Verification Of Results Research Articles

Energy absorption and deformation of cellular structures with dovetail joints

In this paper, the dovetail joint is creatively applied to cellular structural plates. Based on the concave cellular structural dovetail joint plates, several new cellular structural dovetail joints with stable quality are designed by chamfering the joints and bending the rods. Tensile experiments of several dovetail jointed cellular structure plates are carried out to investigate the effects of the connection methods on energy absorption and deformation behavior. Finite element models are established and the finite element simulation results are compared with the experimental results for verification. By comparing the different dovetail connection methods, the results show that under axial tensile conditions, the dovetail-jointed cellular structures generally fail at the rod connections. The chamfering of the rod connection and the bending of the rod at the connection can significantly improve the model load-carrying capacity, and when the chamfering or the degree of rod bending is large, the energy-absorbing characteristics of the model can be significantly improved due to the increased stress and deformation of the model's horizontal rods.

Devising an innovative method for improving decision-making efficiency at polymers processing companies in automotive industry

Enabling the development of technologies for the production of polymer materials directly determines the possibility of achieving the standards of sustainable development in the automotive industry, since polymer composites are used in modern cars as components of almost all assemblies and mechanisms. The expansion of plastics application in the car structure contributes to the reduction of fuel consumption and wear of parts. Technological changes encourage enterprises to constantly improve decision-making methods regarding the introduction of innovative technologies. This paper reports an innovative method devised for making management decisions at enterprises specializing in the processing of polymers for the automotive industry, which makes it possible to increase production efficiency. Based on the Ishikawa diagram and the PDCA cycle application, management tools and procedures for making production and technological decisions have been developed using the methodology of finding root causes and verifying factors influencing existing production problem. A set of indicators that make it possible to reduce the number of management errors has been substantiated; they increase the reliability of verifying received intermediate results of decisions. The selection of verification indicators was carried out taking into account the specificity of polymer production technologies. An improved decision-making management procedure is presented, which was embodied in an updated decision-tracking protocol. Unlike its basic version, it contains additional control points: target date of verification, date of verification, and result of verification. An experimental study showed that the application and observance of the full PDCA cycle increases the overall effectiveness of management by 63 %, which in turn has a positive effect on the company's sustainability in a competitive environment

Parameter Identification of PEMFC Model Using Improved Dung Beetle Optimization Algorithm

A proton exchange membrane fuel cell (PEMFC) is a complex system with multiple inputs and outputs, nonlinearity and strong coupling, and the establishment of an accurate model is the basis for evaluating the performance of PEMFC and developing control strategies. As the majority of the current intelligent algorithms tend to become stuck in local optimum when attempting to determine the PEMFC model’s parameters, resulting in low accuracy of parameter identification and poor model generalization ability, we propose an Improved Dung Beetle Optimization (IDBO) algorithm to identify the PEMFC model’s best parameters. To evaluate the IDBO algorithm’s performance, we identify the model optimal parameters of two typical commercial stacks, BCS 500 W and NedStack PS6, and the self-developed 3 kW PEMFC system, with the minimization of the sum of squared errors between the experimental output voltages and the model output voltages as the objective function. The verification results indicate that the IDBO algorithm has better convergence performance and higher parameter identification exactitude than the DBO algorithm. The robustness and applicability of the IDBO algorithm in addressing the issue of parameter identification of the PEMFC models are verified.

Exploration of Engine Parameters for Emission Reduction in Gasoline-Ethanol Fueled Engines

The main objective of this study is to develop spark ignition engine parameters that allow complete combustion while reducing dependence on fossil fuels. To achieve this goal, optimization of compression ratio, gasoline-ethanol mixture, ignition timing, and spark plug type was used. In addition, this study used water injection that continuously injects water before the intake manifold. In this study, the Taguchi method with the L9 orthogonal array was applied. According to the experimental verification results, the best combination to reduce exhaust emission levels is to utilize gasoline-ethanol (E70), a compression ratio (CR) of 15.6:1, an ignition degree of +4°, and a platinum spark plug. Meanwhile, the presence of water injection at 1.45 ml/s helps reduce vehicle exhaust pollutants.

Decoupling Algorithm for Online Identification of Inductance in Permanent Magnet Synchronous Motors Based on Virtual Axis Injection Method and Sensorless Control

Among the existing sensorless control algorithms for permanent magnet synchronous motors (PMSMs), the model reference adaptive system (MRAS) is widely utilized due to its merits of good robustness, high stability, and rapid response. Nevertheless, the performance of the sensorless control is largely determined by the identified inductance parameters. The virtual-rotary-axis high-frequency injection (VHFSI) method is an online identification strategy for PMSM inductance parameters which is not affected by the observed rotor position errors among inductance identification algorithms. However, the existing PMSM inductance online identification algorithm based on VHFSI has ignored the influence of the error components, resulting in low accuracy and the weak dynamic performance of the inductance identification algorithm. In response to the problems existing in the original method, this paper improves VHFSI by designing a feedforward decoupling algorithm to compensate for the error components. Theoretical verification and simulation results indicate that the improved identification algorithm enhances the accuracy of inductance parameter identification, significantly improves the dynamic tracking performance of inductance identification, and combines it with the MRAS sensorless control method to constitute a sensorless control system for the motor, thereby enhancing control performance and system stability.

Sustainable Smart Education Based on AI Models Incorporating Firefly Algorithm to Evaluate Further Education

With the popularity of higher education and the evolution of the workplace environment, graduate education has become a key choice for students planning their future career paths. Therefore, this study proposes to use the data processing ability and pattern recognition ability of machine learning models to analyze the relevant information of graduate applicants. This study explores three different models—backpropagation neural networks (BPNN), random forests (RF), and logistic regression (LR)—and combines them with the firefly algorithm (FA). Through data selection, the model was constructed and verified. By comparing the verification results of the three composite models, the model whose evaluation results were closest to the actual data was selected as the research result. The experimental results show that the evaluation result of the BPNN-FA model is the best, with an R value of 0.8842 and the highest prediction accuracy. At the same time, the influence of each characteristic parameter on the prediction result was analyzed. The results show that CGPA has the greatest influence on the evaluation results, which provides the evaluation direction and evaluation results for the evaluators to analyze the level of students’ scientific research ability, as well as providing impetus to continue to promote the combination of education and artificial intelligence.

Young Goji Fruit Volatiles Regulate the Oviposition Behavior and Chemosensory Gene Expression of Gravid Female Neoceratitis asiatica.

The goji fruit fly, Neoceratitis asiatica, is a major pest on the well-known medicinal plant Lycium barbarum. Dissecting the molecular mechanisms of the oviposition selection of N. asiatica regarding the host plant will help to identify new strategies for pest fly control. However, the molecular mechanism of chemical communication between the goji fruit fly and the host goji remains unclear. Hence, our study found that young goji fruit volatiles induced the oviposition response of gravid female N. asiatica. After N. asiatica was exposed to young goji fruit volatiles, the expression of six chemosensory genes (NasiOBP56h3 and OBP99a1 in the antennae; OBP99a2, OBP99a3 and CSP2 in the legs; and OBP56a in the ovipositor) was significantly upregulated in different organs of female N. asiatica compared with the group without odor treatment according to transcriptome data. Further results of qPCR verification show that the expression levels of the six selected upregulated genes after the flies were exposed to host plant volatiles were mostly consistent with the results of transcriptome data. We concluded that six upregulated genes may be involved in the recognition of young goji fruit volatiles by gravid female N. asiatica. Our study preliminarily identifies young goji fruit volatiles as a key factor in the oviposition behavior of N. asiatica, which will facilitate further studies on the mechanisms of host oviposition selection in N. asiatica.

Improved Method for the Calculation of the Air Film Thickness of an Air Cushion Belt Conveyor.

The air film thickness is an important parameter of an air cushion belt conveyor, which directly affects the compressed air supply power and operating resistance of the system. Therefore, it is important to calculate the bottom thickness of the gas film accurately in the design stage. A calculation method for the thickness of a conveyor air cushion was derived based on the mathematical model of the air cushion flow field for a multi row uniformly distributed air cushion structure. Meanwhile, the algorithm was validated based on a Fluent 3D flow field numerical simulation and experiments. Through verification, it was found that due to the algorithm's assumption that the increase in the gas flow rate only existed at the axis of the gas hole, there was a sudden change in the calculation results of the gas flow rate at the axis of the gas hole. The sudden change in the gas flow rate had caused the calculation results of the air cushion thickness to experience abrupt and discontinuous changes. Furthermore, the calculation method for air cushion thickness was revised based on the verification results. Compared with the experimental test results, the average error of the calculation results of the algorithm proposed in this paper was 14.27%.

Model-free active noise control using second-order simultaneous perturbation stochastic approximation algorithm for periodic noise

Model-free active noise control (ANC) using simultaneous perturbation stochastic approximation (SPSA) algorithm has recently attracted interest of researchers since it avoids the problem of secondary path modeling (SPM). However, the existing SPSA-based ANC methods are all based on the first-order SPSA algorithm and the convergence rate of these methods are significantly lower than that of the Filtered-x least mean square (FxLMS) algorithm. In order to enhance the convergence performance, this paper introduces the second-order SPSA algorithm into the ANC system. During the implementation of the second-order SPSA algorithm, a novel method of matrix positive definite transformation based on eigenvalue decomposition (EVD) is proposed. Convergence performance and tracking performance of ANC using the second-order SPSA algorithm are compared with ANC using the FxLMS algorithm and the standard SPSA through simulation and experimental verification. Simulation and experimental verification results show that ANC using the second-order SPSA algorithm can not only achieve the convergence rate comparable to that of ANC using the FxLMS algorithm, but also track the secondary path change.

Host lipid metabolism influences the variation in resistance of Pekin ducks to duck hepatitis A virus genotype 3.

Duck viral hepatitis (DVH) is a common and serious acute infectious disease that has a significantly impact on the duck farming industry. Duck hepatitis A virus type 3 (DHAV-3) is the major causative agent of DVH in East Asia. Host factor indicators of resistance to DHAV-3 in Pekin ducks were investigated using resistant (Z7R) and susceptible (Z7S) duck lines. Before DHAV-3 infection, Z7R had significantly higher HDL-C and LDL-C levels than Z7S. The results of population verification showed that Pekin ducks with HDL-C and/or LDL-C concentrations within their maximum 5 % confidence interval were highly resistant to DHAV-3. RNA-seq identified fifteen differentially expressed genes, primarily involved in lipid metabolism. Additionally, lipidomics identified one hundred distinct metabolites involved in glycerophospholipid metabolism. The ACSL6 gene was found to be significantly associated with OAHFA, PC, and PE. ACSL6, PE, PC, HDLC, and LDL-C co-regulated hepatic lipid metabolism. In conclusion, our results reveal that HDL-C and LDL-C may serve as markers of anti-DHAV-3 infection and lipid metabolism may be related to a potential mechanism of antiviral activity in Pekin ducks, providing a theoretical basis for future studies on the interaction between lipid metabolism and DHAV-3.

Prediction and Optimization Design of Porous Structure Properties of Biomass-Derived Biochar Using Machine Learning Methods

Biochar produced from biomass by pyrolysis and activation is a platform porous carbon material that has been widely used in many areas. The porosity properties of biochar such as specific surface area (SSA), total pore volume (Total_PV), micropore volume (Micro_PV), mesopore volume (Meso_PV), and average pore size (Average_PS) are essential to biochar applications. Although previous machine learning (ML) models can precisely predict SSA and Total_PV, these models are unable to comprehensively predict the other porosity characteristics. More importantly, activation, which is a critical process for preparing high-porosity biochar, was generally not considered in previous studies. Here, six single-target models were established first based on pyrolysis & activation conditions for the prediction of SSA, Total_PV, Micro_PV, Meso_PV, Average_PS, and yield, obtaining test R2 of 0.89, 0.86, 0.88, 0.89, 0.76 and 0.91, respectively. Then, a multi-target model was established for simultaneous prediction with an average test R2 of 0.87. ML model interpretation indicated agent type and ratio were crucial to porosity properties. Finally, activation and direct pyrolysis biochar production optimum schemes were derived from ML model for a high porosity. Favorable experimental verification results were obtained with validation R2 of 0.98, indicating the great potential of using ML for biochar engineering.

Belief game: Verifying smart contract functionality in player dynamic interactions

Smart contracts, being security-critical code, facilitate consensus among players and ensure secure and accurate value transfer, and formal verification is necessary to guarantee functional correctness of contracts. Game theory serves as one of the tools in formal verification by assessing whether the outcomes of contract executions meet the expected goals. While most studies employing game theory to verify smart contract functionality assume rational players, in practice, players may invoke and deploy smart contracts involving irrational behavior, casting doubt on the correctness of verification results. The aim of this study is to propose an alternative game model to verify smart contract functionality in dynamic player interactions where irrational behavior is involved. Specifically, a belief-based smart contract execution game (BSC-game) model was introduced, utilizing belief – the probability that a player believes in the irrationality of others – to capture how the irrational behavior of others affects a player’s contract execution decisions. Reasonable economic incentives were introduced to encourage honest behavior of players. Moreover, a computationally feasible method was designed to update players’ beliefs in large-scale dynamic smart contract executions. Theoretical analysis discloses the existence of equilibrium in the BSC-game, as well as the conditions for the number of faulty players within the system’s fault tolerance. We conducted the simulation experiments, and verified the business-oriented smart contract written in G language by the BSC-game model. The results further indicate that although players’ beliefs impact their decisions to execute contracts, reasonable economic incentives can motivate players to execute contracts honestly. This ensures that smart contract functionality aligns with expected goals, showing that the BSC-game model can verify and guarantee the correctness of contract functions. This new approach significantly contributes to bolstering smart contract security and credibility, positively influencing blockchain stability.

Role of anoikis-related gene RAC3 in prognosis, immune microenvironment, and contribution to malignant behavior in vitro and in vivo of bladder urothelial carcinoma.

Anoikis disrupts the normal apoptotic process in cells, leading to abnormal proliferation and migration, thereby promoting tumor formation and development. However, the role of anoikis in bladder urothelial carcinoma (BLCA) still requires further exploration. Anoikis-related genes (ARGs) were retrieved from the GeneCards and Harmonizome databases to distinguish various subtypes of BLCA and develop a predictive model for BLCA. The immune microenvironment and enrichment pathways between various subtypes were also analyzed using consensus clustering. Potential medications were screened by utilizing drug sensitivity analysis. In vitro and vivo, the character of the independent prognostic gene in BLCA was confirmed through cell studies and mouse xenograft models. One hundred thirty differentially expressed genes (DEGs) were identified, and nine of them were chosen to construct predictive models that can accurately forecast the prognosis of BLCA patients. K = 2 was correctly identified as the optimal clustering type for BLCA, showing prominent differences in survival rates between the two subgroups. The immune-related functional studies manifested that the two subtypes' immune cell expressions differed. It was verified that RAC3 is an independent prognostic gene for BLCA. RAC3 shows high expression levels in BLCA, as indicated by its consistent mRNA and protein levels across different gene expressions. The functional verification results of RAC3 in BLCA showed that silencing RAC3 can significantly inhibit BLCA cell proliferation, colony formation, and migration. RAC3 knockdown inhibited the growth and migration of BLCA in vivo. SB505124 exhibited a significant inhibitory effect on the proliferation of BLCA cells. Based on the predictive model developed in this study, BLCA patients' prognoses can be accurately predicted. SB505124 could become an important drug in the treatment of BLCA patients. RAC3 is essential in prognosis, immune microenvironment, and malignant behavior of BLCA in vitro and in vivo. It will also offer the potential for personalized treatment for BLCA patients and generate new research avenues for clinical investigators.

Assessment of Land Damage Status Using Frequency Method on Several Land Use for Biomass Production in Payakumbuh City

Intensive use of land for agricultural businesses causes the land to become very dynamic and causes a decrease in soil quality for producing biomass. This research aims to determine environmental conditions (level of damage to land and soil for biomass production) and the factors that cause soil damage in Payahkumbuh City. Land surveys were carried out using the research method in five sub-districts. Samples taken in the field are disturbed soil samples for analysis of physical properties (texture, BV, TRP, and permeability). In contrast, disturbed soil samples are used to analyze chemical soil properties (c.organic, pH, DLH, Redok). Apart from that, he also observed environmental conditions such as rocks on the surface and the depth of the soil. Next, the soil samples are explained in the soil laboratory of the Department of Soil Science and Land Resources. Data from soil physical and chemical property variables are processed, averaged, and compared with soil health standards following PP No. 150 of 2000. The research results show that the results of land identification and verification show that the land in the five sub-districts of Payakumbuh City in the research area has a Light Damage Status (RI) covering an area of 2391.65 ha and Moderate Damage (RII) covering an area of 1856.76 ha. From 10 observation points, the land in the research area has two levels of soil damage: moderate and light. Soil damage is caused by limiting factors. In moderate damage status, the limiting factors are (RII-vpre), namely soil porosity, degree of air escape, redox reactions, and electrical conductivity. Meanwhile, the limiting factors in soil with mild damage are (RI-vre) soil porosity, redox reactions, and electrical conductivity.

Intelligent Inspection Method for Rebar Installation Quality of Reinforced Concrete Slab Based on Point Cloud Processing and Semantic Segmentation

The rebar installation quality significantly impacts the safety and durability of reinforced concrete (RC) structures. Traditional manual inspection is time-consuming, inefficient, and highly subjective. In order to solve this problem, this study uses a depth camera and aims to develop an intelligent inspection method for the rebar installation quality of an RC slab. The Random Sample Consensus (RANSAC) method is used to extract point cloud data for the bottom formwork, the upper and lower rebar lattices, and individual rebars. These data are utilized to measure the concrete cover thickness, the distance between the upper and lower rebar lattices, and the spacing between rebars in the RC slab. This paper introduces the concept of the “diameter calculation region” and combines point cloud semantic information with rebar segmentation mask information through the relationship between pixel coordinates and camera coordinates to measure the nominal diameter of the rebar. The verification results indicate that the maximum deviations for the concrete cover thickness, the distance between the upper and lower rebar lattices, and the spacing of the double-layer bidirectional rebar in the RC slab are 0.41 mm, 1.32 mm, and 5 mm, respectively. The accuracy of the nominal rebar diameter measurement reaches 98.4%, demonstrating high precision and applicability for quality inspection during the actual construction stage. Overall, this study integrates computer vision into traditional civil engineering research, utilizing depth cameras to acquire point cloud data and color results. It replaces inefficient manual inspection methods with an intelligent and efficient approach, addressing the challenge of detecting double-layer reinforcement. This has significant implications for practical engineering applications and the development of intelligent engineering monitoring systems.

Wagner's law. Comparative verification of global trends and regional policy in the southern regions of the Central Federal District of the Russian Federation

Subject. This article discusses the issues related to the contribution of the State to the formation of the gross domestic product of developed countries. Objectives. The article aims to verify Wagner's law on the basis of a long and medium time series of consolidated regional budget expenditures and gross regional products of the Kursk, Bryansk, Orel, Lipetsk, Voronezh, and Belgorod Oblasts in comparison with global trends. Methods. For the study, we used a statistical analysis of the time series and comparative analysis. Results. The article presents the results of verification of Wagner's law on the statistical data of economically developed countries and confirms its significance. The article finds that the share of the State in the gross product in developed countries and regions of Russia is growing in the long term, but demonstrates cyclicality in the medium term. Conclusions. The development of Russia and its regions is in line with global trends and demonstrates the growing role of the State in the economy.

Neutron detectors in proton therapy: Calibration, operational in situ verification, and comparison with Monte Carlo simulation

The number of proton therapy centers is continuing to increase and the use of suitable neutron detectors is essential in the field of radiation protection to ensure a safe working environment. The neutrons present in proton therapy centers range from thermal neutrons to neutrons with the maximum energy of the irradiated proton beam. Therefore, the neutron detectors must be of extended-range. The first aim of the study is to develop a robust and easy-to-implement method to operationally verify the neutron detectors in a proton therapy facility. The neutron field generated in the facility under specific conditions was used as the neutron reference source against which the neutron detectors were evaluated. For this purpose, an analysis of the detectors' uncertainties and the establishment of indicators that report the correct status of the detectors was performed. In addition, the experiment was characterized in TOPAS (a Monte Carlo toolkit based on Geant4) to validate the experimentally measured data and vice versa. The WENDI-II and LUPIN-II responses were determined in TOPAS. Both detectors were calibrated in an accredited laboratory and the simulation of this calibration in Monte Carlo was performed. An estimated uncertainty of 13.1% (k = 2) was found for the results of the operational verification of the WENDI-II and LUPIN-II detectors. Thus, for the neutron detectors, the investigation interval is the range between 13.1% and 17.2% (k = 3) while the intervention level is set at 17.2%. An overall agreement (within 61%) was obtained among neutron H∗(10) values calculated with TOPAS and H∗(10) measured for the operational verification of the detectors. The calibration factor for WENDI-II coincides with that indicated by the manufacturer, although a discrepancy of 19% was found for LUPIN-II. The verification method for neutron detectors, studied for two different types of irradiations (a single energy and an energy sweep irradiation), is sufficiently conservative to ensure the correct operation of the equipment, fully complies with international recommendations (i.e. IEC 61005:2014) and can be applied in other proton therapy facilities.

Concrete Creep Prediction Based on Improved Machine Learning and Game Theory: Modeling and Analysis Methods

Understanding the impact of creep on the long-term mechanical features of concrete is crucial, and constructing an accurate prediction model is the key to exploring the development of concrete creep under long-term loads. Therefore, in this study, three machine learning (ML) models, a Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting Machine (XGBoost), are constructed, and the Hybrid Snake Optimization Algorithm (HSOA) is proposed, which can reduce the risk of the ML model falling into the local optimum while improving its prediction performance. Simultaneously, the contributions of the input features are ranked, and the optimal model’s prediction outcomes are explained through SHapley Additive exPlanations (SHAP). The research results show that the optimized SVM, RF, and XGBoost models increase their accuracies on the test set by 9.927%, 9.58%, and 14.1%, respectively, and the XGBoost has the highest precision in forecasting the concrete creep. The verification results of four scenarios confirm that the optimized model can precisely capture the compliance changes in long-term creep, meeting the requirements for forecasting the nature of concrete creep.

Antibacterial mechanism analysis of resveratrol against Salmonella typhimurium via metabolomics

Salmonella, a common pathogenic bacterium in food, can have a severe impact on food safety and consumer health. At present, Salmonella infection is controlled primarily by the use of antibiotics, which creates unsafe factors for food safety. Thus, finding a natural antibacterial agent is highly practical. In this study, resveratrol was screened from 17 kinds of polyphenols, and its inhibitory mechanism and effects on metabolites of Salmonella typhimurium were investigated to occur through cell wall and membrane damage and metabolomics analysis. The results revealed that the minimum inhibitory concentration of resveratrol against S. typhimurium was 250 μg/mL. After treatment with resveratrol, the lag period of the strain was prolonged, and the cell wall and membrane structure were destroyed. Scanning electron microscopy (SEM) and transmission electron microscopy (TEM) further confirmed that resveratrol induced damage to the cell walls and cell membrane. The metabolic profile of S. typhimurium following resveratrol treatment was analysed by gas chromatography‒mass spectrometry. In the metabolome evaluation, we screened 23 differentially abundant metabolites, including 11 upregulated and 12 downregulated metabolites. Eight metabolic pathways of S. typhimurium, including pathways important for amino acid metabolism and the tricarboxylic acid (TCA) cycle, exhibited significant changes after resveratrol treatment. The verification results of the citric acid content, cis-aconitase activity, and ATP content further revealed that the tricarboxylic acid cycle and other related metabolic pathways of S. typhimurium were affected. These results could provide a theoretical possibility for the use of resveratrol as a plant-derived bacteriostatic for food safety problems caused by S. typhimurium.Key points• The mechanism of bacteriostasis was studied via metabolomics• Resveratrol causes the death of Salmonella by disrupting the cell wall and membrane

Effective stress intensity factor range for fatigue cracks propagating in mixed mode I-II loading

The actual service axles are often subjected to mixed-mode loading, and predicting the mixed-mode I-II crack propagation behaviour using the mode I effective stress intensity factor (ΔKI) differs from the real service conditions. To effectively predict the fatigue crack propagation behaviour of actual service structures, the I-II stress intensity factor range (ΔKP-R) considering two closure effects was adopted to describe the fatigue crack propagation under mixed mode loading. A test database was established based on monitoring data of mode I and mixed-mode I-II (30/45/60°) crack propagation tests under different stress ratios. Combining domain knowledge and symbolic regression (SR) methods, an angle factor was proposed for constructing correlation functions between ΔKI and ΔKP-R. The results showed that the loading angle (α) only affects the initial projection of the load parallel and perpendicular to the fatigue crack growth (FCG) direction. Compared with the geometric correction factor, the correlation function acquired by the angle factor constructed by the SR method has higher accuracy, and the balance parameters (SCORE) obtained by the former are significantly higher than those obtained by the latter under the same function complexity. The SR verification results demonstrated that constructing mode I and I-II correlation functions with angle factors has a good predictive effect.