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Rubber pad forming is one of the primary ways of manufacturing aviation sheet metal components. In addition to reducing forming die costs, using elastic rubber in rubber pad forming prevents scratching on sheet metal, which is crucial for flight safety. However, during the forming process, the rubber pad primarily applies a vertical force to the sheet metal and cannot effectively exert pressure on the sheet metal laterally. Consequently, the contours of the finished product could fall outside specifications. This can cause severe manufacturing defects such as wrinkling or springback and therefore require a significant amount of labor to remedy and prevent any defects. As such, improvements in production efficiency are necessary. This study presents a rubber pad forming die with a lateral forming force mechanism that provides a lateral compressive stroke to the forming process of sheet metal via wedge-shaped structures; in other words, a lateral forming force to rubber pad forming is applied. In our exploration of stacking rubber pads of varying hardnesses in different orders, the maximum force was a larger-the-better objective, and the thickness compression ratio of the rubber pad was a smaller-the-better objective. We then combined grey relational analysis and the entropy method to identify the rubber pad stacking order and thicknesses with the optimal forming results. Subsequently, we constructed an actual rubber pad forming die with a lateral forming force mechanism as well as performed experiments and verification using 6061-T6 sheet metal with a thickness of 1 mm for aircraft wing reinforcement. The results demonstrated that employing rubber pad forming with a lateral forming force mechanism could reduce the amount of springback and surface wrinkling, meet blueprint requirements, and provide a new method and application of rubber pad forming for aviation sheet metal. [ABSTRACT FROM AUTHOR]

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

Nowadays, sustainable Minimum Quantity Lubrication (MQL) technology has significantly enhanced machinability, surface quality, and productivity comparing to dry machining. Furthermore, it is considered as a key factor that respects the ecological trends in machining and also contributes in reducing the consumption of cutting fluid and machining costs. The present paper brings a comparative research between sustainable dry machining and sustainable MQL machining while turning AISI 52100 steel with CVD coated (GC-2015) metal carbide inserts. First, parametric tests were carried out to evaluate the impact of machining parameters (CS, f, and Doc) on performance factors in terms of tool wear, cutting force, surface roughness, energy consumption, and cutting temperature. Then, a Taguchi L9 (3^3) experimental design was implemented in order to carry out a statistical analysis based on ANOVA and performance modelling (Ra, Fz, Pc, Kc, and MRR) by applying the (RSM) method. Finally, a comparative multi-criteria optimization was performed using the DF method and four other MCDM methods (GRA, EAMR, FUCA, and EDAS) combined with Taguchi's signal-to-noise-ratio (S/N). The results confirmed the effectiveness of MQL technique in enhancing machining performance over dry machining. Furthermore, the EAMR, EDAS, and GRA methods identified the same optimal combination (CS = 300 m/min, Doc = 0.45 mm, and f = 0.08 mm/rev), yielding the best results for (Ra, Kc, and MRR). The FUCA method promotes improvement of energy efficiency during machining by achieving minimal values for (Pc and Fz). Finally, the DF method stands out for its ability to produce balanced results across all performances. [ABSTRACT FROM AUTHOR]

Although the concept of circular economy is a frequently encountered effective tool of sustainable development, its social dimension, the social circular economy, is a topic that has only begun to be discussed. Assessing the social circular economy performance of European Union countries from the sustainable development perspective is critical for monitoring their progress. In this study, a model that evaluates the social circular economy performances of countries for sustainable development is proposed. The importance weights of the evaluation criteria are determined using the interval-valued intuitionistic fuzzy VIKOR method's linguistic scale. Then , the countries are ranked using the grey relational analysis method. In this study, the social circular economy performance of EU member states for 2021 are investigated with the proposed model. According to the results obtained, social circular economy performances are directly proportional to the success of countries in achieving sustainable development goals. The success rating achieved in this study may vary with the improvement activities of the relevant countries. The proposed model updates the ranking, considering each improvement. The findings of this study can help scholars and policymakers better understand the social circularity capabilities of the European Union member countries in the context of sustainable development. In the limited literature, there is no other study in which the social circular economy performance of EU member countries is measured with the relevant evaluation criteria. A model that can evaluate the social circular economy performances of the European Union member countries is proposed to fill the profound gap in this field. We contributed to the literature with a new model that differs with evaluation criteria, real and current data sets. [ABSTRACT FROM AUTHOR]

The current study proposes a new approach that is a combination of methods: fuzzy and decision-making trial and evaluation laboratory (DEMATEL) and distance-based approximation (DBA) so that decision-makers can determine the relationship structures between criteria, how the criteria affect each other, and the weight of each criterion. From that, managers can quickly and easily evaluate and select the best alternative. A previous study using DEMATEL and grey relational analysis (GRA) is presented as an example. Data from this previous study are used to present the implementation process and results of the proposed method and simultaneously show the results of the current problem solved by the fuzzy DEMATEL—TOPPSIS (technique for order of preference by similarity to ideal solution) method. The comparison of results between three methods including DEMATEL—GRA, fuzzy DEMATEL—TOPPSIS, and fuzzy DEMATEL—DBA is performed to demonstrate the efficiency of the proposed method. The results show that there is a slight difference in weights between fuzzy DEMATEL and DEMATEL but the error is extremely low. In addition, the correlation of results between methods is very high. Particularly, correlation coefficients between fuzzy DEMATEL and DEMATEL, between fuzzy DEMATEL—TOPPSIS and fuzzy DEMATEL—BDA, and between DEMATEL—GRA and fuzzy DEMATEL—BDA are 0.99, 0.95, and 0.78 respectively. Finally, all three methods suggest alternative 4 as the best option. [ABSTRACT FROM AUTHOR]

Mode I fracture toughness is the most common amongst the three fracture modes because of its applications in rocks/materials engineering. There are different experimental methods that are available for its measurements and the selection of the available procedures to be used is usually a difficult task and highly multi criteria in nature. Therefore, this study proposed novel applications of two fuzzy-based multi criteria decision making (MCDM) methods for prioritising the experimental procedures. Thirteen different experimental methods were identified and evaluated under three different criteria by the experts. The weights were computed using both expert-assigned weights to each criterion and the fuzzy intuitionistic entropy measure obtained weights. This study reveals that ISRM-suggested cracked chevron notched Brazilian disc method is the most preferred follow by the semi-circular bend specimen and Brazilian disc method. The Pearson's correlation between the models is very strong (˃ 0.9), indicating that either of the two proposed approaches is suitable for this purpose. The sensitivity analysis was conducted by generating twenty-one sets of weights and the outcome ranked ISRM-suggested cracked chevron notched Brazilian disc, the most suitable follow by semi-circular bend specimen and Brazilian disc methods. The correlations amongst the MCDM methods for the sensitivity analysis are also very strong as previously observed. This study shows that the proposed models are suitable in prioritising the most appropriate experimental procedures for mode I fracture toughness. Highlights: Experimental procedures for mode I fracture toughness are found in the literature but users are facing difficulties in selecting the most suitable. Novel fuzzy TOPSIS and fuzzy GRA MCDM methods are used for selecting the best experimental procedure. The ISRM-suggested cracked chevron notched Brazilian disc method is found to be most suitable by the novel methods. [ABSTRACT FROM AUTHOR]

Cauliflower is an important winter crop grown in India, its curds are rich in nutritional profile, containing valuable minerals and vitamins. However, cauliflower harvesting is mainly accomplished by hands, which is time-consuming and requires a high labour force. On the other hand, most developed cauliflower harvesters are once over or single pass type, which harvests all plants irrespective of their maturity. So, the selective harvester could improve the cauliflower curds yield, and then decrease the labour requirement. To improve the cutting performance of the selective cauliflower harvester, the working parameters of the chainsaw cutting mechanism need to be considered and optimized. This research investigates the impact of cutting height, feed (push) force, and cutting speed on the efficiency of the cutter during harvest. The Taguchi approach, together with grey relational analysis (GRA), was employed to identify the most favorable combination of operational parameters. In addition, the variance analysis was conducted to statistically examine the impact of multiple parameters. The findings indicated that the feed force was the major parameter that influenced the cutting force, splitting failure levels, and cutting time. The most effective parameter combination consisted of a cutting height of 15 mm, a feed force of 10 N, and a cutting speed of 5 m/s. The grey relational grade of the ideal parameter combination has shown a 0.322 increase in comparison to the grade achieved with the initially selected parameter combination. This setting was further incorporated in the developed selective cauliflower harvester to improve the performance of its cutting mechanism. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM), also known as 3D printing, is a recent innovation in manufacturing, employing additive techniques rather than traditional subtractive methods. This study focuses on Directed Energy Deposition (DED), utilizing a blend of nickel-based superalloy IN 718 and stainless steel SS316 powders in varying ratios (25%+75%, 50%, and 75%+25%). The objective is to assess the impact of process parameters on quality and optimize them. Mechanical properties of the different powder mixtures are compared. In the study, Taguchi-grey relational analysis is employed for parameter optimization, with four key factors identified: laser power, overlap ratio, powder feed rate, and scanning speed, affecting cladding efficiency, deposition rate, and porosity. Verification experiments confirm optimization repeatability, and further fine-tuning is achieved through one-factor-at-a-time experiments. Optimized parameters yield varied tensile properties among different powder mixtures; for example, a 25% SS316L and 75% IN718 blend demonstrates the highest ultimate tensile strength (499.37 MPa), while a 50% SS316L and 50% IN718 blend exhibits the best elongation (13.53%). This study offers an effective approach for using DED technology to create mixed SS316 and IN718 powders, enabling tailored mechanical performance based on mixing ratios. [ABSTRACT FROM AUTHOR]

Failure Modes, Effects, and Criticality Analysis (FMECA) is a commonly used method for analyzing system reliability. It is frequently applied in identifying weak points in the reliability of CNC machine tools. However, traditional FMECA has issues such as vague descriptions of risk factors, equal treatment of risk factors, and unclear directions for improving weak points. In response to the issue of vague descriptions of risk factors, this paper further expands severity (S) into machine hazard (M) and personal hazard (P), and subdivides detectability (D) into functional structural complexity (D1) and detection cost (D2). In addressing the issue of treating risk factors equally, this paper integrates Distance Analysis Method (DAM) and Grey Relational Analysis (GRA) to propose Distance-Grey Relational Analysis (D-GRA). Subsequently, based on the D-GRA method, the weights of each risk factor were determined by comprehensively considering expert system scores and actual economic loss indicators. In response to the issue of unclear improvement directions for weak points, this paper introduces the BCC model. It treats common failure modes of CNC machine tools as decision-making units within the BCC model, refines risk factors as input indicators, and evaluates the efficiency values of each decision-making unit based on various actual losses as output indicators. Through efficiency value analysis, it proposes improvement directions for weak points. Then, based on the weights of risk factors and the efficiency values of failure modes, a modified calculation method for the new Risk Priority Number (RPN) is proposed to amend the traditional RPN, This paper takes the electric spindle system of a certain machining center as an example, applies the proposed method to rank common failure modes with the new RPN, and compares it with other RPN calculation methods to verify the rationality of the proposed approach. Finally, it presents improvement directions for reliability enhancement. [ABSTRACT FROM AUTHOR]

Slope stability prediction based on the grey entropy relational analysis (GERA) was carried out. First, the entropy weight method was applied to determine the weight of each influencing factor, and the GERA model was established. Then, 30 slope engineering cases were collected from the literature as a reference sample database, and another ten slope engineering cases were used as prediction samples. Finally, the GERA method was used to calculate the grey entropy relational degree between each prediction sample and 30 reference samples to evaluate the stability of the prediction sample slope. The results show that the stability predicted results of 10 slopes obtained by GERA are entirely consistent with the actual slope stability state. Case analysis confirms that the GERA model can effectively predict the slope stability state. [ABSTRACT FROM AUTHOR]

Defect Density (DD) is a cornerstone metric in software quality assessment, influencing decisions across quality planning, testing strategies, and resource allocation. However, inherent uncertainties within software module data significantly impede accurate prediction of DD. This paper proposes a novel predictive framework that leverages the strengths of grey system theory and fuzzy logic to address data imprecision in software defect measurement. Our approach introduces a fuzzy grey relational similarity metric that capitalizes on the benefits of both fuzzy logic membership functions and grey relational analysis for similarity assessment. We perform a rigorous evaluation comparing our model with eight established machine learning algorithms across 28 publicly available software datasets. The proposed framework demonstrates superior performance, particularly in scenarios characterized by high sparsity levels in the DD variable. We further analyze the model's efficacy across varying data sparsity levels, categorizing datasets into four groups based on sparsity ratios. Our findings reveal the model's dominance over alternative prediction methods in datasets exhibiting high and very high sparsity. Notably, traditional techniques like multi-linear regression and multi-layer perceptron exhibit limitations in handling such challenges within this specific problem domain and data landscape. Conversely, ensemble learning techniques emerge as viable alternatives for datasets with lower sparsity levels. [ABSTRACT FROM AUTHOR]

This study aimed to extend the storage time of fresh-cut lotus roots. A quantitative evaluation was conducted using the grey–correlation analysis method based on the variation–coefficient weight to observe the treatment of acetic acid (0, 2%, and 5%) and ozone (0, 0.3 mg/L, and 0.6 mg/L). Their effects on weight loss rate, browning degree, and the content of reducing sugar, ascorbic acid, and free amino acid were determined. Under varied treatments and storage time, the weight loss rate, browning degree, and content of nutritional components in fresh-cut lotus root differed. Result of the weighted correlation degree showed that for fresh-cut lotus root stored for more than 4 days, 0.3 mg/L ozone could be used as preservative to ensure the storage quality. These findings could provide a scientific basis for using acetic acid and ozone as preservative to extend the storage time of fresh-cut lotus root. [ABSTRACT FROM AUTHOR]

The selective catalytic reduction (SCR) system plays a crucial role in reducing diesel exhaust emissions. To further enhance the performance of the SCR system, a three-dimensional numerical simulation model was established and experimentally validated for its reliability. Subsequently, the length (L1) and porosity (ε1) of carrier 1, as well as the length (L2) and porosity (ε2) of carrier 2, were varied to achieve an axial non-uniform distribution of the catalysts. The impact of this axial non-uniform distribution on the SCR system's performance was investigated. Finally, a fuzzy grey relational analysis method was employed to evaluate and optimize the influence of the aforementioned four individual parameters on the SCR system's denitrification (de-NOx) efficiency and pressure drop. The results indicate that variations of L1 have the greatest impact on de-NOx efficiency, while changes of ε2 have the most significant effect on pressure drop. The fuzzy grey relational degrees of L1, L2, ε1, and ε2 on de-NOx efficiency are 0.8267, 0.6288, 0.7457, and 0.8233, respectively. Similarly, the fuzzy grey relational degrees of L1, L2, ε1, and ε2 on pressure drop are 0.3552, 0.4894, 0.1045, and 0.7931, respectively. Compared with uniformly distributed catalysts, axially non-uniform distributed catalysts can improve de-NOx efficiency by 2.22% and reduce pressure drop by 46 Pa. This study provides theoretical and engineering references for improving the SCR catalyst coating process. [ABSTRACT FROM AUTHOR]

Predicting regional carbon dioxide (CO2) emissions is essential for advancing toward global carbon neutrality. This study introduces a novel CO2 emissions prediction model tailored to the unique environmental, economic, and energy consumption of Shanghai Chongming. Utilizing an innovative hybrid approach, the study first applies grey relational analysis to evaluate the influence of economic activity, natural conditions, and energy consumption on CO2 emissions. This is followed by the implementation of a dual-channel pooled convolutional neural network (DCNN) that captures both local and global features of the data, enhanced through feature stacking. Gated recurrent unit (GRU) network then assesses the temporal aspects of these features, culminating in precise CO2 emission predictions for the region. The results indicate: (1) The proposed hybrid model achieves accurate predictions based on accounting data, with high precision, low error, and good stability. (2) The study found an overall increase in Chongming's carbon emissions from 2000 to 2022, with the prediction results being generally consistent with existing research findings. (3) The proposed method, based on Chongming's CO2 emission predictions, addresses issues such as the scarcity of effective accounting data and inaccuracies in traditional calculation methods. The results can provide effective technical support for local government policies on carbon reduction and promote sustainable development. [ABSTRACT FROM AUTHOR]

In the present study, a wear-resistant AA7075-Al2O3 composite coating was efficiently developed on an AA6082 substrate via the friction surfacing technique. Micro-sized Al2O3 particles were used as reinforcement material by filling in the blind holes drilled on the face of the consumable rod (AA7075) to develop the composite coating. The consumable rod rotational speed (1000–1400 rpm), number of blind holes (1–3) and travel speed (140–180 mm/min) were considered as input parameters. The composite coating's dry sliding wear, microhardness and bond strength were chosen as output response characteristics. Grey relational analysis was utilized to multi-objectively optimize the input parameters while the experiments were carried out in accordance with the Taguchi L9 orthogonal array. The multi-objective optimized input parameters were found to be the rod rotational speed of 1200 rpm, a travel speed of 140 mm/min, and three holes, respectively. Under optimal conditions, the bond strength of the AA7075-SiC composite coating is 170.4 MPa, and its wear (weight loss) is 56.02% and 41.6% less than that of AA7075 and AA6082, respectively. The microhardness of the composite coating developed at optimal parametric setting (174.2 HV0.1) was 99.98% and 11.31% higher than that of the substrate (AA6082) material (87.11 HV0.1) and rod (AA7075) material (156.5 HV0.1) respectively. [ABSTRACT FROM AUTHOR]

The present study aims to explore the suitable welding technique for Austenitic Stainless Steel (ASS) material through multi-criteria decision making approach i.e. Grey Relational Analysis (GRA). For this purpose, seven selection criteria are identified with the help of literature and expert' suggestions and assigned the same weightage to all selection criteria. We have identified eight most widely used welding techniques through the comprehensive literature review for stainless steel material. From the application of GRA approach, it is observed that Tungstun Inert Gas welding obtained 1st rank and found the most suitable welding process for ASS material as per selected criteria. This welding exhibits the minimal heat affected zone and better weld strength for ASS material. This study would facilitates to industrial person and engineers in selection of appropriate welding technique for ASS material with the specified performance characteristics. [ABSTRACT FROM AUTHOR]

The main objective of this study was to optimize the friction stir process parameters to fabricate agate particle reinforced AA6061-T6 Aluminum alloy surface composite for hardness and tensile strength. The surface composite samples were made using a CNC milling machine based on Taguchi's mixed L16 orthogonal array. The process parameters such as tool rotational speed, traverse speed, percent of reinforcement, number of passes and pin profiles were selected in this study. The optimal properties for the developed surface composite were obtained when surface composites having 18% reinforcement were fabricated by a two-pass FSP using a squared pin tool with a rotational speed of 1400 rpm and traverse speed of 60 mm/min. At the optimal process parameters, the tensile strength and hardness of the fabricated surface composite were found to be 312.23 MPa and 74.82 HRB respectively. ANOVA analysis performed at a 95% confidence level revealed that all process parameters were significant and the contribution of the number of passes on the properties of surface composite was higher than other process parameters. Microstructural analysis revealed that medium rotational speed and high traverse speed produced a uniform distribution of agate particles which helped to improve the properties. Article Highlights: A new aluminum alloy surface composite produced with friction stir processing. Studied the effects of process parameters on tensile strength and hardness of the surface composite. Grey relational analysis (GRA) identified the best process parameters for optimum properties. [ABSTRACT FROM AUTHOR]

In this research study, the synthesis of poly lactic acid (PLA) based bio composite material factors contributions were investigated through the Taguchi-based grey relational analysis (GRA) technique. Effects of micro crystalline cellulose (MCC) and montmorillonite (MTT) nano clay filler, sorbitol (S) plasticizer, and temperature (T) operating factor on the PLA matrix through melt-mixing preparation method. The tensile strength (TS), Young modulus (YM), flexural strength (FS), flexural modulus (FM), hardness, impact strength (IS), water absorption (WA), and density properties of bio composite material were investigated for each experimental setup (orthogonal array, L16). Additionally, neat PLA and optimal sample structural, thermal, and morphological properties were examined through Fourier transform infrared spectroscopy (FTIR), X-Ray diffraction (X-RD), thermal gravimetry analysis/differential thermal gravimetry (TGA/DTG), and DSC and SEM analyses. The obtained result for optimal mechanical and physical properties of MCC/MTT/S/PLA bio composite was MCC at level 3 (6%), MTT at level 4 (9%), S at level 2 (10%), and T at level 4 (175 °C). Analysis of variance (ANOVA) shows that MTT has the greatest significant effect on mechanical and physical properties of MCC/MTT/S/PLA bio composite followed by MCC, T, and S. The confirmation test indicates that the improvement of weighted grey relational grade (GRG) from 0.7896 to 0.846 and the FTIR, XRD, thermal gravimetry/differential scanning calorimetry (TG/DSC), and scanning electron microscopy (SEM) results indicates that the good interaction between PLA and fillers, improvement of thermal and morphological properties of optimal (6MCC/9MTT/10S/175 °C) bio composite samples. Therefore, the multi-response characteristics of MCC/MTT/S/PLA bio composite can be highly improved by this technique. [ABSTRACT FROM AUTHOR]

With the increasing number of students majoring in art in universities, the improvement of the quality of art teaching in universities is becoming increasingly important. However, due to the unique characteristics of art teaching compared to other majors, in order to improve the quality of art teaching, it is necessary to follow the individual characteristics of art majors and students, and establish corresponding systems to ensure the steady improvement of teaching quality, while taking students as the main body. Thus, the overall quality of cultivating art talents in universities can be improved. The art education quality evaluation in higher education institutions is MAGDM. In recent years, the Exponential TODIM (ExpTODIM) and grey relational analysis (GRA) technique was employed to deal with MAGDM. The 2-tuple linguistic Pythagorean fuzzy sets (2TLPFSs) are employed as an effective tool for expressing uncertain decision information during the art teaching quality evaluation in higher education institutions. In this paper, the 2-tuple linguistic Pythagorean fuzzy number Exponential TODIM-GRA (2TLPFN-ExpTODIM-GRA) technique is implemented to deal with MAGDM under 2TLPFNs. Finally, a numerical example for art teaching quality evaluation in higher education institutions is implemented to verify the proposed technique. The research contributions of this work are outlined: (1) the entropy technique is employed to conduct the attribute's weight; (2) the 2TLPFN-ExpTODIM-GRA technique is implemented to implement the MAGDM under 2TLPFNs; (3) a numerical example for art teaching quality evaluation in higher education institutions and several comparative analysis is employed to validate the 2TLPFN-ExpTODIM-GRA technique. [ABSTRACT FROM AUTHOR]

We attempted to optimize the process-based parameters during friction stir welding of AZ80A - AZ31B Mg alloys with the objective of enhancing the mechanical properties of the fabricated joints. A response surface method based grey relational analysis was employed using three factors and three distinctive levels. A central composite design based multi-objective numerical model using the technique of grey relational analysis was formulated for optimizing the tool dependent parameters, namely tool's rotational speed, its speed of traverse and geometry of the pin. Grey relational grade was determined for all the responses: tensile strength and elongation percentage. Analysis of variance was employed for attaining grey relational grade to determine the most influential parameter of the FSW process. It was observed that the geometry of the tool pin had a greater impact in ascertaining the quality of the fabricated Mg alloy joints, and the tool possessing tapered cylindrical pin geometry exhibited larger values of grey relational grade. Optimized process parameter settings based on the attained GRG values were recorded to be 1100 rpm rotational speed, speed of traverse of 1.5 mm/sec and a tool with taper cylindrical pin geometry. The anticipated values were validated through confirmation investigational runs performed during employment of optimized parameter combinations, which exhibited a perfect agreement with the investigational run values and the confirmatory joint exhibited a tensile strength of 260.42 MPa and elongation percentage of 6.53. [ABSTRACT FROM AUTHOR]

The present research examines the impact of Si3N4–BN hybrid reinforcement on the machinability of a magnesium hybrid composite in wire electrical discharge machine (WEDM). The composite is fabricated through inert gas assisted stir casting route with silicon nitride and boron nitride as reinforcement with varying weight percentages of 0%, 5%, and 10%. Subsequently, the fabricated composites were machined through WEDM according to Taguchi 27 orthogonal array with varying pulse on time (PON), pulse off time (POFF), wire feed rate (WFR), and wire tension (WT). The machinability of the composite was evaluated by measuring Surface Roughness (SR), Kerf Width (KW), and Cutting Velocity (CV) during WEDM. Results reveals that the % of Si3N4 has greater influence over kerf width and cutting velocity whereas BN % has higher influence over surface roughness. The optimization of process parameters using the Taguchi method resulted in different combinations of parameters for each output response. Therefore, Grey Relational Analysis was applied to determine the common optimal process parameters for all three considered output responses. The identified input parameters that yielded the higher CV, minimal SR and KW were as follows: 0% Si3N4 and BN, 6 µs PON, 14 µs POFF, 6 m/min WFR, and 10 g WT. Artificial Neural Network model has been developed to predict the output response CV, SR and KW. The 6–8–3 network model predicts the output responses with better accuracy with overall R2 value of 99.3%. [ABSTRACT FROM AUTHOR]

In order to study the relationship between China's safety production indicators and economic and social indicators, the development trend of indicator data in the past 20 years was statistically analyzed, and qualitative and quantitative research was conducted using grey relational analysis and multiple linear regression analysis methods. In the past two decades, there has been a significant improvement in the number of deaths, work-related injuries, and occupational patients in China's safety production, and the country's three categories of 14 economic and social indicators have achieved rapid development. Using the grey relation analysis method, the grey correlation degree between the number of deaths, work-related injuries, and occupational patients in China over the past twenty years and 14 economic and social indicators was obtained. The ranking of economic and social indicators that affect the number of deaths, work-related injuries, and occupational patients varies greatly. A multiple linear regression model was established for the number of deaths, work-related injuries, occupational diseases, and 14 economic and social indicators. The rationality of the model was verified from four aspects: R2, F-value, P-value, and deviation between actual and fitted values. Provide guidance for the development of safety production indicators and economic and social indicators in China through research. [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]

This study investigates the effects of laser power, scan speed, and hatch distance on the features of aluminium specimens produced using direct metal laser sintering (DMLS). By systematically varying these parameters, we identified optimal combinations for producing high-quality metal components. Our findings were validated through reproducible printing processes. Analysis of variance (ANOVA) and grey relational analysis (GRA) were employed to optimize the production parameters further. We found a significant trade-off between laser power, tensile strength, and fatigue resistance, with laser power having the most substantial impact on mechanical properties, microstructure, and surface roughness. Statistical analysis confirmed that higher laser power improves mechanical characteristics but may increase surface roughness. These insights are crucial for enhancing the efficiency and quality of DMLS-produced metal components. [ABSTRACT FROM AUTHOR]

Accurately predicting the state of health (SOH) of lithium-ion batteries is fundamental in estimating their remaining lifespan. Various parameters such as voltage, current, and temperature significantly influence the battery's SOH. However, existing data-driven methods necessitate substantial data from the target domain for training, which hampers the assessment of lithium-ion battery health at the initial stage. To address these challenges, this paper introduces the multi-head attention-time convolution network (MHAT-TCN), amalgamating multi-head attention learning with random block dropout techniques. Additionally, it employs grey relational analysis (GRA) to select health indicators (HIs) highly correlated with battery capacity, thereby enhancing the accuracy of the model training. Employing leave-one-out crossvalidation (LOOCV), the MHAT-TCN network is pre-trained using data from batteries of the same model to facilitate comprehensive prediction of the target battery throughout its operational period. Results demonstrate that the MHAT-TCN network trained on HIs outperforms other models, enabling precise predictions across the entire operational period. [ABSTRACT FROM AUTHOR]

The present work deals with the influence of the graphite and tin particles in the wear behavior of A356 alloy-based composites. The A356 alloy consists of 92% aluminum and 7% silicon. The A356/Gr/Sn composites were fabricated with the proportion of 10 wt.% Gr and varying wt.% of Sn (0, 5, and 10 wt.%) particles through the bottom pouring stir casting route. The inclusion of Sn with Gr in A356 alloy matrix took significant improvement in the wear characteristics of the composites. Process parameters such as wt.% of tin, normal load, and sliding speed have been considered for performing the wear test. The Box–Behnken design in response surface methodology was chosen for conducting the wear study because of its ability to efficiently explore the response surface with fewer experiments, ensuring statistical rigor in optimizing multiple variables within a balanced experimental domain. Grey relational analysis (GRA) has been used to optimize the wear study parameters. Grey-fuzzy reasoning grade analysis was used to check the uncertainty in the GRA results. The study used main effect plots to analyze the GFRG values for the various input variables. An artificial neural network model was created to create the relationship model between the input conditions and output responses. Scanning electron microscope micrographs were used to test the worn samples' surfaces. [ABSTRACT FROM AUTHOR]

The current study explores the precision enhancement of AISI420 Martensitic Stainless Steel (MSS) using sinking-electrical discharge machining (S-EDM) with Copper electrodes, which is a unique combination of materials and machining process and conducts a comprehensive multivariate analysis to investigate the correlation between machine control variables (MCV) and measured machining performance (MMP) in the context of AISI420 Martensitic Stainless Steel and Sinking-Electrical Discharge Machining. The analysis of variance (ANOVA) establishes the hierarchy of machine control variables influence: Pulse current (B) > Gap voltage (A) > Pulse on Time (C). Remarkably, Pulse current (B) emerges as the paramount parameter, thus constituting a cornerstone of this study's findings. This research article utilizes the RSM–GRA–PCA methodology, which combines response surface methodology (RSM), grey relational analysis (GRA), and principal component analysis (PCA) to optimize the machining process. Using traditional RSM–GRA technique and RSM–GRA–PCA methodology, the experimental Grey Relational Grade (GRGexperiment) are achieved 0.8048 and 0.9817, respectively. The validation test has been performed to confirm the fittest method positions. The percentage significance of significant factor is also improved from 64.63 to 79.71% and error is reduced from 5.22 to 1.68% using RSM–GRA–PCA methodology with improved GRG of 0.068. This integrated approach improves the grey relational grade (GRG) and reduces errors, leading to more accurate and efficient machining. [ABSTRACT FROM AUTHOR]

Drilling precise micro-holes in glass material has brought a new challenge, primarily due to its application in microfluidic devices. It is required to reduce machining time and simultaneously achieve repeatability of the process. The machined hole should have minimum overcut to get desired hole diameter. Maximum hole circularity and minimum heat-affected zone are the essential hole characteristics to achieve controlled machining. The multi-criteria decision-making (MCDM) method is quite effective in selection of best possible combination of outputs from several alternative solutions. The experimental data from the previously published paper is used in the current study. As input parameters, the experiment data include voltage, tool feed rate, and machining time. The radial overcut (ROC), circularity of the machined hole and heat-affected zone (HAZ) were calculated as output responses. The experiments were conducted using copper and nickel-coated copper tools in previous study and response data were used in current work. The present study uses the methods which include hybrid grey relational analysis (GRA), technique for order performance by similarity to ideal solution (TOPSIS), and VIšekriterijumsko KOmpromisno Rangiranje (VIKOR) methods for the selection of the best combination of process parameters in drilling to obtain the optimal geometric characteristic of a hole, i.e. optimal values of ROC, circularity and HAZ, in glass using electrochemical discharge machining (ECDM). In this study, weight calculation for MCDM was proposed using entropy and analytic hierarchy process (AHP) methods and combined to get final weights using the fuzzy logic tool, which determines the importance of AHP and entropy weights using the expert opinion. The final weights were used to calculate the ranks from each MCDM method and determine optimal process parameter selection in ECDM hole drilling with copper and nickel-coated copper tools. The fuzzy logic method determined that the weight contribution from AHP is 0.586 in the present study. Genetic algorithm-based multi-objective optimization was conducted, and a non-dominated Pareto front was generated. The Spearman correlation method was used to determine the relationship between different MCDM methods. The current study will be helpful in selecting the best combination of process parameters in drilling holes during ECDM machining using MCDM methods. [ABSTRACT FROM AUTHOR]

Glass fiber-reinforced polymer (GFRP) composites are extensively used in many sectors because of their high strength, low weight, and resistance to corrosion properties. Drilling operations are needed to make holes for the joining of the components. Various defects were reported during the drilling, and the authors in this study tried to provide an approach to minimize the drilling defects. Selection of the optimum level of the input parameters can reduce defects generated during drilling. This study used grey relational analysis to optimize input parameters during glass fiber-reinforced composite drilling. Three tools (T1, T2, T3) with point angles (118°, 135°, 140°), feed rate in mm/min (10, 20, 30), and spindle speed in rpm (1000, 2000, 3000) were taken as input parameters. Taguchi L27 orthogonal arrays were selected for the three factors at three levels. Machining force (Fm), surface roughness (Ra), peel-up delamination factor (DF1), push-out delamination factor (DF2), hole size error, and circularity error were taken as output responses. Drilling GFRP composite by considering these input parameters combined with all these output responses using grey relational analysis was not explored before. The effect of input parameters on individual output response was analyzed using Taguchi and analysis of variance. Feed rate influenced machining force the most (65.34%), tool geometry influenced surface roughness the most (38.32%), and spindle speed influenced the push-out delamination factor the most (18.11%). SEM analysis was done to explain the aftereffects of drilling. [ABSTRACT FROM AUTHOR]

There is a complex high-dimensional nonlinear mapping relationship between the compressive strength of High-Performance Concrete (HPC) and its components, which has great influence on the accurate prediction of compressive strength. In this paper, an efficient robust software calculation strategy combining BP Neural Network (BPNN), Support Vector Machine (SVM) and Genetic Algorithm (GA) is proposed for the prediction of compressive strength of HPC. 8 features were extracted from the previous literature, and a compressive strength database containing 454 sets of data was constructed. The model was trained and tested, and the performance of 4 Machine Learning (ML) models, namely BPNN, SVM, GA-BPNN and GA-SVM, was compared. The results show that the coupled model is superior to the single model. Moreover, because GA-SVM has better generalization ability and theoretical basis, its convergence speed and prediction accuracy are better than GA-BPNN. Then Grey Relational Analysis (GRA) and Shapley analysis were used to verify the interpretability of the GA-SVM model, which showed that the water-binder ratio had the most significant influence on the compressive strength. Finally, the combination of multiple input variables to evaluate the compressive strength supplemented this research, and again verified the significant influence of water-binder ratio, providing reference value for subsequent research. [ABSTRACT FROM AUTHOR]

The competition between real estate enterprises in the future is likely to be between supply chains, and strategic general contracting is an important part. Implementing strategic construction general contracting procurement is beneficial for improving procurement efficiency and transparency, saving procurement costs, improving procurement quality, achieving economies of scale, and opening up or enhancing the core competitiveness of real estate enterprises. It has practical and guiding significance for the long-term and good development of real estate enterprises. The capability evaluation of real estate general contractors is a multiple-attribute decision-making (MADM) problem. Recently, the Exponential TODIM(ExpTODIM) and (grey relational analysis) GRA technique has been used to cope with MADM issue. The hesitant triangular fuzzy sets (HTFSs) are used as a tool for characterizing uncertain information during the capability evaluation of real estate general contractors. In this paper, the hesitant triangular fuzzy Exponential TODIM-GRA (HTF-ExpTODIM-GRA) technique is built to solve the MADM under HTFSs. In the end, a numerical case study for capability evaluation of real estate general contractors and some comparative analysis is given to validate the proposed technique. The main contribution of this paper is managed: (1) The MEREC technique is constructed to obtain weight values under HTFSs; (2) an integrated HTF-ExpTODIM-GRA technique is constructed to manage the MADM issue; (3) An illustrative example for capability evaluation of real estate general contractors has accomplished to verify the HTF-ExpTODIM-GRA technique. [ABSTRACT FROM AUTHOR]

Fluid leakage caused by debonding at the cement interfaces driven by low-temperature fluids is recognized as a significant challenge to wellbore integrity. Previous studies have primarily focused on casing/cement interface debonding, without thoroughly analyzing the simultaneous propagation of debonding fractures at the casing/cement interface and cement/formation interface. Furthermore, prior investigations have been conducted based on hydro-mechanical coupling analysis, neglecting the influence of injection fluid temperature on interface debonding. Therefore, this paper aims to reveal the debonding mechanism of the two cement interfaces using a coupled thermal–hydrological–mechanical (THM) modeling approach. Considering the effect of injected fluid temperature, a 3D numerical model of casing-cement-formation is developed for the simulation of the fluid-driven debonding based on the coupled pore pressure cohesive zone method. The influences of various factors including injection fluid temperature, flow rate, viscosity, casing pressure and cement Young's modulus on the cement sheath debonding are studied, and grey relational analysis is carried out on the influence degree of different factors. The results indicate that the fracture propagation pressure (FPP) is higher, and the fracture height is lower at the casing/cement interface compared to the cement/formation interface. When the fluid temperature is considered by the model, the FPP at the cement sheath interface is lower, and fracture initiation and propagation are more likely to occur. The results also demonstrate that higher fluid temperature, flow rate, viscosity and casing pressure increase the FPP and reduce the risk of cement sheath interface debonding. Casing pressure and fluid temperature exhibit a more prominent influence on FPP. The method can effectively predict the propagation of debonding failures at the cement sheath interface, and provide a foundation for the optimization of injection parameters. Highlights: This paper investigates the simultaneous propagation of the fluid-driven debonding of two cement interfaces considering the injected fluid temperature. A novel numerical model considering the fully coupled thermal–hydrological–mechanical processes is developed. Effects of injection fluid temperature, flow rate, viscosity, casing pressure and cement Young's modulus on the interface debonding are examined. A grey relational analysis is conducted to rank the influence degree of different factors. [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]

Improving the machining efficiency in form of better surface quality and improved tool life is always challenging. This is due to the high amount of heat produced during machining. Hence effective measures are required to control the heat generated. In this response, conventional coolants are applied to achieve cooling and lubricating effect in machining. However, these cutting fluids affect the operator's health and environmental resources. Considering this, the aim of the work is to assess the role of minimum quantity lubrication (MQL) compared to dry and flood cooling. The turning tests are performed under the defined machining environments to find the most suitable combination of parameters to minimize the tool wear and surface roughness. The Taguchi orthogonal arrays are incorporated to evaluate the 27 different combinations of variable working parameters and assess them. The lubricating conditions are varied as per the experimental design. Three different lubricating conditions are used: dry lubrication, flood lubrication and MQL. A statistical analysis of obtained results is performed to understand the effect of machining variables on output responses. A multi-response optimization is conducted to minimize surface roughness and tool wear by utilizing Grey relational analysis. Finally, the most suitable combination of parameters is suggested based on the obtained grey relational grade. [ABSTRACT FROM AUTHOR]

The Gas Tungsten Arc Welding (GTAW) approach is employed in this research study to join the thin sheet (1.6 mm) of Super Duplex Stainless Steel (SDSS). Process parameters such as welding current (65–85 A), voltage (12–20 V), and welding speed (250–450 mm/min) are used to compute heat input. To anticipate all potential experiments and outcomes, an artificial neural network is deployed. The Taguchi technique and grey relational analysis (GRA) is employed to optimize the welding conditions. To identify the best suitable parameters, the Taguchi L25 orthogonal array technique is employed. The parameters 1-3-4-5 have been established by GRA. The welding current, voltage, welding speed, and gas flow rate were discovered to be 65 A, 16 V, 400 mm/s, and 12 L/min, respectively. After determining the optimal conditions, the butt joint of SDSS material is fabricated using the GTAW technique. The welded joint's microstructure, mechanical characteristics (tensile test, three-point bending, and hardness), and corrosion rate has been investigated. Based on the acquired experimental data, artificial intelligence tools have evolved and the capability of evolved tools has been analyzed. The outcomes of the analysis prove that the model is competent enough to predict the desired process variables. [ABSTRACT FROM AUTHOR]

The present work is the investigating the parametric optimization of FSW parameters for joining ZE42 magnesium alloys by multi-objective grasshopper modelling technique (MOGOA) and non-dominated sorted genetic algorithm-II (NSGA-II). Regression equations were used to predict the optimal tensile strength and hardness of the FSW joints, while tensile and Vickers hardness testing was used to obtain experimental results. Response surface method and Box-Behnken experimental design were used to create the experimental methodology, while the adequacy of the predicted and experimental results was analyzed by Analysis of variance. Five different tool types were tested for different levels of input parametric values. The optimized input parameters were 1150 rpm of tool rotation speed, 60 mm/min of welding speed, 5 kN of axial force and cylindrical threaded tool pin, which exhibited the highest ultimate tensile strength for specimen '2'. It was found that the axial force was the most influential input parameter on the output tensile strength and microhardness, followed by welding speed and the tool pin profile. It was found that the NSGA-II provided better optimization compared to MOGOA. The fractography analysis indicated ductile fracture for specimen '2', which also exhibited the highest ultimate tensile strength 180 MPa and enhanced Vickers hardness of 80.6 HV while the highest hardness noted was 84.3 HV (specimen 20) during the experimental trials. [ABSTRACT FROM AUTHOR]

This research paper presents a novel investigation into optimizing the milling process of S-GFRP composites using a combination of spindle speed, cutting depth, and feed rate. The study utilizes the Taguchi method and a unique Orthogonal Array L9 design to examine the impact of different parameter combinations on cutting force, surface roughness, and delamination. The results of this study are further analyzed through a combination of Grey Relational Analysis and ANOVA to determine the significance of each input parameter. The findings of this work demonstrate that the speed of the spindle is the most influential factor with largest contribution of 66.64% on all three responses, followed by feed with 26.12% contribution and cutting depth contribution 5.78%. this research provides a valuable insights for the optimization of end milling processes in S-GFRP composite materials. The findings can be used to improve the performance and quality of machined S-GFRP composite parts. [ABSTRACT FROM AUTHOR]

This paper complicates the classic security dilemma by considering the notions of ontological security and relational deterrence. It studies how the ontological security dilemma has emerged between the US, China, and Taiwan from the relational perspective and how these spiral chains will further develop in the future. The paper incurs the literature on relational analysis to expound on how different ontological security concerns allude to relational deterrence between the three actors. Taiwan's separatism is more of a threat to China's relational self than to physical security because the separatist does not belong to any already agreed relationship. A geometric model and a few simulations yield three unconventional findings. 1) The less advantaged the US military is over China, the less likely armed unification will occur. 2) The US anti-Chinese tendency is irrelevant in determining the probability of armed unification. 3) What may escalate the spiral are China's anti-Taiwan independence and Taiwan's anti-unification. Case sensitivity indicates the ontological sensibilities of a security dilemma. [ABSTRACT FROM AUTHOR]

The twenty-first century has seen the development and delivery of online programs of behavioral family intervention for disruptive child behavior. Typically, programs evaluate outcomes in terms of change in child functioning and change in parenting ability. Existing research has also articulated the importance of parent–child relational capacity and its role in facilitating change in child functioning, and the importance of parent emotion regulation in the interests of ensuring optimal child development. These factors were explored in a meta-analysis of k = 14 prospective longitudinal research studies of online parenting interventions for disruptive child behavior. Peer reviewed randomized controlled trials with inactive control groups that were published in English between 2000 and 2022 were included in the review if they were delivered online; offered parent self-directed treatment; included as participants families who were screened as having child behavioral difficulties on validated psychometric assessment measures; and assessed child treatment outcomes, parenting ability and parent treatment outcomes. The protocol for this study was pre-registered with PROSPERO (CRD42020215947). Statistical analyses employed random effects models and reported pooled effect sizes (Hedge's g) within and between groups. Results emphasize the importance of child outcomes and parenting ability in program assessment, however, suggest that parents' capacity to develop optimal parent–child relationships and regulate emotion may not be sufficiently reflected in program content. Identified continuous and categorical moderators of treatment outcome were also assessed. Results of the review are discussed in terms of their potential to influence the future development of online programs of behavioral family intervention and, therefore, child development. [ABSTRACT FROM AUTHOR]

To achieve lightweight of automobile body side structure, a body optimization framework is established. The optimization framework includes numerical simulation, wall thickness, and material set parameterization, design of experiment, surrogate model, NSGA-II, and multi-criteria decision making (MCDM). The finite element model of automobile body side collision is established, and the accuracy of the model is verified by side collision experiment. The problem that it is difficult to embed discrete material variables into the optimization model is solved by using material set parameterization technology. The RBF surrogate model and NSGA-II are applied to the multi-objective lightweight optimization of the body side structure. A hybrid weight & GRA decision method is proposed for Pareto front data mining. Compared with other MCDM methods, the decision results using hybrid weight & GRA are more robust and reasonable. Through the integrated optimization of the wall thickness, material, and performance of the body side structure and the Pareto front data mining, the mass of the body side structure is reduced by 9.21 kg, the lightweight rate is up to 15.93%, and the crashworthiness performance indicators meet the design baseline requirements. [ABSTRACT FROM AUTHOR]

In recent years, the concept of entrepreneurial and innovative universities has gained widespread prominence. Many universities have been paying more attention to being entrepreneurial and innovative by improving their organizational systems, advancing their infrastructure, and increasing financial support. Since numerous criteria with different weights exist, ranking universities based on entrepreneurial and innovative performance can be considered a multi-criteria decision-making (MCDM) problem. This article aims to investigate how different multi-criteria decision-making methods with different criterion weights can affect university rankings and to highlight the reasons that contribute to these differences. In this scope, Grey Relational Analysis (GRA) and Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) methods were used to rank and compare the universities in Türkiye according to the 2022 Entrepreneur and Innovative University Index (EIUI). In addition to the current weights of each EIUI dimension, entropy-based weights and equal weights were used in MCDM methods. Three ranking approaches with varying weights provided different rankings for universities. The effect of criterion weights was found to be more important in the ranking difference than the method used. The ranks for universities coded U1 and U2 as the most entrepreneurial and innovative universities remained the same. In addition, the performance of each university according to each dimension was evaluated graphically using the GAIA plane to enable them to identify areas for improvement in their rankings. [ABSTRACT FROM AUTHOR]

In this study, mahogany seed oil was sourced and prepared, and the performances were compared with mineral oils. The extracted oil was characterized to recognize properties related to pyto-chemical, physio-chemical lubricity and thereafter used along with mineral oil for the formulation of cutting fluid using emulsifying agent, anti-corrosive agent, biocides, and anti-foam agent as additives. These additives were added to oil and water by using 24 full factorial design to achieve the optimal combination. In addition, central composite design (CCD) was adopted for the experimental design, and the performance of the mahogany oil-based cutting fluid (MBCF) was investigated in terms of surface finish, cutting temperature, material removal rate, machine sound level, and chips formation and, thereafter, compared with conventional mineral oil (CBCF) in turning of AISI 304 steel under flood cooling technique. Experimental data were analyzed using analysis of variance (ANOVA) and grey relational analysis (GRA). The experimental findings showed that optimal multi-response performance of the MBCF can be achieved using spindle speed, feed rate and depth of cut of 1100 rev/min, 0.27 mm/rev, and 0.23 mm, respectively, while optimal multi-response performance of CBCF can be achieved with spindle speed, feed rate, and depth of cut of 900 rev/min, 0.62 mm/rev, and 0.23 mm, respectively. [ABSTRACT FROM AUTHOR]

Accurately predicting stock prices is crucial for reducing investment-related risks in decision-making. Contemporary challenges to financial behavior, posed by environmental issues such as pollution and climate change, have received limited attention in existing studies on capital market predictability. This paper focuses on the Shanghai Stock Exchange Composite Index (SSEC) and employs air quality and weather data from the Shanghai area as input variables. Subsequently, a hybrid prediction model is constructed by integrating Grey Relational Analysis (GRA), Wavelet Decomposition (WD), and Bidirectional Long Short-Term Memory (BiLSTM) neural networks. The objective is to achieve precise predictions of closing prices. Additionally, this study validates the feasibility of incorporating environmental factors as input variables for stock price prediction, using the Shenzhen Component Index (SZI) and Hang Seng Index (HSI) as case studies, while also assessing the applicability of the GRA-WD-BiLSTM model. The findings demonstrate that the GRA-WD-BiLSTM model exhibits superior applicability and prediction performance in stock price forecasting, with respective prediction accuracies of 95.93%, 93.02%, and 97.07% when accounting for environmental factors. The incorporation of GRA and WD contributes to enhancing single models' performance while integrating air quality and weather factors, which prove valuable in accurately predicting stock prices. The findings also indicate that the impact of regional environmental factors on local stock exchange index prices shows variability. [ABSTRACT FROM AUTHOR]

With the extensive development of nuclear energy, soil uranium contamination has become an increasingly prominent problem. The development of evaluation systems for various uranium contamination levels and soil microhabitats is critical. In this study, the effects of uranium contamination on the carbon source metabolic capacity and microbial community structure of soil microbial communities were investigated using Biolog microplate technology and high-throughput sequencing, and the responses of soil biochemical properties to uranium were also analyzed. Then, ten key biological indicators as reliable input variables, including arylsulfatase, biomass nitrogen, metabolic entropy, microbial entropy, Simpson, Shannon, McIntosh, Nocardioides, Lysobacter, and Mycoleptodisus, were screened by random forest (RF), Boruta, and grey relational analysis (GRA). The optimal uranium-contaminated soil microbiological evaluation model was obtained by comparing the performance of three evaluation methods: partial least squares regression (PLS), support vector regression (SVR), and improved particle algorithm (IPSO-SVR). Consequently, partial least squares regression (PLS) has a higher R2 (0.932) and a lower RMSE value (0.214) compared to the other. This research provides a new evaluation method to describe the relationship between soil ecological effects and biological indicators under nuclear contamination. [ABSTRACT FROM AUTHOR]

The present study investigates the impact of integrating mould vibration with the vibration-assisted cooling slope (VCS) process on the microstructural and mechanical properties of A392 alloy and the optimization of process parameters. The optimization is done by considering process parameters such as slope length (300 mm, 400 mm, 500 mm, 600 mm and 700 mm), pouring temperature (680 °C, 690 °C, 700 °C, 710 °C and 720 °C), slope vibration (0 Hz, 15 Hz, 30 Hz, 45 Hz and 60 Hz), mould vibration (0 Hz, 15 Hz, 30 Hz, 45 Hz and 60 Hz) and slope angle (15°, 30°, 45°, 55° and 60°), using the multi-objective method (Taguchi-based grey relational analysis). The output responses (mechanical and microstructural properties) were converted into single objective responses as grey relational grades, and the obtained grades were analysed using Taguchi and ANOVA (analysis of variance). The results revealed that the integration of mould vibration with the vibration-assisted cooling slope process (VCS) improved the microstructure homogeneity and mechanical properties of the A392 alloy. It was observed that the optimum parameters for the VCS-integrated mould vibration process were a slope length of 600 mm, pouring temperature of 700 °C, slope vibration of 45 Hz, mould vibration of 30 Hz and slope angle of 45°. The confirmation test was done with optimum values. The optimization of process parameters resulted in the further enhancement of the properties. The results are very close to the predicted value with a difference of 9%. The findings of this study could provide valuable insights for optimizing the vibration-assisted cooling slope process (VCS) integrated with mould vibration for A392 alloy and similar materials. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) techniques, such as fused filament fabrication (FFF), play nowadays an important role for processing polymeric materials, owing to their considerable advantages such as its cost-effectiveness and wide range of usable materials. In particular, techniques such as FFF involve multiple parameters that critically influence printing quality, including the choice of filament material. Although, existing literature extensively investigates how these parameters affect print quality and overall process efficiency, the relative influence of specimen weight apart from process parameters is rarely discussed. In this study, we focus on the flexural properties of PETG processed via FFF. Using an orthogonal Taguchi design of experiment, we analyzed the influence of four control factors: infill density, infill pattern, layer height, and printing speed. Flexural properties were evaluated based on the flexural modulus of elasticity, flexural yield strength, and flexural absorbed energy, both in absolute terms and normalized to weight. Following the Taguchi analysis, grey relational analysis (GRA) was used to identify the optimal set of parameters for both absolute and reduced values. This study yields valuable insights into each parameter impact, the efficient fabrication capabilities, while it also provides guidelines for future research. By employing a combination of Taguchi DOE and GRA, the obtained flexural properties of the printed parts were significantly improved and optimized based on different criteria, taking into account the weight of specimens and printing time, and finally, it was deduced that the consideration of reduced values can reveal promising alternative strategies for obtaining optimized parts. [ABSTRACT FROM AUTHOR]

Research presented an experimental investigation to determine the optimal parametric setting during friction stir welding (FSW) of Aluminium AA 6061 alloy. Three critical parameters viz. welding speed, axial force and tool rotational speed at three different levels are taken as the input processing parameters. Experimental runs were designed using Taguchi L9 orthogonal array. Selected process parameters and their levels were chosen after conducting the pilot experiments and also through the literature survey. Based on the importance, tensile strength and tensile elongation are the output quality characteristics considered. The significant process parameters that affect welding performance were determined using the analysis of variance (ANOVA) and F-test in conjunction with the experimental results obtained. Process parameters such as welding speed and Tool rotational speed are found to be the most significant parameters affecting grey relational grade. Considering the significant parameters so obtained, improvement in the quality characteristics for the FSW of Aluminium AA 6061 was done and the improved results were obtained when compared to the original parameters. Multi response optimization was performed on both the output responses using the Taguchi–Grey relational analysis. [ABSTRACT FROM AUTHOR]

Biodiesel production through the synthesis of Datura stramonium L. oil is studied to explore the most efficient approaches to suggest an alternate feedstock for biodiesel production. The main objective of this work is to optimize the process variables of biodiesel synthesis by using some statistical approach (Taguchi method, grey relational analysis (GRA), and response surface methodology (RSM) analyzing three parameters, i.e., alcohol-to-oil molar ratio, catalyst (NaOH) concentration, and process temperature for achieving maximum biodiesel derived from Datura stramonium L. oil. The transesterification process is applied by using an ultrasonic-assisted technique. Grey relational analysis (GRA) was successfully applied with the Taguchi method resulting in the optimum combination of A2B1C1. Based on the findings, the best operating conditions for transesterifying are attained with the RSM approach consisting of a 5.697:1 molar ratio (level 2), 0.3 (wt.%) NaOH concentration (level 1), and 70 °C process temperature (level 1). With a value of 87.02%, these ideal operating conditions produce the maximum yield as compared to grey relational analysis (GRA) yields 83.99%. The obtained results have been verified through the characterization of oil and biodiesel as well. Also, the fuel qualities of DSL biodiesel were identified and assessed. DSL oil was found 137.6 degrees of unsaturation during fatty acid profile analysis. DSL biodiesel was found the best kinematic viscosity (4.2 mm2/s) and acid value (0.49) when compared to Karanja and palm biodiesel. D. stramonium L. was recognized as a suitable species for biodiesel feedstock according to the findings. [ABSTRACT FROM AUTHOR]

The present study embraces two-fold sustainability benefits where in first-fold, minimum power consumption (Pc) and machining time (Mt) escort one pillar of sustainability named as environmental sustainability. Moreover, the highest material removal rate (MRR) reduces manufacturing costs and embraces the second pillar of sustainability named as economic sustainability. The study is experimentally conducted on globally emerging Inconel 825 super alloy with a copper electrode in EDM. The sustainable machining is evaluated under a cluster of 8 input process parameters, i.e., spark gap (Sg), gap voltage (Vg), pulse on time (Ton), pulse off time (Toff), peak current (Ip), servo feed (Sf), depth of cut (Dc), and difficulty index (Di) under multi objective optimization (MOO) domain. The study has originally devised two novel approaches based on Taguchi, i.e., simple additive weighting (SAWTAG) and grey relational analysis (GRATAG). Toff is found as the chief significant parameter having p values of 0.005 and 0.002 respectively. Dc is found as the second significant parameter with p values of 0.061 and 0.071 respectively. Significant improvements of 0.16262 and 0.48371 are recognized in conformity experiment under GRATAG and SAWTAG approaches; optimal parametric setting is found as Sg2Vg3Ton1Toff1Ip3Sf1Dc1Di1, which is experimentally validated as robust decision. Furthermore, the proportional contribution of process parameters is estimated where GRATAG reflected 41.11% and SAWTAG reflected 47.29% of the contribution of pulse off time respectively. The study investigated surface topography for highlighting the effects of process parameters on heat-affected layers, machining debris, and surface cracks. It is examined with an increase in Ip and Vg; MRR increases and machining time decreases. Moreover, it is also noticed that an increase in Vg prevents irregularities such as micro voids and re-solidified droplets in machining. [ABSTRACT FROM AUTHOR]