Grey Relational Analysis Method Research Articles

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.

A decision-making model for blasting risk assessment in mines using FBWM and GRA methods

Due to the extensive use of explosives, the failure to identify hazards and assess risks in blasting may lead to catastrophic consequences. However, classical risk assessment approaches are limited in their ability to address ambiguity and uncertainty, as well as in assigning weights to the criteria involved in the risk assessment process. This study employs a multi-criteria decision-making system to address these limitations and assess the risks associated with blasting. The proposed model integrates Grey Relational Analysis (GRA) to prioritize risks and the Fuzzy Best-Worst Method (FBWM) to assign weights to the criteria critical to the risk assessment process. The findings indicated that “not using personal anti-static protection devices during blasting (R12)”, “placing the explosive fuse near explosive materials (R15)”, and “bringing explosive materials to the explosion site before completing drilling and blasting operations (R23)” were the most significant blasting risks, respectively. These risks stem from operational processes, human factors, and the working environment, thus requiring special attention. The weighting of the study criteria, including Consequence (C), Probability (P), and Exposure (E), revealed that the C criterion, with a final weight of 0.538, was the most influential in the risk assessment process. The P and E criteria, with weights of 0.294 and 0.167, respectively, ranked second and third in importance among the assessment criteria. To ensure the applicability and accuracy of the proposed method, a validation study comprising two distinct parts—sensitivity analysis and comparative analysis—was conducted. The results of these evaluations highlighted the appropriate and reliable performance of the proposed approach. This approach can assist decision-makers, managers, and risk analysts in more accurately identifying and assessing risks by addressing some of the limitations inherent in classical risk assessment methods.

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.

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.

Optimization of preparation parameters of palm oil-based nanofuel with multi wall carbon nanotube (MWCNT) for stability using Taguchi-grey relation analysis (GRA) combination

This research optimizes the preparation parameters of palm oil-based nanofuel and Multi Wall Carbon Nanotube (MWCNT) to produce stable nanofuel. The parameters optimized include stirrer speed, sonication time, sonication power, and surfactant ratio, with stability measured through absorbance and sedimentation ratio (SR). The Taguchi method, using an L9 orthogonal array designed with minitab 19.0 software, was employed for single-objective optimization, while Grey Relation Analysis (GRA) is applied for multi-objective optimization. Experimental results show that the optimal conditions for absorbance are stirrer speed of 1000 rpm, sonication time of 30 minutes, sonication power of 200 watts, and surfactant ratio of 1, whereas for sedimentation ratio the optimal conditions are stirrer speed of 1000 rpm, sonication time of 30 minutes, sonication power of 150 watts, and surfactant ratio of 1. ANOVA analysis reveals that surfactant concentration contributes the most to nanofuel stability, with contributions of 79.63% for absorbance and 82.60% for sedimentation ratio. Multi-objective GRA optimization results also show that surfactant concentration is the most dominant factor, contributing 71.5% to the Grey Relational Grade (GRG). The consistency of optimal parameters yielded by both Taguchi and GRA methods reinforces the validity and consistency of this study's results. This research provides a strong foundation for the development of more stable nanofuels, potentially enhancing energy efficiency and sustainability. These findings offer practical guidelines for real-world applications and make significant contributions to nanofuel technology

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.

Predicting Water Flowing Fracture Zone Height Using GRA and Optimized Neural Networks

As coal mining depths continue to rise, consideration of WFFZ elevations is becoming increasingly important to mine safety. The goal was to accurately predict the height of the WFFZ to effectively prevent and manage possible roof water catastrophes and ensure the ongoing safety of the mine. To achieve this goal, we combined the particle swarm optimisation (PSO) algorithm with a backpropagation neural network (BPNN) in order to enhance the accuracy of the forecast. The present study draws upon the capacity of the PSO algorithm to conduct global searches and the nonlinear mapping capability of the BPNN. Through grey relational analysis (GRA), the order of the correlation degree was as follows: mining thickness > mining depth > overburden structure > mining width > mining dip. GRA has identified the degree of correlation between five influencing factors and the height of the WFFZ, among these, mining thickness, mining depth, overburden structure and mining width all show strong correlations, and the mining dip of the coal seam shows a good correlation. The weight ranking obtained by the PSO-BPNN method was the same as that obtained by the GRA method. Based on two actual cases, the relative errors of the obtained prediction results after PSO implementation were 2.97% and 3.47%, while the relative errors of the BPNN before optimisation were 18.46% and 4.34%, respectively, indicating that the PSO-BPNN method provides satisfactory prediction results and demonstrating that the PSO-optimised BPNN is easy to use and yields reliable results. In this paper, the height of the WFFZ model under the influence of five factors is only established for the Northwest Mining Area. With the continuous progress of technology and research, the neural network can consider more factors affecting the height of hydraulic fracturing development zones in the future to improve the comprehensiveness and accuracy of prediction.

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.

Grey Relation System Applications in Multi-Attribute Decision-Making and Forecasting in Financial Management

Objective: The objective of the paper is to verify the Grey relation analysis methods and compare them with known models in financial management. The first part is devoted to MADM (multi-attribute decision methods) and the second one to grey forecasting methods. Method: Localization GRA and globalization GRA multi-criteria models are described and compared with the TOPSIS method. The forecasting GRA(1,1) model based on cumulative data is described, derived and compared with the linear growth model. The models are verified on the data set. Results and Discussion: The globalisation model based on a pair-wise comparison of alternatives is different, and the ranking of alternatives is fully different in comparison with the TOPSIS model. It was found that the localisation GRA model and TOPSIS, both constructed on similarity measures, present similar and comparable results. So, the grey relation approach considers the uncertainty of input data. The forecasting model GRA(1,1) is computed and compared with the growth model. Results show that the growth model is the trendy model, and the non-trendy series is overestimated or underestimated, so the GRA model looks more robust. Research Implications: The problem of imprecise data is investigated. And it is a realistic condition for practical decision-making. The problem of lack of data is often a phenomenon in decision-making. Traditional statistical methods cannot be applied. Therefore, grey relation methodology is one approach to overcoming the problem of imprecise data and lack of data. Uncertainty and lack of data problems are topics important for research and the managerial community. Originality/Value: Sometimes, in financial decision-making, high-quality data or long-term time series is not at our disposal, and on the other hand, decisions have to be made. This problem is often neglected and solved by not-relevant traditional statistical models. Therefore, methods that allow the solving of such problems are useful. It was shown that a grey relation methodology for multicriteria financial performance evaluation and forecasting shows more robust and stable results and could be more relevant for imprecise data or short time series data in financial decision-making problems.

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.

An improved interval‐grey‐probability QFD method for prioritizing engineering characteristics under vague environment

AbstractAt present, the interval‐grey quality function deployment (QFD) has been widely used to address a vague information at the early stage of product design. However, the existing interval‐grey QFD method suffers from a problem that it is incapable of comparing specific values from any two intervals within overlapping segments associated with the importance of engineering characteristics (ECs) during the process of prioritizing their importance. This problem consequently impedes the effective prioritization of their magnitudes, thus hindering the prioritization of the importance of ECs. Aiming at addressing the problem, an improved interval‐grey‐probability QFD method is proposed. The proposed method integrates interval grey numbers, grey relational analysis (GRA), and interval‐probability ranking method into the interval‐grey QFD framework to prioritize the importance of ECs under a vague environment. Specifically, the interval grey numbers are used to assess the importance of customer requirements (CRs) and the GRA method is adopted to construct the relationship matrix of CRs‐ECs, respectively. Based on the importance of CRs and the relationship matrix of CRs‐ECs, the interval‐probability ranking method is introduced to measure the prioritization of the importance of ECs. The proposed method is validated by a multi‐function flash drive. Results show that the proposed method is more accurate than existing methods in terms of the deviation degree.

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.

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.

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 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.

Multi-objective optimization and influence degree analysis of the thermally integrated HP-ORC carnot battery based on the orthogonal design method and grey relational analysis

Thermally integrated heat pump (HP)-organic Rankine cycle (ORC) Carnot battery is a promising solution for efficient energy storage and waste heat utilization. Firstly, in order to research the influence degree of the operating parameters on the system performance, single-objective optimization is conducted by the orthogonal design method. Then, multi-objective optimization is conducted by combining the grey relational analysis (GRA) and orthogonal design method to assess the comprehensive performance of the system. The importance order and contribution rates of the operating parameters and the optimal operating combinations are determined. Finally, in order to explore the influence mechanism of the parameters on the system performance, the influence of the parameters on the subsystems and the subsystems on the whole system is quantitatively analyzed. The results show that thermal storage temperature difference, waste heat temperature difference and cold source temperature are the most important parameters affecting the comprehensive performance of the whole system. Under the optimal operating conditions, round-trip efficiency, exergy efficiency, energy storage capacity (ESC), energy storage density (ESD) and levelized cost of storage (LCOS) are 47.05 %, 22.66 %, 1943 kW, 2.90 kWh/m3 and 0.38$/kWh, respectively. Compared to the ORC subsystem, the HP subsystem is more important to round-trip efficiency and exergy efficiency.

A combined method for the evaluation of contributing factors to maritime dangerous goods transport accidents

This paper evaluates the contributing factors to maritime dangerous goods (DG) transport accidents by integrating the Entropy Weight (EW) and Grey Relational Analysis (GRA) methods. For this purpose, investigation reports of maritime DG transport accidents that occurred worldwide between 2000 and 2023 are derived from the International Maritime Organization’s Integrated Shipping Information System (IMO GISIS) database’s Marine Casualties and Incidents (MCI) module. Eleven main ship operations and thirteen primary causes were selected by analysing accident investigation reports. The weights of main ship operations are calculated utilizing the EW method. The correlational degrees of the primary causes are then calculated using the GRA method. Most maritime DG transport accidents occur during unberthing, bunkering, and pilotage operations. The most common contributing factors of maritime DG transport accidents are collisions and occupational accidents. Specifically, maritime DG transport accidents are most likely to be caused by collisions during sailing, passage, maneuvering, and bunkering operations, as well as occupational accidents during cargo loading, anchoring, berthing, and mooring operations. The results of this paper can support stakeholders in developing the needed policies to guarantee the safety of maritime DG transport.

How sustainable are agricultural practices in European Union countries? A spherical fuzzy logic based evaluation

ABSTRACT Climate change and population growth have heightened the need for a comprehensive framework to analyse the environmental impacts of agricultural practices. While traditional agricultural activities have detrimental environmental effects such as soil degradation, water pollution, and greenhouse gas emissions, sustainable agriculture is an innovative approach to minimizing humanity’s significant problems, such as depletion of natural resources, climate change, and biodiversity loss. In this study, a model is developed to evaluate the agri-environmental performance of countries. This model evaluates and ranks countries using the spherical fuzzy analytical hierarchy process and grey relational analysis methods within the European Union’s agri-environmental indicators framework. This study is among the pioneering studies to utilise the spherical fuzzy logic approach for determining the relative importance of agri-environmental performance criteria. The findings reveal that crucial factors such as the irrigable utilised agricultural area, the sales of pesticides per utilised agricultural area, and the share of main livestock types in total livestock units are critical to agri-environmental performance. In addition, in the implementation phase of the study, the performances of the European Union member countries are evaluated using the proposed model. This constitutes another contribution of the research. The results of this study indicate that Finland, Lithuania, and Sweden are the frontrunners in agri-environmental performance, while Italy, the Netherlands, and Malta rank lower. The model developed in this study has far-reaching implications for developing targeted environmental protection policies and advancing sustainable agricultural practices on a global scale.