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Gnaphalium affine is a plant with various active properties and has been approved for use as a traditional medicine and food ingredient. In this study, liquid chromatography with electrochemical detection was utilized to separate the antioxidant compounds of G. affine, in conjunction with liquid chromatography-triple quadrupole mass spectrometry for identification. The in vitro antioxidant activity of G. affine was evaluated using DPPH and ABTS radical scavenging capacity assays, as well as the ferric reducing antioxidant power assay. The results showed that a total of 25 antioxidant compounds were screened and identified, among which 14 compounds were quantitatively analyzed. G. affine was found to be rich in phenolic substances, with the total phenolic content ranging from 14.62 to 59.30 mg GAE/g DW, which exhibited high antioxidant potential. The antioxidant activities showed a strong positive correlation with the total phenolic content (r > 0.9). The predominant phenolic compounds in G. affine were 5-O-caffeoylquinic acid, 3-O-caffeoylquinic acid, ferulic acid, luteolin-7-O-glucoside, 3,5-O-dicaffeoylquinic acid, 1,5-O-dicaffeoylquinic acid, and luteolin, with 3,5-O-dicaffeoylquinic acid and 1,5-O-dicaffeoylquinic acid exhibiting the highest levels. Statistical analysis indicated that monosubstituted caffeoylquinic acids, disubstituted caffeoylquinic acids, and luteolin derivatives strongly correlated with the antioxidant potential. This study identified and quantified the major chemical constituents responsible for the antioxidant properties of G. affine, demonstrating its potential as a natural source of antioxidants for the use in foods, pharmaceuticals, and health products. [ABSTRACT FROM AUTHOR]

The study aims to improve the prediction of slope stability in road engineering by combining sophisticated analytical methods, including ABAQUS software-based finite element modeling and gray relational analysis. The aim is to devise a systematic and impartial strategy that reduces the subjective biases linked with conventional empirical techniques, enhancing the precision of safety factor computations for soil slopes across diverse geological and environmental scenarios. This strategy tackles the drawbacks of traditional approaches, which frequently rely on empirical procedures and produce inconsistent findings in various contexts. The methodology entails utilizing ABAQUS software to create a numerical slope model and then calculating safety factors under different operating situations using the finite element strength reduction method. The improved gray relational analysis captures the complexity related to slope stability and offers a more thorough knowledge of variable interdependencies. The study uses ABAQUS to model slope stability problems and, despite its complexity, calculates exact safety factors using the finite element strength reduction approach to assess slope performance under various scenarios. The paper illustrates how the suggested approach may be used practically in engineering projects and how it can enhance the procedures involved in making decisions about slope stability evaluations. [ABSTRACT FROM AUTHOR]

The AA7075 alloy and AA7075/SiC/Gr hybrid composite prepared by the stir-casting method have been further thermo-mechanically processed through unidirectional hot rolling and hot cross-rolling routes to eliminate casting defects like voids, blow holes etc. Three initial sample temperatures, that is, 350℃, 400℃, and 450℃, have been considered for the hot cross-rolling. The Al alloy and its hybrid composite prepared at different rolling temperatures are studied to investigate their micro-machining behavior through micro-electric discharge machining (µ-EDM). The micro-holes are made on the rolled samples along the rolling direction of the final rolling pass using a tool with twist drill bit geometry. Further, multi-objective optimization of the µ-EDM process parameters considering the response parameters viz. material removal rate (MRR) and tool wear rate (TWR) has been carried out using the gray relational analysis (GRA) technique. The µ-EDM parameters chosen to optimize in this study are voltage, pulse on time, and tool rotation. The obtained GRA results are further analyzed through normality and equal variance tests. The R-squared values of 95.02% and 95.53% for AA7075 alloy and hybrid composite, respectively, indicate that the data fit well with the statistical model. In material removal, the different electrical and thermal characteristics of the Al matrix and the reinforcements possibly generate sparks with a different characteristic that ultimately affects the MRR of the composite. Through scanning electron microscopy (SEM), the morphological study confirmed the presence of globules and voids on the machined surface. The formation of globules and voids is more significant in the AA7075 alloy than in the composite. [ABSTRACT FROM AUTHOR]

Aiming to address the complexity and challenges of predicting pure electric vehicle (EV) sales, this paper integrates a time series model, support vector machine and combined model to forecast EV sales in China. Firstly, a seasonal autoregressive integrated moving average (SARIMA) model was constructed using historical EV sales data, and the model was trained on sales statistics to obtain forecasting results. Secondly, variables that were highly correlated with sales were analyzed using gray relational analysis (GRA) and utilized as input parameters for the support vector regression (SVR) model, which was constructed to optimize sales predictions for EVs. Finally, a combined model incorporating different algorithms was verified against market sales data to explore the optimal sales prediction approach. The results indicate that the SARIMA-GRA-SVR model with the squared prediction error and inverse method achieved the best predictive performance, with MAPE, MAE and RMSE values of 12%, 1.45 and 2.08, respectively. This empirical study validates the effectiveness and superiority of the SARIMA-GRA-SVR model in forecasting EV sales. [ABSTRACT FROM AUTHOR]

This study investigates the additive manufacturing (AM) process of 30% ceramic‐reinforced composite PLA material using the fused deposition modeling (FDM) technique. The effects of various printing parameters on tensile strength, build time, and material consumption are comprehensively analyzed through a combination of the Taguchi method, analysis of variance (ANOVA), and gray relational analysis (GRA). Experimental design parameters include nozzle diameter, infill density, infill pattern, wall line count, print speed, and layer height. Statistical analyses reveal significant contributions of these parameters to mechanical properties and production efficiency. Single and multi‐objective optimizations of the responses were performed. The single optimization resulted in a significant increase in tensile strength from 39.9 to 48.10 MPa. Production time was reduced from 16 to 9 min; material consumption decreased from 4.95 to 2.43 g for tensile test specimens. The use of GRA in multi‐objective optimization has led to a significant improvement of 8.31% in the gray relational grade (GRG) when compared to the initial parameter settings. These findings provide valuable insights for optimizing FDM processes in the fabrication of composite PLA materials. This contributes towards the advancement of additive manufacturing technology and its applications across various industries. and its applications across various industries. Highlights: Tensile strength increased while reducing build time and material consumption.Optimal printing parameters were identified for a composite PLA material.Layer height and nozzle diameter were effective parameters. [ABSTRACT FROM AUTHOR]

GDP is a common and essential indicator for evaluating a country's overall economy. However, environmental issues may be overlooked in the pursuit of GDP growth for some countries. It may be beneficial to adopt more sustainable criteria for assessing economic health. In this study, green GDP (GGDP) is discussed using mathematical approaches. Multiple dataset indicators were selected for the evaluation of GGDP and its impact on climate mitigation. The k-means clustering algorithm was utilized to classify 16 countries into three distinct categories for specific analysis. The potential impact of transitioning to GGDP was investigated through changes in a quantitative parameter, the climate impact factor. Ridge regression was applied to predict the impact of switching to GGDP for the three country categories. The consequences of transitioning to GGDP on the quantified improvement of climate indicators were graphically demonstrated over time on a global scale. The entropy weight method (EWM) and TOPSIS were used to obtain the value. Countries in category 2, as divided by k-means clustering, were predicted to show a greater improvement in scores as one of the world's largest carbon emitters, China, which belongs to category 2 countries, plays a significant role in global climate governance. A specific analysis of China was performed after obtaining the EWM-TOPSIS results. Gray relational analysis and Pearson correlation were carried out to analyze the relationships between specific indicators, followed by a prediction of CO2 emissions based on the analyzed critical indicators. [ABSTRACT FROM AUTHOR]

Gnaphalium affine is a plant with various active properties and has been approved for use as a traditional medicine and food ingredient. In this study, liquid chromatography with electrochemical detection was utilized to separate the antioxidant compounds of G. affine, in conjunction with liquid chromatography-triple quadrupole mass spectrometry for identification. The in vitro antioxidant activity of G. affine was evaluated using DPPH and ABTS radical scavenging capacity assays, as well as the ferric reducing antioxidant power assay. The results showed that a total of 25 antioxidant compounds were screened and identified, among which 14 compounds were quantitatively analyzed. G. affine was found to be rich in phenolic substances, with the total phenolic content ranging from 14.62 to 59.30 mg GAE/g DW, which exhibited high antioxidant potential. The antioxidant activities showed a strong positive correlation with the total phenolic content (r > 0.9). The predominant phenolic compounds in G. affine were 5-O-caffeoylquinic acid, 3-O-caffeoylquinic acid, ferulic acid, luteolin-7-O-glucoside, 3,5-O-dicaffeoylquinic acid, 1,5-O-dicaffeoylquinic acid, and luteolin, with 3,5-O-dicaffeoylquinic acid and 1,5-O-dicaffeoylquinic acid exhibiting the highest levels. Statistical analysis indicated that monosubstituted caffeoylquinic acids, disubstituted caffeoylquinic acids, and luteolin derivatives strongly correlated with the antioxidant potential. This study identified and quantified the major chemical constituents responsible for the antioxidant properties of G. affine, demonstrating its potential as a natural source of antioxidants for the use in foods, pharmaceuticals, and health products.

【Objective】As the largest grain sales area in China, the grain security issue of Guangdong Province occupies a special important position in the overall situation of national grain security. The study evaluates the risk of the grain supply chain in Guangdong Province and proposes control strategies for the risks.【Method】Based on the data of National Bureau of Statistics from 2003 to 2021, we used SPSS to conduct factor analysis and constructed a risk assessment index system of grain supply chain from the production end, including 12 indicators such as the area affected by natural disasters, the number of environmental emergencies and the consumer price index for grain in Guangdong Province. By using Stata for entropy weight method - TOPSIS, the sample data were analyzed for grain supply chain risk assessment, and Python was used for gray relation analysis to analyze the causes of changes.【Result】The results of empirical analysis show that the risk of the grain supply chain in Guangdong Province can be summarized as three phases: the relatively higher risk phase (2003-2007), with a risk fit of 0.62 in 2004, reaching the peak in the past 20 years; the risk fluctuation phase (2008-2012), in which the risk of the grain supply chain declined compared with the previous phase, undulating up and down compared with the average level of risk; and the risk stabilization phase (2013-2021), in which the risk fit was between 0.3 and 0.4, maintaining around the average level.【Conclusion】As a whole, the risk of the grain supply chain in Guangdong Province has been on a downward trend since 2003; the external risks are relatively low, and it can effectively safeguard the external supply of grain, but the grain production and reserves are insufficient; the internal supply risks are still high, among which the grain production, the risk-resistant capacity of grain enterprises and the consumer price index for grains are the main factors affecting the risk of the grain supply chain, with gray relations of 0.831, 0.824 and 0.805, respectively. In order to improve the self-sufficiency rate of grain in Guangdong Province and reduce the risk of the grain supply chain, four strategies for controlling the risk of the grain supply chain are proposed: improving the conditions of grain production, transforming the management mindsets of grain enterprises, optimizing the grain logistics system, and broadening the channels of grain imports.

While the quality of parts produced by additive manufacturing is generally evaluated by surface roughness, relative density, and mechanical properties, the issue of dimensional accuracy is not examined sufficiently. However, dimensional accuracy is very important for the final use and finishing of a product. Since the dimensional change mainly occurs due to shrinkage resulting from the heat energy applied during the sintering process, the effect of production parameters in the additive manufacturing method is quite large. To minimize shrinkage and increase dimensional accuracy, manufacturing parameters need to be optimized and meticulously examined. This study was aimed at determining the effects of manufacturing parameters on geometric tolerances in the production of parts using the additive manufacturing method. AlSi10Mg powder alloy and selective laser melting (SLM) technology were used in the additive manufacturing of this alloy in part production. Twelve different laser powers and scanning speeds, as well as fixed scanning range and layer thickness parameters, were used in production. In determining geometric tolerances, features such as hole diameter change, deviation from angularity, deviation from perpendicularity, deviation from flatness, and deviation from parallelism were taken into consideration. As a result of the study, deviation values increased in high and low laser power/scanning speed combinations. Minimum deviation amounts were obtained in the range of 250–310 laser power and 785–974 scanning speed, which are the middle values of the parameters used. The optimum values of different output responses have been obtained with different production parameters, but for the final use and quality control approval of the product, it is necessary to determine the input parameters at which all output responses are optimal. In this process, the gray relational analysis optimization method, which is one of the multi-criteria decision-making methods, was preferred. As a result of the optimization, the optimum manufacturing parameters for geometric tolerances were determined as the 290/911 laser power/scanning speed combination. [ABSTRACT FROM AUTHOR]

Welding techniques are widely utilized for permanent material joining. The tungsten inert gas (TIG) welding process is notable for its exceptional precision, controlled heat input, and affordable equipment. It is widely employed for joining different grades of steel. Currently, extensive research is being conducted to enhance the quality of welded joints. This involves exploring various welding methods and adjusting welding parameters to improve characteristics such as strength, ductility, formability, appearance, and corrosion resistance. The current investigation's main aim consists of studying the effect of the welding parameters, i.e., the welding current and the gas flow rate, on the mechanical properties of welded joints of low-carbon steel with a thickness of 1 mm. The yield strength, tensile strength, and strain at break are selected as responses. The analysis of variance (ANOVA) is utilized to check the impact of welding parameters on the responses, while the Gray Relational Analysis (GRA) is used to optimize the welding parameters to maximize the chosen responses. Results show that welding current possesses the most influence on the three responses, i.e., the yield strength, tensile strength, and strain at break. The combination of 65 A welding current and 15 l/min gas flow rate allows the maximization of the three responses. [ABSTRACT FROM AUTHOR]

Fused Filament Fabrication (FDM) has emerged as a prominent and innovative manufacturing technique, facilitating the creation of intricate, lightweight components at reduced time and cost. Despite its advantages, the productivity and mechanical performance of 3D-printed parts remain challenging areas. Hence, there is a growing interest in investigating the interplay between FDM build time and mechanical properties. Structural parameters such as layer thickness (Lt), raster angle (Ia), and air gap distance (Ifd) can exhibit conflicting effects on productivity and mechanical properties. This study employs GRA and VIKOR techniques for the multi-criteria selection of 3D printing, aiming to optimize both printing time and specific flexural properties of PLA/copper composite beams constructed in the edge direction. We compare the parameters selected by both methods and evaluate their impact on the responses. The behaviors of the two models differ: GRA optimizes two responses the specific stiffness ans specific strength, while VIKOR is more effective for minimising printing time. Both models select the lower raster angle of 0° as optimuim. Lt significantly influences printing time, being the primary parameter in the VIKOR method, whereas Ifd primarily contributes to mechanical properties. [ABSTRACT FROM AUTHOR]

This paper investigates the effects of welding voltage, welding current, wire feeding speed, travelling speed and welding position on aspect ratio, dilution rate, microhardness and fluctuation using orthogonal experiment design. To achieve multi-objective optimization, S/N ratio conversion and grey relational analysis were employed, The model-predicted process parameters were then verified. The study findings indicate that the welding position has the greatest impact on aspect ratio, dilution rate and fluctuation, while the welding current has the greatest impact on the microhardness. Additionally, the force of the droplets at different positions affects the average aspect ratio and fluctuation, the arc shape affects the dilution rate, and the heat input affects the microhardness. Furthermore, the arc shape at different positions exhibits distinct characteristics. Finally, the optimal process parameters for different positions were obtained through an orthogonal test. The optimal combination of process parameters was found to be 70 A for welding current, 25 V for welding voltage, 500 cm.min−1 for wire feeding speed, 50 cm.min−1 for travelling speed, and vertical up welding position. The experimental results indicate that the optimized process parameters can consistently improve the welding quality, with a relative error of only 2.38% of only 2.38% between the grey relational degree and predicted value. This study's findings hold significant practical value for enhancing welding process stability and quality. [ABSTRACT FROM AUTHOR]

Abstract Accurate on‐board state‐of‐health (SOH) prediction is crucial for lithium‐ion battery applications. This study presents an in situ prediction technique for minorly deformed battery SOH, utilizing a Gaussian process regression (GPR) model tuned by a Bayesian optimization algorithm. Unlike previous methods that interpret voltage–time data as incremental capacitance curves, our approach directly operates on raw voltage–time data. We apply gray relational analysis to select feature variables as inputs and train the Bayesian Gaussian process regression (BGPR) model using experimental data from batteries under different working conditions. To demonstrate the performance of the BGPR model, we compare it with stepwise linear regression, neural network, and Bayesian support vector machine (BSVM) models. The performance of these four models is evaluated using different performance indicators: mean absolute percentage error (MAPE), root‐mean‐squared percentage error (RMSPE), and coefficient of determination (R²). The results demonstrate that the BGPR model exhibits superior prediction performance with the lowest MAPE (0.11%), RMSPE (0.12%), and the highest R² (0.9915) for minorly deformed batteries. Furthermore, the BGPR model exhibits excellent robustness for SOH prediction of normal batteries under different conditions. This study provides an effective and robust method for accurate on‐board SOH prediction in lithium‐ion battery applications.

With the rapid development of electronic information technology, higher requirements have been put forward for the dielectric properties and load-bearing capacity of materials. In continuous glass fiber-reinforced thermoplastic composites, polypropylene matrix is a non-polar polymer with a very low dielectric constant and dielectric loss, but polypropylene is extremely flammable which greatly limits its application. Aiming at the better application of flame retardant-modified continuous glass fiber-reinforced polypropylene composites (FR/GF/PP) in the field of electronic communication, the effects of four different kinds of flame retardants (Decabromodiphenyl ethane (DBDPE), halogen-free one-component flame retardant (MONO), halogen-free compound flame retardant (MULTI), and intumescent flame retardant (IFR)) on the properties of FR/GF/PP were compared, including the mechanical properties, dielectric properties and flame-retardant properties. The results showed that among the FR/GF/PP, IFR has the highest performance in mechanical properties, MULTI has better performance in LOI, DBDPE and IFR have better performance in flame retardant rating, and DBDPE and IFR have lower dielectric properties. Finally, gray relational analysis is applied to propose an approach for selecting the optimal combination (flame retardant type and flame-retardant content) of comprehensive performance. In the application exemplified in this paper, the performance of IFR-3-modified GF/PP is optimized. [ABSTRACT FROM AUTHOR]

The present study aimed to improve and assess the electrical discharge coating (EDC) factors with aluminium 7075 alloy as a substrate. The results of input variables like current, composition, and compaction load on EDC responses, including material deposition rate (MDR), tool wear rate (TWR), and surface roughness (Ra) were studied. Taguchi's L9 experiment design was employed with GRA (grey relational analysis), ARAS (Additive Ratio Assessment), and preference value (Ki) was measured. The experiments determined the best parametric setting for more MDR, less TWR, and less Ra. The level of contribution of the process parameters to the response was evaluated by a statistical test known as ANOVA, wherein current appeared as the most noteworthy parameter, followed by composition and compaction load. The results of GRA and ARAS were compared with a prominent MCDM method known as TOPSIS, which substantiated the outcomes of both methods. Successfully attained rates for material deposition, tool wear, and average surface roughness were 0.385 mg/min for material, 4.37 mg/min for tool wear, and 2.101 µm for surface roughness. Finally, a sensitivity study is used to validate the hybrid model's constancy with a strong correlation. The topography of the deposited surface was analyzed by SEM, wherein base material, deposition, and interface were distinctly visible with the occurrence of a few micro-cracks. The XRD contour of the coating exhibited peaks of Al, Al2Cu, Cu, and SiC. Additionally, the pin-on-disc wear result yielded a substantial drop in the wear of the coated samples as compared to the substrate material. [ABSTRACT FROM AUTHOR]

Fragile ecosystems that are affected by erosion from high pollution environmental loads pose a serious threat to human health and well-being. The evaluation of regional environmental loads has become a major issue for eco-environmental conservation and management. As a key region in central China, Hubei Province relies on ecosystems and the environment, which offer an important foundation for sustainable development and continuous improvements in social productivity. For this study, seven influencing factors were selected, and a correlation degree model was applied to assess Hubei Province's environmental loads during the period from 1995-2019. The results show that the overall environmental loads exhibit a fluctuating decreasing trend in response to economic growth and development actions. Moreover, as eco-environmental pollution problems have been addressed and improved over time, the ecosystem operating status has been gradually optimized. Finally, the paper concludes with a proposal of specific measures designed to mitigate Hubei Province's ecological loading from the perspectives of industrial structure, public awareness and technological innovation. [ABSTRACT FROM AUTHOR]

AbstractStructural health monitoring (SHM) system is an important way to evaluate the tunnel structural performance. In practice, the missing data is inevitably induced in the SHM dataset, which may cause deviation or even misleading results of the data analysis, and hence, need to be accurately reconstructed. This study proposes a new KNN data reconstruction method based on a gray correlation measure (GRA-KNN). Compared to the traditional KNN, the GRA-KNN can measure the structural similarity of data better, and the pre-filling can make full use of the information of known data to estimate missing data. By comparing with the other five machine learning methods (i.e., Ridge Regression, Support Vector Regression, Multilayer Perceptron, RandomForest, and XGBoost), the imputation performance of this method is examined using two real-time SHM datasets from Nanjing Yangtze River tunnel and Hong Kong-Zhuhai-Macao Bridge Undersea Tunnel. Results show that the GRA-KNN method possesses a better performance and stronger robustness under a wide range of missing ratios from 10% to 90%, showing great potential in SHM data reconstruction. In particular, when the missing ratio is larger than 60%, the imputation error of the proposed method is the smallest compared to that of the other machine learning methods. [ABSTRACT FROM AUTHOR]

Accurate on‐board state‐of‐health (SOH) prediction is crucial for lithium‐ion battery applications. This study presents an in situ prediction technique for minorly deformed battery SOH, utilizing a Gaussian process regression (GPR) model tuned by a Bayesian optimization algorithm. Unlike previous methods that interpret voltage–time data as incremental capacitance curves, our approach directly operates on raw voltage–time data. We apply gray relational analysis to select feature variables as inputs and train the Bayesian Gaussian process regression (BGPR) model using experimental data from batteries under different working conditions. To demonstrate the performance of the BGPR model, we compare it with stepwise linear regression, neural network, and Bayesian support vector machine (BSVM) models. The performance of these four models is evaluated using different performance indicators: mean absolute percentage error (MAPE), root‐mean‐squared percentage error (RMSPE), and coefficient of determination (R²). The results demonstrate that the BGPR model exhibits superior prediction performance with the lowest MAPE (0.11%), RMSPE (0.12%), and the highest R² (0.9915) for minorly deformed batteries. Furthermore, the BGPR model exhibits excellent robustness for SOH prediction of normal batteries under different conditions. This study provides an effective and robust method for accurate on‐board SOH prediction in lithium‐ion battery applications. [ABSTRACT FROM AUTHOR]

This study utilizes the mold flow analysis technique of the injection compression molding (ICM) process, combined with the Taguchi--gray relational analysis (GRA), for process optimization analysis on the roundness of the lens holes and the flatness of the lens mount in a 4 x 4 planar multilens array mount. After manufacturing simulation analysis, the eccentricity and tilt information of the lens mount was further evaluated optically through spot diagram analysis upon inserting the same glass lenses. The results showed a positive correlation trend between roundness and flatness in structural deformation analysis, indicating that improving the overall flatness of the lens mount can enhance the roundness of the lens holes. In optical analysis, better improvements in the lens tilt angle were achieved through GRA. In conclusion, aiming to simultaneously improve the roundness of the lens holes and the overall flatness of the lens mount, the Taguchi--GRA method can achieve the optimization objectives. In terms of optical performance, by optimizing for roundness, it is possible not only to reduce the diameter of the light spot but also to simultaneously reduce the offset displacement of the light spot center on the screen. The method proposed in this paper can serve as an analytical model for the design and fabrication of plastic multilens mount. [ABSTRACT FROM AUTHOR]

In order to study the root–soil composite system shear characteristics under the action of freeze–thaw cycles in the permafrost regions along the Qinghai–Tibet Highway (QTH) from the Beiluhe–Tuotuohe (B-T) section, the slopes in the permafrost regions along the QTH from the B-T section were selected as the object of the study. The direct shear test of root–soil composite systems under different amounts of freeze–thaw (F-T) cycles and gray correlations were used to analyze the correlation between the number of F-T cycles, water content, root content, and the soil shear strength index. The results show that the cohesion of the soil in the area after F-T cycles exhibits a significant stepwise decrease with an increase in F-T cycles, which can be divided into three stages: the instantaneous stage (a decrease of 46.73–56.42%), the gradual stage (a decrease of 14.80–25.55%), and the stabilization stage (a decrease of 0.61–2.99%). The internal friction angle did not exhibit a regular change. The root–soil composite system showed significant enhancement of soil cohesion compared with soil without roots, with a root content of 0.03 g/cm3 having the most significant effect on soil cohesion (increasing amplitude 65.20–16.82%). With an increase in the number of the F-T cycles, while the water content is greater than 15.0%, the greater the water content of the soil, the smaller its cohesion becomes. Through gray correlation analysis, it was found that the correlation between the number of F-T cycles, water content, root content, and soil cohesion after F-T cycles were 0.63, 0.72, and 0.66, respectively, indicating that water content had the most significant impact on soil cohesion after F-T cycles. The results of this study provide theoretical support for further understanding the variation law of the shear strength of root–soil composite systems in permafrost regions under F-T cycles and the influencing factors of plant roots to enhance soil shear strength under F-T cycles, as well as for the scientific and effective prevention and control of retrogressive thaw slump in the study area, the QTH stretches across the region. [ABSTRACT FROM AUTHOR]

Aiming to address the complexity and challenges of predicting pure electric vehicle (EV) sales, this paper integrates a time series model, support vector machine and combined model to forecast EV sales in China. Firstly, a seasonal autoregressive integrated moving average (SARIMA) model was constructed using historical EV sales data, and the model was trained on sales statistics to obtain forecasting results. Secondly, variables that were highly correlated with sales were analyzed using gray relational analysis (GRA) and utilized as input parameters for the support vector regression (SVR) model, which was constructed to optimize sales predictions for EVs. Finally, a combined model incorporating different algorithms was verified against market sales data to explore the optimal sales prediction approach. The results indicate that the SARIMA-GRA-SVR model with the squared prediction error and inverse method achieved the best predictive performance, with MAPE, MAE and RMSE values of 12%, 1.45 and 2.08, respectively. This empirical study validates the effectiveness and superiority of the SARIMA-GRA-SVR model in forecasting EV sales.

People wear clothes for warmth, survival and necessity in modern life, but in the modern era, eco-friendliness, shortened production times, design and intelligence also matter. To determine the relationship between data series and verify the proximity of each data series, a gray relational analysis, or GRA, is applied to textiles, where seamless bonding technology enhances the bond between components. In this study, a polyurethane prepolymer, 2-hydroxyethyl acrylate (2-HEA) as an end-capping agent and n-octyl acrylate (ODA) as a photoinitiator were used to synthesize a dual-curing polyurethane hot-melt adhesive. Taguchi quality engineering and a gray relational analysis were used to discuss the influence of different mole ratios of NCO:OH and the effect of the molar ratio of the addition of octyl decyl acrylate on the mechanical strength. The Fourier transform infrared spectroscopy (FTIR) results showed the termination of the prepolymer's polymerization reaction and the C=O peak intensity at 1730 cm−1, indicating efficient bonding to the main chain. Advanced Polymer Chromatography (APC) was used to investigate the high-molecular-weight (20,000–30,000) polyurethane polymer bonded with octyl decyl acrylate to achieve a photothermosetting effect. The thermogravimetric analysis (TGA) results showed that the thermal decomposition temperature of the polyurethane hot-melt adhesive also increased, and they showed the highest pyrolysis temperature (349.89 °C) for the polyhydric alcohols. Furthermore, high peel strength (1.68 kg/cm) and shear strength (34.94 kg/cm2) values were detected with the dual-cure photothermosetting polyurethane hot-melt adhesive. The signal-to-noise ratio was also used to generate the gray relational degree. It was observed that the best parameter ratio of NCO:OH was 4:1 with five moles of monomer. The Taguchi quality engineering method was used to find the parameters of single-quality optimization, and then the gray relation calculation was used to obtain the parameter combination of multi-quality optimization for thermosetting the polyurethane hot-melt adhesive. The study aims to meet the requirements of seamless bonding in textile factories and optimize experimental parameter design by setting target values that can effectively increase production speed and reduce processing time and costs as well. [ABSTRACT FROM AUTHOR]

There are many oily sludge treatment technologies with great development prospects, and due to the lack of an objective and scientific technology evaluation system, there have been different opinions and views on the selection of technologies. To address this issue, this paper establishes a multicriteria decision system based on hierarchical analysis and gray decisionmaking, discusses the weights of oily sludge treatment technologies in terms of economic benefits, technology, environment and social indicators, and categorizes the relevant impact indicators into target level, criterion level and program level, etc. So as to analyze them qualitatively and quantitatively. The results show that in the hierarchical analysis decision system, the order of influence of the weight of each indicator on the research objectives is as follows: technical and environmental impact indicators occupy an absolute advantage for the treatment and disposal of oily sludge, with a weight of 0.5886 for environmental indicators and 0.2074 for technical indicators, whose main impact indicators include the resourceization of oily sludge with a weight of 0.6104 and the pollution degree of soil with a weight of 0.5115. Because X2 shows a high correlation in the evaluation model, the preferred oily sludge and blast furnace slag catalytic pyrolysis treatment, a comprehensive evaluation of oily sludge treatment technologies will help researchers to have a good understanding of the recent developments and future research directions. [ABSTRACT FROM AUTHOR]

It is important to predict the sales of an iron and steel company and to identify the variables that influence these sales for future planning. The aim in this study was to identify and model the key factors that influence the sales volume of an iron and steel company using artificial neural networks (ANNs). We attempted to obtain an integrated result from the performance/sales levels of 5 models, to use the ANN approach with hybrid algorithms, and also to present an exemplary application in the base metals industry, where there is a limited number of studies. This study contributes to the literature as the first application of artificial intelligence methods in the iron and steel industry. The ANN models incorporated 6 macroeconomic variables and price-to-sales data and their results were evaluated. An ordinary least squares regression model was also used to facilitate the comparison of results, while gray relational analysis (GRA) was used to draw a comprehensive conclusion based on the ANN results. The results showed that the variables USD/TL exchange rate, product prices, and interest rates, in descending order, had the highest degree of influence in determining the sales of the iron and steel company. Furthermore, these variables are crucial for forecasting future sales and strategic planning. The study showed that the ANN outperformed classical regression models in terms of prediction accuracy. In the model applications conducted for 5 different product groups, it was observed that 3 models (models 2, 3, and 4), including model 4, which sold a higher volume of products than the total of the other products, had an overall performance above 80%. In addition, GRA was found to be a valuable tool for synthesizing insights from different ANN models based on their respective performance levels. [ABSTRACT FROM AUTHOR]

The harmonics of photovoltaic power plants are affected by various factors including temperature, weather, and light amplitude. Traditional power harmonic prediction methods have weak non-linear mapping and poor generalization capability to unknown time series data. In this paper, a Kernel Extreme Learning Machine (KELM) model power harmonic prediction method based on Gray Relational Analysis (GRA) with Variational Mode Decomposition (VMD) coupled with Harris Hawk Optimization (HHO) is proposed. First, the GRA method is used to construct the similar day set in one screening, followed by further using K-means clustering to construct the final similar day set. Then, the VMD method is adopted to decompose the harmonic data of the similar day set, and each decomposition subsequence is input to the HHO-optimized KELM neural network for prediction, respectively. Finally, the prediction results of each subseries are superimposed and numerical evaluation indexes are introduced, and the proposed method is validated by applying the above method in simulation. The results show that the error of the prediction model is reduced by at least 39% compared with the conventional prediction method, so it can satisfy the function of harmonic content prediction of a photovoltaic power plant. [ABSTRACT FROM AUTHOR]

GDP is a common and essential indicator for evaluating a country’s overall economy. However, environmental issues may be overlooked in the pursuit of GDP growth for some countries. It may be beneficial to adopt more sustainable criteria for assessing economic health. In this study, green GDP (GGDP) is discussed using mathematical approaches. Multiple dataset indicators were selected for the evaluation of GGDP and its impact on climate mitigation. The k-means clustering algorithm was utilized to classify 16 countries into three distinct categories for specific analysis. The potential impact of transitioning to GGDP was investigated through changes in a quantitative parameter, the climate impact factor. Ridge regression was applied to predict the impact of switching to GGDP for the three country categories. The consequences of transitioning to GGDP on the quantified improvement of climate indicators were graphically demonstrated over time on a global scale. The entropy weight method (EWM) and TOPSIS were used to obtain the value. Countries in category 2, as divided by k-means clustering, were predicted to show a greater improvement in scores as one of the world’s largest carbon emitters, China, which belongs to category 2 countries, plays a significant role in global climate governance. A specific analysis of China was performed after obtaining the EWM-TOPSIS results. Gray relational analysis and Pearson correlation were carried out to analyze the relationships between specific indicators, followed by a prediction of CO2 emissions based on the analyzed critical indicators.

In recent years, there has been an increase in the quantity of marsh ecological asset (MEA) on the Qinghai-Tibetan Plateau due to climatic warming and humidification. However, the alterations in the quality of the MEA in this region remain unclear. Here, we constructed a marsh quality index (MQI) based on the structural (including marsh fractal dimension, FD; and river network density, RN) and functional (including river runoff, Rr; and Normalized Difference Vegetation Index, NDVI) indices of marshes to assess the change in the quality of MEA between 1980 and 2020 in the pastoral areas of Zoige County. We also examined changes in MEA quantity. And the relationships between the MEA and environmental variables were tested using grey relational analysis. We found that the quantity of MEA experienced an increase of 564.41 km2 from 1980 to 2020 in the pastoral areas of Zoige County. This rise was mainly due to the conversion of grassland into marsh. The MQI results demonstrated that there was a steady improvement in the quality of MEA, which was mainly attributed to the increase in RN, Rr, and NDVI. While the quantity and quality of MEA showed a strong association with climatic factors, their correlation with livestock numbers was found to be weak. These findings suggest that changes in MEA are strongly controlled by climate change and thus nature-based approaches should be highlighted for the conservation and management of MEA in plateau regions. [ABSTRACT FROM AUTHOR]

Desiccation cracking is a common phenomenon for red clay due to alternating wet and dry weathers. The presence of cracks definitely affects the strength of the soil; however, characterizing the development of desiccation cracks inside the soil has been a challenge. The purpose of this study was to propose a method to quantify the volumetric crack rate (VCR) of red clay and investigate the influence of VCR on the unconfined compressive strength (UCS) of red clay. First, the relationship between wave speed and VCR was quantified by a prefabricated crack method to obtain the equivalent VCR (EVCR). Then, the EVCR was compared with the surface crack rate (SCR) to determine the reliability of the method. Finally, unconfined compression tests were carried out to explore the relationship between EVCR and UCS. The results show that the relationship between the prefabricated VCR and wave velocity can be described by a logarithmic equation. The calculated VCR by this equation is linearly correlated to the SCR of red clay, indicating that the logarithmic equation is effective to quantify the VCR of red clay caused by wet and dry cycles. The UCS and residual strength of red clay containing cracks reduce following an exponential function with increasing EVCR. Gray relational analysis showed that the correlation between UCS and EVCR is better than that between UCS and SCR, suggesting that it is more reliable to express the crack rate by EVCR when studying the UCS of red clay. [ABSTRACT FROM AUTHOR]

This experimental research investigates the influence of geopolymerization factors such as Na2O dosages, water and mineral admixture [bentonite (BT), burnt lime (BL), and ground granulated blast furnace slag (GGBS)] on physiomechanical properties of the pelletized fly ash (FA)–based aggregates. Taguchi's L9 orthogonal array was adopted to design the mixing ratios for three kinds of fly ash–based aggregates (in the combinations of FA-BT, FA-BL, and FA-GGBS). The degree of geopolymerization of the produced aggregates was characterized using thermogravimetric analysis (TGA), Fourier transform infrared spectroscopy (FTIR), and a scanning electron microscope (SEM). Most influential response indices in the production of pelletized aggregates were identified using gray relational analysis. The physiomechanical characteristics of the fly-ash aggregates were significantly improved by admixing BL than that of GGBS and BT. However, pelletization efficiency was seen to be superior for GGBS-substituted fly-ash aggregates. The quantified amount of hydration products, i.e., sodium alumino-silicate hydrate (N-A-S-H)/calcium alumino-silicate hydrate (C-A-S-H) for fly ash–based aggregates intensified on increasing Na2O and mineral admixture dosages. The results strongly suggest the existence of a linear relationship between the quantified amount of N-A-S-H/C-A-S-H and individual pellet strength of produced aggregate. The FTIR spectrum showed strong and broadened bands of Si-O terminal for all types of aggregates, representing the conversion of unreacted minerals to chains of aluminosilicate gel (geopolymerized hydration product). Further, it can also be inferred from gray relational analysis that among all other factors, Na2O content significantly impacted the engineering properties of produced fly ash–based aggregates. [ABSTRACT FROM AUTHOR]

Plastic lens arrays (PLAs) have the advantages of a light weight and a compact size. However, their optical performance is often degraded by warpage, caused by the accumulation of residual stress during the injection molding process. Accordingly, the present study employs mold flow analysis simulations and a Taguchi‐Gray relational analysis (GRA) framework to optimize the processing parameters of the injection compression molding (ICM) process. The analyses focus specifically on the effects of the mold temperature, melt temperature, injection velocity, packing pressure, packing time, and compression gap on the optical path difference (OPD) and lens center displacement (LCD) of the fabricated lens array. The results confirm that the optimized processing parameters significantly improve the OPD, LCD, birefringence, and imaging properties of the PLA compared to those of a PLA fabricated using standard molding conditions. [ABSTRACT FROM AUTHOR]

Aerogel fibers utilized in thermal protective apparel exhibit exceptional heat insulation capabilities; however, concerns arise regarding potential degradation due to the detachment of aerogel particles during repeated washing. Accurate prediction of aerogel fiber thermal resistance is critical for assessing hydrothermal aging in aerogel textiles, yet the precision of such predictions is significantly hindered by limited sample data and numerous uncertainties constrained by testing time and expenses. The present study endeavors to ascertain the optimal parameters dictating aerogel fabric thermal resistance post-washing and establish a prediction model based on these variables using small-sample data. Four aerogel fabric candidates were selected and subjected to multiple washing cycles (0, 1, 5, 10, 15, and 20 cycles). Gray relational analysis (GRA) was initially employed to prioritize the primary thermal resistance parameters, thereby identifying the interrelations among various factors and circumventing the unreasonable equal treatment of samples in conventional gray predictions. Subsequently, a discrete gray linear regression (DGLR) algorithm was proposed and validated to estimate thermal resistance using input from four principal fabric parameters: the fabric weight, thickness, air permeability, and surface temperature distribution coefficient. The findings revealed that the GRA-DGLR model achieved relatively high accuracy, closely aligning with the experimental results. Following repeated washing, aerogel fabric thermal resistance diminished, with the air permeability, weight, thickness, and surface temperature distribution coefficient ranked in descending order of significance. This investigation highlights the considerable impact of repeated washing on aerogel fabric thermal resistance and the efficacy of the GRA-DGLR model in estimating this parameter. [ABSTRACT FROM AUTHOR]

In order to study the self-healing performance of macroscopic fractures of asphalt mixtures, semicircular bending (SCB) tests were used to test 90# base asphalt mixtures, SBS (Styrene–Butadiene–Styrene) modified asphalt mixtures, and SBS + CR (Chloroprene Rubber) composite modified asphalt mixtures. The F-H-F (the asphalt mixture specimen was fatigued for a certain number of times, then healed under certain conditions, and then fatigued until destroyed) test was carried out, and the fatigue life recovery rate of the fatigue test before and after healing was defined as the healing index (HI). The gray correlation analysis method was used to judge the influence degree of influencing factors on fatigue–healing according to the correlation index. The results show the type of asphalt has the most significant influence on the healing ability of the asphalt mixture. In the case of complete healing, the fatigue–healing performance of the SBS + CR composite modified asphalt mixture was the best, followed by the SBS-modified asphalt mixture, and 90# base asphalt. When the healing temperature is close to the softening point of asphalt, the healing performance of 90# base asphalt is better when the healing temperature is low. When the healing time is longer, the healing performance is better, and there is an optimal healing time. The healing index decreased with the increase in the degree of damage. When the degree of damage is too large, the asphalt mixture will be difficult to heal. [ABSTRACT FROM AUTHOR]

In recent years, the development and iteration of information technology have prompted the financial industry to transform and upgrade to financial technology (FinTech), which has received emerging attention from the global financial industry. While the FinTech industry is growing rapidly around the world, however, few studies have foucusd on the shortage of talent and difficulties in recruiting talent. First, this paper clarifies the shortage of FinTech talent through expert interviews and a questionnaire survey of 112 financial industry enterprises in Shanghai, China. Following, based on role theory, we construct a talent capability evaluation index system using 5 primary and 17 secondary indicators. Based on the exploration above, a gray optimization model is designed to support talent recruitment strategy for FinTech enterprises. The results indicate that Chinese FinTech talent should have composite abilities with outstanding professional technical skills and learning abilities, innovation and teamwork ability, project experience, and international vision. This study provides methodological guidelines for global FinTech talent evaluation and recruitment strategies and broadens the application of role theory and gray clustering theory. [ABSTRACT FROM AUTHOR]

【Objective】Natural rubber is an important industrial raw materials and strategic materials, and played a very important role in national economic development. As a resource constraint industry, it was mainly produced by Brazilian rubber tree (Hevea brasiliensis) and it has the highest yield and the best quality among the mankind known rubber-producing plants. Harvesting latex is the main economic purpose of Brazilian rubber tree, latex yield is a quantitative trait controlled by minorgene, with many influencing factors, and its phenotypic value is affected by heredity and environmental effect. To determine the relationship among the yield and agronomic traits of the wild germplasm resources of Hevea brasiliensis, this study provides a scientific basis for the evaluation and screening of rubber tree germplasm resources and the selection and breeding of new varieties.【Method】The yield, growth and other related agronomic traits of 13 wild germplasm resources of Hevea brasiliensis planted in the experimental field of Chinese Academy of Tropical Agricultural Sciences (CATAS) were studied and analyzed by grey relational analysis.【Result】The results showed that the association order of the eight main agronomic traits related to the dry rubber yield per plant were stem girth, dry rubber content, average number of laticifer cells, average number of laticifer columns, bark thickness, mean stem girth increment, lateral vein latex grade and gum value ratio, and the weights of each trait were 0.1380, 0.1338, 0.1322, 0.1264, 0.1205, 0.1182, 0.1178, 0.1143. In summary, stem girth, dry rubber content, quantity of laticifers bark thickness and other traits are closely related to and have a great impact on dry rubber yield per plant.【Conclusion】Among the main agronomic traits, the stem girth, dry rubber content and quantity of laticifer should be paid more attention on the evaluation of rubber tree germplasm resources, at the same time the potential impact of other traits on yield also need to be laid some stress on.

Adjusting the energy structure of various industries is crucial for achieving China’s carbon peak and carbon neutrality goals. Given the significant proportion of carbon emissions from the logistics industry in the tertiary sector, the research on predicting the carbon emissions of the logistics industry is of great significance for China to achieve its “Dual carbon” target. In this paper, the gray relational analysis (GRA) methodology is adopted to screen the influencing factors of carbon emissions in the logistics industry firstly. Then, the particle swarm optimization (PSO) algorithm was used to optimize the penalty coefficientand kernel function range parameter of the support vector regression (SVR) model (i.e. PSO- SVR model). The data from 2000 to 2021 regarding carbon emissions and related influencing factors in China’s logistics industry are analyzed, and the mean absolute percentage error (MAPE) of the PSO-SVR model is 0.82%, which shows that the proposed PSO-SVR model in this paper is effective. Finally, instructive suggestions are provided for China to achieve the “Dual Carbon” goal and upgrading of the logistics industry.

Electroless Ni-B (ENB) coatings have industrial importance due to their excellent mechanical properties. The inclusion of W (ENB-W) to the coatings further enhances their mechanical properties and thermal stability. Nevertheless, the ENB or ENB-W coatings are deposited from a heavy metal-stabilized bath, and the very commonly used stabilizer is lead nitrate. The present work is an attempt to obtain an ENB-W coating with enhanced mechanical properties and elimination of the stabilizer from the bath. To achieve this, the coating bath temperature, the heat treatment temperature and the heat treatment duration were varied systematically following a strategy adopted from Taguchi's experimental design. The mechanical properties targeted include surface microhardness and scratch hardness. Multi-objective optimization was performed using gray relational methodology. The predicted bath temperature was 85 °C, while the predicted heat treatment temperature was 450 °C and there was 3 h of heat treatment time. The optimized lead-free ENB-W coatings had a microhardness of 1096.2 HV100 and scratch hardness of 13.86 GPa. In fact, single-objective optimization for surface microhardness and scratch hardness by Taguchi's methodology also predicted the same optimal parametric condition for both scratch hardness and surface microhardness. This was comparable to that of a lead-stabilized ENB-W coating and higher than the as-plated stabilizer free ENB-W coatings. The coating failure of the optimized ENB-W alloy was tested using a progressive scratch test, which showed that there was no chevron or transverse cracks within the load range considered. Analysis of corrosion resistance revealed that the corrosion potential of the optimized coatings was −407 mV, and this was comparable to that of a lead-based ENB-W coating. [ABSTRACT FROM AUTHOR]

In this paper, Ti-6Al-4 V alloy coatings were fabricated by the laser cladding process on TC4 alloys using a coaxial powder feed method. By setting orthogonal test, surface quality evaluation basis, and grey relational analysis method, the effect of each parameter on the cladding process was investigated and the optimal parameters for laser cladding were gained. Furthermore, a comprehensive study was performed on the different powder feed rates on morphology, microstructure, micro-hardness and tribological properties of the coatings. The results show that the optimized process can produce better cladding coatings. The microstructure of the Ti-6Al-4 V coatings is mainly composed of columnar β-grains and martensites α′. It was also seen that the micro-hardness of the coatings prepared at a powder feed rate of 16 g min−1 was greater than TC4 substrate and other claddings because the amount of martensites α' increases initially and then decreases. As a result, the friction coefficient and wear loss of the coatings decrease initially and then increase with the increase in the powder feed rate, indicating that the wear resistance of the coatings generated at 16 g min−1 is best. [ABSTRACT FROM AUTHOR]

In this present study, the effect of processing parameters on cutting width (kerf), material removal rate (MRR), Ra (arithmetic mean deviation), Rq (root mean square deviation) and Rz (maximum height) values ​​as a result of wire electrical discharge machining (WEDM) of Ti-6Al-4V alloy was investigated. It is aimed to determine the optimum values of the cutting parameters to obtain the highest MRR value with the lowest kerf, Ra, Rq, Rz. Cutting experiments were carried out using three different voltages (46, 56, 66 V), three different dielectric fluid pressures (10, 12, 14 kg/cm2) and three different wire feed rates (8, 10, 12 m/min). The parameters used in the experiments were designed according to the Taguchi L9 (33) orthogonal array in order to reduce the experimental cost. Gray Relational Analysis (GRA), one of the multi-criteria decision-making methods, has been applied to optimize the machining parameters in the cutting process with the wire erosion machine. Analysis of variance (ANOVA) was used to determine the effect percentages of the processing parameters. By using the data obtained from the experiments, the prediction study of the experimental data was carried out with the Artificial Neural Networks (ANN) model. High correlation coefficients were obtained in the regression model created using the ANN technique, and it was observed that both models were suitable and usable to predict the answers. As a result of GRA, the most ideal sequence was determined as VG1LQ1WS1. Ideal conditions were determined as 46 V voltage, 10 kg/cm2 dielectric fluid pressure and 8 m/min wire feed rate. Using the optimum machining parameters, an improvement of 4.22%, 54.65%, 28.77%, 31.94% and 35.24% was obtained for kerf, MRR, Ra, Rq, and Rz, respectively. As for the results obtained from ANOVA the contribution rate of the voltage was 72.18%. However, the effect of wire feed rate and dielectric fluid pressure was not statistically significant. [ABSTRACT FROM AUTHOR]

This series of studies developed a nanographene modified dyeable polypropylene yarn with far-infrared properties making it suitable for winter clothing fabrics. In this paper, part II, the dyeable polypropylene granules developed in part I are used as a base material and, with the addition of graphene nanopowder, melt-mixed and melt-spun to produce 75 d/24 f fully drawn yarn. The physical properties of the yarn, namely, yarn count, tensile strength, elongation at break, far-infrared emissivity, and far-infrared temperature rise, are investigated, and the impact of the melt-spinning process parameters, namely, graphene nanopowder content, mold temperature, melt temperature, gear pump speed, hot roller speed and take-up speed on the quality of the yarn, is determined. The Taguchi method, combined with gray relational analysis, is used to design experiments through which an optimal set of melt-spinning process parameters maximizing the multi-characteristic quality of the yarn is obtained. This optimized nanographene modified dyeable polypropylene yarn has a tensile strength of 3.5 g/d, elongation at break of 41.0%, yarn count of 75.3 d, far-infrared emissivity of 82%, far-infrared temperature rise of 21.0°C, washing fastness grade of 3–4 and surface resistance of 3 × 108 Ω. [ABSTRACT FROM AUTHOR]

As an important branch of modern decision-making theory, multi-attribute decision-making (MADM) plays an important role in various fields. Classic MADM methods can provide a ranking of alternatives, and decision-makers need to evaluate the level subjectively based on the ranking results. Because of the limitation of knowledge, this is likely to lead to potential individual losses. Three-way decision (3WD) theory has good classification ability. Therefore, this paper proposes a dynamic hybrid multi-attribute 3WD (MA3WD) model. First, a new scheme for constructing loss functions is proposed from the perspective of gray relational analysis (GRA), which is an accurate and objective way to describe the relationship between loss functions and attribute values. Then, conditional probabilities are determined by employing the gray relational analysis technique for order preference by similarity to the ideal solution (GRA-TOPSIS). With these discussions, a GRA-based hybrid MA3WD model for a single period is proposed by considering multi-source information. Furthermore, by extending the single-period scenario to a multi-period one, we construct a dynamic hybrid MA3WD model, which can obtain the final three-way decision rules as well as the results of each period and each attribute. Finally, the proposed method is applied to the case of performance evaluation of elderly-care services to demonstrate the effectiveness of the method, and comparative analyses are given to verify the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Composting is an environment-friendly and sustainable way to transform grape branches (GBs) into a useful product. Different parameters can differently affect fertilizer quality. Here, the compost product nutrient content was evaluated using an L9 orthogonal array (parameters, nitrogen source: chicken manure, sheep manure, urea; stirring temperature: 50, 60, 70 °C; initial pH: 6, 7, 8; conditioning agent: calcium superphosphate, zeolite, and copper sulfate). Among the treatments, the T3 (chicken manure, 70 °C, pH = 8, copper sulfate), T2 (chicken manure, 60 °C, pH = 7, zeolite), and T9 (urea, 70 °C, pH = 7, calcium superphosphate) had high gray relational grades (0.7424, 0.7132, 0.7110, respectively). The nitrogen source type (R = 0.1140) had the greatest influence on the nutrient content of the final product, followed by the stirring temperature (R = 0.1104), the conditioning agent (R = 0.0522), and the initial pH (R = 0.0408). Finally, the best nitrogen source of the grape branch compost was chicken manure, the best stirring temperature was 70 °C, the best initial pH was 7, and the best conditioning agent was zeolite. An experimental verification showed that the weighted correlation degree of the optimal treatment predicted by the orthogonal experiment increased by 3.63%. [ABSTRACT FROM AUTHOR]

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The Grunow–Finke epidemiological assessment tool (GFT) has several limitations in its ability to differentiate between natural and man‐made epidemics. Our study aimed to improve the GFT and analyze historical epidemics to validate the model. Using a gray relational analysis (GRA), we improved the GFT by revising the existing standards and adding five new standards. We then removed the artificial weights and final decision threshold. Finally, by using typically unnatural epidemic events as references, we used the GRA to calculate the unnatural probability and obtain assessment results. Using the advanced tool, we conducted retrospective and case analyses to test its performance. In the validation set of 13 historical epidemics, unnatural and natural epidemics were divided into two categories near the unnatural probability of 45%, showing evident differences (p < 0.01) and an assessment accuracy close to 100%. The unnatural probabilities of the Ebola virus disease of 2013 and Middle East Respiratory Syndrome of 2012 were 30.6% and 36.1%, respectively. Our advanced epidemic assessment tool improved the accuracy of the original GFT from approximately 55% to approximately 100% and reduced the impact of human factors on these outcomes effectively. [ABSTRACT FROM AUTHOR]

2D woven auxetic fabrics (AF) developed by weave design modification provide a continuous, commercial, and low-cost manufacturing process for high strength and stable auxetic structures. However, AFs developed by previous methods have a highly folded surface, which is problematic in printing, laminating, and stitching. For the first time, we studied the auxeticity in a few yarn-based auxetic structures, which provides a uniform surface texture. Reentrant honey-comb geometry was chosen to fabricate auxetic woven fabrics, as such geometry shows the best auxetic nature. Different parameters of auxetic geometry were optimized based on their mechanical response using the statistical tool: gray relational analysis (GRA). The mechanical properties of AFs were compared with conventional (3/1 twill woven) non-auxetic fabric (NAF), having the same specifications. The smaller unit cell (3E) with a reorientation area of 4P showed optimum auxeticity and mechanical properties. The optimized auxetic structure showed superior mechanical properties as compared to conventional fabric. GRA grade of optimized AF was 67.3% higher than the NAF. Most importantly, the tensile strength was 23% higher for optimized AF structure. The bidimensional energy dissipation ability of the AF provides a key advantage in the superior mechanical response.