emphasizes layer count and orientation,often neglecting the potential of microstructural design,constraints in the layup process,and performance reliability.This study,therefore,introduces a multiscale reliability-based design optimization method for carbon fiber-reinforced plastic(CFRP)drive shafts.Initially,parametric modeling of the microscale cell was performed,and its elastic performance parameters were predicted using two homogenization methods,examining the impact of fluctuations in microscale cell parameters on composite material performance.A finite element model of the CFRP drive shaft was then constructed,achieving parameter transfer between microscale and macroscale through Python programming.This enabled an investigation into the influence of both micro and macro design parameters on the CFRP drive shaft’s performance.The Multi-Objective Particle Swarm Optimization(MOPSO)algorithm was enhanced for particle generation and updating strategies,facilitating the resolution of multi-objective reliability optimization problems,including composite material layup process constraints.Case studies demonstrated that this approach leads to over 30%weight reduction in CFRP drive shafts compared to metallic counterparts while satisfying reliability requirements and offering insights for the lightweight design of other vehicle components.
基金support from the National Natural Science Foundation of China(No.22105031)National Key Research and Development Program of China(No.2019YFE0121600)+2 种基金Sichuan Science and Technology Program(No.2021YFH0054,2023JDGD0011)Fundamental Research Funds for the Central Universities(ZYGX2020J028)Z.M.W.acknowledges the National Key Research and Development Program of China(No.2019YFB2203400)and the“111 Project”(No.B20030).
文摘Photocatalytic and photoelectrochemical water splitting using semiconductor materials are effective approaches for converting solar energy into hydrogen fuel.In the past few years,a series of photocatalysts/photoelectrocatalysts have been developed and optimized to achieve efficient solar hydrogen production.Among various optimization strategies,the regulation of spin polarization can tailor the intrinsic optoelectronic properties for retarding charge recombination and enhancing surface reactions,thus improving the solar-to-hydrogen(STH)efficiency.This review presents recent advances in the regulation of spin polarization to enhance spin polarized-dependent solar hydrogen evolution activity.Specifically,spin polarization manipulation strategies of several typical photocatalysts/photoelectrocatalysts(e.g.,metallic oxides,metallic sulfides,non-metallic semiconductors,ferroelectric materials,and chiral molecules)are described.In the end,the critical challenges and perspectives of spin polarization regulation towards future solar energy conversion are briefly provided.
基金financial support of Natural Science Foundation of China(No.61971102,62132004)MOST Major Research and Development Project(No.2021YFB2900204)+1 种基金Sichuan Science and Technology Program(No.2022YFH0022)Key Research and Development Program of Zhejiang Province(No.2022C01093)。
文摘Integrated data and energy transfer(IDET)is capable of simultaneously delivering on-demand data and energy to low-power Internet of Everything(Io E)devices.We propose a multi-carrier IDET transceiver relying on superposition waveforms consisting of multi-sinusoidal signals for wireless energy transfer(WET)and orthogonal-frequency-divisionmultiplexing(OFDM)signals for wireless data transfer(WDT).The outdated channel state information(CSI)in aging channels is employed by the transmitter to shape IDET waveforms.With the constraints of transmission power and WDT requirement,the amplitudes and phases of the IDET waveform at the transmitter and the power splitter at the receiver are jointly optimised for maximising the average directcurrent(DC)among a limited number of transmission frames with the existence of carrier-frequencyoffset(CFO).For the amplitude optimisation,the original non-convex problem can be transformed into a reversed geometric programming problem,then it can be effectively solved with existing tools.As for the phase optimisation,the artificial bee colony(ABC)algorithm is invoked in order to deal with the nonconvexity.Iteration between the amplitude optimisation and phase optimisation yields our joint design.Numerical results demonstrate the advantage of our joint design for the IDET waveform shaping with the existence of the CFO and the outdated CSI.
基金Science and Technology Innovation Program of Hunan Province,Grant/Award Number:2021RC3021Project of State Key Laboratory of Environment‐Friendly Energy Materials,Grant/Award Numbers:18ZD320304,21fksy24+2 种基金Natural Science Foundation of Hunan Province,Grant/Award Number:2021JJ40780National Natural Science Foundation of China,Grant/Award Numbers:51902346,52172239Start‐up Funding of Yangtze Region Institute(Huzhou),University of Electronic Science and Technology,Grant/Award Number:U03220102。
文摘Lithium-sulfur batteries(LSBs)have been regarded as one of the promising candidates for the next-generation“lithium-ion battery beyond”owing to their high energy density and due to the low cost of sulfur.However,the main obstacles encountered in the commercial implementation of LSBs are the notorious shuttle effect,retarded sulfur redox kinetics,and uncontrolled dendrite growth.Accordingly,single-atom catalysts(SACs),which have ultrahigh catalytic efficiency,tunable coordination configuration,and light weight,have shown huge potential in the field of LSBs to date.This review summarizes the recent research progress of SACs applied as multifunctional components in LSBs.The design principles and typical synthetic strategies of SACs toward effective Li–S chemistry as well as the working mechanism promoting sulfur conversion reactions,inhibiting the lithium polysulfide shuttle effect,and regulating Li+nucleation are comprehensively illustrated.Potential future directions in terms of research on SACs for the realization of commercially viable LSBs are also outlined.
基金supported by the Natural Science foundation of China(51972043)the Sichuan-Hong Kong Collaborative Research Fund(2021YFH0184)the Natural Science foundation of Sichuan Province(2023NSFSC0417)。
文摘Lithium-ion batteries(LIBs)require separators with high performance and safety to meet the increasing demands for energy storage applications.Coating electrochemically inert ceramic materials on conventional polyolefin separators can enhance stability but comes at the cost of increased weight and decreased capacity of the battery.Herein,a novel separator coated with lithium iron phosphate(LFP),an active cathode material,is developed via a simple and scalable process.The LFP-coated separator exhibits superior thermal stability,mechanical strength,electrolyte wettability,and ionic conductivity than the conventional polyethylene(PE)separator.Moreover,the LFP coating can actively participate in the electrochemical reaction during the charge-discharge process,thus enhancing the capacity of the battery.The results show that the LFP-coated separator can increase the cell capacity by 26%,and improve the rate capability by 29%at 4 C compared with the conventional PE separator.The LFP-coated separator exhibits only 1.1%thermal shrinkage at 140°C,a temperature even above the melting point of PE.This work introduces a new strategy for designing separators with dual functions for the next-generation LIBs with improved performance and safety.
基金financially supported by the National Natural Science Foundation of China(52079026)the National Key Research and Development Program of China(2021YFC3201100)+4 种基金the National Natural Science Foundation of China(41830863 and 61976044)Sichuan Science and Technology Program(2020YFH0037)the Belt and Road Fund on Water and Sustainability of the State Key Laboratory of Hydrology–Water Resources and Hydraulic Engineering(2019nkzd02)the Open Research Fund of State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin(IWHR-SKL-201911)the Fundamental Research Funds for the Central Universities(ZYGX2019Z014)。
文摘径流预报对防洪具有重要意义。然而,由于径流过程的复杂性和随机性,对日径流量进行准确预测是困难的,尤其是对峰值径流量的预测。为了解决这一问题,本研究提出了一种用于径流预测的增强型长短期记忆(LSTM)模型,其中引入了新的损失函数并集成了特征提取器。设计了峰值误差tanh(peak error tanh,PET)和峰值误差swish(peak error swish,PES)两个损失函数,增强了峰值径流预测的重要性,弱化了正常径流预测的权重。为每个气象站建立由3个LSTM网络组成的特征提取器,目的是提取每个气象站输入数据的时间特征。以中国淮河上游为例,利用增强型LSTM模型对1960—2016年的日径流量进行了预测。结果表明,改进后的LSTM模型表现良好,在验证期内(2005年11月至2016年12月),Nash-Sutcliffe效率(NSE)系数在0.917-0.924之间,优于广泛使用的集总水文模型(Australian Water Balance model(AWBM)、Sacramento、Sim Hyd和Tank模型)和数据驱动模型(人工神经网络(ANN)、支持向量回归(SVR)和门控循环单元(GRU))。以PES为损失函数的增强型LSTM对洪水极端径流的预测效果最好,平均NSE为0.873。此外,海拔较高的气象站降水对径流预测的贡献比最近的气象站更大。该研究为流域日径流预测提供了有效工具,有利于流域防洪和水安全管理。
基金supported by the National Natural Science Foundation of China(No.52103308)the Natural Science Foundation of Jiangsu Province of China(No.BK20210826)+4 种基金Outstanding Youth Foundation of Jiangsu Province of China(No.BK20211548)National Key Research and Development Program of China(No.2017YFE0115900)Innovative Science and Technology Platform Project of Cooperation between Yangzhou City and Yangzhou University(No.YZ2020266)Lvyang Jinfeng Plan for Excellent Doctor of Yangzhou City,Special Funds for Self-Made Experimental Equipment of Yangzhou Universitythe Doctor of Suzhou University Scientific Research Foundation Project(No.2022BSK003).
文摘2D MXenes are highly attractive for fabricating high-precision gas sensors operated at room temperature(RT)due to their high surface-to-volume ratio.However,the limited selectivity and low sensitivity are still long-standing challenges for their further applications.Herein,the self-assembly of 0D-2D heterostructure for highly sensitive NO_(2) detection was achieved by integrating ZnO nanoparticles on Ti_(3)C_(2)Tx MXene-derived TiO_(2) nanosheets(designated as ZnO@MTiO_(2)).ZnO nanoparticles can not only act as spacers to prevent the restacking of MTiO_(2) nanosheets and ensure effective transfer for gas molecules,but also enhance the sensitivity of the sensor the through trapping effect on electrons.Meanwhile,MTiO_(2) nanosheets facilitate gas diffusion for rapid sensor response.Benefiting from the synergistic effect of individual components,the ZnO@MTiO_(2)0D-2D heterostructure-based sensors revealed remarkable sensitivity and excellent selectivity to low concentration NO_(2) at RT.This work may facilitate the sensing application of MXene derivative and provide a new avenue for the development of high-performance gas sensors in safety assurance and environmental monitoring.
基金supported by the National Natural Science Foundation of China(42027805)the National Aeronautical Fund(ASFC-20172080005)。
文摘Convolutional neural networks(CNNs)are well suited to bearing fault classification due to their ability to learn discriminative spectro-temporal patterns.However,gathering sufficient cases of faulty conditions in real-world engineering scenarios to train an intelligent diagnosis system is challenging.This paper proposes a fault diagnosis method combining several augmentation schemes to alleviate the problem of limited fault data.We begin by identifying relevant parameters that influence the construction of a spectrogram.We leverage the uncertainty principle in processing time-frequency domain signals,making it impossible to simultaneously achieve good time and frequency resolutions.A key determinant of this phenomenon is the window function's choice and length used in implementing the shorttime Fourier transform.The Gaussian,Kaiser,and rectangular windows are selected in the experimentation due to their diverse characteristics.The overlap parameter's size also influences the outcome and resolution of the spectrogram.A 50%overlap is used in the original data transformation,and±25%is used in implementing an effective augmentation policy to which two-stage regular CNN can be applied to achieve improved performance.The best model reaches an accuracy of 99.98%and a cross-domain accuracy of 92.54%.When combined with data augmentation,the proposed model yields cutting-edge results.
基金supported by the National Natural Science Foundation of China(52002052)the Startup funds of Outstanding Talents of UESTC(A1098531023601205)+1 种基金the National Youth Talents Plan of China(G05QNQR049)the Foundation of State Key Laboratory of Silicon Materials(SKL2021-12)。
文摘Aqueous Zinc-based energy storage devices are considered as one of the potential candidates in future power technologies.Nevertheless,poor low temperature performance and uncontrollable Zn dendrite growth lead to the limited energy storage capability.Herein,an anti-hydrolysis,cold-resistant,economical,safe,and environmentally friendly electrolyte is developed by utilizing water,ethylene glycol(EG),and ZnCl_(2)with high ionic conductivity(7.9 mS cm^(-1)in glass fiber membrane at-20℃).The spectra data and DFT calculations show the competitive coordination of EG and Cl-to induce a unique solvation configuration of Zn^(2+),conducive to effectively inhibiting the hydrolysis of Zn^(2+),suppressing the dendrite growth,and broadening the working voltage range and temperature range of ZnCl_(2)electrolyte.The isotope tracing data confirm that Cl^(-)could effectively destroy the ZnO passivation film,promoting the formation of Zn nuclei and improving its reaction activity.Compared to the corresponding ZnSO4electrolyte,the Cu/Zn half-cell with the ZnCl_(2)electrolyte exhibits a stable cycle life of more than 1600 h at-20℃,even at the current density of 5 mA cm^(-2).The assembled Zn-ion hybrid capacitor possesses an average capacity of 42.68 m A h g^(-1)under-20℃at a current density of 5 A g^(-1),3.5 times than that of the modified ZnSO4electrolyte.Our work proposes a new approach for optimizing aqueous electrolytes to meet low temperature energy storage applications.
基金supported by the National Natural Science Foundation of China(Grant Nos.61771104 and 62071106)Jiangxi Innovative Talent Program,and Sichuan Science and Technology Program(Grant No.2021JDTD0026)。
文摘With the help of the first principle calculation,the solid-state reaction experiment was conducted to investigate the alteration in the sintering and the microwave dielectric properties of Mg_(3)B_(2)O_(6)ceramic with many Zn^(2+)substitutions.These properties were characterized using the scanning electron microscopy,network analyzer,X-ray diffraction,Raman spectroscopy,energy-dispersive spectroscopy,and thermomechanical and differential-thermal analyses.The coexistence of Mg_(3)B_(2)O_(6),Mg_(2)B_(2)O_(5)and ZnO ceramics could be observed with increasing Zn^(2+)addition,and the lattice distortion occurred in the Mg_(2)B_(2)O_(5)and Mg_(3)B_(2)O_(6)ceramics due to the substitution of Mg^(2+)with Zn^(2+).The electron density and the bond property of the MgO_(6)octahedron changed,and a quantitative method was used to discuss the variation in sintering,substitution and phase formation properties.The densification window was decreased to 1100℃,and the dielectric properties improved with the formation of a three-phase borate solid solution(dielectric constant=6.73,quality factor=112,000 GHz at 16 GHz(Q=7000),temperature coefficient of resonant frequency=-61.2 ppm℃^(-1),and relative density=97.0%).
基金supported by the Sichuan Science and Technology Program under Grants No.2022YFQ0052 and No.2021YFQ0009.
文摘At present,knowledge embedding methods are widely used in the field of knowledge graph(KG)reasoning,and have been successfully applied to those with large entities and relationships.However,in research and production environments,there are a large number of KGs with a small number of entities and relations,which are called sparse KGs.Limited by the performance of knowledge extraction methods or some other reasons(some common-sense information does not appear in the natural corpus),the relation between entities is often incomplete.To solve this problem,a method of the graph neural network and information enhancement is proposed.The improved method increases the mean reciprocal rank(MRR)and Hit@3 by 1.6%and 1.7%,respectively,when the sparsity of the FB15K-237 dataset is 10%.When the sparsity is 50%,the evaluation indexes MRR and Hit@10 are increased by 0.8%and 1.8%,respectively.
基金supported by NRPU Project No.20-16091awarded by Higher Education Commission,PakistanThe title of the project is“University Education and Occupational Skills Mismatch (A Case Study of SMEs in Khyber Pakhtunkhwa)”,by the National Natural Science Foundation of China (Grant No.61370073)the National High Technology Research and Development Program of China,the project of Science and Technology Department of Sichuan Province (Grant No.2021YFG0322).
文摘Stock market forecasting has drawn interest from both economists and computer scientists as a classic yet difficult topic.With the objective of constructing an effective prediction model,both linear and machine learning tools have been investigated for the past couple of decades.In recent years,recurrent neural networks(RNNs)have been observed to perform well on tasks involving sequence-based data in many research domains.With this motivation,we investigated the performance of long-short term memory(LSTM)and gated recurrent units(GRU)and their combination with the attention mechanism;LSTM+Attention,GRU+Attention,and LSTM+GRU+Attention.The methods were evaluated with stock data from three different stock indices:the KSE 100 index,the DSE 30 index,and the BSE Sensex.The results were compared to other machine learning models such as support vector regression,random forest,and k-nearest neighbor.The best results for the three datasets were obtained by the RNN-based models combined with the attention mechanism.The performances of the RNN and attention-based models are higher and would be more effective for applications in the business industry.
基金supported by the National Key Research and Development Program of China under Grant No.2016YFF0102100the Pre-Research Project of Civil Aerospace Technology of China under Grant No.D040109.
文摘In this study,we investigated the abatement of volatile organic compounds(VOCs)by the atmospheric pressure microwave plasma torch(AMPT).To study the treatment efficiency of AMPT,we used the toluene and water-based varnish to simulate VOCs,respectively.By measuring the compounds and contents of the mixture gas before/after the microwave plasma process,we have calculated the treatment efficiency of AMPT.The experimental results show that the treatment efficiency of AMPT for toluene with a concentration of 17.32×10^(4) ppm is up to 60 g/kWh with the removal rate of 86%.For the volatile compounds of water-based varnish,the removal efficiency is up to 97.99%.We have demonstrated the higher potential for VOCs removal of the AMPT process.
基金This work was supported by the National Natural Science Foundation of China(21822601,22176029,22172019)the Sichuan Natural Science Foundation for Distinguished Scholars(2021JDJQ0006)+2 种基金the 111 Project(B20030)the Funda-mental Research Funds for the Central Universities(ZYGX2019Z021)KHK acknowledges support made by a grant from the National Research Foundation of Korea(NRF)funded by the Ministry Of Science And ITC(MSIT)Of The Kor-ean Government(Grant No:2021R1A3B1068304).
文摘It is crucial to efficiently separate and transport photo-induced charge carriers for the effective implementation of photocatalysis toward environmental remediation.A rational design strategy is proposed to validate such proposition through the construction of an interfacial structure in the form of LDH/Zn_(2)SnO_(4) heterostructures in this research.The interfacial charge transfer on LDH/Zn_(2)SnO_(4) is greatly promoted via the unique charge transfer pathway,as characterized by transient photocurrent responses,X-ray photoelectron spectroscopy,electron paramagnetic resonance spectrum,and photoluminescence analysis.As such,it contributes to the generation of reactive oxygen species(ROS)and the activation of reactants for the mineralization of toluene.According to the in situ DRIFTS spectra analysis,the accumulation of benzoic acid takes place possibly through the partial oxidation of the methyl group on toluene at the interface of the LDH/Zn 2 SnO 4 heterostructure.This process can greatly promote the photocatalytic oxidation of toluene with the enhanced ring-opening efficiency.The LDH/Zn 2 SnO 4 is thus demonstrated as superior photocatalyst against toluene(removal efficiency of 89.5%;mineralization of 83.1%;and quantum efficiency of 4.55×10^(−6) molecules/photon).As such,the performance of this composite far exceeds that of their individual components(e.g.,P25,pure Mg-Al LDH,or Zn_(2)SnO_(4)).This study is expected to offer a new path to the interfacial charge transfer mechanism based on the design of highly efficient photocatalysts for air purification.