Geological prospecting and the identification of adverse geological features are essential in tunnel construction,providing critical information to ensure safety and guide engineering decisions.As tunnel projects exte...Geological prospecting and the identification of adverse geological features are essential in tunnel construction,providing critical information to ensure safety and guide engineering decisions.As tunnel projects extend into deeper and more mountainous terrains,engineers face increasingly complex geological conditions,including high water pressure,intense geo-stress,elevated geothermal gradients,and active fault zones.These conditions pose substantial risks such as high-pressure water inrush,largescale collapses,and tunnel boring machine(TBM)blockages.Addressing these challenges requires advanced detection technologies capable of long-distance,high-precision,and intelligent assessments of adverse geology.This paper presents a comprehensive review of recent advancements in tunnel geological ahead prospecting methods.It summarizes the fundamental principles,technical maturity,key challenges,development trends,and real-world applications of various detection techniques.Airborne and semi-airborne geophysical methods enable large-scale reconnaissance for initial surveys in complex terrain.Tunnel-and borehole-based approaches offer high-resolution detection during excavation,including seismic ahead prospecting(SAP),TBM rock-breaking source seismic methods,fulltime-domain tunnel induced polarization(TIP),borehole electrical resistivity,and ground penetrating radar(GPR).To address scenarios involving multiple,coexisting adverse geologies,intelligent inversion and geological identification methods have been developed based on multi-source data fusion and artificial intelligence(AI)techniques.Overall,these advances significantly improve detection range,resolution,and geological characterization capabilities.The methods demonstrate strong adaptability to complex environments and provide reliable subsurface information,supporting safer and more efficient tunnel construction.展开更多
Accurate seasonal precipitation forecasts,especially for extreme events,are crucial to preventing meteorological hazards and their potential impacts on national development,social activity,and security.However,the int...Accurate seasonal precipitation forecasts,especially for extreme events,are crucial to preventing meteorological hazards and their potential impacts on national development,social activity,and security.However,the intensity of summer precipitation is often largely underestimated in many current dynamic models.This study uses a deep learning method called Cycle-Consistent Generative Adversarial Networks(CycleGAN)to improve the seasonal forecasts for June-JulyAugust precipitation in southeastern China by the Nanjing University of Information Science and Technology Climate Forecast System(NUIST-CFS 1.0).The results suggest that the CycleGAN-based model significantly improves the accuracy in predicting the spatiotemporal distribution of summer precipitation compared to the traditional quantile mapping(QM)method.Using the unpaired bias-correction model,we can also obtain advanced forecasts of the frequency,intensity,and duration of extreme precipitation events over the dynamic model predictions.This study expands the potential applications of deep learning models toward improving seasonal precipitation forecasts.展开更多
With the widespread use of Chinese globally, the number of Chinese learners has been increasing, leading to various grammatical errors among beginners. Additionally, as domestic efforts to develop industrial informati...With the widespread use of Chinese globally, the number of Chinese learners has been increasing, leading to various grammatical errors among beginners. Additionally, as domestic efforts to develop industrial information grow, electronic documents have also proliferated. When dealing with numerous electronic documents and texts written by Chinese beginners, manually written texts often contain hidden grammatical errors, posing a significant challenge to traditional manual proofreading. Correcting these grammatical errors is crucial to ensure fluency and readability. However, certain special types of text grammar or logical errors can have a huge impact, and manually proofreading a large number of texts individually is clearly impractical. Consequently, research on text error correction techniques has garnered significant attention in recent years. The advent and advancement of deep learning have paved the way for sequence-to-sequence learning methods to be extensively applied to the task of text error correction. This paper presents a comprehensive analysis of Chinese text grammar error correction technology, elaborates on its current research status, discusses existing problems, proposes preliminary solutions, and conducts experiments using judicial documents as an example. The aim is to provide a feasible research approach for Chinese text error correction technology.展开更多
Photoinduced[2+2]cycloaddition of biomass-derived cycloolefin is a promising approach to synthesize high-energy bio-fuels,however,the conversion efficiency and selectivity are still low.Herein,we provide an acid-promo...Photoinduced[2+2]cycloaddition of biomass-derived cycloolefin is a promising approach to synthesize high-energy bio-fuels,however,the conversion efficiency and selectivity are still low.Herein,we provide an acid-promoted photocycloaddition approach to synthesize a new kind of spiral fuel from biomass-derived cyclohexanone (CHOE) and camphene (CPE).BrΦnsted acids show higher catalytic activity than Lewis acids,and acetic acid (HOAc) possesses the best catalytic performance,with CHOE conversion up to 99.1%.Meanwhile,the HOAc-catalytic effect has been confirmed for[2+2]photocycloaddition of other biomass-derived ketenes and olefins.The catalytic mechanism and dynamics have been investigated,and show that HOAc can bond with C=O groups of CHOE to form H–CHOE complex,which leads to higher light adsorption and longer triplet lifetime.Meanwhile,H–CHOE complex reduces the energy gap between CHOE LUMO and CPE HOMO,shortens the distance of ring-forming atoms,and then decreases the energy barrier (from 103.3 kcal mol^(-1)to 95.8 kcal mol^(-1)) of rate-limiting step.After hydrodeoxygenation,the targeted bio-spiral fuel shows high density of 0.992 g cm^(-3),high neat heat of combustion of 41.89 MJ L^(-1),low kinetic viscosity of 5.69 mm^(2)s^(-1)at 20℃,which is very promising to serve as high-performance aerospace fuel.展开更多
The wireless signals emitted by base stations serve as a vital link connecting people in today’s society and have been occupying an increasingly important role in real life.The development of the Internet of Things(I...The wireless signals emitted by base stations serve as a vital link connecting people in today’s society and have been occupying an increasingly important role in real life.The development of the Internet of Things(IoT)relies on the support of base stations,which provide a solid foundation for achieving a more intelligent way of living.In a specific area,achieving higher signal coverage with fewer base stations has become an urgent problem.Therefore,this article focuses on the effective coverage area of base station signals and proposes a novel Evolutionary Particle Swarm Optimization(EPSO)algorithm based on collective prediction,referred to herein as ECPPSO.Introducing a new strategy called neighbor-based evolution prediction(NEP)addresses the issue of premature convergence often encountered by PSO.ECPPSO also employs a strengthening evolution(SE)strategy to enhance the algorithm’s global search capability and efficiency,ensuring enhanced robustness and a faster convergence speed when solving complex optimization problems.To better adapt to the actual communication needs of base stations,this article conducts simulation experiments by changing the number of base stations.The experimental results demonstrate thatunder the conditionof 50 ormore base stations,ECPPSOconsistently achieves the best coverage rate exceeding 95%,peaking at 99.4400%when the number of base stations reaches 80.These results validate the optimization capability of the ECPPSO algorithm,proving its feasibility and effectiveness.Further ablative experiments and comparisons with other algorithms highlight the advantages of ECPPSO.展开更多
Ensemble learning,a pivotal branch of machine learning,amalgamates multiple base models to enhance the overarching performance of predictive models,capitalising on the diversity and collective wisdom of the ensemble t...Ensemble learning,a pivotal branch of machine learning,amalgamates multiple base models to enhance the overarching performance of predictive models,capitalising on the diversity and collective wisdom of the ensemble to surpass individual models and mitigate overfitting.In this review,a four-layer research framework is established for the research of ensemble learning,which can offer a comprehensive and structured review of ensemble learning from bottom to top.Firstly,this survey commences by introducing fundamental ensemble learning techniques,including bagging,boosting,and stacking,while also exploring the ensemble's diversity.Then,deep ensemble learning and semi-supervised ensemble learning are studied in detail.Furthermore,the utilisation of ensemble learning techniques to navigate challenging datasets,such as imbalanced and highdimensional data,is discussed.The application of ensemble learning techniques across various research domains,including healthcare,transportation,finance,manufacturing,and the Internet,is also examined.The survey concludes by discussing challenges intrinsic to ensemble learning.展开更多
Silicon-based materials are considered as the next generation anode to replace graphite due to their low cost and ultra-high theoretical capacity.However,significant volume expansion and contraction occur during charg...Silicon-based materials are considered as the next generation anode to replace graphite due to their low cost and ultra-high theoretical capacity.However,significant volume expansion and contraction occur during charging and discharging processes,leading to the instability of electrode structure and susceptibility to peeling and damage,limiting its application.Constructing controllable molecular artificial solid electrolyte interphase(CMASEI)is an effective approach to address the commercialization of silicon-based anode materials[1].Improving the performance of silicon-based anodes through CMASEI is a multifaceted outcome.展开更多
Dilatancy is referred to the phenomenon of volume increase that occurs when a material is deformed.Dilatancy theory originated in geomechanics for the study of the behavior of granular materials.Later it is expanded t...Dilatancy is referred to the phenomenon of volume increase that occurs when a material is deformed.Dilatancy theory originated in geomechanics for the study of the behavior of granular materials.Later it is expanded to the case of more brittle materials like rocks when it is subjected to the load of varying effective stress and starts to crack and deform,then named the dilatancy-diffusion hypothesis.This hypothesis was developed to explain the changes in rock volume and pore pressure that occur prior to and during fault slip,which can influence earthquake dynamics.Dilatancy-fluid diffusion is a significant concept in understanding the seismogenic process and has served as the major theoretical pillar for earthquake prediction by its classic definition.This paper starts with the recount of fundamental laboratory experiments on granular materials and rocks,then conducts review and examination of the history for using the dilatancy-diffusion hypothesis to interpret the‘prediction’of the 1975 Haicheng Earthquake and other events.The Haicheng Earthquake is the first significant event to be interpreted with the dilatancy-diffusion hypothesis in the world.As one pivotal figure in the development of the dilatancy-diffusion hypothesis for earthquake prediction Professor Amos Nur of Stanford University worked tirelessly to attract societal attention to this important scientific and humanistic issue.As a deterministic physical model the dilatancy-diffusion hypothesis intrinsically bears the deficit to interpret the stochastic seismogenic process.With the emergence of deep learning and its successful applications to many science and technology fields,we may see a possibility to overcome the shortcoming of the current state of the theory with the addition of empirical statistics to push the operational earthquake forecasting approach with the addition of the physicallyinformed neural networks which adopt the dilatancy-diffusion hypothesis as one of its embedded physical relations,to uplift the seismic risk reduction to a new level for saving lives and reducing the losses.展开更多
Deep-buried tunnels traversing complex hydrogeological zones with clay-sand-filled structures are highly susceptible to water inrush hazards.High ground temperature,high in-situ stress,and highwater pressure render th...Deep-buried tunnels traversing complex hydrogeological zones with clay-sand-filled structures are highly susceptible to water inrush hazards.High ground temperature,high in-situ stress,and highwater pressure render these events a complex thermohydro-mechanical coupling problem.However,current research on water inrush often insufficiently investigates the multi-field coupled instability mechanisms within highly permeable filling media and frequently neglects the influence of temperature.This study aims to investigate the evolutionary mechanism of seepage instability in filling structures that trigger water inrush hazards under the complex conditions of deep-buried tunnels.Laboratory tests were conducted using a large-scale triaxial thermo-hydro-mechanical system,and a DEM-CFD coupled model was established to numerically simulate the seepage process.The influences of temperature,particle size distribution,and confining pressure were analyzed on the seepage characteristics of the filling media.By examining the variations in water inflow rate,discharged clay-sand particle mass,porosity and permeability,we analyzed the entire process of seepage behavior and instability evolution under the thermohydro-mechanical coupling effect.The results show that:(1)Temperature significantly affects water inflow,discharged particle mass,porosity,and permeability.Higher temperatures remarkably increase porosity and permeability,with the maximum permeability coefficient of filling media at 90℃being 1.6 times that at 45℃.(2)The Talbol power index exhibits a positive correlation with water inflow rate and discharged particle mass,while confining pressure is negatively correlated with water inflow rate.(3)For filling materials dominated by clay-sand particles or with favorable gradation,the seepage instability process exhibits distinct phase characteristics,with different stages reflected in changes in water inflow,porosity,and permeability.The experimental results are consistent with the numerical simulation results.(4)In high ground temperature environments,temperature enhances convective heat transfer and energy exchange between water and filling media,thereby accelerating the process of water inrush caused by seepage instability.The findings provide scientific support for risk assessment,early warning,and prevention of water inrush hazards in deep-buried tunnels crossing clay-sand-filled structures.展开更多
In this work,a flexible battery structure is fabricated using soft lithography and three-dimensional(3D) printing technology.Ga_(52.5)Sn_(39.5)Zn_(8) anode material,Bi_(67)In_(33) cathode material,and alkaline hydroge...In this work,a flexible battery structure is fabricated using soft lithography and three-dimensional(3D) printing technology.Ga_(52.5)Sn_(39.5)Zn_(8) anode material,Bi_(67)In_(33) cathode material,and alkaline hydrogel electrolyte are introduced to form the flexible battery.A variety of circuit structures are fabricated to realize the series-parallel integration of different numbers of single cells and achieve the fabrication of batteries with different voltages and powers,with a maximum open-circuit voltage(OCV) of 4.6 V and a maximum output power of 1.193 mW.A reconfigurable soft battery group is proposed,and the regulation of the battery voltage has been realized through the microfluidic perfusion process without the need for an external variable-voltage circuit.We have also fabricated an EGaIn-NaOH microfluidic switch to achieve the control of the light emitting diode(LED).In addition,a wristband with a flexible battery is demonstrated to realize power supply to a liquid crystal display(LCD) with a clock or a temperature sensor.展开更多
It is a challenge to determine the dominant topological characteristics of mechanical properties of adhesive interfaces.In this paper,we used graph theory and molecular dynamics simulation to investigate the influence...It is a challenge to determine the dominant topological characteristics of mechanical properties of adhesive interfaces.In this paper,we used graph theory and molecular dynamics simulation to investigate the influence of topological characteristics on the strength and toughness of highly cross-linked polymer interface systems.Based on the microstructure of the adhesive system,we extracted the dominant topological characteristics,including the connectivity degree(D)that determines the yield strength,and the average node-path(P)and the simple cycles proportions(R)that determine the deformability and load-bearing capacity during the void propagation respectively,which co-determine the toughness.The influence of the wall-effect on the dominant topological characteristics was also analyzed.The results showed that the interfacial yield strength increases with the increase of D,while the toughness increases with the increase of P and R.The wall-effect has a significant influence on D,P,and R.The strong wall-effect causes the enrichment of amino groups near the wall and insufficient cross-linking away from the wall,leading to the lower D and R,i.e.,the lower yield strength and load-bearing capacity during the void propagation.With the attenuation of the wall-effect,the D increases gradually,while the P and the R first increase and then decrease,showing an optimized wall-effect for the toughness of the adhesive interface.This paper reveals the dominant topological characteristics of adhesive interfacial strength and toughness,providing a new way to modulate the mechanical properties of polymer adhesive interface systems.展开更多
(Mg,Fe)SiO_(3) is primarily located in the mantle and has a substantial impact on geophysical and geochemical processes.Here,we employ molecular dynamics simulations to investigate the structural and transport propert...(Mg,Fe)SiO_(3) is primarily located in the mantle and has a substantial impact on geophysical and geochemical processes.Here,we employ molecular dynamics simulations to investigate the structural and transport properties of(Mg,Fe)SiO_(3) with varying iron contents at temperatures up to 5000 K and pressures up to 135 GPa.We thoroughly examine the effects of pressure,temperature,and iron content on the bond lengths,coordination numbers,viscosities,and electrical conductivities of(Mg,Fe)SiO_(3).Our calculations indicate that the increase of pressure leads to the shortening of the O-O and Mg-O bond lengths,while the Si-O bond lengths exhibit the initial increase with pressure up to 40 GPa,after which they are almost unchanged.The coordination numbers of Si transition from four-fold to six-fold and eventually reach eight-fold coordination at 135 GPa.The enhanced pressure causes the decrease of the diffusion coefficients and the increase of the viscosities of(Mg,Fe)SiO_(3).The increased temperatures slightly decrease the coordination numbers and viscosities,as well as obviously increase the diffusion coefficients and electrical conductivities of(Mg,Fe)SiO_(3).Additionally,iron doping facilitates the diffusion of Si and O,reduces the viscosities,and enhances the electrical conductivities of(Mg,Fe)SiO_(3).These findings advance fundamental understanding of the structural and transport properties of(Mg,Fe)SiO_(3) under high temperature and high pressure,which provide novel insights for unraveling the complexities of geological processes within the Earth's mantle.展开更多
In fluorescence flow cytometry,spectral overlap among multiple fluorescent labels cannot be avoided,and thus detected fluorescent intensities need to be compensated.Although fluorescent compensation in flow cytometry ...In fluorescence flow cytometry,spectral overlap among multiple fluorescent labels cannot be avoided,and thus detected fluorescent intensities need to be compensated.Although fluorescent compensation in flow cytometry has been widely used for many years,it still lacks quantitative evaluations to validate its effectiveness.Using a home-developed nine-color fluorescence flow cytometer,this study first obtains calibration curves by assaying gradient concentrations of nine different fluorochromes individually,with the fluorescent intensities of the highest concentrations of each fluorochrome being used to obtain a spillover matrix.Mixed fluorescent solutions are analyzed by flow cytometry in which the obtained fluorescent intensities are compensated by the spillover matrix,translated to specific concentrations based on calibration curve and compared with nominal values.Three mixed solutions of Brilliant Violet 650 and Brilliant Violet 711,of Alexa Fluor 488 and PE,and of Pacific Orange,Alexa Fluor 488,and PE are tested,with fluorescent compensation being observed to reduce excessive signals due to spectral overlap.Specifically,concentration deviations(before vs after compensation)in comparison with nominal values for Brilliant Violet 711 and Alexa Fluor 488 are quantified as 40.6%vs 14.9%and 6.7%vs 1.9%,respectively.The results presented here provide a quantitative reference for fluorescent compensation that can be used to effectively address the issue of spectral overlap in fluorescence flow cytometry.展开更多
Precise droplet manipulation is critical in material synthesis,biochemical detection,and tissue engineering.However,the droplet velocity and volume manipulated by magnetic techniques are restricted owing to the low ma...Precise droplet manipulation is critical in material synthesis,biochemical detection,and tissue engineering.However,the droplet velocity and volume manipulated by magnetic techniques are restricted owing to the low magnetic force exerted on magnetic particles and beads.Furthermore,magnetic particles are prone to contaminate droplets owing to residues and corrosion.To address these issues,this paper proposes a hydrophilic hard-magnetic soft robot(HMSR)with strong magnetic controllability and chemical stability for precise droplet manipulation.A porous HMSR was synthesized by incorporating NdFeB particles and sacrificial sugar particles into an Ecoflex elastomer.Oxygen plasma treatment was applied to make the HMSR become hydrophilic and thus enhance the driving force exerted on droplets.Three forms of droplet manipulation were demonstrated:droplet transport,droplet splitting,and robot–magnet detachment.Theoretical analysis and experimental results revealed that the critical HMSR speed requisite for droplet transport and splitting was inversely proportional to the droplet volume.Notably,a 50μl droplet was transported in a 20 mT magnetic field at a maximum velocity of 200 mm/s.The maximum droplet volume that the HMSR could transport reached 900μl.Benefiting from its chemical stability,HMSR successfully manipulated chemical reactions of acidic and alkaline droplets.Additionally,the HMSR achieved targeted removal of microparticles through droplet adhesion to them.This HMSR with precise droplet manipulation capability holds broad prospects for applications in biochemical detection,material synthesis,and surgical robotics.展开更多
The complexity of cancer therapy has led to the emergence of combination therapy as a promising approach to enhance treatment efficacy and safety.The integration of glutathione(GSH)-activatable two-photon photodynamic...The complexity of cancer therapy has led to the emergence of combination therapy as a promising approach to enhance treatment efficacy and safety.The integration of glutathione(GSH)-activatable two-photon photodynamic therapy(TP-PDT)and chemodynamic therapy(CDT)offers the possibility to advance precision and efficacy in anti-cancer treatments.In this study,a GSH-activatable photosensitizer(PS),namely copper-elsinochrome(CuEC),is synthesized and utilized for combination second nearinfrared(NIR-II)TP-PDT/CDT.The Cu^(2+)acts as a“lock”,suppressing the fluorescence and^(1)O_(2)generation ability of EC in a normal physiological environment(“OFF”state).However,the overexpressed GSH in the tumor microenvironment acts as the“key”,resulting in the release of EC(“ON”state)and Cu^(+)(reduced by GSH).The released EC can be utilized for fluorescence imaging and TP-PDT under NIR-II(λ=1000 nm)two-photon excitation,while Cu+can generate highly toxic hydroxyl radicals(•OH)via Fenton-like reaction for CDT.Additionally,this process consumes GSH and diminishes the tumor’s antioxidant capacity,thereby augmenting the efficacy of combination therapy.The CuEC achieves significant tumor cell ablation in both 2D monolayer cells and 3D multicellular tumor spheres through the combination of NIR-II TP-PDT and CDT.展开更多
In recent years,torrential rain events caused by extratropical cyclones(ETCs)during the boreal midsummer(July-August)in Central and Eastern China have shown an increasing trend.For instence,in August 2024,two ETCs bro...In recent years,torrential rain events caused by extratropical cyclones(ETCs)during the boreal midsummer(July-August)in Central and Eastern China have shown an increasing trend.For instence,in August 2024,two ETCs brought large-scale heavy rainfall to North China,with daily precipitation exceeding 100 mm.Using reanalysis datasets and gridded precipitation data,the ETCs that affected Central and Eastern China during the boreal midsummer from 1981 to 2020 were objectively identified and tracked.ETCs causing precipitation were classified based on maximum daily precipitation,resulting in datasets for ETCs with torrential rain(daily precipitation exceeding 100 mm,referred to as ETC_R100)and heavy rain(daily precipitation exceeding 25 mm,referred to as ETC_R25).Comparative analysis can help highlight the characteristics of ETC_R100.This study compares the spatial distribution,movement paths,weather impacts,large-scale atmospheric circulation,and environmental conditions of these two types of precipitation-related ETCs.The following findings emerged:(1)ETC_R100 is driven by the combined forcing of upper-level troughs and warm-moist airflows at lower levels,exhibiting stronger thermal forcing than ETC_R25.(2)The moisture source for ETC_R100 are the Bay of Bengal and the Northwest Pacific,with moisture transported via the South China Sea.Compared to ETCs with nonextreme rainfall,ETC_R100 is characterized by greater atmospheric instability and better moisture conditions,resulting in higher precipitation intensity.(3)Regardless of the precipitation level,ETCs affected different regions but contributed significantly to precipitation in northern China,accounting for approximately 50%of the total precipitation.The results indicate that ETC_R100 differs significantly from ETCs with varying levels of precipitation in terms of statistical characteristics,weather impact,environmental conditions,and cyclogenesis conditions.展开更多
Ammonia(NH_(3))plays an important role in the world economy and its demand is steadily rising alongside the progress of modern society.The electrocatalytic nitrogen reduction reaction(NRR)is presently regarded as a hi...Ammonia(NH_(3))plays an important role in the world economy and its demand is steadily rising alongside the progress of modern society.The electrocatalytic nitrogen reduction reaction(NRR)is presently regarded as a high-po-tential method for the synthesis of NH_(3).Nev-ertheless,the development of efficient NRR electrocatalysts remains a challenging task.In this study,various transition metal(TM)sin-gle atoms(TM=Sc-Zn,Y-Cd except Tc,and Ta-Pt)anchored onγ-graphyne(γ-GY)are sys-tematically investigated as NRR electrocatalysts using density functional theory(DFT)cal-culations.According to several criteria regarding the adsorption stability of isolated TM sin-gle atoms onγ-graphyne,the adsorption properties of N_(2) on these TM single atoms,the ad-sorption competition between N_(2) and H,and the free energy change in the initial protonation process for N_(2),we find that Os@γ-GY and Re@γ-GY may be suitable electrocatalysts for NRR,and analyze the reasons why the two types of single atoms can well adsorb and activate N_(2) molecules.From the reaction pathways of NRR catalyzed by the two single-atom systems,we further find that NRR is hindered by the step of*NH_(2) hydrogenation to*NH_(3) on Re@γ-GY but can proceed well on Os@γ-GY.Thus,Os@γ-GY behaves best in catalyzing NRR among theγ-GY anchored single atom systems studied.This work has the potential to offer valuable recommendations for the development of novel and highly effective NRR electrocat-alysts.展开更多
Accurate battery health diagnostics are essential for timely maintenance,replacement,and the safe operation of electric vehicles(EVs).For on-road EVs,leveraging operational data for accurate state-of-health(SOH)estima...Accurate battery health diagnostics are essential for timely maintenance,replacement,and the safe operation of electric vehicles(EVs).For on-road EVs,leveraging operational data for accurate state-of-health(SOH)estimation remains challenging due to varied degradation patterns across different driving conditions,vehicle types,and battery chemistries.Thus,developing an on-road-specific efficient feature system and a generalized SOH estimation framework adaptable to diverse EV models and chemistries is essential.To address these limitations,this study proposes a vehicle operational data-driven approach that integrates multi-dimensional feature fusion with a hybrid deep neural network architecture.Specifically,12.83 million on-road data points spanning a wide range of vehicle types and battery chemistries are processed.Capturing representational,driving behavioral,and electrochemical characteristics,this study proposes a three-dimensional feature system comprising shallow,intermediate,and deep descriptors.To tackle challenges posed by long time spans and the limited effectiveness of Transformer models on multivariate inputs,a hybrid framework combining temporal convolutional networks with an enhanced iTransformer is developed,incorporating a differential attention mechanism to suppress attention noise.Experimental results demonstrate that the proposed method achieves high accuracy across two test sets,with an average R^(2),MAPE,MAE,and RMSE of 98.88%,0.35%,0.31%,and 0.40%,respectively.This represents an 81.4%reduction in RMSE compared to the bestperforming baseline.Data scarcity experiments using reduced training data demonstrate that even when the training set is decreased from 80%to 30%,model performance remains stable,with the RMSE remaining below 0.16%.Feature attribution analysis using Shapley additive explanations(SHAP)confirms the indispensability of all three feature dimensions,with driving behavior features being particularly influential.Following feature optimization,training time is reduced by 17.3%.This study presents a robust SOH estimation framework tailored for intelligent cloud battery management systems,proactive maintenance,and the safe operation of EV batteries in practical environments.展开更多
With the rapid development and widespread adoption of Internet of Things(IoT)technology,the innovative concept of the Internet of Vehicles(IoV)has emerged,ushering in a new era of intelligent transportation.Since vehi...With the rapid development and widespread adoption of Internet of Things(IoT)technology,the innovative concept of the Internet of Vehicles(IoV)has emerged,ushering in a new era of intelligent transportation.Since vehicles are mobile entities,they move across different domains and need to communicate with the Roadside Unit(RSU)in various regions.However,open environments are highly susceptible to becoming targets for attackers,posing significant risks of malicious attacks.Therefore,it is crucial to design a secure authentication protocol to ensure the security of communication between vehicles and RSUs,particularly in scenarios where vehicles cross domains.In this paper,we propose a provably secure cross-domain authentication and key agreement protocol for IoV.Our protocol comprises two authentication phases:intra-domain authentication and cross-domain authentication.To ensure the security of our protocol,we conducted rigorous analyses based on the ROR(Real-or-Random)model and Scyther.Finally,we show in-depth comparisons of our protocol with existing ones from both security and performance perspectives,fully demonstrating its security and efficiency.展开更多
Chitosan(CS)-based nanocomposites have been studied in various fields,requiring a more facile and efficient technique to fabricate nanoparticles with customized structures.In this study,Ag@methacrylamide CS/poly(ethyl...Chitosan(CS)-based nanocomposites have been studied in various fields,requiring a more facile and efficient technique to fabricate nanoparticles with customized structures.In this study,Ag@methacrylamide CS/poly(ethylene glycol)diacrylate(Ag@MP)micropatterns are successfully fabricated by femtosecond laser maskless optical projection lithography(Fs-MOPL)for the first time.The formation mechanism of core-shell nanomaterial is demonstrated by the local surface plasmon resonances and the nucleation and growth theory.Amino and hydroxyl groups greatly affect the number of Ag@MP nanocomposites,which is further verified by replacing MCS with methacrylated bovine serum albumin and hyaluronic acid methacryloyl,respectively.Besides,the performance of the surface-enhanced Raman scattering,cytotoxicity,cell proliferation,and antibacterial was investigated on Ag@MP micropatterns.Therefore,the proposed protocol to prepare hydrogel core-shell micropattern by the home-built Fs-MOPL technique is prospective for potential applications in the biomedical and biotechnological fields,such as biosensors,cell imaging,and antimicrobial.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.52021005,52325904,and 51991391)。
文摘Geological prospecting and the identification of adverse geological features are essential in tunnel construction,providing critical information to ensure safety and guide engineering decisions.As tunnel projects extend into deeper and more mountainous terrains,engineers face increasingly complex geological conditions,including high water pressure,intense geo-stress,elevated geothermal gradients,and active fault zones.These conditions pose substantial risks such as high-pressure water inrush,largescale collapses,and tunnel boring machine(TBM)blockages.Addressing these challenges requires advanced detection technologies capable of long-distance,high-precision,and intelligent assessments of adverse geology.This paper presents a comprehensive review of recent advancements in tunnel geological ahead prospecting methods.It summarizes the fundamental principles,technical maturity,key challenges,development trends,and real-world applications of various detection techniques.Airborne and semi-airborne geophysical methods enable large-scale reconnaissance for initial surveys in complex terrain.Tunnel-and borehole-based approaches offer high-resolution detection during excavation,including seismic ahead prospecting(SAP),TBM rock-breaking source seismic methods,fulltime-domain tunnel induced polarization(TIP),borehole electrical resistivity,and ground penetrating radar(GPR).To address scenarios involving multiple,coexisting adverse geologies,intelligent inversion and geological identification methods have been developed based on multi-source data fusion and artificial intelligence(AI)techniques.Overall,these advances significantly improve detection range,resolution,and geological characterization capabilities.The methods demonstrate strong adaptability to complex environments and provide reliable subsurface information,supporting safer and more efficient tunnel construction.
基金supported by the National Key Research and Development Program of China(Grant No.2020YFA0608000)the National Natural Science Foundation of China(Grant No.42030605)+1 种基金CAAI-MindSpore Academic Fund Research Projects(CAAIXSJLJJ2023MindSpore11)the program of China Scholarships Council(No.CXXM2101180001)。
文摘Accurate seasonal precipitation forecasts,especially for extreme events,are crucial to preventing meteorological hazards and their potential impacts on national development,social activity,and security.However,the intensity of summer precipitation is often largely underestimated in many current dynamic models.This study uses a deep learning method called Cycle-Consistent Generative Adversarial Networks(CycleGAN)to improve the seasonal forecasts for June-JulyAugust precipitation in southeastern China by the Nanjing University of Information Science and Technology Climate Forecast System(NUIST-CFS 1.0).The results suggest that the CycleGAN-based model significantly improves the accuracy in predicting the spatiotemporal distribution of summer precipitation compared to the traditional quantile mapping(QM)method.Using the unpaired bias-correction model,we can also obtain advanced forecasts of the frequency,intensity,and duration of extreme precipitation events over the dynamic model predictions.This study expands the potential applications of deep learning models toward improving seasonal precipitation forecasts.
文摘With the widespread use of Chinese globally, the number of Chinese learners has been increasing, leading to various grammatical errors among beginners. Additionally, as domestic efforts to develop industrial information grow, electronic documents have also proliferated. When dealing with numerous electronic documents and texts written by Chinese beginners, manually written texts often contain hidden grammatical errors, posing a significant challenge to traditional manual proofreading. Correcting these grammatical errors is crucial to ensure fluency and readability. However, certain special types of text grammar or logical errors can have a huge impact, and manually proofreading a large number of texts individually is clearly impractical. Consequently, research on text error correction techniques has garnered significant attention in recent years. The advent and advancement of deep learning have paved the way for sequence-to-sequence learning methods to be extensively applied to the task of text error correction. This paper presents a comprehensive analysis of Chinese text grammar error correction technology, elaborates on its current research status, discusses existing problems, proposes preliminary solutions, and conducts experiments using judicial documents as an example. The aim is to provide a feasible research approach for Chinese text error correction technology.
基金the support from National Key R&D Program of China (2021YFC2103704)the National Natural Science Foundation of China (22222808)+4 种基金the Natural Science Foundation of Shandong Province (ZR2023QB152)the Youth Innovation Team Plan of Shandong Province (2022KJ270)the China National Postdoctoral Program for Innovative Talents (BX20240251)the Aeronautical Science Foundation of China (2023Z073048003)the Haihe Laboratory of Sustainable Chemical Transformations。
文摘Photoinduced[2+2]cycloaddition of biomass-derived cycloolefin is a promising approach to synthesize high-energy bio-fuels,however,the conversion efficiency and selectivity are still low.Herein,we provide an acid-promoted photocycloaddition approach to synthesize a new kind of spiral fuel from biomass-derived cyclohexanone (CHOE) and camphene (CPE).BrΦnsted acids show higher catalytic activity than Lewis acids,and acetic acid (HOAc) possesses the best catalytic performance,with CHOE conversion up to 99.1%.Meanwhile,the HOAc-catalytic effect has been confirmed for[2+2]photocycloaddition of other biomass-derived ketenes and olefins.The catalytic mechanism and dynamics have been investigated,and show that HOAc can bond with C=O groups of CHOE to form H–CHOE complex,which leads to higher light adsorption and longer triplet lifetime.Meanwhile,H–CHOE complex reduces the energy gap between CHOE LUMO and CPE HOMO,shortens the distance of ring-forming atoms,and then decreases the energy barrier (from 103.3 kcal mol^(-1)to 95.8 kcal mol^(-1)) of rate-limiting step.After hydrodeoxygenation,the targeted bio-spiral fuel shows high density of 0.992 g cm^(-3),high neat heat of combustion of 41.89 MJ L^(-1),low kinetic viscosity of 5.69 mm^(2)s^(-1)at 20℃,which is very promising to serve as high-performance aerospace fuel.
基金supported by the National Natural Science Foundation of China(Nos.62272418,62102058)Basic Public Welfare Research Program of Zhejiang Province(No.LGG18E050011)the Major Open Project of Key Laboratory for Advanced Design and Intelligent Computing of the Ministry of Education under Grant ADIC2023ZD001,National Undergraduate Training Program on Innovation and Entrepreneurship(No.202410345054).
文摘The wireless signals emitted by base stations serve as a vital link connecting people in today’s society and have been occupying an increasingly important role in real life.The development of the Internet of Things(IoT)relies on the support of base stations,which provide a solid foundation for achieving a more intelligent way of living.In a specific area,achieving higher signal coverage with fewer base stations has become an urgent problem.Therefore,this article focuses on the effective coverage area of base station signals and proposes a novel Evolutionary Particle Swarm Optimization(EPSO)algorithm based on collective prediction,referred to herein as ECPPSO.Introducing a new strategy called neighbor-based evolution prediction(NEP)addresses the issue of premature convergence often encountered by PSO.ECPPSO also employs a strengthening evolution(SE)strategy to enhance the algorithm’s global search capability and efficiency,ensuring enhanced robustness and a faster convergence speed when solving complex optimization problems.To better adapt to the actual communication needs of base stations,this article conducts simulation experiments by changing the number of base stations.The experimental results demonstrate thatunder the conditionof 50 ormore base stations,ECPPSOconsistently achieves the best coverage rate exceeding 95%,peaking at 99.4400%when the number of base stations reaches 80.These results validate the optimization capability of the ECPPSO algorithm,proving its feasibility and effectiveness.Further ablative experiments and comparisons with other algorithms highlight the advantages of ECPPSO.
基金supported in part by National Natural Science Foundation of China No.92467109,U21A20478National Key R&D Program of China 2023YFA1011601the Major Key Project of PCL(Grant PCL2024A05).
文摘Ensemble learning,a pivotal branch of machine learning,amalgamates multiple base models to enhance the overarching performance of predictive models,capitalising on the diversity and collective wisdom of the ensemble to surpass individual models and mitigate overfitting.In this review,a four-layer research framework is established for the research of ensemble learning,which can offer a comprehensive and structured review of ensemble learning from bottom to top.Firstly,this survey commences by introducing fundamental ensemble learning techniques,including bagging,boosting,and stacking,while also exploring the ensemble's diversity.Then,deep ensemble learning and semi-supervised ensemble learning are studied in detail.Furthermore,the utilisation of ensemble learning techniques to navigate challenging datasets,such as imbalanced and highdimensional data,is discussed.The application of ensemble learning techniques across various research domains,including healthcare,transportation,finance,manufacturing,and the Internet,is also examined.The survey concludes by discussing challenges intrinsic to ensemble learning.
基金supported by the Nanxun Scholars Program for Young Scholars of ZJWEU(No.RC2023021315)the start-up funding for Scientific Research for High-level Talents(No.88106324004)the National Natural Science Foundation of China(No.62304070).
文摘Silicon-based materials are considered as the next generation anode to replace graphite due to their low cost and ultra-high theoretical capacity.However,significant volume expansion and contraction occur during charging and discharging processes,leading to the instability of electrode structure and susceptibility to peeling and damage,limiting its application.Constructing controllable molecular artificial solid electrolyte interphase(CMASEI)is an effective approach to address the commercialization of silicon-based anode materials[1].Improving the performance of silicon-based anodes through CMASEI is a multifaceted outcome.
基金sponsored by the National Research Foundation of Korea(RS-2023-00220913).
文摘Dilatancy is referred to the phenomenon of volume increase that occurs when a material is deformed.Dilatancy theory originated in geomechanics for the study of the behavior of granular materials.Later it is expanded to the case of more brittle materials like rocks when it is subjected to the load of varying effective stress and starts to crack and deform,then named the dilatancy-diffusion hypothesis.This hypothesis was developed to explain the changes in rock volume and pore pressure that occur prior to and during fault slip,which can influence earthquake dynamics.Dilatancy-fluid diffusion is a significant concept in understanding the seismogenic process and has served as the major theoretical pillar for earthquake prediction by its classic definition.This paper starts with the recount of fundamental laboratory experiments on granular materials and rocks,then conducts review and examination of the history for using the dilatancy-diffusion hypothesis to interpret the‘prediction’of the 1975 Haicheng Earthquake and other events.The Haicheng Earthquake is the first significant event to be interpreted with the dilatancy-diffusion hypothesis in the world.As one pivotal figure in the development of the dilatancy-diffusion hypothesis for earthquake prediction Professor Amos Nur of Stanford University worked tirelessly to attract societal attention to this important scientific and humanistic issue.As a deterministic physical model the dilatancy-diffusion hypothesis intrinsically bears the deficit to interpret the stochastic seismogenic process.With the emergence of deep learning and its successful applications to many science and technology fields,we may see a possibility to overcome the shortcoming of the current state of the theory with the addition of empirical statistics to push the operational earthquake forecasting approach with the addition of the physicallyinformed neural networks which adopt the dilatancy-diffusion hypothesis as one of its embedded physical relations,to uplift the seismic risk reduction to a new level for saving lives and reducing the losses.
基金funded by National Natural Science Foundation of China(Grant No.52278404)Taishan Young Scholar Program of Shandong Province(Grant No.tsqn202103002),which collectively funded this project。
文摘Deep-buried tunnels traversing complex hydrogeological zones with clay-sand-filled structures are highly susceptible to water inrush hazards.High ground temperature,high in-situ stress,and highwater pressure render these events a complex thermohydro-mechanical coupling problem.However,current research on water inrush often insufficiently investigates the multi-field coupled instability mechanisms within highly permeable filling media and frequently neglects the influence of temperature.This study aims to investigate the evolutionary mechanism of seepage instability in filling structures that trigger water inrush hazards under the complex conditions of deep-buried tunnels.Laboratory tests were conducted using a large-scale triaxial thermo-hydro-mechanical system,and a DEM-CFD coupled model was established to numerically simulate the seepage process.The influences of temperature,particle size distribution,and confining pressure were analyzed on the seepage characteristics of the filling media.By examining the variations in water inflow rate,discharged clay-sand particle mass,porosity and permeability,we analyzed the entire process of seepage behavior and instability evolution under the thermohydro-mechanical coupling effect.The results show that:(1)Temperature significantly affects water inflow,discharged particle mass,porosity,and permeability.Higher temperatures remarkably increase porosity and permeability,with the maximum permeability coefficient of filling media at 90℃being 1.6 times that at 45℃.(2)The Talbol power index exhibits a positive correlation with water inflow rate and discharged particle mass,while confining pressure is negatively correlated with water inflow rate.(3)For filling materials dominated by clay-sand particles or with favorable gradation,the seepage instability process exhibits distinct phase characteristics,with different stages reflected in changes in water inflow,porosity,and permeability.The experimental results are consistent with the numerical simulation results.(4)In high ground temperature environments,temperature enhances convective heat transfer and energy exchange between water and filling media,thereby accelerating the process of water inrush caused by seepage instability.The findings provide scientific support for risk assessment,early warning,and prevention of water inrush hazards in deep-buried tunnels crossing clay-sand-filled structures.
基金supported by the Science and Technology Program from State Grid Corporation of China through the Development of Flexible Liquid Metal Based Micro-Sensor with Anti-Electromagnetic Interference Ability for Power Engineering Applications under Grant 5700-202155453A-00-00。
文摘In this work,a flexible battery structure is fabricated using soft lithography and three-dimensional(3D) printing technology.Ga_(52.5)Sn_(39.5)Zn_(8) anode material,Bi_(67)In_(33) cathode material,and alkaline hydrogel electrolyte are introduced to form the flexible battery.A variety of circuit structures are fabricated to realize the series-parallel integration of different numbers of single cells and achieve the fabrication of batteries with different voltages and powers,with a maximum open-circuit voltage(OCV) of 4.6 V and a maximum output power of 1.193 mW.A reconfigurable soft battery group is proposed,and the regulation of the battery voltage has been realized through the microfluidic perfusion process without the need for an external variable-voltage circuit.We have also fabricated an EGaIn-NaOH microfluidic switch to achieve the control of the light emitting diode(LED).In addition,a wristband with a flexible battery is demonstrated to realize power supply to a liquid crystal display(LCD) with a clock or a temperature sensor.
基金supported by the National Key R&D Program of China(Grant No.2021YFA0719200)the National Natural Science Foundation of China(Grant Nos.12272391,12232020,and 11672314)the CAS Project for Young Scientists in Basic Research(Grant No.YSBR-096).
文摘It is a challenge to determine the dominant topological characteristics of mechanical properties of adhesive interfaces.In this paper,we used graph theory and molecular dynamics simulation to investigate the influence of topological characteristics on the strength and toughness of highly cross-linked polymer interface systems.Based on the microstructure of the adhesive system,we extracted the dominant topological characteristics,including the connectivity degree(D)that determines the yield strength,and the average node-path(P)and the simple cycles proportions(R)that determine the deformability and load-bearing capacity during the void propagation respectively,which co-determine the toughness.The influence of the wall-effect on the dominant topological characteristics was also analyzed.The results showed that the interfacial yield strength increases with the increase of D,while the toughness increases with the increase of P and R.The wall-effect has a significant influence on D,P,and R.The strong wall-effect causes the enrichment of amino groups near the wall and insufficient cross-linking away from the wall,leading to the lower D and R,i.e.,the lower yield strength and load-bearing capacity during the void propagation.With the attenuation of the wall-effect,the D increases gradually,while the P and the R first increase and then decrease,showing an optimized wall-effect for the toughness of the adhesive interface.This paper reveals the dominant topological characteristics of adhesive interfacial strength and toughness,providing a new way to modulate the mechanical properties of polymer adhesive interface systems.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.12174352 and 12111530103)the Fundamental Research Funds for the Central Universities,China University of Geosciences(Wuhan)(Grant No.G1323523065)。
文摘(Mg,Fe)SiO_(3) is primarily located in the mantle and has a substantial impact on geophysical and geochemical processes.Here,we employ molecular dynamics simulations to investigate the structural and transport properties of(Mg,Fe)SiO_(3) with varying iron contents at temperatures up to 5000 K and pressures up to 135 GPa.We thoroughly examine the effects of pressure,temperature,and iron content on the bond lengths,coordination numbers,viscosities,and electrical conductivities of(Mg,Fe)SiO_(3).Our calculations indicate that the increase of pressure leads to the shortening of the O-O and Mg-O bond lengths,while the Si-O bond lengths exhibit the initial increase with pressure up to 40 GPa,after which they are almost unchanged.The coordination numbers of Si transition from four-fold to six-fold and eventually reach eight-fold coordination at 135 GPa.The enhanced pressure causes the decrease of the diffusion coefficients and the increase of the viscosities of(Mg,Fe)SiO_(3).The increased temperatures slightly decrease the coordination numbers and viscosities,as well as obviously increase the diffusion coefficients and electrical conductivities of(Mg,Fe)SiO_(3).Additionally,iron doping facilitates the diffusion of Si and O,reduces the viscosities,and enhances the electrical conductivities of(Mg,Fe)SiO_(3).These findings advance fundamental understanding of the structural and transport properties of(Mg,Fe)SiO_(3) under high temperature and high pressure,which provide novel insights for unraveling the complexities of geological processes within the Earth's mantle.
基金support from the National Natural Science Foundation of China(Grant Nos.62331025 and 62121003).
文摘In fluorescence flow cytometry,spectral overlap among multiple fluorescent labels cannot be avoided,and thus detected fluorescent intensities need to be compensated.Although fluorescent compensation in flow cytometry has been widely used for many years,it still lacks quantitative evaluations to validate its effectiveness.Using a home-developed nine-color fluorescence flow cytometer,this study first obtains calibration curves by assaying gradient concentrations of nine different fluorochromes individually,with the fluorescent intensities of the highest concentrations of each fluorochrome being used to obtain a spillover matrix.Mixed fluorescent solutions are analyzed by flow cytometry in which the obtained fluorescent intensities are compensated by the spillover matrix,translated to specific concentrations based on calibration curve and compared with nominal values.Three mixed solutions of Brilliant Violet 650 and Brilliant Violet 711,of Alexa Fluor 488 and PE,and of Pacific Orange,Alexa Fluor 488,and PE are tested,with fluorescent compensation being observed to reduce excessive signals due to spectral overlap.Specifically,concentration deviations(before vs after compensation)in comparison with nominal values for Brilliant Violet 711 and Alexa Fluor 488 are quantified as 40.6%vs 14.9%and 6.7%vs 1.9%,respectively.The results presented here provide a quantitative reference for fluorescent compensation that can be used to effectively address the issue of spectral overlap in fluorescence flow cytometry.
基金supported by the Science and Technology Program from the State Grid Corporation of China(Grant No.5700-202155453A-0-0-00):“Development of flexible liquid metal based micro-sensor with anti-electromagnetic interference ability for power engineering applications.”。
文摘Precise droplet manipulation is critical in material synthesis,biochemical detection,and tissue engineering.However,the droplet velocity and volume manipulated by magnetic techniques are restricted owing to the low magnetic force exerted on magnetic particles and beads.Furthermore,magnetic particles are prone to contaminate droplets owing to residues and corrosion.To address these issues,this paper proposes a hydrophilic hard-magnetic soft robot(HMSR)with strong magnetic controllability and chemical stability for precise droplet manipulation.A porous HMSR was synthesized by incorporating NdFeB particles and sacrificial sugar particles into an Ecoflex elastomer.Oxygen plasma treatment was applied to make the HMSR become hydrophilic and thus enhance the driving force exerted on droplets.Three forms of droplet manipulation were demonstrated:droplet transport,droplet splitting,and robot–magnet detachment.Theoretical analysis and experimental results revealed that the critical HMSR speed requisite for droplet transport and splitting was inversely proportional to the droplet volume.Notably,a 50μl droplet was transported in a 20 mT magnetic field at a maximum velocity of 200 mm/s.The maximum droplet volume that the HMSR could transport reached 900μl.Benefiting from its chemical stability,HMSR successfully manipulated chemical reactions of acidic and alkaline droplets.Additionally,the HMSR achieved targeted removal of microparticles through droplet adhesion to them.This HMSR with precise droplet manipulation capability holds broad prospects for applications in biochemical detection,material synthesis,and surgical robotics.
基金supported by the project of the National Key Research and Development Program of China(No.2022YFA1207600)the National Natural Science Foundation of China(Nos.62005294,62375272)TIPC Director’s Fund.
文摘The complexity of cancer therapy has led to the emergence of combination therapy as a promising approach to enhance treatment efficacy and safety.The integration of glutathione(GSH)-activatable two-photon photodynamic therapy(TP-PDT)and chemodynamic therapy(CDT)offers the possibility to advance precision and efficacy in anti-cancer treatments.In this study,a GSH-activatable photosensitizer(PS),namely copper-elsinochrome(CuEC),is synthesized and utilized for combination second nearinfrared(NIR-II)TP-PDT/CDT.The Cu^(2+)acts as a“lock”,suppressing the fluorescence and^(1)O_(2)generation ability of EC in a normal physiological environment(“OFF”state).However,the overexpressed GSH in the tumor microenvironment acts as the“key”,resulting in the release of EC(“ON”state)and Cu^(+)(reduced by GSH).The released EC can be utilized for fluorescence imaging and TP-PDT under NIR-II(λ=1000 nm)two-photon excitation,while Cu+can generate highly toxic hydroxyl radicals(•OH)via Fenton-like reaction for CDT.Additionally,this process consumes GSH and diminishes the tumor’s antioxidant capacity,thereby augmenting the efficacy of combination therapy.The CuEC achieves significant tumor cell ablation in both 2D monolayer cells and 3D multicellular tumor spheres through the combination of NIR-II TP-PDT and CDT.
基金National Natural Science Foundation of China(42375014,42088101,42030605)Joint Research Project for Meteorological Capacity Improvement(24NLTSZ010)Young Elite Scientists Sponsorship Program by BAST(BYESS2023205)。
文摘In recent years,torrential rain events caused by extratropical cyclones(ETCs)during the boreal midsummer(July-August)in Central and Eastern China have shown an increasing trend.For instence,in August 2024,two ETCs brought large-scale heavy rainfall to North China,with daily precipitation exceeding 100 mm.Using reanalysis datasets and gridded precipitation data,the ETCs that affected Central and Eastern China during the boreal midsummer from 1981 to 2020 were objectively identified and tracked.ETCs causing precipitation were classified based on maximum daily precipitation,resulting in datasets for ETCs with torrential rain(daily precipitation exceeding 100 mm,referred to as ETC_R100)and heavy rain(daily precipitation exceeding 25 mm,referred to as ETC_R25).Comparative analysis can help highlight the characteristics of ETC_R100.This study compares the spatial distribution,movement paths,weather impacts,large-scale atmospheric circulation,and environmental conditions of these two types of precipitation-related ETCs.The following findings emerged:(1)ETC_R100 is driven by the combined forcing of upper-level troughs and warm-moist airflows at lower levels,exhibiting stronger thermal forcing than ETC_R25.(2)The moisture source for ETC_R100 are the Bay of Bengal and the Northwest Pacific,with moisture transported via the South China Sea.Compared to ETCs with nonextreme rainfall,ETC_R100 is characterized by greater atmospheric instability and better moisture conditions,resulting in higher precipitation intensity.(3)Regardless of the precipitation level,ETCs affected different regions but contributed significantly to precipitation in northern China,accounting for approximately 50%of the total precipitation.The results indicate that ETC_R100 differs significantly from ETCs with varying levels of precipitation in terms of statistical characteristics,weather impact,environmental conditions,and cyclogenesis conditions.
基金supported by the National Natural Science Foundation of China(No.21825302)the Strate-gic Priority Research Program of the Chinese Academy of Sciences(XDB0450101)USTC-SCC,SCCAS,Tianjin,and Shanghai Supercomputer Centers.
文摘Ammonia(NH_(3))plays an important role in the world economy and its demand is steadily rising alongside the progress of modern society.The electrocatalytic nitrogen reduction reaction(NRR)is presently regarded as a high-po-tential method for the synthesis of NH_(3).Nev-ertheless,the development of efficient NRR electrocatalysts remains a challenging task.In this study,various transition metal(TM)sin-gle atoms(TM=Sc-Zn,Y-Cd except Tc,and Ta-Pt)anchored onγ-graphyne(γ-GY)are sys-tematically investigated as NRR electrocatalysts using density functional theory(DFT)cal-culations.According to several criteria regarding the adsorption stability of isolated TM sin-gle atoms onγ-graphyne,the adsorption properties of N_(2) on these TM single atoms,the ad-sorption competition between N_(2) and H,and the free energy change in the initial protonation process for N_(2),we find that Os@γ-GY and Re@γ-GY may be suitable electrocatalysts for NRR,and analyze the reasons why the two types of single atoms can well adsorb and activate N_(2) molecules.From the reaction pathways of NRR catalyzed by the two single-atom systems,we further find that NRR is hindered by the step of*NH_(2) hydrogenation to*NH_(3) on Re@γ-GY but can proceed well on Os@γ-GY.Thus,Os@γ-GY behaves best in catalyzing NRR among theγ-GY anchored single atom systems studied.This work has the potential to offer valuable recommendations for the development of novel and highly effective NRR electrocat-alysts.
基金supported by the National Key Research and Development Program of China(2024YFE0115800)the Department of Science and Technology of Guangdong Province(2023ZT10L145)。
文摘Accurate battery health diagnostics are essential for timely maintenance,replacement,and the safe operation of electric vehicles(EVs).For on-road EVs,leveraging operational data for accurate state-of-health(SOH)estimation remains challenging due to varied degradation patterns across different driving conditions,vehicle types,and battery chemistries.Thus,developing an on-road-specific efficient feature system and a generalized SOH estimation framework adaptable to diverse EV models and chemistries is essential.To address these limitations,this study proposes a vehicle operational data-driven approach that integrates multi-dimensional feature fusion with a hybrid deep neural network architecture.Specifically,12.83 million on-road data points spanning a wide range of vehicle types and battery chemistries are processed.Capturing representational,driving behavioral,and electrochemical characteristics,this study proposes a three-dimensional feature system comprising shallow,intermediate,and deep descriptors.To tackle challenges posed by long time spans and the limited effectiveness of Transformer models on multivariate inputs,a hybrid framework combining temporal convolutional networks with an enhanced iTransformer is developed,incorporating a differential attention mechanism to suppress attention noise.Experimental results demonstrate that the proposed method achieves high accuracy across two test sets,with an average R^(2),MAPE,MAE,and RMSE of 98.88%,0.35%,0.31%,and 0.40%,respectively.This represents an 81.4%reduction in RMSE compared to the bestperforming baseline.Data scarcity experiments using reduced training data demonstrate that even when the training set is decreased from 80%to 30%,model performance remains stable,with the RMSE remaining below 0.16%.Feature attribution analysis using Shapley additive explanations(SHAP)confirms the indispensability of all three feature dimensions,with driving behavior features being particularly influential.Following feature optimization,training time is reduced by 17.3%.This study presents a robust SOH estimation framework tailored for intelligent cloud battery management systems,proactive maintenance,and the safe operation of EV batteries in practical environments.
基金supported by the Startup Foundation for Introducing Talent of Nanjing University of Information Science and Technology and Natural Science Foundation of Shandong Province,China(Grant no.ZR202111230202).
文摘With the rapid development and widespread adoption of Internet of Things(IoT)technology,the innovative concept of the Internet of Vehicles(IoV)has emerged,ushering in a new era of intelligent transportation.Since vehicles are mobile entities,they move across different domains and need to communicate with the Roadside Unit(RSU)in various regions.However,open environments are highly susceptible to becoming targets for attackers,posing significant risks of malicious attacks.Therefore,it is crucial to design a secure authentication protocol to ensure the security of communication between vehicles and RSUs,particularly in scenarios where vehicles cross domains.In this paper,we propose a provably secure cross-domain authentication and key agreement protocol for IoV.Our protocol comprises two authentication phases:intra-domain authentication and cross-domain authentication.To ensure the security of our protocol,we conducted rigorous analyses based on the ROR(Real-or-Random)model and Scyther.Finally,we show in-depth comparisons of our protocol with existing ones from both security and performance perspectives,fully demonstrating its security and efficiency.
基金the National Natural Science Foundation of China(NSFC,Grant Nos.61975213,61475164,51901234,and 61205194)National Key R&D Program of China(Grant Nos.2017YFB1104300and 2016YFA0200500)+2 种基金International Partnership Program of Chinese Academy of Sciences(GJHZ2021130)Cooperative R&D Projects between Austria,FFG and China,CAS(GJHZ1720)supported by JSPS Bilateral Program Number JPJSBP120217203。
文摘Chitosan(CS)-based nanocomposites have been studied in various fields,requiring a more facile and efficient technique to fabricate nanoparticles with customized structures.In this study,Ag@methacrylamide CS/poly(ethylene glycol)diacrylate(Ag@MP)micropatterns are successfully fabricated by femtosecond laser maskless optical projection lithography(Fs-MOPL)for the first time.The formation mechanism of core-shell nanomaterial is demonstrated by the local surface plasmon resonances and the nucleation and growth theory.Amino and hydroxyl groups greatly affect the number of Ag@MP nanocomposites,which is further verified by replacing MCS with methacrylated bovine serum albumin and hyaluronic acid methacryloyl,respectively.Besides,the performance of the surface-enhanced Raman scattering,cytotoxicity,cell proliferation,and antibacterial was investigated on Ag@MP micropatterns.Therefore,the proposed protocol to prepare hydrogel core-shell micropattern by the home-built Fs-MOPL technique is prospective for potential applications in the biomedical and biotechnological fields,such as biosensors,cell imaging,and antimicrobial.
