An Improved Machine Learning Technique with Effective Heart Disease Prediction System

Heart disease is the leading cause of death worldwide.Predicting heart disease is challenging because it requires substantial experience and knowledge.Several research studies have found that the diagnostic accuracy of heart disease is low.The coronary heart disorder determines the state that influences the heart valves,causing heart disease.Two indications of coronary heart disorder are strep throat with a red persistent skin rash,and a sore throat covered by tonsils or strep throat.This work focuses on a hybrid machine learning algorithm that helps predict heart attacks and arterial stiffness.At first,we achieved the component perception measured by using a hybrid cuckoo search particle swarm optimization(CSPSO)algorithm.With this perception measure,characterization and accuracy were improved,while the execution time of the proposed model was decreased.The CSPSO-deep recurrent neural network algorithm resolved issues that state-of-the-art methods face.Our proposed method offers an illustrative framework that helps predict heart attacks with high accuracy.The proposed technique demonstrates the model accuracy,which reached 0.97 with the applied dataset.

Study on time effect and prediction model of shear strength of root-soil complex under dry-wet cycle

Triaxial compression tests were conducted on the alfalfa root-loess complex at different growthperiods obtained through artificial planting.The research focused on analyzing the time variation law of the shear strength index and deformation index of the alfalfa root-loess complex under dry-wet cycles.Additionally,the time effect of the shear strength index of the alfalfa root-loess complex under dry-wet cycles was analyzed and its prediction model was proposed.The results show that the PG-DWC(dry-wet cycle caused by plant water management during plant growth period)causes the peak strength of plain soil to change in a"V"shape with the increase of growth period,and the peak strength of alfalfa root-loess complex is higher than that of plain soil at the same growth period.The deterioration of the peak strength of alfalfa root-loess complex in the same growth period is aggravated with the increase of drying and wetting cycles.Compared with the 0 days growth period,the effective cohesion of alfalfa root-loess complex under different dry-wet cycles maximum increase rate is at the 180 days,which are 33.88%,46.05%,30.12%and 216.02%,respectively.When the number of dry-wet cycles is constant,the effective cohesion of the alfalfa root-loess complex overall increases with the growth period.However,it gradually decreases comparedwith the previous growth period,and the minimum increase rate are all at the 180 days.For the same growth period,the effective cohesion of the alfalfa root-loess complex decreases with the increase of the number of dry-wet cycles.This indicates that EC-DWC(the dry-wet cycles caused by extreme natural conditions such as continuous rain)have a detrimental effect on the time effect of the shear strength of the alfalfa root-loess complex.Finally,based on the formula of total deterioration,a prediction model for the shear strength of the alfalfa root-loess complex under dry-wet cycles was proposed,which exhibits high prediction accuracy.The research results provide useful guidance for the understanding of mechanical behavior and structural damage evolution of root-soil composite.

Factors influencing pore-pressure prediction in complex carbonates based on effective medium theory

A calculation model based on effective medium theory has been developed for predicting elastic properties of dry carbonates with complex pore structures by integrating the Kuster-Toksǒz model with a differential method.All types of pores are simultaneously introduced to the composite during the differential iteration process according to the ratio of their volume fractions.Based on this model,the effects of pore structures on predicted pore-pressure in carbonates were analyzed.Calculation results indicate that cracks with low pore aspect ratios lead to pore-pressure overestimation which results in lost circulation and reservoir damage.However,moldic pores and vugs with high pore aspect ratios lead to pore-pressure underestimation which results in well kick and even blowout.The pore-pressure deviation due to cracks and moldic pores increases with an increase in porosity.For carbonates with complex pore structures,adopting conventional pore-pressure prediction methods and casing program designs will expose the well drilling engineering to high uncertainties.Velocity prediction models considering the influence of pore structure need to be built to improve the reliability and accuracy of pore-pressure prediction in carbonates.

Grain size effect on cyclic deformation behavior and springback prediction of Ni-based superalloy foil

In order to clarify the influence of grain size on cyclic deformation response of superalloy sheets and springback behavior,cyclic loading-unloading and shearing tests were performed on the superalloy foils with 0.2 mm in thickness and diverse grain sizes.The results show that,the decline ratio of elastic modulus is weakened with increasing grain size,and the Bauschinger effect becomes evident with decreasing grain size.Meanwhile,U-bending test results determine that the springback is diminished with increasing grain size.The Chaboche,Anisotropic Nonlinear Kinematic(ANK)and Yoshida-Uemori(Y-U)models were utilized to fit the shear stress-strain curves of specimens.It is found that Y-U model is sufficient of predicting the springback.However,the prediction accuracy is degraded with increasing grain size.

Time effect and prediction of broken rock bulking coefficient on the base of particle discrete element method

Bulking characteristics of gangue are of great significance for the stability of goafs in mining overburden in the caving zones.In this paper,a particle discrete element method with clusters to represent gangue was adopted to explore the bulking coefficient time effect of the broken rock in the caving zone under three-dimensional triaxial compression condition.The phenomena of stress corrosion,deformation,and failure of rock blocks were simulated in the numerical model.Meanwhile,a new criterion of rock fragments damage was put forward.It was found that the broken rock has obvious viscoelastic properties.A new equation based on the Burgers creep model was proposed to predict the bulking coefficient of broken rock.A deformation characteristic parameter of the prediction equation was analyzed,which can be set as a fixed value in the mid-and long-term prediction of the bulking coefficient.There are quadratic function relationships between the deformation characteristic parameter value and Talbot gradation index,axial pressure and confining pressure.

Analysis on Explanation Effect of the European Numerical Prediction on Temperature

[Objective] The research aimed to analyze explanation effect of the European numerical prediction on temperature. [Method] Based on CMSVM regression method, by using 850 hPa grid point data of the European numerical prediction from 2003 to 2009 and actual data of the maximum and minimum temperatures at 8 automatic stations in Qingyang City, prediction model of the temperature was established, and running effect of the business from 2008 to 2010 was tested and evaluated. [Result] The method had very good guidance role in real-time business running of the temperature prediction. Test and evaluation found that as forecast time prolonged, prediction accuracies of the maximum and minimum temperatures declined. When temperature anomaly was higher (actual temperature was higher than historical mean), prediction accuracy increased. Influence of the European numerical prediction was bigger. [Conclusion] Compared with other methods, operation of the prediction method was convenient, modeling was automatic, running time was short, system was stable, and prediction accuracy was high. It was suitable for implementing of the explanation work for numerical prediction product at meteorological station.

Effect Evaluation and Intelligent Prediction of Power Substation Project Considering New Energy

The evaluation of the implementation effect of the power substation project can find out the problems of the project more comprehensively,which has important practical significance for the further development of the power substation project.To ensure accuracy and real-time evaluation,this paper proposes a novel hybrid intelligent evaluation and prediction model based on improved TOPSIS and Long Short-Term Memory(LSTM)optimized by a Sperm Whale Algorithm(SWA).Firstly,under the background of considering the development of new energy,the influencing factors of power substation project implementation effect are analyzed from three aspects of technology,economy and society.Moreover,an evaluation model based on improved TOPSIS is constructed.Then,an intelligent prediction model based on SWA optimized LSTM is designed.Finally,the scientificity and accuracy of the proposed model are verified by empirical analysis,and the important factors affecting the implementation effect of power substation projects are pointed out.

A New Contribution to the Theory of Anode Effect in Aluminium Electrolysis

This paper discusses the general characteristics of anode effect in aluminium electrolysis and pre- vious theories about the mechanism of anode effect. On the basis of laboratory experiments,the author suggests a new contribution to the primary and di- rect reason for anode effect.

A MODEL OF BUTTERFLY CATASTROPHE FOR ANODE EFFECT IN ALUMINA ELECTROLYSIS

A model of butterfly catastrophe is confirmed based on the matching and fitting the previous experimental data from alumina electrolysis when anode effect occurs.The complicated behaviour of the cryolite-alumina melt system with varying parameters could be generally described by this model.Therefore,the anode effect and its occurrence may be thoroughly understood.

Extended Anode Effect for Tube Inner Coating of Non-Conductive Ceramics by Pulsed Coaxial Magnetron Plasma

For uniform tube inner coating of non-conductive thin films, the double-ended coaxial magnetron pulsed plasma (DCMPP) method was investigated. In this study, coating of TiN and TiO2 was performed. It was clearly shown that the extended anode effect was strongly influenced by the electric resistance of the coated thin films on the inner surface of an insulator tube. Additionally, high frequency (100 kHz) was better for relatively high plasma density. On the other hand, in the case of titanium oxide deposition, negative ion productions drastically decrease the deposition rate and the shifting velocity of plasma main position for coated TiO2 films.

Residual lifetime prediction model of nonlinear accelerated degradation data with measurement error

For the product degradation process with random effect (RE), measurement error (ME) and nonlinearity in step-stress accelerated degradation test (SSADT), the nonlinear Wiener based degradation model with RE and ME is built. An analytical approximation to the probability density function (PDF) of the product's lifetime is derived in a closed form. The process and data of SSADT are analyzed to obtain the relation model of the observed data under each accelerated stress. The likelihood function for the population-based observed data is constructed. The population-based model parameters and its random coefficient prior values are estimated. According to the newly observed data of the target product in SSADT, an analytical approximation to the PDF of its residual lifetime (RL) is derived in accordance with its individual degradation characteristics. The parameter updating method based on Bayesian inference is applied to obtain the posterior value of random coefficient of the RL model. A numerical example by simulation is analyzed to verify the accuracy and advantage of the proposed model.

Battery impedance spectrum prediction from partial charging voltage curve by machine learning

Electrochemical impedance spectroscopy(EIS) is an effective technique for Lithium-ion battery state of health diagnosis, and the impedance spectrum prediction by battery charging curve is expected to enable battery impedance testing during vehicle operation. However, the mechanistic relationship between charging curves and impedance spectrum remains unclear, which hinders the development as well as optimization of EIS-based prediction techniques. In this paper, we predicted the impedance spectrum by the battery charging voltage curve and optimized the input based on electrochemical mechanistic analysis and machine learning. The internal electrochemical relationships between the charging curve,incremental capacity curve, and the impedance spectrum are explored, which improves the physical interpretability for this prediction and helps define the proper partial voltage range for the input for machine learning models. Different machine learning algorithms have been adopted for the verification of the proposed framework based on the sequence-to-sequence predictions. In addition, the predictions with different partial voltage ranges, at different state of charge, and with different training data ratio are evaluated to prove the proposed method have high generalization and robustness. The experimental results show that the proper partial voltage range has high accuracy and converges to the findings of the electrochemical analysis. The predicted errors for impedance spectrum are less than 1.9 mΩ with the proper partial voltage range selected by the corelative analysis of the electrochemical reactions inside the batteries. Even with the voltage range reduced to 3.65–3.75 V, the predictions are still reliable with most RMSEs less than 4 mO.

Seismic hazard prediction using multispectral amplification maps in a complex topographic area: A case study of Qiaozhuang town, Sichuan Province, Southwest China

Earthquakes can cause widely distributed slope failures and damage in mountainous areas.The accurate prediction of ground motions in mountainous areas is essential for managing the seismic risk of urban cities near mountains but is restricted primarily by complex seismic site amplification effects in areas of uneven terrain.This study selected Qiaozhuang town located in the Qingchuan–Pingwu fault zone,Southwest China,as a case study.A simulator for mapped seismic responses using a hybrid model(Si Se RHMap)was applied to compute the multispectral seismic topographic amplification maps at the three slope units surrounding Qiaozhuang town(Weigan hill,Mt.Dong,and Mt.Shizi).Post-earthquake damage survey maps,1 D seismic site response spectral ratios,and H/V spectral ratios of earthquake data were used to validate the computed seismic site amplification factors and resonance frequencies.The results suggest that strong topographic amplification effects usually occur at distinct slope locations,such as hilltops,convex slope positions,upslope,and narrow ridges.The computed topographic amplification factors in the study area reached up to 2.4 at upslope or hilltops,and the resonance frequencies were between 3 and 10 Hz.Topographic effects can be as important as stratigraphic effects when assessing seismic amplification effects in the study area.We conclude that both topographic and stratigraphic effects should be considered in the comprehensive seismic hazard assessment of the study area or other similar mountain towns.

Genomic prediction of yield performance among single-cross maize hybrids using a partial diallel cross design

Genomic prediction(GP)in plant breeding has the potential to predict and identify the best-performing hybrids based on the genotypes of their parental lines.In a GP experiment,34 elite inbred lines were selected to make 285 single-cross hybrids in a partial-diallel cross design.These lines represented a mini-core collection of Chinese maize germplasm and comprised 18 inbred lines from the Stiff Stalk heterotic group and 16 inbred lines from the Non-Stiff Stalk heterotic group.The parents were genotyped by sequencing and the 285 hybrids were phenotyped for nine yield and yield-related traits at two locations in the summer sowing area(SUS)and three locations in the spring sowing area(SPS)in the main maizeproducing regions of China.Multiple GP models were employed to assess the accuracy of trait prediction in the hybrids.By ten-fold cross-validation,the prediction accuracies of yield performance of the hybrids estimated by the genomic best linear unbiased prediction(GBLUP)model in SUS and SPS were 0.51 and 0.46,respectively.The prediction accuracies of the remaining yield-related traits estimated with GBLUP ranged from 0.49 to 0.86 and from 0.53 to 0.89 in SUS and SPS,respectively.When additive,dominance,epistasis effects,genotype-by-environment interaction,and multi-trait effects were incorporated into the prediction model,the prediction accuracy of hybrid yield performance was improved.The ratio of training to testing population and size of training population optimal for yield prediction were determined.Multiple prediction models can improve prediction accuracy in hybrid breeding.

Polar motion prediction using the combination of SSA and ARMA

High-precision polar motion(PM) prediction is of important significance in astronomy, geodesy, aviation,hydrographic mapping, interstellar navigation, and so on. SSA can effectively extract the trend and period terms of PM,in the process of achieving high-precision medium-and long-term polar motion prediction,it is necessary to solve the end effect problem and overfitting problem of SSA forecasting method;therefore, ARMA was applied to decreasethe end effect, and a suitable combination of reconstructed components was determined to avoid the high variance reaction of SSA overfitting. Based on the decomposition and reconstruction of the PM by SSA, the reconstructed components are determined to participate in the SSA iterative fitting model according to the variance contribution rate. The combination of the reconstructed components representing the polar motion period term and the trend term is determined according to the correlation analysis of the selected reconstructed components. After the above work, the principal component prediction sequence is obtained by fitting the period term and the trend term to convergence, respectively, and then, the SSA end effect is modified, and the residual term is predicted based on ARMA. The test results show that he prediction accuracy of SSA + ARMA at the front of the X and Y directions are improved by 96.90% and 97.53% compared with those of SSA, respectively,and the forecast accuracy of 365 days are improved by 37.93% and 19.53% in the X and Y directions compared with those of Bulletin A, respectively.

Loss prediction of three-level amplified spontaneous emission sources in radiation environment

A model of three-level amplified spontaneous emission(ASE)sources,considering radiation effect,is proposed to predict radiation induced loss of output power in radiation environment.Radiation absorption parameters of ASE sources model are obtained by the fitting of color centers generation and recovery process of gain loss data at lower dose rate.Gain loss data at higher dose is applied for self-validating.This model takes both the influence of erbium ions absorption and photon bleaching effect into consideration,which makes the prediction of different dose and dose rate more accurate and flexible.The fitness value between ASE model and gain loss data is 99.98%,which also satisfies the extrapolation at the low dose rate.The method and model may serve as a valuable tool to predict ASE performance in harsh environment.

Control of Facies and Potential on Jurassic Hydrocarbon Accumulation and Prediction of Favorable Targets in the Hinterland Region of the Junggar Basin

Exploration practices show that the Jurassic System in the hinterland region of the Junggar Basin has a low degree of exploration but huge potential, however the oil/gas accumulation rule is very complicated, and it is difficult to predict hydrocarbon-bearing properties. The research indicates that the oil and gas is controlled by structure facies belt and sedimentary system distribution macroscopically, and hydrocarbon-bearing properties of sand bodies are controlled by lithofacies and petrophysical facies microscopically. Controlled by ancient and current tectonic frameworks, most of the discovered oil and gas are distributed in the delta front sedimentary system of a palaeo-tectonic belt and an ancient slope belt. Subaqueous branch channels and estuary dams mainly with medium and fine sandstone are the main reservoirs and oil production layers, and sand bodies of high porosity and high permeability have good hydrocarbon-bearing properties; the facies controlling effect shows a reservoir controlling geologic model of relatively high porosity and permeability. The hydrocarbon distribution is also controlled by relatively low potential energy at the high points of local structure macroscopically, while most of the successful wells are distributed at the high points of local structure, and the hydrocarbon-bearing property is good at the place of relatively low potential energy; the hydrocarbon distribution is in close connection with faults, and the reservoirs near the fault in the region of relatively low pressure have good oil and gas shows; the distribution of lithologic reservoirs at the depression slope is controlled by the distribution of sand bodies at positions of relatively high porosity and permeability. The formation of the reservoir of the Jurassic in the Junggar Basin shows characteristics of favorable facies and low-potential coupling control, and among the currenffy discovered reservoirs and industrial hydrocarbon production wells, more than 90% are developed within the scope of facies- potential index FPI〉0.5, while the FPI and oil saturation of the discovered reservoir and unascertained traps have relatively good linear correlation. By establishing the relation model between hydrocarbon- bearing properties of traps and FPI, totally 43 favorable targets are predicted in four main target series of strata and mainly distributed in the Badaowan Formation and the Sangonghe Formation, and the most favorable targets include the north and east of the Shinan Sag, the middle and south of the Mobei Uplift, Cai-35 well area of the Cainan Oilfield, and North-74 well area of the Zhangbei fault-fold zone.

Drag Characteristics Prediction for Wet Multi-Disk Brakes

To improve the characteristics of wet multi-disc brakes (WMDBs), the WMDBs of the drive axles of mining trucks were studied. A model was established to predict the phenomenon of drag characteristics during wet brake non-engagement by considering the combined effect of surface grooves, film shrinkage, and laminar Navier-Stokes (N-S) equations. The model was used to study drag torque and temperature variation of the wet brakes for different volume flows, dynamic viscosities, and friction pair clearances. The simulation results indicated that the peak torque decreased when the clearance of the friction pair increased. Additionally, the peak torque increased when the volume flow increased and when the cooling liquid dynamic viscosity increased. The model was more accurate than a traditional forecasting system when considering the role of surface grooves and oil film shrinkage in actual working conditions.

Electrode/Electrolyte Interfacial Chemistry Modulated by Chelating Effect for High-Performance Zinc Anode

Although Zn metal has been regarded as the most promising anode for aqueous batteries,its practical application is still restricted by side reactions and dendrite growth.Herein,an in-situ solid electrolyte interphase(SEI)film formed on the interface of electrode/electrolyte during the plating/stripping of zinc anodes by introducing trace amounts of multidentate ligand sodium diethyldithiocarbamate(DDTC)additive into 1 M ZnSO_(4).The synergistic effect of in-situ solid electrolyte interphase forming and chelate effect endows Zn^(2+)with uniform and rapid interface-diffusion kinetics against dendrite growth and surface side reactions.As a result,the Zn anode in 1 M ZnSO_(4)+DDTC electrolytes displays an ultra-high coulombic efficiency of 99.5%and cycling stability(more than 2000 h),especially at high current density(more than 600 cycles at 40 mA cm^(-2)).Moreover,the Zn//MnO_(2)full cells in the ZnSO_(4)+DDTC electrolyte exhibit outstanding cyclic stability(with 98.6%capacity retention after 2000 cycles at 10 C).This electrode/electrolyte interfacial chemistry modulated strategy provides new insight into enhancing zinc anode stability for high-performance aqueous zinc batteries.