Machine learning(ML)has become a powerful tool for accelerating the design and development of new materials.Among various traditional ML algorithms,decision tree-based ensemble learning methods are frequently chosen f...Machine learning(ML)has become a powerful tool for accelerating the design and development of new materials.Among various traditional ML algorithms,decision tree-based ensemble learning methods are frequently chosen for their strong predictive capabilities.However,decision trees are limited in regression tasks to interpolating within the data range of the training set,which restricts their usefulness for designing materials with enhanced properties.Herein,we focused on predicting and optimizing the L1_(2)-phase solvus temperature(T_(L12))and density,two critical properties for multi-principal-element superalloys(MPESAs).To achieve this,we employed the piecewise symbolic regression tree(PS-Tree),which demonstrates excellent extrapolation capability.Our model successfully predicted high T_(L12)values exceeding the training data range(1242℃),with four candidate alloys achieving TL12values of 1246,1249,1254,and 1274℃.Experimental validation confirmed the accuracy of these predictions,verifying the robust extrapolative capability of the PS-Tree method.Notably,one alloy exhibited a T_(L12)of 1267℃and a density of 7.94 g cm^(-3),outperforming most MPESAs.Additionally,another alloy exhibited a compressive yield strength of 897 MPa at 750℃,with a specific yield strength at this temperature higher than that of most L1_(2)-strengthened alloys and Co/Ni-based superalloys.Moreover,the model provided generalized insights,indicating that alloys with δ_(r)>5.3 and ΔH_(mix)<-12.8 J mol^(-1)K^(-1)tend to favor higher T_(L12).展开更多
This paper investigates the optimal reinsurance-investment strategy for an insurer whose premium is subject to extrapolative bias.In other words,the insurance premium is dynamically updated by a weighted average of pr...This paper investigates the optimal reinsurance-investment strategy for an insurer whose premium is subject to extrapolative bias.In other words,the insurance premium is dynamically updated by a weighted average of prior claims and the initial estimation of claims.The insurer’s surplus follows a diffusion approximation process.He purchases proportional reinsurance or acquires new business to manage insurance risk,and invests his surplus in the financial market,containing a risk-free asset and a risky asset(stock).The price of the risky asset is described by a constant elasticity of variance(CEV)model.The insurer is uncertain about the models of claims and risky asset.In order to derive robust optimal reinsurance-investment strategies,we establish an optimal control problem by maximizing the insurer’s expected exponential utility of terminal wealth and solve the optimization problem explicitly.Finally,we present several numerical examples to illustrate our theoretical results.展开更多
This study proposes a novel radar echo extrapolation algorithm,OF-ConvGRU,which integrates Optical Flow(OF)and Convolutional Gated Recurrent Unit(ConvGRU)methods for improved nowcasting.Using the Standardized Radar Da...This study proposes a novel radar echo extrapolation algorithm,OF-ConvGRU,which integrates Optical Flow(OF)and Convolutional Gated Recurrent Unit(ConvGRU)methods for improved nowcasting.Using the Standardized Radar Dataset of the Guangdong-Hong Kong-Macao Greater Bay Area,the performance of OF-ConvGRU was evaluated against OF and ConvGRU methods.Threat Score(TS)and Bias Score(BIAS)were employed to assess extrapolation accuracy across various echo intensities(20-50 dBz)and weather phenomena.Results demonstrate that OF-ConvGRU significantly enhances prediction accuracy for moderate-intensity echoes(30-40 dBz),effectively combining OF s precise motion estimation with ConvGRU s nonlinear learning capabilities.However,challenges persist in low-intensity(20 dBz)and high-intensity(50 dBz)echo predictions.The study reveals distinct advantages of each method in specific contexts,highlighting the importance of multi-method approaches in operational nowcasting.OF-ConvGRU shows promise in balancing short-term accuracy with long-term stability,particularly for complex weather systems.展开更多
Understanding the wind power potential of a site is essential for designing an optimal wind power conditioning system. The Weibull distribution and wind speed extrapolation methods are powerful mathematical tools for ...Understanding the wind power potential of a site is essential for designing an optimal wind power conditioning system. The Weibull distribution and wind speed extrapolation methods are powerful mathematical tools for efficiently predicting the frequency distribution of wind speeds at a site. Hourly wind speed and direction data were collected from the National Aeronautics and Space Administration (NASA) website for the period 2013 to 2023. MATLAB software was used to calculate the distribution parameters using the graphical method and to plot the corresponding curves, while WRPLOTView software was used to construct the wind rose. The average wind speed obtained is 3.33 m/s and can reach up to 5.71 m/s at a height of 100 meters. The wind energy is estimated to be 1315.30 kWh/m2 at a height of 100 meters. The wind rose indicates the prevailing winds (ranging from 3.60 m/s to 5.70 m/s) in the northeast-east direction.展开更多
Seismic anisotropy has been extensively acknowledged as a crucial element that influences the wave propagation characteristic during wavefield simulation,inversion and imaging.Transversely isotropy(TI)and orthorhombic...Seismic anisotropy has been extensively acknowledged as a crucial element that influences the wave propagation characteristic during wavefield simulation,inversion and imaging.Transversely isotropy(TI)and orthorhombic anisotropy(OA)are two typical categories of anisotropic media in exploration geophysics.In comparison of the elastic wave equations in both TI and OA media,pseudo-acoustic wave equations(PWEs)based on the acoustic assumption can markedly reduce computational cost and complexity.However,the presently available PWEs may experience SV-wave contamination and instability when anisotropic parameters cannot satisfy the approximated condition.Exploiting pure-mode wave equations can effectively resolve the above-mentioned issues and generate pure P-wave events without any artifacts.To further improve the computational accuracy and efficiency,we develop two novel pure qP-wave equations(PPEs)and illustrate the corresponding numerical solutions in the timespace domain for 3D tilted TI(TTI)and tilted OA(TOA)media.First,the rational polynomials are adopted to estimate the exact pure qP-wave dispersion relations,which contain complicated pseudo-differential operators with irrational forms.The polynomial coefficients are produced by applying a linear optimization algorithm to minimize the objective function difference between the expansion formula and the exact one.Then,the developed optimized PPEs are efficiently implemented using the finite-difference(FD)method in the time-space domain by introducing a scalar operator,which can help avoid the problem of spectral-based algorithms and other calculation burdens.Structures of the new equations are concise and corresponding implementation processes are straightforward.Phase velocity analyses indicate that our proposed optimized equations can lead to reliable approximation results.3D synthetic examples demonstrate that our proposed FD-based PPEs can produce accurate and stable P-wave responses,and effectively describe the wavefield features in complicated TTI and TOA media.展开更多
Extrapolation on Temporal Knowledge Graphs(TKGs)aims to predict future knowledge from a set of historical Knowledge Graphs in chronological order.The temporally adjacent facts in TKGs naturally form event sequences,ca...Extrapolation on Temporal Knowledge Graphs(TKGs)aims to predict future knowledge from a set of historical Knowledge Graphs in chronological order.The temporally adjacent facts in TKGs naturally form event sequences,called event evolution patterns,implying informative temporal dependencies between events.Recently,many extrapolation works on TKGs have been devoted to modelling these evolutional patterns,but the task is still far from resolved because most existing works simply rely on encoding these patterns into entity representations while overlooking the significant information implied by relations of evolutional patterns.However,the authors realise that the temporal dependencies inherent in the relations of these event evolution patterns may guide the follow-up event prediction to some extent.To this end,a Temporal Relational Context-based Temporal Dependencies Learning Network(TRenD)is proposed to explore the temporal context of relations for more comprehensive learning of event evolution patterns,especially those temporal dependencies caused by interactive patterns of relations.Trend incorporates a semantic context unit to capture semantic correlations between relations,and a structural context unit to learn the interaction pattern of relations.By learning the temporal contexts of relations semantically and structurally,the authors gain insights into the underlying event evolution patterns,enabling to extract comprehensive historical information for future prediction better.Experimental results on benchmark datasets demonstrate the superiority of the model.展开更多
Finite-volume extrapolation is an important step for extracting physical observables from lattice calculations.However,it is a significant challenge for systems with long-range interactions.We employ symbolic regressi...Finite-volume extrapolation is an important step for extracting physical observables from lattice calculations.However,it is a significant challenge for systems with long-range interactions.We employ symbolic regression to regress the finite-volume extrapolation formula for both short-range and long-range interactions.The regressed formula still holds the exponential form with a factor L^(n) in front of it.The power decreases with the decreasing range of the force.When the range of the force becomes sufficiently small,the power converges to-1,recovering the short-range formula as expected.Our work represents a significant advancement in leveraging machine learning to probe uncharted territories within particle physics.展开更多
Proteolysis-targeting chimeras(PROTACs)represent a promising class of drugs that can target disease-causing proteins more effectively than traditional small molecule inhibitors can,potentially revolutionizing drug dis...Proteolysis-targeting chimeras(PROTACs)represent a promising class of drugs that can target disease-causing proteins more effectively than traditional small molecule inhibitors can,potentially revolutionizing drug discovery and treatment strategies.However,the links between in vitro and in vivo data are poorly understood,hindering a comprehensive understanding of the absorption,distribution,metabolism,and excretion(ADME)of PROTACs.In this work,14C-labeled vepdegestrant(ARV-471),which is currently in phase III clinical trials for breast cancer,was synthesized as a model PROTAC to characterize its preclinical ADME properties and simulate its clinical pharmacokinetics(PK)by establishing a physiologically based pharmacokinetics(PBPK)model.For in vitro–in vivo extrapolation(IVIVE),hepatocyte clearance correlated more closely with in vivo rat PK data than liver microsomal clearance did.PBPK models,which were initially developed and validated in rats,accurately simulate ARV-471's PK across fed and fasted states,with parameters within 1.75-fold of the observed values.Human models,informed by in vitro ADME data,closely mirrored postoral dose plasma profiles at 30 mg.Furthermore,no human-specific metabolites were identified in vitro and the metabolic profile of rats could overlap that of humans.This work presents a roadmap for developing future PROTAC medications by elucidating the correlation between in vitro and in vivo characteristics.展开更多
This paper studies a strongly convergent inertial forward-backward-forward algorithm for the variational inequality problem in Hilbert spaces.In our convergence analysis,we do not assume the on-line rule of the inerti...This paper studies a strongly convergent inertial forward-backward-forward algorithm for the variational inequality problem in Hilbert spaces.In our convergence analysis,we do not assume the on-line rule of the inertial parameters and the iterates,which have been assumed by several authors whenever a strongly convergent algorithm with an inertial extrapolation step is proposed for a variational inequality problem.Consequently,our proof arguments are different from what is obtainable in the relevant literature.Finally,we give numerical tests to confirm the theoretical analysis and show that our proposed algorithm is superior to related ones in the literature.展开更多
We use the extrapolated Tikhonov regularization to deal with the ill-posed problem of 3D density inversion of gravity gradient data. The use of regularization parameters in the proposed method reduces the deviations b...We use the extrapolated Tikhonov regularization to deal with the ill-posed problem of 3D density inversion of gravity gradient data. The use of regularization parameters in the proposed method reduces the deviations between calculated and observed data. We also use the depth weighting function based on the eigenvector of gravity gradient tensor to eliminate undesired effects owing to the fast attenuation of the position function. Model data suggest that the extrapolated Tikhonov regularization in conjunction with the depth weighting function can effectively recover the 3D distribution of density anomalies. We conduct density inversion of gravity gradient data from the Australia Kauring test site and compare the inversion results with the published research results. The proposed inversion method can be used to obtain the 3D density distribution of underground anomalies.展开更多
Tikhonov regularization（TR） method has played a very important role in the gravity data and magnetic data process. In this paper, the Tikhonov regularization method with respect to the inversion of gravity data is d...Tikhonov regularization（TR） method has played a very important role in the gravity data and magnetic data process. In this paper, the Tikhonov regularization method with respect to the inversion of gravity data is discussed. and the extrapolated TR method（EXTR） is introduced to improve the fitting error. Furthermore, the effect of the parameters in the EXTR method on the fitting error, number of iterations, and inversion results are discussed in details. The computation results using a synthetic model with the same and different densities indicated that. compared with the TR method, the EXTR method not only achieves the a priori fitting error level set by the interpreter but also increases the fitting precision, although it increases the computation time and number of iterations. And the EXTR inversion results are more compact than the TR inversion results, which are more divergent. The range of the inversion data is closer to the default range of the model parameters, and the model features and default model density distribution agree well.展开更多
Autoregressive (AR) modeling is applied to data extrapolation of radio frequency (RF) echo signals, and Burg algorithm, which can be computed in small amount and lead to a stable prediction filter, is used to estimate...Autoregressive (AR) modeling is applied to data extrapolation of radio frequency (RF) echo signals, and Burg algorithm, which can be computed in small amount and lead to a stable prediction filter, is used to estimate the prediction parameters of AR modeling. The complex data samples are directly extrapolated to obtain the extrapolated echo data in the frequency domain. The small rotating angle data extrapolation and the large rotating angular data extrapolation are considered separately in azimuth domain. The method of data extrapolation for the small rotating angle is the same as that in frequency domain, while the amplitude samples of large rotating angle echo data are extrapolated to obtain extrapolated echo amplitude, and the complex data of large rotating angle echo samples are extrapolated to get the extrapolated echo phase respectively. The calculation results show that the extrapolated echo data obtained by the above mentioned methods are accurate.展开更多
This paper deals with the recommendation system in the so-called user-centric payment environment where users,i.e.,the payers,can make payments without providing self-information to merchants.This service maintains on...This paper deals with the recommendation system in the so-called user-centric payment environment where users,i.e.,the payers,can make payments without providing self-information to merchants.This service maintains only the minimum purchase information such as the purchased product names,the time of purchase,the place of purchase for possible refunds or cancellations of purchases.This study aims to develop AI-based recommendation system by utilizing the minimum transaction data generated by the user-centric payment service.First,we developed a matrix-based extrapolative collaborative filtering algorithm based on open transaction data.The recommendation methodology was verified with the real transaction data.Based on the experimental results,we confirmed that the recommendation performance is satisfactory only with the minimum purchase information.展开更多
A series of SnO2‐based catalysts modified by Mn, Zr, Ti and Pb oxides with a Sn/M （M=Mn, Zr, Ti and Pb） molar ratio of 9/1 were prepared by a co‐precipitation method and used for CH4 and CO oxidation. The Mn3＋, ...A series of SnO2‐based catalysts modified by Mn, Zr, Ti and Pb oxides with a Sn/M （M=Mn, Zr, Ti and Pb） molar ratio of 9/1 were prepared by a co‐precipitation method and used for CH4 and CO oxidation. The Mn3＋, Zr4＋, Ti4＋and Pb4＋cations are incorporated into the lattice of tetragonal rutile SnO2 to form a solid solution structure. As a consequence, the surface area and thermal stability of the catalysts are improved. Moreover, the oxygen species of the modified catalysts become easier to be reduced. Therefore, the oxidation activity over the catalysts was improved, except for the one modified by Pb oxide. Manganese oxide demonstrates the best promotional effects for SnO2. Using an X‐ray diffraction extrapolation method, the lattice capacity of SnO2 for Mn2O3 was 0.135 g Mn2O3/g SnO2, which indicates that to form stable solid solution, only 21%Sn4＋cations in the lattice can be maximally replaced by Mn3＋. If the amount of Mn3＋cations is over the capacity, Mn2O3 will be formed, which is not favorable for the activity of the catalysts. The Sn rich samples with only Sn‐Mn solid solution phase show higher activity than the ones with excess Mn2O3 species.展开更多
Extending the lead time of precipitation nowcasts is vital to improvements in heavy rainfall warning, flood mitigation, and water resource management. Because the TREC vector （tracking radar echo by correlation） rep...Extending the lead time of precipitation nowcasts is vital to improvements in heavy rainfall warning, flood mitigation, and water resource management. Because the TREC vector （tracking radar echo by correlation） represents only the instantaneous trend of precipitation echo motion, the approach using derived echo motion vectors to extrapolate radar reflectivity as a rainfall forecast is not satisfactory if the lead time is beyond 30 minutes. For longer lead times, the effect of ambient winds on echo movement should be considered. In this paper, an extrapolation algorithm that extends forecast lead times up to 3 hours was developed to blend TREC vectors with model-predicted winds. The TREC vectors were derived from radar reflectivity patterns in 3 km height CAPPI （constant altitude plan position indicator） mosaics through a cross-correlation technique. The background steering winds were provided by predictions of the rapid update assimilation model CHAF （cycle of hourly assimilation and forecast）. A similarity index was designed to determine the vertical level at which model winds were applied in the extrapolation process, which occurs via a comparison between model winds and radar vectors. Based on a summer rainfall case study, it is found that the new algorithm provides a better forecast.展开更多
A new radar echo tracking algorithm known as multi-scale tracking radar echoes by cross-correlation （MTREC） was developed in this study to analyze movements of radar echoes at different spatial scales. Movement of r...A new radar echo tracking algorithm known as multi-scale tracking radar echoes by cross-correlation （MTREC） was developed in this study to analyze movements of radar echoes at different spatial scales. Movement of radar echoes, particularly associated with convective storms, exhibits different characteristics at various spatial scales as a result of complex interactions among meteorological systems leading to the formation of convective storms. For the null echo region, the usual correlation technique produces zero or a very small magnitude of motion vectors. To mitigate these constraints, MTREC uses the tracking radar echoes by correlation （TREC） technique with a large ＂box＂ to determine the systematic movement driven by steering wind, and MTREC applies the TREC technique with a small ＂box＂ to estimate small-scale internal motion vectors. Eventually, the MTREC vectors are obtained by synthesizing the systematic motion and the small-scale internal motion. Performance of the MTREC technique was compared with TREC technique using case studies： the Khanun typhoon on 11 September 2005 observed by Wenzhou radar and a squall-line system on 23 June 2011 detected by Beijing radar. The results demonstrate that more spatially smoothed and continuous vector fields can be generated by the MTREC technique, which leads to improvements in tracking the entire radar reflectivity pattern. The new multi-scMe tracking scheme was applied to study its impact on the performance of quantitative precipitation nowcasting. The location and intensity of heavy precipitation at a 1-h lead time was more consistent with quantitative precipitation estimates using radar and rain gauges.展开更多
It is widely reported that CALPHAD is an extrapolation method when the thermodynamic properties of a multicomponent system are approximated by its subsystems.In this work the meaning of the words extrapolation and int...It is widely reported that CALPHAD is an extrapolation method when the thermodynamic properties of a multicomponent system are approximated by its subsystems.In this work the meaning of the words extrapolation and interpolation is discussed in context of the CALPHAD method.When assessing the properties in binary and ternary systems,extrapolation method is indeed often used.However,after assessment,the Gibbs energies are in fact interpolated from the lower order systems into the higher order systems in the compositional space.The metastable melting temperatures of bcc and hep in Re-W and the liquid miscibility gap in Mg-Zr system are predicted to illustrate the difference between interpolation and extrapolation.展开更多
Transient control law ensures that the aeroengine transits to the command operating state rapidly and reliably. Most of the existing approaches for transient control law design have complicated principle and arithmeti...Transient control law ensures that the aeroengine transits to the command operating state rapidly and reliably. Most of the existing approaches for transient control law design have complicated principle and arithmetic. As a result, those approaches are not convenient for application. This paper proposes an extrapolation approach based on the set-point parameters to construct the transient control law, which has a good practicability. In this approach, the transient main fuel control law for acceleration and deceleration process is designed based on the main fuel flow on steady operating state. In order to analyze the designing feature of the extrapolation approach, the simulation results of several different transient control laws designed by the same approach are compared together. The analysis indicates that the aeroengine has a good performance in the transient process and the designing feature of the extrapolation approach conforms to the elements of the turbofan aeroengine.展开更多
基金financially supported by the National Natural Science Foundation of China(Nos.52371007 and 52301042)the National Key R&D Program of China(No.2020YFB0704503)+2 种基金Shenzhen Science and Technology Program(No.SGDX20210823104002016)Guangdong Basic and Applied Basic Research Foundation(No.2021B1515120071)Shenzhen Basic Research Project(No.JCYJ20241202123504007)
文摘Machine learning(ML)has become a powerful tool for accelerating the design and development of new materials.Among various traditional ML algorithms,decision tree-based ensemble learning methods are frequently chosen for their strong predictive capabilities.However,decision trees are limited in regression tasks to interpolating within the data range of the training set,which restricts their usefulness for designing materials with enhanced properties.Herein,we focused on predicting and optimizing the L1_(2)-phase solvus temperature(T_(L12))and density,two critical properties for multi-principal-element superalloys(MPESAs).To achieve this,we employed the piecewise symbolic regression tree(PS-Tree),which demonstrates excellent extrapolation capability.Our model successfully predicted high T_(L12)values exceeding the training data range(1242℃),with four candidate alloys achieving TL12values of 1246,1249,1254,and 1274℃.Experimental validation confirmed the accuracy of these predictions,verifying the robust extrapolative capability of the PS-Tree method.Notably,one alloy exhibited a T_(L12)of 1267℃and a density of 7.94 g cm^(-3),outperforming most MPESAs.Additionally,another alloy exhibited a compressive yield strength of 897 MPa at 750℃,with a specific yield strength at this temperature higher than that of most L1_(2)-strengthened alloys and Co/Ni-based superalloys.Moreover,the model provided generalized insights,indicating that alloys with δ_(r)>5.3 and ΔH_(mix)<-12.8 J mol^(-1)K^(-1)tend to favor higher T_(L12).
基金supported by National Natural Science Foundation of China[72171056].
文摘This paper investigates the optimal reinsurance-investment strategy for an insurer whose premium is subject to extrapolative bias.In other words,the insurance premium is dynamically updated by a weighted average of prior claims and the initial estimation of claims.The insurer’s surplus follows a diffusion approximation process.He purchases proportional reinsurance or acquires new business to manage insurance risk,and invests his surplus in the financial market,containing a risk-free asset and a risky asset(stock).The price of the risky asset is described by a constant elasticity of variance(CEV)model.The insurer is uncertain about the models of claims and risky asset.In order to derive robust optimal reinsurance-investment strategies,we establish an optimal control problem by maximizing the insurer’s expected exponential utility of terminal wealth and solve the optimization problem explicitly.Finally,we present several numerical examples to illustrate our theoretical results.
基金Scientific Research and Development Project of Hebei Meteorological Bureau(23ky08).
文摘This study proposes a novel radar echo extrapolation algorithm,OF-ConvGRU,which integrates Optical Flow(OF)and Convolutional Gated Recurrent Unit(ConvGRU)methods for improved nowcasting.Using the Standardized Radar Dataset of the Guangdong-Hong Kong-Macao Greater Bay Area,the performance of OF-ConvGRU was evaluated against OF and ConvGRU methods.Threat Score(TS)and Bias Score(BIAS)were employed to assess extrapolation accuracy across various echo intensities(20-50 dBz)and weather phenomena.Results demonstrate that OF-ConvGRU significantly enhances prediction accuracy for moderate-intensity echoes(30-40 dBz),effectively combining OF s precise motion estimation with ConvGRU s nonlinear learning capabilities.However,challenges persist in low-intensity(20 dBz)and high-intensity(50 dBz)echo predictions.The study reveals distinct advantages of each method in specific contexts,highlighting the importance of multi-method approaches in operational nowcasting.OF-ConvGRU shows promise in balancing short-term accuracy with long-term stability,particularly for complex weather systems.
文摘Understanding the wind power potential of a site is essential for designing an optimal wind power conditioning system. The Weibull distribution and wind speed extrapolation methods are powerful mathematical tools for efficiently predicting the frequency distribution of wind speeds at a site. Hourly wind speed and direction data were collected from the National Aeronautics and Space Administration (NASA) website for the period 2013 to 2023. MATLAB software was used to calculate the distribution parameters using the graphical method and to plot the corresponding curves, while WRPLOTView software was used to construct the wind rose. The average wind speed obtained is 3.33 m/s and can reach up to 5.71 m/s at a height of 100 meters. The wind energy is estimated to be 1315.30 kWh/m2 at a height of 100 meters. The wind rose indicates the prevailing winds (ranging from 3.60 m/s to 5.70 m/s) in the northeast-east direction.
基金supported by the National Key R&D Program of China(2021YFA0716902)National Natural Science Foundation of China(NSFC)under contract number 42374149 and 42004119National Science and Technology Major Project(2024ZD1002907)。
文摘Seismic anisotropy has been extensively acknowledged as a crucial element that influences the wave propagation characteristic during wavefield simulation,inversion and imaging.Transversely isotropy(TI)and orthorhombic anisotropy(OA)are two typical categories of anisotropic media in exploration geophysics.In comparison of the elastic wave equations in both TI and OA media,pseudo-acoustic wave equations(PWEs)based on the acoustic assumption can markedly reduce computational cost and complexity.However,the presently available PWEs may experience SV-wave contamination and instability when anisotropic parameters cannot satisfy the approximated condition.Exploiting pure-mode wave equations can effectively resolve the above-mentioned issues and generate pure P-wave events without any artifacts.To further improve the computational accuracy and efficiency,we develop two novel pure qP-wave equations(PPEs)and illustrate the corresponding numerical solutions in the timespace domain for 3D tilted TI(TTI)and tilted OA(TOA)media.First,the rational polynomials are adopted to estimate the exact pure qP-wave dispersion relations,which contain complicated pseudo-differential operators with irrational forms.The polynomial coefficients are produced by applying a linear optimization algorithm to minimize the objective function difference between the expansion formula and the exact one.Then,the developed optimized PPEs are efficiently implemented using the finite-difference(FD)method in the time-space domain by introducing a scalar operator,which can help avoid the problem of spectral-based algorithms and other calculation burdens.Structures of the new equations are concise and corresponding implementation processes are straightforward.Phase velocity analyses indicate that our proposed optimized equations can lead to reliable approximation results.3D synthetic examples demonstrate that our proposed FD-based PPEs can produce accurate and stable P-wave responses,and effectively describe the wavefield features in complicated TTI and TOA media.
基金supported in part by the National Natural Science Foundation of China(No.62302507)and the funding of Harbin Institute of Technology(Shenzhen)(No.20210035).
文摘Extrapolation on Temporal Knowledge Graphs(TKGs)aims to predict future knowledge from a set of historical Knowledge Graphs in chronological order.The temporally adjacent facts in TKGs naturally form event sequences,called event evolution patterns,implying informative temporal dependencies between events.Recently,many extrapolation works on TKGs have been devoted to modelling these evolutional patterns,but the task is still far from resolved because most existing works simply rely on encoding these patterns into entity representations while overlooking the significant information implied by relations of evolutional patterns.However,the authors realise that the temporal dependencies inherent in the relations of these event evolution patterns may guide the follow-up event prediction to some extent.To this end,a Temporal Relational Context-based Temporal Dependencies Learning Network(TRenD)is proposed to explore the temporal context of relations for more comprehensive learning of event evolution patterns,especially those temporal dependencies caused by interactive patterns of relations.Trend incorporates a semantic context unit to capture semantic correlations between relations,and a structural context unit to learn the interaction pattern of relations.By learning the temporal contexts of relations semantically and structurally,the authors gain insights into the underlying event evolution patterns,enabling to extract comprehensive historical information for future prediction better.Experimental results on benchmark datasets demonstrate the superiority of the model.
基金supported in part by the National Natural Science Foundation of China(Grant Nos.12375072,12375073,12275259,and 12135011)supported by Guangdong Provincial Funding(Grant No.2019QN01X172)supported by the National Security Academic Fund(Grant No.U2330401)。
文摘Finite-volume extrapolation is an important step for extracting physical observables from lattice calculations.However,it is a significant challenge for systems with long-range interactions.We employ symbolic regression to regress the finite-volume extrapolation formula for both short-range and long-range interactions.The regressed formula still holds the exponential form with a factor L^(n) in front of it.The power decreases with the decreasing range of the force.When the range of the force becomes sufficiently small,the power converges to-1,recovering the short-range formula as expected.Our work represents a significant advancement in leveraging machine learning to probe uncharted territories within particle physics.
基金supported by the National Natural Science Foundation of China(Grant Nos.:82373938,82104275,and 82204585)Key Technologies R&D Program of Guangdong Province,China(Grant No.:2023B1111030004)National Key R&D Program of China(Grant No.:2022YFF1202600).
文摘Proteolysis-targeting chimeras(PROTACs)represent a promising class of drugs that can target disease-causing proteins more effectively than traditional small molecule inhibitors can,potentially revolutionizing drug discovery and treatment strategies.However,the links between in vitro and in vivo data are poorly understood,hindering a comprehensive understanding of the absorption,distribution,metabolism,and excretion(ADME)of PROTACs.In this work,14C-labeled vepdegestrant(ARV-471),which is currently in phase III clinical trials for breast cancer,was synthesized as a model PROTAC to characterize its preclinical ADME properties and simulate its clinical pharmacokinetics(PK)by establishing a physiologically based pharmacokinetics(PBPK)model.For in vitro–in vivo extrapolation(IVIVE),hepatocyte clearance correlated more closely with in vivo rat PK data than liver microsomal clearance did.PBPK models,which were initially developed and validated in rats,accurately simulate ARV-471's PK across fed and fasted states,with parameters within 1.75-fold of the observed values.Human models,informed by in vitro ADME data,closely mirrored postoral dose plasma profiles at 30 mg.Furthermore,no human-specific metabolites were identified in vitro and the metabolic profile of rats could overlap that of humans.This work presents a roadmap for developing future PROTAC medications by elucidating the correlation between in vitro and in vivo characteristics.
文摘This paper studies a strongly convergent inertial forward-backward-forward algorithm for the variational inequality problem in Hilbert spaces.In our convergence analysis,we do not assume the on-line rule of the inertial parameters and the iterates,which have been assumed by several authors whenever a strongly convergent algorithm with an inertial extrapolation step is proposed for a variational inequality problem.Consequently,our proof arguments are different from what is obtainable in the relevant literature.Finally,we give numerical tests to confirm the theoretical analysis and show that our proposed algorithm is superior to related ones in the literature.
基金supported by National major special equipment development(No.2011YQ120045)The National Natural Science Fund(No.41074050 and 41304023)
文摘We use the extrapolated Tikhonov regularization to deal with the ill-posed problem of 3D density inversion of gravity gradient data. The use of regularization parameters in the proposed method reduces the deviations between calculated and observed data. We also use the depth weighting function based on the eigenvector of gravity gradient tensor to eliminate undesired effects owing to the fast attenuation of the position function. Model data suggest that the extrapolated Tikhonov regularization in conjunction with the depth weighting function can effectively recover the 3D distribution of density anomalies. We conduct density inversion of gravity gradient data from the Australia Kauring test site and compare the inversion results with the published research results. The proposed inversion method can be used to obtain the 3D density distribution of underground anomalies.
基金supported by the National Scientific and Technological Plan(Nos.2009BAB43B00 and 2009BAB43B01)
文摘Tikhonov regularization（TR） method has played a very important role in the gravity data and magnetic data process. In this paper, the Tikhonov regularization method with respect to the inversion of gravity data is discussed. and the extrapolated TR method（EXTR） is introduced to improve the fitting error. Furthermore, the effect of the parameters in the EXTR method on the fitting error, number of iterations, and inversion results are discussed in details. The computation results using a synthetic model with the same and different densities indicated that. compared with the TR method, the EXTR method not only achieves the a priori fitting error level set by the interpreter but also increases the fitting precision, although it increases the computation time and number of iterations. And the EXTR inversion results are more compact than the TR inversion results, which are more divergent. The range of the inversion data is closer to the default range of the model parameters, and the model features and default model density distribution agree well.
文摘Autoregressive (AR) modeling is applied to data extrapolation of radio frequency (RF) echo signals, and Burg algorithm, which can be computed in small amount and lead to a stable prediction filter, is used to estimate the prediction parameters of AR modeling. The complex data samples are directly extrapolated to obtain the extrapolated echo data in the frequency domain. The small rotating angle data extrapolation and the large rotating angular data extrapolation are considered separately in azimuth domain. The method of data extrapolation for the small rotating angle is the same as that in frequency domain, while the amplitude samples of large rotating angle echo data are extrapolated to obtain extrapolated echo amplitude, and the complex data of large rotating angle echo samples are extrapolated to get the extrapolated echo phase respectively. The calculation results show that the extrapolated echo data obtained by the above mentioned methods are accurate.
基金supported under the framework of international cooperation program managed by the National Research Foundation of Korea(NRF 2020K2A9A2A06069972,FY2020)supported by the BK21 FOUR(Fostering Outstanding Universities for Research)funded by the Ministry of Education of the Republic of Korea and National Research Foundation of Korea(NRF)supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea(NRF-2020S1A5B8103855).
文摘This paper deals with the recommendation system in the so-called user-centric payment environment where users,i.e.,the payers,can make payments without providing self-information to merchants.This service maintains only the minimum purchase information such as the purchased product names,the time of purchase,the place of purchase for possible refunds or cancellations of purchases.This study aims to develop AI-based recommendation system by utilizing the minimum transaction data generated by the user-centric payment service.First,we developed a matrix-based extrapolative collaborative filtering algorithm based on open transaction data.The recommendation methodology was verified with the real transaction data.Based on the experimental results,we confirmed that the recommendation performance is satisfactory only with the minimum purchase information.
基金supported by the National Natural Science Foundation of China (21263015,21567016 and 21503106)the Education Department Foundation of Jiangxi Province (KJLD14005 and GJJ150016)the Natural Science Foundation of Jiangxi Province (20142BAB213013 and 20151BBE50006),which are greatly acknowledged by the authors~~
文摘A series of SnO2‐based catalysts modified by Mn, Zr, Ti and Pb oxides with a Sn/M （M=Mn, Zr, Ti and Pb） molar ratio of 9/1 were prepared by a co‐precipitation method and used for CH4 and CO oxidation. The Mn3＋, Zr4＋, Ti4＋and Pb4＋cations are incorporated into the lattice of tetragonal rutile SnO2 to form a solid solution structure. As a consequence, the surface area and thermal stability of the catalysts are improved. Moreover, the oxygen species of the modified catalysts become easier to be reduced. Therefore, the oxidation activity over the catalysts was improved, except for the one modified by Pb oxide. Manganese oxide demonstrates the best promotional effects for SnO2. Using an X‐ray diffraction extrapolation method, the lattice capacity of SnO2 for Mn2O3 was 0.135 g Mn2O3/g SnO2, which indicates that to form stable solid solution, only 21%Sn4＋cations in the lattice can be maximally replaced by Mn3＋. If the amount of Mn3＋cations is over the capacity, Mn2O3 will be formed, which is not favorable for the activity of the catalysts. The Sn rich samples with only Sn‐Mn solid solution phase show higher activity than the ones with excess Mn2O3 species.
基金This study was provided by Natural Science Foundation of Guangdong Province under Grant No. 5001121the China Meteorological Administration under Grant Nos. CMATG2005Y05 and CMATG2008Z10the Guangdong Meteorological Bureau under Grant Nos. 2007A2 and GRMC2007Z03
文摘Extending the lead time of precipitation nowcasts is vital to improvements in heavy rainfall warning, flood mitigation, and water resource management. Because the TREC vector （tracking radar echo by correlation） represents only the instantaneous trend of precipitation echo motion, the approach using derived echo motion vectors to extrapolate radar reflectivity as a rainfall forecast is not satisfactory if the lead time is beyond 30 minutes. For longer lead times, the effect of ambient winds on echo movement should be considered. In this paper, an extrapolation algorithm that extends forecast lead times up to 3 hours was developed to blend TREC vectors with model-predicted winds. The TREC vectors were derived from radar reflectivity patterns in 3 km height CAPPI （constant altitude plan position indicator） mosaics through a cross-correlation technique. The background steering winds were provided by predictions of the rapid update assimilation model CHAF （cycle of hourly assimilation and forecast）. A similarity index was designed to determine the vertical level at which model winds were applied in the extrapolation process, which occurs via a comparison between model winds and radar vectors. Based on a summer rainfall case study, it is found that the new algorithm provides a better forecast.
基金This study was supported by the Special Fund for Basic Research and Operation of Chinese Academy of Meteorological Science:Development on quantitative precipitation forecasts for 0-6 h lead times by blending radar-based extrapolation and GRAPES-meso,Observation and retrieval methods of micro-physics，the National Natural Science Foundation of China
文摘A new radar echo tracking algorithm known as multi-scale tracking radar echoes by cross-correlation （MTREC） was developed in this study to analyze movements of radar echoes at different spatial scales. Movement of radar echoes, particularly associated with convective storms, exhibits different characteristics at various spatial scales as a result of complex interactions among meteorological systems leading to the formation of convective storms. For the null echo region, the usual correlation technique produces zero or a very small magnitude of motion vectors. To mitigate these constraints, MTREC uses the tracking radar echoes by correlation （TREC） technique with a large ＂box＂ to determine the systematic movement driven by steering wind, and MTREC applies the TREC technique with a small ＂box＂ to estimate small-scale internal motion vectors. Eventually, the MTREC vectors are obtained by synthesizing the systematic motion and the small-scale internal motion. Performance of the MTREC technique was compared with TREC technique using case studies： the Khanun typhoon on 11 September 2005 observed by Wenzhou radar and a squall-line system on 23 June 2011 detected by Beijing radar. The results demonstrate that more spatially smoothed and continuous vector fields can be generated by the MTREC technique, which leads to improvements in tracking the entire radar reflectivity pattern. The new multi-scMe tracking scheme was applied to study its impact on the performance of quantitative precipitation nowcasting. The location and intensity of heavy precipitation at a 1-h lead time was more consistent with quantitative precipitation estimates using radar and rain gauges.
基金financial support from the National Natural Science Foundation of China (Nos. 51671118 and 51871143)Young Elite Scientists Sponsorship Program by CAST (No. 2017QNRC001)+2 种基金the “Chenguang” project supported by Shanghai Municipal Education Commission and Shanghai Education Development Foundation (No. 17CG42)Science and Technology Committee of Shanghai (No. 16520721800)Aeronautical Science Fund ¨Integrated computational research of the additive manufacturing for ultra-high strength Ti alloys¨(No. 2017ZF25022)
文摘It is widely reported that CALPHAD is an extrapolation method when the thermodynamic properties of a multicomponent system are approximated by its subsystems.In this work the meaning of the words extrapolation and interpolation is discussed in context of the CALPHAD method.When assessing the properties in binary and ternary systems,extrapolation method is indeed often used.However,after assessment,the Gibbs energies are in fact interpolated from the lower order systems into the higher order systems in the compositional space.The metastable melting temperatures of bcc and hep in Re-W and the liquid miscibility gap in Mg-Zr system are predicted to illustrate the difference between interpolation and extrapolation.
基金support of Aeronautical Science Foundation of China (2011ZB51019)
文摘Transient control law ensures that the aeroengine transits to the command operating state rapidly and reliably. Most of the existing approaches for transient control law design have complicated principle and arithmetic. As a result, those approaches are not convenient for application. This paper proposes an extrapolation approach based on the set-point parameters to construct the transient control law, which has a good practicability. In this approach, the transient main fuel control law for acceleration and deceleration process is designed based on the main fuel flow on steady operating state. In order to analyze the designing feature of the extrapolation approach, the simulation results of several different transient control laws designed by the same approach are compared together. The analysis indicates that the aeroengine has a good performance in the transient process and the designing feature of the extrapolation approach conforms to the elements of the turbofan aeroengine.
