Classification Method and Application of Rock Fracture Ability by Supercritical CO2 Blasting

Light pollution is a serious and growing problem worldwide. How to develop a widely applicable light pollution indicator analysis model to help people monitor and mitigate light pollution is a question worthy of our consideration. This paper uses the TOPSIS integrated evaluation and decision method based on Entropy Weight Method (EWM) and Analytic Hierarchy Process (AHP) to identify indicators and establish a widely applicable LPI (Light Pollution Index) analyze Model for the global light pollution problem. Then this model is used for analyzing specific differentiated areas and develop optimal intervention strategies. In order to build an accurate model to assess global light pollution hazard levels, this paper have to take three key aspects into account: Light Trespass, Glare, Spill Light. For each aspect, six to eight sub-indicators were selected to measure different aspects of light pollution. This paper evaluated all indicators and by combining EWM and AHP, this paper obtained a Light Pollution Index Analyze Model. To verify the rationality and reliability of this model, this paper uses TOPSIS integrated evaluation and decision method based on EWM to predict and rank 20 countries worldwide in terms of light pollution risk index. The results show that comparing our data with monitoring data of Light Pollution Map, the two rankings show a high degree of agreement. Therefore, the model of this paper is reasonable and reliable. And the research of this paper can provide effective help to the research and monitoring of light pollution.

During the construction of tunnel, the observational classification of rock mass quality is the most direct method to evaluate the quality of the tunnel face surrounding rock. It is also an important basis for preventing geological disasters in tunnel construction, deciding construction excavation method and supporting measures. The traditional Q method and BQ method of national standard rock mass quality grading evaluation method require on-site and indoor laboratory tests and analysis, which results in the lag time of rock mass quality evaluation, often reduces the construction efficiency, or misses the window time to prevent sudden construction geological disasters. Rapidly and accurately to rock mass quality classification of tunnel face is the primary problem to be solved in observational rock mass classification of highway tunnel during construction period. The artificial intelligence algorithm provides a method to solve the real-time and accurate evaluation of the rock mass of the tunnel. Taking Yanqing-Chongli highway for Beijing Winter Olympic Games as an example, we adopt a method of using photogrammetry and artificial intelligence rock structure parameter to identify working face. In this method, seven index parameters system are established. We use the KNN intelligent algorithm to evaluate the quality of the tunnel rock and select 150 samples from 40 working faces in 8 tunnels for the training and learning. Another 50 samples are selected for rock mass quality evaluation and verification. Compared with the rock mass quality evaluation results of BQ, the accuracy reachs 90%. Conclusions are as follows. (1)KNN method for observational classification of rock mass quality of highway tunnel is a fast and efficient method for observational classification of rock mass quality by using artificial intelligence technology, which can obtain real-time rock mass evaluation results on site. (2)KNN classification method selects seven discriminant indicators, considering the occurrence of rock environment, geological structure, rock structure and other characteristics, and reflecting the operability and applicability of these indicators in the actual engineering. (3)KNN classification method misjudgment rate is very low. It has strong discriminant ability when marking discriminant classification by excluding the influence of artificial factors. This method provides a new way to solve the rock mass grade determination and classification.

Many evaluation problems in product scheme design belong to multi-person, multi-level and multi-objective comprehensive evaluation problems. At present, the analytic hierarchy process (AHP) is widely used in the evaluation of complex product design scheme. In the establishment of judgment matrix, experts compare the value of each evaluation index and give them scores, with strong subjective factors. And another commonly used method of determining the weight -- the entropy weight method is to determine the objective weight according to the size of the index variance. According to the characteristics of complex product scheme evaluation and the actual needs, this paper will establish the decision-making model with the AHP method and entropy method, realizing the comprehensive evaluation of the scheme of complex products.

It is an important requirement for the operators to control the power grid to evaluate the operation performance of the smart power distribution network reasonably and accurately. This paper from the perspective of the smart grid efficiency, effect and benefit, build up a set of evaluation index of the performance of the smart grid distribution network state system. First, through the expert method, experts from the perspective of three index system screen the center index and the non-central index, next using the improved fuzzy clustering method to correct the center index and the non-central index again, and then using the analytic hierarchy process (AHP) to improve the group gray correlation method to correct the rest of the center again and eliminate redundant indicators. The mixed weight obtained by analytic hierarchy process and entropy weight method is used to evaluate the final determined evaluation system. Finally, an example of a regional power grid is used to verify that the evaluation system and method proposed in this paper can easily and accurately reflect the smart power grid efficiency status.

In this study, potential quality markers(Q-markers) of Schisandrae Chinensis Fructus for treating bronchial asthma were predicted based on analytic hierarchy process(AHP), entropy weight method(EWM), fingerprint, and network pharmacology. AHPEWM was employed to quantitatively identify the Q-markers of Schisandrae Chinensis Fructus. AHP was used to weight the primary indicators(effectiveness, measurability, and specificity), while EWM was employed to analyze the secondary indicators of each primer indicator. Further, through fingerprint combined with network pharmacology, a ″component-target-pathway″ network was constructed to screen the components of Schisandrae Chinensis Fructus for treating bronchial asthma. It was finally determined that schisandrol A,schisandrin A, and schisandrin B were potential Q-markers of Schisandrae Chinensis Fructus in the treatment of bronchial asthma. This study is the first to comprehensively use AHP-EWM, fingerprint, and network pharmacology to screen the key Q-markers of Schisandrae Chinensis Fructus in the treatment of bronchial asthma. This study provides a scientific basis for improving the quality standard of Schisandrae Chinensis Fructus and lays a foundation for studying its material basis in treating bronchial asthma.

At present, the operation and management of power grid enterprises is facing a complicated situation. Facing the new market-oriented reform situation and development trend, power grid companies must strengthen the awareness of input and output efficiency, fully integrate the key factors that determine investment and cost allocation and the evaluation results of enterprise production and operation, and do a good job in production and operation and grid development. This paper takes the county companies in the S area as the empirical analysis object, proposes a power grid management development evaluation index system, and combines the analytic hierarchy process(AHP) and the entropy weight method(EWM) to determine the weight, uses the TOPSIS theory to comprehensively evaluate the actual situation of the county companies, and through comparative analysis, The rationality of the indicator system and evaluation method is verified, and it can provide support and reference for further improving the level of fine investment management.

With the rapid development of the electric vehicle (EV) industry, various issues, such as the suboptimal deployment of charging facilities and inadequate distribution, hinder improvements in user satisfaction with EVs. Moreover, the interests of multiple stakeholders, including power grid companies and transportation departments, are not sufficiently addressed. To tackle these challenges, this paper proposes a comprehensive evaluation method for EV charging network service quality that integrates user satisfaction. First, considering the coupled dynamics of the EV charging network, we construct a service quality evaluation index system. Then, using a combination of the entropy weight method (EWM) and the analytic hierarchy process (AHP) with game theory, we determine the composite weights for the index system. Finally, we use the improved R-method evaluation model to theoretically verify the charging network in a particular area. The results show that the evaluation method proposed in this paper realizes differentiated evaluation for different planning schemes, highlights the weak links in the operation process of the charging network, and provides a theoretical reference for decision making about charging station planning.

<div class="section abstract"><div class="htmlview paragraph">Millions of configurations of power-split hybrid powertrain can be generated due to variation in number of planetary-gear sets (PG), difference in number, type and installation location of shift actuators (clutches or brakes), and difference in connection positions of power components. Considering the large number of configurations, complex structures and control modes, it is vital to construct an appropriate multi-index system evaluation method, which directly affects the requirement fulfillment, the time and cost of 2-PG system configuration design. Considering one-sidedness (dynamics and economic performance), simplicity (linear combination of indicators) and subjectivity (relying on expert experience) of previous system evaluation method of 2-PG system design, a more systematic evaluation method is proposed in this paper. The proposed evaluation system consists of five aspects, involving dynamic, economy, comfort, reliability and cost, and more than 20 indexes. To reduce the influence of the different experts' subjective opinions and the one-sidedness of objective data, a comprehensive system evaluation method based on fuzzy analytic hierarchy process (FAHP), entropy weight method (EWM) and grey relational analysis (GRA) is put forward. The consistency of subjective judgment and evaluation results is enhanced by FAHP; the objectivity and rationality are enhanced by EWM based on a large number of sample data; finally, the candidate solutions are obtained and ranked using GRA. The system evaluation method proposed in this paper is applied to a case study of 2-PG system configuration evaluation, and the evaluation results not only give the ranking of candidate solutions, but also give suggestions for improving the design. The evaluation method integrates the advantages of three evaluation methods, the calculation process is simple, and the results are intuitive. It provides a new evaluation method for power-split hybrid system design.</div></div>

Evaluation of key nodes is a hot issue in social networks. Existing research primarily evaluates the importance of nodes in social networks based on centrality metrics, neglecting the node’s own attributes. After analyzing the topology attributes and the basic attributes of nodes, this article proposes a key nodes evaluation method for social networks, which is based on analytic hierarchy process (AHP) and improved Vise Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR), termed AE-VIKOR. Considering global attributes, local attributes, and positional attributes of nodes, three evaluation metrics are constructed. The subjective and objective weights are computed by AHP and entropy weight method, respectively. The comprehensive weights of metrics are determined by combination weighting method based on square sums of distance. Due to the excessive weight of specific metrics and excessive difference in data distribution, the computation of individual regret value depends too much on a single metric in VIKOR method, individual regret value is optimized by weighted sum of closeness between the scheme to be evaluated and the negative ideal scheme. Multimetric evaluation schemes are ranked to achieve the evaluation of key nodes. Experiments on two real social network datasets show that the key nodes evaluated by AE-VIKOR have stronger information spread ability and more fans than the ones of the existing methods. In addition, the validity of the three metrics and the two improvements on the VIKOR method are verified by ablation experiments.

At present, complex electromechanical systems are widely used in modern society. By evaluating complex electromechanical systems, it is of great value to improve and upgrade them. Given the complex and interrelated characteristics of performance indicators, this paper establishes the performance evaluation indicator system of a complex electromechanical system, proposes to calculate the weight of each indicator by an improved analytic hierarchy process, revises the indicator weight by entropy weight method, and then adopts fuzzy synthesis evaluation method to evaluate the comprehensive effectiveness of a complex electromechanical system quantitatively. Through the example analysis, it is proved that this method provides an effective way for the effectiveness evaluation of complex electromechanical systems.

Scientific dynamic evaluation of the power load supply capacity of thermal power units is crucial for the safe and economic dispatch of the power grid. This article constructs an evaluation index system for the power load supply capacity of thermal power units. A dynamic evaluation and correction method for the power load supply capacity of thermal power units is proposed based on entropy weight method, analytic hierarchy process, and differential theory, and case calculations are conducted using basic data from three thermal power units. Research has shown that for every 0.1 increase in equipment reliability indicators, the average power supply capacity increases by 1.2 - 3.5 percentage points. And optimizing coal quality indicators by 0.2 units can increase power supply capacity by 3.9 - 6.8 percentage points. The work done in this article has certain reference value for improving the power guarantee capability of thermal power units.

In order to carry out fine management of the distribution network and evaluate the power supply performance of the distribution network, it is necessary to construct corresponding evaluation indexes and methods based on the practical and available business data. Firstly, this paper analyzes the actual power grid data, and establishes an evaluation index system from three dimensions: power supply status, power grid structure and equipment level. The combined evaluation algorithm of analytic hierarchy process (AHP), entropy weight method and VIKOR is proposed to evaluate the power supply performance of the distribution network at the same time. Firstly, the algorithm combines AHP and entropy weight method to analyze and get the combined weight of each evaluation index, so that the evaluation results can not only reflect the subjective experience of experts, but also reflect the distribution law of data itself. Secondly, the VIKOR method is used to evaluate the selected annual planning schemes of distribution networks to obtain the power supply performance score value of each scheme. Taking the urban distribution network of a certain city as an example, through the analysis of the evaluation results, this method can provide a reference for the construction and transformation of distribution networks grid planning.

The relevance of the work is due to the existence of a large number of empirical methods for predicting the condition of a rock mass and the need to apply them at the design and operational stage of mining enterprises to minimize costs during construction and future maintenance. Research objective is to analyze and compare existing empirical methods for determining the strength and deformation properties of rock mass. Methods of research include the analysis, systematization and synthesis of existing empirical methods of rock mass condition assessment and their applicability to real-world sites. Recommendations for further application of empirical rock mass assessment methods in real-world conditions are determined by generalization of theory and practice, research results and analytical calculations based on the assessment of fracture formation in rock masses. Results. The main empirical methods such as Deere's method (RQD) of structural failure evaluation, the method relating strain rate to RMR rating system and the method relating strain rate to geomechanical classification of rock mass Q were found and chosen for comparison. Several types of rocks such as limestone, diorite, granite, basalt, tuff, andesite in different geotechnical conditions are chosen for comparison of the methods. Comparison of different empirical methods of rock mass condition assessment is carried out. Evaluations of each method are given. Ways of application of empirical research methods are recommended. It is concluded that it is necessary to apply several methods in order to get a more accurate picture of the rock mass condition. Recommendations for the initial assessment of the state of rocks are given. Conclusions. There are a large number of empirical methods of rock mass condition estimation. The results obtained by some methods for some types of rocks under different mining and geological conditions are very different from each other, this is due to the fact that the considered methods were created on the basis of different sources. It is necessary to take into account the stresses acting in the rock mass, which also influence the results obtained.

The higher education plays an important role in the development of a country. Research on this issue may serve as a reference for countries to build a healthy and sustainable higher education system. We reviewed the literature and created a dataset Higher Education Evaluation Dataset (HEED) for this problem based on the collected data and interpolation algorithms. Based on the Analytic Hierarchy Process (AHP) and the Entropy Weighting Method (EWM), we established the weights of the dimension and indicator layers, and then used the TOPSIS method to calculate the health score of the national higher education system, obtaining a model capable of assessing the health of higher education in any country. In addition, the combination of the subjectivity provided by the AHP and the objectivity of the EWM and TOPSIS methods results in better performance of the model evaluation. To verify the validity of the model, we clustered 20 countries using hierarchical clustering algorithm. We found that the clustering results are generally consistent with the ratings obtained from the model, verifying that the model is applicable to the higher education evaluation system. Finally, the paper summarizes the model, which combines subjective indicators with quantitative and objective data and is the first attempt to use such a model for higher education evaluation.

This paper combines the analytic hierarchy process (AHP), entropy weight method (EWM), and Monte Carlo method to propose a multi-attribute decision-making model through popularity, accessibility, fairness, gender equality, Safety, and security, innovation, and sustainability six criteria to evaluate the selection process for Olympic sports. The AHP-EWM model dynamically determines the optimal balance between AHP and EWM weights, further improving the model's reliability. The model's validity in confirming its consistency with Olympic values is verified through tests on recent Olympic Games (2020, 2024, 2028) and traditional sports since 1988. It provides a comprehensive framework for making all choices and following Olympic principles. In addition to evaluating existing sports, the paper identifies potential new inclusions or reintroductions for the 2032 Brisbane Olympics, prioritizing Baseball, Skateboarding, and Electronic Sports. Suggestions for possible new sports for the 2036 Olympics are also provided, reflecting emerging global trends. Sensitivity analysis uses Monte Carlo simulations to ensure the model's robustness. By introducing random noise into the data and recalculating scores, the study evaluates the stability and distribution of results, offering insights into how variations in input data impact final decisions.