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Multi-criteria decision-making (MCDM) methods have gained increased attention in sustainable engineering, where complex decision-making problems require consideration of multiple criteria and stakeholder perspectives. This review paper provides a comprehensive overview of the different MCDM methods, their applications in sustainable engineering, and their strengths and weaknesses. The paper discusses the concept of sustainable engineering, its principles, and the different areas where MCDM methods have been applied, including energy, manufacturing, transportation, and environmental engineering. Case studies of real-world applications are presented and analyzed, highlighting the main findings and implications for engineering practice. Finally, the challenges and limitations of MCDM methods in sustainable engineering are discussed, and future research directions are proposed. This review contributes to the understanding of the role of MCDM methods in sustainable engineering and provides guidance for researchers and practitioners. [ABSTRACT FROM AUTHOR]

This paper describes the conceptual design phase in the product development of a natural fibre composites cyclist helmet liner, beginning with idea generation and ending with selecting the best design concept. The integrated Theory of Inventive Problem Solving (TRIZ), Biomimetic methods, and the Grey Relational Analysis (GRA) method are demonstrated in this paper. This work aims to produce nature-inspired design concepts and determine the best design concept for the composite cyclist helmet liner. Following that, four design concepts were generated using the TRIZ-Biomimetic method, and the variance of concepts was developed using a morphological chart. The GRA method was chosen as the multiple criteria decision-making tool to compare their cost and weight criteria. The design concept C1 was selected as the best design concept for the natural fibre composites of cyclist helmet liner conceptual design when the highest grey relational grade (GRG) value and rank with a value of 1.0000 satisfied the GRA method conditions. This paper demonstrates how the integrated method of TRIZ-Biomimetics-Morphological Chart and GRA helps researchers and engineers develop designs inspired by nature and select the best design concept during the conceptual design stage using a systematic strategy and justified solutions. [ABSTRACT FROM AUTHOR]

This research paper presents an advanced exploration into the utilization of ultrasound-assisted extraction (UAE) combined with natural deep eutectic solvents (NADESs) to achieve higher concentrations of polyphenols from Graševina grape pomace. Focusing on optimizing extraction parameters to maximize the yield of polyphenols, this study evaluates their potential industrial applications, particularly within the food and cosmetics sectors. The effectiveness of betaine/glucose (BGlc) NADESs in producing stable, ready-to-use extracts with enhanced bioavailability and eco-friendly attributes is underscored. The integration of UAE with BGlc NADESs has shown significant scalability and applicability for industrial use, as evidenced by the extracts' collagenase-inhibitory effects, determined using a ninhydrin-based colorimetric assay showing the significant inhibition of gelatine degradation and scratch tests on cultured skin cells, demonstrating enhanced cell migration and wound healing, indicating their potential in anti-aging cosmetic products. Additionally, the results from PAMPA tests demonstrated that NADES extraction significantly enhances the intestinal absorption of polyphenols from grape pomace extracts compared to conventional solvents, highlighting the potential of NADESs to improve the bioavailability of these compounds and offering promising implications for their application in the food industry. Furthermore, the research highlights the practicality of directly incorporating these extracts into products, such as anti-aging creams and functional foods, supporting sustainability initiatives within the cosmetic and food industries. This work aims to provide a comprehensive guide to green extraction techniques on a preparative scale, showcasing the versatility and innovative applications of NADES-extracted compounds across various industries, thereby paving the way for the development of eco-conscious and effective products. [ABSTRACT FROM AUTHOR]

The selection of suppliers alone is one of the most important competitive challenges facing organizations today. With today's increasing awareness and facing many significant environmental pressures, the process of selecting the right suppliers for green supply chain management is even more difficult for decision-makers. The process of developing and implementing decision-making tools seek to face these challenges is rapidly evolving, especially in a fuzzy environment whose data are incomplete. The purpose of this article is to investigate prospective green chain suppliers for a laurel soap producing company based on a framework using MCDA fuzzy technologies such as Fuzzy GRA, Fuzzy TOPSIS, and Fuzzy VIKOR. During this paper, a discussion of fuzzy set was discussed with Fuzzy MCDM. Within a realistic case study, where a comparison was made between the tools, and it was noted that the results were consistent between the two tools Fuzzy TOPSIS and Fuzzy GRA, and the arrival of similar results and a slight difference with results was observed in Fuzzy VIKOR interpreted. The most detailed analyses were conducted on distance measurements, linguistic variables, assembly functions, and confusion processing. This study introduces the Fuzzy MCDM method to help researchers choose a more effective approach for green supplier selection and the conclusions and other study directions are offered toward the conclusion of the paper. [ABSTRACT FROM AUTHOR]

Role assignment is an important and complex task in collaboration and management. Group role assignment (GRA) pursues the optimal team performance by assigning pertinent roles to agents in a team from the team’s viewpoint. There are many factors that should be considered when conducting role assignment. Budgets are such a factor. This paper presents a challenging role assignment problem in collaboration, called GRA with budget constraints (GRABC). This paper contributes a thorough investigation of the GRABC problem including: 1) the first formalization of the proposed problem; 2) a set of practical solutions to different forms of this problem by using the IBM ILOG CPLEX optimization package (CPLEX); 3) a set of necessary conditions for these problems to possess feasible solutions to improve the CPLEX solutions; and 4) a synthesized process to deal with GRABC problems. Contribution 2) actually clarifies a list of requirements in solving the GRABC problem. Simulations, experiments and a case study are used to verify the practicability and efficiency of the proposed solutions. [ABSTRACT FROM AUTHOR]

When the government decides to use the public-private partnership (PPP) in projects, such as infrastructure construction, selecting a private-sector partner that meets the project's needs is one of the key factors for successful implementation, but this is ignored in existing research. On this basis, this paper proposes a GRA-VIKOR method based on the combined weight of the improved CRITIC-entropy weight method (EWM). The traditional CRITIC method is improved by combining it with EWM. The combined weights are measured, which reduces the limitations of the single weight. Considering the inherent correlation of evaluation indicators, the VIKOR method is combined with the grey relational analysis (GRA), which makes the results of the selection assessment more reasonable. This paper carries out a case study of the first inland navigation PPP project in China. Through preliminary tracking studies, it tests the effectiveness of the proposed method, the superiority of which is demonstrated by comparing the evaluation results with those of the traditional GRA methods, VIKOR, and TOPSIS. [ABSTRACT FROM AUTHOR]

At present, photodetectors based on traditional semiconductor materials no longer meet the needs of the rapid development of science and technology. Two-dimensional materials have attracted wide attention in academia and industry because of their excellent electrical and optical properties. In addition, WS2 is considered a promising photodetector material because of its tunable band structure, high stability, and high carrier mobility. In this paper, a metal-semiconductor-metal photodetector based on mechanically stripped WS2 material is designed, with a metal used as electrode material. However, the Schottky barrier and trap state between metal-semiconductor structures will lead to decreasing carrier collection efficiency. To further improve the performance of the detector, we consider graphene as the electrode material and design a photodetector with Au–WS2–Graphene (Gra)–Au structure. According to experiments, using graphene as an electrode can significantly improve the photocurrent gain and reduce the dark current. At 1 V positive bias and 3.9 nW illumination power, the responsivity of the Au–WS2–Gra–Au structure is as high as 29.0 A/W, which indicates that it has excellent ability in light response and weak light detection. This research makes it possible to realize photodetectors with low power consumption and ultra-high light responsiveness, which have great application prospects in practical electronic devices. [ABSTRACT FROM AUTHOR]

Abrasive Water Jet Machining (AWJM) is a popular machining method used to machine polymer matrix composites that are sensitive to temperature. This method is non-thermal, and each input parameter has a significant effect on output parameters, such as material removal rate, kerf width, surface roughness, and the potential for delamination. To ensure high-quality machining, it is crucial to set these input parameters at their optimal level. This paper proposes a simple approach to predict the optimum process parameters of water jet machining operations on jute fiber-reinforced polymer composite (JFRPC). The process parameters considered are standoff distance (SOD), traverse speed (TS), and abrasive material flow rate (MFR). Conversely, surface roughness (Ra) and delamination (Da) are the output parameters. Process parameters are set using Taguchi's L27 array, with consideration given to three levels of each input parameter. The best value for process parameters is found using grey relational analysis (GRA), and an ANOVA on GRA illustrates the impact of each input variable. After a confirmation test, it was found that the suggested parameters guarantee the best possible results. [ABSTRACT FROM AUTHOR]

DSS has low machinability characteristics due to its high strength, machining is complicated, and careful attention is required when selecting machining parameters. The main criteria discussed in this paper concern the turning optimization parameters and machining time reduction of DSS 2205 as the work material. The input parameters are cutting velocity, feeds, cutting depth, and tooltip nose radius of the cutting tool. The design of experiments methodology is employed to design the experiments using Design-Expert V12 software. The second-order mathematical model was developed, and analysis of variance was performed to analyze the performance characteristics to recognize the critical variables influencing the output parameter. An artificial neural network (ANN) backpropagation algorithm using MATLAB software was used to develop the mathematical model and optimize the output. The model was developed, and the results were optimized using MATLAB software's ANN back propagation method to find the best possible solutions. The generated models were significant based on the analysis of variance and the R-squared value, and these results indicate that the cutting velocity is the most critical factor. For a low machining time, the cutting velocity should be between 100 and 140 m/min, and the tooltip nose radius should be 2.8 mm. The optimal parameter settings are validated by performing a lower is better confirmation test using gray relational analysis (GRA). The GRA exposed the lower machining time at a cutting velocity of 140 m/min, rate of feed of 0.5 mm/rev, cutting depth of 0.5 mm, and tooltip nose radius of 2.4 mm. The predicted values were close to the experimental values, and the result indicates the optimal level of the highest GRA grade of the machining variable. [ABSTRACT FROM AUTHOR]

This paper reports the investigation of suitable input parameters for grade 316 stainless steel, which has numerous home and industrial applications. Various input parameters like ball diameter, speed, feed, vertical step depth (VSD) and lubrication were used in many combinations using L27 orthogonal array. Through experimental investigation various outputs like wall angle, formability, surface finish and spring back were obtained. From these outputs, optimized input parameters were obtained using grey relational analysis (GRA) and technique for order preference by similarity to ideal solution method (TOPSIS). Analysis of variance was also used to find the influence of individual parameters. Different optimized parameters were obtained from both GRA and TOPSIS. However, TOPSIS gave better predicted closeness values compared to GRA. The most influencing factor was also found using analysis of variance (ANOVA). [ABSTRACT FROM AUTHOR]

During lower-extremity rehabilitation training, muscle activity status needs to be monitored in real time to adjust the assisted force appropriately, but it is a challenging task to obtain muscle force noninvasively. Mechanomyography (MMG) signals offer unparalleled advantages over sEMG, reflecting the intention of human movement while being noninvasive. Therefore, in this paper, based on MMG, a combined scheme of gray relational analysis (GRA) and support vector regression optimized by an improved cuckoo search algorithm (ICS-SVR) is proposed to estimate the knee joint extension force. Firstly, the features reflecting muscle activity comprehensively, such as time-domain features, frequency-domain features, time–frequency-domain features, and nonlinear dynamics features, were extracted from MMG signals, and the relational degree was calculated using the GRA method to obtain the correlation features with high relatedness to the knee joint extension force sequence. Then, a combination of correlated features with high relational degree was input into the designed ICS-SVR model for muscle force estimation. The experimental results show that the evaluation indices of the knee joint extension force estimation obtained by the combined scheme of GRA and ICS-SVR were superior to other regression models and could estimate the muscle force with higher estimation accuracy. It is further demonstrated that the proposed scheme can meet the need of muscle force estimation required for rehabilitation devices, powered prostheses, etc. [ABSTRACT FROM AUTHOR]

To improve the accuracy of the multiple channel integration quality (MCIQ) evaluation, this paper proposes a comprehensive evaluation method using the nonlinear autoregressive exogenous model (NARX) and constructs an index system. First, the entropy method is used to determine the objective weight of each indicator. The indicators used in this paper are process consistency, information consistency, emotional value, procedural value, service structure transparency, online result value, business relevance, and online purchase intention. Second, an improved gray relational analysis (GRA) algorithm is used to obtain the comprehensive gray relational degree between the above eight indicators' standard samples and the tested samples. Then, this study uses the dataset preprocessed with the GRA algorithm for training the NARX model. Then, this study uses the trained model to evaluate the quality of multiple channel integration comprehensively. Next, this study uses standardized methods to quantify the evaluation results to provide new ideas and theoretical guidance for teaching traditional retailers to use the advantages of multiple channels to expand their online business. This paper uses 50,000 consecutive samples of a product for 3 months as a dataset in the experimental part. Through the GRA method and the NARX model, the comprehensive gray relational degree between the test sample and the ideal sample is obtained, and the results are quantified. Experiments show that, compared with the GRA method, this paper's method has a higher degree of fit between the output value and the target value. [ABSTRACT FROM AUTHOR]

The factors that affect the performance of the equipment are numerous and complicated, which makes it difficult to establish a performance calculation model. This paper puts forward a data-driven modeling method with reverse process for this problem. Based on the partial least squares (PLS) algorithm and the gray relational analysis (GRA) method, the analysis method of the performance related factors, the extraction method of characteristic variables, and the performance modeling method are studied. The related factors of the energy consumption of an industrial steam turbine are analyzed, and an energy consumption calculation model is established, and the effectiveness of the above-mentioned modeling methods is verified with sample data, which provides a basis for the energy-saving optimization of the steam turbine. [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]

In this paper, we propose a robust ABC classification for inventories using a hybrid technique for order of preference by similarity to ideal solution-alternative factor extraction approach (TOPSIS-AFEA) as the cornerstone method to calculate and rank importance scores for each item in stock. This is done to mitigate multicollinearity that may exist among different inventory criteria, which artificially inflates total data variance. Besides, and differently from previous research, information reliability techniques such as information entropy and gray relational analysis (GRA) are used as an auxiliary tool to differentiate alternative ABC methods proposed in the literature in terms of the principle of maximal entropy. This principle states that the probability distribution that best represents the current state of knowledge given prior data is the one with largest entropy. Results suggest that the proposed robust TOPSIS-AFEA provides an adequate representation of score ranks that may be computed on different datasets by using existing alternative ABC inventory classification models. [ABSTRACT FROM AUTHOR]

This paper presents a study to evaluate the comparative positioning accuracy of Surface Vehicle (SV) using Dead Reckoning (DR), Geodetic Least-Squares Adjustment (GLSA), Geodetic Robust Adjustment (GRA), and External Kalman Filter (EKF) methods. This involved simulating the results of navigational measurements subject to errors (including gross errors) used to position the SV swimming along a given trajectory in the vicinity of three beacons. We showed an apparent increase in the SV positioning accuracy, from approximately 9 m of Root Mean Square (RMS) obtained by DR and GLSA methods, to approximately 2 m (RMS), achieved using GRA and EKF methods. We also showed that, by interchanging GRA and EKF methods, it is still possible to increase the positioning accuracy of the SV up to 1.14 m (RMS). However, such an interchange should occur after the experimentally determined limit of the mean error of the position coordinates estimated by the GRA method has been exceeded. [ABSTRACT FROM AUTHOR]

Modern manufacturing's objective is to guarantee quality while lowering production costs and increasing productivity. In this context, the selection of cutting materials is important in increasing productivity due to their wide range of applications. In this paper, when machining AISI 4140 alloy steel, an experimental study is presented to find the cutting conditions that result in minimum Ra, Vb, and maximum MRR values. Different optimization methods are used, namely, Taguchi, grey relational analysis (GRA), technique by order of preference by similarity to ideal solution (TOPSIS), and multi-objective optimization ratio analysis (MOORA). Taguchi's L16 design was used to arrange 16 experiments. Cutting speed (Vc), feed rate (f), and depth of cut (ap) were the input parameters, with four levels for each cutting parameter. The feed rate and cutting speed had the greatest effect on Ra and Vb, according to an ANOVA analysis of the experimental results. MRR was significantly affected by the depth of cut and feed rate. To achieve the minimum surface roughness, flank wear, and maximum material removal rate, the cutting speed, feed rate, and depth of cut were required to be 250 m/min, 0.11 mm/rev, and 1.4 mm, respectively, according to the optimization results. [ABSTRACT FROM AUTHOR]

The traditional altimetry satellites based on pulse-limited radar altimeter only calculate along-track deflection of the vertical (DOV), which results in poorer precision of the prime vertical component than that of the meridian component and limits the precision of the marine gravity field inversion. We expect an improvement in the higher precision prime vertical component using the Ice, Cloud and land Elevation Satellite 2 (ICESat-2) sea surface height (SSH) data. In this paper, the 2′ × 2′ gridded DOVs derived from along-beam DOVs, cross-beam DOVs, and joint along-cross beam DOVs in the South China Sea (SCS; 0°–23°N, 103°–120°E) are calculated with the weighted least squares method, respectively. The inverse Vening–Meinesz (IVM) formula is applied to derive 2′ × 2′ gravity anomalies over the SCS from ICESat-2-derived gridded DOVs. In addition, the XGM2019e_2159-DOV and SIO V31.1-DOV models are used to assess the precision of the gridded DOVs. The XGM2019e_2159-GRA, SIO V31.1-GRA models, and ship-borne gravity anomalies are also adopted to evaluate the quality of gravity anomalies. The results show that the gridded DOVs calculated by the joint along-cross beam DOVs have the highest precision among the three gridded DOVs determined by ICESat-2. The precision of difference between gravity anomalies derived from the joint along-cross beam DOV and the above verification data are higher than those derived from the along-beam and cross-beam DOVs. We conclude that the joint along-cross beam DOV can effectively improve the precision of the gridded DOV, which is conducive to the inversion of a high-precision marine gravity field. [ABSTRACT FROM AUTHOR]

Polymer composites are extensively accepted in the material fraternity due to their lightweight, high-strength quality. The carbon nanomaterials (CMNs) significantly enhance the thermal resistivity, anti-corrosion, mechanical, and physical features of polymer (epoxy) composites. This paper emphasizes the machining aspects of Zero-dimensional Carbon nano onion (0D-CNO) reinforced polymer composites. The influence of four varying constraints, namely, weight% of CNO, Spindle speed, feed rate, and depth of cut, was examined through the Grey integrated JAYA algorithm (GRA- JAYA). The desired value of Surface roughness (Ra) and Material Removal Rate (MRR) was controlled through process constraints. The Milling experimentation was executed through the Taguchi L27 orthogonal array (OA). The conflicting machining performances were aggregated by Grey theory to develop the algorithm fitness function. To obtain a better work efficiency, higher spindle speed, feed rate, depth of cut, and lower weight% of nanomaterial addition are proposed in this study. Hence, the result demonstrated that the weight% of CNO reinforcement and feed rate are the most influential factors for optimal machining performance results. The finding of the proposed module has compared with other metaheuristics algorithms, which demonstrate the higher application potential to acquire the desired machining characteristics. It can be suggested for the enhancement of quality and productivity indices of the polymer manufacturing sector. [ABSTRACT FROM AUTHOR]

The optimal amount of fertilizer application which was needed by the trees and the factors that influence the fertilization have an intricated nonlinear relationship. According to the problems that the traditional fertilization prediction model has, such as lacking of the scalability and practicality, this paper initiates an accurate fertilization prediction model that was based on the GRA-PSO-BP neural network which can make the accurate fertilization come true and improve the economic benefits of forest industry. This paper uses the GRA method to determine the input of the neural network as the site index and make the forest age, nutrient content of the advantage trees, biomass of the advantage trees, biomass of average trees, and target yield as the output numbers of the Actual amount of fertilizer applied. During the calculation process, the global particle swarm optimization algorithm is used to optimize the initial numbers and threshold numbers of BP neural network which build a phased GRA-PSO-BP accurate fertilization model. Compared with the prediction algorithm of full input variate that is based on the single BP neural network and the prediction algorithm of full input variate that is based on PSO-BP Neural Network, the GRA method can determine the key factors that influence the amount of fertilizer applied in different forest areas and modify the prediction model to improve the scalability and accuracy of the prediction and finally achieve the precision fertilization as the data of different forests updated, so we can see that the prediction result of this paper is more accurate. The result demonstrates that the GRA-PSO-BP neural network Segment fertilization model is more accurate than the traditional BP neural network and BP Neural Network that was optimized by the PSO algorithm, and specifically, the error of the predicted amount of fertilizer application and the actual amount of fertilizer application is less than 5%, which can effectively guide the fertilization in stages. [ABSTRACT FROM AUTHOR]

Foundations are one of the oldest organizational forms globally; their number and resources, as well as their socio-political and economic importance, have steadily continued to grow. Yet, foundations' attributes, activities, and actual achievements remain underexplored and poorly understood. This is particularly noticeable in the context of global policy and transnational administration, an area where foundations tend to be subliminal players, acting as a widely unrecognized socio-political undercurrent. Addressing the resulting need for better and alternative conceptualizations of foundations, our paper uses French pragmatic sociology of critique (FPSC), a non-structuralist, post-Bourdesian, approach to sociology, to theorize philanthropic foundations within the policy agora. Through FPSC, we present foundations as a composite setup of activity, where critically reflexive actors bring normative ideologies and knowledge to policy, providing a new avenue for how scholarship can interpret and critique foundations and their influence. [ABSTRACT FROM AUTHOR]

This paper shows the novel approach of Taguchi-Based Grey Relational Analysis of Ti6Al4V Machining parameter. Ti6Al4V metal matrix composite has been fabricated using the powder metallurgy route. Here, all the components of TI6Al4V machining forces, including longitudinal force (Fx), radial force (Fy), tangential force (Fz), surface roughness and material removal rate (MRR) are measured during the facing operation. The effect of three process parameters, cutting speed, tool feed and cutting depth, is being studied on the matching responses. Orthogonal design of experiment (Taguchi L9) has been adopted to execute the process parameters in each level. To validate the process output parameters, the Grey Relational Analysis (GRA) optimization approach was applied. The percentage contribution of machining parameters to the parameter of response performance was interpreted through variance analysis (ANOVA). Through the GRA process, the emphasis was on the fact that for TI6Al4V metal matrix composite among all machining parameters, tool feed serves as the highest contribution to the output responses accompanied by the cutting depth with the cutting speed in addition. From optimal testing, it is found that for minimization of machining forces, maximization of MRR and minimization of Ra, the best combinations of input parameters are the 2nd stage of cutting speed (175 m/min), the 3rd stage of feed (0.25 mm/edge) as well as the 2nd stage of cutting depth (1.2 mm). It is also found that hardness of Ti6Al4V MMC is 59.4 HRA and composition of that material remain the same after milling operation. [ABSTRACT FROM AUTHOR]

In many of the least developed and developing countries, a multitude of infants continue to suffer and die from vaccine-preventable diseases and malnutrition. Lamentably, the lack of official identification documentation makes it exceedingly difficult to track which infants have been vaccinated and which infants have received nutritional supplements. Answering these questions could prevent this infant suffering and premature death around the world. To that end, we propose Infant-Prints, an end-to-end, low-cost, infant fingerprint recognition system. Infant-Prints is comprised of our (i) custom built, compact, low-cost (85 USD), high-resolution (1,900 ppi), ergonomic fingerprint reader, and (ii) high-resolution infant fingerprint matcher. To evaluate the efficacy of Infant-Prints, we collected a longitudinal infant fingerprint database captured in four different sessions over a 12-month time span (December 2018 to January 2020), from 315 infants at the Saran Ashram Hospital, a charitable hospital in Dayalbagh, Agra, India. Our experimental results demonstrate, for the first time, that Infant-Prints can deliver accurate and reliable recognition (over time) of infants enrolled between the ages of 2-3 months, in time for effective delivery of vaccinations, healthcare, and nutritional supplements (TAR = 95.2% @ FAR = 1.0% for infants aged 8-16 weeks at enrollment and authenticated 3 months later). 1. A preliminary version of this paper was present at CVPRW Computer Vision for Global Challenges, Long Beach, CA, 2019. [ABSTRACT FROM AUTHOR]

Ferroelectric tunnel junction (FTJ) has attracted considerable attention for its potential applications in nonvolatile memory and neuromorphic computing. However, the experimental exploration of FTJs with high ON/OFF ratios is a challenging task due to the vast search space comprising of ferroelectric and electrode materials, fabrication methods and conditions and so on. Here, machine learning (ML) is demonstrated to be an effective tool to guide the experimental search of FTJs with high ON/OFF ratios. A dataset consisting of 152 FTJ samples with nine features and one target attribute (i.e., ON/OFF ratio) is established for ML modeling. Among various ML models, the gradient boosting classification model achieves the highest prediction accuracy. Combining the feature importance analysis based on this model with the association rule mining, it is extracted that the utilizations of {graphene/graphite (Gra) (top), LaNiO3 (LNO) (bottom)} and {Gra (top), Ca 0. 9 6 Ce 0. 0 4 MnO3 (CCMO) (bottom)} electrode pairs are likely to result in high ON/OFF ratios in FTJs. Moreover, two previously unexplored FTJs: Gra/BaTiO3 (BTO)/LNO and Gra/BTO/CCMO, are predicted to achieve ON/OFF ratios higher than 1000. Guided by the ML predictions, the Gra/BTO/LNO and Gra/BTO/CCMO FTJs are experimentally fabricated, which unsurprisingly exhibit ≥ 1000 ON/OFF ratios (∼ 8540 and ∼ 7890, respectively). This study demonstrates a new paradigm of developing high-performance FTJs by using ML. [ABSTRACT FROM AUTHOR]

The shear transfer mechanism of steel fiber reinforced concrete (SFRC) beams without stirrups is still not well understood. This is demonstrated herein by examining the accuracy of typical empirical formulas for 488 SFRC beam test records compiled from the literature. To steer clear of these cognitive limitations, this study turned to artificial intelligence (AI) models. A gray relational analysis (GRA) was first conducted to evaluate the importance of different parameters for the problem at hand. The outcomes indicate that the shear capacity depends heavily on the material properties of concrete, the amount of longitudinal reinforcement, the attributes of steel fibers, and the geometrical and loading characteristics of SFRC beams. After this, AI models, including back-propagation artificial neural network, random forest and multi-gene genetic programming, were developed to capture the shear strength of SFRC beams without stirrups. The findings unequivocally show that the AI models predict the shear strength more accurately than do the empirical formulas. A parametric analysis was performed using the established AI model to investigate the effects of the main influential factors (determined by GRA) on the shear capacity. Overall, this paper provides an accurate, instantaneous and meaningful approach for evaluating the shear capacity of SFRC beams containing no stirrups. [ABSTRACT FROM AUTHOR]

In multivariate chaotic time series prediction, correlation analysis is important for reducing input dimensions and improving prediction performance. Grey relational analysis (GRA) has proved to be an effective method for data correlation analysis, especially for inexact data and incomplete data. In GRA, points are usually regarded as objects, and the distance between points or the concave and convex degree are mostly used to measure the correlations. However, with discrete variables, correlation analysis results always tend to have some deviations when using prior GRA methods. Furthermore, GRA methods cannot directly use vector datasets. Therefore, in this paper, an improved GRA method is proposed based on vector projections. The input and output variables are expressed as vectors by linking two adjacent points. The vectors, instants of the points, are regarded as the objects, and the projection length of input variables to output variables is used to measure the correlations. The smaller the difference between the projection length and the input variables, the higher the correlation. Then, a hybrid variable selection and prediction model is proposed based on the improved GRA method for multivariate chaotic time series predictions, in order to overcome the negative effects of irrelevant and redundant variables caused by phase-space reconstruction. The experimental results based on the gas furnace dataset and San Francisco river runoff dataset demonstrate that the improved GRA method is effective for data correlation analysis, and the prediction accuracy is better than prior GRA-based methods. [ABSTRACT FROM AUTHOR]

Apicomplexan parasites cause severe diseases in human and livestock. Dense granule proteins (GRAs), specific to the Apicomplexa, participate in the maintenance of intracellular parasitism of host cells. GRAs have better immunogenicity and they can be emerged as important players in vaccine development. Although studies on GRAs have increased gradually in recent years, due to incompleteness and complexity of data collection, biologists have difficulty in the comprehensive utilization of information. Thus, there is a desperate need of user-friendly resource to integrate with existing GRAs. In this paper, we developed the Dense Granule Protein Database (DGPD), the first knowledge database dedicated to the integration and analysis of typical GRAs properties. The current version of DGPD includes annotated GRAs metadata of 245 samples derived from multiple web repositories and literature mining, involving five species that cause common diseases (Plasmodium falciparum, Toxoplasma gondii, Hammondia hammondi, Neospora caninum and Cystoisospora suis). We explored the baseline characteristics of GRAs and found that the number of introns and transmembrane domains in GRAs are markedly different from those of non-GRAs. Furthermore, we utilized the data in DGPD to explore the prediction algorithms for GRAs. We hope DGPD will be a good database for researchers to study GRAs. Database URL : http://dgpd.tlds.cc/DGPD/index/ [ABSTRACT FROM AUTHOR]

A proper CNC machine selection problem is an important issue for manufacturing companies under competitive market conditions. The selection of an improper machine tool can cause many problems such as production capabilities and productivity indicators considering time and money industrially and practically. In this paper, a comprehensive solution approach is presented for the CNC machine tool selection problem according to the determined criteria. Seven main and thirteen sub-criteria were determined for the evaluation of the seven alternatives. To purify the selection process from subjectivity, instead of a single decision-maker, the opinions of six different experts on the importance of the criteria were taken and evaluated using the Best-Worst method. According to the evaluations, the order of importance of the main criteria has been determined as cost, productivity, flexibility, and dimensions. After the weighting of the criteria, three different ranking methods (GRA, COPRAS, and MULTIMOORA) were preferred due to the high investment costs of the selected alternatives. The findings obtained by solving the problem of selection of the CNC machine are close to those obtained by past researchers. As a result, using the suggested methodology, effective alternative decision-making solutions are obtained. [ABSTRACT FROM AUTHOR]

In the present study, we present a multi-attribute-decision-making (MADM) strategy in Single Valued Pentapartitioned Neutrosophic Set (SVPNS) environment based on Grey Relational Analysis (GRA) which we call SVPNS- MADM strategy. We define Hamming distance between two single valued pentapartitioned neutrosophic sets and prove its basic properties. The notion of pentapartitioned neutrosophic set is a powerful mathematical tool to deal with incomplete, indeterminate, ignorance, and inconsistent information. In this paper, we extend the neutrosophic GRA strategy to pentapartitioned neutrosophic GRA strategy. Then we employ it to an MADM strategy. Further, we demonstrate the developed MADM strategy by solving an illustrative numerical example that reflects the efficiency and applicability of the proposed strategy. [ABSTRACT FROM AUTHOR]

Electrical discharge machines (EDM) are widely employed in machining components containing complex profiles of hard-to-cut and machining materials. However, the fabrication-of-tool time for the EDM process is excessively high in the traditional machining method, which significantly affects the machining rate. Therefore, in this paper, a powder metallurgy (PM) technique is employed to fabricate the tool electrode using copper (Cu), titanium carbide (TiC), and zirconium silicate (ZrSiO4) for different combinations. An L18 orthogonal array (OA) is planned using the following input parameters: three types of tools (Cu, Cu90, Cu80), peak current (PC) [A], pulse on time (PT) [µs], and gap voltage (GV) [V]. The performance of EDM is evaluated through the material removal rate (MRR), tool wear rate (TWR), and surface roughness (SR). The process parameters are optimized using two different techniques: the technique for order of preference by similarity to the ideal solution (TOPSIS) and grey relational analysis (GRA). TOPSIS and GRA optimization techniques produce the same optimal parametric solution for less TWR, SR, and higher MRR with the combination of the Cu90 tool, E8 APC, 15 µs pulse PT, and 75 V GV. Based on the ANOVA table of TOPSIS, pulse on time plays a major role, contributing 46.8 % of the machining performance; peak current shows the most significant contribution of 39.3 % of the machining performance using GRA values. Furthermore, the scanning electron microscope (SEM) image analyses are carried out on the machined workpiece surface to understand the effect of tools on machining quality. [ABSTRACT FROM AUTHOR]

It is widely agreed that properly establishing a sustainable supply chain strategy to yield competitive advantages is essential for business enterprises, and a number of research papers on sustainable supply chains have been produced over the last two decades. However, many past studies on sustainable supply chain strategies emphasized either classification schemes or various coordination mechanisms, and few of them have focused on an integrated framework for sustainable supply chains. Therefore, the objective of this study is to develop a strategic framework for the sustainable supply chain management. The study is based on the abductive reasoning process through literature review to establish a strategic framework which is ranked through grey relational analysis (GRA). The weighted data of various strategies collected from the elite interview prove to be comprehensive and evaluable, so it can create values for supply chain members in practice. The results further suggest that each sustainable supply chain in different fields can select the best combination of strategies through GRA to constantly facilitate performance of sustainability. The main contribution is the submission of a strategic framework which makes up the insufficiency of past research papers lacking an integrated strategic framework. At the same time, the proposed strategic framework has also been illustrated through a case study. [ABSTRACT FROM AUTHOR]

This paper presents the effect of cutting parameters on the mist particle diameters under the minimum quantity lubrication (MQL) for reducing the air pollution and harm to operators caused by the mist particles produced during the MQL process. In this study, turning experiments were conducted on AISI 304 stainless steel using MQL, for which 16 groups of Taguchi experiments were utilized. The results show that by using the MQL, the contribution rates of the cutting speed and air pressure to the mist particle diameters were 54.62% and 25.34%, respectively. The increase in air pressure caused an increase in the overall proportion of mist particles with diameters in the range of 2.5~10 μm, from 88 up to 96%. The increase in cutting speed aided the conversion of mist particles with a diameter in the range of 2.5~10 μm to a diameter of 2.5 μm, about 16 to 27% of the 2.5~10 μm. However, the overall proportion of the mist particles was not changed. Finally, by targeting the cutting efficiency, surface roughness, and deposition of the cutting fluid mist, the four parameters were optimized using a multi-objective optimization algorithm. Compared to the 14th group experiment with a similar cutting efficiency, the verification experiment results show that the surface roughness decreased 16% and the deposition of cutting fluid mist increased 173% by using the optimized parameters. [ABSTRACT FROM AUTHOR]

Workshops would avoid losing their customers due to a long wait on the line. Some workshops initially provide more waiting areas than they actually need to put them in the safe side, and reducing the areas as the time goes on safe space. However, waiting areas alone would not solve a problem when customers withdraw and go to the competitor's door; the service time may need to be improved. This shows a need of a numerical model for the worshop management to understand the situation better. This paper aims to show that queuing theory satisfies the model when tested with a real-case scenario. We obtained the data from a workshop in Agra. We then derive the arrival rate, service rate, utilization rate, waiting time in queue and the probability of potential customers to balk based on the data using Little's Theorem and M/M/1 queuing model. The arrival rate at Ford workshop during its busiest period of the day is 2.22 customers per minute (cpm) while the service rate is 2.24 cpm. The average number of customers in the workshop is 122 and the utilization period is 0.991. We conclude the paper by discussing the benefits of performing queuing analysis to a busy workshop. [ABSTRACT FROM AUTHOR]

Lightweight design is one of the important ways to reduce automobile fuel consumption and exhaust emissions. At the same time, the fatigue life of automobile parts also greatly affects vehicle safety. This paper proposes a multi-objective reliability optimization method by integrating Monte Carlo simulation (MCS) with the NSGA-II algorithm coupled with entropy weighted grey relational analysis (GRA) for lightweight design of the lower control arm of automobile Macpherson suspension. The dynamic load histories of the control arm were extracted through dynamic simulations of a rigid-flexible coupling vehicle model on virtual proving ground. Then, the nominal stress method was used to predict its fatigue life. Six design variables were defined to describe the geometric dimension of the control arm, while mass and fatigue life were taken as optimization objectives. The multi-objective optimization design of the control arm was carried out based on the Kriging surrogate model and NSGA-II algorithm. Aiming at the uncertainty of design variables, the reliability constraint was added to the multi-objective optimization to improve the reliability of the fatigue life of the control arm. The optimal design of the control arm was determined from Pareto solutions by entropy weighted grey relational analysis (GRA). The optimization results show that the mass of the control arm was reduced by 4.1% and the fatigue life was increased by 215.8% while its reliability increased by 7.8%. The proposed multi-objective reliability optimization method proved to be feasible and effective for lightweight design of a suspension control arm. [ABSTRACT FROM AUTHOR]

In this paper, a 2-D turbulent closure model, based on the pollutant mass conservation equation, is adopted to estimate the local and background pollutants in the predominant wind direction for the stable atmosphere during winter mornings. The background concentration of pollutants can severely affect the regional pollution level, and its monitoring is a challenging task. Here, the turbulent closure model is employed across three cities in India, viz., Patiala, Delhi, and Agra, to estimate SO2 and NOx concentration along the predominant wind direction to demonstrate the potential of numerical models. The direction of the prevailing wind in this area during January 2003 was NNW (330°). Patiala is followed by Delhi and then Agra in the predominant wind direction. The sensitivity analysis of surface temperature on pollutant concentration reveals that concentration would increase by its square as temperature dips. So, during low or no horizontal wind, pollution episodes will be inevitable. Thus, the pollution hotspots are also identified in these three cities. Delhi had a high pollution load. So, the impact of local pollution in Delhi, through dispersion, was found significant in Agra. NOx hot spots (exceed the 30 μg/m3 limit) are found all across Delhi, except IGI Airport and two other locations. However, no SO2 hotspot (exceed the 60 μg/m3 limit) is found in Delhi. The proposed model output is verified with the WRF-CFD model results. Compared to the WRF-CFD model, the proposed model has overestimated NOx and SO2 concentration maximum by 14.4% and 23.5%, respectively. The overestimation occurred primarily due to ignoring atmospheric chemical reactions (e.g., acid condensation, etc.) for which the atmospheric factors were not so conducive. [ABSTRACT FROM AUTHOR]

In the present manuscript, authors have made an attempt to find the effect of aluminium powder on the electric discharge machining (EDM) of EN-19 alloy steel. Brass metal is used as electrode material to conduct the experiments. The novelty of paper lies in machining of EN-19 steel using aluminium powder mixed EDM, which is not attempted so far. During experimentation, peak current, pulse on time, gap voltage and concentration of powder are considered as input process parameters for investigating their effect on material removal rate (MRR) and tool wear rate (TWR). Using the surface response modelling, a relationship has been established among the responses and process parameters in EDM. Optimisation of the machining parameters that are responsible for the best combination of MRR and TWR are determined with the help of fuzzy-grey relational analysis. Furthermore, the Micro structural and chemical composition analysis were characterised through Scanning Electron Microscope, Energy Dispersive Spectroscopy and X-Ray Diffraction, respectively. Abbreviations: CCD: Central Composite Design; DF: Degree of Freedom; EDM: Electrical Discharge Machining; EDS: Energy Dispersive Spectroscopy; GRA: Grey Relational Analysis; GRC: Grey Relational Coefficients; GRG: Grey Relational Grade; MRR: Material Removal Rate; MS: Mean of Squares; PMEDM: Powder Mixed Electrical Discharge Machining; RSM: Response Surface methodology; SEM: Scanning Electron Microscope; SR: Surface Roughness; SS: Sum of Squares; TEM: Transmission electron microscopy; TWR: Tool Wear Rate; XRD: X- Ray Diffraction [ABSTRACT FROM AUTHOR]

The actualization of the befitting sampling strategy and the application of an appropriate evaluation algorithm have been elementary issues in the coordinate metrology. The decisions regarding their choice for a given geometrical feature customarily rely upon the user's instinct or experience. As a consequence, the measurement results have to be accommodated between the accuracy and the inspection time. Certainly, a reliable and efficient sampling plan is imperative to accomplish a dependable inspection in minimal time, effort, and cost. This paper deals with the determination of an optimal sampling plan that minimizes the inspection cost, while still promising a measurement quality. A cylindrical-shaped component has been utilized in this work to achieve the desired objective. The inspection quality of the cylinder using a coordinate measuring machine (CMM) can be enhanced by controlling the three main parameters, which are used as input variables in the data file, namely, point distribution schemes, total number of points, and form evaluation algorithms. These factors affect the inspection output, in terms of cylindricity and measurement time, which are considered as target variables. The dataset, which comprises input and intended parameters, has been acquired through experimentation on the CMM machine. This work has utilized state-of-the-art machine learning algorithms to establish predictive models, which can predict the inspection output. The different algorithms have been examined and compared to seek for the most relevant machine learning regression method. The best performance has been observed using the support vector regression for cylindricity, with a mean absolute error of 0.000508 mm and a root-mean-squared error of 0.000885 mm. Likewise, the best prediction performance for measuring time has been demonstrated by the decision trees. Finally, the optimal parameters are estimated by employing the grey relational analysis (GRA) and the fuzzy technique for order performance by similarity to ideal solution (FTOPSIS). It has been approved that the values obtained from GRA are comparable with those of the FTOPSIS. Moreover, the quality of the optimal results is bettered by incorporating the measurement uncertainty in the outcome. [ABSTRACT FROM AUTHOR]

Composite tape winding is an important forming process of composite materials, which can ensure good performance of composite products. Selection and control of key process parameters in the winding process have a great influence on the properties of products, such as void content and residual stress of products. Through experimental analysis, the residual stress and void content of the composite products are not the minimum when the prepreg winding process is carried out by using the empirical process parameters. To solve this multiobjective optimization problem, experiments were conducted using the Box–Behnken design. The multiobjective optimization problem is converted to a single-objective problem using grey relational analysis (GRA). Principal component analysis (PCA) is used to quantify the relative contributions of residual stress and void content. Regression analysis of grey relational grade (GRG) based on the experimental data was used to develop a second-order GRG prediction model. The winding process parameters were optimized with response surface methodology (RSM), and the winding experiments were carried out with these parameters. The experimental results show that the best combination of process parameters yields the best GRG results with better void content and residual stress. [ABSTRACT FROM AUTHOR]

Mutual interference among the radar sensors has become a serious concern due to the extensive growth of self driving vehicles (SDV) equipped with such sensors. The problem becomes more severe with the increase in traffic density, where a large number of SDVs gather within the proximity of inter-vehicular radars. Thereby, the number of resources required to maintain the neighbourhood orthogonality increases with traffic density and further leads to the problem of radar blindness. In this paper, we propose a graph-based resource allocation (GRA) scheme to assign resources to the running SDV pool. GRA assures that two closely located SDVs may not simultaneously use the same resource. Also, we integrate the notion of traffic-based dynamic-range approach (TDA) with GRA. Then, through simulation results, it is shown that GRA outperforms the state of art random allocation approach. Further, it is shown that GRA, along with TDA, may eliminate the problem of radar blindness. [ABSTRACT FROM AUTHOR]

Granzyme A (GrA) has long been recognized as one of the key players in the induction of cell death of neoplastic, foreign or infected cells after granule delivery by cytotoxic cells. While the cytotoxic potential of GrA is controversial in current literature, accumulating evidence now indicates roles for extracellular GrA in modulating inflammation and inflammatory diseases. This paper aims to explore the literature presenting current knowledge on GrA as an extracellular modulator of inflammation by summarizing (i) the presence and role of extracellular GrA in several inflammatory diseases, and (ii) the potential molecular mechanisms of extracellular GrA in augmenting inflammation. [ABSTRACT FROM AUTHOR]

Wave-equation-based traveltime tomography has been extensively applied in both global tomography and seismic exploration. Typically, the traveltime Fréchet derivative is obtained using the first-order Born approximation, which is only satisfied for weak velocity perturbations and small phase shifts (i.e. the weak-scattering assumption). Although the small phase-shift restriction can be handled with the Rytov approximation, the weak velocity-perturbation assumption is still a major limitation. The recently developed generalized Rytov approximation (GRA) method can achieve an improved phase accuracy of the forward-scattered wavefield, in the presence of large-scale and strong velocity perturbations. In this paper, we combine GRA with the classical finite-frequency theory and propose a GRA-based traveltime sensitivity kernel (GRA-TSK), which overcomes the weak-scattering limitation of the conventional finite-frequency methods. Numerical examples demonstrate that the accumulated time delay of forward-scattered waves caused by large-scale smooth perturbations can be correctly handled by the GRA-TSK, regardless of the magnitude of the velocity perturbations. Then, we apply the new sensitivity kernel to solve the traveltime inverse problem, and we propose a matrix-free Gauss–Newton method that has a faster convergence rate compared with the gradient-based method. Numerical tests show that, compared with the conventional adjoint traveltime tomography, the proposed GRA-based traveltime tomography can obtain a more accurate model with a faster convergence rate, making it more suited for recovering the large-intermediate scale of the velocity model, even for strong-perturbation and complex subsurface structures. [ABSTRACT FROM AUTHOR]

This study aims to evaluate online product categories (electronics, apparels, personal care, kitchen appliances and stationery & school/offices supplies) association with key purchase criteria to illustrate the relative positioning of five product categories across specific purchase criteria. The study includes a sample of 600 respondents from three tier 2 cities of north India-Kota, Agra, and Jalandhar. Correspondence analysis was used to depict purchase criteria based comparative analysis of the top selling online product categories. The findings of the paper helps to reflect on three aspects - (i) similarity in the positioning of product categories, (ii) specific purchase criteria association with product categories and (iii) dominant purchase criteria. This study provides meaningful insights for multi-category players and market space aggregator to create an efficient/effective marketing programs for obtaining sustainable competitive advantage. The study also presents an opportunity to reflect on the efficacy of its current strategy and optimally reposition itself. Also, the study presents a meaningful ground for future researchers who wish to investigate the growing potential of e-tailing in small town India. [ABSTRACT FROM AUTHOR]

Increasingly, spatial decision support systems (SDSS) help consumers, businesses and governmental entities make decisions involving geospatial data. Understanding if, and how, user- and task-characteristics impact decision-performance will allow developers of SDSS to maximize decision-making performance. Furthermore, scholars can benefit from a more comprehensive understanding of what specific characteristics influence decision-making when using an SDSS. This paper provides a synthesis of relevant research and presents a two-factor experiment (n = 200) designed to measure the impact of user- and task-characteristics on decision-performance. Using Cognitive Fit Theory (CFT) as the theoretical framework, we investigate the effect of geospatial reasoning ability (GRA), input complexity, task complexity, and user perceptions of task-technology fit (PTTF), on geospatial decision-making performance. Specifically, we measure GRA as a user-characteristic, while input- and problem-complexity are measured as task-characteristics. A partial least squares (PLS) analysis reveals the statistical significance of user- and task-characteristics on geospatial decision-making performance. Theoretical and managerial implications are discussed in detail. [ABSTRACT FROM AUTHOR]

Despite efforts to prevent and correct unsafe behavior on construction sites, such behavior persists among construction workers. Much of what makes unsafe behavior so difficult to eradicate is its contagious nature. The contagion pattern of unsafe behavior is not only based on the group characteristics of construction workers but also influenced by government regulatory agencies (GRA) and general contractors (GC). In this study, we established a contagion model of construction workers' unsafe behavior based on group-level factors and the dual influence of GRA and GC and employed evolutionary game theory and epidemic models to explore the contagion patterns and interventions of unsafe behavior. We find that, although the strong and weak regulation strategies by GRA suppress the contagion of unsafe behavior, the strong regulation strategy exhibits a better preventive effect; further, the active management strategies by GC have a dampening effect on the contagion of unsafe behavior and vice versa, enhanced perceived social benefits of GRA prevent unsafe behavior more than enhanced perceived image loss, and a high degree of group identity perception effectively inhibits the contagion of unsafe behavior when construction workers have relatively high levels of safety awareness. By portraying the contagion state of construction workers' unsafe behavior in actual situations, this study provides a theoretical basis for the decision-making of GRA and GC and helps to improve the work efficiency of GRA as well as the safety management level of GC. [ABSTRACT FROM AUTHOR]

Accurate and rapid prediction of water quality is crucial for the protection of aquatic ecosystems. This study aims to enhance the prediction of total phosphorus (TP) concentrations in the middle reaches of the Yangtze River by integrating advanced modeling techniques. Using operational and discharge data from the Three Gorges Reservoir (TGR), along with water quality parameters from downstream sections, we used Grey Relational Analysis (GRA) to rank the factors contributing to TP concentrations. The analysis identified turbidity, permanganate index (CODMn), total nitrogen (TN), water temperature, chlorophyll a, upstream water level variation, and discharge from the Three Gorges Dam (TGD) as the top contributors. Subsequently, a coupled neural network model was established, incorporating these key contributors, to predict TP concentrations under the dynamic water level control during flood periods in the TGR. The proposed GRA-CEEMDAN-CN1D-LSTM-DBO model was compared with conventional models, including BP, LSTM, and GRU. The results indicated that the GRA-CEEMDAN-CN1D-LSTM-DBO model significantly outperformed the others, achieving a correlation coefficient (R) of 0.784 and a root mean square error (RMSE) of 0.004, compared to 0.58 (R) and 0.007 (RMSE) for the LSTM model, 0.576 (R) and 0.007 (RMSE) for the BP model, and 0.623 (R) and 0.006 (RMSE) for the GRU model. The model's accuracy and applicability further validated in two sections: YC (Yunchi) in Yichang City and LK (Liukou) in Jingzhou City, where it performed satisfactorily in predicting TP in YC (R = 0.776, RMSE = 0.007) and LK (R = 0.718, RMSE = 0.007). Additionally, deep learning analysis revealed that as the distance away from dam increased, prediction accuracy gradually decreased, indicating a reduced impact of TGR operations on downstream TP concentrations. In conclusion, the GRA-CEEMDAN-CN1D-LSTM-DBO model demonstrates superior performance in predicting TP concentration in the middle reaches of the Yangtze River, offering valuable insights for dynamic water level control during flood seasons and contributing of smart to the advancement of water management in the Yangtze River. [ABSTRACT FROM AUTHOR]

Hastelloy B2 is a high-performance nickel-molybdenum alloy and has been used in micro reactor, pump, pipe, and corrosion-resistant equipment. Square holes are required in the above application components. Although square holes are easily produced by spending more electrodes wear in Electrical Discharge Machining (EDM). Moreover, the square hole with small electrode wear achieved is a challenging task in thermal machining. Hence, in this work, the effects of EDM process parameters on electrode taper angle, electrode radius, electrode wear length and cutting time of Hastelloy B2 are studied. Taguchi based VišekriterijumskoKompromisnoRangiranje (VIKOR) method is used to find the optimum process parameters and compared with Taguchi based Grey Relational Analysis (GRA) method for validation. The results showed that current and pulse duration greatly affects the electrode wear out surface and edges. The square electrodes with sharp edges and surfaces are fully transformed into hemispherical shapes at electrode ends due to an unstable spark with diffusion of heat. Also, the VIKOR method provides an alternative multi-criteria optimization method that enhances the process performance over the grey relational analysis due to comparability, uniqueness, assessment and prediction. [ABSTRACT FROM AUTHOR]

In this paper, the bump-type foil bearing is considered to analyze and optimize its performance characteristics with various foil materials. For the study, the operating parameters such as friction coefficient F1, F2 and loading pressure P are considered variable input parameters. The response surface methodology-based BBD model is used to design and analyze these variable parameters. The structural stiffness and equivalent stress of bump-type foil bearings are considered as the output responses in the present study. The multiobjective GRA technique is used to evaluate the optimum value of operating parameters for each foil material and compare its results. These results show that the loading pressure is the most influencing operating parameter on the output responses, followed by friction coefficient F1 and then F2. By the GRA technique, the Inconel X750 is found to be the most significant foil material, followed by Spring Steel EN42J for bump-type foil bearings. Moreover, it is also observed that the dissimilar foil material in bump-type foil bearings provides better results compared to similar foil material. The combination of stainless steel 316 (bump foil) and phosphor bronze (top foil) offers better results than similar foil results. In addition, the fabrication cost of bump-type foil bearings should be reduced with dissimilar foil materials. [ABSTRACT FROM AUTHOR]

This paper presents a spreadsheet-based decision support system (DSS) for any parameter optimization problem, in the small- and medium-sized enterprises to help the managers to make better decisions. Microsoft Excel is used as a DSS development platform. The DSS application requires the quality characteristics and the level of parameters affecting the problem. The proposed system considers three multi-criteria decision-making methods: TOPSIS, VIKOR and GRA. These methods are integrated into the Taguchi method to convert the multi-response optimization problem to a single-response problem. The DSS suggests proper Taguchi experimental designs and provides the decision maker with an opportunity to use different metrics and to validate the experimental results. Several issues and an application are provided for illustrative purposes. The proposed DSS is tested on a case study (the performance of the mixed integer programming (MIP) formulation solver) and the results highlight that the system is capable of offering satisfactory outcomes. Using such a quick and flexible DSS might help to reduce the daily workload of the decision makers. The different metrics used for the response variables which results with the different parameter combination. Using the optimal parameter combination of TOPSIS (come to the fore in case MinBest metric used), the MIP formulation solver gives the best integer objective function value of 609 and a GAP value of 1.93%, both of which are less than the values obtained using the other methods. Using the optimal parameter combination of GRA (come to the fore in case OptBest metric used), the MIP formulation gives a best integer objective function value of 632 and a GAP value of 6.52%, both of which are less than the values obtained by using the other methods. [ABSTRACT FROM AUTHOR]

Neutrosophic hesitant fuzzy set (NHFS) is a convincing tool that deals with uncertain information. In this paper, we propose an NH-MADM strategy for solving MADM with NHFSs based on extended GRA. We assume that the information of attributes is partially known or completely unknown. We develop two models to determine the weights of attributes. Then we rank the alternatives based on the strategy. Further, we extend the strategy into MADM in interval neutrosophic hesitant fuzzy set environment which we call INH-MADM strategy. Finally, we provide two illustrative examples to show the validity and effectiveness of the proposed strategies. [ABSTRACT FROM AUTHOR]