A simulation-based nonlinear site amplification model for ground-motion prediction equations in Japan

In this manuscript we present a nonlinear site amplification model for ground-motion prediction equations(GMPEs)in Japan,using a site period-based site class and a site impedance ratio as site parameters.We used a large number of shear-wave velocity profiles from the Kiban-Kyoshin network(KiK-net)and the Kyoshin network(K-NET)to construct the one-dimensional(1D)numerical models.The strong-motion records from rock-sites in Japan with different earthquake categories and taken from the Pacific Earthquake Engineering Research Center dataset were used in this study.We fit a set of 1D site amplification models using the spectral amplification ratios derived from 1D equivalent linear analyses.Parameters of site impedance ratios for both linear and nonlinear site response were included in the 1D model.The 1D model could be implemented into GMPEs using a new proposed adjustment method.The adjusted site amplification ratios retain the nonlinear characteristics of the 1D model for strong motions and match the linear amplification ratio in GMPE for weak motions.The nonlinearity of the present site model is reasonably similar to that of the historical models,and the present site model could satisfactorily capture the nonlinear site response in empirical data.

Continuous-Time Channel Prediction Based on Tensor Neural Ordinary Differential Equation

Channel prediction is critical to address the channel aging issue in mobile scenarios.Existing channel prediction techniques are mainly designed for discrete channel prediction,which can only predict the future channel in a fixed time slot per frame,while the other intra-frame channels are usually recovered by interpolation.However,these approaches suffer from a serious interpolation loss,especially for mobile millimeter-wave communications.To solve this challenging problem,we propose a tensor neural ordinary differential equation(TN-ODE)based continuous-time channel prediction scheme to realize the direct prediction of intra-frame channels.Specifically,inspired by the recently developed continuous mapping model named neural ODE in the field of machine learning,we first utilize the neural ODE model to predict future continuous-time channels.To improve the channel prediction accuracy and reduce computational complexity,we then propose the TN-ODE scheme to learn the structural characteristics of the high-dimensional channel by low-dimensional learnable transform.Simulation results show that the proposed scheme is able to achieve higher intra-frame channel prediction accuracy than existing schemes.

Prediction of epidemics dynamics on networks with partial differential equations:A case study for COVID-19 in China

Since December 2019,the COVID-19 epidemic has repeatedly hit countries around the world due to various factors such as trade,national policies and the natural environment.To closely monitor the emergence of new COVID-19 clusters and ensure high prediction accuracy,we develop a new prediction framework for studying the spread of epidemic on networks based on partial differential equations(PDEs),which captures epidemic diffusion along the edges of a network driven by population flow data.In this paper,we focus on the effect of the population movement on the spread of COVID-19 in several cities from different geographic regions in China for describing the transmission characteristics of COVID-19.Experiment results show that the PDE model obtains relatively good prediction results compared with several typical mathematical models.Furthermore,we study the effectiveness of intervention measures,such as traffic lockdowns and social distancing,which provides a new approach for quantifying the effectiveness of the government policies toward controlling COVID-19 via the adaptive parameters of the model.To our knowledge,this work is the first attempt to apply the PDE model on networks with Baidu Migration Data for COVID-19 prediction.

PREDICTION OF THE PHASE BEHAVIOR OF GAS CONDENSATES USING PATEL-TEJA EQUATION OF STATE AND GAMMA DISTRIBUTION FUNCTION

The three-parameter Petal-Teja equation of state coupled with a characterization proceduref0r C7+-fraction based on gamma distribution function was employed to predict the phase behaviorof eight gas condensates.The lumping of the subdivided single carbon number（SCN）hydrocarbons inthe plus-fraction and the choice of empirical correlations for calculating the critical properties andacentric factor of SCN hydrocarbons were discussed.

Improved Quality Prediction Model for Multistage Machining Process Based on Geometric Constraint Equation

Product variation reduction is critical to improve process efficiency and product quality, especially for multistage machining process（MMP）. However, due to the variation accumulation and propagation, it becomes quite difficult to predict and reduce product variation for MMP. While the method of statistical process control can be used to control product quality, it is used mainly to monitor the process change rather than to analyze the cause of product variation. In this paper, based on a differential description of the contact kinematics of locators and part surfaces, and the geometric constraints equation defined by the locating scheme, an improved analytical variation propagation model for MMP is presented. In which the influence of both locator position and machining error on part quality is considered while, in traditional model, it usually focuses on datum error and fixture error. Coordinate transformation theory is used to reflect the generation and transmission laws of error in the establishment of the model. The concept of deviation matrix is heavily applied to establish an explicit mapping between the geometric deviation of part and the process error sources. In each machining stage, the part deviation is formulized as three separated components corresponding to three different kinds of error sources, which can be further applied to fault identification and design optimization for complicated machining process. An example part for MMP is given out to validate the effectiveness of the methodology. The experiment results show that the model prediction and the actual measurement match well. This paper provides a method to predict part deviation under the influence of fixture error, datum error and machining error, and it enriches the way of quality prediction for MMP.

Breakdown Voltage Prediction by Utilizing the Behavior of Natural Ester for Transformer Applications

This research investigates the dielectric performance of Natural Ester(NE)using the Partial Differential Equation(PDE)tool and analyzes dielectric performance using fuzzy logic.NE nowadays is found to replace Mineral Oil(MO)due to its extensive dielectric properties.Here,the heat-tolerant Natural Esters Olive oil(NE1),Sunflower oil(NE2),and Ricebran oil(NE3)are subjected to High Voltage AC(HVAC)under different electrodes configurations.The break-down voltage and leakage current of NE1,NE2,and NE3 under Point-Point(P-P),Sphere-Sphere(S-S),Plane-Plane(PL-PL),and Rod-Rod(R-R)are measured,and survival probability is presented.The electricfield distribution is analyzed using the Partial Differential Equation(PDE)tool.NE shows better HVAC with stand capacity under all the electrodes configuration,especially in the S-S shape geometry.The exponential function is developed for the oils under different elec-trode geometry;NE shows a higher survival probability.Likewise,the most influ-ential dielectric properties such as breakdown voltage,kinematic viscosity,and water content are used to develop a Mamdani-based control system model that combines two variables to produce the surface model.The surface model indi-cates that the NE subjected for investigation is less susceptible to moisture effect and higher kinematic viscosity.Based on the surface models of PDE and fuzzy,it is concluded that NE possesses a high survival rate since its breakdown voltage does not react to changes in water content.Hence the application of NE in the transformer application is highly safe and possesses extended life.

A Logistic-growth-equation-based Intensity Prediction Scheme for Western North Pacific Tropical Cyclones

Accurate prediction of tropical cyclone(TC)intensity remains a challenge due to the complex physical processes involved in TC intensity changes.A seven-day TC intensity prediction scheme based on the logistic growth equation(LGE)for the western North Pacific(WNP)has been developed using the observed and reanalysis data.In the LGE,TC intensity change is determined by a growth term and a decay term.These two terms are comprised of four free parameters which include a time-dependent growth rate,a maximum potential intensity(MPI),and two constants.Using 33 years of training samples,optimal predictors are selected first,and then the two constants are determined based on the least square method,forcing the regressed growth rate from the optimal predictors to be as close to the observed as possible.The estimation of the growth rate is further refined based on a step-wise regression(SWR)method and a machine learning(ML)method for the period 1982−2014.Using the LGE-based scheme,a total of 80 TCs during 2015−17 are used to make independent forecasts.Results show that the root mean square errors of the LGE-based scheme are much smaller than those of the official intensity forecasts from the China Meteorological Administration(CMA),especially for TCs in the coastal regions of East Asia.Moreover,the scheme based on ML demonstrates better forecast skill than that based on SWR.The new prediction scheme offers strong potential for both improving the forecasts for rapid intensification and weakening of TCs as well as for extending the 5-day forecasts currently issued by the CMA to 7-day forecasts.

A model for predicting SST in limited region-I. The dynamical equations

-Starting from physical oceanology characteristics of the China seas and for the short-term operational prediction of SST in the region, a two-dimensional (vertically integrated) primitive equation model, physically reasonable and operationally feasible,on the upper mixed layer is constructed and given here, which consists of three parts, the nondivergent residual current (the monthly mean field of the Kuroshio and its branches) equations, the dynamic forecasting equations, and the equation of model's physics consisting of surface heat flux, coolings of the upper mixed layer due to the Ekman pumping and the entrainment by gale. This model may be used primarily to forecast the sea surface temperature, and to give estimations of the mean wind-driven current and the sea level, for a period of 3-5 d. In part 1 of this series, the physical conditions for establishing model equations are discussed first, that is, 1. the existence of the upper well mixed layer in the region; 2. the distinguishability of currents of all kinds; 3. the splitting of thermodynamical equation. The equations of nondivergent residual current, and the dynamic forecasting equations with initial values and boundary conditions are also discussed.

A Modification of α in SRK Equation of State and Vapor-Liquid Equilibria Prediction

Based on results of saturated vapor pressures of pure substances calculated by SRK equation of state, the factor α in attractive pressure term was modified. Vapor-liquid equilibria of mixtures were calculated by original and modified SRK equation of state combined with MHV1 mixing rule and UNIFAC model, respectively. For 1447 saturated pressure points of 37 substance including alkanes; organics containing chlorine, fluorine, and oxygen; in-organic gases and water, the original SRK equation of state predicted pressure with an average deviation of 2.521% and modified one 1.673%. Binary vapor-liquid equilibria of alcohols containing mixtures and water containing mixtures also indicated that the SRK equation of state with the modified α had a better precision than that with the original one.

Prediction of the Conductance of Strong Electrolytes and the Calculation of the Ionization Constant of Weak Electrolytes in a Dilute Solution by a New Equation

In order to predict the conductance for dilute 1 1 valent electrolyte solutions, a new conductance equation was proposed based on the Onsager and Onsagar Fuoss Chen conductance equation. It has only one parameter A , which can be obtained directly from the data of ionic limiting molar conductivity Λ ∞ m, and its expression is very simple. The new equation has been verified by the experimental molar conductivities of some single strong electrolyte and mixed electrolyte solutions at 298.15 K reported in literatures. The results are in good agreement with the experimental data. Meanwhile the ionization constants of some weak electrolyte solutions were calculated by a modified equation of this new equation, and it was also found that the calculation results are in good agreement with the data in the literature.

Sensitivity analysis of ground motion prediction equation using next generation attenuation dataset

Seismic hazard assessment is an important stage in earthquake engineering.Since the ground motion prediction equation(GMPE)has a key role in seismic hazard assessment,it is necessary to determine this equation as well as possible.Some mutual seismic parameters,such as earthquake magnitude,distance,fault type,and site effect,are applied in GMPEs.The ridge regression method is applied to find the relation between input seismic parameters and targets(PGA,PGV,PGD,SA(T=0.02 s),and SA(T=1 s)).Results of regression show that equations obtained in this study have a root mean square error(RMSE)of 90%.In the last part,GMPEs are inspected by sensitivity analysis through two methods:Sobol and Delta Moment-Independent Measure.The results from the two methods were very similar to each other for each equation.Furthermore,the analysis shows that the most effective parameters in determining GMPEs are moment magnitude and shear wave velocity.

Performance prediction of expressway pavement in high maintenance level areas based on cosine deterioration equation: A case study of Zhejiang Province in China

Accurate prediction of performance decay law is an important basis for long-term planning of maintenance strategy.The statistical regression prediction model is the most widely employed method to calculate pavement performance due to its advantages such as the small amount of calculation and good accuracy,but the traditional prediction model seems not applicable to the high maintenance level areas with excellent pavement conditions.In this paper,the service life and the cumulative number of the axle load were determined as the independent variables of prediction models of pavement performance.The pavement condition index(PCI)and rutting depth index(RDI)were selected as maintenance decision control indexes to establish the unified prediction model of PCI and RDI respectively by applying the cosine deterioration equation.Results reveal that the deterioration law of PCI presents an anti-S type or concave type and the deterioration law of RDI shows an obvious concave type.The prediction model proposed in this study added the pavement maintenance standard factor d,which brings the model parameterα(reflecting the road life)and the deterioration equations are more applicable than the traditional standard equations.It is found that the fitting effects of PCI and RDI prediction models with different traffic grades are relatively similar to the actual service state of the pavements.

Hysteretic energy prediction method for mainshock-aftershock sequences

Structures located in seismically active regions may be subjected to mainshock-aftershock（MSAS）sequences.present study selected two kinds of MSAS sequences,with one aftershock and two aftershocks,respectively.The aftershocksThe MSAS sequence with one aftershock exhibited a 10%to 30%hysteretic energy increase,whereas the MSAS sequence with two aftershocks presented a 20%to 40%hysteretic energy increase.Finally,a hysteretic energy prediction equation is proposed as a function of the vibration period,ductility value,and damping ratio to estimate hysteretic energy for mainshockaftershock sequences.

Machine learning seismic reservoir prediction method based on virtual sample generation

Seismic reservoir prediction plays an important role in oil exploration and development.With the progress of artificial intelligence,many achievements have been made in machine learning seismic reservoir prediction.However,due to the factors such as economic cost,exploration maturity,and technical limitations,it is often difficult to obtain a large number of training samples for machine learning.In this case,the prediction accuracy cannot meet the requirements.To overcome this shortcoming,we develop a new machine learning reservoir prediction method based on virtual sample generation.In this method,the virtual samples,which are generated in a high-dimensional hypersphere space,are more consistent with the original data characteristics.Furthermore,at the stage of model building after virtual sample generation,virtual samples screening and model iterative optimization are used to eliminate noise samples and ensure the rationality of virtual samples.The proposed method has been applied to standard function data and real seismic data.The results show that this method can improve the prediction accuracy of machine learning significantly.

Quantification and prediction of enteric methane emissions from Chinese lactating Holstein dairy cows fed diets with different dietary neutral detergent fiber/non-fibrous carbohydrate(NDF/NFC)ratios

Methane(CH_(4))emissions from ruminant production are a significant source of anthropogenic greenhouse gas production,but few studies have examined the enteric CH_(4)emissions of lactating dairy cows under different feeding regimes in China.This study aimed to investigate the influence of different dietary neutral detergent fiber/non-fibrous carbohydrate(NDF/NFC)ratios on production performance,nutrient digestibility,and CH_(4)emissions for Holstein dairy cows at various stages of lactation.It evaluated the performance of CH_(4)prediction equations developed using local dietary and milk production variables compared to previously published prediction equations developed in other production regimes.For this purpose,36 lactating cows were assigned to one of three treatments with differing dietary NDF/NFC ratios:low(NDF/NFC=1.19),medium(NDF/NFC=1.54),and high(NDF/NFC=1.68).A modified acid-insoluble ash method was used to determine nutrient digestibility,while the sulfur hexafluoride technique was used to measure enteric CH4 emissions.The results showed that the dry matter(DM)intake of cows at the early,middle,and late stages of lactation decreased significantly(P<0.01)from 20.9 to 15.4 kg d^(–1),15.3 to 11.6 kg d^(–1),and 16.4 to 15.0 kg d^(–1),respectively,as dietary NDF/NFC ratios increased.Across all three treatments,DM and gross energy(GE)digestibility values were the highest(P<0.05)for cows at the middle and late lactation stages.Daily CH_(4)emissions increased linearly(P<0.05),from 325.2 to 391.9 kg d^(–1),261.0 to 399.8 kg d^(–1),and 241.8 to 390.6 kg d^(–1),respectively,as dietary NDF/NFC ratios increased during the early,middle,and late stages of lactation.CH_(4)emissions expressed per unit of metabolic body weight,DM intake,NDF intake,or fat-corrected milk yield increased with increasing dietary NDF/NFC ratios.In addition,CH_(4)emissions expressed per unit of GE intake increased significantly(P<0.05),from 4.87 to 8.12%,5.16 to 9.25%,and 5.06 to 8.17%respectively,as dietary NDF/NFC ratios increased during the early,middle,and late lactation stages.The modelling results showed that the equation using DM intake as the single variable yielded a greater R^(2)than equations using other dietary or milk production variables.When data obtained from each lactation stage were combined,DM intake remained a better predictor of CH_(4)emissions(R^(2)=0.786,P=0.026)than any other variables tested.Compared to the prediction equations developed herein,previously published equations had a greater root mean square prediction error,reflecting their inability to predict CH_(4)emissions for Chinese Holstein dairy cows accurately.The quantification of CH_(4)production by lactating dairy cows under Chinese production systems and the development of associated prediction equations will help establish regional or national CH_(4)inventories and improve mitigation approaches to dairy production.

FULLY COUPLED FORWARD-BACKWARD STOCHASTIC DIFFERENTIAL EQUATIONS WITH GENERAL MARTINGALE

The article first studies the fully coupled Forward-Backward Stochastic Differential Equations （FBSDEs） with the continuous local martingale. The article is mainly divided into two parts. In the first part, it considers Backward Stochastic Differential Equations （BSDEs） with the continuous local martingale. Then, on the basis of it, in the second part it considers the fully coupled FBSDEs with the continuous local martingale. It is proved that their solutions exist and are unique under the monotonicity conditions.

Continuous-Time Prediction of Industrial Paste Thickener System With Differential ODE-Net

It is crucial to predict the outputs of a thickening system,including the underflow concentration(UC)and mud pressure,for optimal control of the process.The proliferation of industrial sensors and the availability of thickening-system data make this possible.However,the unique properties of thickening systems,such as the non-linearities,long-time delays,partially observed data,and continuous time evolution pose challenges on building data-driven predictive models.To address the above challenges,we establish an integrated,deep-learning,continuous time network structure that consists of a sequential encoder,a state decoder,and a derivative module to learn the deterministic state space model from thickening systems.Using a case study,we examine our methods with a tailing thickener manufactured by the FLSmidth installed with massive sensors and obtain extensive experimental results.The results demonstrate that the proposed continuous-time model with the sequential encoder achieves better prediction performances than the existing discrete-time models and reduces the negative effects from long time delays by extracting features from historical system trajectories.The proposed method also demonstrates outstanding performances for both short and long term prediction tasks with the two proposed derivative types.

Bi-variable damage model for fatigue life prediction of metal components

Based on the theory of continuum damage mechanics,a bi-variable damage mechanics model is developed,which,according to thermodynamics,is accessible to derivation of damage driving force,damage evolution equation and damage evolution criteria. Furthermore,damage evolution equations of time rate are established by the generalized Drucker＇s postulate. The damage evolution equation of cycle rate is obtained by integrating the time damage evolution equations,and the fatigue life prediction method for smooth specimens under repeated loading with constant strain amplitude is constructed. Likewise,for notched specimens under the repeated loading with constant strain amplitude,the fatigue life prediction method is obtained on the ground of the theory of conservative integral in damage mechanics. Thus,the material parameters in the damage evolution equation can be obtained by reference to the fatigue test results of standard specimens with stress concentration factor equal to 1,2 and 3.

Conductivity Prediction of Sodium and Potassium Hydrogen Tartrates in Aqueous Solution at Low Concentration

1 INTRODUCTIONAcid salt is one kind of important compound,andstudies on its solution conductivity behavior are verynecessary in chemical analysis and medicine,biologyand food Industry.However,in aqueous solution,theconductivity behavior of the acid salt is quite compli-cated due to the existence of dissociation and asso-ciation equilibria among the species,which makes itdifficult to predict or correlate the molar

Soil Plugging Effect on Drivability Prediction of Offshore Platform Piles

Field measurements of driving resistances and heights of soil core during driving were made offshore and onshore of steel pipe piles. Measured data show that the height of soil core varies differently for piles of different diameters with the increase of penetration. Dynamic plugging could be assumed never to occur for steel pipe piles with diameters over 900 mm. Soil resistances at the time of continuous driving (SRD) are back analyzed from blow counts with an empirical distribution of resistances suppported by many early dynamic measurements. A method of predicting SRD is finally suggested.