Ocean surface waves and upper sea circulation are primarily propelled by wind force and are usually expressed in terms of sea surface drag coefficient(c_(d))that increases with sea surface roughness and wind speed.Thi...Ocean surface waves and upper sea circulation are primarily propelled by wind force and are usually expressed in terms of sea surface drag coefficient(c_(d))that increases with sea surface roughness and wind speed.This work discussed the c_(d)parameterization at Aiyetoro,Ilaje Local Government Area,Ondo State,Southwestern Nigeria,to quantify the exchange of momentum in this region,The dependence of cd on some one hourly averaged variables sourced from ERA5 Reanalysis over a 71 year period(1950-2020)was clearly analysed.Results of the monthly mean and variability of cd and u10 over the study area showed that November had the lowest monthly mean cd and u10,with values of 0.000825 and 3.38 m/s,respectively,and August had the highest values of 0.001031 and 5.66 m/s,respectively.Furthermore,the cd variability is lowest(63.24%)in November and highest(106.35%)in August.The variability for u10 is lowest in March(198.18%)and greatest in October(304.37%).For the study location,five parameterizations,were statistically evaluated for the predictive power of c_(d) on an annual,seasonal and monthly basis.Furthermore,the cd showed improved performance when using monthly values than when using annual and seasonal values.The equations yielded better performance in the wet season than in the dry season.展开更多
Combining a linear regression and a temperature budget formula, a multivariate regression model is proposed to parameterize and estimate sea surface temperature（SST） cooling induced by tropical cyclones（TCs）. Thre...Combining a linear regression and a temperature budget formula, a multivariate regression model is proposed to parameterize and estimate sea surface temperature（SST） cooling induced by tropical cyclones（TCs）. Three major dynamic and thermodynamic processes governing the TC-induced SST cooling（SSTC）, vertical mixing, upwelling and heat flux, are parameterized empirically using a combination of multiple atmospheric and oceanic variables：sea surface height（SSH）, wind speed, wind curl, TC translation speed and surface net heat flux. The regression model fits reasonably well with 10-year statistical observations/reanalysis data obtained from 100 selected TCs in the northwestern Pacific during 2001–2010, with an averaged fitting error of 0.07 and a mean absolute error of 0.72°C between diagnostic and observed SST cooling. The results reveal that the vertical mixing is overall the pre dominant process producing ocean SST cooling, accounting for 55% of the total cooling. The upwelling accounts for 18% of the total cooling and its maximum occurs near the TC center, associated with TC-induced Ekman pumping. The surface heat flux accounts for 26% of the total cooling, and its contribution increases towards the tropics and the continental shelf. The ocean thermal structures, represented by the SSH in the regression model,plays an important role in modulating the SST cooling pattern. The concept of the regression model can be applicable in TC weather prediction models to improve SST parameterization schemes.展开更多
Aimed at attaining to an integrated and effective pattern to guide the port design process, this paper puts forward a new conception of feature solution, which is based on the parameterized feature modeling. With this...Aimed at attaining to an integrated and effective pattern to guide the port design process, this paper puts forward a new conception of feature solution, which is based on the parameterized feature modeling. With this solution, the overall port pre-design process can be conducted in a virtual pattern. Moreover, to evaluate the advantages of the new design pattern, an application of port system has been involved in this paper; and in the process of application a computational fluid dynamic analysis is concerned. An ideal effect of cleanness, high efficiency and high precision has been achieved.展开更多
Soil respiration(R_(S))represents the largest carbon flux from terrestrial ecosystems to the atmosphere,substantially influencing the global carbon budget and climate change.R_(S)exhibits vital yet complex nonlinear d...Soil respiration(R_(S))represents the largest carbon flux from terrestrial ecosystems to the atmosphere,substantially influencing the global carbon budget and climate change.R_(S)exhibits vital yet complex nonlinear dependencies on soil temperature and moisture,while its response to these factors demonstrates pronounced spatiotemporal heterogeneity.Most land carbon cycle models use fixed temperature and moisture sensitivities to project R_(S)changes,which may induce substantial uncertainty in R_(S)estimations and projections.This paper reviews recent progress in understanding spatiotemporal variations of R_(S)sensitivities,their responses to global warming,and advances in parameterizing these sensitivities.The exponential temperature response and parabolic moisture response of R_(S)are summarized,alongside their spatiotemporal sensitivities.Although some models have made progress in parameterizing spatiotemporally heterogeneous temperature and moisture sensitivities of R_(S),critical challenges persist,including insufficient mechanistic explanations,suboptimal validation performance,and poor cross-model consistency.Additionally,limitations in parameterizing the interactive effects of soil temperature and moisture on R_(S)may lead to notable biases in R_(S)estimations.This paper advocates expanding in situ measurements of R_(S)across climatic zones and land cover types,and further deepening the analysis of these data with advanced techniques(e.g.,artificial intelligence)to establish more comprehensive relationships between R_(S)and soil temperature and moisture.Such improvements would optimize land carbon cycle model parameterization,reduce estimation biases,enhance simulation precision,and ultimately provide robust scientific foundations for global carbon budgeting and climate policy formulation to support carbon neutrality goals.展开更多
We investigate the null tests of cosmic accelerated expansion by using the baryon acoustic oscillation(BAO)data measured by the dark energy spectroscopic instrument(DESI)and reconstruct the dimensionless Hubble parame...We investigate the null tests of cosmic accelerated expansion by using the baryon acoustic oscillation(BAO)data measured by the dark energy spectroscopic instrument(DESI)and reconstruct the dimensionless Hubble parameter E(z)from the DESI BAO Alcock-Paczynski(AP)data using Gaussian process to perform the null test.We find strong evidence of accelerated expansion from the DESI BAO AP data.By reconstructing the deceleration parameter q(z) from the DESI BAO AP data,we find that accelerated expansion persisted until z■0.7 with a 99.7%confidence level.Additionally,to provide insights into the Hubble tension problem,we propose combining the reconstructed E(z) with D_(H)/r_(d) data to derive a model-independent result r_(d)h=99.8±3.1 Mpc.This result is consistent with measurements from cosmic microwave background(CMB)anisotropies using the ΛCDM model.We also propose a model-independent method for reconstructing the comoving angular diameter distance D_(M)(z) from the distance modulus μ,using SNe Ia data and combining this result with DESI BAO data of D_(M)/r_(d) to constrain the value of r_(d).We find that the value of r_(d),derived from this model-independent method,is smaller than that obtained from CMB measurements,with a significant discrepancy of at least 4.17σ.All the conclusions drawn in this paper are independent of cosmological models and gravitational theories.展开更多
Accurately predicting fatigue life under multiaxial fatigue damage conditions is essential for ensuring the safety of critical components in service.However,due to the complexity of fatigue failure mechanisms,achievin...Accurately predicting fatigue life under multiaxial fatigue damage conditions is essential for ensuring the safety of critical components in service.However,due to the complexity of fatigue failure mechanisms,achieving accurate multiaxial fatigue life predictions remains challenging.Traditional multiaxial fatigue prediction models are often limited by specific material properties and loading conditions,making it difficult to maintain reliable life prediction results beyond these constraints.This paper presents a study on the impact of seven key feature quantities on multiaxial fatigue life,using Convolutional Neural Networks(CNN),Long Short-Term Memory Networks(LSTM),and Fully Connected Neural Networks(FCNN)within a deep learning framework.Fatigue test results from eight metal specimens were analyzed to identify these feature quantities,which were then extracted as critical time-series features.Using a CNN-LSTM network,these features were combined to form a feature matrix,which was subsequently input into an FCNN to predict metal fatigue life.A comparison of the fatigue life prediction results from the STFAN model with those from traditional prediction models—namely,the equivalent strain method,the maximum shear strain method,and the critical plane method—shows that the majority of predictions for the five metal materials and various loading conditions based on the STFAN model fall within an error band of 1.5 times.Additionally,all data points are within an error band of 2 times.These findings indicate that the STFAN model provides superior prediction accuracy compared to the traditional models,highlighting its broad applicability and high precision.展开更多
This study introduces a new ocean surface friction velocity scheme and a modified Thompson cloud microphysics parameterization scheme into the CMA-TYM model.The impact of these two parameterization schemes on the pred...This study introduces a new ocean surface friction velocity scheme and a modified Thompson cloud microphysics parameterization scheme into the CMA-TYM model.The impact of these two parameterization schemes on the prediction of the movement track and intensity of Typhoon Kompasu in 2021 is examined.Additionally,the possible reasons for their effects on tropical cyclone(TC)intensity prediction are analyzed.Statistical results show that both parameterization schemes improve the predictions of Typhoon Kompasu’s track and intensity.The influence on track prediction becomes evident after 60 h of model integration,while the significant positive impact on intensity prediction is observed after 66 h.Further analysis reveals that these two schemes affect the timing and magnitude of extreme TC intensity values by influencing the evolution of the TC’s warm-core structure.展开更多
This study investigates the stratification of soil thermal properties induced by soil organic carbon (SOC) and its impacts on the parameterization of the thermal properties. Soil parameters were measured for alpine gr...This study investigates the stratification of soil thermal properties induced by soil organic carbon (SOC) and its impacts on the parameterization of the thermal properties. Soil parameters were measured for alpine grassland stations and North China flux stations, with a total of 34 stations and 77 soil profiles. Measured data indicate that the topsoils of alpine grasslands contain high SOC contents than underlying soil layers, which leads to higher soil porosity values and lower thermal conductivity and bulk density values in the topsoils. However, this stratification is not evident at the lowland stations due to low SOC contents. Evaluations against measured data show that three thermal conductivity schemes used in land surface models severely overestimate the values for soils with high SOC content (i.e. topsoils of alpine grassland), but they are better for soils with low SOC content. A new parameterization is then developed to take the impacts of SOC into account. The new one can well estimate the soil thermal conductivity values in both low and high SOC content cases, and therefore, it is a potential candidate of thermal conductivity scheme to be used in land surface models.展开更多
Deep neural networks(DNNs)are effective in solving both forward and inverse problems for nonlinear partial differential equations(PDEs).However,conventional DNNs are not effective in handling problems such as delay di...Deep neural networks(DNNs)are effective in solving both forward and inverse problems for nonlinear partial differential equations(PDEs).However,conventional DNNs are not effective in handling problems such as delay differential equations(DDEs)and delay integrodifferential equations(DIDEs)with constant delays,primarily due to their low regularity at delayinduced breaking points.In this paper,a DNN method that combines multi-task learning(MTL)which is proposed to solve both the forward and inverse problems of DIDEs.The core idea of this approach is to divide the original equation into multiple tasks based on the delay,using auxiliary outputs to represent the integral terms,followed by the use of MTL to seamlessly incorporate the properties at the breaking points into the loss function.Furthermore,given the increased training dificulty associated with multiple tasks and outputs,we employ a sequential training scheme to reduce training complexity and provide reference solutions for subsequent tasks.This approach significantly enhances the approximation accuracy of solving DIDEs with DNNs,as demonstrated by comparisons with traditional DNN methods.We validate the effectiveness of this method through several numerical experiments,test various parameter sharing structures in MTL and compare the testing results of these structures.Finally,this method is implemented to solve the inverse problem of nonlinear DIDE and the results show that the unknown parameters of DIDE can be discovered with sparse or noisy data.展开更多
To meet the requirements of electromagnetic(EM)theory and applied physics,this study presents an overview of the state-of-the-art research on obtaining the EM properties of media and points out potential solutions tha...To meet the requirements of electromagnetic(EM)theory and applied physics,this study presents an overview of the state-of-the-art research on obtaining the EM properties of media and points out potential solutions that can break through the bottlenecks of current methods.Firstly,based on the survey of three mainstream approaches for acquiring EM properties of media,we identify the difficulties when implementing them in realistic environments.With a focus on addressing these problems and challenges,we propose a novel paradigm for obtaining the EM properties of multi-type media in realistic environments.Particularly,within this paradigm,we describe the implementation approach of the key technology,namely“multipath extraction using heterogeneous wave propagation data in multi-spectrum cases”.Finally,the latest measurement and simulation results show that the EM properties of multi-type media in realistic environments can be precisely and efficiently acquired by the methodology proposed in this study.展开更多
Model predictive control(MPC)has been deemed as an attractive control method in motor drives by virtue of its simple structure,convenient multi-objective optimization,and satisfactory dynamic performance.However,the s...Model predictive control(MPC)has been deemed as an attractive control method in motor drives by virtue of its simple structure,convenient multi-objective optimization,and satisfactory dynamic performance.However,the strong reliance on mathematical models seriously restrains its practical application.Therefore,improving the robustness of MPC has attained significant attentions in the last two decades,followed by which,model-free predictive control(MFPC)comes into existence.This article aims to reveal the current state of MFPC strategies for motor drives and give the categorization from the perspective of implementation.Based on this review,the principles of the reported MFPC strategies are introduced in detail,as well as the challenges encountered in technology realization.In addition,some of typical and important concepts are experimentally validated via case studies to evaluate the performance and highlight their features.Finally,the future trends of MFPC are discussed based on the current state and reported developments.展开更多
Transcutaneous electrical acupoint stimulation(TEAS)is a kind of physical therapy that use electric cur-rent through the electrodes placed on the surface of acupoints to produce clinical effects in the human body,whic...Transcutaneous electrical acupoint stimulation(TEAS)is a kind of physical therapy that use electric cur-rent through the electrodes placed on the surface of acupoints to produce clinical effects in the human body,which is characterized by less adverse reaction and convenient operation.It has been widely used in the treatment of various diseases.This review introduces six major clinical applications of TEAS,named analgesia,regulation of gastrointestinal function,improvement of reproductive function,enhancement of cognitive function,promotion of limb function recovery and relief of fatigue.Besides,TEAS has been ap-plied to the treatment of other chronic diseases such as hypertension and diabetes,achieving satisfactory clinical effects.However,two crucial challenges are encountered in the development of TEAS.One is the lack of standardization in the selection of parameters such as waveform,frequency,intensity and stimula-tion duration.The other is the limitation on the flexibility in the acupoint selection.This review analyzes key issues that need to be addressed in the current clinical application of TEAS,such as the selection of parameters and acupoints,and this review provides a certain reference value for optimizing regimens of TEAS and promoting its development and application.展开更多
This study primarily aimed to investigate the prevalence of human papillomavirus(HPV)and other common pathogens of sexually transmitted infections(STIs)in spermatozoa of infertile men and their effects on semen parame...This study primarily aimed to investigate the prevalence of human papillomavirus(HPV)and other common pathogens of sexually transmitted infections(STIs)in spermatozoa of infertile men and their effects on semen parameters.These pathogens included Ureaplasma urealyticum,Ureaplasma parvum,Chlamydia trachomatis,Mycoplasma genitalium,herpes simplex virus 2,Neisseria gonorrhoeae,Enterococcus faecalis,Streptococcus agalactiae,Pseudomonas aeruginosa,and Staphylococcus aureus.A total of 1951 men of infertile couples were recruited between 23 March 2023,and 17 May 2023,at the Department of Reproductive Medicine of The First People’s Hospital of Yunnan Province(Kunming,China).Multiplex polymerase chain reaction and capillary electrophoresis were used for HPV genotyping.Polymerase chain reaction and electrophoresis were also used to detect the presence of other STIs.The overall prevalence of HPV infection was 12.4%.The top five prevalent HPV subtypes were types 56,52,43,16,and 53 among those tested positive for HPV.Other common infections with high prevalence rates were Ureaplasma urealyticum(28.3%),Ureaplasma parvum(20.4%),and Enterococcus faecalis(9.5%).The prevalence rates of HPV coinfection with Ureaplasma urealyticum,Ureaplasma parvum,Chlamydia trachomatis,Mycoplasma genitalium,herpes simplex virus 2,Neisseria gonorrhoeae,Enterococcus faecalis,Streptococcus agalactiae,and Staphylococcus aureus were 24.8%,25.4%,10.6%,6.4%,2.4%,7.9%,5.9%,0.9%,and 1.3%,respectively.The semen volume and total sperm count were greatly decreased by HPV infection alone.Coinfection with HPV and Ureaplasma urealyticum significantly reduced sperm motility and viability.Our study shows that coinfection with STIs is highly prevalent in the semen of infertile men and that coinfection with pathogens can seriously affect semen parameters,emphasizing the necessity of semen screening for STIs.展开更多
The mechanical parameters and failure characteristics of sandstone under compressive-shear stress states provide crucial theoretical references for underground engineering construction.In this study,a series of varied...The mechanical parameters and failure characteristics of sandstone under compressive-shear stress states provide crucial theoretical references for underground engineering construction.In this study,a series of varied angle shear tests(VASTs)were designed using acoustic emission(AE)detection and digital image correlation technologies to evaluate the mechanical behaviors of typical red sandstone.AE signal parameters revealed differences in the number and intensity of microcracks within the sandstone,with a test angle(α)of 50°identified as a significant turning point for its failure properties.Whenα³50°,microcrack activity intensified,and the proportion of tensile cracks increased.Asαincreased,the number of fragments generated after failure decreased,fragment sizes became smaller,and the crack network simplified.Cracks extended from the two cut slits at the ends of the rock,gradually penetrating along the centerline towards the central location,as observed from the evolution of the strain concentration field.Both cohesion(c)and internal friction angle(ϕ)measured in VAST were lower than those measured under conventional triaxial compression.展开更多
The reaction rate constant is a crucial kinetic parameter that governs the charge and discharge performance of batteries,particularly in high-rate and thick-electrode applications.However,conventional estimation or fi...The reaction rate constant is a crucial kinetic parameter that governs the charge and discharge performance of batteries,particularly in high-rate and thick-electrode applications.However,conventional estimation or fitting methods often overestimate the charge transfer overpotential,leading to substantial errors in reaction rate constant measurements.These inaccuracies hinder the accurate prediction of voltage profiles and overall cell performance.In this study,we propose the characteristic time-decomposed overpotential(CTDO)method,which employs a single-layer particle electrode(SLPE)structure to eliminate interference overpotentials.By leveraging the distribution of relaxation times(DRT),our method effectively isolates the characteristic time of the charge transfer process,enabling a more precise determination of the reaction rate constant.Simulation results indicate that our approach reduces measurement errors to below 2%,closely aligning with theoretical values.Furthermore,experimental validation demonstrates an 80% reduction in error compared to the conventional galvanostatic intermittent titration technique(GITT)method.Overall,this study provides a novel voltage-based approach for determining the reaction rate constant,enhancing the applicability of theoretical analysis in electrode structural design and facilitating rapid battery optimization.展开更多
Austenitic stainless steel(ASS)is a common material used in high-pressure hydrogen systems.Prolonged exposure to high-pressure hydrogen can cause hydrogen embrittlement(HE),raising significant safety concerns.Selectiv...Austenitic stainless steel(ASS)is a common material used in high-pressure hydrogen systems.Prolonged exposure to high-pressure hydrogen can cause hydrogen embrittlement(HE),raising significant safety concerns.Selective Laser Melting(SLM),known for its high precision,is a promising additive manufacturing technology that has been widely adopted across various industries.Studies have reported that under certain SLM manufacturing conditions and process parameters,the HE resistance of SLM ASS is significantly better than that of conventionally manufactured(CM)ASS,showing great potential for application in high-pressure hydrogen systems.Thus,studying the HE of SLM ASS is crucial for further improving the safety of high-pressure hydrogen systems.This paper provides an overview of the SLM process,reviews the mechanisms of HE and their synergistic effects,and analyzes the HE characteristics of SLM ASS.Additionally,it examines the influence of unique microstructures and SLM process variables on HE of SLM ASS and offers recommendations for future research to enhance the safety of high-pressure hydrogen systems.展开更多
The endpoint carbon content in the converter is critical for the quality of steel products,and accurately predicting this parameter is an effective way to reduce alloy consumption and improve smelting efficiency.Howev...The endpoint carbon content in the converter is critical for the quality of steel products,and accurately predicting this parameter is an effective way to reduce alloy consumption and improve smelting efficiency.However,most scholars currently focus on modifying methods to enhance model accuracy,while overlooking the extent to which input parameters influence accuracy.To address this issue,in this study,a prediction model for the endpoint carbon content in the converter was developed using factor analysis(FA)and support vector machine(SVM)optimized by improved particle swarm optimization(IPSO).Analysis of the factors influencing the endpoint carbon content during the converter smelting process led to the identification of 21 input parameters.Subsequently,FA was used to reduce the dimensionality of the data and applied to the prediction model.The results demonstrate that the performance of the FA-IPSO-SVM model surpasses several existing methods,such as twin support vector regression and support vector machine.The model achieves hit rates of 89.59%,96.21%,and 98.74%within error ranges of±0.01%,±0.015%,and±0.02%,respectively.Finally,based on the prediction results obtained by sequentially removing input parameters,the parameters were classified into high influence(5%-7%),medium influence(2%-5%),and low influence(0-2%)categories according to their varying degrees of impact on prediction accuracy.This classi-fication provides a reference for selecting input parameters in future prediction models for endpoint carbon content.展开更多
A conceptual model of intermittent joints is introduced to the cyclic shear test in the laboratory to explore the effects of loading parameters on its shear behavior under cyclic shear loading.The results show that th...A conceptual model of intermittent joints is introduced to the cyclic shear test in the laboratory to explore the effects of loading parameters on its shear behavior under cyclic shear loading.The results show that the loading parameters(initial normal stress,normal stiffness,and shear velocity)determine propagation paths of the wing and secondary cracks in rock bridges during the initial shear cycle,creating different morphologies of macroscopic step-path rupture surfaces and asperities on them.The differences in stress state and rupture surface induce different cyclic shear responses.It shows that high initial normal stress accelerates asperity degradation,raises shear resistance,and promotes compression of intermittent joints.In addition,high normal stiffness provides higher normal stress and shear resistance during the initial cycles and inhibits the dilation and compression of intermittent joints.High shear velocity results in a higher shear resistance,greater dilation,and greater compression.Finally,shear strength is most sensitive to initial normal stress,followed by shear velocity and normal stiffness.Moreover,average dilation angle is most sensitive to initial normal stress,followed by normal stiffness and shear velocity.During the shear cycles,frictional coefficient is affected by asperity degradation,backfilling of rock debris,and frictional area,exhibiting a non-monotonic behavior.展开更多
Atlantic Meridional Overturning Circulation(AMOC)plays a central role in long-term climate variations through its heat and freshwater transports,which can collapse under a rapid increase of greenhouse gas forcing in c...Atlantic Meridional Overturning Circulation(AMOC)plays a central role in long-term climate variations through its heat and freshwater transports,which can collapse under a rapid increase of greenhouse gas forcing in climate models.Previous studies have suggested that the deviation of model parameters is one of the major factors in inducing inaccurate AMOC simulations.In this work,with a low-resolution earth system model,the authors try to explore whether a reasonable adjustment of the key model parameter can help to re-establish the AMOC after its collapse.Through a new optimization strategy,the extra freshwater flux(FWF)parameter is determined to be the dominant one affecting the AMOC’s variability.The traditional ensemble optimal interpolation(EnOI)data assimilation and new machine learning methods are adopted to optimize the FWF parameter in an abrupt 4×CO_(2) forcing experiment to improve the adaptability of model parameters and accelerate the recovery of AMOC.The results show that,under an abrupt 4×CO_(2) forcing in millennial simulations,the AMOC will first collapse and then re-establish by the default FWF parameter slowly.However,during the parameter adjustment process,the saltier and colder sea water over the North Atlantic region are the dominant factors in usefully improving the adaptability of the FWF parameter and accelerating the recovery of AMOC,according to their physical relationship with FWF on the interdecadal timescale.展开更多
The centroid coordinate serves as a critical control parameter in motion systems,including aircraft,missiles,rockets,and drones,directly influencing their motion dynamics and control performance.Traditional methods fo...The centroid coordinate serves as a critical control parameter in motion systems,including aircraft,missiles,rockets,and drones,directly influencing their motion dynamics and control performance.Traditional methods for centroid measurement often necessitate custom equipment and specialized positioning devices,leading to high costs and limited accuracy.Here,we present a centroid measurement method that integrates 3D scanning technology,enabling accurate measurement of centroid across various types of objects without the need for specialized positioning fixtures.A theoretical framework for centroid measurement was established,which combined the principle of the multi-point weighing method with 3D scanning technology.The measurement accuracy was evaluated using a designed standard component.Experimental results demonstrate that the discrepancies between the theoretical and the measured centroid of a standard component with various materials and complex shapes in the X,Y,and Z directions are 0.003 mm,0.009 mm,and 0.105 mm,respectively,yielding a spatial deviation of 0.106 mm.Qualitative verification was conducted through experimental validation of three distinct types.They confirmed the reliability of the proposed method,which allowed for accurate centroid measurements of various products without requiring positioning fixtures.This advancement significantly broadened the applicability and scope of centroid measurement devices,offering new theoretical insights and methodologies for the measurement of complex parts and systems.展开更多
基金supported by Department of Engineering,University of Campania Luigi Vanvitelli,81031 Aversa,Italy.
文摘Ocean surface waves and upper sea circulation are primarily propelled by wind force and are usually expressed in terms of sea surface drag coefficient(c_(d))that increases with sea surface roughness and wind speed.This work discussed the c_(d)parameterization at Aiyetoro,Ilaje Local Government Area,Ondo State,Southwestern Nigeria,to quantify the exchange of momentum in this region,The dependence of cd on some one hourly averaged variables sourced from ERA5 Reanalysis over a 71 year period(1950-2020)was clearly analysed.Results of the monthly mean and variability of cd and u10 over the study area showed that November had the lowest monthly mean cd and u10,with values of 0.000825 and 3.38 m/s,respectively,and August had the highest values of 0.001031 and 5.66 m/s,respectively.Furthermore,the cd variability is lowest(63.24%)in November and highest(106.35%)in August.The variability for u10 is lowest in March(198.18%)and greatest in October(304.37%).For the study location,five parameterizations,were statistically evaluated for the predictive power of c_(d) on an annual,seasonal and monthly basis.Furthermore,the cd showed improved performance when using monthly values than when using annual and seasonal values.The equations yielded better performance in the wet season than in the dry season.
基金The Major National Basic Research Development Program of China under contract No.2016YFA0202704the National Natural Science Foundation of China under contract Nos 41476008 and 41576018+1 种基金the Basic Fund of Chinese Academy of Meteorological Sciences under contract No.2017Z017the Strategic Priority Research Program of the Chinese Academy of Sciences under contract No.XDA11010303
文摘Combining a linear regression and a temperature budget formula, a multivariate regression model is proposed to parameterize and estimate sea surface temperature（SST） cooling induced by tropical cyclones（TCs）. Three major dynamic and thermodynamic processes governing the TC-induced SST cooling（SSTC）, vertical mixing, upwelling and heat flux, are parameterized empirically using a combination of multiple atmospheric and oceanic variables：sea surface height（SSH）, wind speed, wind curl, TC translation speed and surface net heat flux. The regression model fits reasonably well with 10-year statistical observations/reanalysis data obtained from 100 selected TCs in the northwestern Pacific during 2001–2010, with an averaged fitting error of 0.07 and a mean absolute error of 0.72°C between diagnostic and observed SST cooling. The results reveal that the vertical mixing is overall the pre dominant process producing ocean SST cooling, accounting for 55% of the total cooling. The upwelling accounts for 18% of the total cooling and its maximum occurs near the TC center, associated with TC-induced Ekman pumping. The surface heat flux accounts for 26% of the total cooling, and its contribution increases towards the tropics and the continental shelf. The ocean thermal structures, represented by the SSH in the regression model,plays an important role in modulating the SST cooling pattern. The concept of the regression model can be applicable in TC weather prediction models to improve SST parameterization schemes.
文摘Aimed at attaining to an integrated and effective pattern to guide the port design process, this paper puts forward a new conception of feature solution, which is based on the parameterized feature modeling. With this solution, the overall port pre-design process can be conducted in a virtual pattern. Moreover, to evaluate the advantages of the new design pattern, an application of port system has been involved in this paper; and in the process of application a computational fluid dynamic analysis is concerned. An ideal effect of cleanness, high efficiency and high precision has been achieved.
基金supported by the National Natural Science Foundation of China(Grant No.42175064)。
文摘Soil respiration(R_(S))represents the largest carbon flux from terrestrial ecosystems to the atmosphere,substantially influencing the global carbon budget and climate change.R_(S)exhibits vital yet complex nonlinear dependencies on soil temperature and moisture,while its response to these factors demonstrates pronounced spatiotemporal heterogeneity.Most land carbon cycle models use fixed temperature and moisture sensitivities to project R_(S)changes,which may induce substantial uncertainty in R_(S)estimations and projections.This paper reviews recent progress in understanding spatiotemporal variations of R_(S)sensitivities,their responses to global warming,and advances in parameterizing these sensitivities.The exponential temperature response and parabolic moisture response of R_(S)are summarized,alongside their spatiotemporal sensitivities.Although some models have made progress in parameterizing spatiotemporally heterogeneous temperature and moisture sensitivities of R_(S),critical challenges persist,including insufficient mechanistic explanations,suboptimal validation performance,and poor cross-model consistency.Additionally,limitations in parameterizing the interactive effects of soil temperature and moisture on R_(S)may lead to notable biases in R_(S)estimations.This paper advocates expanding in situ measurements of R_(S)across climatic zones and land cover types,and further deepening the analysis of these data with advanced techniques(e.g.,artificial intelligence)to establish more comprehensive relationships between R_(S)and soil temperature and moisture.Such improvements would optimize land carbon cycle model parameterization,reduce estimation biases,enhance simulation precision,and ultimately provide robust scientific foundations for global carbon budgeting and climate policy formulation to support carbon neutrality goals.
基金supported in part by the National Key Research and Development Program of China (Grant No.2020YFC2201504)the National Natural Science Foundation of China (Grant Nos.12588101 and 12535002)。
文摘We investigate the null tests of cosmic accelerated expansion by using the baryon acoustic oscillation(BAO)data measured by the dark energy spectroscopic instrument(DESI)and reconstruct the dimensionless Hubble parameter E(z)from the DESI BAO Alcock-Paczynski(AP)data using Gaussian process to perform the null test.We find strong evidence of accelerated expansion from the DESI BAO AP data.By reconstructing the deceleration parameter q(z) from the DESI BAO AP data,we find that accelerated expansion persisted until z■0.7 with a 99.7%confidence level.Additionally,to provide insights into the Hubble tension problem,we propose combining the reconstructed E(z) with D_(H)/r_(d) data to derive a model-independent result r_(d)h=99.8±3.1 Mpc.This result is consistent with measurements from cosmic microwave background(CMB)anisotropies using the ΛCDM model.We also propose a model-independent method for reconstructing the comoving angular diameter distance D_(M)(z) from the distance modulus μ,using SNe Ia data and combining this result with DESI BAO data of D_(M)/r_(d) to constrain the value of r_(d).We find that the value of r_(d),derived from this model-independent method,is smaller than that obtained from CMB measurements,with a significant discrepancy of at least 4.17σ.All the conclusions drawn in this paper are independent of cosmological models and gravitational theories.
基金supported by Key Program of National Natural Science Foundation of China(U2368215)the Science and Technology Research and Development Program Project of China Railway Group Co.,Ltd.(N2023J056).
文摘Accurately predicting fatigue life under multiaxial fatigue damage conditions is essential for ensuring the safety of critical components in service.However,due to the complexity of fatigue failure mechanisms,achieving accurate multiaxial fatigue life predictions remains challenging.Traditional multiaxial fatigue prediction models are often limited by specific material properties and loading conditions,making it difficult to maintain reliable life prediction results beyond these constraints.This paper presents a study on the impact of seven key feature quantities on multiaxial fatigue life,using Convolutional Neural Networks(CNN),Long Short-Term Memory Networks(LSTM),and Fully Connected Neural Networks(FCNN)within a deep learning framework.Fatigue test results from eight metal specimens were analyzed to identify these feature quantities,which were then extracted as critical time-series features.Using a CNN-LSTM network,these features were combined to form a feature matrix,which was subsequently input into an FCNN to predict metal fatigue life.A comparison of the fatigue life prediction results from the STFAN model with those from traditional prediction models—namely,the equivalent strain method,the maximum shear strain method,and the critical plane method—shows that the majority of predictions for the five metal materials and various loading conditions based on the STFAN model fall within an error band of 1.5 times.Additionally,all data points are within an error band of 2 times.These findings indicate that the STFAN model provides superior prediction accuracy compared to the traditional models,highlighting its broad applicability and high precision.
基金supported by the National Key R&D Program of China[grant number 2023YFC3008004]。
文摘This study introduces a new ocean surface friction velocity scheme and a modified Thompson cloud microphysics parameterization scheme into the CMA-TYM model.The impact of these two parameterization schemes on the prediction of the movement track and intensity of Typhoon Kompasu in 2021 is examined.Additionally,the possible reasons for their effects on tropical cyclone(TC)intensity prediction are analyzed.Statistical results show that both parameterization schemes improve the predictions of Typhoon Kompasu’s track and intensity.The influence on track prediction becomes evident after 60 h of model integration,while the significant positive impact on intensity prediction is observed after 66 h.Further analysis reveals that these two schemes affect the timing and magnitude of extreme TC intensity values by influencing the evolution of the TC’s warm-core structure.
基金supported by National Natural Science Foundation of China (Grant No. 41105003)Key Project of Chinese Academy of Sciences (Grant No.KZCX2-YW-Q10-2)+1 种基金National Natural Science Foundation of China (Grant No. 91025004)Open Fund from the State Key Laboratory of Remote Sensing Science (Grant No. OFSLRSS201108) that is cosponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University
文摘This study investigates the stratification of soil thermal properties induced by soil organic carbon (SOC) and its impacts on the parameterization of the thermal properties. Soil parameters were measured for alpine grassland stations and North China flux stations, with a total of 34 stations and 77 soil profiles. Measured data indicate that the topsoils of alpine grasslands contain high SOC contents than underlying soil layers, which leads to higher soil porosity values and lower thermal conductivity and bulk density values in the topsoils. However, this stratification is not evident at the lowland stations due to low SOC contents. Evaluations against measured data show that three thermal conductivity schemes used in land surface models severely overestimate the values for soils with high SOC content (i.e. topsoils of alpine grassland), but they are better for soils with low SOC content. A new parameterization is then developed to take the impacts of SOC into account. The new one can well estimate the soil thermal conductivity values in both low and high SOC content cases, and therefore, it is a potential candidate of thermal conductivity scheme to be used in land surface models.
文摘Deep neural networks(DNNs)are effective in solving both forward and inverse problems for nonlinear partial differential equations(PDEs).However,conventional DNNs are not effective in handling problems such as delay differential equations(DDEs)and delay integrodifferential equations(DIDEs)with constant delays,primarily due to their low regularity at delayinduced breaking points.In this paper,a DNN method that combines multi-task learning(MTL)which is proposed to solve both the forward and inverse problems of DIDEs.The core idea of this approach is to divide the original equation into multiple tasks based on the delay,using auxiliary outputs to represent the integral terms,followed by the use of MTL to seamlessly incorporate the properties at the breaking points into the loss function.Furthermore,given the increased training dificulty associated with multiple tasks and outputs,we employ a sequential training scheme to reduce training complexity and provide reference solutions for subsequent tasks.This approach significantly enhances the approximation accuracy of solving DIDEs with DNNs,as demonstrated by comparisons with traditional DNN methods.We validate the effectiveness of this method through several numerical experiments,test various parameter sharing structures in MTL and compare the testing results of these structures.Finally,this method is implemented to solve the inverse problem of nonlinear DIDE and the results show that the unknown parameters of DIDE can be discovered with sparse or noisy data.
基金supported by the Beijing Natural Science Foundation(No.L212029)the National Natural Science Foundation of China(No.62271043).
文摘To meet the requirements of electromagnetic(EM)theory and applied physics,this study presents an overview of the state-of-the-art research on obtaining the EM properties of media and points out potential solutions that can break through the bottlenecks of current methods.Firstly,based on the survey of three mainstream approaches for acquiring EM properties of media,we identify the difficulties when implementing them in realistic environments.With a focus on addressing these problems and challenges,we propose a novel paradigm for obtaining the EM properties of multi-type media in realistic environments.Particularly,within this paradigm,we describe the implementation approach of the key technology,namely“multipath extraction using heterogeneous wave propagation data in multi-spectrum cases”.Finally,the latest measurement and simulation results show that the EM properties of multi-type media in realistic environments can be precisely and efficiently acquired by the methodology proposed in this study.
基金supported in part by the National Natural Science Foundation of China under Grant 52077002。
文摘Model predictive control(MPC)has been deemed as an attractive control method in motor drives by virtue of its simple structure,convenient multi-objective optimization,and satisfactory dynamic performance.However,the strong reliance on mathematical models seriously restrains its practical application.Therefore,improving the robustness of MPC has attained significant attentions in the last two decades,followed by which,model-free predictive control(MFPC)comes into existence.This article aims to reveal the current state of MFPC strategies for motor drives and give the categorization from the perspective of implementation.Based on this review,the principles of the reported MFPC strategies are introduced in detail,as well as the challenges encountered in technology realization.In addition,some of typical and important concepts are experimentally validated via case studies to evaluate the performance and highlight their features.Finally,the future trends of MFPC are discussed based on the current state and reported developments.
基金Supported by Shanghai 2020“Science and Technology Innovation Action Plan”Medical Innovation Research Special Program:20Y21902800Shanghai Municipal Health Commission Shanghai Three-Year Action Plan to Further Accelerate the Development of Traditional Chinese Medicine Inheritance and Innovation:ZY(2021-2023)−0302)+1 种基金Shanghai Key Specialty(Acupuncture)Construction Project:shslczdzk04701Shanghai 2024"Science and Technology Innovation Action Plan"star cultivation(Sail special):24YF2740600.
文摘Transcutaneous electrical acupoint stimulation(TEAS)is a kind of physical therapy that use electric cur-rent through the electrodes placed on the surface of acupoints to produce clinical effects in the human body,which is characterized by less adverse reaction and convenient operation.It has been widely used in the treatment of various diseases.This review introduces six major clinical applications of TEAS,named analgesia,regulation of gastrointestinal function,improvement of reproductive function,enhancement of cognitive function,promotion of limb function recovery and relief of fatigue.Besides,TEAS has been ap-plied to the treatment of other chronic diseases such as hypertension and diabetes,achieving satisfactory clinical effects.However,two crucial challenges are encountered in the development of TEAS.One is the lack of standardization in the selection of parameters such as waveform,frequency,intensity and stimula-tion duration.The other is the limitation on the flexibility in the acupoint selection.This review analyzes key issues that need to be addressed in the current clinical application of TEAS,such as the selection of parameters and acupoints,and this review provides a certain reference value for optimizing regimens of TEAS and promoting its development and application.
基金supported by the Yunnan Provincial Key Laboratory of Clinical Virology(No.202002AG070062)the Key Projects of Yunnan Province Science and Technology Department(No.202302AA310044)the National Natural Science Foundation of China(No.82060664).
文摘This study primarily aimed to investigate the prevalence of human papillomavirus(HPV)and other common pathogens of sexually transmitted infections(STIs)in spermatozoa of infertile men and their effects on semen parameters.These pathogens included Ureaplasma urealyticum,Ureaplasma parvum,Chlamydia trachomatis,Mycoplasma genitalium,herpes simplex virus 2,Neisseria gonorrhoeae,Enterococcus faecalis,Streptococcus agalactiae,Pseudomonas aeruginosa,and Staphylococcus aureus.A total of 1951 men of infertile couples were recruited between 23 March 2023,and 17 May 2023,at the Department of Reproductive Medicine of The First People’s Hospital of Yunnan Province(Kunming,China).Multiplex polymerase chain reaction and capillary electrophoresis were used for HPV genotyping.Polymerase chain reaction and electrophoresis were also used to detect the presence of other STIs.The overall prevalence of HPV infection was 12.4%.The top five prevalent HPV subtypes were types 56,52,43,16,and 53 among those tested positive for HPV.Other common infections with high prevalence rates were Ureaplasma urealyticum(28.3%),Ureaplasma parvum(20.4%),and Enterococcus faecalis(9.5%).The prevalence rates of HPV coinfection with Ureaplasma urealyticum,Ureaplasma parvum,Chlamydia trachomatis,Mycoplasma genitalium,herpes simplex virus 2,Neisseria gonorrhoeae,Enterococcus faecalis,Streptococcus agalactiae,and Staphylococcus aureus were 24.8%,25.4%,10.6%,6.4%,2.4%,7.9%,5.9%,0.9%,and 1.3%,respectively.The semen volume and total sperm count were greatly decreased by HPV infection alone.Coinfection with HPV and Ureaplasma urealyticum significantly reduced sperm motility and viability.Our study shows that coinfection with STIs is highly prevalent in the semen of infertile men and that coinfection with pathogens can seriously affect semen parameters,emphasizing the necessity of semen screening for STIs.
基金Project(52374150)supported by the National Natural Science Foundation of ChinaProject(2021RC3007)supported by the Science and Technology Innovation Program of Hunan Province,China。
文摘The mechanical parameters and failure characteristics of sandstone under compressive-shear stress states provide crucial theoretical references for underground engineering construction.In this study,a series of varied angle shear tests(VASTs)were designed using acoustic emission(AE)detection and digital image correlation technologies to evaluate the mechanical behaviors of typical red sandstone.AE signal parameters revealed differences in the number and intensity of microcracks within the sandstone,with a test angle(α)of 50°identified as a significant turning point for its failure properties.Whenα³50°,microcrack activity intensified,and the proportion of tensile cracks increased.Asαincreased,the number of fragments generated after failure decreased,fragment sizes became smaller,and the crack network simplified.Cracks extended from the two cut slits at the ends of the rock,gradually penetrating along the centerline towards the central location,as observed from the evolution of the strain concentration field.Both cohesion(c)and internal friction angle(ϕ)measured in VAST were lower than those measured under conventional triaxial compression.
基金supported by the National Key R&D Program of China 2022YFB2404300the National Natural Science Foundation of China U22B2069the China Postdoctoral Science Foundation 2024M761006。
文摘The reaction rate constant is a crucial kinetic parameter that governs the charge and discharge performance of batteries,particularly in high-rate and thick-electrode applications.However,conventional estimation or fitting methods often overestimate the charge transfer overpotential,leading to substantial errors in reaction rate constant measurements.These inaccuracies hinder the accurate prediction of voltage profiles and overall cell performance.In this study,we propose the characteristic time-decomposed overpotential(CTDO)method,which employs a single-layer particle electrode(SLPE)structure to eliminate interference overpotentials.By leveraging the distribution of relaxation times(DRT),our method effectively isolates the characteristic time of the charge transfer process,enabling a more precise determination of the reaction rate constant.Simulation results indicate that our approach reduces measurement errors to below 2%,closely aligning with theoretical values.Furthermore,experimental validation demonstrates an 80% reduction in error compared to the conventional galvanostatic intermittent titration technique(GITT)method.Overall,this study provides a novel voltage-based approach for determining the reaction rate constant,enhancing the applicability of theoretical analysis in electrode structural design and facilitating rapid battery optimization.
基金finncially supported by the National Natural Science Foundation of China(No.52075183)the Guangdong Basic and Applied Research Fundamental(No.2023A1515010692)the Key-Area Research and Development Program of Guangdong Province(Nos.2024B1111080002 and 2020B0404020004).
文摘Austenitic stainless steel(ASS)is a common material used in high-pressure hydrogen systems.Prolonged exposure to high-pressure hydrogen can cause hydrogen embrittlement(HE),raising significant safety concerns.Selective Laser Melting(SLM),known for its high precision,is a promising additive manufacturing technology that has been widely adopted across various industries.Studies have reported that under certain SLM manufacturing conditions and process parameters,the HE resistance of SLM ASS is significantly better than that of conventionally manufactured(CM)ASS,showing great potential for application in high-pressure hydrogen systems.Thus,studying the HE of SLM ASS is crucial for further improving the safety of high-pressure hydrogen systems.This paper provides an overview of the SLM process,reviews the mechanisms of HE and their synergistic effects,and analyzes the HE characteristics of SLM ASS.Additionally,it examines the influence of unique microstructures and SLM process variables on HE of SLM ASS and offers recommendations for future research to enhance the safety of high-pressure hydrogen systems.
基金financially supported by the National Natural Science Foundation of China(No.52174297).
文摘The endpoint carbon content in the converter is critical for the quality of steel products,and accurately predicting this parameter is an effective way to reduce alloy consumption and improve smelting efficiency.However,most scholars currently focus on modifying methods to enhance model accuracy,while overlooking the extent to which input parameters influence accuracy.To address this issue,in this study,a prediction model for the endpoint carbon content in the converter was developed using factor analysis(FA)and support vector machine(SVM)optimized by improved particle swarm optimization(IPSO).Analysis of the factors influencing the endpoint carbon content during the converter smelting process led to the identification of 21 input parameters.Subsequently,FA was used to reduce the dimensionality of the data and applied to the prediction model.The results demonstrate that the performance of the FA-IPSO-SVM model surpasses several existing methods,such as twin support vector regression and support vector machine.The model achieves hit rates of 89.59%,96.21%,and 98.74%within error ranges of±0.01%,±0.015%,and±0.02%,respectively.Finally,based on the prediction results obtained by sequentially removing input parameters,the parameters were classified into high influence(5%-7%),medium influence(2%-5%),and low influence(0-2%)categories according to their varying degrees of impact on prediction accuracy.This classi-fication provides a reference for selecting input parameters in future prediction models for endpoint carbon content.
基金financially supported by the National Natural Science Foundation of China(Grant No.42172292)Taishan Scholars Project Special Funding,and Shandong Energy Group(Grant No.SNKJ 2022A01-R26).
文摘A conceptual model of intermittent joints is introduced to the cyclic shear test in the laboratory to explore the effects of loading parameters on its shear behavior under cyclic shear loading.The results show that the loading parameters(initial normal stress,normal stiffness,and shear velocity)determine propagation paths of the wing and secondary cracks in rock bridges during the initial shear cycle,creating different morphologies of macroscopic step-path rupture surfaces and asperities on them.The differences in stress state and rupture surface induce different cyclic shear responses.It shows that high initial normal stress accelerates asperity degradation,raises shear resistance,and promotes compression of intermittent joints.In addition,high normal stiffness provides higher normal stress and shear resistance during the initial cycles and inhibits the dilation and compression of intermittent joints.High shear velocity results in a higher shear resistance,greater dilation,and greater compression.Finally,shear strength is most sensitive to initial normal stress,followed by shear velocity and normal stiffness.Moreover,average dilation angle is most sensitive to initial normal stress,followed by normal stiffness and shear velocity.During the shear cycles,frictional coefficient is affected by asperity degradation,backfilling of rock debris,and frictional area,exhibiting a non-monotonic behavior.
基金supported by the National Key R&D Program of China [grant number 2023YFF0805202]the National Natural Science Foun-dation of China [grant number 42175045]the Strategic Priority Research Program of the Chinese Academy of Sciences [grant number XDB42000000]。
文摘Atlantic Meridional Overturning Circulation(AMOC)plays a central role in long-term climate variations through its heat and freshwater transports,which can collapse under a rapid increase of greenhouse gas forcing in climate models.Previous studies have suggested that the deviation of model parameters is one of the major factors in inducing inaccurate AMOC simulations.In this work,with a low-resolution earth system model,the authors try to explore whether a reasonable adjustment of the key model parameter can help to re-establish the AMOC after its collapse.Through a new optimization strategy,the extra freshwater flux(FWF)parameter is determined to be the dominant one affecting the AMOC’s variability.The traditional ensemble optimal interpolation(EnOI)data assimilation and new machine learning methods are adopted to optimize the FWF parameter in an abrupt 4×CO_(2) forcing experiment to improve the adaptability of model parameters and accelerate the recovery of AMOC.The results show that,under an abrupt 4×CO_(2) forcing in millennial simulations,the AMOC will first collapse and then re-establish by the default FWF parameter slowly.However,during the parameter adjustment process,the saltier and colder sea water over the North Atlantic region are the dominant factors in usefully improving the adaptability of the FWF parameter and accelerating the recovery of AMOC,according to their physical relationship with FWF on the interdecadal timescale.
基金supported by National Natural Science Foundation of China(No.52176122).
文摘The centroid coordinate serves as a critical control parameter in motion systems,including aircraft,missiles,rockets,and drones,directly influencing their motion dynamics and control performance.Traditional methods for centroid measurement often necessitate custom equipment and specialized positioning devices,leading to high costs and limited accuracy.Here,we present a centroid measurement method that integrates 3D scanning technology,enabling accurate measurement of centroid across various types of objects without the need for specialized positioning fixtures.A theoretical framework for centroid measurement was established,which combined the principle of the multi-point weighing method with 3D scanning technology.The measurement accuracy was evaluated using a designed standard component.Experimental results demonstrate that the discrepancies between the theoretical and the measured centroid of a standard component with various materials and complex shapes in the X,Y,and Z directions are 0.003 mm,0.009 mm,and 0.105 mm,respectively,yielding a spatial deviation of 0.106 mm.Qualitative verification was conducted through experimental validation of three distinct types.They confirmed the reliability of the proposed method,which allowed for accurate centroid measurements of various products without requiring positioning fixtures.This advancement significantly broadened the applicability and scope of centroid measurement devices,offering new theoretical insights and methodologies for the measurement of complex parts and systems.
