Fuzzy Matter-element Research Articles

Improved Evidence Fusion Theory for the Safety Assessment of Prestressed Concrete Bridges

The safety condition assessment of prestressed concrete bridges is currently subject to great uncertainty due to the subjectivity of data collection and data types. This study proposes an improved evidence fusion method, improving the conventional Dempster–Shafer fusion method to reduce assessment inaccuracies caused by data uncertainty. Firstly, the uncertain analytic hierarchy process was applied to construct a three-level safety assessment model for 15 different indicators with their initial weights. Secondly, the fuzzy matter element theory was proposed to obtain basic probability assignments required for the evidence fusion. Finally, an improved evidence fusion method was proposed based on the evidence credibility and preprocessing corrections for highly conflicting evidence. In this study, a prestressed concrete bridge in eastern China was used as a case study to perform a comprehensive safety assessment and verify the effectiveness and practicality of the proposed method. The assessment results demonstrate that the improved fusion method in this study can deal with conflicting evidence better than existing fusion methods. Compared with conventional fuzzy AHP method, it has greater sensitivity to certain indicators with severe damages, which prevents those indicators from being overshadowed by other well-performing ones in the overall assessment.

Evaluation of sustainable livelihood of reservoir resettlement based on the fuzzy matter-element model

Introduction: The study of sustainable livelihoods for reservoir resettlers is of great significance for consolidating the poverty alleviation achievements of China and promoting the realization of the major strategy of China’s rural revitalization.Methods: On the basis of the Sustainable Livelihood Analysis Framework of the UK Agency for International Development and the general requirements of the rural revitalization strategy on industry, ecology, rural ethos, governance and life, this paper selects the material basis, living environment and Social Adaptability which are more close to China’s national conditions and can more accurately measure the resettlers’ livelihood. These three factors have established a Comprehensive Evaluation Index System for the sustainable livelihood of the reservoir resettlers. A mathematical model based on complex fuzzy matter-element and analytic hierarchy process (AHP) is established to assess the sustainable livelihood of reservoir resettlers. Furthermore, it conducts an empirical study on the resettlement of the Danjiangkou Henan Reservoir area of the South-to-North Water Diversion Project, which takes the whole of immigration in Henan Province as the research object.Results: The results show that the current survey value of the overall livelihood of the resettlers in the Danjiangkou Henan Reservoir Area of the South-to-North Water Diversion Project is 0.0085 lower than the maintaining livelihood target and 0.1725 lower than the sustainable livelihood target. Such a result indicates that the livelihood of Henan resettlers in the Danjiangkou Henan Reservoir Area of the South-to-North Water Diversion Project has reached a sustainable level overall, but its gap with the sustainable livelihood goal remains obvious, which is primarily demonstrated by economic foundation and social adaptability.Discussion: Therefore, immigration management agencies should make efforts to adjust the industrial structure, increase the income of resettlers, help resettlers integrate into the local society sooner, and promote the sustainable development of resettlers’ livelihoods.

Study on quantitative estimation method of energy saving and emission reduction of renewable energy system based on fuzzy matter-element method

The quantitative estimation method of energy saving and emission reduction of renewable energy system based on fuzzy matter-element method is studied, which provides reliable basis for the popularization and use of renewable energy system in practical work. Through quantitative estimation, a scientific evaluation index system and evaluation model can be established, transforming abstract and subjective concepts of energy conservation and emission reduction into concrete and objective numerical indicators. These indicators can reflect the energy-saving and emission reduction effects of renewable energy systems, making the evaluation results scientific, comparable, and traceable. The quantitative estimation index system of energy saving and emission reduction of renewable energy system and the index judgment standard are constructed, which are mainly composed of resource utilization, energy consumption and environmental pollution. On this basis, the fuzzy matter-element quantitative estimation model of energy saving and emission reduction of renewable energy system is established based on fuzzy matter-element and entropy weight theory, and the weight of energy saving and emission reduction index of renewable energy system is determined based on information entropy, and the energy saving and emission reduction effect of renewable energy system is comprehensively and quantitatively estimated with closeness as a reliable basis and reference. The experimental results show that this method can quantitatively evaluate the energy-saving and emission reduction effects of renewable energy systems, and it is feasible to comprehensively and quantitatively evaluate the energy-saving and emission reduction effects of renewable energy systems, with a maximum emission reduction of 99%. It can provide reliable reference for the energy-saving and emission reduction of renewable energy systems.

Can Technological Innovation and Financial Agglomeration Promote the Growth of Real Economy? Evidence from China

In the backdrop of China’s evolving economic landscape, the real economy confronts a myriad of challenges, both domestically and on the global front. Technological innovation, characterized by its capital intensity and the unpredictable nature of its returns, stands as a pivotal force poised to rejuvenate nascent sectors and overhaul the existing industrial framework. Parallel to this, financial agglomeration emerges with a bifurcated function: it not only directly propels the real economic trajectory but also exerts an indirect influence via the channels of technological advancement. Delving deep into this interplay, our study dissected data collated from 30 major provinces and cities across mainland China, spanning the years 2011 to 2018. We employed the nuanced techniques of fuzzy matter–element analysis combined with the location entropy method. By anchoring our findings on a spatial econometric model, we uncovered the intricate dynamics of how technological ingenuity and financial clustering drive real economic growth, shedding light on the spatial reverberations that ripple across regions. Building on the tangible empirical evidence reflecting the trajectory of technological innovation and financial agglomeration within China, this article distills and presents the salient conclusions drawn from the investigation.

Construction of an Early Warning System Based on a Fuzzy Matter-Element Model for Diagnosing the Health of Alpine Grassland: A Case Study of Henan County, Qinghai, China

To maintain alpine grassland in a healthy and sustainable state, a sound warning system was developed to diagnose its potential degradation risk. Data related to grassland quality (six indicators), habitat (six indicators), and eco-carrying capacity (three indicators) at eight sampling plots were collected from Henan Mongol Autonomous County of West China in 2014 and 2017, representing five types of grassland and three grazing styles. Compared to the warning level in 2014, alpine grassland had a higher warning level in 2017, demonstrating the degradation of grassland ecosystems. Kobresia tibetica exhibited the lowest level of warning, while Kobresia humilis had the highest, indicating its corresponding safety and unsafety under the environmental change. Grassland quality is the most important index for grassland health, and soil total carbon and available phosphorus are the most important indices of habitat quality, which finally greatly influence the warning level of alpine grassland. Further analysis results suggested that winter grazing is beneficial for the health of grassland, and moderate grazing can accelerate the self-recovery of the alpine grassland due to the increase in organic matter. This study is crucial for understanding the health level of alpine grassland and its further change trends, and providing an important scientific basis for rational grazing.

Evaluating the Service Capacity of Port-Centric Intermodal Transshipment Hub

Port-centric intermodal transshipment hubs are significant nodes in the global freight network and are likewise the gateway to a country’s external communications. It is vital to increase the service capacity of PCITHs, and it is necessary to assess the service capacity of port-centric intermodal transshipment hubs to respond to the growth of economies and global freight needs. This study provides a detailed definition of port-centric intermodal transshipment hubs through a review of relevant kinds of works from the literature and analyzes their primary functions. Based on the research perspective of sea–rail intermodal transportation, the three evaluation dimensions of service capacity of port-centric intermodal transshipment hubs are divided into radiation scale capacity, transportation connection capacity, and resource integration capacity, focusing on the functions of cargo aggregation, cargo transfer, and connection of different transportation modes. The service capacity evaluation indicators were then selected based on the three dimensions. The subjective and objective weightings were calculated by the G1 weighting method and the modified CRITIC method, and the combination weightings were determined based on game theory. The service capability of port-centric intermodal transshipment hubs was evaluated by the fuzzy matter element method, and the evaluation results were quantified by the Euclidean closeness degree. Finally, through the barrier degree model, the current indicators of PCITHs that urgently need improvement were explored, and targeted improvement suggestions are proposed in this paper. The results show that Tianjin Port has the highest service capacity, followed by Ningbo Zhoushan Port. The port rail dedicated line mileage is the most critical area that needs attention in Ningbo Zhoushan Port and Qingdao Port. Tianjin Port needs to improve the container sea–rail transportation volume, while Guangzhou Port and Xiamen Port need to improve the sea–rail container handling capacity.

Analyzing Resource and Environment Carrying Capacity of Kunming City Based on Fuzzy Matter–Element Model

The determination of the sustainable development of a region requires estimating its carrying capacity in terms of resources and environment. It is essential to investigate the carrying capacity of Kunming City to comprehend its rapid development and create a resource and environment-friendly society. This research involved the selection of a set of 35 evaluation indicators from three categories: resources, environment, and social economy. These indicators were chosen based on statistical data obtained from Kunming City between 2011 and 2020. An evaluation system was established using the entropy weight method to determine the weight of these indicators. Subsequently, the fuzzy matter–element analysis method was utilized to construct the European closeness model of Kunming’s resource and environmental carrying capacity. The correlation between the carrying capacity of resources and environment and sub-carrying capacities was analyzed using Pearson’s correlation coefficient to determine the degree of influence of different aspects on the carrying capacity of resources and environment in Kunming. The results show a consistent upward trend in the carrying capacity of resources and environment in Kunming City from 2011 to 2019. However, in 2020, due to national policy adjustments and the impact of COVID-19 on the social economy, the resource and environment carrying capacity index in Kunming City slightly decreased.

Dynamic evaluation of regional economic resilience under major public emergencies: based on an improved dynamic evaluation model of grey incidence projection-fuzzy matter element

The sudden outbreak of COVID-19 in 2020 causes great impact on the economic development of all countries and even the whole world. Under the background of major public emergencies, a timely dynamic evaluation of regional economic resilience can provide an objective basis for economic regulation and control behavior. Based on the existing evaluation model, an improved dynamic evaluation model of grey incidence projection- fuzzy matter element is proposed in this study. The improved model is a universal evaluation model that can be used in different contexts. This model method can, not only limited to analyzing economic resilience, but also be applied to other different contexts. The evaluation indexes are selected (from the market, industry, investment, foreign trade and finance) to construct an evaluation index system of regional economic resilience under major public emergencies. The improved dynamic evaluation model of grey incidence projection- fuzzy matter element is applied to evaluate economic resilience of Hubei province (with its neighboring areas) in context of COVID-19. At the same time, the relative validity of the model is tested based on the empirical evaluation results.

Overall Resilient Evaluation of Surrounding Rock of In-Service High-Speed Railway Tunnel Based on Information Fusion-Improved Fuzzy Matter-Element

Once the high-speed railway tunnel is put into use, its resilience will determine the possibility of permanent safety of the tunnel due to the closure of the structural space of the high-speed railway tunnel in service. Resilience theory is introduced into a risk analysis of operating high-speed rail tunnels to improve the ability to respond to risks in operating high-speed rail tunnels and to relieve the aging phenomenon caused by changes in the tunnel with time. First, an evaluation framework for the safety resilience of existing high-speed railway tunnels is constructed. Starting from the attributes of resilience such as resistance, adaptability, and resilience, and considering the characteristics of high-speed railway tunnels, protective measures, emergency management measures, and other factors, we fit the risk factors and probability of accident type of the high-speed railway tunnel and establish a tunnel safety resilience evaluation index system with 10 indexes. Secondly, the method of information fusion is used to combine subjective weighting and objective weighting. Then, the comprehensive weight of the evaluation index is obtained based on the principle of minimum discriminant information. Thirdly, the system resilience evaluation model based on the TOPSIS improved fuzzy matter-element is constructed to determine the classification criteria of resilience. On this basis, based on the temporal and spatial variability of the ductile tunnel, the concepts of ductile transition and ductile attenuation are introduced and the tunnel toughness optimization model is established to suppress the attenuation situation, enhance the transition ability, and then improve the system resilience level. On this basis, an optimal lifting scheme is obtained. Finally, taking Ai-Min tunnel of Ha-Mu high-speed railway as the engineering background, the flexibility of the resilience system is calculated, and the resilience grade (3) of the rock system surrounding the tunnel is obtained. Combined with the numerical model, improvement measures for specific tunnel facilities are proposed. The results show that the Ai-Min tunnel system has a general ability to resist external intrusion and prevent disasters, and the resilience level is general. It should focus on improving the resilience level of the transition index. The resilience evaluation results of the evaluation model are consistent with the actual situation of the project.

A Rockburst Prediction Model Based on Fuzzy Matter Element of Compound Hierarchy

A Rockburst Prediction Model Based on Fuzzy Matter Element of Compound Hierarchy

Risk assessment of complex footbridge based on Dempster–Shafer evidence theory using Fuzzy matter–element method

Owing to the landscapes of cities and the requirements of subchannels, the construction of large-span footbridges is complex and extremely risky. These risks may lead to the collapse of a footbridge during construction, resulting in casualties and other catastrophic consequences, delays in projects and significant property losses, and potential safety hazards during follow-up maintenance. At present, China’s urban footbridge construction has not been included in the overall urban construction category, and its risk assessment is static and temporary, mainly relying on the subjective judgment of experts and construction personnel, and the different cases are difficult to learn from each other. To address this issue, a new risk assessment method and a novel system quality management framework are proposed herein. In this system, real-time engineering quality data under 4M1E (Man, Method, Material, Machine, Environment) framework is used to replace traditional risk factors. An improved Dempster–Shafer theory is adopted to effectively combine heterogeneous data from the 4M1E framework and calculate the dynamic total risk index. Finally, the proposed method is verified. The research results show that the new system can predict the risk level of each stage of bridge construction objectively and effectively by using real-time monitoring data, and has high robustness in different project testing.

Research on a lightweight unmanned sightseeing vehicle frame based on multi-condition and multi-objective optimization

A design method for a lightweight unmanned sightseeing vehicle frame was proposed based on multi-condition and multi-objective optimization to improve the vehicle’s range and reduce its production cost. First, a finite element model of the frame is established, and its static and dynamic characteristics are analyzed. Then, the wall thickness of the middle beam of the frame was selected as the design variable, and its sensitivity was analyzed. Sample points were generated from the design variables using the Latin hypercube sampling method, and the corresponding response values of the sample points were calculated. A Kriging approximation model was established using the sample points and response values and replaced the actual model for optimization. Finally, a multi-condition and multi-objective optimization mathematical model of the frame was established with the minimum mass, maximum first-order natural frequency, and the minimum stresses under full load bending and torsion conditions as the objectives. The multi-objective genetic algorithm was used for the lightweight design by comparing the fuzzy matter element and analytic hierarchy process methods to select the optimal design and to verify the rationality of the final design scheme. The results show that this method results in an optimized frame meeting the strength requirements under various working conditions and reducing the frame mass by 5.4%.

Reservoir Permeability Prediction Based on Analogy and Machine Learning Methods: Field Cases in DLG Block of Jing’an Oilfield, China

Abstract Reservoir permeability, generally determined by experimental or well testing methods, is an essential parameter in the oil and gas field development. In this paper, we present a novel analogy and machine learning method to predict reservoir permeability. Firstly, the core test and production data of other 24 blocks (analog blocks) are counted according to the DLG block (target block) of Jing’an Oilfield, and the permeability analogy parameters including porosity, shale content, reservoir thickness, oil saturation, liquid production, and production pressure difference are optimized by Pearson and principal component analysis. Then, the fuzzy matter element method is used to calculate the similarity between the target block and analog blocks. According to the similarity calculation results, reservoir permeability of DLG block is predicted by reservoir engineering method (the relationship between core permeability and porosity of QK-D7 in similar blocks) and machine learning method (random forest, gradient boosting decision tree, light gradient boosting machine, and categorical boosting). By comparing the prediction accuracy of the two methods through the evaluation index determination coefficient (R2) and root mean square error (RMSE), the CatBoost model has higher accuracy in predicting reservoir permeability, with R2 of 0.951 and RMSE of 0.139. Finally, the CatBoost model is selected to predict reservoir permeability of 121 oil wells in the DLG block. This work uses simple logging and production data to quickly and accurately predict reservoir permeability without coring and testing. At the same time, the prediction results are well applied to the formulation of DLG block development technology strategy, which provides a new idea for the application of machine learning to predict oilfield parameters.

Identifying the habitat quality of Scutellaria baicalensis based on baicalin content using the fuzzy matter element model

Plants form the basis of sophisticated traditional medicine systems, and information on the spatial distribution of high-quality medicinal herbs is a prerequisite and foundation for their effective utilization and conservation. Scutellaria baicalensis Georgi is a traditional Chinese medicinal plant, and baicalin is the main active ingredient of S. baicalensis. This research collected 470 samples of S. baicalensis through fieldwork and extracted baicalin content by high-performance liquid chromatography. We also collected 240 distribution sites from previous studies, which did not include effective content information. Then, based on 240 distribution sites of S. baicalensis and corresponding environmental variables via the MaxEnt method, we determined the distribution region of wild S. baicalensis. Next, a fuzzy matter element model was used to build the relationship between environmental variables and baicalin content. Finally, we integrated all the results to assess the habitat quality of the baicalin content in S. baicalensis across China. The results showed that compared with the genuine producing area of S. baicalensi, the high-quality habitats for a high baicalin content are further south. And the areas of habitat with high content of wild S. baicalensis based on baicalin in China were approximately 3.1 × 105 km2. The habitat of each category covered a large area in the distribution and the baicalin content differed from category to category obviously. Climatic factors wouldn’t only be an indicator for identifying the potential distribution area, but also for forming some secondary metabolites such as baicalin. In conclusion, it is necessary to label the produce place of S. baicalensis plant when used as herbal medicine. Our results provide a reference for the establishment of S. baicalensi plantations.

Research on security risk assessment mechanism of important\xa0event based on multi-source data

In order to effectively assess all types of security risks in the important event, the important decision-making basis for security risk warning and emergency management of important event is provided by analyzing the coupling relationship and evolution mechanism between various risks. The criminal causes, management defects, security and emergency system construction are analyzed from the possibility of accidents and risks. The multi-source data risk assessment system based on five subsystems and its index set of human factors, management factors, site factors, event factors and audit factors are proposed. The weight of each index in the assessment system is determined by the method of information entropy, and then the risk grade of important event is determined according to the weight calculation function and the improved fuzzy matter element model. The verification with an example shows that: the risk assessment model is optimized by combining entropy weight with fuzzy matter-element model, the influence of weight data extreme value was weakened, the qualitative description and quantitative analysis of multi-source data could be combined, and the subjective error was reduced. The risk grade of important event is reasonably evaluated, and the assessment effect is basically consistent with the expert inspection analysis, which shows that the method had certain application value.

Multi response assessment of EN19 alloy steel in CNC milling process using Fuzzy matter element methodology

In this research work, development of a multi response assessment technique was considered using Fuzzy matter element methodology for material removal rate(MRR) and surface roughness(SR) of milling on CNC Vertical Machining Set up. EN19 alloy steel was selected as work material for experimentation. Material removal rate (MRR) and surface roughness (SR) were investigated in milling operation after varying the level statistically of cutting speed, depth of cut and feed rate. Two types of milling cutters (titanium nitride coated carbide and solid carbide end mill) were selected to do the operation. Fuzzy matter element methodology was found to be successful to select the best tool for this experiment.

Application of Fuzzy Matter Element Analysis to the Evaluation of Circulation Efficiency of Agricultural Products

This article builds an evaluation indicator system based on the process of agricultural circulation and the balance of many-sided benefits. A new classification of indicator types was made from the perspective of benefit balance, and gross profit margin and inventory-sales ratio were classified as interval indicators, which enriched the indicator types. The matter-element analysis method is used to measure the circulation efficiency of agricultural product wholesale markets. The study found that in terms of geographical location, in the vertical direction of Aihui-Tengchong Line, the closer to the southeast, the higher the circulation efficiency; Since 2016, the overall circulation efficiency has almost stagnated, mainly because the wholesale industry has strengthened its ability to control upstream and downstream relationships, and the entry barriers of agricultural products have been continuously increased.

Research and Optimization of Computer Intelligent Evaluation Model under C2C Mode by Fuzzy Matter-Element

This paper discusses the establishment of credit evaluation model suitable for the characteristics of C2C e-commerce, taking the C2C model as an example, so as to reduce the risk cost in e-commerce trade. This model synthesizes the advantages of Hamming nearness degree and variable weight theory, and puts forward the design idea of variable entropy weight with fuzzy matter-element, which has the characteristics of simple operation, strong applicability, and close to reality. From the results of empirical analysis, the model calculates the credit level of different e-commerce, and effectively solves the problem of multi-index evaluation incompatibility in the evaluation of e-commerce credit. This model provides a feasible method for selecting the best supplier, improving the scientific decision, and achieve a good application prospect.

Technologies selection to prevent and control excrement pollution from livestock and poultry farming in Huang-Huai-Hai Region

Taking nitrogen, phosphorus, heavy metals and antibiotics as selection indicators, the fuzzy matter element analysis based on the entropy weight method was used to evaluate and select the technologies to prevent and control excrement pollution from intensive livestock and poultry farming in the Huang-Huai-Hai (HHH) Region, with the combination of Euclid approach degree. The results showed that the N change rate and the passivation rate of heavy metals in the excrement pollution treatment technologies accounted for higher weights, indicating that the difference between N and heavy metals in different treatment technologies was large. When choosing treatment technology, priority could be given to the technologies to prevent and control N and heavy metals. According to the final evaluation results, the technology of multi-level ecological and in-depth treatment was better for the liquid manure, while the technology of reactor composting was better for the treatment of solid manure.

A Bayesian network with fuzzy mathematics for species habitat suitability analysis: A case with limited Angelica sinensis (Oliv.) Diels data

There are many different types of species distribution models (SDMs) that are widely used in the field of ecology. In this research, we explored a new advanced mechanism for predicting the distribution of species based on fuzzy membership function, principle of maximum entropy, fuzzy mathematics comprehensive evaluation, and the framework of Bayesian networks. We use fuzzy mathematics and Bayesian network model (FBM) to simulate relationships between species’ habitats and environmental variables, and the relationship may be difficult to quantify effectively. FBM, which combines species data, environmental data, expert experience, and machine learning, could reduce the data and system error. In the case of medicinal plant, Angelica sinensis (Oliv.) Diels, many approaches have been applied, including nine learning sequence of sampling sites, three FBM models, two types of information classification by fuzzy mathematical classification (FMC) and equal interval classification (EIC), and the evaluation of AIC and log-likelihood. Through the comparison of reasoning results between FBM and fuzzy matter element model (FME) in testing sites, the result shows that the combination of objective data and empirical model structure makes FBM have better result output. Besides, FBM sensitivity analysis helps researchers explore in detail the impact of environmental factors on each level of species habitat suitability. The temperature factor has an important influence on the highly suitable, moderately suitable, and lowly suitable habitats of A. sinensis. Through FMC and sensitivity analysis, annual mean temperature (Bio1) in 5.92 °C-9.05 °C and mean temperature of warmest quarter (Bio10) in 14.80 °C-18.60 °C are the highly suitable habitat temperature range of A. sinensis.