The paper deals with state estimation problem of nonlinear non-Gaussian discrete dynamic systems for improvement of accuracy and consistency. An efficient new algorithm called the adaptive Gaussian-sum square-root cub...The paper deals with state estimation problem of nonlinear non-Gaussian discrete dynamic systems for improvement of accuracy and consistency. An efficient new algorithm called the adaptive Gaussian-sum square-root cubature Kalman filter（AGSSCKF） with a split-merge scheme is proposed. It is developed based on the squared-root extension of newly introduced cubature Kalman filter（SCKF） and is built within a Gaussian-sum framework. Based on the condition that the probability density functions of process noises and initial state are denoted by a Gaussian sum using optimization method, a bank of SCKF are used as the sub-filters to estimate state of system with the corresponding weights respectively, which is adaptively updated. The new algorithm consists of an adaptive splitting and merging procedure according to a proposed split-decision model based on the nonlinearity degree of measurement. The results of two simulation scenarios（one-dimensional state estimation and bearings-only tracking） show that the proposed filter demonstrates comparable performance to the particle filter with significantly reduced computational cost.展开更多
The fast solution of linear equations has always been one of the hot spots in scientific computing.A kind of the diagonal matrix splitting iteration methods are provided,which is different from the classical matrix sp...The fast solution of linear equations has always been one of the hot spots in scientific computing.A kind of the diagonal matrix splitting iteration methods are provided,which is different from the classical matrix splitting methods.Taking the decomposition of the diagonal elements for coefficient matrix as the key point,some new preconditioners are constructed.Taking the tri-diagonal coefficient matrix as an example,the convergence domains and optimal relaxation factor of the new method are analyzed theoretically.The presented new iteration methods are applied to solve linear algebraic equations,even 2D and 3D diffusion problems with the fully implicit discretization.The results of numerical experiments are matched with the theoretical analysis,and show that the iteration numbers are reduced greatly.The superiorities of presented iteration methods exceed some classical iteration methods dramatically.展开更多
Merged satellite altimeter products are widely used in ocean-related fields.Currently,the altimeter merged products of archiving validation and interpretation of satellite oceanographic(AVISO)data are widely used inte...Merged satellite altimeter products are widely used in ocean-related fields.Currently,the altimeter merged products of archiving validation and interpretation of satellite oceanographic(AVISO)data are widely used internationally.Chinese National Satellite Ocean Application Service also released merged altimeter products(ALT MUL)in 2023.However,there are few studies on the quality assessment of ALT MUL.Based on the data of AVISO merged products,Jason3 satellite,tide gauge and drifter buoy,the quality assessment and effect analysis of ALT MUL merged products were carried out by means of error evaluation index,interpolation along rails,velocity inversion and power spectrum.The result shows that the average sea level anomaly(SLA)of ALT MUL is about 2 cm smaller than that of AVISO.And they are consistent with the large-scale characteristics and spatial distribution.These two SLA products are both in accordance with normal distribution.Results indicate a lesser congruence between ALT MUL and Jason3 satellite compared to AVISO.This difference may be attributed to the fact that AVISO products use Jason3 satellite as crosscalibrated reference satellite during the merged process.Comparing the matching effect of the two merged products with the tide gauge and drifter buoy,ALT MUL merged products are superior to AVISO in general.The energy spectral density was calculated by using Jason3 satellite data along the orbit,and the two merged products were interpolated to the data points along the orbit.The effective resolution of AVISO and ALT MUL merged products was 180 km and 210 km respectively through spectral calculation,indicating that AVISO merged products have higher effective resolution.展开更多
Electrochemical water splitting represents a sustainable technology for hydrogen(H_(2))production.However,its large-scale implementation is hindered by the high overpotentials required for both the cathodic hydrogen e...Electrochemical water splitting represents a sustainable technology for hydrogen(H_(2))production.However,its large-scale implementation is hindered by the high overpotentials required for both the cathodic hydrogen evolution reaction(HER)and the anodic oxygen evolution reaction(OER).Transition metal-based catalysts have garnered significant research interest as promising alternatives to noble-metal catalysts,owing to their low cost,tunable composition,and noble-metal-like catalytic activity.Nevertheless,systematic reviews on their application as bifunctional catalysts for overall water splitting(OWS)are still limited.This review comprehensively outlines the principal categories of bifunctional transition metal electrocatalysts derived from electrospun nanofibers(NFs),including metals,oxides,phosphides,sulfides,and carbides.Key strategies for enhancing their catalytic performance are systematically summarized,such as heterointerface engineering,heteroatom doping,metal-nonmetal-metal bridging architectures,and single-atom site design.Finally,current challenges and future research directions are discussed,aiming to provide insightful perspectives for the rational design of high-performance electrocatalysts for OWS.展开更多
Herein,we have developed a straightforward wet-chemical method to synthesize a series of Pd-based alloy nanowires(NWs),including Pd Pt NWs,Pd Au NWs,Pd Ir NWs,and Pd Ru NWs,which exhibits high mass activity and turnov...Herein,we have developed a straightforward wet-chemical method to synthesize a series of Pd-based alloy nanowires(NWs),including Pd Pt NWs,Pd Au NWs,Pd Ir NWs,and Pd Ru NWs,which exhibits high mass activity and turnover frequency(TOF) for HER,surpassing Pt/C by 4.6-fold and 1.5-fold in acidic and alkaline electrolytes,respectively.It also demonstrates high stability in alkaline electrolyte at a current density of 220 m A/cm^(2) for 280 h,highlighting its potential for practical applications under industrial current conditions.Pd Pt NWs exhibited ultrathin structures with head-to-tail kinks and inherent defects,significantly increasing the density of active sites and precisely tuning the electronic structure,which could accelerate reaction kinetics and boost water-splitting electrocatalytic performance.This study highlights the potential of Pd Pt NWs as highly efficient catalysts,offering outstanding catalytic performance and stability for practical applications.展开更多
Split Learning(SL)has been promoted as a promising collaborative machine learning technique designed to address data privacy and resource efficiency.Specifically,neural networks are divided into client and server subn...Split Learning(SL)has been promoted as a promising collaborative machine learning technique designed to address data privacy and resource efficiency.Specifically,neural networks are divided into client and server subnetworks in order to mitigate the exposure of sensitive data and reduce the overhead on client devices,thereby making SL particularly suitable for resource-constrained devices.Although SL prevents the direct transmission of raw data,it does not alleviate entirely the risk of privacy breaches.In fact,the data intermediately transmitted to the server sub-model may include patterns or information that could reveal sensitive data.Moreover,achieving a balance between model utility and data privacy has emerged as a challenging problem.In this article,we propose a novel defense approach that combines:(i)Adversarial learning,and(ii)Network channel pruning.In particular,the proposed adversarial learning approach is specifically designed to reduce the risk of private data exposure while maintaining high performance for the utility task.On the other hand,the suggested channel pruning enables the model to adaptively adjust and reactivate pruned channels while conducting adversarial training.The integration of these two techniques reduces the informativeness of the intermediate data transmitted by the client sub-model,thereby enhancing its robustness against attribute inference attacks without adding significant computational overhead,making it wellsuited for IoT devices,mobile platforms,and Internet of Vehicles(IoV)scenarios.The proposed defense approach was evaluated using EfficientNet-B0,a widely adopted compact model,along with three benchmark datasets.The obtained results showcased its superior defense capability against attribute inference attacks compared to existing state-of-the-art methods.This research’s findings demonstrated the effectiveness of the proposed channel pruning-based adversarial training approach in achieving the intended compromise between utility and privacy within SL frameworks.In fact,the classification accuracy attained by the attackers witnessed a drastic decrease of 70%.展开更多
Designing a highly active and stable bifunctional catalyst is essential for achieving superior overall water splitting(OWS).In this study,a three-dimensional(3D)core-shell structure Co_(3)S_(4)/CuS@NiFe LDH nanocoral ...Designing a highly active and stable bifunctional catalyst is essential for achieving superior overall water splitting(OWS).In this study,a three-dimensional(3D)core-shell structure Co_(3)S_(4)/CuS@NiFe LDH nanocoral spheres electrocatalyst was constructed on nickel foam(NF)via an interfacial engineering strategy.This 3D core-shell heterostructure maximizes the exposure of active sites,optimizes the charge transport pathway and accelerates gas release rates.The protective shell strategy of NiFe LDH provides favorable stability,which contributes to inhibiting the electrochemical corrosion of the electrocatalyst and mitigating the toxic effects of Cl^(-) and other microorganisms during the seawater splitting process.Moreover,the introduction of NiFe LDH induces a change in the OER mechanism from an adsorption evolution mechanism(AEM)to a lattice oxygen mechanism(LOM),which improves the intrinsic activity of the catalyst.Consequently,Co_(3)S_(4)/CuS@NiFe LDH demonstrates exceptional performance in the oxygen evolution reaction(OER)(η100=251 mV)and in the hydrogen evolution reaction(HER)(η100=254 mV),alongside remarkable stability over 100 h.For OWS,it exhibits a voltage of 1.46 V at 10 mA/cm^(2) and maintain stability for 100 h.Impressively,Co_(3)S_(4)/CuS@NiFe LDH still possesses outstanding activity and stability in natural alkaline seawater.This work proposes interfacial engineering to construct bifunctional catalysts with core-shell heterostructures,providing instructive guidelines for the design of highly efficient electrocatalysts toward seawater electrolysis.展开更多
The Internet of Vehicles,or IoV,is expected to lessen pollution,ease traffic,and increase road safety.IoV entities’interconnectedness,however,raises the possibility of cyberattacks,which can have detrimental effects....The Internet of Vehicles,or IoV,is expected to lessen pollution,ease traffic,and increase road safety.IoV entities’interconnectedness,however,raises the possibility of cyberattacks,which can have detrimental effects.IoV systems typically send massive volumes of raw data to central servers,which may raise privacy issues.Additionally,model training on IoV devices with limited resources normally leads to slower training times and reduced service quality.We discuss a privacy-preserving Federated Split Learning with Tiny Machine Learning(TinyML)approach,which operates on IoV edge devices without sharing sensitive raw data.Specifically,we focus on integrating split learning(SL)with federated learning(FL)and TinyML models.FL is a decentralisedmachine learning(ML)technique that enables numerous edge devices to train a standard model while retaining data locally collectively.The article intends to thoroughly discuss the architecture and challenges associated with the increasing prevalence of SL in the IoV domain,coupled with FL and TinyML.The approach starts with the IoV learning framework,which includes edge computing,FL,SL,and TinyML,and then proceeds to discuss how these technologies might be integrated.We elucidate the comprehensive operational principles of Federated and split learning by examining and addressingmany challenges.We subsequently examine the integration of SL with FL and various applications of TinyML.Finally,exploring the potential integration of FL and SL with TinyML in the IoV domain is referred to as FSL-TM.It is a superior method for preserving privacy as it conducts model training on individual devices or edge nodes,thereby obviating the necessity for centralised data aggregation,which presents considerable privacy threats.The insights provided aim to help both researchers and practitioners understand the complicated terrain of FL and SL,hence facilitating advancement in this swiftly progressing domain.展开更多
NiFe layered double hydroxide(NiFe LDH)has emerged as a promising catalyst for the oxygen evolution reaction(OER);however,its hydrogen evolution reaction(HER)activity remains suboptimal due to unfavorable electronic s...NiFe layered double hydroxide(NiFe LDH)has emerged as a promising catalyst for the oxygen evolution reaction(OER);however,its hydrogen evolution reaction(HER)activity remains suboptimal due to unfavorable electronic structures,particularly the d-electron density of metal sites,which impede water dissociation and lead to poor hydrogen adsorption/desorption capabilities.Herein,we introduce an efficient cooperative d-electron density regulation(CDDR)engineering to comprehensively optimize the delectron density of NiFe LDH by grafting MoO_(x) -modified NiFe LDH nanosheets onto porous nickel particles(PNPs).The PNPs facilitate d-electron density modulation along the edges of the nanosheets,while the MoO_(x) species enable d-electron density modulation across the plane of the nanosheets,thus cooperatively constructing enriched d-electron density in NiFe LDH.Theoretical studies validate the CDDR process and reveal that the enriched d-electron density accelerates water dissociation and optimizes the hydrogen adsorption behavior of NiFe LDH.As a result,the engineered catalyst exhibits significantly improved HER activity,achieving an ultra-low overpotential of 38 mV at 10 mA cm^(-2)in 1 M KOH.Additionally,the CDDR-optimized catalyst also exhibits good OER performance,demonstrating excellent bifunctional performance for overall water splitting in both alkaline freshwater and seawater electrolytes.This work presents a novel CDDR strategy for engineering NiFe LDH into efficient HER catalysts without compromising its OER activity,potentially paving the way for the development of active and robust electrocatalysts for sustainable energy applications.展开更多
The personalized fine-tuning of large languagemodels(LLMs)on edge devices is severely constrained by limited computation resources.Although split federated learning alleviates on-device burdens,its effectiveness dimin...The personalized fine-tuning of large languagemodels(LLMs)on edge devices is severely constrained by limited computation resources.Although split federated learning alleviates on-device burdens,its effectiveness diminishes in few-shot reasoning scenarios due to the low data efficiency of conventional supervised fine-tuning,which leads to excessive communication overhead.To address this,we propose Language-Empowered Split Fine-Tuning(LESFT),a framework that integrates split architectures with a contrastive-inspired fine-tuning paradigm.LESFT simultaneously learns frommultiple logically equivalent but linguistically diverse reasoning chains,providing richer supervisory signals and improving data efficiency.This process-oriented training allows more effective reasoning adaptation with fewer samples.Extensive experiments demonstrate that LESFT consistently outperforms strong baselines such as SplitLoRA in task accuracy.LESFT consistently outperforms strong baselines on GSM8K,CommonsenseQA,and AQUA_RAT,with the largest gains observed on Qwen2.5-3B.These results indicate that LESFT can effectively adapt large language models for reasoning tasks under the computational and communication constraints of edge environments.展开更多
Despite intensive research on solar-driven photocatalytic overall water splitting(POWS),the overall efficiencies remain insufficient to meet commercial standards.As a central challenge in realizing this technology mai...Despite intensive research on solar-driven photocatalytic overall water splitting(POWS),the overall efficiencies remain insufficient to meet commercial standards.As a central challenge in realizing this technology mainly lies in the precise tuning and rational designing of highly efficient materials and photocatalytic systems,which is paramount for its unlocking scalable,practical applications.However,novel materials fabrication and advanced photocatalytic systems are essential for overcoming intrinsic limitations of conventional catalysts by enabling this green technology to resolve global energy crisis.Therefore,this review critically explores the engineering developments in POWS process and novel photocatalyst designing,via shifting from simple bandgap engineering to more advanced charge carrier dynamics control via utilizing one/two-step photocatalytic excitation system,surface phase junctions i.e.,Z-scheme and S-scheme heterojunctions,surface modification,morphological tuning,and the role of co-catalysts,to control sluggish kinetics,promote oxygen evolution reaction(OER)and suppress undesirable H2/O2,backward reaction with superior visible light absorption capacity to produce remarkable energy production.Moreover,we critically discuss the recent trend of POWS from a materials discovery phase to demanding engineering and mechanistic optimization phase with viable economic viability,which requires bridging the gap between excellent lab-scale performance to stringent stability,cost,and high efficiency demands of industrial-scale solar fuel production.In addition,the currents challenges and future directions are also enclosed in detail for sustainable energy production.展开更多
This insightful review explores the electrochemical principles and energy potential of electrocatalytic water splitting(EWS).It highlights recent advancements,identifies key challenges,and underscores the pivotal role...This insightful review explores the electrochemical principles and energy potential of electrocatalytic water splitting(EWS).It highlights recent advancements,identifies key challenges,and underscores the pivotal role of EWS in enabling the transition to sustainable energy systems.This work contextualizes the significance of green hydrogen in global decarbonization pathways and examines the historical progression of electrocatalysis.The fundamental thermodynamics and mechanistic pathways governing both the hydrogen and oxygen evolution reactions(HER and OER)are analyzed,highlighting energy barriers and rate-determining steps.Various electrode architectures and electrochemical cell configurations are evaluated,including a comparative assessment of key electrolyzer technologies and their performance characteristics.Furthermore,we critically examine recent advances and persistent limitations across the landscape of electrocatalysts,spanning noble metal-based materials,earth-abundant transition metal compounds,and emerging materials.Design principles and mechanistic insights drawn from electronic structu re modulation,defect engineering,doping strategies,and na noscale morphology control are elucidated to establish robust structure-property-performance relationships.Major challenges including sluggish oxygen evolution kinetics,catalyst degradation mechanisms,and the integration of devices with intermittent renewable energy sources are thoroughly examined.This work also debates advanced strategies such as hybrid photoelectrochemical systems,flexible device architectures,and the direct utilization of non-traditional water sources(e.g.,seawater,wastewater)as promising pathways for future development.Finally,it is specifically distinguished by its critical focus on bridging the gap between fundamental electrocatalysts development and practical system-level integration,addressing the challenges of scalability and deployment under industrially relevant conditions.This comprehensive review provides a strategic outlook and identifies key scientific priorities for optimizing EWS systems toward efficient,robust,and scalable hydrogen generation.展开更多
Energy shortage has become one of themost concerning issues in the world today,and improving energy utilization efficiency is a key area of research for experts and scholars worldwide.Small-diameter heat exchangers of...Energy shortage has become one of themost concerning issues in the world today,and improving energy utilization efficiency is a key area of research for experts and scholars worldwide.Small-diameter heat exchangers offer advantages such as reduced material usage,lower refrigerant charge,and compact structure.However,they also face challenges,including increased refrigerant pressure drop and smaller heat transfer area inside the tubes.This paper combines the advantages and disadvantages of both small and large-diameter tubes and proposes a combined-diameter heat exchanger,consisting of large and small diameters,for use in the indoor units of split-type air conditioners.There are relatively few studies in this area.In this paper,A theoretical and numerical computation method is employed to establish a theoretical-numerical calculation model,and its reliability is verified through experiments.Using this model,the optimal combined diameters and flow path design for a combined-diameter heat exchanger using R32 as the working fluid are derived.The results show that the heat transfer performance of all combined diameter configurations improves by 2.79%to 8.26%compared to the baseline design,with the coefficient of performance(COP)increasing from 4.15 to 4.27~4.5.These designs can save copper material,but at the cost of an increase in pressure drop by 66.86%to 131.84%.The scheme IIIH,using R32,is the optimal combined-diameter and flow path configuration that balances both heat transfer performance and economic cost.展开更多
Bismuth vanadate(BiVO_(4))is regarded as a promising photoanode for photoelectrochemical(PEC)water splitting.Despite its advantage in band gap and visible-light response,the BiVO_(4)exhibits an unsatisfactory achievin...Bismuth vanadate(BiVO_(4))is regarded as a promising photoanode for photoelectrochemical(PEC)water splitting.Despite its advantage in band gap and visible-light response,the BiVO_(4)exhibits an unsatisfactory achieving water splitting due to severe charge recombination.Herein,we elucidate an innovative approach involving the incorporation of single Ru atom with a CoFe-LDH cocatalyst(Ru_(0.51)-CoFe-LDH)and integrating it onto the BiVO_(4)semiconductor substrate.The resulting Ru_(0.51)-CoFe-LDH/BiVO_(4)photoanode film demonstrates commendable charge injection efficiency(76%)and charge collection efficiency(100%).Interestingly,the yield of hydrogen and oxygen increases linearly at a stoichiometric ratio of about 2:1,reaching 158.6 and 67.4μmol after140 min of irradiation,respectively.According to experimental characterization and density functional theory calculation,this remarkable performance results from single Ru atoms triggering the electron rearrangement of Ru_(0.51)-CoFe-LDH to engineer active sites and optimize interfacial energetics.Additionally,the negative shift of Ru_(0.51)-CoFe-LDH band edge gives rise to more conspicuous band bending of the n-n junction formed with BiVO_(4),expediting the separation and transfer of photogenerated electron-hole pairs at the interface.This work furnishes a new preparation perspective for PEC water splitting systems to construct single atoms in the semiconductor substrate.展开更多
Amorphous metal-based catalysts are highly promising for water splitting due to their abundance of unsaturated active sites.Herein,we report a one-step,surfactant-free synthesis of amorphous nickel nanoparticles(NPs)e...Amorphous metal-based catalysts are highly promising for water splitting due to their abundance of unsaturated active sites.Herein,we report a one-step,surfactant-free synthesis of amorphous nickel nanoparticles(NPs)encapsulated in nitrogen-doped carbon shells(A-Ni@NC)via pulsed laser ablation in liquid(PLAL).The synergistic integration of the amorphous Ni core and a defect-rich N-doped carbon shell markedly enhanced the catalytic activities for both the hydrogen evolution reaction(HER)and oxygen evolution reaction(OER),with low overpotentials of 182 mV for HER and 288 mV for OER at 10 mA cm^(-2)in 1.0 m KOH.Furthermore,the bifunctional catalyst achieved a current density of 10 mA cm^(-2)at 1.63 V and retained 98.9%of its initial performance after 100 h of operation.The nitrogen-rich carbon shell not only offered abundant active sites and structural protection but also promoted charge transport.Density functional theory(DFT)calculations revealed that N-doping optimized intermediate adsorption energies,while the amorphous Ni core facilitated efficient electron transfer.This green and scalable synthesis strategy provides a promising platform for developing a wide range of transition metal@N-doped carbon hybrid catalysts for sustainable energy conversion applications.展开更多
目的:对比三维多回波恢复梯度回波(3D MERGE)、三维可变反转角快速自旋回波(3D SPACE STIR)序列在腰椎间盘突出症(LDH)检查中的应用效果。方法:选择2020年1月~2022年11月收治的135例LDH患者,回顾性分析患者临床和磁共振成像(MRI)资料,...目的:对比三维多回波恢复梯度回波(3D MERGE)、三维可变反转角快速自旋回波(3D SPACE STIR)序列在腰椎间盘突出症(LDH)检查中的应用效果。方法:选择2020年1月~2022年11月收治的135例LDH患者,回顾性分析患者临床和磁共振成像(MRI)资料,所有患者均接受常规MRI扫描及3D MERGE、3D SPACE STIR序列扫描,对比3D MERGE、3D SPACE STIR序列测量神经根直径的一致性,评价两种序列的图像质量参数[信噪比(SNR)、对比噪声比(CNR)]、图像清晰度评分。结果:3D MERGE和3D SPACE STIR序列测量的L3~S1神经根直径比较差异无统计学意义(P>0.05),且两组序列测量的L3、L4、L5和S1直径均显示出较高相关性(r=0.957,0.986,0.975,0.972,P<0.05);3D MERGE序列的SNR及CNR均高于3D SPACE STIR序列,神经根显示分级、图像清晰度评分优于3D SPACE STIR序列,差异有统计学意义(P<0.05)。结论:3D MERGE、3D SPACE STIR序列在LDH神经根直径测量中具有极高一致性,3D MERGE序列较3D SPACE STIR序列能够更清晰显示神经跟的解剖形态,图像质量更好。展开更多
基金supported by the National Natural Science Foundation of China(No. 61032001)Shandong Provincial Natural Science Foundation of China (No. ZR2012FQ004)
文摘The paper deals with state estimation problem of nonlinear non-Gaussian discrete dynamic systems for improvement of accuracy and consistency. An efficient new algorithm called the adaptive Gaussian-sum square-root cubature Kalman filter（AGSSCKF） with a split-merge scheme is proposed. It is developed based on the squared-root extension of newly introduced cubature Kalman filter（SCKF） and is built within a Gaussian-sum framework. Based on the condition that the probability density functions of process noises and initial state are denoted by a Gaussian sum using optimization method, a bank of SCKF are used as the sub-filters to estimate state of system with the corresponding weights respectively, which is adaptively updated. The new algorithm consists of an adaptive splitting and merging procedure according to a proposed split-decision model based on the nonlinearity degree of measurement. The results of two simulation scenarios（one-dimensional state estimation and bearings-only tracking） show that the proposed filter demonstrates comparable performance to the particle filter with significantly reduced computational cost.
基金The National Natural Science Foundations of China (12202219)the Natural Science Foundations of Ningxia (2024AAC02009, 2023AAC05001)the Ningxia Youth Top Talents Training Project。
文摘The fast solution of linear equations has always been one of the hot spots in scientific computing.A kind of the diagonal matrix splitting iteration methods are provided,which is different from the classical matrix splitting methods.Taking the decomposition of the diagonal elements for coefficient matrix as the key point,some new preconditioners are constructed.Taking the tri-diagonal coefficient matrix as an example,the convergence domains and optimal relaxation factor of the new method are analyzed theoretically.The presented new iteration methods are applied to solve linear algebraic equations,even 2D and 3D diffusion problems with the fully implicit discretization.The results of numerical experiments are matched with the theoretical analysis,and show that the iteration numbers are reduced greatly.The superiorities of presented iteration methods exceed some classical iteration methods dramatically.
基金The National Key R&D Program of China under contract No.2021YFC3101503the National Natural Science Foundation of China under contract Nos 42276205 and 42406195+1 种基金the Hunan Provincial Natural Science Foundation of China under contract No.2023JJ10053the Youth Independent Innovation Science Foundation under contract No.ZK24-54.
文摘Merged satellite altimeter products are widely used in ocean-related fields.Currently,the altimeter merged products of archiving validation and interpretation of satellite oceanographic(AVISO)data are widely used internationally.Chinese National Satellite Ocean Application Service also released merged altimeter products(ALT MUL)in 2023.However,there are few studies on the quality assessment of ALT MUL.Based on the data of AVISO merged products,Jason3 satellite,tide gauge and drifter buoy,the quality assessment and effect analysis of ALT MUL merged products were carried out by means of error evaluation index,interpolation along rails,velocity inversion and power spectrum.The result shows that the average sea level anomaly(SLA)of ALT MUL is about 2 cm smaller than that of AVISO.And they are consistent with the large-scale characteristics and spatial distribution.These two SLA products are both in accordance with normal distribution.Results indicate a lesser congruence between ALT MUL and Jason3 satellite compared to AVISO.This difference may be attributed to the fact that AVISO products use Jason3 satellite as crosscalibrated reference satellite during the merged process.Comparing the matching effect of the two merged products with the tide gauge and drifter buoy,ALT MUL merged products are superior to AVISO in general.The energy spectral density was calculated by using Jason3 satellite data along the orbit,and the two merged products were interpolated to the data points along the orbit.The effective resolution of AVISO and ALT MUL merged products was 180 km and 210 km respectively through spectral calculation,indicating that AVISO merged products have higher effective resolution.
基金Supported by the National Natural Science Foundation of China(No.52273056)the Science and Technology Development Program of Jilin Province,China(No.YDZJ202501ZYTS305)。
文摘Electrochemical water splitting represents a sustainable technology for hydrogen(H_(2))production.However,its large-scale implementation is hindered by the high overpotentials required for both the cathodic hydrogen evolution reaction(HER)and the anodic oxygen evolution reaction(OER).Transition metal-based catalysts have garnered significant research interest as promising alternatives to noble-metal catalysts,owing to their low cost,tunable composition,and noble-metal-like catalytic activity.Nevertheless,systematic reviews on their application as bifunctional catalysts for overall water splitting(OWS)are still limited.This review comprehensively outlines the principal categories of bifunctional transition metal electrocatalysts derived from electrospun nanofibers(NFs),including metals,oxides,phosphides,sulfides,and carbides.Key strategies for enhancing their catalytic performance are systematically summarized,such as heterointerface engineering,heteroatom doping,metal-nonmetal-metal bridging architectures,and single-atom site design.Finally,current challenges and future research directions are discussed,aiming to provide insightful perspectives for the rational design of high-performance electrocatalysts for OWS.
基金the financial support from the National Natural Science Foundation of China (Nos.21805170,22172093)Natural Science Foundation of Shandong Province (Nos.ZR2023QB219,ZR2021QB161)Qingdao Postdoctoral Innovation Project (No.QDBSH20220202031)。
文摘Herein,we have developed a straightforward wet-chemical method to synthesize a series of Pd-based alloy nanowires(NWs),including Pd Pt NWs,Pd Au NWs,Pd Ir NWs,and Pd Ru NWs,which exhibits high mass activity and turnover frequency(TOF) for HER,surpassing Pt/C by 4.6-fold and 1.5-fold in acidic and alkaline electrolytes,respectively.It also demonstrates high stability in alkaline electrolyte at a current density of 220 m A/cm^(2) for 280 h,highlighting its potential for practical applications under industrial current conditions.Pd Pt NWs exhibited ultrathin structures with head-to-tail kinks and inherent defects,significantly increasing the density of active sites and precisely tuning the electronic structure,which could accelerate reaction kinetics and boost water-splitting electrocatalytic performance.This study highlights the potential of Pd Pt NWs as highly efficient catalysts,offering outstanding catalytic performance and stability for practical applications.
基金supported by a grant(No.CRPG-25-2054)under the Cybersecurity Research and Innovation Pioneers Initiative,provided by the National Cybersecurity Authority(NCA)in the Kingdom of Saudi Arabia.
文摘Split Learning(SL)has been promoted as a promising collaborative machine learning technique designed to address data privacy and resource efficiency.Specifically,neural networks are divided into client and server subnetworks in order to mitigate the exposure of sensitive data and reduce the overhead on client devices,thereby making SL particularly suitable for resource-constrained devices.Although SL prevents the direct transmission of raw data,it does not alleviate entirely the risk of privacy breaches.In fact,the data intermediately transmitted to the server sub-model may include patterns or information that could reveal sensitive data.Moreover,achieving a balance between model utility and data privacy has emerged as a challenging problem.In this article,we propose a novel defense approach that combines:(i)Adversarial learning,and(ii)Network channel pruning.In particular,the proposed adversarial learning approach is specifically designed to reduce the risk of private data exposure while maintaining high performance for the utility task.On the other hand,the suggested channel pruning enables the model to adaptively adjust and reactivate pruned channels while conducting adversarial training.The integration of these two techniques reduces the informativeness of the intermediate data transmitted by the client sub-model,thereby enhancing its robustness against attribute inference attacks without adding significant computational overhead,making it wellsuited for IoT devices,mobile platforms,and Internet of Vehicles(IoV)scenarios.The proposed defense approach was evaluated using EfficientNet-B0,a widely adopted compact model,along with three benchmark datasets.The obtained results showcased its superior defense capability against attribute inference attacks compared to existing state-of-the-art methods.This research’s findings demonstrated the effectiveness of the proposed channel pruning-based adversarial training approach in achieving the intended compromise between utility and privacy within SL frameworks.In fact,the classification accuracy attained by the attackers witnessed a drastic decrease of 70%.
基金supported by the National Natural Science Foundation of China(No.52274304).
文摘Designing a highly active and stable bifunctional catalyst is essential for achieving superior overall water splitting(OWS).In this study,a three-dimensional(3D)core-shell structure Co_(3)S_(4)/CuS@NiFe LDH nanocoral spheres electrocatalyst was constructed on nickel foam(NF)via an interfacial engineering strategy.This 3D core-shell heterostructure maximizes the exposure of active sites,optimizes the charge transport pathway and accelerates gas release rates.The protective shell strategy of NiFe LDH provides favorable stability,which contributes to inhibiting the electrochemical corrosion of the electrocatalyst and mitigating the toxic effects of Cl^(-) and other microorganisms during the seawater splitting process.Moreover,the introduction of NiFe LDH induces a change in the OER mechanism from an adsorption evolution mechanism(AEM)to a lattice oxygen mechanism(LOM),which improves the intrinsic activity of the catalyst.Consequently,Co_(3)S_(4)/CuS@NiFe LDH demonstrates exceptional performance in the oxygen evolution reaction(OER)(η100=251 mV)and in the hydrogen evolution reaction(HER)(η100=254 mV),alongside remarkable stability over 100 h.For OWS,it exhibits a voltage of 1.46 V at 10 mA/cm^(2) and maintain stability for 100 h.Impressively,Co_(3)S_(4)/CuS@NiFe LDH still possesses outstanding activity and stability in natural alkaline seawater.This work proposes interfacial engineering to construct bifunctional catalysts with core-shell heterostructures,providing instructive guidelines for the design of highly efficient electrocatalysts toward seawater electrolysis.
文摘The Internet of Vehicles,or IoV,is expected to lessen pollution,ease traffic,and increase road safety.IoV entities’interconnectedness,however,raises the possibility of cyberattacks,which can have detrimental effects.IoV systems typically send massive volumes of raw data to central servers,which may raise privacy issues.Additionally,model training on IoV devices with limited resources normally leads to slower training times and reduced service quality.We discuss a privacy-preserving Federated Split Learning with Tiny Machine Learning(TinyML)approach,which operates on IoV edge devices without sharing sensitive raw data.Specifically,we focus on integrating split learning(SL)with federated learning(FL)and TinyML models.FL is a decentralisedmachine learning(ML)technique that enables numerous edge devices to train a standard model while retaining data locally collectively.The article intends to thoroughly discuss the architecture and challenges associated with the increasing prevalence of SL in the IoV domain,coupled with FL and TinyML.The approach starts with the IoV learning framework,which includes edge computing,FL,SL,and TinyML,and then proceeds to discuss how these technologies might be integrated.We elucidate the comprehensive operational principles of Federated and split learning by examining and addressingmany challenges.We subsequently examine the integration of SL with FL and various applications of TinyML.Finally,exploring the potential integration of FL and SL with TinyML in the IoV domain is referred to as FSL-TM.It is a superior method for preserving privacy as it conducts model training on individual devices or edge nodes,thereby obviating the necessity for centralised data aggregation,which presents considerable privacy threats.The insights provided aim to help both researchers and practitioners understand the complicated terrain of FL and SL,hence facilitating advancement in this swiftly progressing domain.
基金financially supported from the National Key Research and Development Program of China(2022YFB3803600)the National Natural Science Foundation of China(52301272,22309168,12564025,and 52472205)+7 种基金the Fundamental Research Funds for the Central Universities(CCNU25ZH006)the National College Student Innovation and Entrepreneurship Training Project(202510513082)the Research Program of HBNU(2025X082 and2025Y145)the Foundation of Hubei Key Laboratory of Photoelectric Materials and Devices(PMD202404)the General Program of Open Project of the State Key Laboratory of Precision Welding and Joining of Materials Structures(MSWJ-25M-18)the Key Research Project of Hubei Provincial Department of Education(No.D20252503)the Key Project of Hubei Provincial Natural Science Foundation of China(2025AFD002)the Foundation of National Laboratory of Solid State Microstructures(M37087)。
文摘NiFe layered double hydroxide(NiFe LDH)has emerged as a promising catalyst for the oxygen evolution reaction(OER);however,its hydrogen evolution reaction(HER)activity remains suboptimal due to unfavorable electronic structures,particularly the d-electron density of metal sites,which impede water dissociation and lead to poor hydrogen adsorption/desorption capabilities.Herein,we introduce an efficient cooperative d-electron density regulation(CDDR)engineering to comprehensively optimize the delectron density of NiFe LDH by grafting MoO_(x) -modified NiFe LDH nanosheets onto porous nickel particles(PNPs).The PNPs facilitate d-electron density modulation along the edges of the nanosheets,while the MoO_(x) species enable d-electron density modulation across the plane of the nanosheets,thus cooperatively constructing enriched d-electron density in NiFe LDH.Theoretical studies validate the CDDR process and reveal that the enriched d-electron density accelerates water dissociation and optimizes the hydrogen adsorption behavior of NiFe LDH.As a result,the engineered catalyst exhibits significantly improved HER activity,achieving an ultra-low overpotential of 38 mV at 10 mA cm^(-2)in 1 M KOH.Additionally,the CDDR-optimized catalyst also exhibits good OER performance,demonstrating excellent bifunctional performance for overall water splitting in both alkaline freshwater and seawater electrolytes.This work presents a novel CDDR strategy for engineering NiFe LDH into efficient HER catalysts without compromising its OER activity,potentially paving the way for the development of active and robust electrocatalysts for sustainable energy applications.
基金supported in part by the National Natural Science Foundation of China(NSFC)under Grant 62276109The authors extend their appreciation to the Deanship of Scientific Research at King Saud University for funding this work through the Research Group Project number(ORF-2025-585).
文摘The personalized fine-tuning of large languagemodels(LLMs)on edge devices is severely constrained by limited computation resources.Although split federated learning alleviates on-device burdens,its effectiveness diminishes in few-shot reasoning scenarios due to the low data efficiency of conventional supervised fine-tuning,which leads to excessive communication overhead.To address this,we propose Language-Empowered Split Fine-Tuning(LESFT),a framework that integrates split architectures with a contrastive-inspired fine-tuning paradigm.LESFT simultaneously learns frommultiple logically equivalent but linguistically diverse reasoning chains,providing richer supervisory signals and improving data efficiency.This process-oriented training allows more effective reasoning adaptation with fewer samples.Extensive experiments demonstrate that LESFT consistently outperforms strong baselines such as SplitLoRA in task accuracy.LESFT consistently outperforms strong baselines on GSM8K,CommonsenseQA,and AQUA_RAT,with the largest gains observed on Qwen2.5-3B.These results indicate that LESFT can effectively adapt large language models for reasoning tasks under the computational and communication constraints of edge environments.
基金the Taizhou University,Zhejiang,China for funding(No.T20250101215)the Deanship of research and Graduate Studies at King Khalid University for funding this work through Large Research Project(R.G.P.2/398/46).
文摘Despite intensive research on solar-driven photocatalytic overall water splitting(POWS),the overall efficiencies remain insufficient to meet commercial standards.As a central challenge in realizing this technology mainly lies in the precise tuning and rational designing of highly efficient materials and photocatalytic systems,which is paramount for its unlocking scalable,practical applications.However,novel materials fabrication and advanced photocatalytic systems are essential for overcoming intrinsic limitations of conventional catalysts by enabling this green technology to resolve global energy crisis.Therefore,this review critically explores the engineering developments in POWS process and novel photocatalyst designing,via shifting from simple bandgap engineering to more advanced charge carrier dynamics control via utilizing one/two-step photocatalytic excitation system,surface phase junctions i.e.,Z-scheme and S-scheme heterojunctions,surface modification,morphological tuning,and the role of co-catalysts,to control sluggish kinetics,promote oxygen evolution reaction(OER)and suppress undesirable H2/O2,backward reaction with superior visible light absorption capacity to produce remarkable energy production.Moreover,we critically discuss the recent trend of POWS from a materials discovery phase to demanding engineering and mechanistic optimization phase with viable economic viability,which requires bridging the gap between excellent lab-scale performance to stringent stability,cost,and high efficiency demands of industrial-scale solar fuel production.In addition,the currents challenges and future directions are also enclosed in detail for sustainable energy production.
基金Higher Education Commission(HEC)of Pakistan for financial support under grants#377-IPFP-Ⅱ/Batch-1st/SRGP-NAHE/HEC-2022-27 along with ASIP-Support Award Letter#ASIP/R&D/HEC/2024/10006/83387/127。
文摘This insightful review explores the electrochemical principles and energy potential of electrocatalytic water splitting(EWS).It highlights recent advancements,identifies key challenges,and underscores the pivotal role of EWS in enabling the transition to sustainable energy systems.This work contextualizes the significance of green hydrogen in global decarbonization pathways and examines the historical progression of electrocatalysis.The fundamental thermodynamics and mechanistic pathways governing both the hydrogen and oxygen evolution reactions(HER and OER)are analyzed,highlighting energy barriers and rate-determining steps.Various electrode architectures and electrochemical cell configurations are evaluated,including a comparative assessment of key electrolyzer technologies and their performance characteristics.Furthermore,we critically examine recent advances and persistent limitations across the landscape of electrocatalysts,spanning noble metal-based materials,earth-abundant transition metal compounds,and emerging materials.Design principles and mechanistic insights drawn from electronic structu re modulation,defect engineering,doping strategies,and na noscale morphology control are elucidated to establish robust structure-property-performance relationships.Major challenges including sluggish oxygen evolution kinetics,catalyst degradation mechanisms,and the integration of devices with intermittent renewable energy sources are thoroughly examined.This work also debates advanced strategies such as hybrid photoelectrochemical systems,flexible device architectures,and the direct utilization of non-traditional water sources(e.g.,seawater,wastewater)as promising pathways for future development.Finally,it is specifically distinguished by its critical focus on bridging the gap between fundamental electrocatalysts development and practical system-level integration,addressing the challenges of scalability and deployment under industrially relevant conditions.This comprehensive review provides a strategic outlook and identifies key scientific priorities for optimizing EWS systems toward efficient,robust,and scalable hydrogen generation.
基金supported by Supported by the Scientific Research Foundation for High-Level Talents of Zhoukou Normal University(ZKNUC2024018).
文摘Energy shortage has become one of themost concerning issues in the world today,and improving energy utilization efficiency is a key area of research for experts and scholars worldwide.Small-diameter heat exchangers offer advantages such as reduced material usage,lower refrigerant charge,and compact structure.However,they also face challenges,including increased refrigerant pressure drop and smaller heat transfer area inside the tubes.This paper combines the advantages and disadvantages of both small and large-diameter tubes and proposes a combined-diameter heat exchanger,consisting of large and small diameters,for use in the indoor units of split-type air conditioners.There are relatively few studies in this area.In this paper,A theoretical and numerical computation method is employed to establish a theoretical-numerical calculation model,and its reliability is verified through experiments.Using this model,the optimal combined diameters and flow path design for a combined-diameter heat exchanger using R32 as the working fluid are derived.The results show that the heat transfer performance of all combined diameter configurations improves by 2.79%to 8.26%compared to the baseline design,with the coefficient of performance(COP)increasing from 4.15 to 4.27~4.5.These designs can save copper material,but at the cost of an increase in pressure drop by 66.86%to 131.84%.The scheme IIIH,using R32,is the optimal combined-diameter and flow path configuration that balances both heat transfer performance and economic cost.
基金financially supported by the Hunan Provincial Natural Science Foundation for Distinguished Young Scholars(2025JJ20019)the National Key R&D Program of China(2025YFE0107600)。
文摘Bismuth vanadate(BiVO_(4))is regarded as a promising photoanode for photoelectrochemical(PEC)water splitting.Despite its advantage in band gap and visible-light response,the BiVO_(4)exhibits an unsatisfactory achieving water splitting due to severe charge recombination.Herein,we elucidate an innovative approach involving the incorporation of single Ru atom with a CoFe-LDH cocatalyst(Ru_(0.51)-CoFe-LDH)and integrating it onto the BiVO_(4)semiconductor substrate.The resulting Ru_(0.51)-CoFe-LDH/BiVO_(4)photoanode film demonstrates commendable charge injection efficiency(76%)and charge collection efficiency(100%).Interestingly,the yield of hydrogen and oxygen increases linearly at a stoichiometric ratio of about 2:1,reaching 158.6 and 67.4μmol after140 min of irradiation,respectively.According to experimental characterization and density functional theory calculation,this remarkable performance results from single Ru atoms triggering the electron rearrangement of Ru_(0.51)-CoFe-LDH to engineer active sites and optimize interfacial energetics.Additionally,the negative shift of Ru_(0.51)-CoFe-LDH band edge gives rise to more conspicuous band bending of the n-n junction formed with BiVO_(4),expediting the separation and transfer of photogenerated electron-hole pairs at the interface.This work furnishes a new preparation perspective for PEC water splitting systems to construct single atoms in the semiconductor substrate.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(NRF-2023R1A2C1005419).
文摘Amorphous metal-based catalysts are highly promising for water splitting due to their abundance of unsaturated active sites.Herein,we report a one-step,surfactant-free synthesis of amorphous nickel nanoparticles(NPs)encapsulated in nitrogen-doped carbon shells(A-Ni@NC)via pulsed laser ablation in liquid(PLAL).The synergistic integration of the amorphous Ni core and a defect-rich N-doped carbon shell markedly enhanced the catalytic activities for both the hydrogen evolution reaction(HER)and oxygen evolution reaction(OER),with low overpotentials of 182 mV for HER and 288 mV for OER at 10 mA cm^(-2)in 1.0 m KOH.Furthermore,the bifunctional catalyst achieved a current density of 10 mA cm^(-2)at 1.63 V and retained 98.9%of its initial performance after 100 h of operation.The nitrogen-rich carbon shell not only offered abundant active sites and structural protection but also promoted charge transport.Density functional theory(DFT)calculations revealed that N-doping optimized intermediate adsorption energies,while the amorphous Ni core facilitated efficient electron transfer.This green and scalable synthesis strategy provides a promising platform for developing a wide range of transition metal@N-doped carbon hybrid catalysts for sustainable energy conversion applications.
文摘目的:对比三维多回波恢复梯度回波(3D MERGE)、三维可变反转角快速自旋回波(3D SPACE STIR)序列在腰椎间盘突出症(LDH)检查中的应用效果。方法:选择2020年1月~2022年11月收治的135例LDH患者,回顾性分析患者临床和磁共振成像(MRI)资料,所有患者均接受常规MRI扫描及3D MERGE、3D SPACE STIR序列扫描,对比3D MERGE、3D SPACE STIR序列测量神经根直径的一致性,评价两种序列的图像质量参数[信噪比(SNR)、对比噪声比(CNR)]、图像清晰度评分。结果:3D MERGE和3D SPACE STIR序列测量的L3~S1神经根直径比较差异无统计学意义(P>0.05),且两组序列测量的L3、L4、L5和S1直径均显示出较高相关性(r=0.957,0.986,0.975,0.972,P<0.05);3D MERGE序列的SNR及CNR均高于3D SPACE STIR序列,神经根显示分级、图像清晰度评分优于3D SPACE STIR序列,差异有统计学意义(P<0.05)。结论:3D MERGE、3D SPACE STIR序列在LDH神经根直径测量中具有极高一致性,3D MERGE序列较3D SPACE STIR序列能够更清晰显示神经跟的解剖形态,图像质量更好。
