Relational Analysis Method Research Articles

Analysis of vehicle carbon emission characteristics on expressways in mountainous plateau areas based on the coupled simulation of CarSim/TruckSim and MOVES.

Expressways in mountainous plateau areas exhibit complex driving conditions and harsh climatic characteristics that continuously impact vehicle carbon emissions throughout their entire lifecycle and determine the carbon emission levels of expressways during the operational period. To study the carbon emission characteristics of expressways in the western Sichuan Plateau mountainous area, a coupling simulation analysis method combining CarSim/TruckSim simulation software and the Motor Vehicle Emissions Simulator (MOVES) was employed to analyze the spatial distribution characteristics of vehicle-specific power (VSP). This analysis was based on the alignment data and operational environment of the Wenchuan‒Barkam Expressway in the mountainous area of the western Sichuan Plateau. A conversion formula was established to calculate the cumulative VSP per second and total carbon emissions of each unit. Statistical analysis was conducted on the distribution of carbon emissions equivalent per kilometer across the different alignment units. Distribution ranges of carbon emissions equivalent per kilometer for light-duty vehicles (LDVs) and heavy-duty vehicles (HDVs) at each alignment unit were 60‒240 g and 100‒1600 g, respectively. The grey relational analysis method was used to quantify the relationship between the carbon emissions equivalent per kilometer of alignment units, circular curves radius, and average grade. Based on the vertical variations in climatic changes with altitude in the mountainous plateau area, a comparative analysis was performed on the trends in the effects of altitude, season, and vehicle starting frequency on carbon emissions. Frequent vehicle starts significantly impacted carbon emissions, and this impact was significantly higher in winter than in summer. Carbon emissions equivalent of the LDV and HDV starting twice on average in summer were approximately 1.09- and 1.04-times higher, respectively, than that when vehicles were started 0.5 times; whereas, in winter, they were 1.17- and 1.07-times higher, respectively.

Identification of drivers of global trade in plastic waste based on GCA and ISM-MICMAC model: Taking China, USA and South Africa as cases.

Plastic waste's dual characteristics of "resource" and "pollution" led to the prevalence of trade. The Global Plastic Waste Trade Network (GPWTN) is heterogeneous, and its structure is susceptible to the influence of key countries within it. However, there is a shortage of research on the key countries and trade drivers influencing GPWTN evolution. In this paper, three typical countries (China: importer - exporter; USA: exporter; South Africa: potential importer) are selected as cases. Through an extensive literature review, a comprehensive set of 22 trade drivers (TDs), including economic and social factors, environmental pressure, plastic industry, plastic waste, and policy, were identified and then evaluated and ranked using the Grey Relational Analysis (GRA) method. The results reveal that the TDs with the highest trade relevant to China are environmental taxation and NOx (Grey relation correlation, GRC=0.84). The USA was influenced by the environmental policy stringency index (GRC=0.69) and South Africa by the environmental taxation of waste and plastic products (GRC=0.91). This shows that carbon trading policies have become the highest impact driver on the USA's plastic waste trade. The coupled ISM-MICMAC (Interpretive structural modelling-Cross-Impact Matrix Multiplication Applied to Classification) method was used to identify the relationship between trade and drivers and to explore the TDs of different countries. The identified TDs are influenced by policies. The policies to reduce plastic waste production and primary plastic use, to achieve zero plastics in landfills, and to control CO2 emissions could cut the amount of plastic waste exported from China. Both multi-driver controls and single-CO2 emission controls could significantly reduce plastic waste exports, with reductions exceeding 94% in 2060. Controlling CO2 emissions would be the best option for China. For South Africa, the multi-drivers and recycling scenarios could reduce import rates by 70.83% and 66.3% in 2060. In South Africa, the implementation of policies to increase recycling rates, taxing plastics and plastic waste, etc. is the optimal choice to reduce imports.

From Connection to Isolation: The Role of TikTok Algorithmic Personalization in Computational Media and Cross-cultural Communication

With the popularity of social media platforms, recommendation algorithms play an important role in user content exposure. However, while the personalized recommendation mechanism of the algorithm may promote cross-cultural communication and understanding, it could also strengthen information barriers and affect users' cultural cognition and cross-cultural communication ability. This paper focuses on TikTok's recommendation algorithm and uses a related studies analysis method to explore its impact on multicultural content exposure and cross-cultural communication. According to the findings of numerous related studies, TikTok's algorithm enhances the probability of users being exposed to specific cultural content through strengthening interest - based matching. Moreover, TikTok's algorithm restricts the extensive dissemination of cross - cultural content on account of its "loneliness effect", thus diminishing the depth and quality of cross - cultural understanding. This paper has revealed the two-sided nature of recommendation algorithms in cross-cultural communication and provides inspiration for improving algorithm design and promoting cross-cultural communication.

Integrated Best-Worst Method and Gray Relational Analysis for Hospital Location Selection: The Case of Burdur Province

The hospital location (site) selection problem involves determining the most suitable location for a healthcare facility. As a multi-criteria decision-making (MCDM) problem, it requires the evaluation of several factors, including strategic, economic, and social considerations, due to the direct impact hospital locations have on public health. To make an optimal decision, MCDM methods are typically employed to accurately assess the relevant criteria. These methods assist decision-makers in evaluating various factors and determining the best location for the hospital. This study focuses on selecting a site for a private hospital planned for Burdur Province, where no private hospital currently exists. In this context, three alternative locations were evaluated based on expert opinions from academicians, health managers, and urban planners. In the first phase of the study, the Best-Worst Method (BWM) was used to calculate the criteria weights, allowing for the subjective evaluation of multiple factors. Following this, the Gray Relational Analysis (GRA) method was applied to determine the most appropriate location by assessing the degree of similarity or dissimilarity between the alternatives. This combination of methods enabled the ranking of the alternatives, ultimately identifying the best site for the hospital.

PHILOSOPHY OF EDUCATION IN THE DIGITAL AGE: TRANSFORMATION OF KNOWLEDGE AND LEARNING

The article examines the transformational process in the philosophy of education driven by digitalization, which necessitates a critical re-evaluation of traditional epistemological and ontological categories. The study addresses the dichotomy between classical educational paradigms and emerging approaches that respond to digital innovations. This research aims to analyze the impact of digital technologies on the structure of knowledge, educational institutions, and cognitive interaction methods. It applies poststructuralist and relational analysis methods to conceptualize knowledge as a contingent and variable phenomenon. By employing a multi-discursive approach, the study demonstrates how digital media not only reshape the learning process but also alter the nature of knowledge itself. The relevance of this research is underscored by the context of epistemological relativism that arises in the era of educational virtualization.The study finds that knowledge no longer operates as a static logos but takes on the form of a fluctuating, networked substance, interacting with the subject through multichannel simulations. Traditional educational hierarchies give way to hypertextual and multi-agent forms of knowledge transmission, where the authenticity and credibility of knowledge increasingly depend on dynamic algorithmic and simulacral constructs. The practical significance of this work lies in defining new axiological imperatives for education that focus on enhancing adaptability and critical reflexivity in the learner. The study proposes heuristic methods designed to foster cognitive resilience against informational entropy and to cultivate “flexible thinking” capable of navigating the evolving digital environment.In conclusion, the study emphasizes that the philosophy of education in the age of digital expansion undergoes profound ontological and epistemological changes, transforming the learning process into an interactive and multidimensional phenomenon. Knowledge loses its stability, evolving into a series of cognitive impulses that continually shift in form and structure. This evolution calls for the formation of a new type of educational subject, capable of integrating and critically navigating virtually dispersed informational environments. Accordingly, the article offers a foundational analysis and innovative approaches to the philosophy of education, reflecting the integration of digital technologies as the basis of the contemporary cognitive process, and highlights the importance of harmonizing technocratic and humanistic elements in the educational discourse.

Optimal selection of software reliability growth model for open-source software using weighted Grey relational analysis method

Abstract Given the complexity of software development and testing environments, the establishment of software reliability growth models (SRGMs) is diverse. To date, no SRGM can be applied and implemented in all software development and testing environments. Therefore, how to choose an appropriate SRGM for software reliability evaluation in the current software development and testing environment is an important practical issue. In this study, we proposed a weighted Grey relational analysis method to select the optimal SRGMs, including closed- and open-source SRGMs, as well as perfect and imperfect debugging SRGMs. To effectively validate the effectiveness of the proposed method, we used 12 SRGMs, 11 model evaluation criteria, and 2 successive versions of open-source software fault datasets. Results of this study indicated that the proposed method can select the optimal SRGM in the current software development and testing environment. To conclude, this study has important practical significance for actual software development and testing and makes important contributions to assisting developers or testers in selecting the optimal SRGM for software reliability assessment.

Establishing a Marine Gravimeter Test Site in the South China Sea to Validate the Performance of Different Marine Gravimeters

Marine gravity anomalies play an important role in geophysics applications. To observe high-precision and high-resolution gravity anomalies, a high-performance marine gravimeter is key. At present, the marine gravimeters widely used in the world have different measurement principles, such as the two-axis stable platform gravimeter, gimbaled inertial navigation gravimeter, and strapdown gravimeter. However, the performances of marine gravimeters with different measurement principles show different precision levels in real applications. A synchronized comparison test on the same platform is the most direct method for evaluating their performance, which is a relative analysis method. To absolutely evaluate the performance of different kinds of marine gravimeters, a new method is presented to remove the residual noise from the measured free air gravity anomaly to establish an “air truth” free air gravity anomaly. Synchronous measurements with different gravimeters were carried out in the north area of the South China Sea, measured three times over a round trip, and the highest-precision free air gravity anomaly measured by GT-2M, SAG-2M, and ZL11-1A was chosen to establish the “air truth” free air gravity anomaly. The external consistency of the free air gravity anomaly upon the removal of residual noise of frequencies 0.03 Hz to 0.06 Hz improved, and the three separate standard free air gravity anomalies of each gravimeter were the same with no deviation. The weighted result of the three average values of GT-2M, SAG-2M, and ZL11-1A is the established “air truth” free air gravity anomaly, which can be used as a standard to estimate the performance of marine gravimeters with different kinds of principles.

Propagation Law of Hydraulic Fractures in Continental Shale Reservoirs with Sandstone–Shale Interaction

There are significant lithological and stress differences between continental shale layers, posing challenges for hydraulic fractures (HFs) to propagate through the formations, leading to weak fracture effects. To address this, this article adopts the finite element and cohesive force element methods to formulate a three-dimensional numerical model for hydraulic fracture (HF) propagation through layers, considering interlayer lithology and stress variations. The accuracy of the model was verified by physical experiments, and the one-factor analysis method was used to creatively reveal the complex mechanism of the effect of geological and engineering variables on the diffusion of HFs in continental shale reservoirs. The results show that high interlayer stress difference, high interlayer tensile strength difference, low interlayer Young’s modulus difference and large interlayer thickness are not conducive to the penetration of HFs, but increasing the injection rate and the viscosity of fracturing fluid can effectively improve the penetration of HFs. The influence ranking of each factor was determined using the grey relational degree analysis method: interlayer stress difference > interlayer Young’s modulus difference > interlayer tensile strength difference > interlayer thickness > injection rate > fracturing fluid viscosity.

Cutting of 316L stainless steel for metal mesh implant using CO2 laser cutting: Analysis and optimization

This study investigates the effects of CO2 laser cutting parameters on the quality of 316L stainless steel used for metal mesh implants. A Taguchi L9 orthogonal array was employed to design the experiments with three factors: laser power (100-200 W), cutting speed (50-150 mm/min), and gas pressure (3-9 bar). The dimensions (Dim) and heat-affected zone (HAZ) of the cuts were analyzed using both Taguchi and Hybrid Taguchi-Grey Relational Analysis (T-GRA) methods. Results from Taguchi analysis identified the optimal parameters for minimizing Dim as 100 W, 100 mm/min, and 6 bar, and for HAZ as 100 W, 50 mm/min, and 6 bar. ANOVA indicated that laser power significantly influenced both Dim (41.11%) and HAZ (28.10%). T-GRA optimization predicted the optimal parameters as 200 W, 50 mm/min, and 3 bar, confirmed through validation experiments with a Grey Relational Grade (GRG) of 0.969. The study concludes that laser power is the most critical parameter, followed by gas pressure and cutting speed, providing a comprehensive optimization strategy for laser cutting 316L stainless steel for medical implants.

Variability of Interpolation Errors and Mutual Enhancement of Different Interpolation Methods

Data interpolation methods are important statistical analysis tools that can fill in data gaps and missing areas by predicting and estimating unknown data points, thereby improving the accuracy and credibility of data analysis and research. Different interpolation methods are widely used in related fields, but the error between different interpolation methods and their interpolation fusion optimization have a significant impact on the interpolation accuracy, which still deserves further exploration. This study is based on two different types of point data: PM2.5 (PM2.5 refers to particulate matter in the atmosphere with a diameter of 2.5 μm or less, also known as inhalable particles or fine particulate matter) in Xinyang City, Henan Province, and the elevation of typical gullies in Yuanmou County, Yunnan Province. Using relative difference coefficients and hotspot analysis methods, the differences in error characteristics among four interpolation methods, ordinary kriging (OK), universal kriging (UK), inverse distance weighted (IDW), and radial basis functions (RBFs), were compared, and the influence of interpolation fusion methods on the accuracy of interpolation results was explored. The results show that after interpolation of PM2.5 concentration and gully elevation, the error difference between OK and UK is the smallest in both datasets. For PM2.5 concentration data, IDW and UK interpolation errors have the largest difference; for elevation data, the differences between RBF and UK interpolation are the largest. The weighted fusion results show that the interpolation error accuracy of PM2.5 concentration data with an interpolation point density of 0.009 points per square kilometer is improved, and the root mean square error (RMSE) after fusion is reduced from 0.374 μg/m3 to 0.004 μg/m3. However, the error accuracy of the elevation data of the gully with an interpolation point density of 0.76 points/m2 did not improve significantly. This indicates that characteristics such as the density of the original data are important factors that affect the accuracy of interpolation. In the case of sparse interpolation points, it is possible to consider fusing the interpolation results with different error patterns to improve their accuracy. This study provides a new idea for improving the accuracy of interpolation errors.

Physics-informed spatio-temporal hybrid neural networks for predicting remaining useful life in aircraft engine

Aircraft engines have complex structures and various operating conditions, which leads to the health monitoring data of aircraft engines’ high coupling in the spatial domain and time invariance in the temporal domain. Existing methods for remaining useful life prediction of aircraft engines are deficient in handling spatial data and spatial–temporal data fusion. To address this problem, a novel method named physics-informed spatio-temporal hybrid neural networks (PI-STHNN) is proposed. In this study, a spatial domain data association graph for aircraft engines is constructed by utilizing a thermal cycle physical model as prior knowledge and combining it with the grey relational grade analysis method. Specifically, the proposed spatio-temporal hybrid neural network framework extracts the temporal features and spatial coupling features of the data in parallel. Then attention mechanism is employed to weigh and fuse all feature representations. Finally, the method is validated on the two aircraft engine datasets, the results show that PI-STHNN outperforms current state-of-the-art methods. Furthermore, our analysis sheds light on the advantages of incorporating physical information into graph neural network models and visually presents the attention weights to elucidate the contributions of time-domain and spatial-domain models within our hybrid approach.

Investigation of the effects of contour process parameters on mechanical properties and part quality in SLM

AlSi10Mg is an aluminium alloy preferred in the automotive, medical, and aerospace industries due to its engineering properties such as lightness, high corrosion resistance, and excellent castability. In metal-based additive manufacturing, offset value, laser power, and scanning speed are important manufacturing parameters. These parameters significantly impact the formation of defects that adversely affect the microstructure, mechanical properties, surface quality, and dimensional accuracy of the final product. Therefore, it is imperative to select these parameters correctly during the manufacturing process. In this context, the study focuses on improving product quality in the additive manufacturing of AlSi10Mg alloy, and samples were produced using different contour process parameters. The production of samples was carried out according to the Taguchi L27 orthogonal experimental design. Three different offset values (0.140–0.175–0.210 mm), three different laser powers (160–200-240 W), and three different scanning speeds (280–350-420 mm/s) were utilized as contour process parameters. At the conclusion of the study, it was determined that there are ideal process parameters for achieving high mechanical properties, low surface roughness, and dimensional accuracy according to nominal values. It was observed that the product quality decreased in samples produced with non-ideal parameters. Yield strength decreased by 4.12 % with an increase in the offset value from 0.140 mm to 0.175 mm, and by 3.83 % with an increase from 0.175 to 0.210 mm. Increasing the offset value from 0.140 mm to 0.175 mm resulted in a decrease in tensile strength by 1.94 % and increasing it from 0.175 mm to 0.210 mm resulted in a decrease of 2.83 %. The decrease in mechanical properties was attributed to the occurring defects. The lowest Ra value (2.011 µm) was measured at a 0.140 mm offset value, with a laser power of 200 W, and a scanning speed of 280 mm/s. For all offset values, the deviation in thickness measurement varies between approximately −5.13 % and + 4.96 %, and the deviation in width measurement varies between −1.89 and + 1.33. Multi-response optimization was conducted for mechanical properties, surface roughness, and dimensional accuracy using the Taguchi-based gray relational analysis method. According to the experimental results, the optimal contour process parameters for additive manufacturing of AlSi10Mg alloy were obtained as 0.140 mm offset value, 200 W laser power, and 280 mm/s scanning speed.

Safety Risk Assessment Method of In-Service Stage Suspension Equipment Based on Grey Fuzzy Comprehensive Evaluation

Performance safety is one of the important goals for the high-quality development of modern performance services. The in-service stage suspension equipment that has been put into use is one of the most frequently used and most closely related stage machinery in performances, and there are often significant safety hazards during its use. In response to the current lack of safety risk assessment methods and incomplete assessment techniques for in-service stage suspension equipment, this paper proposes a safety risk assessment method for in-service stage suspension equipment based on grey fuzzy comprehensive evaluation, with professional theaters as the target scenario. This method first identifies risk factors based on Failure Mode and Effects Analysis (FMEA), then uses the grey relational analysis (GRA) method for risk factor analysis, and finally adopts the fuzzy comprehensive evaluation (FCE) method to achieve safety risk level assessment. By constructing and analyzing an evaluation model for professional theater stage suspension equipment, the safety risk levels and corresponding safety risk factor rankings of performance accidents such as electric shock, falling, and failure can be obtained, and measures to reduce risks can be provided based on the most important risk factors. The research results show that more attention should be paid to the influence of human factors in the safety assessment and detection of in-service stage suspension systems. The research in this article is of great significance for improving the safe use of in-service stage suspension equipment, enhancing the level of performance safety management, and improving the quality of performance equipment services, laying the foundation for the formation of relevant regulatory systems and standards.

Spatial benefit assessment and marine climate response of coastal zone in Fujian Province under cross-system influence.

To gain a scientific understanding of the cross-system impact of coastal zones and promote the sustainable development and protection of coastal areas, we constructed a spatial benefit evaluation system that encompassed both terrestrial and marine systems, focusing on the ecological, economic, and social dimensions. We employed the entropy method, moving average method, and Mann-Kendall trend test to quantitatively characterize the spatial benefits of the coastal zone in Fujian Province, China, and the evolution of the marine climate from 2005 to 2020. Building on this, the grey relational analysis method was applied to investigate the correlation between spatial benefits and marine climate and to explore the trends and magnitude of the impact of marine climate on spatial benefits. During the study period, the spatial benefits of the coastal zone in Fujian Province exhibited a fluctuating pattern of an initial increase followed by a decrease, with spatial benefits varying among cities. The role of the economic system in enhancing spatial benefits was not considerable. Changes in the marine climate aligned with the global warming trend, with the most considerable changes observed in sea level and tropical cyclone frequency and intensity, which are sensitive to human activities. There was a high degree of correlation between coastal zone spatial benefits and marine climate, with seawater salinity being most closely related to spatial benefits, while tropical cyclones showed the weakest correlation. The results of this study support sustainable development efforts in coastal zones.

Lifetime prediction of epoxy coating using convolutional neural networks and post processing image recognition methods

The rapid failure of organic coatings in deep-sea environments complicates accurate lifetime prediction. Given the rapid cracking characteristic on the coating surface in this environment, a comprehensive “performance-structure” failure model was established. Initially, a targeted image recognition approach containing convolutional neural network (CNN) and post-processing was constructed for the crack area detection. An overall precision of 82.81% demonstrated the network’s good accuracy. The length distribution and the statistical evolution of cracks were extracted from SEM images to obtain the kinetic equation of the cracks related to coating structure degradation. In addition, the kinetics of water diffusion and coating adhesion were examined, as they represent critical parameters of coating performance. Based on this achievement, a failure model incorporating three dominant factors was integrated by the gray relational analysis method. The average prediction error of the model was 2.60%, which lays the groundwork for developing image-based methods to predict coating life.

Multiresponse optimization of hole number and surface roughness in drilling processes for 316l stainless steel material using Taguchi-grey relational analysis method

Multiresponse optimization of hole number and surface roughness in drilling processes for 316l stainless steel material using Taguchi-grey relational analysis method

A comprehensive validation of GRA with ant colony optimization for robust optimal design: A case study on the forming process

Sheet metal forming design involves numerous factors and controlling all these parameters to prevent cracks is a major challenge to maintain the quality. Therefore, the careful selection of process parameters is critical for ensuring product longevity. Several researchers employed different techniques for optimal selection of process parameters but consistency among the techniques and validation of results in real time scenarios will play a pivotal role in determining the suitable parameters. Therefore, the current work presents a validation method of Grey Relational Analysis (GRA) results with Ant Colony Optimization (ACO) for the robust optimum design with an illustrative example of forming process. Al 6061 – T6 alloy was chosen as a material for deep drawing process and L27 Orthogonal Array was planned to conduct experiments with four input parameters such as blank thickness, Die and Blank Temperature, Die Speed, Lubrication at three levels each. Punch force and Thickness variation were considered as the output parameters. GRA Technique was implemented to find the best optimal combination and regression equation was developed to predict the nature of objective function. These results were given as an input to ACO, a nature inspired algorithm to search for the optimal solution with in the design space. Sensitivity analysis has also been performed to highlight the effectiveness of the proposed solution in terms of solution quality and computational efficiency. The ACO method, GRA technique and the experimental results are almost similar with a margin of acceptable deviation. The results indicated the robustness of the projected approach in achieving the quality of solution.

Multi-Objective Optimization of Dry Sliding Wear in Cryogenically Treated High-Performance AISI 9310 Steel: An Integrated Approach Using Grey Relational Analysis and Taguchi Method

AISI 9310 steel is commonly employed in the automotive, aerospace, space, and defense sectors because of its excellent corrosion resistance and durability under pressure and impact. In this study, to enhance the surface properties and to extend the service life of the parts made from AISI 9310 steel, shallow (SCT) and deep (DCT) cryogenic treatments were applied to this material. The impact of various processing parameters on wear resistance of the material was assessed using a ball-on-disc tribometer in reciprocating mode and analyzed using analysis of variance (ANOVA). To determine the optimal processing parameters that provide the lowest volume loss and friction coefficient in dry sliding wear tests, an integrated approach of the Taguchi method and Gray Relational Analysis (GRA) has been employed. The results showed a significant increase in hardness of the material due to the cryogenic treatments; a 12% increase with SCT-applied samples, a 30% increase with DCT-applied samples. However, wear volume decreased by 7% for SCT and 14% for DCT treatments. The coefficient of friction improved by 5% with SCT and 9% with DCT process. ANOVA results indicated that cryogenic treatment had the greatest effect on volume loss (63,03%) and friction coefficient (88,75%), while load had a lesser impact. Regression analysis revealed that the model fit was excellent, with R² values of 97,63% for volume loss and 99,42% for the friction coefficient. These findings demonstrate that cryogenic treatments significantly enhance the wear resistance of the AISI 9310 steel and improved its performance across varying load conditions. These results underscore the critical role of cryogenic treatments in prolonging the service life of materials used in industrial settings.

Evaluation and comparison for higher temperature performance index of emulsified asphalt mastic using coal gangue powder

In order to investigate the rheological behavior and the influence of solid waste coal gangue powder on the high-temperature performance of cold recycled Emulsified Asphalt Mastics (EAM), Dynamic Shear Rheometer (DSR) and Multiple Stress Creep Recovery (MSCR) tests were conducted using three different fillers and four powder-to-binder ratios of EAM. Evaluation indexes such as rutting factor (G*/sinδ), improved rutting factor (G*/1−1/sinδ∙tanδ), Unrecoverable Creep Compliance (Jnr), Cumulative Strain (CS) and Creep Recovery Ratio (R) were used to analyze the influence of filler type and dosage on high temperature of EAM, and the effectiveness of these indexes to evaluate high temperature is considered. Moreover, atomic force microscopy experiments were carried out to study on interaction mechanism between the fillers and emulsified asphalt. Finally, grey relational analysis method was used for quantitative assessment of these indexes for EAM. The results indicate that all three fillers effectively improve the high-temperature performance, with a larger improvement observed as the filler dosage increases, among which the tunneling coal gangue powder is the most significant. Under a stress level of 0.1 KPa, the coal gangue filler mastic appears the best deformation recovery ability, whereas under a stress level of 3.2 KPa, the cement filler mastics appears the best deformation recovery ability. The additional fillers significantly influence the nano-scale surface morphology of the residues, and the tunneling coal gangue powder appears a stronger adsorption capacity for free polar components compared to the cement and limestone mineral powder. It is suggested that the Jnr and CS can be used as evaluation indexes of high temperature evaluation index of EAM, instead of G*/1−1/sinδ∙tanδ or G*/sinδ or R.

Evaluation system of vocational education construction in China based on linked TOPSIS analysis

In response to the task of vocational education reform in the new era, in order to cultivate innovative skilled talents, it is necessary to improve traditional evaluation methods to enhance their efficiency. This study proposes an evaluation model for vocational education construction that combines grey relational analysis and approximate ideal point method (TOPSIS). By selecting indicators and combining subjective analytic hierarchy process and objective entropy weight method for weight allocation, the weights of the indicators are determined using subjective analytic hierarchy process and objective entropy weight method. Through experimental analysis, the results showed that among the 11 analysis indicators, the weight of policy environment indicators was the highest at 0.124, while the weight of employment rate and contract signing rate indicators was the lowest at 0.050. The above results indicate that the TOPSIS comprehensive evaluation system based on grey correlation analysis has better application effects in practical scenarios and is more suitable for the development requirements of vocational education.