The magnetic properties and Kondo effect in Ce3TiBi5 with a quasi-one-dimensional structure were investigated using in situ high-pressure resistivity measurements up to 48 GPa.At ambient pressure,Ce_(3)TiBi_(5) underg...The magnetic properties and Kondo effect in Ce3TiBi5 with a quasi-one-dimensional structure were investigated using in situ high-pressure resistivity measurements up to 48 GPa.At ambient pressure,Ce_(3)TiBi_(5) undergoes an antiferromagnetic(AFM)transition at T_(N)∼5 K.Under high pressures within 8.9 GPa,we find that Kondo scattering contributes differently to the high-temperature resistance,R(T),depending on the applied current direction,demonstrating a significantly anisotropic Kondo effect.The complete P–T phase diagram has been constructed,in which the pressure dependence of T_(N) exhibits a dome-like shape.The AFM order remains robust under pressure,even when the coherence temperature T^(*) far exceeds 300 K.We attribute the observed anisotropic Kondo effect and the robust AFM to the underlying anisotropy in electronic hybridization under high pressure.展开更多
As the proportion of composite materials used in aircraft continues to increase, the electromagnetic Shielding Effectiveness (SE) of these materials becomes a critical factor in the electromagnetic safety design of ai...As the proportion of composite materials used in aircraft continues to increase, the electromagnetic Shielding Effectiveness (SE) of these materials becomes a critical factor in the electromagnetic safety design of aircraft structures. The assessment of electromagnetic SE for Slotted Composite Structures(SCSs) is particularly challenging due to their complex geometries and there remains a lack of suitable models for accurately predicting the SE performance of these intricate configurations. To address this issue, this paper introduces SCS-Net, a Deep Neural Network (DNN) method designed to accurately predict the SE of SCS. This method considers the impacts of various structural parameters, material properties and incident wave parameters on the SE of SCSs. In order to better model the SCS, an improved Nicolson-Ross-Weir (NRW) method is introduced in this paper to provide an equivalent flat structure for the SCS and to calculate the electromagnetic parameters of the equivalent structure. Additionally, the prediction of SE via DNNs is limited by insufficient test data, which hinders support for large-sample training. To address the issue of limited measured data, this paper develops a Measurement-Computation Fusion (MCF) dataset construction method. The predictions based on the simulation results show that the proposed method maintains an error of less than 0.07 dB within the 8–10 GHz frequency range. Furthermore, a new loss function based on the weighted L1-norm is established to improve the prediction accuracy for these parameters. Compared with traditional loss functions, the new loss function reduces the maximum prediction error for equivalent electromagnetic parameters by 47%. This method significantly improves the prediction accuracy of SCS-Net for measured data, with a maximum improvement of 23.88%. These findings demonstrate that the proposed method enables precise SE prediction and design for composite structures while reducing the number of test samples needed.展开更多
Climate change is significantly influenced by both clouds and Earth’s surface temperature(EST).While numerous studies have investigated clouds and EST separately,the extent of clouds’impact on EST remains unclear.Ba...Climate change is significantly influenced by both clouds and Earth’s surface temperature(EST).While numerous studies have investigated clouds and EST separately,the extent of clouds’impact on EST remains unclear.Based on the inspiration and limitation of cloud radiative effect(CRE),this study provides a pioneering attempt to propose a novel indicator,cloud radiative effect on surface temperature(CREST),aiming to quantify how clouds affect EST globally while also analyzing the physical mechanism.Using reanalysis and remotely sensed data,a phased machine learning scheme in combination of surface energy balance theory is proposed to estimate EST under all-sky and hypothetical clear-sky conditions in stages,thereby estimating the newly defined CREST by subtracting the hypothetical clear-sky EST from the all-sky EST.The inter-annual experiments reveal the significant spatial heterogeneity in CREST across land,ocean,and ice/snow regions.As a global offset of the heterogeneity,clouds exhibit a net warming effect on global surface temperature on an annual scale(e.g.,0.26 K in 1981),despite their ability to block sunlight.However,the net warming effect has gradually weakened to nearly zero over the past four decades(e.g.,only 0.06 K in 2021),and it’s even possible to transform into a cooling effect,which might be good news for mitigating the global warming.展开更多
With the continuous upgrading of traditional manufacturing industries and the rapid rise of emerging technology fields,the performance requirements for the permanent magnet synchronous motors(PMSMs)have become higher ...With the continuous upgrading of traditional manufacturing industries and the rapid rise of emerging technology fields,the performance requirements for the permanent magnet synchronous motors(PMSMs)have become higher and higher.The importance of fast and accurate electromagnetic thermal coupling analysis of such motors becomes more and more prominent.In view of this,the surfacemounted PMSM(SPMSM)equipped with unequally thick magnetic poles is taken as the main object and its electromagnetic thermal coupling analytical model(ETc AM)is investigated.First,the electromagnetic analytical model(EAM)is studied based on the modified subdomain method.It realizes the fast calculation of key electromagnetic characteristics.Subsequently,the 3D thermal analytical model(TAM)is developed by combining the EAM,the lumped parameter thermal network method(LPTNM),and the partial differential equation of heat flux.It realizes the fast calculation of key thermal characteristics in 3D space.Further,the information transfer channel between EAM and TAM is built with reference to the intrinsic connection between electromagnetic field and temperature field.Thereby,the novel ETcAM is proposed to realize the fast and accurate prediction of electromagnetic and temperature fields.Besides,ETcAM has a lot to commend it.One is that it well accounts for the complex structure,saturation,and heat exchange behavior.Second,it saves a lot of computer resources.It offers boundless possibilities for initial design,scheme evaluation,and optimization of motors.Finally,the validity,accuracy,and practicality of this study are verified by simulation and experiment.展开更多
This study examines the influence of magnetic field and temperature on the transient voltage of a polycrystalline silicon radial junction solar cell in a dynamic regime under multispectral illumination. Radial junctio...This study examines the influence of magnetic field and temperature on the transient voltage of a polycrystalline silicon radial junction solar cell in a dynamic regime under multispectral illumination. Radial junction solar cells represent a major advancement in photovoltaic technologies, as they optimize light absorption and charge collection efficiency. The focus is on the impact of the magnetic field and temperature on the decay of transient voltage, which provides crucial information on recombination processes and the lifetime of minority carriers. The results reveal that the magnetic field tends to increase the transient voltage by directly affecting the transient electron density. Indeed, for B > 7 × 10−5 T, the magnetic field prolongs the relaxation time by increasing the transient voltage amplitude. Additionally, rising temperatures accelerate (ranging from 290 K to 450 K) recombination processes, thereby reducing the transient voltage, although this effect is moderated by the presence of a magnetic field. The study highlights the complex interaction between magnetic field and temperature, with significant impacts on the transient behaviour.展开更多
As a critical material in construction engineering,concrete requires accurate prediction of its outlet temperature to ensure structural quality and enhance construction efficiency.This study proposes a novel hybrid pr...As a critical material in construction engineering,concrete requires accurate prediction of its outlet temperature to ensure structural quality and enhance construction efficiency.This study proposes a novel hybrid prediction method that integrates a heat conduction physical model with a multilayer perceptron(MLP)neural network,dynamically fused via a weighted strategy to achieve high-precision temperature estimation.Experimental results on an independent test set demonstrated the superior performance of the fused model,with a root mean square error(RMSE)of 1.59℃ and a mean absolute error(MAE)of 1.23℃,representing a 25.3%RMSE reduction compared to conventional physical models.Ambient temperature and coarse aggregate temperature were identified as the most influential variables.Furthermore,the model-based temperature control strategy reduced costs by 0.81 CNY/m^(3),showing significant potential for improving resource efficiency and supporting sustainable construction practices.展开更多
The transverse momentum spectra of different types of particles produced in central and peripheral gold–gold(Au–Au)and inelastic proton–proton(pp)collisions at the Relativistic Heavy Ion Collider,as well as in cent...The transverse momentum spectra of different types of particles produced in central and peripheral gold–gold(Au–Au)and inelastic proton–proton(pp)collisions at the Relativistic Heavy Ion Collider,as well as in central and peripheral lead-lead(Pb–Pb)and pp collisions at the Large Hadron Collider,are analyzed by the multi-component standard(Boltzmann–Gibbs,Fermi–Dirac,and Bose–Einstein)distributions.The obtained results from the standard distribution give an approximate agreement with the measured experimental data by the STAR,PHENIX,and ALICE Collaborations.The behavior of the effective(kinetic freeze-out)temperature,transverse flow velocity,and kinetic freeze-out volume for particles with different masses is obtained,which observes the early kinetic freezeout of heavier particles as compared to the lighter particles.The parameters of emissions of different particles are observed to be different,which reveals a direct signature of the mass-dependent differential kinetic freeze-out.It is also observed that the peripheral nucleus–nucleus(AA)and pp collisions at the same center-of-mass energy per nucleon pair are in good agreement in terms of the extracted parameters.展开更多
In permanent magnet synchronous machine(PMSM) drives, temperature information is critical to achieve reliable and high-performance control. The popular model-based estimation methods are based on extracting temperatur...In permanent magnet synchronous machine(PMSM) drives, temperature information is critical to achieve reliable and high-performance control. The popular model-based estimation methods are based on extracting temperature dependent terms from the voltages using the machine model. The estimation accuracy under low speed or load can be greatly affected by the model uncertainty and noise due to low signal-tonoise ratio. This paper presents a high frequency(HF) position offset injection-based winding and permanent magnet(PM) temperature decoupled estimation approach for PMSMs to achieve accurate and robust temperature estimation among a wide speed range especially under low-speed conditions. In the proposed approach, a small HF position offset is injected into the machine to construct a decoupled winding and PM temperature estimation model, in which the winding and PM temperatures are independently estimated from HF excitations. The temperature estimation is independent from the fundamental model and parameter variation, and it achieves high signal-tonoise ratio under low-speed conditions. Moreover, the temperature estimation is also not affected by magnetic saturation and inverter distortion, which can improve the accuracy and robustness of temperature estimation. The proposed approach is validated with experiments and comparisons on a laboratory machine under various operating conditions.展开更多
Thermal management system is highly desirable to guarantee the performance and thermal safety of lithium-ion batteries,but it reduces the energy density of battery modules and even is unable to provide highly effectiv...Thermal management system is highly desirable to guarantee the performance and thermal safety of lithium-ion batteries,but it reduces the energy density of battery modules and even is unable to provide highly effective protection.Here,a thermal management function integrated material is presented based on high-temperature resistant aerogel and phase change material and is applied at both charge–discharge process and thermal runaway condition.In this sandwich structure Paraffin@SiC nanowire/Aerogel sheet (denoted as PA@SAS) system,SiC nanowires endow the middle aerogel sheet (SAS) a dual nano-network structure.The enhanced mechanical properties of SAS were studied by compressive tests and dynamic mechanical analysis.Besides,the thermal conductivity of SAS at 600°C is only 0.042 W/(m K).The surface phase change material layers facilitate temperature uniformity of batteries (surface temperature difference less than 1.82°C) through latent heat.Moreover,a large-format battery module with four 58 Ah LiNi0.5Co0.2Mn0.3O2LIBs was assembled.PA@SAS successfully prevents thermal runaway propagation,yielding a temperature gap of 602°C through the 2 mm-thick cross section.PA@SAS also exhibits excellent performance in other safety issues such as temperature rise rate,flame heat flux,etc.The lightweight property and effective insulation performance achieves significant safety enhancement with mass and volume energy density reduction of only 0.79%and 5.4%,respectively.The originality of the present research stems from the micro and macro structure design of the proposed thermal management material and the combination of intrinsic advantages of every component.This work provides a reliable design of achieving the integration of thermal management functions into an aerogel composite and improves the thermal safety of lithium-ion batteries.展开更多
Aqueous zinc-ion batteries exhibit significant promise for practical energy storage owing to their costeffective materials and inherent safety.However,the practical application at low temperatures is hindered by the s...Aqueous zinc-ion batteries exhibit significant promise for practical energy storage owing to their costeffective materials and inherent safety.However,the practical application at low temperatures is hindered by the sluggish interfacial kinetics at the Zn electrode.Here,a localized cation-anion clustering chemistry is developed by introducing cyclopentyl methyl ether(CPME)as a diluent to improve the low-temperature interface kinetics at the Zn anode.In this configuration,CPME does not participate in solvation shell formation but instead facilitates the selective integration of trifluoro-methane-sulfonate anions(OTF^(-))into the solvation sheaths of Zn^(2+)ions,accelerating desolvation kinetics at the zinc metal interface.Furthermore,the enhanced interaction between Zn^(2+)and OTF^(-)anions drives preferential anion decomposition,yielding a ZnF_(2)-rich interfacial layer,which enhances Zn^(2+)diffusion at the Zn electrode interface under cryogenic conditions.Notably,Zn//Cu cells employing this optimized electrolyte achieve corrosion-resistant zinc stripping/plating of over 1200 cycles at-40℃,with an average Coulombic efficiency of 99.74%.Moreover,Zn//NaV_(3)O_(8)·1.5H_(2)O(NVO)full cells demonstrate exceptional stability,retaining 90.91%of their initial capacity after 2000 cycles at-40℃.This work offers new insights into the rational regulation of interfacial kinetics in aqueous zinc-ion batteries at low temperatures.展开更多
This study focused on investigating the effects of various factors on the mechanical properties of superconducting matrix composites reinforced with ferromagnetic particles and interface phases when exposed to externa...This study focused on investigating the effects of various factors on the mechanical properties of superconducting matrix composites reinforced with ferromagnetic particles and interface phases when exposed to external magnetic fields.A micromechanical model was created by simplifying the basic properties and composition of the interface,utilizing principles such as Eshelby’s equivalent inclusion theory and Hooke’s law,as well as applying uniform stress boundary conditions.Through the development of equations,the study predicted changes in effective mechanical properties,highlighting the significant influence of parameters like the interface phase,inclusions,and magnetic field on the effective elastic modulus and magnetostriction of the composite material.By shedding light on these relationships,the research offers valuable insights for the manufacture and application of ferromagnetic particle-reinforced superconducting matrix composites with interface phases,providing a foundation for future research in this area.展开更多
In the electroslag remelting(ESR)process,it mainly relies on thermal experiments or analysis via mechanistic models to realize the physical fields simulation of the electromagnetic field and temperature field coupled ...In the electroslag remelting(ESR)process,it mainly relies on thermal experiments or analysis via mechanistic models to realize the physical fields simulation of the electromagnetic field and temperature field coupled transfer,which has the limitations of high cost,a large amount of calculating data and high computing power requirements.A novel network based on physics-informed neural network(PINN)was designed to realize the fast and high-fidelity prediction of the distribution of electromagnetic field and temperature field in ESR process.The physical laws were combined with the deep learning network through PINN,and physical constraints were embedded to achieve effective solution of partial differential equations(PDEs).PINN was used to minimize the loss function consisting of data error,physical information error and boundary condition error.The physical laws and boundary condition constraints in the ESR process were considered to maintain high PDE solution accuracy under different spatial and temporal resolutions.Automatic differentiation(Autodiff)technique and gradient descent algorithm were used to optimize the network parameters.The experimental results show that compared with the mechanistic models,PINN can effectively replace thermal experiments to realize the physical field simulation of ESR process with only a few experimental data,which can avoid the disadvantages of pure data-driven network simulation that requires a large amount of training data.Moreover,the solution of PINN has good physical interpretability and reliability of simulation results.For simulating electromagnetic field and temperature field distribution,the training time of the network is only 140 and 203 s,and the regression indicators of root mean square error can reach 12.65 and 13.76,respectively.展开更多
The magnetoplasmadynamic thruster(MPDT) is characterized by its high specific impulse and substantial thrust density, making it a promising propulsion system for deep space exploration missions. In both laboratory exp...The magnetoplasmadynamic thruster(MPDT) is characterized by its high specific impulse and substantial thrust density, making it a promising propulsion system for deep space exploration missions. In both laboratory experiments and practical applications, cathode ablation has emerged as a critical concern. An optical diagnostic approach based on monochromatic radiation temperature measurement, utilizing plume emission spectra and the selection of an appropriate test band, has been successfully employed. This method provides an accurate temperature distribution across the cathode surface, offering a novel testing technique for the optimization and evaluation of magnetic plasma thruster designs.展开更多
The results of the study of the effect of partial substitution of Fe by Mn in the La Fe_(11.2-x)Mn_(x)Co_(0.7)Si_(1.1)system on magnetization,specific heat,magnetostriction and magnetocaloric effect are presented.Dire...The results of the study of the effect of partial substitution of Fe by Mn in the La Fe_(11.2-x)Mn_(x)Co_(0.7)Si_(1.1)system on magnetization,specific heat,magnetostriction and magnetocaloric effect are presented.Direct measurements of the adiabatic temperature change(ΔT_(ad))were carried out in alternating magnetic fields(AMF)using the magnetic field modulation method.Partial substitution of Fe atoms by Mn atoms leads to a shift in the Curie temperature(T_(C))towards lower temperatures without a noticeable deterioration in magnetic properties.A correlation was found between the structural component of the magnetocaloric effect and the stability of the frequency of theΔT_(ad)in the AMFs—an increase in the manganese concentration leads to a decrease in magnetostriction and to a lower dependence ofΔT_(ad)on the frequency of the magnetic field.Estimates of the specific cooling power Q_(C)as a function of the frequency of the AMF showed that the highest value of Q_(C)at f=20 Hz in a magnetic field of 12k Oe is 26.3 W g^(-1)and is observed for the composition with x=0.1.This value is higher than that of Gd,for which,under the same conditions,Q_(C)=21.6 W g^(-1).All the samples studied show stability of the value ofΔT_(ad)without any sign of deterioration of the effect up to 60,000cycles of switching on/off of the magnetic field of 12 k Oe.The discovered frequency and cyclic stability ofΔT_(ad)of the studied samples increase their prospects for application in magnetic cooling technology.展开更多
Tomato(Solanum lycopersicum)is an extensively cultivated vegetable,and its growth and fruit quality can be significantly impaired by low temperatures.The widespread presence of N^(6)-methyladenosine(m^(6)A)modificatio...Tomato(Solanum lycopersicum)is an extensively cultivated vegetable,and its growth and fruit quality can be significantly impaired by low temperatures.The widespread presence of N^(6)-methyladenosine(m^(6)A)modification on RNA is involved in a diverse range of stress response processes.There is a significant knowledge gap regarding the precise roles of m^(6)A modification in tomato,particularly for cold stress response.Here,we assessed the m^(6)A modification landscape of S.lycopersicum'Micro-Tom'leaves in response to low-temperature stress.Furthermore,we investigated the potential relationship among m^(6)A modification,transcriptional regulation,alternative polyadenylation events,and protein translation via MeRIP-seq,RNA-seq,and protein mass spectrometry.After omic date analysis,11378 and 10735 significant m^(6)A peak associated genes were identified in the control and cold treatment tomato leaves,respectively.Additionally,we observed a UGUACAK(K=G/U)motif under both conditions.Differential m^(6)A site associated genes most likely play roles in protein translation regulatory pathway.Besides directly altering gene expression levels,m^(6)A also leads to differential poly(A)site usage under low-temperature.Finally,24 important candidate genes associated with cold stress were identified by system-level multi-omic analysis.Among them,m^(6)A modification levels were increased in SBPase(Sedoheptulose-1,7-bisphosphatase,Solyc05g052600.4)mRNA,causing distal poly(A)site usage,downregulation of mRNA expression level,and increased protein abundance.Through these,tomato leaves try to maintain normal photo synthetic carbon assimilation and nitro gen metabolism under low-temperature condition.The comprehensive investigation of the m^(6)A modification landscape and multi-omics analysis provide valuable insights into the epigenetic regulatory mechanisms in tomato cold stress response.展开更多
Dividing wall batch distillation with middle vessel(DWBDM)is a new type of batch distillation column,with outstanding advantages of low capital cost,energy saving and flexible operation.However,temperature control of ...Dividing wall batch distillation with middle vessel(DWBDM)is a new type of batch distillation column,with outstanding advantages of low capital cost,energy saving and flexible operation.However,temperature control of DWBDM process is challenging,since inherently dynamic and highly nonlinear,which make it difficult to give the controller reasonable set value or optimal temperature profile for temperature control scheme.To overcome this obstacle,this study proposes a new strategy to develop temperature control scheme for DWBDM combining neural network soft-sensor with fuzzy control.Dynamic model of DWBDM was firstly developed and numerically solved by Python,with three control schemes:composition control by PID and fuzzy control respectively,and temperature control by fuzzy control with neural network soft-sensor.For dynamic process,the neural networks with memory functions,such as RNN,LSTM and GRU,are used to handle with time-series data.The results from a case example show that the new control scheme can perform a good temperature control of DWBDM with the same or even better product purities as traditional PID or fuzzy control,and fuzzy control could reduce the effect of prediction error from neural network,indicating that it is a highly feasible and effective control approach for DWBDM,and could even be extended to other dynamic processes.展开更多
The impact of Y content on the microstructure,mechanical properties,and electromagnetic interference shielding effectiveness(EMI SE)of the Mg-6Zn-xY-1La-0.5Zr alloy was investigated.After the extrusion treatment of Mg...The impact of Y content on the microstructure,mechanical properties,and electromagnetic interference shielding effectiveness(EMI SE)of the Mg-6Zn-xY-1La-0.5Zr alloy was investigated.After the extrusion treatment of Mg-6Zn-xY-1La-0.5Zr alloy,the large grains that did not experience dynamic recrystallization were elongated along the extrusion direction,and the small-sized dynamic recrystallized grains were distributed around the large grains.The Mg-6Zn-1Y-1La-0.5Zr alloy demonstrated a favorable balance between strength and plasticity,exhibiting ultimate tensile strength,yield strength,and elongation values of 332.3 MPa,267.3 MPa,and 16.2%,respectively.Moreover,the EMI SE within the frequency range of 30-1500 MHz changes from 79 to 110 dB,aligning with the electromagnetic shielding requirements of many high-strength applications.展开更多
Although generative conversational artificial intelligence(AI)can answer questions well and hold conversations as a person,the semantic ambiguity inherent in text-based communication poses challenges to effective use....Although generative conversational artificial intelligence(AI)can answer questions well and hold conversations as a person,the semantic ambiguity inherent in text-based communication poses challenges to effective use.Effective use reflects the users’utilization of generative conversational AI to achieve their goals,which has not been previously studied.Drawing on the media naturalness theory,we examined how generative conversational AI’s content and style naturalness affect effective use.A two-wave survey was conducted to collect data from 565 users of generative conversational AI.Two techniques were used in this study.Initially,partial least squares structural equation modeling(PLS-SEM)was applied to determine the variables that significantly affected the mechanisms(i.e.,cognitive effort and communication ambiguity)and effective use.Secondly,an artificial neural network model was used to evaluate the relative importance of the significant predictors of mechanisms and effective use identified from the PLS-SEM analysis.The results revealed that the naturalness of content and style differed in their effects on cognitive effort and communication ambiguity.Additionally,cognitive effort and communication ambiguity negatively affected effective use.This study advances the literature on effective use by uncovering the psychological mechanisms underlying effective use and their antecedents.In addition,this study offers insights into the design of generative conversational AI.展开更多
Optical temperature sensor materials face great challenges in terms of temperature measurement sensitivity and applicability in extreme environments.To overcome these problems,Er^(3+)∕Yb^(3+)co-doped La_(2)O_(3)-TiO_...Optical temperature sensor materials face great challenges in terms of temperature measurement sensitivity and applicability in extreme environments.To overcome these problems,Er^(3+)∕Yb^(3+)co-doped La_(2)O_(3)-TiO_(2)-Ga_(2)O_(3)-ZrO_(2)(LTGZ)glasses were designed and synthesized using the aerodynamic levitation method.In the glass system,the strongest intensity of upconversion luminescence was measured on 3.0Yb^(3+)∕0.5Er^(3+)(mole fraction)co-doped LTGZ glasses.In the temperature range of 300 to 700 K,the maximum relative and absolute sensitivities were 2.71%and 0.56%K^(−1),respectively.The temperature reliability was proved through variable temperature cycling tests.More importantly,to our knowledge,it is the first time to investigate the optical temperature measurement capability under a high magnetic field in this as-designed sensor.By applying the magnetic field up to 42 T,the relative sensitivity changes from 1.79%to 1.58%K^(−1),revealing that the temperature sensitivity of the sensor remains stable even in high magnetic fields.The results of the study provide a reference for the selection of temperature measurement materials in the field of optical temperature sensing,and the designed temperature sensor can be used for temperature measurement in extreme environments,especially in strong magnetic field conditions,which provides an important value for the development of special optical temperature sensors.展开更多
Net primary productivity(NPP)is the net accumulation of organic matter by vegetation through photosynthesis and serves as a key indicator for exploring vegetation responses to climate change.Considering the remote and...Net primary productivity(NPP)is the net accumulation of organic matter by vegetation through photosynthesis and serves as a key indicator for exploring vegetation responses to climate change.Considering the remote and local impacts of soil heat capacities on vegetation growth through pathways of atmospheric circulation and land–atmosphere interaction,this paper develops a statistical prediction model for NPP from April to June(AMJ)across the middle-to-high latitudes of Eurasia.The model introduces two physically meaningful predictors:the snow water equivalent(SWE)from February to March(FM)over central Europe and the FM local soil temperature(ST).The positive phase of FM SWE triggers anomalous eastward-propagating Rossby waves,leading to an anomalous low-pressure system and cooling in the middle-to-high latitudes of Eurasia.This effect persists into spring through snow feedback to the atmosphere and affects subsequent NPP changes.The ST is closely related to the AMJ temperature and precipitation.With positive ST anomalies,the AMJ temperature and precipitation exhibit an east–west dipole anomaly distribution in this region.The single-factor prediction scheme using ST as the predictor is much better than using SWE as the predictor.Independent validation results from 2009 to 2014 demonstrate that the ST scheme alone has good predictive performance for the spatial distribution and interannual variability of NPP.The predictive skills of the multi-factor prediction schemes can be improved by about 13%if the ST predictor is included.The findings confirm that local ST is a predictor that must be included for NPP prediction.展开更多
基金supported by the National Key Research and Development Program of Chinathe National Natural Science Foundation of China (Grant Nos.2024YFA1408000,12474097,and2023YFA1406001)+2 种基金the Guangdong Provincial Quantum Science Strategic Initiative (Grant No.GDZX2201001)the Center for Computational Science and Engineering at Southern University of Science and Technology,the Major Science and Technology Infrastructure Project of Material Genome Big-science Facilities Platform supported by Municipal Development and Reform Commission of Shenzhen(for J.L.Z.and Y.L.)the Chinese funding sources applied via HPSTAR。
文摘The magnetic properties and Kondo effect in Ce3TiBi5 with a quasi-one-dimensional structure were investigated using in situ high-pressure resistivity measurements up to 48 GPa.At ambient pressure,Ce_(3)TiBi_(5) undergoes an antiferromagnetic(AFM)transition at T_(N)∼5 K.Under high pressures within 8.9 GPa,we find that Kondo scattering contributes differently to the high-temperature resistance,R(T),depending on the applied current direction,demonstrating a significantly anisotropic Kondo effect.The complete P–T phase diagram has been constructed,in which the pressure dependence of T_(N) exhibits a dome-like shape.The AFM order remains robust under pressure,even when the coherence temperature T^(*) far exceeds 300 K.We attribute the observed anisotropic Kondo effect and the robust AFM to the underlying anisotropy in electronic hybridization under high pressure.
基金supported by the National Natural Science Foundation of China(Nos.62101020 and 62141405)the Special Scientific Research Project of Civil Aircraft,China(No.MJZ5-2N22).
文摘As the proportion of composite materials used in aircraft continues to increase, the electromagnetic Shielding Effectiveness (SE) of these materials becomes a critical factor in the electromagnetic safety design of aircraft structures. The assessment of electromagnetic SE for Slotted Composite Structures(SCSs) is particularly challenging due to their complex geometries and there remains a lack of suitable models for accurately predicting the SE performance of these intricate configurations. To address this issue, this paper introduces SCS-Net, a Deep Neural Network (DNN) method designed to accurately predict the SE of SCS. This method considers the impacts of various structural parameters, material properties and incident wave parameters on the SE of SCSs. In order to better model the SCS, an improved Nicolson-Ross-Weir (NRW) method is introduced in this paper to provide an equivalent flat structure for the SCS and to calculate the electromagnetic parameters of the equivalent structure. Additionally, the prediction of SE via DNNs is limited by insufficient test data, which hinders support for large-sample training. To address the issue of limited measured data, this paper develops a Measurement-Computation Fusion (MCF) dataset construction method. The predictions based on the simulation results show that the proposed method maintains an error of less than 0.07 dB within the 8–10 GHz frequency range. Furthermore, a new loss function based on the weighted L1-norm is established to improve the prediction accuracy for these parameters. Compared with traditional loss functions, the new loss function reduces the maximum prediction error for equivalent electromagnetic parameters by 47%. This method significantly improves the prediction accuracy of SCS-Net for measured data, with a maximum improvement of 23.88%. These findings demonstrate that the proposed method enables precise SE prediction and design for composite structures while reducing the number of test samples needed.
基金carried out under the co-funding of the National Natural Science Foundation of China(NSFC)project(Grant No.42022008)Zhuhai basic and applied research project(Grant No.ZH22017003200009PWC)the Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)(Grant No.311022003).
文摘Climate change is significantly influenced by both clouds and Earth’s surface temperature(EST).While numerous studies have investigated clouds and EST separately,the extent of clouds’impact on EST remains unclear.Based on the inspiration and limitation of cloud radiative effect(CRE),this study provides a pioneering attempt to propose a novel indicator,cloud radiative effect on surface temperature(CREST),aiming to quantify how clouds affect EST globally while also analyzing the physical mechanism.Using reanalysis and remotely sensed data,a phased machine learning scheme in combination of surface energy balance theory is proposed to estimate EST under all-sky and hypothetical clear-sky conditions in stages,thereby estimating the newly defined CREST by subtracting the hypothetical clear-sky EST from the all-sky EST.The inter-annual experiments reveal the significant spatial heterogeneity in CREST across land,ocean,and ice/snow regions.As a global offset of the heterogeneity,clouds exhibit a net warming effect on global surface temperature on an annual scale(e.g.,0.26 K in 1981),despite their ability to block sunlight.However,the net warming effect has gradually weakened to nearly zero over the past four decades(e.g.,only 0.06 K in 2021),and it’s even possible to transform into a cooling effect,which might be good news for mitigating the global warming.
基金supported by the Project of National Natural Science Foundation of China under Grant 52077122。
文摘With the continuous upgrading of traditional manufacturing industries and the rapid rise of emerging technology fields,the performance requirements for the permanent magnet synchronous motors(PMSMs)have become higher and higher.The importance of fast and accurate electromagnetic thermal coupling analysis of such motors becomes more and more prominent.In view of this,the surfacemounted PMSM(SPMSM)equipped with unequally thick magnetic poles is taken as the main object and its electromagnetic thermal coupling analytical model(ETc AM)is investigated.First,the electromagnetic analytical model(EAM)is studied based on the modified subdomain method.It realizes the fast calculation of key electromagnetic characteristics.Subsequently,the 3D thermal analytical model(TAM)is developed by combining the EAM,the lumped parameter thermal network method(LPTNM),and the partial differential equation of heat flux.It realizes the fast calculation of key thermal characteristics in 3D space.Further,the information transfer channel between EAM and TAM is built with reference to the intrinsic connection between electromagnetic field and temperature field.Thereby,the novel ETcAM is proposed to realize the fast and accurate prediction of electromagnetic and temperature fields.Besides,ETcAM has a lot to commend it.One is that it well accounts for the complex structure,saturation,and heat exchange behavior.Second,it saves a lot of computer resources.It offers boundless possibilities for initial design,scheme evaluation,and optimization of motors.Finally,the validity,accuracy,and practicality of this study are verified by simulation and experiment.
文摘This study examines the influence of magnetic field and temperature on the transient voltage of a polycrystalline silicon radial junction solar cell in a dynamic regime under multispectral illumination. Radial junction solar cells represent a major advancement in photovoltaic technologies, as they optimize light absorption and charge collection efficiency. The focus is on the impact of the magnetic field and temperature on the decay of transient voltage, which provides crucial information on recombination processes and the lifetime of minority carriers. The results reveal that the magnetic field tends to increase the transient voltage by directly affecting the transient electron density. Indeed, for B > 7 × 10−5 T, the magnetic field prolongs the relaxation time by increasing the transient voltage amplitude. Additionally, rising temperatures accelerate (ranging from 290 K to 450 K) recombination processes, thereby reducing the transient voltage, although this effect is moderated by the presence of a magnetic field. The study highlights the complex interaction between magnetic field and temperature, with significant impacts on the transient behaviour.
基金funded by National Key Research and Development Plan(2018YFC0406703)Supported by the National Natural Science Foundation of China(51779277)+4 种基金Chinese Academy of Water Sciences(SD0145B072021)Supported by the State Key Laboratory of Flow Water Cycle Simulation and Regulation,SKL2022ZD05Support provided by the fund of State Key Laboratory of Water Cycle and Water Security,IWHR(Grant No.SKL2024YJZD05)Support provided by the fund of Power China(DJ-ZDXM-2020-50)Support provided by the fund of Research and Application of Intelligent Simulation and Intelligent Control Technology for Structural States of Gravity DAMS in Jingling Reservoir Project,Zhejiang Province(JLSKFW-2024113).
文摘As a critical material in construction engineering,concrete requires accurate prediction of its outlet temperature to ensure structural quality and enhance construction efficiency.This study proposes a novel hybrid prediction method that integrates a heat conduction physical model with a multilayer perceptron(MLP)neural network,dynamically fused via a weighted strategy to achieve high-precision temperature estimation.Experimental results on an independent test set demonstrated the superior performance of the fused model,with a root mean square error(RMSE)of 1.59℃ and a mean absolute error(MAE)of 1.23℃,representing a 25.3%RMSE reduction compared to conventional physical models.Ambient temperature and coarse aggregate temperature were identified as the most influential variables.Furthermore,the model-based temperature control strategy reduced costs by 0.81 CNY/m^(3),showing significant potential for improving resource efficiency and supporting sustainable construction practices.
基金supported by the National Natural Science Foundation of China(Nos.11575103 and 11947418)the Chinese Government Scholarship(China Scholarship Council)+2 种基金the Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi(STIP)(No.201802017)the Shanxi Provincial Natural Science Foundation(No.201901D111043)the Fund for Shanxi‘‘1331 Project’’Key Subjects Construction。
文摘The transverse momentum spectra of different types of particles produced in central and peripheral gold–gold(Au–Au)and inelastic proton–proton(pp)collisions at the Relativistic Heavy Ion Collider,as well as in central and peripheral lead-lead(Pb–Pb)and pp collisions at the Large Hadron Collider,are analyzed by the multi-component standard(Boltzmann–Gibbs,Fermi–Dirac,and Bose–Einstein)distributions.The obtained results from the standard distribution give an approximate agreement with the measured experimental data by the STAR,PHENIX,and ALICE Collaborations.The behavior of the effective(kinetic freeze-out)temperature,transverse flow velocity,and kinetic freeze-out volume for particles with different masses is obtained,which observes the early kinetic freezeout of heavier particles as compared to the lighter particles.The parameters of emissions of different particles are observed to be different,which reveals a direct signature of the mass-dependent differential kinetic freeze-out.It is also observed that the peripheral nucleus–nucleus(AA)and pp collisions at the same center-of-mass energy per nucleon pair are in good agreement in terms of the extracted parameters.
基金supported by Shenzhen Science and Technology Program under Grant JCYJ20250604175412017the National Natural Science Foundation of China under Grant 62473387+1 种基金the Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) under Grant SML2024SP007in part by the Department of Science and Technology of Guangdong Province under Grant. 2021QN020085。
文摘In permanent magnet synchronous machine(PMSM) drives, temperature information is critical to achieve reliable and high-performance control. The popular model-based estimation methods are based on extracting temperature dependent terms from the voltages using the machine model. The estimation accuracy under low speed or load can be greatly affected by the model uncertainty and noise due to low signal-tonoise ratio. This paper presents a high frequency(HF) position offset injection-based winding and permanent magnet(PM) temperature decoupled estimation approach for PMSMs to achieve accurate and robust temperature estimation among a wide speed range especially under low-speed conditions. In the proposed approach, a small HF position offset is injected into the machine to construct a decoupled winding and PM temperature estimation model, in which the winding and PM temperatures are independently estimated from HF excitations. The temperature estimation is independent from the fundamental model and parameter variation, and it achieves high signal-tonoise ratio under low-speed conditions. Moreover, the temperature estimation is also not affected by magnetic saturation and inverter distortion, which can improve the accuracy and robustness of temperature estimation. The proposed approach is validated with experiments and comparisons on a laboratory machine under various operating conditions.
基金Collaborative Innovation University Project of Anhui Province (GXXT-2022-018)National Natural Science Foundation of China (52374238 and 52074253)+3 种基金Natural Science Foundation of Anhui Province (2108085J28)Taishan Industrial Leading Talent Project (2019TSCYCX-27)Major Science and Technology Projects of Anhui Province(202103a05020011)Youth Innovation Promotion Association(CX2320007001)。
文摘Thermal management system is highly desirable to guarantee the performance and thermal safety of lithium-ion batteries,but it reduces the energy density of battery modules and even is unable to provide highly effective protection.Here,a thermal management function integrated material is presented based on high-temperature resistant aerogel and phase change material and is applied at both charge–discharge process and thermal runaway condition.In this sandwich structure Paraffin@SiC nanowire/Aerogel sheet (denoted as PA@SAS) system,SiC nanowires endow the middle aerogel sheet (SAS) a dual nano-network structure.The enhanced mechanical properties of SAS were studied by compressive tests and dynamic mechanical analysis.Besides,the thermal conductivity of SAS at 600°C is only 0.042 W/(m K).The surface phase change material layers facilitate temperature uniformity of batteries (surface temperature difference less than 1.82°C) through latent heat.Moreover,a large-format battery module with four 58 Ah LiNi0.5Co0.2Mn0.3O2LIBs was assembled.PA@SAS successfully prevents thermal runaway propagation,yielding a temperature gap of 602°C through the 2 mm-thick cross section.PA@SAS also exhibits excellent performance in other safety issues such as temperature rise rate,flame heat flux,etc.The lightweight property and effective insulation performance achieves significant safety enhancement with mass and volume energy density reduction of only 0.79%and 5.4%,respectively.The originality of the present research stems from the micro and macro structure design of the proposed thermal management material and the combination of intrinsic advantages of every component.This work provides a reliable design of achieving the integration of thermal management functions into an aerogel composite and improves the thermal safety of lithium-ion batteries.
基金This research was financially supported by the National Natural Science Foundation of China(22209071,22309081)the Natural Science Foundation of Jiangsu Province(BK20220339,BK20230320)+2 种基金the Natural Science Research in Colleges and Universities of Jiangsu Province(22KJB150006,22KJB430005)the China Postdoctoral Science Foundation funded project(2023M731641)the Open Project Fund from Jiangsu Province Large Scientific Instruments。
文摘Aqueous zinc-ion batteries exhibit significant promise for practical energy storage owing to their costeffective materials and inherent safety.However,the practical application at low temperatures is hindered by the sluggish interfacial kinetics at the Zn electrode.Here,a localized cation-anion clustering chemistry is developed by introducing cyclopentyl methyl ether(CPME)as a diluent to improve the low-temperature interface kinetics at the Zn anode.In this configuration,CPME does not participate in solvation shell formation but instead facilitates the selective integration of trifluoro-methane-sulfonate anions(OTF^(-))into the solvation sheaths of Zn^(2+)ions,accelerating desolvation kinetics at the zinc metal interface.Furthermore,the enhanced interaction between Zn^(2+)and OTF^(-)anions drives preferential anion decomposition,yielding a ZnF_(2)-rich interfacial layer,which enhances Zn^(2+)diffusion at the Zn electrode interface under cryogenic conditions.Notably,Zn//Cu cells employing this optimized electrolyte achieve corrosion-resistant zinc stripping/plating of over 1200 cycles at-40℃,with an average Coulombic efficiency of 99.74%.Moreover,Zn//NaV_(3)O_(8)·1.5H_(2)O(NVO)full cells demonstrate exceptional stability,retaining 90.91%of their initial capacity after 2000 cycles at-40℃.This work offers new insights into the rational regulation of interfacial kinetics in aqueous zinc-ion batteries at low temperatures.
基金supported by the National Natural Science Foundation of China(No.12262020).
文摘This study focused on investigating the effects of various factors on the mechanical properties of superconducting matrix composites reinforced with ferromagnetic particles and interface phases when exposed to external magnetic fields.A micromechanical model was created by simplifying the basic properties and composition of the interface,utilizing principles such as Eshelby’s equivalent inclusion theory and Hooke’s law,as well as applying uniform stress boundary conditions.Through the development of equations,the study predicted changes in effective mechanical properties,highlighting the significant influence of parameters like the interface phase,inclusions,and magnetic field on the effective elastic modulus and magnetostriction of the composite material.By shedding light on these relationships,the research offers valuable insights for the manufacture and application of ferromagnetic particle-reinforced superconducting matrix composites with interface phases,providing a foundation for future research in this area.
基金supported by National Natural Science Foundation of China(52274323 and 524743495)the Postdoctoral Fellowship Program of CPSF under Grant Number GZC20240231.
文摘In the electroslag remelting(ESR)process,it mainly relies on thermal experiments or analysis via mechanistic models to realize the physical fields simulation of the electromagnetic field and temperature field coupled transfer,which has the limitations of high cost,a large amount of calculating data and high computing power requirements.A novel network based on physics-informed neural network(PINN)was designed to realize the fast and high-fidelity prediction of the distribution of electromagnetic field and temperature field in ESR process.The physical laws were combined with the deep learning network through PINN,and physical constraints were embedded to achieve effective solution of partial differential equations(PDEs).PINN was used to minimize the loss function consisting of data error,physical information error and boundary condition error.The physical laws and boundary condition constraints in the ESR process were considered to maintain high PDE solution accuracy under different spatial and temporal resolutions.Automatic differentiation(Autodiff)technique and gradient descent algorithm were used to optimize the network parameters.The experimental results show that compared with the mechanistic models,PINN can effectively replace thermal experiments to realize the physical field simulation of ESR process with only a few experimental data,which can avoid the disadvantages of pure data-driven network simulation that requires a large amount of training data.Moreover,the solution of PINN has good physical interpretability and reliability of simulation results.For simulating electromagnetic field and temperature field distribution,the training time of the network is only 140 and 203 s,and the regression indicators of root mean square error can reach 12.65 and 13.76,respectively.
文摘The magnetoplasmadynamic thruster(MPDT) is characterized by its high specific impulse and substantial thrust density, making it a promising propulsion system for deep space exploration missions. In both laboratory experiments and practical applications, cathode ablation has emerged as a critical concern. An optical diagnostic approach based on monochromatic radiation temperature measurement, utilizing plume emission spectra and the selection of an appropriate test band, has been successfully employed. This method provides an accurate temperature distribution across the cathode surface, offering a novel testing technique for the optimization and evaluation of magnetic plasma thruster designs.
基金financially supported by Russian Science Foundation(No.24-43-00156,https://rscf.ru/en/project/24-43-00156/)the National Natural Science Foundation of China(No.52171169)the State Key Laboratory for Advanced Metals and Materials(No.2023-ZD01)。
文摘The results of the study of the effect of partial substitution of Fe by Mn in the La Fe_(11.2-x)Mn_(x)Co_(0.7)Si_(1.1)system on magnetization,specific heat,magnetostriction and magnetocaloric effect are presented.Direct measurements of the adiabatic temperature change(ΔT_(ad))were carried out in alternating magnetic fields(AMF)using the magnetic field modulation method.Partial substitution of Fe atoms by Mn atoms leads to a shift in the Curie temperature(T_(C))towards lower temperatures without a noticeable deterioration in magnetic properties.A correlation was found between the structural component of the magnetocaloric effect and the stability of the frequency of theΔT_(ad)in the AMFs—an increase in the manganese concentration leads to a decrease in magnetostriction and to a lower dependence ofΔT_(ad)on the frequency of the magnetic field.Estimates of the specific cooling power Q_(C)as a function of the frequency of the AMF showed that the highest value of Q_(C)at f=20 Hz in a magnetic field of 12k Oe is 26.3 W g^(-1)and is observed for the composition with x=0.1.This value is higher than that of Gd,for which,under the same conditions,Q_(C)=21.6 W g^(-1).All the samples studied show stability of the value ofΔT_(ad)without any sign of deterioration of the effect up to 60,000cycles of switching on/off of the magnetic field of 12 k Oe.The discovered frequency and cyclic stability ofΔT_(ad)of the studied samples increase their prospects for application in magnetic cooling technology.
基金financially supported by the National Natural Science Foundation of China(Grant Nos.32202518 and 32070601)Shandong University of Technology PhD Start-up Fund(418097)。
文摘Tomato(Solanum lycopersicum)is an extensively cultivated vegetable,and its growth and fruit quality can be significantly impaired by low temperatures.The widespread presence of N^(6)-methyladenosine(m^(6)A)modification on RNA is involved in a diverse range of stress response processes.There is a significant knowledge gap regarding the precise roles of m^(6)A modification in tomato,particularly for cold stress response.Here,we assessed the m^(6)A modification landscape of S.lycopersicum'Micro-Tom'leaves in response to low-temperature stress.Furthermore,we investigated the potential relationship among m^(6)A modification,transcriptional regulation,alternative polyadenylation events,and protein translation via MeRIP-seq,RNA-seq,and protein mass spectrometry.After omic date analysis,11378 and 10735 significant m^(6)A peak associated genes were identified in the control and cold treatment tomato leaves,respectively.Additionally,we observed a UGUACAK(K=G/U)motif under both conditions.Differential m^(6)A site associated genes most likely play roles in protein translation regulatory pathway.Besides directly altering gene expression levels,m^(6)A also leads to differential poly(A)site usage under low-temperature.Finally,24 important candidate genes associated with cold stress were identified by system-level multi-omic analysis.Among them,m^(6)A modification levels were increased in SBPase(Sedoheptulose-1,7-bisphosphatase,Solyc05g052600.4)mRNA,causing distal poly(A)site usage,downregulation of mRNA expression level,and increased protein abundance.Through these,tomato leaves try to maintain normal photo synthetic carbon assimilation and nitro gen metabolism under low-temperature condition.The comprehensive investigation of the m^(6)A modification landscape and multi-omics analysis provide valuable insights into the epigenetic regulatory mechanisms in tomato cold stress response.
基金supported by Beijing Natural Science Foundation(2222037)the Special Educating Project of the Talent for Carbon Peak and Carbon Neutrality of University of Chinese Academy of Sciences(Innovation of talent cultivation model for“dual carbon”in chemical engineering industry,E3E56501A2).
文摘Dividing wall batch distillation with middle vessel(DWBDM)is a new type of batch distillation column,with outstanding advantages of low capital cost,energy saving and flexible operation.However,temperature control of DWBDM process is challenging,since inherently dynamic and highly nonlinear,which make it difficult to give the controller reasonable set value or optimal temperature profile for temperature control scheme.To overcome this obstacle,this study proposes a new strategy to develop temperature control scheme for DWBDM combining neural network soft-sensor with fuzzy control.Dynamic model of DWBDM was firstly developed and numerically solved by Python,with three control schemes:composition control by PID and fuzzy control respectively,and temperature control by fuzzy control with neural network soft-sensor.For dynamic process,the neural networks with memory functions,such as RNN,LSTM and GRU,are used to handle with time-series data.The results from a case example show that the new control scheme can perform a good temperature control of DWBDM with the same or even better product purities as traditional PID or fuzzy control,and fuzzy control could reduce the effect of prediction error from neural network,indicating that it is a highly feasible and effective control approach for DWBDM,and could even be extended to other dynamic processes.
基金supported from the National Key R&D Program of China(No.2021YFB3701100)the National Natural Science Foundation of China(No.52225101)+1 种基金the Fundamental Research Funds for the Central Universities of China(Nos.2023CDJYXTD-002,2020CDJDPT001)the Graduate Research and Innovation Foundation of Chongqing,China(No.CYB23037).
文摘The impact of Y content on the microstructure,mechanical properties,and electromagnetic interference shielding effectiveness(EMI SE)of the Mg-6Zn-xY-1La-0.5Zr alloy was investigated.After the extrusion treatment of Mg-6Zn-xY-1La-0.5Zr alloy,the large grains that did not experience dynamic recrystallization were elongated along the extrusion direction,and the small-sized dynamic recrystallized grains were distributed around the large grains.The Mg-6Zn-1Y-1La-0.5Zr alloy demonstrated a favorable balance between strength and plasticity,exhibiting ultimate tensile strength,yield strength,and elongation values of 332.3 MPa,267.3 MPa,and 16.2%,respectively.Moreover,the EMI SE within the frequency range of 30-1500 MHz changes from 79 to 110 dB,aligning with the electromagnetic shielding requirements of many high-strength applications.
基金supported by the National Natural Science Foundation of China(NSFC)(Grant No.72171095)the National Social Science Foundation of China(Grant No.22VRC153)the Wuhan Textile University Fund(Grant Nos.2024289 and 2024380)。
文摘Although generative conversational artificial intelligence(AI)can answer questions well and hold conversations as a person,the semantic ambiguity inherent in text-based communication poses challenges to effective use.Effective use reflects the users’utilization of generative conversational AI to achieve their goals,which has not been previously studied.Drawing on the media naturalness theory,we examined how generative conversational AI’s content and style naturalness affect effective use.A two-wave survey was conducted to collect data from 565 users of generative conversational AI.Two techniques were used in this study.Initially,partial least squares structural equation modeling(PLS-SEM)was applied to determine the variables that significantly affected the mechanisms(i.e.,cognitive effort and communication ambiguity)and effective use.Secondly,an artificial neural network model was used to evaluate the relative importance of the significant predictors of mechanisms and effective use identified from the PLS-SEM analysis.The results revealed that the naturalness of content and style differed in their effects on cognitive effort and communication ambiguity.Additionally,cognitive effort and communication ambiguity negatively affected effective use.This study advances the literature on effective use by uncovering the psychological mechanisms underlying effective use and their antecedents.In addition,this study offers insights into the design of generative conversational AI.
基金supported by the Ministry of Human Resources and Social Security of the People’s Republic of China(Grant No.S20240022)the Hubei Provincial Natural Science Foundation for Innovation and Development(Grant No.2025AFD325)the Interdisciplinary Program of Wuhan National High Magnetic Field Center(Grant No.WHMFC202129).
文摘Optical temperature sensor materials face great challenges in terms of temperature measurement sensitivity and applicability in extreme environments.To overcome these problems,Er^(3+)∕Yb^(3+)co-doped La_(2)O_(3)-TiO_(2)-Ga_(2)O_(3)-ZrO_(2)(LTGZ)glasses were designed and synthesized using the aerodynamic levitation method.In the glass system,the strongest intensity of upconversion luminescence was measured on 3.0Yb^(3+)∕0.5Er^(3+)(mole fraction)co-doped LTGZ glasses.In the temperature range of 300 to 700 K,the maximum relative and absolute sensitivities were 2.71%and 0.56%K^(−1),respectively.The temperature reliability was proved through variable temperature cycling tests.More importantly,to our knowledge,it is the first time to investigate the optical temperature measurement capability under a high magnetic field in this as-designed sensor.By applying the magnetic field up to 42 T,the relative sensitivity changes from 1.79%to 1.58%K^(−1),revealing that the temperature sensitivity of the sensor remains stable even in high magnetic fields.The results of the study provide a reference for the selection of temperature measurement materials in the field of optical temperature sensing,and the designed temperature sensor can be used for temperature measurement in extreme environments,especially in strong magnetic field conditions,which provides an important value for the development of special optical temperature sensors.
基金funded by the National Natural Science Foundation of China[grant numbers 42075115 and 41991285]the Joint Open Project of KLME&CIC-FEMD[grant number KLME201901]。
文摘Net primary productivity(NPP)is the net accumulation of organic matter by vegetation through photosynthesis and serves as a key indicator for exploring vegetation responses to climate change.Considering the remote and local impacts of soil heat capacities on vegetation growth through pathways of atmospheric circulation and land–atmosphere interaction,this paper develops a statistical prediction model for NPP from April to June(AMJ)across the middle-to-high latitudes of Eurasia.The model introduces two physically meaningful predictors:the snow water equivalent(SWE)from February to March(FM)over central Europe and the FM local soil temperature(ST).The positive phase of FM SWE triggers anomalous eastward-propagating Rossby waves,leading to an anomalous low-pressure system and cooling in the middle-to-high latitudes of Eurasia.This effect persists into spring through snow feedback to the atmosphere and affects subsequent NPP changes.The ST is closely related to the AMJ temperature and precipitation.With positive ST anomalies,the AMJ temperature and precipitation exhibit an east–west dipole anomaly distribution in this region.The single-factor prediction scheme using ST as the predictor is much better than using SWE as the predictor.Independent validation results from 2009 to 2014 demonstrate that the ST scheme alone has good predictive performance for the spatial distribution and interannual variability of NPP.The predictive skills of the multi-factor prediction schemes can be improved by about 13%if the ST predictor is included.The findings confirm that local ST is a predictor that must be included for NPP prediction.
