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The purpose of this paper is to explore a new grey relational analysis model to measure the coupling relationship between the indicators for the water environment status assessment. Meanwhile, the model deals with the problem that the changing of indicator order may result in the changing of the degree of grey relation.

The binary index submatrix of the sample matrix is given first. Then the product of the matrix and its own transpose is used to measure the characteristics of the index and the coupling relationship between the indicators. Thirdly, the grey relational coefficient is defined based on the matrix norm, and a grey coupling relational analysis model is proposed.

The paper provides a novel grey relational analysis model based on the norm of matrix. The properties, normalization, symmetry, relational order invariance to the multiplicative, are studied. The paper also shows that the model performs very well on the water environment status assessment in the eight cities along the Yangtze River.

The model in this paper has supplemented and improved the grey relational analysis theory for panel data.

The Grey System Theory (GST) is an emerging area of research within artificial intelligence. Since its founding in 1982, it has seen a lot of multidisciplinary applications. In just a short period, it has garnered some considerable strengths. Based on the 1987–2021 data collected from the Web of Science (WoS), the current study reports the advancement of the GST.

Research papers utilizing the GST in the fields of economics and education were retrieved from the Web of Science (WoS) platform using a set of predetermined keywords. In the final stage of the process, the papers that underwent analysis were manually chosen, with selection criteria based on the information presented in the titles and abstracts.

The study identifies prominent authors, institutions, publications and journals closely associated with the subject. In terms of authors, two major clusters are identified around Liu SF and Wang ZX, while the institution with the highest number of publications is Nanjing University of Aeronautics and Astronautics. Moreover, significant keywords, trends and research directions have been extracted and analyzed. Additionally, the study highlights the regions where the theory holds substantial influence.

The study is subject to certain limitations stemming from factors such as the language employed in the chosen literature, the papers included within the Web of Science (WoS) database, the designation of works categorized as “articles” in the database, the specific selection of keywords and keyword combinations, and the meticulous manual process employed for paper selection. While the manual selection process itself is not inherently limiting, it demands a greater investment of time and meticulous attention, contributing to the overall limitations of the study.

The significance of the study extends not only to scholars and practitioners but also to readers who observe the development of emerging scientific disciplines.

The analysis of trends revealed a growing emphasis on the application of GST in diverse domains, including supply chain management, manufacturing and economic development. Notably, the emergence of COVID-19 as a new research focal point among GST scholars is evident. The heightened interest in COVID-19 can be attributed to its global impact across various academic disciplines. However, it is improbable that this interest will persist in the long term, as the pandemic is gradually brought under control.

Grey relational analysis (GRA) has already proved itself as an efficient tool for multi-objective optimization of many of the machining processes. In GRA, the distinguishing coefficient (ξ) plays an important role in identifying the optimal parametric combinations of the machining processes and almost all the past researchers have considered its value as 0.5. In this paper, based on past experimental data, the application of GRA is extended to dynamic GRA (DGRA) to optimize two electrochemical machining (ECM) processes.

Instead of a static distinguishing coefficient, this paper considers dynamic distinguishing coefficient for each of the responses for both the ECM processes under consideration. Based on these coefficients, the application of DGRA leads to determination of the dynamic grey relational grade (DGRG) and grey relational standard deviation (GRSD), helping in initial ranking of the alternative experimental trials. Considering the ranks obtained by DGRG and GRSD, a composite rank in terms of rank product score is obtained, aiding in final rankings of the experimental trials for both the ECM processes.

In the first example, the maximum material removal rate (MRR) would be obtained at an optimal combination of ECM parameters as electrolyte concentration = 2 mol/l, voltage = 16V and current = 4A, while another parametric intermix as electrolyte concentration = 2 mol/l, voltage = 14V and current = 2A would result in minimum radial overcut and delamination. For the second example, an optimal combination of ECM parameters as electrode temperature = 30°C, voltage = 12V, duty cycle = 90% and electrolyte concentration = 15 g/l would simultaneously maximize MRR and minimize surface roughness and conicity.

In this paper, two ECM operations are optimized using a newly developed but yet to be popular multi-objective optimization tool in the form of the DGRA technique. For both the examples, the derived rankings of the ECM experiments exactly match with those obtained by the past researchers. Thus, DGRA can be effectively adopted to solve parametric optimization problems in any of the machining processes.

In the present-day highly customer-conscious service environment, supply chain management has become a critical component of health-care industry, helping in fulfilling patient expectation, optimizing inventory and automating departmental activities. Supplier selection is one of the crucial elements of health-care supplier chain, establishing mutually beneficial relationships with the reliable suppliers that provide the most value of money. Health-care supplier selection with feasible sets of alternatives and conflicting criteria can be treated as a multi-criteria decision making (MCDM) problem. Among the MCDM methods, grey relational analysis (GRA) appears as a potent tool due to its simple computational steps and ability to deal with imprecise data. The purpose of this paper is to explore the applicability of a newly developed MCDM tool for solving a health-care supplier selection problem.

In GRA, the distinguishing coefficient (ξ) plays a contributive role in final ranking of the alternative suppliers and almost all the past researchers have considered its value as 0.5. In this paper, a newly developed MCDM tool, i.e. dynamic GRA (DGRA), is adopted to evaluate the relative performance of 25 leading pharmaceutical suppliers for a health-care unit based on nine important financial metrics. Instead of static value of ξ, DGRA treats it as a dynamic variable dependent on grey relational variator and ranks the health-care suppliers using their computed rank product scores.

Based on rank product scores and developed exponential curve, DGRA classifies all the suppliers into reliable, moderately reliable and unreliable clusters, helping the health-care unit in identifying the best performing suppliers for subsequent order allocation. Among the reliable suppliers, alternatives A₂ and A₁₁ occupy the top two positions having almost the same performance with respect to the considered financial metrics.

Application of DGRA along with determination of the most reliable suppliers would help in effectively adopting multi-sourcing strategy to increase resilience while diversifying the supply portfolio, thereby enabling the health-care unit to minimize chances of sudden disruption in the supply chain. It can act as an intelligent decision-making framework aiding in solving health-care supplier selection problems considering perceived risks and dynamic input data.

The purpose of this study to identify and optimize the machining of polyvinyl butyral (PVB) material for industrial uses. The research is based on input machining parameters of rotary ultrasonic machining for better understand the output response surface roughness (SR) property of polyvinyl butyral (PVB) by using the Taguchi approach. The grey relational grade analysis (GRG) is also implemented to resolve the complex interrelationship of SR data for optimization and predicting and validate the results.

In experimental work, the input parameters, namely, concentration, abrasives, power rate, grit size, tool material and hydrofluoric (HF) acid has been selected. The experiment’s design was created using MINITAB Software; the L27 orthogonal array was selected for the experimentation. SR was examined with the GRG technique for process optimization. On the other hand, for single parameter optimization analysis of variance (ANOVA) has been used.

ANOVA optimization technique gives the best result on concentration (40%) of abrasive (Al2O3+SiC+B4C), power rate (40%), grit size (600), HF acid (1.5%) and tool material (D2 alloy) are the optimal parameters to provide the slightest degree of SR. GRG optimization of multi-response parameter setting: 40% concentration, SiC+B4C mixed abrasive slurry, 40% of power rating, 280 grit size, 0.5% HF acid and high-speed tool steel tool material gives better results. The SR of PVB glass material improved by 20% after grey relational analysis.

There are several practical applications in a variety of material processing sectors, including metallurgy, machinery, electronics and transportation. These real-world applications have produced substantial and discernible economic benefits.

The analytical and optimization results will be used in the various material processing sectors, including metallurgy, machinery, electronics and transportation.

The ANOVA and grey theory approaches offer the reader a primary picture of the machining research and process parameter optimization. Combined abrasive slurry of Al₂O3+SiC+B₄C with a high power-rating exhibits lower SR. Similarly, grit size is vital; larger grits produce better SR. Ra – 0. 611 m is the lowest SR value at the hole found in trial 25 after the experimentation.

In order to solve the decision-making problem that the attributive weight and attributive value are both interval grey numbers, this paper tries to construct a multi-attribute grey decision-making model based on generalized greyness of interval grey number.

Firstly, according to the nature of the generalized gresness of interval grey number, the generalized weighted greyness distance between interval grey numbers is given, and the transformation relationship between greyness distance and real number distance is analyzed. Then according to the objective function that the square sum of generalized weighted greyness distances from the decision scheme to the best scheme and the worst scheme is the minimum, a multi-attribute grey decision-making model is constructed, and the simplified form of the model is given. Finally, the grey decision-making model proposed in this paper is applied to the evaluation of technological innovation capability of 6 provinces in China to verify the effectiveness of the model.

The results show that the grey decision-making model proposed in this paper has a strict mathematical foundation, clear physical meaning, simple calculation and easy programming. The application example shows that the grey decision model in this paper is feasible and effective. The research results not only enrich the grey system theory, but also provide a new way for the decision-making problem that the attributive weights and attributive values are interval grey numbers.

The decision-making model proposed in this paper does not need to seek the optimal solution of the attributive weight and the attributive value, and can save the decision-making labor and capital investment. The model in this paper is also suitable for the decision-making problem that deals with the coexistence of interval grey numbers and real numbers.

The paper succeeds in realizing the multi-attribute grey decision-making model based on generalized gresness and its simplified forms, which provide a new method for grey decision analysis.

This study aims to investigate the effect of the copresence of ferrous (Fe²⁺) ions and sodium dodecyl sulfate (SDS) on the activity of an amylase enzyme during the desizing of greige viscose fabric for potential industrial applications. The removal of starches is an essential step before processing the fabric for dyeing and finishing operations. The authors tend to accomplish it in eco-friendly and sustainable ways.

The experiments were designed under the Taguchi approach, and the results were analyzed using grey relational analysis to optimize the process. The textile properties of absorbency, reducing sugars, bending length, weight loss, Tegawa rating and tensile strength were assessed using the standard protocols. The control and optimized viscose specimens were investigated for certain surface chemical properties using advanced analytical techniques including scanning electron microscopy (SEM), X-ray diffraction (XRD) and thermal gravimetric analysis (TGA).

The results demonstrate that the Fe²⁺ concentration and process time were the main influencing factors in the amylolytic desizing of viscose fabric. The optimized process conditions were found to be 0.1 mm Fe²⁺ ions, 3 mm SDS, 80°C, 7 pH and 30 min process time. The copresence of Fe²⁺ ions and SDS promoted the biodesizing of viscose fabric. The SEM, Fourier transform infrared spectroscopy, XRD and TGA results demonstrated that the sizing agent has efficiently been removed from the fabric surface.

The amylase desizing of viscose fabric in the presence of certain metal ions and surfactants is a significant subject as the enzyme may face them due to their prevalence in the water systems. This could also support the biodesizing and bioscouring operations to be done in one bath, thus making the textile pretreatment process both economical and environmentally sustainable.

The authors found no report on the biodesizing of viscose fabric in the copresence of Fe²⁺ ions and the SDS surfactant under statistical multiresponse optimization. The biodesized viscose fabric has been investigated using both conventional and analytical approaches.

Data breaches in the US healthcare sector have more than tripled in the last decade across all states. However, to this day, no established framework ranks all states from most to least at risk for healthcare data breaches. This gap has led to a lack of proper risk identification and understanding of cyber environments at state levels.

Based on the security action cycle, the National Institute of Standards and Technology (NIST) cybersecurity framework, the risk-planning model, and the multicriteria decision-making (MCDM) literature, the paper offers an integrated multicriteria framework for prioritization in cybersecurity to address this lack and other prioritization issues in risk management in the field. The study used historical breach data between 2015 and 2021.

The findings showed that California, Texas, New York, Florida, Indiana, Pennsylvania, Massachusetts, Minnesota, Ohio, and Georgia are the states most at risk for healthcare data breaches.

The findings highlight each US state faces a different level of healthcare risk. The findings are informative for patients, crucial for privacy officers in understanding the nuances of their risk environment, and important for policy-makers who must grasp the grave disconnect between existing issues and legislative practices. Furthermore, the study suggests an association between positioning state risk and such factors as population and wealth, both avenues for future research.

Theoretically, the paper offers an integrated framework, whose basis in established security models in both academia and industry practice enables utilizing it in various prioritization scenarios in the field of cybersecurity. It further emphasizes the importance of risk identification and brings attention to different healthcare cybersecurity environments among the different US states.

In the recent post-pandemic era, the globe has been anxious for the sustainable disposal of healthcare waste to protect public health, protect the environment and enhance future preparedness. Developing countries, in particular, have struggled to dispose of healthcare waste (HCW) to eradicate the hazardous effects of medical waste generated during and after the deadly COVID-19 pandemic. Hence the purpose of the research paper is to develop a hybrid decision-making framework to identify various barriers for sustainable disposal of healthcare waste use of Grey-Decision Making Trial and Evaluation Laboratory (G-DEMATEL) and Analytical Network Process (ANP).

A hybrid framework of Grey-Decision Making Trial and Evaluation Laboratory (G-DEMATEL) and Analytical Network Process (ANP) has been used to rank barriers and sub-barriers in the disposal of healthcare waste.

The study’s findings suggest that lack of segregation practices, absence of green procurement policy, obsolete technologies and resistance to adopting change management are the topmost causal barriers influencing the remaining barriers. Lack of commitment among healthcare administrations, lack of standard performance measures and resistance to adopting change appear to be the topmost crucial barriers.

The study’s finding enables all stakeholders to prioritize the barriers systematically for better performance and save resources during the process. The policymakers can use the results to design a clear regulatory framework.

The literature has highlighted the factors and their association with the disposal of healthcare waste mainly in isolation. The results are validated against the Grey-Analytical Hierarchy Process (G-AHP) to ensure the robustness of the proposed framework. This paper is one of the preliminary attempts to propose a framework of the interrelationships of the factors that have a direct role in survival for management education.

The purpose of this paper is to construct a grey incidence model for panel data that can reflect the incidence direction and degree between indicators.

Firstly, this paper introduces the concept of a negative matrix and preprocesses the data of each indicator matrix to eliminate differences in dimensions and magnitudes between indicators. Then a model is constructed to measure the incidence direction and degree between indicators, and the properties of the model are studied. Finally, the model is applied to a practical problem.

The grey-directed incidence degree is 1 if and only if corresponding elements between the feature indicator matrix and the factor indicator matrix have a positive linear relationship. This degree is −1 if and only if corresponding elements between the feature indicator matrix and the factor indicator matrix have a negative linear relationship.

The example shows the number of days with good air quality is negatively correlated with the annual average concentration of each pollutant index. PM_2.5, PM₁₀ and O₃ are the main pollutants affecting air quality in northern Henan.

This paper introduces the negative matrix and constructs a model from the holistic perspective to measure the incidence direction and level between indicators. This model can effectively measure the incidence between the feature indicator and factor indicator by integrating information from the point, row, column and matrix.