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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]

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]

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]

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]

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]

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 study aims to enhance the interest of university classrooms and the learning efficiency of students. From deep learning, gesture recognition algorithms (GRA) and long short-term memory (LSTM) are adopted to build prediction modules. After the system process is completed, a brand-new human-computer interaction (HCI) system is established. The deep learning GRA augments the operation of the recognition algorithm of the pattern matching method created on the previous image segmentation. Its enticements on two merits of gesture estimation are as follows: rapid detection of gesture joint features and convolutional neural network (CNN) image classification, thereby improving GRA. The recognition rates of "take", "pinch", and "point" are 98.6%, 99.5%, and 99.4%, respectively. When the data volume of the LSTM network model is less than 10, the prediction accuracy can reach 70%, and the performance is relatively stable. The designed HCI system can better recognize the teacher's gesture intent in teaching, execute gesture commands, and optimize the interactive teaching method between the teacher and the computer. This system is a derivative form of gamification teaching. While it improves the interest in teaching, it is also of great significance for improving the teaching efficiency of general higher education. [ABSTRACT FROM AUTHOR]

Hybrid titanium metal matrix composites (HTMMCs) are advanced composite materials that can be tailored to a variety of applications. Because of their decreased fuel consumption and cost, they are popular in the transportation industry. Using multi-objective optimization and Taguchi-based Grey relational analysis (TGRA), this study investigates the impact of hybrid reinforced HTMMCs synthesized using powder metallurgy on their physic mechanical properties. The research investigates reinforcements such as B4C, SiC, ZrO2, and MoS2 at various compaction pressures, milling durations, and sintering temperatures. The best powder metallurgy control parameters for HTMMC synthesis, with a milling time of 5 h, a compaction pressure of 40 MPa, a sintering temperature of 1200 °C, and a sintering time of 1 h, and a compaction time of 40 min. According to validation results, HTMMC material with optimized process parameters had experimental densities, porosities, hardness, compressive strength, and wear rates of 4.29 gm/cm3, 0.1178%, 71.53RHN, 2782.36 MPa, and 0.1519 mm3 correspondingly. The material hardness was increased by 1.99% and compressive strength by 2.87%. The use of Taguchi and GRA techniques strongly verified that the impact of milling duration and sintering temperature was the greatest of all five factors. The novel synthesized hybrid reinforcing HTMMCs outperformed pure Ti grade 5 and single and double fortified HTMMCs in terms of physic mechanical characteristics. As a result, the newly developed tetra hybrid reinforced HTMMC material is expected to be used in heavy-duty vehicles, aerospace, automobiles, maritime, and other industries. Highlights: Developed new HTMMCs engineering material using powder metallurgy. In a multi-response optimization process, Taguchi-based Grey relational analysis (TGRA) was used to determine the most effective combinations of process variables. The high relative density, robust interface, and minimal cavities/porosity (less than 1%) of the sintered specimens make them acceptable engineering materials for use in the automotive and aerospace industries. The optimized HTMMC material exhibited improved hardness and compressive strength examination results, with an improvement of 1.99% in hardness and 2.87% in compressive strength. [ABSTRACT FROM AUTHOR]

Urban green spaces (UGSs) improve the environmental value of the city as well as the quality of life for citizens. Unfortunately, many major cities in India are lagging behind the minimum standard of UGS required in the city. For example, Agra city in India, one of the most famous tourist destinations in the world, represents two distinct realities of UGS in the same city. One is the focus of tourism, and the rest is the place of common people with very limited public green spaces, leading to multiple social issues (like spatial polarization, etc.). The secondary data concludes that the PPGC of Agra is lower than the standards, and the ongoing schemes are approached through quantitative methods. Despite having many UGS development schemes, Agra's UGS shows stalled growth. This research examines the policies and programs of UGS development (AMRUT, SCM, etc.) and reviews them to understand the unique gaps and possible regulatory interventions. The study includes an assessment of stakeholders' readiness to accept plausible UGS strategies using an analytical analysis approach. The primary data shows that PPP is the requirement for the integrated development of UGSs. The planners can make policies highlighting citizen's rights and responsibilities to enhance UGSs in Agra. [ABSTRACT FROM AUTHOR]

With the rapid development of cloud computing, artificial intelligence, etc., data centers have become a flexible load due to their adjustable capabilities and are able to take on power service. However, the existing communication architecture is monolithic and poorly adapted between services and communications, which poses a challenge for data centers to participate in power service. Firstly, this article constructs a multi-domain communication architecture for data center management systems that includes local and remote communication, and analyzes the key technologies supporting this architecture. Secondly, based on differentiated service types and communication requirements, an evaluation system for the adaptability between service and communication technology was constructed, and a communication mode adaptation method based on fuzzy analytic hierarchy process (FAHP)-improved CRITIC combined weighting model and grey Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) was proposed to achieve the analysis of the adaptability between differentiated service and multi-domain communication technology. According to the analysis of examples, the architecture and adaptation algorithm proposed in this article provide an effective theoretical basis and solution for the selection of multi-domain communication technologies for data center management system service. [ABSTRACT FROM AUTHOR]

(1) Monitoring salinized soil in saline–alkali land is essential, requiring regional-scale soil salinity inversion. This study aims to identify sensitive variables for predicting electrical conductivity (EC) in soil, focusing on effective feature selection methods. (2) The study systematically selects a feature subset from Sentinel-1 C SAR, Sentinel-2 MSI, and SRTM DEM data. Various feature selection methods (correlation analysis, LASSO, RFE, and GRA) are employed on 79 variables. Regression models using random forest regression (RF) and partial least squares regression (PLSR) algorithms are constructed and compared. (3) The results highlight the effectiveness of the RFE algorithm in reducing model complexity. The model incorporates significant environmental factors like soil moisture, topography, and soil texture, which play an important role in modeling. Combining the method with RF improved soil salinity prediction (R2 = 0.71, RMSE = 1.47, RPD = 1.84). Overall, salinization in Minqin oasis soils was evident, especially in the unutilized land at the edge of the oasis. (4) Integrating data from different sources to construct characterization variables overcomes the limitations of a single data source. Variable selection is an effective means to address the redundancy of variable information, providing insights into feature engineering and variable selection for soil salinity estimation in arid and semi-arid regions. [ABSTRACT FROM AUTHOR]

The goal of this research is to optimize the input parameters utilized in dry CNC drilling of forging steel to attain sustainable machining. Particular emphasis will be placed on achieving high productivity while minimizing the impact on surface quality. To achieve the aforementioned goal, three Taguchi-based multi-criteria decision-making (MCDM) approaches, such as traditional gray relational analysis (GRA), weighted gray relational analysis (WGRA), and data envelopment analysis ranking (DEAR), were used for simultaneous optimization of the MRR and Ra. In Taguchi's L12 (24) orthogonal array design, the cutting mode parameters—such as cutting speed, depth of cut, feed rate, and point angle—have been chosen as the input parameters for the modeling and analysis of the drilling process characteristics. The process of determining the effect of the input parameters on the output parameters was carried out with the use of analysis of variance (ANOVA). The best results from the studies were Ra = 2.19 and MRR = 375 mm3/s, which corresponded to Taguchi's single optimization levels, S2F1D1A2 and S2F2D2A1, respectively. In the next step, the performance values obtained for each MCDM technique were reoptimized using the Taguchi method, and the optimal levels were obtained: for traditional GRA, the level S2F1D2A1 (Ra = 2.52 µm, MRR = 125 mm3/s); for WGRA, the level S2F1D1A1 (Ra = 2.31 µm, MRR = 83 mm3/s); and for DEAR, the level S2F2D2A1 (Ra = 4.42 µm, MRR = 375 mm3/s), respectively. Lastly, in order to compare the experiments' performance, validation tests were carried out. The results of the experiments using multi-objective optimization show that traditional GRA improved the overall quality response characteristics by 29.86% compared to the initial setup parameters, while weighted GRA improved them by 34.48%, with the DEAR method providing an improvement of 96%. Based on the findings of this investigation, the DEAR optimization method outperforms the GRA method. As a result, the proposed methods are useful tools for multi-objective optimization of cutting parameters. [ABSTRACT FROM AUTHOR]

The animation industry is an important part of China's cultural and creative industries. In fact, it is the leading cultural and creative industry in China. However, there is insufficient research on the audience's views in China's animation industry, which has become an important research gap. Thus, an integrated approach of FAHP and GRA is proposed in this study, to analyse and evaluate the key selection factors for the Chinese animation industry from the perspective of a Chinese audience. In this research, in both FAHP and GRA models, factors such as visual appealing character, interesting performance of character animation, and easy-to-understand storyline are prioritised conditions for the selection of Chinese animation from the perspective of Chinese audiences. The main contribution of this research is to underscore the value of the hybrid MCDM model to aid Chinese animation companies in aligning their productions with audience expectations and making informed decisions. Finally, this study offers a systematic and objective model for Chinese animation selection, providing practical insights that can be applied in the industry and can serve as a valuable reference for future research in similar domains. [ABSTRACT FROM AUTHOR]

In this paper, slit fin with lateral perforations are presented and the optimal parameters of the perforation on the silt fin are determined. The effects of the three design parameters of perforation length H , perforation height P and tilt angle θ on the j and f -factor are analyzed using gray relation analysis (GRA) base on the Taguchi method (TM). Numerical simulations were performed for different combinations of the three geometric parameters for 16 cases. Based on the Taguchi method (single-objective optimization) analysis, out of the three perforation parameters, the perforation length H exhibited the most pronounced impact on the j -factor, boasting a substantial contribution ratio of 33.1 %. The contribution ratios of the perforation length H , height P , and tilt angle θ influencing the f -factor were determined to be 31.1 %, 46.2 %, and 18.4 %, respectively. The optimal combination of parameters for perforation on the slit fins was obtained by Gray relation analysis (multi-objective optimization) with values of H = 1/3, P = 1 mm and θ = 90°, representing the highest j -factor and the lowest f -factor of the parameter configuration, in contrast to the remaining parameter combinations, the SNR values for its j and f -factor stand at −49.14 and 35.1, respectively while the JF -factor also outperformed those of the other 16 groups of cases. Finally, the results obtained by Taguchi method and GRA are analyzed in conjunction with flow behavior. [ABSTRACT FROM AUTHOR]

The benefits provided by ecological assets play a crucial role in enhancing human well-being. However, there is a scarcity of viable methods for assessing their status. This study is grounded in Land Use and Land Cover Change (LUCC) and integrates both the quantity and quality aspects of these assets. Utilizing the ecological asset index (EQ and EQi), this study conducts a quantitative assessment of the assets in the Shibing karst and the Libo-Huanjiang karst heritage sites, while also qualitatively analyzing their influencing factors. The findings reveal that: (1) In the Shibing heritage site, forest and impervious surface assets exhibited an upward trend, whereas cropland and grassland experienced a decline; meanwhile, shrub and water body assets remained relatively stable. The total area of assets rated as excellent or good increased by 95.371 km2, resulting in an EQ enhancement of 45.427. (2) Likewise, in the Libo-Huanjiang heritage site, forest and impervious surface assets demonstrated an upward trajectory, while shrub assets declined. Cropland, grassland, and water body assets experienced minimal variation. The total area of assets rated as excellent or good expanded by 168.227 km2, resulting in an EQ enhancement of 80.806. (3) The execution of a series of ecological protection projects and management plans for heritage site conservation primarily accounts for the enhancement of regional assets. Notably, ecological resources, socio-economic conditions, human resources, and conservation management policies serve as pivotal drivers influencing the alterations in heritage site assets. [ABSTRACT FROM AUTHOR]

The escalating prevalence of antibiotic-resistant bacteria poses a grave threat to human health, necessitating the exploration of novel alternatives to conventional antibiotics. This study investigated the impact of extracts derived from the supernatant of four lactic acid bacteria strains on factors contributing to the pathogenicity of three Staphylococcus aureus strains. The study evaluated the influence of lactic acid bacteria supernatant extracts on the growth, biofilm biomass formation, biofilm metabolic activity, and biofilm integrity of the S. aureus strains. Additionally, the impact on virulence factors (hemolysin and coagulase) was examined. Gas chromatography coupled with mass spectrometry was used to identify the bioactive compounds in the extracts, while molecular docking analyses explored potential interactions. Predominantly, the extracts contain eight 2,5-diketopiperazines, which are cyclic forms of peptides. The extracts demonstrated inhibitory effects on biofilm formation, the ability to disrupt mature biofilms, and reduce the biofilm cell metabolic activity of the S. aureus strains. Furthermore, they exhibited the ability to inhibit α-hemolysin production and reduce coagulase activity. An in silico docking analysis reveals promising interactions between 2,5-diketopiperazines and key proteins (SarA and AgrA) in S. aureus, confirming their antivirulence and antibiofilm activities. These findings suggest that 2,5-diketopiperazines could serve as a promising lead compound in the fight against antibiotic-resistant S. aureus. [ABSTRACT FROM AUTHOR]

The irrigation sector in the Kingdom of Saudi Arabia (KSA) confronts a range of obstacles, such as scarce water resources, the elevated salinity and alkalinity of irrigation water, inefficient irrigation practices, and inter-sectoral competition for water resources. These challenges have led to diminishing agricultural yields and abandonment of arable lands. Internet of Things (IoT)-based irrigation systems present a promising remedy for these issues. By curbing water wastage and ensuring precise water delivery to crops, IoT-based irrigation systems offer a viable solution to the challenges entrenched in traditional irrigation methodologies in KSA. However, the widespread implementation of an IoT-based Smart Irrigation System (I-SIMS) poses a multifaceted and intricate challenge in KSA. This study is focused on the identification of the factors and challenges through a systematic review and ranking of the challenges/factors that exert a significant influence on the adoption of I-SIMS. Ranking aids in determining the importance of various alternatives. It enables locating the best options that support the required objectives in complex decision situations. The study employs both Grey Relational Analysis (GRA) and Analytical Hierarchical Process (AHP) methodologies to prioritize these factors. The study's conclusive findings indicate that among the challenges, technical expertise and security measures emerge as the foremost concerns that demand attention. [ABSTRACT FROM AUTHOR]

The global spread of the SARS-CoV-2 pandemic, originating in Wuhan, China, has had profound consequences on both health and the economy. Traditional alignment-based phylogenetic tree methods for tracking epidemic dynamics demand substantial computational power due to the growing number of sequenced strains. Consequently, there is a pressing need for an alignment-free approach to characterize these strains and monitor the dynamics of various variants. In this work, we introduce a swift and straightforward tool named GenoSig, implemented in C++. The tool exploits the Di and Tri nucleotide frequency signatures to delineate the taxonomic lineages of SARS-CoV-2 by employing diverse machine learning (ML) and deep learning (DL) models. Our approach achieved a tenfold cross-validation accuracy of 87.88% (± 0.013) for DL and 86.37% (± 0.0009) for Random Forest (RF) model, surpassing the performance of other ML models. Validation using an additional unexposed dataset yielded comparable results. Despite variations in architectures between DL and RF, it was observed that later clades, specifically GRA, GRY, and GK, exhibited superior performance compared to earlier clades G and GH. As for the continental origin of the virus, both DL and RF models exhibited lower performance than in predicting clades. However, both models demonstrated relatively higher accuracy for Europe, North America, and South America compared to other continents, with DL outperforming RF. Both models consistently demonstrated a preference for cytosine and guanine over adenine and thymine in both clade and continental analyses, in both Di and Tri nucleotide frequencies signatures. Our findings suggest that GenoSig provides a straightforward approach to address taxonomic, epidemiological, and biological inquiries, utilizing a reductive method applicable not only to SARS-CoV-2 but also to similar research questions in an alignment-free context. [ABSTRACT FROM AUTHOR]

Variations in the concentration of glucoraphanin (GRA) and glucoerucin (GER), as well as the corresponding breakdown products, isothiocyanates (ITCs) and nitriles, were investigated during the growth of broccoli sprouts. The concentrations of GRA and GER decreased sharply from 33.66 µmol/g to 11.48 µmol/g and 12.98 µmol/g to 8.23 µmol/g, respectively, after seed germination. From the third to the seventh day, both GRA and GER were maintained as relatively stable. The highest concentrations of sulforaphane (17.16 µmol/g) and erucin (12.26 µmol/g) were observed on the first day. Hereafter, the concentrations of nitrile hydrolyzed from GRA or GER were higher than those of the corresponding ITCs. Moreover, the ratio of sulforaphane to sulforaphane nitrile decreased from 1.35 to 0.164 from 1 d to 5 d, with a similar trend exhibited for erucin/erucin nitrile after 2 d. RNA-seq analysis showed that BolMYB28 and BolCYP83A1, involved in aliphatic glucosinolate (GSL) biosynthesis, remained largely unexpressed until the third day. In contrast, the genes operating within the GSL-myrosinase hydrolysis pathway were highly expressed right from the beginning, with their expression levels increasing significantly after the third day. Additionally, we identified two BolESPs and six BolNSPs that might play important roles in promoting the production of nitriles during the development of broccoli sprouts. [ABSTRACT FROM AUTHOR]

The qualitative and quantitative assessment of groundwater is one of the important aspects for determining the suitability of potable water. Therefore, the present study has been performed to evaluate the groundwater quality for Achhnera block in the city of Taj, Agra, India, where groundwater is an important water resource. The groundwater samples, 50 in number were collected and analyzed for major ions along with some important trace element. This study has further investigated for the applicability of groundwater quality index (GWQI), and the principal component analysis (PCA) to mark out the major geochemical solutes responsible for origin and release of geochemical solutes into the groundwater. The results confirm that, majority of the collected groundwater samples were alkaline in nature. The variation of concentration of anions in collected groundwater samples were varied in the sequence as, HCO3− > Cl− > SO42− > F− while in contrast the sequence of cations in the groundwater as Na > Ca > Mg > K. The Piper diagram demonstrated the major hydro chemical facies which were found in groundwater (sodium bicarbonate or calcium chloride type). The plot of Schoellar diagram reconfirmed that the major cations were Na+ and Ca2+ ions, while in contrast; major anions were bicarbonates and chloride. The results showed water quality index mostly ranged between 105 and 185, hence, the study area fell in the category of unsuitable for drinking purpose category. The PCA showed pH, Na+, Ca2+, HCO3− and fluoride with strong loading, which pointed out geogenic source of fluoride contamination. Therefore, it was inferred that the groundwater of the contaminated areas must be treated and made potable before consumption. The outcomes of the present study will be helpful for the regulatory boards and policymaker for defining the actual impact and remediation goal. [ABSTRACT FROM AUTHOR]

The advancement of quality and environmentally sustainable materials and products made from them has improved significantly over the last few years. However, a research gap is the lack of a developed model that allows for the simultaneous analysis of quality and environmental criteria in the life-cycle assessment (LCA) for the selection of materials in newly designed products. Therefore, the objective of the research was to develop a model that supports the prediction of the environmental impact and expected quality of materials and products made from them according to the design solution scenarios considering their LCA. The model implements the GRA method and environmental impact analysis according to the LCA based on ISO 14040. The model test was carried out for light passenger vehicles of BEV with a lithium-ion battery (LiFePO4) and for ICEV. The results indicated a relatively comparable level of quality, but in the case of the environmental impact throughout the life-cycle, the predominant amount of CO2 emissions in the use phase for combustion vehicles. The originality of the developed model to create scenarios of design solutions is created according to which the optimal direction of their development in terms of quality and environment throughout LCA can be predicted. [ABSTRACT FROM AUTHOR]

The optimization of machining processes is a deciding factor when increasing productivity and ensuring product quality. The response characteristics, such as surface roughness, material removal rate, tool wear, and cutting time, of the finish turning process have been simultaneously optimized. We used the Taguchi-based design of experiments L9(34) in this study to test and find the best values for process parameters like cutting speed, feed rate, depth of cut, and nose radius. The Taguchi-based multi-objective grey relational approach (GRA) method was used to address the turning problem of Inconel 718 alloy to increase productivity, i.e., by simultaneously minimizing surface roughness, tool wear, and machining time. GRA and the S/N ratio derived from the Taguchi approach were utilized to combine many response characteristics into a single response. The grey relational grade (GRG) produces results such as estimations of the optimal level of input parameters and their proportional significance to specific quality characteristics. By employing ANOVA, the significance of parameters with respect to individual responsibility and the overall quality characteristics of the cutting process were ascertained. The single-objective optimization yielded the following results: minimal surface roughness of 0.167 µm, tool wear of 44.65 µm, minimum cutting time of 19.72 s, and maximum material speed of 4550 mm3/min. While simultaneously optimizing the Inconel 718 superalloy at a cutting speed of 100 m/min, depth of cut of 0.4 mm, feed rate of 0.051 mm/rev, and tool nose radius of 0.4 mm, the results of the multi-objective optimization showed that all investigated response characteristics reached their optimal values (minimum/maximum). To validate the results, confirmatory experiments with the most favorable outcomes were conducted and yielded a high degree of concurrence. [ABSTRACT FROM AUTHOR]

Single-image super-resolution (SISR) techniques have found wide applications in semiconductor defect inspection. Enhancing image resolution to improve inspection sensitivity and accuracy holds great significance. A novel SISR algorithm, called cross-convolutional residual network (CCRN), is proposed in this study. CCRN comprises a cross-convolutional module (CCM), which incorporates a cross-sharing mechanism that facilitates the fusion of features from different stages, enabling the extraction of more information from the image. Moreover, a global residual aggregation structure (GRA) is introduced. GRA captures and transfers different levels of residual features acquired from learning each CCM to the reconstruction layer. Experimental results demonstrate that the proposed SR algorithm outperforms existing state-of-the-art SR algorithms in terms of both visual and quantitative metrics when applied to optical, SEM, and TEM images of microfluidic chips, CMOS image sensors, and quantum dots, respectively. Additionally, CCRN significantly improves the accuracy of defect classification and inspection of unpatterned wafers, as evaluated using the WM-811K dataset. Notably, an increase in local defection testing accuracy from 79.00% to 89.00% and an improvement in classification accuracy from 93.69% to 96.06% are achieved. These findings underscore the potential applications of the proposed algorithm in improving semiconductor defect inspection and classification accuracies. [ABSTRACT FROM AUTHOR]

Background: In diabetes, high blood glucose induces glucotoxicity, resulting in the further damage of pancreatic beta-cells and then precipitating diabetic complications. This study was aimed to investigate the relationship between glucotoxicity with the level of adipokines, diabetic cardiomyopathy, and hematological markers. Moreover, the study examined the potential modulatory effect of coenzyme Q10 (CoQ10) on the aforementioned markers associated with the sequelae of diabetes mellitus. Material and methods: Twenty-four male rats were randomly assigned to receive an injection of STZ to induce diabetes (n = 16) or to remain uninduced (n = 8). The hyperglycemic status was induced in fasting rats by single intraperitoneal injection of STZ (45 mg /kg b.w.) dissolved in citrate buffer (pH 4.5). Three days after STZ injection, rats were divided into three groups; Normal control group (A), Diabetic control group (B), and CoQ10- treated diabetic group (C). The group (C) was fed with the basal diet supplemented with 5 g of CoQ10 per kilogram of diet for three weeks after the diabetes induction. After 21 days, the blood and serum samples were taken to conduct biochemical analyses. Blood glucose was determined by Blood Glucose Monitoring System. Adipokines or cytokines were evaluated by ELISA from a serum sample. Cardiac myopathy biomarkers were estimated by UP-Converting Phosphor Immunoassay Analyzer, and hematological parameters were measured by automatic hematology analyzer. Results: In hyperglycemic rats, the level of fasting blood glucose, and serum level of resistin, omentin, TNF-α, and cardiomyopathy biomarkers significantly increased (P < 0.05). The treatment with CoQ10 significantly decreased the profile of adipokines and cardiomyopathy markers (cardiac enzymes and LPPLA2) in diabetic rats and also reduced glucose levels (P < 0.05). Lymphocyte percentages significantly decreased while significant increases were observed in granulocytes and MID percentages in hyperglycemic rats. Conclusion: Diabetic rats had higher serum levels of adipokines and cardiomyopathy markers. Among the hematological markers, GRA% and MID% increased while LYM% decreased. The profile of adipokines and cardiomyopathy markers improved when CoQ10 was supplemented. The study suggests that CoQ10 may have a beneficial effect on improving diabetic complications. [ABSTRACT FROM AUTHOR]