Principal Method Research Articles

Relationship between mental health and stressors among fathers of children with chronic illnesses and cognitive structure of fathers’ stress experiences

To clarify the relationship between stressor and mental health of the fathers with children with chronic illnesses and to examine the cognitive structure of fathers’ stress experiences. This study employs a cross-sectional research design. A self-reported questionnaire survey was conducted on 137 respondents. The dependent variables were the stressors of the fathers and depression. Focusing on the data of 51 fathers of children with chronic illness, for 21 items related to the stressful experiences had been identified in a previous study, an exploratory factor analysis using the principal factor method was performed. Logistic regression analysis results showed that the items “There are children with chronic illness” and “The large number of medical treatments required for the children” were significantly associated with the fathers’ poor mental health. In the factor analysis, the following three factors were extracted as a recognition of the fathers’ stress experiences. The fathers positively recognized their role as a father and a husband, perceiving it in a positive light despite their negative feelings. The results also suggested that it should be essential for nurses to re-evaluate those fathers who are exposed to daily stressors as care subjects, as well as to positively include them in the support activities.

Predicting Temperature and Humidity in Roadway with Water Trickling Using Principal Component Analysis-Long Short-Term Memory-Genetic Algorithm Method

The heat dissipated from high geo-temperature underground surrounding rocks is the main heat source of working faces, while thermal water upwelling and trickling into the roadway will notably deteriorate the mine’s climate and thermal comfort. Predicting airflow temperature and relative humidity (RH) is conductive to intelligent control of air conditioning cooling and ventilation regulation. To accommodate this issue, an intelligent technique was proposed, integrating a genetic algorithm (GA) and long short-term memory (LSTM) based on rock temperature, inlet air temperature, water temperature, water flow rate, RH, and ventilation time. A total of 21 input features including over 200 pieces of data were collected from an independently developed modeling roadway to construct a dataset. Principal component analysis (PCA) was conducted to reduce features, and GA was used to tune the LSTM and PCA-LSTM architectures for best performance. The following research results were yielded. The proposed PCA-LSTM-GA model is more reliable and efficient than the single LSTM model or hybrid LSTM-GA model in predicting the air temperature Tfout and humidity RHout at the end of the water trickling roadway. The importance scores (ISs) indicate that Tfout is mainly influenced by the surrounding rock temperature (IS 0.661) and the inlet air temperature (IS 0.264). While RHout is primarily influenced by the rock temperature in the water trickling section (IS 0.577), the inlet air temperature (IS 0.187), and the trickling water temperature and flow rate (total IS 0.136), and it has an evident time effect. In addition, we developed relevant equipment and provided engineering practice methods to use the machine learning model. The proposed model, which can predict the mine microclimate, serves to facilitate coal and geothermal resource co-mining as well as thermal hazard control.

Governance and competitiveness evaluation of China's financial asset management corporations.

With the development of non-performing assets market and the requirement of maintaining national financial security and stability, asset management corporations (AMC) have set up in recent years, facing the dilemma of governance reform and competitiveness improvement. This paper puts forward the Governance-Performance-Competitiveness theory of asset management corporations, and constructs the comprehensive competitiveness evaluation index system of based on internal and external governance mechanisms, and studies the factors and degrees of internal and external governance affecting competitiveness by combining principal component analysis method and grey correlation analysis method. The empirical results show that the asset scale and profitability of asset management corporations directly determine their competitiveness level to a large extent, and the correlation between external governance mechanism and competitiveness is stronger than that of internal governance mechanism. Clustering and grouping are conducted based on the size of competitiveness, and China's local asset management corporation system presents a diamond structure. China's local financial asset management corporations should strengthen the top-level design of internal governance, attach importance to external governance, expand and strengthen the asset scale, and carry out full-chains business while implementing a differentiated development model to achieve sustainable development when adhering to the principal business of non-performing assets.

Coordinated Development of Civic Education and Student Management in Colleges and Universities Incorporating Principal Component Analysis Approach

Abstract This paper observes the relationship between variables that cannot be explained through the structure of variable explanatory variance-covariance and explores the method of applying the theoretical model of principal component analysis and factor analysis by using the method of hypothesis. After studying the scope of application of the principal component analysis method and factor analysis method, both of which can be used in the general model of comprehensive evaluation of variables, the scientific management approach of planned behavior for student management in the classroom of college and university civic education is proposed. By empirically analyzing the Civics learning management and education evaluation of students and teachers in L colleges and universities, the experimental results show that the cumulative contribution rate of the three principal components, namely, high moral character, perfect personality and good communication, is 87.4% as the main observed variables. In the cluster analysis of the Civics performance, the best Civics performance was found in the category 2 and category 1 students in college L, with a total of 9 students conforming to it. The four principal component factors of the first-ranked teachers did not differ much, respectively 1.6485, 0.4653, 1.7463, −0.645, except for the teacher numbered 1, who had a big difference in the ranking place under the two evaluation methods, and the rest of the teachers did not have any difference, and on the whole, the quality of Civic and Political education in College L was better, and the harmonization with the student management was good.

Investigation and optimization of plasma arc cutting process parameters for AISI 304 by integrating principal component analysis and composite desirability method

Investigation and optimization of plasma arc cutting process parameters for AISI 304 by integrating principal component analysis and composite desirability method

Multi-objective design and optimization of squeezed branch pile based on orthogonal test

In recent years, the emergence of the squeezed branch pile has presented a new avenue for civil engineering, offering a distinctive structure and favorable mechanical characteristics. Squeezed branch piles have strong compressive, uplift, and horizontal load resistance capabilities. Due to the existence of discs, the geometric parameters of squeezed branch piles are abundant but important. This article selects number of discs, disc diameter, disc squeeze angle, and disc spacing as the main influencing factors on the bearing capacity of squeezed branch piles and conducts a qualitative analysis of their mechanical properties. The aim of this article is to analyze the different bearing performances of squeezed branch piles through orthogonal experimental design, simulate test conditions using finite element software ABAQUS, obtain relevant data, and finally determine the weight ranking and optimal combination of influencing factors through range analysis to provide better guidance for engineering practices. Through multi-objective optimization design, six optimization objectives including compressive performance, compressive economic efficiency, uplift performance, uplift economic efficiency, maximum horizontal displacement and maximum bending moment of pile body were analyzed. The analysis methods used included comprehensive balance method, queue scoring method, principal component analysis method, entropy weight method, and analytic hierarchy process. The conclusions obtained are similar, and based on the judgment, the squeezed branch pile with 4 discs, disc diameter of 2.5D, disc squeeze angle of 35°, and disc spacing of 3D is considered as the optimal combination under consideration of all optimization objectives.

Identifying gravity zones of higher school centers by digital traces of social media users

In the information society, social media data, along with official statistics and sociological surveys, are becoming an important source of information about population migration. The article analyzes the possibilities and experience of using «VKontakte» user profiles for studying internal educational migration. The main purpose of the study is to identify the leading higher school centers that attract interregional educational migrants. The principal research method used is analysis of digital traces of social network users. As a result, there have been identified the influence zones of 23 cities being the leading higher education centers in the Russian Federation. Among them are the cities that meet one of the following criteria: having a university with the «Federal» status or being included in the top 10 in Russia according to the QS World University Rankings 2022, population of the city— over 1 million people. The ratio of the number of the social network users is calculated for each zone: students in regions of the gravity center, students in all or only in «their» leading center of higher education. Territorial proximity has turned out not to be a determining factor when forming higher education areas in modern Russia. Formation of educational flows is influenced by a combination of factors: prestige of university, quality of education received, proposed areas of training, low cost of education, presence of a big city, socio-economic conditions, and others. It is concluded that the use of data from the «VKontakte» social network for the study of educational migration makes it possible to make a significant progress in studying these processes.

Убеждения педагогов о слабоуспевающих учащихся старших классов

<p>The article presents the results of a study that explores the teachers’ beliefs concerning low-performing senior schoolchildren in one of the regions of Kazakhstan. Until recently, the issue of low-achieving students was largely absent from the agenda of secondary education and has not got attention from Kazakhstani researchers. A case study methodology was applied as the principal research method, facilitating an in-depth examination of teachers’ belief systems concerning low-performing students. 22 high school and first-year college teachers participated in a qualitative study in May and June 2022. We analyzed teachers’ ideas about low-achieving students within an ecological framework using an inductive thematic analysis of individual interviews and focus group discussions. We identidied the categories of teachers’ individual, in-school and systemic beliefs. These beliefs collectively contribute to the conceptualisation of a low-performing students, their educational trajectories, and their potential for success or failure in life.</p>

Determining basic characteristics of bulk carriers at the early stages of design

This paper considers the task to determine the basic characteristics of bulker vessels. Available studies that define the main characteristics of ships were analyzed. The complexity of the task to choose a prototype in the process of determining the basic characteristics of bulk carriers at the initial stages of design has been shown. The accepted assumption implied that each ship in the sample (a ship database) was designed in such a way that some operational qualities and/or economic characteristics of a vessel are optimal in a certain sense. To solve the task, stochastic factor analysis was used, and the method of principal components was applied, which makes it possible to identify numerical characteristics that affect the parameters of the entire system but are not clearly observed. The factors were compiled that are dimensionless sets of characteristics of sample vessels, which are, in fact, new phase orthogonal coordinates of the problem with unequal variances of the sample along the coordinate directions. Evaluating the significance of correlations of factors and vectors of the sample data makes it possible to discard factors that have a weak influence on the values of the parameters of the sample vessels. Thus, the model of the problem is simplified while preserving essential connections. The analysis of the calculation data revealed that the number of significant factors due to the use of the principal component method decreased from 7 to 3. It was determined that the ships of the same designers are indeed grouped by the value of the factor sets. In the absence of an original algorithm for determining the main characteristics of the vessel at the initial stage of design, the application of the described factor analysis procedure should help with the definition of the main factors that affect the quality of the design. Further, it is possible to apply more subtle mechanisms for project optimization according to selected economic and technical parameters. The data of the numerical calculation of integrated characteristics of the ship are given

Effects of Different Heterogeneous Nutrient Environments on the Growth and Activities of Enzymes in the Roots of Fokienia hodginsii Families.

Currently, research on the F. hodginsii asexual lineage primarily focuses on the screening of growth traits and the control of single fertilizer applications. The effects of the heterogeneity of soil nutrients on root growth and activity have not been studied in detail. Therefore, we propose forest management measures to improve the foraging ability of forest trees in conjunction with stand productivity. In this experiment, annual containerized seedlings of 10 free-pollinated F. hodginsii lines from a primary asexual seed orchard were used as test subjects, and three heterogeneous nutrient environments of nitrogen (N), phosphorus (P), and potassium (K) were constructed. In contrast, homogeneous nutrient environments were used as the control to carry out potting experiments, to study the growth of F. hodginsii lines and the differences in the activities of root enzymes under the three heterogeneous nutrient environments, and to carry out the comprehensive evaluation using the principal component and cluster analysis method. The results were as follows: (1) The seedling height of F. hodginsii family lines under a homogeneous nutrient environment was significantly higher than that of all heterogeneous nutrient environments; the diameter of the ground was the highest under N heterogeneous nutrient environment and significantly higher than that of all the other nutrient environments; the biomass of the root system was the highest under P heterogeneous nutrient environment, which was significantly higher than that of homogeneous nutrient environment and K heterogeneous nutrient environment. The catalase (CAT) activity of F. hodginsii roots was higher than that of homogeneous nutrients in all heterogeneous nutrient environments but not significant, and the superoxide dismutase (SOD) activity was slightly higher than that of K heterogeneous and homogeneous nutrient environments in N and P heterogeneous nutrient environments. SOD activity was slightly higher than that of K heterogeneous and homogeneous nutrient environments under N, and P. peroxidase (POD) activity in the F. hodginsii root system was the highest under the P heterogeneous nutrient environment, which was significantly higher than that of the other nutrient environments. Unlike the activities of the enzymes, the content of malondialdehyde (MDA) in the roots of F. hodginsii was higher in the heterogeneous environment than in all the other nutrient environments. (2) Under N and P heterogeneous nutrient environments, lines 552 and 590 had higher seedling height, ground diameter, and root enzyme activity, while root biomass was highest in line 544; and under K heterogeneous nutrient environments, line 591 had higher seedling height, ground diameter, and root enzyme activity while root biomass was highest in line 551. In contrast to the patterns of seedling height, accumulation of root biomass and activities of root enzymes, family No. 590 had the highest ground diameter of all the F. hodginsii families under the heterogeneous nutrient environments. Family No. 547 had the highest MDA content. In conclusion, it can be seen that N heterogeneous and homogeneous nutrient environments can significantly increase the seedling height and diameter of F. hodginsii compared with P and K heterogeneous nutrient environments, and N and P heterogeneous nutrient environments can also increase the root biomass, root enzyme activities and significantly reduce the MDA content of F. hodginsii. According to the principal component analysis and cluster analysis, it can be seen that among the 10 F. hodginsii family lines, family lines 590 and 552 have higher evaluation in growth, root biomass accumulation, and enzyme activity.

Multispectral Remote Sensing Data Application in Modelling Non-Extensive Tsallis Thermodynamics for Mountain Forests in Northern Mongolia.

The imminent threat of Mongolian montane forests facing extinction due to climate change emphasizes the pressing need to study these ecosystems for sustainable development. Leveraging multispectral remote sensing data from Landsat 8 OLI TIRS (2013-2021), we apply Tsallis non-extensive thermodynamics to assess spatiotemporal fluctuations in the absorbed solar energy budget (exergy, bound energy, internal energy increment) and organizational parameters (entropy, information increment, q-index) within the mountain taiga-meadow landscape. Using the principal component method, we discern three functional subsystems: evapotranspiration, heat dissipation, and a structural-informational component linked to bioproductivity. The interplay among these subsystems delineates distinct landscape cover states. By categorizing ecosystems (pixels) based on these processes, discrete states and transitional areas (boundaries and potential disturbances) emerge. Examining the temporal dynamics of ecosystems (pixels) within this three-dimensional coordinate space facilitates predictions of future landscape states. Our findings indicate that northern Mongolian montane forests utilize a smaller proportion of received energy for productivity compared to alpine meadows, which results in their heightened vulnerability to climate change. This approach deepens our understanding of ecosystem functioning and landscape dynamics, serving as a basis for evaluating their resilience amid ongoing climate challenges.

Uncovering a causal connection between the Lachnoclostridium genus in fecal microbiota and non-alcoholic fatty liver disease: a two-sample Mendelian randomization analysis.

Previous observational studies have indicated that an imbalance in gut microbiota may contribute to non-alcoholic fatty liver disease (NAFLD). However, given the inevitable bias and unmeasured confounders in observational studies, the causal relationship between gut microbiota and NAFLD cannot be deduced. Therefore, we employed a two-sample Mendelian randomization (TSMR) study to assess the causality between gut microbiota and NAFLD. The gut microbiota-related genome-wide association study (GWAS) data of 18,340 individuals were collected from the International MiBioGen consortium. The GWAS summary data for NAFLD from the Anstee cohort (1,483 cases and 17,781 controls) and the FinnGen consortium (894 cases and 217,898 controls) were utilized in the discovery and verification phases, respectively. The inverse variance weighted (IVW) method was used as the principal method in our Mendelian randomization (MR) study, with sensitivity analyses using the MR-Egger, weighted median, simple mode, and weighted mode methods. The MR-Egger intercept test, Cochran's Q test, and leave-one-out analysis were conducted to identify heterogeneity and pleiotropy. Moreover, a fixed-effect meta-analysis was conducted to verify the robustness of the results. The gene prediction results showed that at the genus level, four gut microbiota were causally associated with NAFLD in the GWAS conducted by Anstee et al. The relative abundance of Intestinimonas (OR: 0.694, 95%CI: 0.533-0.903, p = 0.006, IVW), Lachnoclostridium (OR: 0.420, 95%CI: 0.245-0.719, p = 0.002, IVW), and Senegalimassilia (OR: 0.596, 95%CI: 0.363-0.978, p = 0.041, IVW) was negatively associated with NAFLD. The relative abundance of Ruminococcus1 (OR: 1.852, 95%CI: 1.179-2.908, p = 0.007, IVW) was positively correlated with NAFLD. Among them, the Lachnoclostridium genus was validated in FinnGen GWAS (OR: 0.53, 95%CI: 0.304-0.928, p = 0.026, IVW). The Lachnoclostridium genus was also significantly associated with NAFLD risk in the meta-analyses (OR: 0.470, 95%CI: 0.319-0.692, p = 0.0001, IVW). No heterogeneity or pleiotropy was observed. This study provided new evidence of the relationship between the Lachnoclostridium genus and NAFLD, suggesting that augmentation of the relative abundance of the Lachnoclostridium genus through the oral administration of probiotics or fecal microbiota transplantation could be an effective way to reduce the risk of NAFLD.

Application of Principal Component Analysis and Maximum Likelihood Estimation Method to Identify the Determinant Factors Intention to Use of Paylater in E-Commerce

This research aims to analyze which factors are most determining in influencing interest in using pay-later. This type of research is multivariate analytical research using a quantitative approach. All pay-later service customers who engage in e-commerce make up the study's population. Purposive sampling and non-probability sampling were employed in the sample-taking process based on this study. There are one hundred samples. Researchers have made observations to gather data. The author intends to employ principal component analysis and maximum likelihood estimation techniques for data analysis. Following the completion of the study, the following findings were made. The principal component analysis method's RMSE value is 0.02. The maximum likelihood estimates method's root mean square error is 0.015. Therefore, it can be said that the principal component analysis approach is appropriate for usage because it can explain factor formation due to its strong factor loading value and low RMSE value. The influence of advantages is the most important element determining interest in pay-later utilizing the principal component analysis method since it has the highest loading factor value in decision-making.

Kernel principal component analysis fault diagnosis method based on improving Golden Jackal optimization algorithm

Aiming at the shortcomings of the Golden Jackal optimization algorithm, such as low convergence accuracy and easy falling into the optimal local solution, an improved Golden Jackal optimization algorithm was proposed. First, sine and piecewise linear (SPM) chaotic mapping was introduced to increase the population number to achieve the purpose of initial population diversity. The self-adaptive weight and sine–cosine algorithm improved the position update formula of the Golden Jackal optimization algorithm, so the global search ability of the golden jackal algorithm is improved, and avoid the algorithm that fell into local optimality. Second, simulation experiments with eight standard test functions are performed to prove that the algorithm has excellent optimization ability. The improved Golden Jackal optimization algorithm was applied to optimize the kernel parameters of hybrid kernel principal component analysis. A fault diagnosis model is proposed to improve the golden jackal algorithm to optimize the kernel principal component analysis. Finally, the proposed method is used to fault diagnosis in the hot strip mill process. According to the study of simulation results, the faulty data can be identified effectively by this method, the accuracy is up to 100%, and the fault false alarm rate is greatly reduced.

The Impact of the Digital Economy on the Integration and Development of Manufacturing and Logistics Industries

Industrial integration is an effective way to promote economic growth in the era of digital economy and an important part of the high-quality development of the manufacturing industry. In the context of the vigorous development of the digital economy, this paper uses the principal component analysis method to construct the digital economy development index system and the coupled coordination degree model to measure the integration level of the manufacturing industry and logistics industry, and empirically analyzes the impact of the digital economy on the integration and development of the manufacturing industry and logistics industry by using the panel data of 30 provinces (municipalities and districts) in China from 2013 to 2020. It is found that the digital economy significantly promotes the integration level of manufacturing and logistics. The digital economy can promote the integrated development of manufacturing and logistics through both enterprise technology innovation and a digital platform. Heterogeneity analysis shows that the digital economy has a significant promotion effect on the eastern region and western region, with the western region > the eastern region.

Performance of Principal Components Estimation Method on the Quality of Factor Analysis without and with Varimax Rotation

The objective of this work was to evaluate the performance of the principal components estimation method on the quality of estimates of some parameters of exploratory factor analysis (EFA), without and with varimax rotation. To this end, 18 parametric correlation matrices (r) were imposed. These matrices were obtained from combinations between six different values of parametric communalities of four normally distributed random variables with three different proportions of distances between the parametric factorial loadings of the first two orthogonal factors. For each matrix r, the following parameters were defined: the first two eigenvalues (l1 and l2), the matrix of factor loadings (G), the four communalities () and the matrix of the specific factors (Y). After the 36 factor analyses, the respective estimates of these parameters were obtained, and their respective absolute deviations between the estimates obtained a posteriori and the parameters known a priori were evaluated. With the results of the Student's t test at 5% significance applied to the response surface analysis, it was concluded that the principal components estimation method for estimating orthogonal factors was not adequate and the varimax rotation improved relatively little the quality of the SFA estimates.

Research on the enhancement path of talent cultivation in the integration of dual-creation education and professional education for teacher training majors in colleges and universities based on multivariate statistical analysis

Abstract In today’s society, under the demand for high-quality talents, the professionals cultivated in colleges and universities should not only master professional skills but also need to have the spirit of innovation and entrepreneurial ability in professional aspects. In this paper, firstly, the multivariate statistical analysis method is explained, and the basic principles of the factor analysis model, principal component analysis method and systematic clustering method are given to analyze the data in the following article. Secondly, starting from the necessity of the integration of bi-initiative education and professional education of teacher training majors in colleges and universities, we constructed the 4344 specialized and innovative integrated talent cultivation model of teacher training majors and constructed an evaluation system to evaluate the effectiveness. Finally, based on the evaluation system, the data analysis of the specialized and creative integrated talent cultivation model was carried out using factor analysis, principal component analysis and systematic clustering method. The results show that in the factor analysis, the highest loading value of the first principal factor is 0.917, the contribution rate of the first principal factor is 39.67%, and the loading value of the principal component factor reaches the highest value of 0.925. The clustering analysis is based on the results of the factor analysis, and the respondents are divided into 4 clusters. The number of people in the 2nd category is more than 30 people, which accounts for about 31.41% of the total number of people. The method of multivariate statistical analysis can be used to analyze the data effectively for the specialized and integrated personnel training mode of teacher training in colleges and universities and also gives the path of specialized and integrated personnel training for teacher training.

Nutritional and Sensory Quality Evaluation of Low-Temperature Yogurt Sold in the Market Based on Principal Component Analysis and Cluster Analysis

In order to explore the quality differences of various brands of low-temperature yogurt sold in the market, the nutritional components and physical characteristics of 11 kinds of stirred lowtemperature commercial yogurt were analyzed by principal component analysis (PCA), including protein content, total sugar content, fat content, sodium content, calcium content, probiotic content, solid content, pH value, L*, a*, b*, viscosity and whey precipitation rate. In addition, combined with the method of sensory evaluation, the quality evaluation model is initially constructed. The results show that the cumulative variance contribution rate of the four principal components extracted by principal component analysis method reaches 90.74%. Based on the comprehensive evaluation function of principal component analysis method, the calculated results are significantly positively correlated with the sensory evaluation results (r = 0.909, P <0.001), which reflects the high reliability and effectiveness of the evaluation model. By cluster analysis, 11 kinds of yogurt can be divided into four groups. The first group is characterized by high protein and mellow taste, the second group is characterized by balanced nutrition and popular taste, the third group is characterized by no additive and original flavor, and the fourth group is characterized by moderate quality in all aspects. The research results provide a theoretical basis for the quality evaluation of stirred low-temperature yogurt and the research and development of fermented dairy products.

Quantitation of olefins in sustainable aviation fuel intermediates using principal component analysis coupled with vacuum ultraviolet spectroscopy

Olefins, a common intermediate from biomass conversion processes, are undesirable in jet fuel because of their poor thermal stability. This paper presents an approach for olefin quantitation using 2D gas chromatography coupled with vacuum ultraviolet spectroscopy. Principal component analysis was used to reduce the dimensionality of the spectroscopic data from a highly olefinic fuel intermediate. A principal component template was created that enabled olefin quantitation, which was compared to the existing GCxGC-VUV approach from the literature. The principal component method was able to identify and quantify trace amounts of cyclodienes, which were present at only 0.01 wt% in the fuel sample. The principal component approach also identifies species that fall outside of the GCxGC template. For instance, quantitation with the literature method resulted in an olefin concentration of 0.95 times that of the principal component method due to olefins falling outside of the expected GCxGC regions. The principal component results were compared with 13C and 1H NMR data, which confirmed that the fuel had a high concentration of olefins and alkanes with little aromatic content.

Crack classification and quantitative evaluation based on dimensionality reduction optimization model of multifeature weak magnetic signal

In this paper, we propose a classification and quantitative method based on the optimization model of dimensionality reduction for detecting crack defects in superalloy turbine disks using multifeature weak magnetic signals. First, the experiment on magnetic weak nondestructive testing was conducted on the defects, resulting in the acquisition of 112 sets of preset magnetic anomaly eigenvalue data. The dimensionality of the multidimensional weak magnetic signal features was then reduced using the principal component analysis method to construct the weak magnetic feature sample library. On this basis, a support vector machine (SVM) model was established to classify and quantitatively evaluate the defects. Genetic algorithm and grid search method were used to optimize the model. Finally, the validity of the optimized SVM model was verified by adding artificial and natural crack defect specimens outside of the sample library. Results show that compared with the original model, the feature dimension reduction and optimization of the two methods enhance the classification accuracy of superalloy surface defects and the quantitative accuracy of defect length, width, and depth. The genetic algorithm significantly improved the classification accuracy, increasing it by 39.33 %. Specifically, the genetic algorithm improved the quantitative accuracy of length and width by 17.9 % and 28.56 % respectively. On the other hand, the grid search method improved the quantitative accuracy of depth the most, with an improvement of 49.83 %. For specimens with natural cracks, the optimized SVM model still demonstrated good classification and quantitative effectiveness in detecting defects, with an accuracy rate of over 85 %.